Hospital Medicine

Substance use disorders (SUD) represent a national epidemic with death rates exceeding those of HIV at its peak.¹ Hospitals are increasingly filled with people suffering from medical complications of addiction.^2,3 While the US health system spends billions of dollars annually on hospital care for medical problems resulting from SUD,⁴ most hospitals lack expertise or care systems to directly address SUD or connect people to treatment after discharge. ^5,6

Patients with SUD often feel stigmatized in healthcare settings and want providers who understand SUD and how to treat it.⁷ Providers feel underprepared⁸ and commonly have negative attitudes toward patients with SUD.^9,10 Caring for patients can be a source of resentment, dissatisfaction, and burnout.⁹ Such negative attitudes can adversely affect patient care. Studies show that patients who perceive discrimination by providers are less likely to complete treatment¹¹ and providers’ negative attitudes may disempower patients.⁹

Evaluations of hospital interventions for adults with SUD focus primarily on patient-level outcomes of SUD severity,¹² healthcare utilization,¹³ and treatment engagement.^14,15 Little is known about how such interventions can affect interprofessional providers’ attitudes and experiences, or how systems-level interventions influence hospital culture.¹⁶

We performed a qualitative study of multidisciplinary hospital providers to 1) understand the challenges that hospital providers face in managing care for patients with SUD, and 2) explore how integrating SUD treatment in a hospital setting affects providers’ attitudes, experiences, and perceptions of the care environment. This study was part of a formative evaluation of the Improving Addiction Care Team (IMPACT). IMPACT includes a hospital-based, interprofessional addiction medicine consultation service and rapid-access pathways to community addiction care after hospitalization.¹⁷. IMPACT is an intensive intervention that includes SUD assessments, withdrawal management, medications for addiction (eg, methadone, buprenorphine induction), counseling and behavioral SUD treatment, peer engagement and support, and linkages to community-based addiction care. We described the rationale and design of IMPACT in earlier publications.^7,17

Setting

We conducted in-person interviews and focus groups (FGs) with interprofessional hospital providers at a single urban academic medical center between February and July 2016, six months after starting IMPACT implementation. Oregon Health and Science University’s (OHSU) institutional review board approved the protocol.

Participants

We conducted 12 individual informant interviews (IIs) and 6 (FGs) (each comprising 3-6 participants) with a wide range of providers, including physicians, nurses, social workers, residents, patient advocates, case managers, and pharmacists. In total, 34 providers participated. We used purposive sampling to choose participants with experience both caring for patients with SUD and with exposure to IMPACT. Participant characteristics are summarized in Table 1.

Data Collection

We employed 2 different types of interviews. In situations where multiple providers occupied a similar role (eg, social workers), we chose to use a focus group format to elicit a range of perspectives and experiences through participant interaction.¹⁸ We conducted individual interviews to gain input from key informants who had unique roles in the program (eg, a cardiac surgeon) and to include providers who would otherwise be unable to participate due to scheduling barriers (eg, residents). We interviewed all participants using a semi-structured interview guide that was developed by an interdisciplinary team, including expert qualitative researchers, IMPACT clinical team members, and other OHSU clinicians (Appendix A). An interviewer who was not a part of the IMPACT clinical team asked all participants about their experience caring for patients with SUD, their experience with IMPACT, and how they might improve care. FGs lasted between 41-57 minutes, and individual key informant interviews lasted between 11-38 minutes. We ended recruitment after reaching theme saturation. Our goal was to achieve saturation across the sample as a whole and not within distinct participant groups. We noted if certain themes were more salient for 1 particular group. We audio-recorded all interviews and FGs. Recordings were transcribed, de-identified, and transferred to ATLAS.ti for data analysis.

Analysis

We conducted a thematic analysis using an inductive approach at the semantic level.¹⁹ Using an iterative process, we generated a preliminary coding schema after reviewing an initial selection of transcripts. Coders then independently coded transcripts and met in dyads to both discuss and reconcile codes, and resolve any discrepancies through discussion until reaching a consensus. One coder (DC) coded all transcripts; 3 coders (EP, SPP, MR) divided the transcripts evenly. All authors met periodically to discuss codebook revisions and emergent themes. We identified themes that represented patterns, had meaning to study participants, and captured important findings related to our research questions.¹⁹

As expected, the style of IIs differed from that of FGs and informants were able to provide information specific to their roles. Overall, the information provided by IIs was complementary to that of FGs and helped triangulate findings. Thus, we combined them in the results.¹⁸

We organized our findings into 3 main groupings, including (1) care before IMPACT, (2) care with IMPACT, and (3) perceived limitations of IMPACT. We included a table (Table 2) with additional quotations, beyond those in the body of the results, to support emergent themes described below.

Care before IMPACT

Providers felt hospitalization did not address addiction for many reasons, including ethical and legal concerns, medical knowledge gaps, and lack of treatment options.

Before IMPACT, many participants noted that hospitalization ignored or avoided addressing addiction, leading to a chaotic care environment that adversely affected patient care and provider experience. As one social worker stated, “prior to IMPACT we provided assessments, and we provided resources. But we didn’t address addiction.”

Providers cited multiple explanations for this, including the common misperception that using methadone to treat withdrawal violated federal regulations, and concerns about the ethicality of using opioids in patients with SUD. Across disciplines, providers described a “huge knowledge gap” and little confidence in addressing withdrawal, complex chronic pain, medications for addiction, and challenging patient behaviors. Providers also described limited expertise and scarce treatment options as a deterrent. As one attending reflected, “I would ask those questions [about SUD] before, but then … I had the information, but I couldn’t do anything with it.”

Providers felt the failure to address SUD adversely affected patient care, leading to untreated withdrawal, disruptive behaviors, and patients leaving against medical advice (AMA).

Participants across disciplines described wide variability in the medical management of SUD, particularly around the management of opioid withdrawal and pain, with some providers who “simply wouldn’t prescribe methadone or any opiates” and others who prescribed high doses without anticipating risks. As one attending recalled:

“You would see this pattern, especially in the intravenous drug-using population: left AMA, left AMA, left AMA … nine times out of ten, nobody was dealing with the fact that they were gonna go into withdrawal.”

Respondents recalled that disruptive behaviors from patients’ active use or withdrawal frequently threatened safety; imposed a tremendous burden on staff time and morale; and were a consistent source of providers’ distress. As one patient advocate explained:

“[Providers] get called to the unit because the person is yelling and throwing things or comes back after being gone for a long period and appears impaired … it often blows up, and they get discharged or they leave against medical advice or they go out and don’t come back. We don’t really know what happened to them, and they’re vulnerable. And the staff are vulnerable. And other patients are distressed by the disruption and commotion.”

Absent standards and systems to address SUD, providers felt they were “left to their own,” resulting in a reactive and chaotic care environment.

Providers noted inconsistent rules and policies regarding smoke breaks, room searches, and visitors. As a result, care felt “reckless and risky” and led to a “nonalliance” across disciplines. Providers frequently described inconsistent and loose expectations until an event -- often active use – triggered an ad hoc ratcheting up of the rules, damaging patient-provider relationships and limiting providers’ ability to provide medical care. Facing these conflicts, “staff gets escalated, and everybody gets kind of spun up.” As one attending reflected:

“I could not get any sort of engagement even in just her medical issues … I was trying to talk to her and educate her about heart failure and salt intake and food intake, but every time I walked in the room … I’d have to come in and be like, ‘your UDS [urine drug screen] was positive again, so here’s the changes to your behavioral plan, and OK, let’s talk about your heart failure …’ At that point, the relationship had completely disintegrated until it was very nonproductive.”

Providers described widespread “moral distress,” burnout, and feelings of futility before IMPACT.

Consequently, providers felt that caring for people with SUD was “very emotionally draining and very time consuming.” As one patient advocate described:

“We’ve been watching staff try to manage these patients for years without the experts and the resources and the skills that they need … As a result, there was a crescendo effect of moral distress, and [staff] bring in all of their past experiences which influence the interaction … Some staff are very skilled, but you also saw some really punitive responses.”

Many felt that providing intensive medical care without addressing people’s underlying SUD was a waste of time and resources. As one cardiac surgeon reflected:

“[Patients] ended up either dead or reinfected. Nobody wanted to do stuff because we felt it was futile. Well, of course, it’s futile …. you’re basically trying to fix the symptoms. It’s like having a leaky roof and just running around with a bunch of buckets, which is like surgery. You gotta fix the roof…otherwise they will continue to inject bacteria into their bodies.”

Care with IMPACT:

Providers felt integrating hospital-based systems to address SUD legitimized addiction as a treatable disease.

Participants described IMPACT as a “sea change” that “completely reframes” addiction as “a medical condition that actually has a treatment.” As one social worker observed, “when it’s somebody in a white coat with expertise who’s talking to another doctor it really can shift mindsets in an amazing way.” Others echoed this, stating that an addiction team “legitimized the fact that this is an actual disease that we need to treat - and a failure to treat it is a failure to be a good doctor.”

Providers felt that by addressing addiction directly, “IMPACT elevated the consciousness of providers and nurses … that substance use disorders are brain disorders and not bad behavior.” They described that this legitimization, combined with seeing firsthand the stabilizing effects of medications for addiction, allowed providers to understand SUD as a chronic disease, and not a moral failing.

Providers felt IMPACT improved patient engagement and humanized care by treating withdrawal, directly communicating about SUD, and modeling compassionate care.

Providers noted that treating withdrawal had a dramatic effect on patient engagement and care. One surgeon explained, “by managing their opioid dependence and other substance abuse issues … it’s easier for the staff to take care of them, it’s safer, and the patients feel better taken care of because the staff will engage with them.” Many noted that conflict-ridden “conversations were able to go to the side, and we were able to talk about other things to build rapport.” Others noted that this shift felt like “more productive time.”

In addition, providers repeatedly emphasized that having clear hospital standards and a process to engage patients “really helps … establish rapport with patients: ‘This is how we work this. These are your boundaries. And this is what will happen if you push those boundaries.’ There it is.” Providers attributed improved patient-provider communication to “frank conversation,” “the right amount of empathy,” and a less judgmental environment. As one attending described, “I don’t know if it gives them a voice or allows us to hear them better … but something’s happening with communication.”

Many participants highlighted that IMPACT modeled compassionate bedside interactions, exposed the role of trauma in many patients’ lives, and helped providers see SUD as a disease spectrum. One attending noted that to “actually appreciate the subtleties – just the severity of the disorder – has been powerful.” One resident said:

“There’s definitely a lot of stigma around patients with use disorders that probably shows itself in subtle ways throughout their hospitalization. I think IMPACT does a good job … keeping the patient in the center and keeping their use disorder contextualized in the greater person … [IMPACT] role models bedside interactions and how to treat people like humans.”

Providers valued post-hospital SUD treatment pathways.

Providers valued previously nonexistent post-hospital SUD treatment pathways, stating “this relationship with [community treatment] … it’s like an answer to prayers,” and “this isn’t just like we’re being nicer.” One attending described:

“Starting them on [methadone or buprenorphine-naloxone] and then making the next step in the outpatient world happen has been huge. That transition is so critical … that’s been probably the biggest impact.”

Providers felt relief after IMPACT implementation.

Providers felt that by addressing SUD treatment gaps and providing addiction expertise, IMPACT helped alleviate the previously widespread feelings of “moral distress.” One resident explained “having [IMPACT] as a lifeline, it just feels so good.” As an infectious disease consultant noted, “it makes people more open to treating people if they don’t feel isolated and out of their depth.” Others noted that IMPACT supported better multidisciplinary collaboration, which “reduced a lot of tension between the teams.” One nurse summarized:

“I think you feel more empowered when you’ve got the right medication, … the knowledge, and you feel like you have the resources. You actually feel like you’re making a difference.”

Respondents acknowledged that even with IMPACT, some patients leave AMA or relapse. However, by understanding addiction as a relapsing and remitting disease, providers reconceptualized “success,” further reducing feelings of emotional burnout and stress: “there will be ups and downs, it’s not gonna be a straight linear success.” One case manager reflected,

“Maybe that’s part of the nature of the illness, you progress, and then you kind of hold your breath and then it slips again … at least with IMPACT at the table I can say we’ve done the best we can for this person.”

Perceived limitations of IMPACT:

Providers noted several key limitations of IMPACT, including that hospital-based interventions do not address poverty and have limited ability to address socioeconomic determinants such as “social support, … housing, or nutrition.” Providers also felt that IMPACT had limited ability to alleviate patients’ feelings of boredom and isolation associated with prolonged hospitalization, and that IMPACT had limited effectiveness for highly ambivalent patients (Table 2).

Finally, while many described increased confidence managing SUD after working with IMPACT, others cautioned against deferring too much to specialists. As one resident doctor said:

“We shouldn’t forget that all providers should know how to handle some form of people with addiction … I just don’t want it to be like, ‘oh, well, no, I don’t need to think about this … because we have an addiction specialist.’”

Participants across disciplines repeatedly suggested formal, ongoing initiatives to educate and train providers to manage SUD independently.

This study explores provider perspectives on care for hospitalized adults with SUD. Before IMPACT, providers felt care was chaotic, unsafe, and frustrating. Providers perceived variable care quality, resulting in untreated withdrawal, inconsistent care plans, and poor patient outcomes, leading to widespread “moral distress” and feelings of futility among providers. Yet this experience was not inevitable. Providers described that a hospital-based intervention to treat SUD reframed addiction as a treatable chronic disease, transformed culture, and improved patient care and provider experience.

Our findings are consistent with and build on previous research in several ways. First, widespread anxiety and difficulty managing patients with SUD was not unique to our hospital. In a systematic review, van Boekel and colleagues describe that healthcare providers perceived violence, manipulation, and poor motivation as factors impeding care for patients with SUD.⁹ Our study demonstrates the resulting feelings of powerlessness and frustration may be alleviated through an intervention that provides SUD care.

Second, our study is consistent with a recent survey-based study by Wakeman and colleagues that found that a hospital-based SUD intervention improved providers’ feelings of preparedness and satisfaction.²⁰ Our study provides a rich qualitative description and elucidates mechanisms by which such interventions may work.

The finding that a hospital-based SUD intervention can shift providers’ views of addiction is important. Earlier studies have shown that providers who perceive addiction as a choice are more likely to have negative attitudes toward people with SUD.¹¹ While our intervention did not provide formal education aimed at changing attitudes, participants reported that seeing firsthand effects of treatment on patient behaviors was a powerful tool that radically shifted providers’ understanding and reduced stigma.

Stigma can occur at both individual and organizational levels. Structural stigma refers to practices, policies, and norms of institutions that exclude needs of a particular group.²¹ The absence of systems to address SUD sends a message to both patients and providers that addiction is a not a treatable or worthy disease. IMPACT was in and of itself a systems-level intervention; by creating a consultation service, hospital-wide policies, and pathways to care after hospitalization, IMPACT ‘legitimized’ SUD and reduced institutional stigma.

Several studies have shown the feasibility and effectiveness of starting medications for addiction (MAT) in the hospital.^13-15 Our study builds on this work by highlighting systems-level elements valued by providers. These elements may be important to support and scale widespread adoption of MAT in hospitals. Specifically, providers felt that IMPACT’s attention to hospital policies, use of addiction medicine specialists, and direct linkages to outpatient SUD treatment proved instrumental in shifting care systems.

Our study has several limitations. As a single-site study, our goal was not generalizability, but transferability. As such, we aimed to obtain rich, in-depth information that can inform implementation of similar efforts. Because our study was conducted after the implementation of IMPACT, providers’ perspectives on care before IMPACT may have been influenced by the intervention. However, this also strengthens our findings by allowing participants the opportunity for insights under a different system. It likely leads to distinct findings compared to what we might have uncovered in a pre-post study. While respondents noted perceived limitations of IMPACT, there were few instances of negative remarks in the data we collected. It is possible that providers with more negative interpretations chose not to participate in interviews; however, we elicited wide viewpoints and encouraged participants to share both strengths and weaknesses. Finally, IMPACT implementation depends on regional as well as local factors such as Medicaid expansion, community treatment resources, and the existence of addiction medicine expertise that will differ across settings.

Despite these limitations, our study has several important implications. For clinical practice, our findings highlight the importance of treating withdrawal to address challenging patient behaviors and the value of integrating MAT into the hospital setting. Our findings also underscore the role of expert consultation for addiction. Importantly, our results emphasize that reframing SUD as a brain disease can have significant implications for clinical care and providers’ well-being. Provider distress is not inevitable and can change with the right support and systems.

At the hospital and health systems level, our findings suggest that hospitals can and should address SUD. This may include forming interprofessional teams with SUD expertise, providing standardized guidelines for addiction care such as patient safety plans and methadone policies, and creating rapid-access pathways to outpatient SUD care. By addressing SUD, hospitals may simultaneously improve care and reduce provider burnout. Providers’ important concerns about shifting SUD treatment to a specialty team and their discomfort managing SUD pre-IMPACT suggest the need to integrate SUD education across all levels of interprofessional education. Furthermore, provider concerns that IMPACT has limited ability to engage ambivalent patients underscores the need for hospital-based approaches that emphasize harm reduction strategies.

As the SUD epidemic worsens, SUD-related hospitalizations are skyrocketing, and people are dying at unprecedented rates.^2,3 Many efforts to address SUD have been in primary care or community settings. While important, many people with SUD are unable or unlikely to seek primary care. ²² Hospitals need a workforce and systems that can address both the physical and behavioral health needs of this population. By implementing SUD improvements, hospitals can support staff and reduce burnout, better engage patients, improve care, and reduce stigma from this devastating disease.

Disclosures

The authors have no conflicts of interest to disclose.

Substance use disorders (SUD) represent a national epidemic with death rates exceeding those of HIV at its peak.¹ Hospitals are increasingly filled with people suffering from medical complications of addiction.^2,3 While the US health system spends billions of dollars annually on hospital care for medical problems resulting from SUD,⁴ most hospitals lack expertise or care systems to directly address SUD or connect people to treatment after discharge. ^5,6

Patients with SUD often feel stigmatized in healthcare settings and want providers who understand SUD and how to treat it.⁷ Providers feel underprepared⁸ and commonly have negative attitudes toward patients with SUD.^9,10 Caring for patients can be a source of resentment, dissatisfaction, and burnout.⁹ Such negative attitudes can adversely affect patient care. Studies show that patients who perceive discrimination by providers are less likely to complete treatment¹¹ and providers’ negative attitudes may disempower patients.⁹

Evaluations of hospital interventions for adults with SUD focus primarily on patient-level outcomes of SUD severity,¹² healthcare utilization,¹³ and treatment engagement.^14,15 Little is known about how such interventions can affect interprofessional providers’ attitudes and experiences, or how systems-level interventions influence hospital culture.¹⁶

We performed a qualitative study of multidisciplinary hospital providers to 1) understand the challenges that hospital providers face in managing care for patients with SUD, and 2) explore how integrating SUD treatment in a hospital setting affects providers’ attitudes, experiences, and perceptions of the care environment. This study was part of a formative evaluation of the Improving Addiction Care Team (IMPACT). IMPACT includes a hospital-based, interprofessional addiction medicine consultation service and rapid-access pathways to community addiction care after hospitalization.¹⁷. IMPACT is an intensive intervention that includes SUD assessments, withdrawal management, medications for addiction (eg, methadone, buprenorphine induction), counseling and behavioral SUD treatment, peer engagement and support, and linkages to community-based addiction care. We described the rationale and design of IMPACT in earlier publications.^7,17

Setting

We conducted in-person interviews and focus groups (FGs) with interprofessional hospital providers at a single urban academic medical center between February and July 2016, six months after starting IMPACT implementation. Oregon Health and Science University’s (OHSU) institutional review board approved the protocol.

Participants

We conducted 12 individual informant interviews (IIs) and 6 (FGs) (each comprising 3-6 participants) with a wide range of providers, including physicians, nurses, social workers, residents, patient advocates, case managers, and pharmacists. In total, 34 providers participated. We used purposive sampling to choose participants with experience both caring for patients with SUD and with exposure to IMPACT. Participant characteristics are summarized in Table 1.

Data Collection

We employed 2 different types of interviews. In situations where multiple providers occupied a similar role (eg, social workers), we chose to use a focus group format to elicit a range of perspectives and experiences through participant interaction.¹⁸ We conducted individual interviews to gain input from key informants who had unique roles in the program (eg, a cardiac surgeon) and to include providers who would otherwise be unable to participate due to scheduling barriers (eg, residents). We interviewed all participants using a semi-structured interview guide that was developed by an interdisciplinary team, including expert qualitative researchers, IMPACT clinical team members, and other OHSU clinicians (Appendix A). An interviewer who was not a part of the IMPACT clinical team asked all participants about their experience caring for patients with SUD, their experience with IMPACT, and how they might improve care. FGs lasted between 41-57 minutes, and individual key informant interviews lasted between 11-38 minutes. We ended recruitment after reaching theme saturation. Our goal was to achieve saturation across the sample as a whole and not within distinct participant groups. We noted if certain themes were more salient for 1 particular group. We audio-recorded all interviews and FGs. Recordings were transcribed, de-identified, and transferred to ATLAS.ti for data analysis.

Analysis

We conducted a thematic analysis using an inductive approach at the semantic level.¹⁹ Using an iterative process, we generated a preliminary coding schema after reviewing an initial selection of transcripts. Coders then independently coded transcripts and met in dyads to both discuss and reconcile codes, and resolve any discrepancies through discussion until reaching a consensus. One coder (DC) coded all transcripts; 3 coders (EP, SPP, MR) divided the transcripts evenly. All authors met periodically to discuss codebook revisions and emergent themes. We identified themes that represented patterns, had meaning to study participants, and captured important findings related to our research questions.¹⁹

As expected, the style of IIs differed from that of FGs and informants were able to provide information specific to their roles. Overall, the information provided by IIs was complementary to that of FGs and helped triangulate findings. Thus, we combined them in the results.¹⁸

We organized our findings into 3 main groupings, including (1) care before IMPACT, (2) care with IMPACT, and (3) perceived limitations of IMPACT. We included a table (Table 2) with additional quotations, beyond those in the body of the results, to support emergent themes described below.

Care before IMPACT

Providers felt hospitalization did not address addiction for many reasons, including ethical and legal concerns, medical knowledge gaps, and lack of treatment options.

Before IMPACT, many participants noted that hospitalization ignored or avoided addressing addiction, leading to a chaotic care environment that adversely affected patient care and provider experience. As one social worker stated, “prior to IMPACT we provided assessments, and we provided resources. But we didn’t address addiction.”

Providers cited multiple explanations for this, including the common misperception that using methadone to treat withdrawal violated federal regulations, and concerns about the ethicality of using opioids in patients with SUD. Across disciplines, providers described a “huge knowledge gap” and little confidence in addressing withdrawal, complex chronic pain, medications for addiction, and challenging patient behaviors. Providers also described limited expertise and scarce treatment options as a deterrent. As one attending reflected, “I would ask those questions [about SUD] before, but then … I had the information, but I couldn’t do anything with it.”

Providers felt the failure to address SUD adversely affected patient care, leading to untreated withdrawal, disruptive behaviors, and patients leaving against medical advice (AMA).

Participants across disciplines described wide variability in the medical management of SUD, particularly around the management of opioid withdrawal and pain, with some providers who “simply wouldn’t prescribe methadone or any opiates” and others who prescribed high doses without anticipating risks. As one attending recalled:

“You would see this pattern, especially in the intravenous drug-using population: left AMA, left AMA, left AMA … nine times out of ten, nobody was dealing with the fact that they were gonna go into withdrawal.”

Respondents recalled that disruptive behaviors from patients’ active use or withdrawal frequently threatened safety; imposed a tremendous burden on staff time and morale; and were a consistent source of providers’ distress. As one patient advocate explained:

“[Providers] get called to the unit because the person is yelling and throwing things or comes back after being gone for a long period and appears impaired … it often blows up, and they get discharged or they leave against medical advice or they go out and don’t come back. We don’t really know what happened to them, and they’re vulnerable. And the staff are vulnerable. And other patients are distressed by the disruption and commotion.”

Absent standards and systems to address SUD, providers felt they were “left to their own,” resulting in a reactive and chaotic care environment.

Providers noted inconsistent rules and policies regarding smoke breaks, room searches, and visitors. As a result, care felt “reckless and risky” and led to a “nonalliance” across disciplines. Providers frequently described inconsistent and loose expectations until an event -- often active use – triggered an ad hoc ratcheting up of the rules, damaging patient-provider relationships and limiting providers’ ability to provide medical care. Facing these conflicts, “staff gets escalated, and everybody gets kind of spun up.” As one attending reflected:

“I could not get any sort of engagement even in just her medical issues … I was trying to talk to her and educate her about heart failure and salt intake and food intake, but every time I walked in the room … I’d have to come in and be like, ‘your UDS [urine drug screen] was positive again, so here’s the changes to your behavioral plan, and OK, let’s talk about your heart failure …’ At that point, the relationship had completely disintegrated until it was very nonproductive.”

Providers described widespread “moral distress,” burnout, and feelings of futility before IMPACT.

Consequently, providers felt that caring for people with SUD was “very emotionally draining and very time consuming.” As one patient advocate described:

“We’ve been watching staff try to manage these patients for years without the experts and the resources and the skills that they need … As a result, there was a crescendo effect of moral distress, and [staff] bring in all of their past experiences which influence the interaction … Some staff are very skilled, but you also saw some really punitive responses.”

Many felt that providing intensive medical care without addressing people’s underlying SUD was a waste of time and resources. As one cardiac surgeon reflected:

“[Patients] ended up either dead or reinfected. Nobody wanted to do stuff because we felt it was futile. Well, of course, it’s futile …. you’re basically trying to fix the symptoms. It’s like having a leaky roof and just running around with a bunch of buckets, which is like surgery. You gotta fix the roof…otherwise they will continue to inject bacteria into their bodies.”

Care with IMPACT:

Providers felt integrating hospital-based systems to address SUD legitimized addiction as a treatable disease.

Participants described IMPACT as a “sea change” that “completely reframes” addiction as “a medical condition that actually has a treatment.” As one social worker observed, “when it’s somebody in a white coat with expertise who’s talking to another doctor it really can shift mindsets in an amazing way.” Others echoed this, stating that an addiction team “legitimized the fact that this is an actual disease that we need to treat - and a failure to treat it is a failure to be a good doctor.”

Providers felt that by addressing addiction directly, “IMPACT elevated the consciousness of providers and nurses … that substance use disorders are brain disorders and not bad behavior.” They described that this legitimization, combined with seeing firsthand the stabilizing effects of medications for addiction, allowed providers to understand SUD as a chronic disease, and not a moral failing.

Providers felt IMPACT improved patient engagement and humanized care by treating withdrawal, directly communicating about SUD, and modeling compassionate care.

Providers noted that treating withdrawal had a dramatic effect on patient engagement and care. One surgeon explained, “by managing their opioid dependence and other substance abuse issues … it’s easier for the staff to take care of them, it’s safer, and the patients feel better taken care of because the staff will engage with them.” Many noted that conflict-ridden “conversations were able to go to the side, and we were able to talk about other things to build rapport.” Others noted that this shift felt like “more productive time.”

In addition, providers repeatedly emphasized that having clear hospital standards and a process to engage patients “really helps … establish rapport with patients: ‘This is how we work this. These are your boundaries. And this is what will happen if you push those boundaries.’ There it is.” Providers attributed improved patient-provider communication to “frank conversation,” “the right amount of empathy,” and a less judgmental environment. As one attending described, “I don’t know if it gives them a voice or allows us to hear them better … but something’s happening with communication.”

Many participants highlighted that IMPACT modeled compassionate bedside interactions, exposed the role of trauma in many patients’ lives, and helped providers see SUD as a disease spectrum. One attending noted that to “actually appreciate the subtleties – just the severity of the disorder – has been powerful.” One resident said:

“There’s definitely a lot of stigma around patients with use disorders that probably shows itself in subtle ways throughout their hospitalization. I think IMPACT does a good job … keeping the patient in the center and keeping their use disorder contextualized in the greater person … [IMPACT] role models bedside interactions and how to treat people like humans.”

Providers valued post-hospital SUD treatment pathways.

Providers valued previously nonexistent post-hospital SUD treatment pathways, stating “this relationship with [community treatment] … it’s like an answer to prayers,” and “this isn’t just like we’re being nicer.” One attending described:

“Starting them on [methadone or buprenorphine-naloxone] and then making the next step in the outpatient world happen has been huge. That transition is so critical … that’s been probably the biggest impact.”

Providers felt relief after IMPACT implementation.

Providers felt that by addressing SUD treatment gaps and providing addiction expertise, IMPACT helped alleviate the previously widespread feelings of “moral distress.” One resident explained “having [IMPACT] as a lifeline, it just feels so good.” As an infectious disease consultant noted, “it makes people more open to treating people if they don’t feel isolated and out of their depth.” Others noted that IMPACT supported better multidisciplinary collaboration, which “reduced a lot of tension between the teams.” One nurse summarized:

“I think you feel more empowered when you’ve got the right medication, … the knowledge, and you feel like you have the resources. You actually feel like you’re making a difference.”

Respondents acknowledged that even with IMPACT, some patients leave AMA or relapse. However, by understanding addiction as a relapsing and remitting disease, providers reconceptualized “success,” further reducing feelings of emotional burnout and stress: “there will be ups and downs, it’s not gonna be a straight linear success.” One case manager reflected,

“Maybe that’s part of the nature of the illness, you progress, and then you kind of hold your breath and then it slips again … at least with IMPACT at the table I can say we’ve done the best we can for this person.”

Perceived limitations of IMPACT:

Providers noted several key limitations of IMPACT, including that hospital-based interventions do not address poverty and have limited ability to address socioeconomic determinants such as “social support, … housing, or nutrition.” Providers also felt that IMPACT had limited ability to alleviate patients’ feelings of boredom and isolation associated with prolonged hospitalization, and that IMPACT had limited effectiveness for highly ambivalent patients (Table 2).

Finally, while many described increased confidence managing SUD after working with IMPACT, others cautioned against deferring too much to specialists. As one resident doctor said:

“We shouldn’t forget that all providers should know how to handle some form of people with addiction … I just don’t want it to be like, ‘oh, well, no, I don’t need to think about this … because we have an addiction specialist.’”

Participants across disciplines repeatedly suggested formal, ongoing initiatives to educate and train providers to manage SUD independently.

This study explores provider perspectives on care for hospitalized adults with SUD. Before IMPACT, providers felt care was chaotic, unsafe, and frustrating. Providers perceived variable care quality, resulting in untreated withdrawal, inconsistent care plans, and poor patient outcomes, leading to widespread “moral distress” and feelings of futility among providers. Yet this experience was not inevitable. Providers described that a hospital-based intervention to treat SUD reframed addiction as a treatable chronic disease, transformed culture, and improved patient care and provider experience.

Our findings are consistent with and build on previous research in several ways. First, widespread anxiety and difficulty managing patients with SUD was not unique to our hospital. In a systematic review, van Boekel and colleagues describe that healthcare providers perceived violence, manipulation, and poor motivation as factors impeding care for patients with SUD.⁹ Our study demonstrates the resulting feelings of powerlessness and frustration may be alleviated through an intervention that provides SUD care.

Second, our study is consistent with a recent survey-based study by Wakeman and colleagues that found that a hospital-based SUD intervention improved providers’ feelings of preparedness and satisfaction.²⁰ Our study provides a rich qualitative description and elucidates mechanisms by which such interventions may work.

The finding that a hospital-based SUD intervention can shift providers’ views of addiction is important. Earlier studies have shown that providers who perceive addiction as a choice are more likely to have negative attitudes toward people with SUD.¹¹ While our intervention did not provide formal education aimed at changing attitudes, participants reported that seeing firsthand effects of treatment on patient behaviors was a powerful tool that radically shifted providers’ understanding and reduced stigma.

Stigma can occur at both individual and organizational levels. Structural stigma refers to practices, policies, and norms of institutions that exclude needs of a particular group.²¹ The absence of systems to address SUD sends a message to both patients and providers that addiction is a not a treatable or worthy disease. IMPACT was in and of itself a systems-level intervention; by creating a consultation service, hospital-wide policies, and pathways to care after hospitalization, IMPACT ‘legitimized’ SUD and reduced institutional stigma.

Several studies have shown the feasibility and effectiveness of starting medications for addiction (MAT) in the hospital.^13-15 Our study builds on this work by highlighting systems-level elements valued by providers. These elements may be important to support and scale widespread adoption of MAT in hospitals. Specifically, providers felt that IMPACT’s attention to hospital policies, use of addiction medicine specialists, and direct linkages to outpatient SUD treatment proved instrumental in shifting care systems.

Our study has several limitations. As a single-site study, our goal was not generalizability, but transferability. As such, we aimed to obtain rich, in-depth information that can inform implementation of similar efforts. Because our study was conducted after the implementation of IMPACT, providers’ perspectives on care before IMPACT may have been influenced by the intervention. However, this also strengthens our findings by allowing participants the opportunity for insights under a different system. It likely leads to distinct findings compared to what we might have uncovered in a pre-post study. While respondents noted perceived limitations of IMPACT, there were few instances of negative remarks in the data we collected. It is possible that providers with more negative interpretations chose not to participate in interviews; however, we elicited wide viewpoints and encouraged participants to share both strengths and weaknesses. Finally, IMPACT implementation depends on regional as well as local factors such as Medicaid expansion, community treatment resources, and the existence of addiction medicine expertise that will differ across settings.

Despite these limitations, our study has several important implications. For clinical practice, our findings highlight the importance of treating withdrawal to address challenging patient behaviors and the value of integrating MAT into the hospital setting. Our findings also underscore the role of expert consultation for addiction. Importantly, our results emphasize that reframing SUD as a brain disease can have significant implications for clinical care and providers’ well-being. Provider distress is not inevitable and can change with the right support and systems.

At the hospital and health systems level, our findings suggest that hospitals can and should address SUD. This may include forming interprofessional teams with SUD expertise, providing standardized guidelines for addiction care such as patient safety plans and methadone policies, and creating rapid-access pathways to outpatient SUD care. By addressing SUD, hospitals may simultaneously improve care and reduce provider burnout. Providers’ important concerns about shifting SUD treatment to a specialty team and their discomfort managing SUD pre-IMPACT suggest the need to integrate SUD education across all levels of interprofessional education. Furthermore, provider concerns that IMPACT has limited ability to engage ambivalent patients underscores the need for hospital-based approaches that emphasize harm reduction strategies.

As the SUD epidemic worsens, SUD-related hospitalizations are skyrocketing, and people are dying at unprecedented rates.^2,3 Many efforts to address SUD have been in primary care or community settings. While important, many people with SUD are unable or unlikely to seek primary care. ²² Hospitals need a workforce and systems that can address both the physical and behavioral health needs of this population. By implementing SUD improvements, hospitals can support staff and reduce burnout, better engage patients, improve care, and reduce stigma from this devastating disease.

Disclosures

The authors have no conflicts of interest to disclose.

Substance use disorders (SUD) represent a national epidemic with death rates exceeding those of HIV at its peak.¹ Hospitals are increasingly filled with people suffering from medical complications of addiction.^2,3 While the US health system spends billions of dollars annually on hospital care for medical problems resulting from SUD,⁴ most hospitals lack expertise or care systems to directly address SUD or connect people to treatment after discharge. ^5,6

Patients with SUD often feel stigmatized in healthcare settings and want providers who understand SUD and how to treat it.⁷ Providers feel underprepared⁸ and commonly have negative attitudes toward patients with SUD.^9,10 Caring for patients can be a source of resentment, dissatisfaction, and burnout.⁹ Such negative attitudes can adversely affect patient care. Studies show that patients who perceive discrimination by providers are less likely to complete treatment¹¹ and providers’ negative attitudes may disempower patients.⁹

Evaluations of hospital interventions for adults with SUD focus primarily on patient-level outcomes of SUD severity,¹² healthcare utilization,¹³ and treatment engagement.^14,15 Little is known about how such interventions can affect interprofessional providers’ attitudes and experiences, or how systems-level interventions influence hospital culture.¹⁶

We performed a qualitative study of multidisciplinary hospital providers to 1) understand the challenges that hospital providers face in managing care for patients with SUD, and 2) explore how integrating SUD treatment in a hospital setting affects providers’ attitudes, experiences, and perceptions of the care environment. This study was part of a formative evaluation of the Improving Addiction Care Team (IMPACT). IMPACT includes a hospital-based, interprofessional addiction medicine consultation service and rapid-access pathways to community addiction care after hospitalization.¹⁷. IMPACT is an intensive intervention that includes SUD assessments, withdrawal management, medications for addiction (eg, methadone, buprenorphine induction), counseling and behavioral SUD treatment, peer engagement and support, and linkages to community-based addiction care. We described the rationale and design of IMPACT in earlier publications.^7,17

Setting

We conducted in-person interviews and focus groups (FGs) with interprofessional hospital providers at a single urban academic medical center between February and July 2016, six months after starting IMPACT implementation. Oregon Health and Science University’s (OHSU) institutional review board approved the protocol.

Participants

We conducted 12 individual informant interviews (IIs) and 6 (FGs) (each comprising 3-6 participants) with a wide range of providers, including physicians, nurses, social workers, residents, patient advocates, case managers, and pharmacists. In total, 34 providers participated. We used purposive sampling to choose participants with experience both caring for patients with SUD and with exposure to IMPACT. Participant characteristics are summarized in Table 1.

Data Collection

We employed 2 different types of interviews. In situations where multiple providers occupied a similar role (eg, social workers), we chose to use a focus group format to elicit a range of perspectives and experiences through participant interaction.¹⁸ We conducted individual interviews to gain input from key informants who had unique roles in the program (eg, a cardiac surgeon) and to include providers who would otherwise be unable to participate due to scheduling barriers (eg, residents). We interviewed all participants using a semi-structured interview guide that was developed by an interdisciplinary team, including expert qualitative researchers, IMPACT clinical team members, and other OHSU clinicians (Appendix A). An interviewer who was not a part of the IMPACT clinical team asked all participants about their experience caring for patients with SUD, their experience with IMPACT, and how they might improve care. FGs lasted between 41-57 minutes, and individual key informant interviews lasted between 11-38 minutes. We ended recruitment after reaching theme saturation. Our goal was to achieve saturation across the sample as a whole and not within distinct participant groups. We noted if certain themes were more salient for 1 particular group. We audio-recorded all interviews and FGs. Recordings were transcribed, de-identified, and transferred to ATLAS.ti for data analysis.

Analysis

We conducted a thematic analysis using an inductive approach at the semantic level.¹⁹ Using an iterative process, we generated a preliminary coding schema after reviewing an initial selection of transcripts. Coders then independently coded transcripts and met in dyads to both discuss and reconcile codes, and resolve any discrepancies through discussion until reaching a consensus. One coder (DC) coded all transcripts; 3 coders (EP, SPP, MR) divided the transcripts evenly. All authors met periodically to discuss codebook revisions and emergent themes. We identified themes that represented patterns, had meaning to study participants, and captured important findings related to our research questions.¹⁹

As expected, the style of IIs differed from that of FGs and informants were able to provide information specific to their roles. Overall, the information provided by IIs was complementary to that of FGs and helped triangulate findings. Thus, we combined them in the results.¹⁸

We organized our findings into 3 main groupings, including (1) care before IMPACT, (2) care with IMPACT, and (3) perceived limitations of IMPACT. We included a table (Table 2) with additional quotations, beyond those in the body of the results, to support emergent themes described below.

Care before IMPACT

Providers felt hospitalization did not address addiction for many reasons, including ethical and legal concerns, medical knowledge gaps, and lack of treatment options.

Before IMPACT, many participants noted that hospitalization ignored or avoided addressing addiction, leading to a chaotic care environment that adversely affected patient care and provider experience. As one social worker stated, “prior to IMPACT we provided assessments, and we provided resources. But we didn’t address addiction.”

Providers cited multiple explanations for this, including the common misperception that using methadone to treat withdrawal violated federal regulations, and concerns about the ethicality of using opioids in patients with SUD. Across disciplines, providers described a “huge knowledge gap” and little confidence in addressing withdrawal, complex chronic pain, medications for addiction, and challenging patient behaviors. Providers also described limited expertise and scarce treatment options as a deterrent. As one attending reflected, “I would ask those questions [about SUD] before, but then … I had the information, but I couldn’t do anything with it.”

Providers felt the failure to address SUD adversely affected patient care, leading to untreated withdrawal, disruptive behaviors, and patients leaving against medical advice (AMA).

Participants across disciplines described wide variability in the medical management of SUD, particularly around the management of opioid withdrawal and pain, with some providers who “simply wouldn’t prescribe methadone or any opiates” and others who prescribed high doses without anticipating risks. As one attending recalled:

“You would see this pattern, especially in the intravenous drug-using population: left AMA, left AMA, left AMA … nine times out of ten, nobody was dealing with the fact that they were gonna go into withdrawal.”

Respondents recalled that disruptive behaviors from patients’ active use or withdrawal frequently threatened safety; imposed a tremendous burden on staff time and morale; and were a consistent source of providers’ distress. As one patient advocate explained:

“[Providers] get called to the unit because the person is yelling and throwing things or comes back after being gone for a long period and appears impaired … it often blows up, and they get discharged or they leave against medical advice or they go out and don’t come back. We don’t really know what happened to them, and they’re vulnerable. And the staff are vulnerable. And other patients are distressed by the disruption and commotion.”

Absent standards and systems to address SUD, providers felt they were “left to their own,” resulting in a reactive and chaotic care environment.

Providers noted inconsistent rules and policies regarding smoke breaks, room searches, and visitors. As a result, care felt “reckless and risky” and led to a “nonalliance” across disciplines. Providers frequently described inconsistent and loose expectations until an event -- often active use – triggered an ad hoc ratcheting up of the rules, damaging patient-provider relationships and limiting providers’ ability to provide medical care. Facing these conflicts, “staff gets escalated, and everybody gets kind of spun up.” As one attending reflected:

“I could not get any sort of engagement even in just her medical issues … I was trying to talk to her and educate her about heart failure and salt intake and food intake, but every time I walked in the room … I’d have to come in and be like, ‘your UDS [urine drug screen] was positive again, so here’s the changes to your behavioral plan, and OK, let’s talk about your heart failure …’ At that point, the relationship had completely disintegrated until it was very nonproductive.”

Providers described widespread “moral distress,” burnout, and feelings of futility before IMPACT.

Consequently, providers felt that caring for people with SUD was “very emotionally draining and very time consuming.” As one patient advocate described:

“We’ve been watching staff try to manage these patients for years without the experts and the resources and the skills that they need … As a result, there was a crescendo effect of moral distress, and [staff] bring in all of their past experiences which influence the interaction … Some staff are very skilled, but you also saw some really punitive responses.”

Many felt that providing intensive medical care without addressing people’s underlying SUD was a waste of time and resources. As one cardiac surgeon reflected:

“[Patients] ended up either dead or reinfected. Nobody wanted to do stuff because we felt it was futile. Well, of course, it’s futile …. you’re basically trying to fix the symptoms. It’s like having a leaky roof and just running around with a bunch of buckets, which is like surgery. You gotta fix the roof…otherwise they will continue to inject bacteria into their bodies.”

Care with IMPACT:

Providers felt integrating hospital-based systems to address SUD legitimized addiction as a treatable disease.

Participants described IMPACT as a “sea change” that “completely reframes” addiction as “a medical condition that actually has a treatment.” As one social worker observed, “when it’s somebody in a white coat with expertise who’s talking to another doctor it really can shift mindsets in an amazing way.” Others echoed this, stating that an addiction team “legitimized the fact that this is an actual disease that we need to treat - and a failure to treat it is a failure to be a good doctor.”

Providers felt that by addressing addiction directly, “IMPACT elevated the consciousness of providers and nurses … that substance use disorders are brain disorders and not bad behavior.” They described that this legitimization, combined with seeing firsthand the stabilizing effects of medications for addiction, allowed providers to understand SUD as a chronic disease, and not a moral failing.

Providers felt IMPACT improved patient engagement and humanized care by treating withdrawal, directly communicating about SUD, and modeling compassionate care.

Providers noted that treating withdrawal had a dramatic effect on patient engagement and care. One surgeon explained, “by managing their opioid dependence and other substance abuse issues … it’s easier for the staff to take care of them, it’s safer, and the patients feel better taken care of because the staff will engage with them.” Many noted that conflict-ridden “conversations were able to go to the side, and we were able to talk about other things to build rapport.” Others noted that this shift felt like “more productive time.”

In addition, providers repeatedly emphasized that having clear hospital standards and a process to engage patients “really helps … establish rapport with patients: ‘This is how we work this. These are your boundaries. And this is what will happen if you push those boundaries.’ There it is.” Providers attributed improved patient-provider communication to “frank conversation,” “the right amount of empathy,” and a less judgmental environment. As one attending described, “I don’t know if it gives them a voice or allows us to hear them better … but something’s happening with communication.”

Many participants highlighted that IMPACT modeled compassionate bedside interactions, exposed the role of trauma in many patients’ lives, and helped providers see SUD as a disease spectrum. One attending noted that to “actually appreciate the subtleties – just the severity of the disorder – has been powerful.” One resident said:

“There’s definitely a lot of stigma around patients with use disorders that probably shows itself in subtle ways throughout their hospitalization. I think IMPACT does a good job … keeping the patient in the center and keeping their use disorder contextualized in the greater person … [IMPACT] role models bedside interactions and how to treat people like humans.”

Providers valued post-hospital SUD treatment pathways.

Providers valued previously nonexistent post-hospital SUD treatment pathways, stating “this relationship with [community treatment] … it’s like an answer to prayers,” and “this isn’t just like we’re being nicer.” One attending described:

“Starting them on [methadone or buprenorphine-naloxone] and then making the next step in the outpatient world happen has been huge. That transition is so critical … that’s been probably the biggest impact.”

Providers felt relief after IMPACT implementation.

Providers felt that by addressing SUD treatment gaps and providing addiction expertise, IMPACT helped alleviate the previously widespread feelings of “moral distress.” One resident explained “having [IMPACT] as a lifeline, it just feels so good.” As an infectious disease consultant noted, “it makes people more open to treating people if they don’t feel isolated and out of their depth.” Others noted that IMPACT supported better multidisciplinary collaboration, which “reduced a lot of tension between the teams.” One nurse summarized:

“I think you feel more empowered when you’ve got the right medication, … the knowledge, and you feel like you have the resources. You actually feel like you’re making a difference.”

Respondents acknowledged that even with IMPACT, some patients leave AMA or relapse. However, by understanding addiction as a relapsing and remitting disease, providers reconceptualized “success,” further reducing feelings of emotional burnout and stress: “there will be ups and downs, it’s not gonna be a straight linear success.” One case manager reflected,

“Maybe that’s part of the nature of the illness, you progress, and then you kind of hold your breath and then it slips again … at least with IMPACT at the table I can say we’ve done the best we can for this person.”

Perceived limitations of IMPACT:

Providers noted several key limitations of IMPACT, including that hospital-based interventions do not address poverty and have limited ability to address socioeconomic determinants such as “social support, … housing, or nutrition.” Providers also felt that IMPACT had limited ability to alleviate patients’ feelings of boredom and isolation associated with prolonged hospitalization, and that IMPACT had limited effectiveness for highly ambivalent patients (Table 2).

Finally, while many described increased confidence managing SUD after working with IMPACT, others cautioned against deferring too much to specialists. As one resident doctor said:

“We shouldn’t forget that all providers should know how to handle some form of people with addiction … I just don’t want it to be like, ‘oh, well, no, I don’t need to think about this … because we have an addiction specialist.’”

Participants across disciplines repeatedly suggested formal, ongoing initiatives to educate and train providers to manage SUD independently.

This study explores provider perspectives on care for hospitalized adults with SUD. Before IMPACT, providers felt care was chaotic, unsafe, and frustrating. Providers perceived variable care quality, resulting in untreated withdrawal, inconsistent care plans, and poor patient outcomes, leading to widespread “moral distress” and feelings of futility among providers. Yet this experience was not inevitable. Providers described that a hospital-based intervention to treat SUD reframed addiction as a treatable chronic disease, transformed culture, and improved patient care and provider experience.

Our findings are consistent with and build on previous research in several ways. First, widespread anxiety and difficulty managing patients with SUD was not unique to our hospital. In a systematic review, van Boekel and colleagues describe that healthcare providers perceived violence, manipulation, and poor motivation as factors impeding care for patients with SUD.⁹ Our study demonstrates the resulting feelings of powerlessness and frustration may be alleviated through an intervention that provides SUD care.

Second, our study is consistent with a recent survey-based study by Wakeman and colleagues that found that a hospital-based SUD intervention improved providers’ feelings of preparedness and satisfaction.²⁰ Our study provides a rich qualitative description and elucidates mechanisms by which such interventions may work.

The finding that a hospital-based SUD intervention can shift providers’ views of addiction is important. Earlier studies have shown that providers who perceive addiction as a choice are more likely to have negative attitudes toward people with SUD.¹¹ While our intervention did not provide formal education aimed at changing attitudes, participants reported that seeing firsthand effects of treatment on patient behaviors was a powerful tool that radically shifted providers’ understanding and reduced stigma.

Stigma can occur at both individual and organizational levels. Structural stigma refers to practices, policies, and norms of institutions that exclude needs of a particular group.²¹ The absence of systems to address SUD sends a message to both patients and providers that addiction is a not a treatable or worthy disease. IMPACT was in and of itself a systems-level intervention; by creating a consultation service, hospital-wide policies, and pathways to care after hospitalization, IMPACT ‘legitimized’ SUD and reduced institutional stigma.

Several studies have shown the feasibility and effectiveness of starting medications for addiction (MAT) in the hospital.^13-15 Our study builds on this work by highlighting systems-level elements valued by providers. These elements may be important to support and scale widespread adoption of MAT in hospitals. Specifically, providers felt that IMPACT’s attention to hospital policies, use of addiction medicine specialists, and direct linkages to outpatient SUD treatment proved instrumental in shifting care systems.

Our study has several limitations. As a single-site study, our goal was not generalizability, but transferability. As such, we aimed to obtain rich, in-depth information that can inform implementation of similar efforts. Because our study was conducted after the implementation of IMPACT, providers’ perspectives on care before IMPACT may have been influenced by the intervention. However, this also strengthens our findings by allowing participants the opportunity for insights under a different system. It likely leads to distinct findings compared to what we might have uncovered in a pre-post study. While respondents noted perceived limitations of IMPACT, there were few instances of negative remarks in the data we collected. It is possible that providers with more negative interpretations chose not to participate in interviews; however, we elicited wide viewpoints and encouraged participants to share both strengths and weaknesses. Finally, IMPACT implementation depends on regional as well as local factors such as Medicaid expansion, community treatment resources, and the existence of addiction medicine expertise that will differ across settings.

Despite these limitations, our study has several important implications. For clinical practice, our findings highlight the importance of treating withdrawal to address challenging patient behaviors and the value of integrating MAT into the hospital setting. Our findings also underscore the role of expert consultation for addiction. Importantly, our results emphasize that reframing SUD as a brain disease can have significant implications for clinical care and providers’ well-being. Provider distress is not inevitable and can change with the right support and systems.

At the hospital and health systems level, our findings suggest that hospitals can and should address SUD. This may include forming interprofessional teams with SUD expertise, providing standardized guidelines for addiction care such as patient safety plans and methadone policies, and creating rapid-access pathways to outpatient SUD care. By addressing SUD, hospitals may simultaneously improve care and reduce provider burnout. Providers’ important concerns about shifting SUD treatment to a specialty team and their discomfort managing SUD pre-IMPACT suggest the need to integrate SUD education across all levels of interprofessional education. Furthermore, provider concerns that IMPACT has limited ability to engage ambivalent patients underscores the need for hospital-based approaches that emphasize harm reduction strategies.

As the SUD epidemic worsens, SUD-related hospitalizations are skyrocketing, and people are dying at unprecedented rates.^2,3 Many efforts to address SUD have been in primary care or community settings. While important, many people with SUD are unable or unlikely to seek primary care. ²² Hospitals need a workforce and systems that can address both the physical and behavioral health needs of this population. By implementing SUD improvements, hospitals can support staff and reduce burnout, better engage patients, improve care, and reduce stigma from this devastating disease.

Disclosures

The authors have no conflicts of interest to disclose.

Hall et al. draw attention to the important question of whether some patients may benefit from a naloxone prescription when discharged from the hospital with a short-term opioid prescription for acute pain. Although all members of the working group agreed that naloxone is appropriate in some cases, we were hesitant to recommend this as a standard practice for several reasons.

First, the intent of our Consensus Statement¹ was to synthesize and summarize the areas of consensus in existing guidelines; none of the existing guidelines included in our systematic review make a recommendation for naloxone prescription in the setting of short-term opioid use for acute pain.² We believe that this may relate to the fact that the risk factors for overdose and the threshold of risk above which naloxone would be beneficial have yet to be defined for this population and are likely to differ from those defined in patients using opioids chronically.

Additionally, if practitioners follow the recommendations to limit prescribing for acute pain to the minimum dose and duration of an opioid that was presumably administered in the hospital with an observed response, then the risk of overdose and the potential benefit of naloxone will decrease. Furthermore, emerging data from randomized controlled trials demonstrating noninferiority of nonopioid analgesics in the management of acute pain suggest that we should not so readily presume opioids to be the necessary or the best option.^3-5 Data questioning the benefits of opioids over other safer therapies have particularly important implications for patients in whom the risks are felt to be high enough to warrant consideration of naloxone.

Disclosures

Dr. Herzig reports receiving compensation from the Society of Hospital Medicine for her editorial role in the Journal of Hospital Medicine (unrelated to the present work). None of the other authors have any conflicts of interest to disclose.

Funding

Dr. Herzig is funded by a grant number K23AG042459 from the National Institute on Aging. Dr. Mosher is supported in part by the Department of Veterans Affairs Office of Academic Affiliations and the Office of Research and Development and Health Services Research and Development Service (HSR&D) through the Comprehensive Access and Delivery Research and Evaluation Center (CIN 13-412). The views expressed in this manuscript do not necessarily represent the views of the funding agencies.

Hall et al. draw attention to the important question of whether some patients may benefit from a naloxone prescription when discharged from the hospital with a short-term opioid prescription for acute pain. Although all members of the working group agreed that naloxone is appropriate in some cases, we were hesitant to recommend this as a standard practice for several reasons.

First, the intent of our Consensus Statement¹ was to synthesize and summarize the areas of consensus in existing guidelines; none of the existing guidelines included in our systematic review make a recommendation for naloxone prescription in the setting of short-term opioid use for acute pain.² We believe that this may relate to the fact that the risk factors for overdose and the threshold of risk above which naloxone would be beneficial have yet to be defined for this population and are likely to differ from those defined in patients using opioids chronically.

Additionally, if practitioners follow the recommendations to limit prescribing for acute pain to the minimum dose and duration of an opioid that was presumably administered in the hospital with an observed response, then the risk of overdose and the potential benefit of naloxone will decrease. Furthermore, emerging data from randomized controlled trials demonstrating noninferiority of nonopioid analgesics in the management of acute pain suggest that we should not so readily presume opioids to be the necessary or the best option.^3-5 Data questioning the benefits of opioids over other safer therapies have particularly important implications for patients in whom the risks are felt to be high enough to warrant consideration of naloxone.

Disclosures

Dr. Herzig reports receiving compensation from the Society of Hospital Medicine for her editorial role in the Journal of Hospital Medicine (unrelated to the present work). None of the other authors have any conflicts of interest to disclose.

Funding

Dr. Herzig is funded by a grant number K23AG042459 from the National Institute on Aging. Dr. Mosher is supported in part by the Department of Veterans Affairs Office of Academic Affiliations and the Office of Research and Development and Health Services Research and Development Service (HSR&D) through the Comprehensive Access and Delivery Research and Evaluation Center (CIN 13-412). The views expressed in this manuscript do not necessarily represent the views of the funding agencies.

Hall et al. draw attention to the important question of whether some patients may benefit from a naloxone prescription when discharged from the hospital with a short-term opioid prescription for acute pain. Although all members of the working group agreed that naloxone is appropriate in some cases, we were hesitant to recommend this as a standard practice for several reasons.

First, the intent of our Consensus Statement¹ was to synthesize and summarize the areas of consensus in existing guidelines; none of the existing guidelines included in our systematic review make a recommendation for naloxone prescription in the setting of short-term opioid use for acute pain.² We believe that this may relate to the fact that the risk factors for overdose and the threshold of risk above which naloxone would be beneficial have yet to be defined for this population and are likely to differ from those defined in patients using opioids chronically.

Additionally, if practitioners follow the recommendations to limit prescribing for acute pain to the minimum dose and duration of an opioid that was presumably administered in the hospital with an observed response, then the risk of overdose and the potential benefit of naloxone will decrease. Furthermore, emerging data from randomized controlled trials demonstrating noninferiority of nonopioid analgesics in the management of acute pain suggest that we should not so readily presume opioids to be the necessary or the best option.^3-5 Data questioning the benefits of opioids over other safer therapies have particularly important implications for patients in whom the risks are felt to be high enough to warrant consideration of naloxone.

Disclosures

Dr. Herzig reports receiving compensation from the Society of Hospital Medicine for her editorial role in the Journal of Hospital Medicine (unrelated to the present work). None of the other authors have any conflicts of interest to disclose.

Funding

Dr. Herzig is funded by a grant number K23AG042459 from the National Institute on Aging. Dr. Mosher is supported in part by the Department of Veterans Affairs Office of Academic Affiliations and the Office of Research and Development and Health Services Research and Development Service (HSR&D) through the Comprehensive Access and Delivery Research and Evaluation Center (CIN 13-412). The views expressed in this manuscript do not necessarily represent the views of the funding agencies.

We read with great interest the consensus statement on improving the safety of opioid use for acute noncancer pain by Herzig et al.¹ We strongly support the recommendations outlined in the document.

However, we would like to advocate for an additional recommendation that was considered but not included by the authors. Given the proven benefit—with minimal risk—in providing naloxone to patients and family members, we encourage naloxone prescriptions at discharge for all patients at risk for opioid overdose independent of therapy duration.² Even opioid-naive patients who are prescribed opioids at hospital discharge have a significantly higher risk for chronic opioid use.³

We support extrapolating recommendations from the Centers for Disease Control and Prevention and Substance Abuse and Mental Health Services Administration to prescribe naloxone to all patients at discharge who are at risk for an opioid overdose, including those with a history of overdose or substance use disorder as well as those receiving a prescription of ≥50 mg morphine equivalents per day or who use opioids and benzodiazepines.^4,5

Given the current barriers to healthcare access, prescribing naloxone at discharge may be a rare opportunity to provide a potential life-saving intervention to prevent a fatal opioid overdose.

Disclosures

We have no relevant conflicts of interest to report. No payment or services from a third party were received for any aspect of this submitted work. We have no financial relationships with entities in the biomedical arena that could be perceived to influence, or that give the appearance of potentially influencing, what was written in this submitted work.

We read with great interest the consensus statement on improving the safety of opioid use for acute noncancer pain by Herzig et al.¹ We strongly support the recommendations outlined in the document.

However, we would like to advocate for an additional recommendation that was considered but not included by the authors. Given the proven benefit—with minimal risk—in providing naloxone to patients and family members, we encourage naloxone prescriptions at discharge for all patients at risk for opioid overdose independent of therapy duration.² Even opioid-naive patients who are prescribed opioids at hospital discharge have a significantly higher risk for chronic opioid use.³

We support extrapolating recommendations from the Centers for Disease Control and Prevention and Substance Abuse and Mental Health Services Administration to prescribe naloxone to all patients at discharge who are at risk for an opioid overdose, including those with a history of overdose or substance use disorder as well as those receiving a prescription of ≥50 mg morphine equivalents per day or who use opioids and benzodiazepines.^4,5

Given the current barriers to healthcare access, prescribing naloxone at discharge may be a rare opportunity to provide a potential life-saving intervention to prevent a fatal opioid overdose.

Disclosures

We have no relevant conflicts of interest to report. No payment or services from a third party were received for any aspect of this submitted work. We have no financial relationships with entities in the biomedical arena that could be perceived to influence, or that give the appearance of potentially influencing, what was written in this submitted work.

We read with great interest the consensus statement on improving the safety of opioid use for acute noncancer pain by Herzig et al.¹ We strongly support the recommendations outlined in the document.

However, we would like to advocate for an additional recommendation that was considered but not included by the authors. Given the proven benefit—with minimal risk—in providing naloxone to patients and family members, we encourage naloxone prescriptions at discharge for all patients at risk for opioid overdose independent of therapy duration.² Even opioid-naive patients who are prescribed opioids at hospital discharge have a significantly higher risk for chronic opioid use.³

We support extrapolating recommendations from the Centers for Disease Control and Prevention and Substance Abuse and Mental Health Services Administration to prescribe naloxone to all patients at discharge who are at risk for an opioid overdose, including those with a history of overdose or substance use disorder as well as those receiving a prescription of ≥50 mg morphine equivalents per day or who use opioids and benzodiazepines.^4,5

Given the current barriers to healthcare access, prescribing naloxone at discharge may be a rare opportunity to provide a potential life-saving intervention to prevent a fatal opioid overdose.

Disclosures

We have no relevant conflicts of interest to report. No payment or services from a third party were received for any aspect of this submitted work. We have no financial relationships with entities in the biomedical arena that could be perceived to influence, or that give the appearance of potentially influencing, what was written in this submitted work.

On September 12, 2017, a son walked into his mother’s room in the surgical intensive care unit (ICU) of Dartmouth-Hitchcock Medical Center (DHMC) in Lebanon, New Hampshire, and shot her with a handgun. As an actively practicing hospitalist and the Chief Clinical Officer for DHMC, I immediately became involved with our hospitals’ response to domestic violence, a homicide, and an issue that to this point we felt lived outside our walls.

Several hospital systems are struggling with violence entering their institutions, particularly in their psychiatry and emergency service areas, fueled in part by untreated mental health and the rising opioid epidemic. Although gun violence in hospitals is indeed rare, inside the hospital, it occurs often in the emergency department.¹ In New Hampshire, we suffer from a woefully underfunded state mental health infrastructure and one of the highest opioid death rates in the United States.²

DHMC is a 400-bed academic medical center, level 1 trauma center, and a National Cancer Institute (NCI)-designated cancer center that serves New Hampshire and eastern Vermont with its community and critical access hospitals and community group practices across the two states. With a wide geographic catchment area, our academic hospital at DHMC has one of the highest case-mix indices in the northeastern United States and is in the top 30 among hospitals of >300 beds in the United States.

After the shooting, the patient’s son left the ICU without targeting anyone else, and despite video surveillance systems, he was not seen leaving the hospital. At the same time, a Code Blue was called to address the victim and her needs. The Critical Care staff struggled to attend to and resuscitate the victim, and my Medicine team, on call that day, was paged and rushed to the ICU to assist. In a unit trained to manage the sequelae of trauma, this event was painfully surreal. Ultimately, the surgical critical-care physician, attending to the patient, ended the resuscitation efforts when it was clear that the patient, now a homicide victim, could not be saved.

With the shooter’s whereabouts unknown, a Code Silver (Active Shooter alert) was called. Then, following our “Run-Hide-Fight” training protocol, staff, patients, and visitors exited the building in large numbers and those that could not, sheltered in place. The operating room and the emergency department were secured and continued to function.

More than 160 law enforcement officers, including trained tactical and SWAT teams, from 13 different agencies arrived on scene. Ninety minutes after the shooting, the son was apprehended at a police traffic checkpoint, attempting to leave the hospital campus.

Our involvement in this event did not end at this point. Concerned about the possibility of other suspects or devices left in the hospital, the law enforcement officers swept our hospital. With a 1.2 million square foot campus, this would take another two hours, during which we still provided care to our patients and asked the staff and families to continue to seek safe shelter.

The shock of this terrible day was immediate and profound, leading to a thorough debrief and systematic analysis of how we might improve our processes and in turn help other organizations that might unfortunately face similar situations.

We reflected on how to better secure our hospital and to strengthen our coordination and collaboration with law enforcement. We increased our security presence not only in the ICU but also in our emergency department and developed individual unit-based security measures. We fast-tracked a unit-based shutdown plan that was already in process and increased our commitments to plan and drill for larger scenarios in conjunction with law enforcement agencies.

The physical location of our hospital was important in how our response unfolded. DHMC’s unique rural location in northern New England added challenges specific to our location, which may provide an opportunity for other hospitals to consider. Although we were able to provide care, water, and transport during this tragedy on a warm day in September, caring for thousands of people outside a hospital during a typical subzero February would be a different story.

Communication during the event and how specifically to ask people to act were identified as a key area of improvement. We realized that our language and training around the various codes lacked clarity and specificity. As is familiar to many, in our hospital with Red, Blue, Black, Purple, and White codes, some staff (and certainly families and visitors) were not sure what to do in a “Code Silver.” We worked to better define our language so that in a future event or in a drill, we would state in plain language that we have “an active shooter” or a “violence with weapons” event in progress with clear instructions on next steps. Our term “Run-Hide-Fight” was changed to “Avoid-Hide-Fight” to better reflect updated training and best practice for a future event. We revised our teaching and training materials and protocols, so that in the event of a similar situation, we could provide information in plain language, across numerous formats, and with some frequency to keep people apprised, even if the situation is not changing.

Our methods of ongoing communications were also reassessed. In our reviews, it became clear that the notification systems and the computer-based alerts seen on the computers of hospital staff were different from those at the medical school. Communication protocols on pagers and mobile phones and across social media such as Facebook and Twitter were redesigned. Though our institution has long had the ability to provide cell phone notifications during emergencies, not all employees and staff had elected to activate this feature. We also improved our speaker systems so that overhead paging and alerts could be heard outside the building.

Having improved personal reference materials on hand is important. We updated the cards attached to our ID badges with clear instructions about “active shooter” or “violence with weapon” situations. We also developed different response scenarios dependent on the campus location. An event in the ICU, for example, might require leaving the scene, although sheltering-in-place might be more appropriate for an offsite administrative building.

A significant challenge to our active-shooter situation was making sure that our staff, patients, visitors, and their families were adequately supported following the event. Learning from the experiences of other hospitals and communities, we undertook a deliberate process of preparedness and healing.³ From our surgical ICU to our distant community group practices, we provided communication and avenues for personal support. Our Employee Assistance Program provided 24/7 support in a conference room in the surgical ICU and in other areas, on and off site, for all staff at Dartmouth-Hitchcock. The shooting affected those in the vicinity, as well as far away. Staff who had experienced domestic and other violence in their past were impacted in ways that required special care and attention. Some who were in adjacent rooms during the event were able to return to work immediately, whereas other staff, in separate units and more distant clinics, struggled and required leaves of absence. Through this event, we witnessed the personal and deep psychological impact of such violence. We held town halls, updated daily communications from our Incident Command Team, and maintained an open dialog across the organization.

In reflection, it is challenging to face this experience without the greater context of what we unfortunately experience all too often in America today. We have seen the spectrum from the shootings at Marjory Stoneman Douglas High School in Parkland, Florida, to the isolated events that rarely reach our national news and collective consciousness. It seems that we have already experienced a shooting at a school every week in the US.

There is even an overlap seen in domestic and mass shootings as we saw in the Sandy Hook Elementary School shootings in 2012, in which the tragic event was preceded by the shooter murdering his mother in her home.⁴ Today, in the US, women are disproportionally the subject of domestic violence, and more than half of all killed are done so by a male family member. The presence of a gun in domestic violence situations increases the risk for homicide for women by 500%.^{5- 7} Our experience indeed mirrored this reality.

Many readers of this piece will recognize how similar their situation is to that of our hospital, that this happens elsewhere, not here. Although my institution has faced this as a tragedy that has tested our organization, one cannot also be deeply troubled by the greater impact of domestic and gun violence on healthcare and the American society today. Our staff and physicians have been witness and at times subject to such violence, and this experience has now made it even more poignant. Ultimately, and sadly, we feel that we are more prepared.

Disclosures

The author has nothing to disclose.

On September 12, 2017, a son walked into his mother’s room in the surgical intensive care unit (ICU) of Dartmouth-Hitchcock Medical Center (DHMC) in Lebanon, New Hampshire, and shot her with a handgun. As an actively practicing hospitalist and the Chief Clinical Officer for DHMC, I immediately became involved with our hospitals’ response to domestic violence, a homicide, and an issue that to this point we felt lived outside our walls.

Several hospital systems are struggling with violence entering their institutions, particularly in their psychiatry and emergency service areas, fueled in part by untreated mental health and the rising opioid epidemic. Although gun violence in hospitals is indeed rare, inside the hospital, it occurs often in the emergency department.¹ In New Hampshire, we suffer from a woefully underfunded state mental health infrastructure and one of the highest opioid death rates in the United States.²

DHMC is a 400-bed academic medical center, level 1 trauma center, and a National Cancer Institute (NCI)-designated cancer center that serves New Hampshire and eastern Vermont with its community and critical access hospitals and community group practices across the two states. With a wide geographic catchment area, our academic hospital at DHMC has one of the highest case-mix indices in the northeastern United States and is in the top 30 among hospitals of >300 beds in the United States.

After the shooting, the patient’s son left the ICU without targeting anyone else, and despite video surveillance systems, he was not seen leaving the hospital. At the same time, a Code Blue was called to address the victim and her needs. The Critical Care staff struggled to attend to and resuscitate the victim, and my Medicine team, on call that day, was paged and rushed to the ICU to assist. In a unit trained to manage the sequelae of trauma, this event was painfully surreal. Ultimately, the surgical critical-care physician, attending to the patient, ended the resuscitation efforts when it was clear that the patient, now a homicide victim, could not be saved.

With the shooter’s whereabouts unknown, a Code Silver (Active Shooter alert) was called. Then, following our “Run-Hide-Fight” training protocol, staff, patients, and visitors exited the building in large numbers and those that could not, sheltered in place. The operating room and the emergency department were secured and continued to function.

More than 160 law enforcement officers, including trained tactical and SWAT teams, from 13 different agencies arrived on scene. Ninety minutes after the shooting, the son was apprehended at a police traffic checkpoint, attempting to leave the hospital campus.

Our involvement in this event did not end at this point. Concerned about the possibility of other suspects or devices left in the hospital, the law enforcement officers swept our hospital. With a 1.2 million square foot campus, this would take another two hours, during which we still provided care to our patients and asked the staff and families to continue to seek safe shelter.

The shock of this terrible day was immediate and profound, leading to a thorough debrief and systematic analysis of how we might improve our processes and in turn help other organizations that might unfortunately face similar situations.

We reflected on how to better secure our hospital and to strengthen our coordination and collaboration with law enforcement. We increased our security presence not only in the ICU but also in our emergency department and developed individual unit-based security measures. We fast-tracked a unit-based shutdown plan that was already in process and increased our commitments to plan and drill for larger scenarios in conjunction with law enforcement agencies.

The physical location of our hospital was important in how our response unfolded. DHMC’s unique rural location in northern New England added challenges specific to our location, which may provide an opportunity for other hospitals to consider. Although we were able to provide care, water, and transport during this tragedy on a warm day in September, caring for thousands of people outside a hospital during a typical subzero February would be a different story.

Communication during the event and how specifically to ask people to act were identified as a key area of improvement. We realized that our language and training around the various codes lacked clarity and specificity. As is familiar to many, in our hospital with Red, Blue, Black, Purple, and White codes, some staff (and certainly families and visitors) were not sure what to do in a “Code Silver.” We worked to better define our language so that in a future event or in a drill, we would state in plain language that we have “an active shooter” or a “violence with weapons” event in progress with clear instructions on next steps. Our term “Run-Hide-Fight” was changed to “Avoid-Hide-Fight” to better reflect updated training and best practice for a future event. We revised our teaching and training materials and protocols, so that in the event of a similar situation, we could provide information in plain language, across numerous formats, and with some frequency to keep people apprised, even if the situation is not changing.

Our methods of ongoing communications were also reassessed. In our reviews, it became clear that the notification systems and the computer-based alerts seen on the computers of hospital staff were different from those at the medical school. Communication protocols on pagers and mobile phones and across social media such as Facebook and Twitter were redesigned. Though our institution has long had the ability to provide cell phone notifications during emergencies, not all employees and staff had elected to activate this feature. We also improved our speaker systems so that overhead paging and alerts could be heard outside the building.

Having improved personal reference materials on hand is important. We updated the cards attached to our ID badges with clear instructions about “active shooter” or “violence with weapon” situations. We also developed different response scenarios dependent on the campus location. An event in the ICU, for example, might require leaving the scene, although sheltering-in-place might be more appropriate for an offsite administrative building.

A significant challenge to our active-shooter situation was making sure that our staff, patients, visitors, and their families were adequately supported following the event. Learning from the experiences of other hospitals and communities, we undertook a deliberate process of preparedness and healing.³ From our surgical ICU to our distant community group practices, we provided communication and avenues for personal support. Our Employee Assistance Program provided 24/7 support in a conference room in the surgical ICU and in other areas, on and off site, for all staff at Dartmouth-Hitchcock. The shooting affected those in the vicinity, as well as far away. Staff who had experienced domestic and other violence in their past were impacted in ways that required special care and attention. Some who were in adjacent rooms during the event were able to return to work immediately, whereas other staff, in separate units and more distant clinics, struggled and required leaves of absence. Through this event, we witnessed the personal and deep psychological impact of such violence. We held town halls, updated daily communications from our Incident Command Team, and maintained an open dialog across the organization.

In reflection, it is challenging to face this experience without the greater context of what we unfortunately experience all too often in America today. We have seen the spectrum from the shootings at Marjory Stoneman Douglas High School in Parkland, Florida, to the isolated events that rarely reach our national news and collective consciousness. It seems that we have already experienced a shooting at a school every week in the US.

There is even an overlap seen in domestic and mass shootings as we saw in the Sandy Hook Elementary School shootings in 2012, in which the tragic event was preceded by the shooter murdering his mother in her home.⁴ Today, in the US, women are disproportionally the subject of domestic violence, and more than half of all killed are done so by a male family member. The presence of a gun in domestic violence situations increases the risk for homicide for women by 500%.^{5- 7} Our experience indeed mirrored this reality.

Many readers of this piece will recognize how similar their situation is to that of our hospital, that this happens elsewhere, not here. Although my institution has faced this as a tragedy that has tested our organization, one cannot also be deeply troubled by the greater impact of domestic and gun violence on healthcare and the American society today. Our staff and physicians have been witness and at times subject to such violence, and this experience has now made it even more poignant. Ultimately, and sadly, we feel that we are more prepared.

Disclosures

The author has nothing to disclose.

On September 12, 2017, a son walked into his mother’s room in the surgical intensive care unit (ICU) of Dartmouth-Hitchcock Medical Center (DHMC) in Lebanon, New Hampshire, and shot her with a handgun. As an actively practicing hospitalist and the Chief Clinical Officer for DHMC, I immediately became involved with our hospitals’ response to domestic violence, a homicide, and an issue that to this point we felt lived outside our walls.

Several hospital systems are struggling with violence entering their institutions, particularly in their psychiatry and emergency service areas, fueled in part by untreated mental health and the rising opioid epidemic. Although gun violence in hospitals is indeed rare, inside the hospital, it occurs often in the emergency department.¹ In New Hampshire, we suffer from a woefully underfunded state mental health infrastructure and one of the highest opioid death rates in the United States.²

DHMC is a 400-bed academic medical center, level 1 trauma center, and a National Cancer Institute (NCI)-designated cancer center that serves New Hampshire and eastern Vermont with its community and critical access hospitals and community group practices across the two states. With a wide geographic catchment area, our academic hospital at DHMC has one of the highest case-mix indices in the northeastern United States and is in the top 30 among hospitals of >300 beds in the United States.

After the shooting, the patient’s son left the ICU without targeting anyone else, and despite video surveillance systems, he was not seen leaving the hospital. At the same time, a Code Blue was called to address the victim and her needs. The Critical Care staff struggled to attend to and resuscitate the victim, and my Medicine team, on call that day, was paged and rushed to the ICU to assist. In a unit trained to manage the sequelae of trauma, this event was painfully surreal. Ultimately, the surgical critical-care physician, attending to the patient, ended the resuscitation efforts when it was clear that the patient, now a homicide victim, could not be saved.

With the shooter’s whereabouts unknown, a Code Silver (Active Shooter alert) was called. Then, following our “Run-Hide-Fight” training protocol, staff, patients, and visitors exited the building in large numbers and those that could not, sheltered in place. The operating room and the emergency department were secured and continued to function.

More than 160 law enforcement officers, including trained tactical and SWAT teams, from 13 different agencies arrived on scene. Ninety minutes after the shooting, the son was apprehended at a police traffic checkpoint, attempting to leave the hospital campus.

Our involvement in this event did not end at this point. Concerned about the possibility of other suspects or devices left in the hospital, the law enforcement officers swept our hospital. With a 1.2 million square foot campus, this would take another two hours, during which we still provided care to our patients and asked the staff and families to continue to seek safe shelter.

The shock of this terrible day was immediate and profound, leading to a thorough debrief and systematic analysis of how we might improve our processes and in turn help other organizations that might unfortunately face similar situations.

We reflected on how to better secure our hospital and to strengthen our coordination and collaboration with law enforcement. We increased our security presence not only in the ICU but also in our emergency department and developed individual unit-based security measures. We fast-tracked a unit-based shutdown plan that was already in process and increased our commitments to plan and drill for larger scenarios in conjunction with law enforcement agencies.

The physical location of our hospital was important in how our response unfolded. DHMC’s unique rural location in northern New England added challenges specific to our location, which may provide an opportunity for other hospitals to consider. Although we were able to provide care, water, and transport during this tragedy on a warm day in September, caring for thousands of people outside a hospital during a typical subzero February would be a different story.

Communication during the event and how specifically to ask people to act were identified as a key area of improvement. We realized that our language and training around the various codes lacked clarity and specificity. As is familiar to many, in our hospital with Red, Blue, Black, Purple, and White codes, some staff (and certainly families and visitors) were not sure what to do in a “Code Silver.” We worked to better define our language so that in a future event or in a drill, we would state in plain language that we have “an active shooter” or a “violence with weapons” event in progress with clear instructions on next steps. Our term “Run-Hide-Fight” was changed to “Avoid-Hide-Fight” to better reflect updated training and best practice for a future event. We revised our teaching and training materials and protocols, so that in the event of a similar situation, we could provide information in plain language, across numerous formats, and with some frequency to keep people apprised, even if the situation is not changing.

Our methods of ongoing communications were also reassessed. In our reviews, it became clear that the notification systems and the computer-based alerts seen on the computers of hospital staff were different from those at the medical school. Communication protocols on pagers and mobile phones and across social media such as Facebook and Twitter were redesigned. Though our institution has long had the ability to provide cell phone notifications during emergencies, not all employees and staff had elected to activate this feature. We also improved our speaker systems so that overhead paging and alerts could be heard outside the building.

Having improved personal reference materials on hand is important. We updated the cards attached to our ID badges with clear instructions about “active shooter” or “violence with weapon” situations. We also developed different response scenarios dependent on the campus location. An event in the ICU, for example, might require leaving the scene, although sheltering-in-place might be more appropriate for an offsite administrative building.

A significant challenge to our active-shooter situation was making sure that our staff, patients, visitors, and their families were adequately supported following the event. Learning from the experiences of other hospitals and communities, we undertook a deliberate process of preparedness and healing.³ From our surgical ICU to our distant community group practices, we provided communication and avenues for personal support. Our Employee Assistance Program provided 24/7 support in a conference room in the surgical ICU and in other areas, on and off site, for all staff at Dartmouth-Hitchcock. The shooting affected those in the vicinity, as well as far away. Staff who had experienced domestic and other violence in their past were impacted in ways that required special care and attention. Some who were in adjacent rooms during the event were able to return to work immediately, whereas other staff, in separate units and more distant clinics, struggled and required leaves of absence. Through this event, we witnessed the personal and deep psychological impact of such violence. We held town halls, updated daily communications from our Incident Command Team, and maintained an open dialog across the organization.

In reflection, it is challenging to face this experience without the greater context of what we unfortunately experience all too often in America today. We have seen the spectrum from the shootings at Marjory Stoneman Douglas High School in Parkland, Florida, to the isolated events that rarely reach our national news and collective consciousness. It seems that we have already experienced a shooting at a school every week in the US.

There is even an overlap seen in domestic and mass shootings as we saw in the Sandy Hook Elementary School shootings in 2012, in which the tragic event was preceded by the shooter murdering his mother in her home.⁴ Today, in the US, women are disproportionally the subject of domestic violence, and more than half of all killed are done so by a male family member. The presence of a gun in domestic violence situations increases the risk for homicide for women by 500%.^{5- 7} Our experience indeed mirrored this reality.

Many readers of this piece will recognize how similar their situation is to that of our hospital, that this happens elsewhere, not here. Although my institution has faced this as a tragedy that has tested our organization, one cannot also be deeply troubled by the greater impact of domestic and gun violence on healthcare and the American society today. Our staff and physicians have been witness and at times subject to such violence, and this experience has now made it even more poignant. Ultimately, and sadly, we feel that we are more prepared.

Disclosures

The author has nothing to disclose.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

A nticoagulation is the cornerstone of acute venous thromboembolism (VTE) management. Nonetheless, the use of inferior vena cava (IVC) filters in addition to anticoagulation is increasing, with wide variation in practice patterns and a growing recognition of filter-related complications. Rigorous randomized controlled data demonstrating that IVC filters, particularly the increasingly commonly placed retrievable filters, provide a mortality benefit are sparse. Given our review of IVC filter use and the lack of evidence demonstrating that IVC filters provide a mortality benefit, we recommend using anticoagulation alone for stable medical service patients admitted with acute VTE. In nuanced cases, hospitalists should engage in multidisciplinary care to develop individualized treatment options.

A 65-year-old woman with a history of diabetes mellitus, metastatic breast cancer, and peptic ulcer disease presents to the Emergency Department for the evaluation of right thigh swelling, chest pain, and dyspnea after a transcontinental flight. Physical examination is notable for a pulse of 114 beats per minute, blood pressure of 136/93 mm Hg, respiratory rate of 14 breaths per minute, oxygen saturation of 95% on room air, and swelling of the right thigh. Computerized tomography imaging demonstrates multiple bilateral pulmonary emboli. Emergency department physicians begin anticoagulation and inform you that they have ordered the placement of a retrievable inferior vena cava (IVC) filter.

Acute venous thromboembolism (VTE) accounts for more than 500,000 hospitalizations in the United States each year.¹ Although the management of VTE centers around anticoagulation, the concurrent use of IVC filters has increased over the past several decades.² Several observational studies have attempted to quantify IVC filter usage and have shown that overall filter placement has increased at an impressive rate. Within two decades, the number of patients undergoing IVC filter placement has increased nearly 25 times from 2,000 in 1979 to 49,000 in 1999.² Recent Medicare data show that claims for IVC filter placement procedures have increased from 30,756 in 1999 to 65,041 in 2008.³ IVC filter placement rates are higher in the US than in other developed countries; one review projected that in 2012, the IVC filter placement rate in a given population in the US is 25 times higher than that in a similar population in Europe.⁴

The guidelines for IVC filter usage are largely based on expert opinion, and solid data regarding this intervention are lacking. This combination is problematic, especially because the practice is becoming commonplace, and filter-related complications are increasingly recognized. Additionally, the appropriateness of filter use varies among providers, as evidenced by a retrospective study in which three VTE experts reviewed medical records to determine the appropriateness of filter placement. They unanimously agreed that filter use was appropriate in 51% of the cases, unanimously agreed that filter use was inappropriate in 26% of the cases, and lacked consensus on the appropriateness of filter use in 23% of the cases.⁵ The striking lack of consensus among experts underscores the wide range of opinion regarding the appropriateness of IVC filter placement on a case-by-case basis. Moreover, evidence suggests that physician adherence to guidelines for appropriate IVC filter use is suboptimal. One single-center study showed that only 43.5% of filters placed by interventional radiology practitioners met the guidelines established by the American College of Chest Physicians (ACCP), with a slightly increased percentage of filter placement meeting guidelines if the requesting provider is an IM-trained physician.⁶

In theory, the concept of IVC filters makes intuitive sense—filters block the ascent of any thrombus from the lower extremities to prevent the feared complication of a pulmonary embolism (PE). Unfortunately, rigorous data are limited, and consensus guidelines vary between different specialty organizations, further obfuscating the role of IVC filter placement in the management of VTE. For example, the ACCP recommends against the use of IVC filters in most patients with VTE receiving anticoagulation and does not list any prophylactic indications.^7,8 Meanwhile, the Society of Interventional Radiology lists prophylactic indications for IVC filter placement in certain patient populations, such patients with a risk of VTE and a high risk of bleeding, and notes numerous relative indications for IVC filter placement.⁸ Notably, these differences in expert opinion likely influence practice patterns, as evidenced by the increase in IVC filter placement for relative indications.^9,10

The Prevention du Risque d’Embolie Pulmonaire par Interruption Cave (PRECIP) trial is the most robust study supporting the 2016 ACCP recommendation against IVC filter use in patients that can receive anticoagulation.^7,11 This study randomized 400 patients with deep vein thrombosis (DVT) at high risk for PE to anticoagulation with or without permanent filter placement to address VTE and mortality rates associated with IVC filter placement. The trial showed that the VTE burden shifts in the presence of IVC filters. At 2-year follow-up, the group with IVC filters had nonsignificantly fewer PEs than the control group and an increased incidence of DVT. Mortality rates did not differ between groups.¹¹ At eight-year follow-up this shift in VTE burden is again seen given that the number of PEs in patients who received IVC filters decreased and the incidence of DVTs increased. Again, mortality did not differ between groups.¹² A subsequent study randomized 399 patients with DVT and acute symptomatic PE with at least one additional marker of severity to anticoagulation with or without retrievable IVC filter placement and showed no difference in recurrent PE or mortality at 3 or 6 months.¹³ These results argue against placing retrievable filters in patients receiving anticoagulation.

The identification of associated adverse events further favor the judicious use of IVC filters. A retrospective review of the long-term complications of IVC filters based on imaging data showed a 14% fracture rate, 13% IVC thrombosis rate, and a 48% perforation rate.¹⁴ Multiple studies have shown that the associated complication rates of retrievable filters are higher than those of permanent filters; such an association is concerning given that retrievable filter usage exceeds permanent filter usage.^14,15 The increase in retrievable filter usage is likely attributable to their attractive risk-benefit calculation. In theory, retrievable IVC filters should be perfect for patients who have conditions that increase VTE risk but create temporary contraindications, such as trauma or major surgery, to anticoagulation. However, anticoagulation is preferred over IVC filters in the long term because the complication rates of IVC filters increase with dwell time.¹⁶ Given the reports of adverse events and concern that IVC filters are not appropriately removed, the Food and Drug Administration recommends removing retrievable IVC filters once the risk of filters outweighs the benefits, which appears to be 29-54 days after implantation.¹⁷ However, successful retrieval rates are low, both because of the low rates of removal attempts and because of the interference of complications, such as embedded or thrombosed filters, with removal.^10,18 As an example, in a retrospective review of all patients who received an IVC filter at an academic medical center over the period of 2003-2011, nearly 25% of patients were discharged on anticoagulation after IVC filter placement.¹⁰ This suggests that their contraindication to anticoagulation and need for IVC placement have passed by the time of discharge. Nevertheless, clinicians attempted filter retrieval in only 9.6% of these patients, representing a significant missed opportunity of treatment with anticoagulation rather than IVC filters.¹⁰

Factors such as filter plan documentation, hematology involvement, patient age ≤70 years, and establishment of dedicated IVC filter clinics are correlated with improved rates of filter removal; these correlations emphasize the importance of a clear follow-up plan in the timely removal of these devices.^18,19

IVC filter placement is inappropriate in the vast majority of patients with VTE who can be anticoagulated. However the ACCP does acknowledge that a small subset of patients – specifically, those with severe or massive PE – may fall outside this guideline.⁷ Clinicians fear that these patients have low cardiopulmonary reserve and may experience hemodynamic collapse and death with another “hit” from a recurrent PE. This recommendation is consistent with the evidence that in unstable patients with PE, IVC filter placement is associated with decreased in-hospital mortality.²⁰ Data remain limited for this situation, and the decision to place an IVC filter in anticoagulated but unstable patients is an individualized one.

In stable patients admitted to the medical service with VTE and who can be anticoagulated, there is little evidence that placement of an IVC filter will improve short- or long-term mortality. Hospitalists should anticoagulate these patients with a vitamin-K antagonist, heparin product, or novel oral anticoagulants.

• Anticoagulate hemodynamically stable patients who are admitted to the medical service with VTE and who do not have a contraindication to anticoagulation. Do not place a permanent or retrievable IVC filter.
• IVC filter placement may benefit unstable patients who may experience hemodynamic collapse with an increased PE burden. IVC filter placement should be discussed with a multidisciplinary team.
• When discharging a patient with an IVC filter, hospitalists should improve retrieval rates by scheduling subsequent removal. The discharge summary should contain information about the IVC filter, as well as clear instructions regarding the plan for removal. The instructions should include radiology follow-up information and the designation of responsible physicians in case of questions.

Although IVC filter use is increasing, the evidence does not support their use in hemodynamically stable patients who can be anticoagulated. The patient described in the initial case has no contraindication to systemic anticoagulation. Therefore, she should be started on anticoagulation, and an IVC filter should not be placed.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing[email protected].

Disclosures

The authors do not have any conflicts of interest to disclose

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

A nticoagulation is the cornerstone of acute venous thromboembolism (VTE) management. Nonetheless, the use of inferior vena cava (IVC) filters in addition to anticoagulation is increasing, with wide variation in practice patterns and a growing recognition of filter-related complications. Rigorous randomized controlled data demonstrating that IVC filters, particularly the increasingly commonly placed retrievable filters, provide a mortality benefit are sparse. Given our review of IVC filter use and the lack of evidence demonstrating that IVC filters provide a mortality benefit, we recommend using anticoagulation alone for stable medical service patients admitted with acute VTE. In nuanced cases, hospitalists should engage in multidisciplinary care to develop individualized treatment options.

A 65-year-old woman with a history of diabetes mellitus, metastatic breast cancer, and peptic ulcer disease presents to the Emergency Department for the evaluation of right thigh swelling, chest pain, and dyspnea after a transcontinental flight. Physical examination is notable for a pulse of 114 beats per minute, blood pressure of 136/93 mm Hg, respiratory rate of 14 breaths per minute, oxygen saturation of 95% on room air, and swelling of the right thigh. Computerized tomography imaging demonstrates multiple bilateral pulmonary emboli. Emergency department physicians begin anticoagulation and inform you that they have ordered the placement of a retrievable inferior vena cava (IVC) filter.

Acute venous thromboembolism (VTE) accounts for more than 500,000 hospitalizations in the United States each year.¹ Although the management of VTE centers around anticoagulation, the concurrent use of IVC filters has increased over the past several decades.² Several observational studies have attempted to quantify IVC filter usage and have shown that overall filter placement has increased at an impressive rate. Within two decades, the number of patients undergoing IVC filter placement has increased nearly 25 times from 2,000 in 1979 to 49,000 in 1999.² Recent Medicare data show that claims for IVC filter placement procedures have increased from 30,756 in 1999 to 65,041 in 2008.³ IVC filter placement rates are higher in the US than in other developed countries; one review projected that in 2012, the IVC filter placement rate in a given population in the US is 25 times higher than that in a similar population in Europe.⁴

The guidelines for IVC filter usage are largely based on expert opinion, and solid data regarding this intervention are lacking. This combination is problematic, especially because the practice is becoming commonplace, and filter-related complications are increasingly recognized. Additionally, the appropriateness of filter use varies among providers, as evidenced by a retrospective study in which three VTE experts reviewed medical records to determine the appropriateness of filter placement. They unanimously agreed that filter use was appropriate in 51% of the cases, unanimously agreed that filter use was inappropriate in 26% of the cases, and lacked consensus on the appropriateness of filter use in 23% of the cases.⁵ The striking lack of consensus among experts underscores the wide range of opinion regarding the appropriateness of IVC filter placement on a case-by-case basis. Moreover, evidence suggests that physician adherence to guidelines for appropriate IVC filter use is suboptimal. One single-center study showed that only 43.5% of filters placed by interventional radiology practitioners met the guidelines established by the American College of Chest Physicians (ACCP), with a slightly increased percentage of filter placement meeting guidelines if the requesting provider is an IM-trained physician.⁶

In theory, the concept of IVC filters makes intuitive sense—filters block the ascent of any thrombus from the lower extremities to prevent the feared complication of a pulmonary embolism (PE). Unfortunately, rigorous data are limited, and consensus guidelines vary between different specialty organizations, further obfuscating the role of IVC filter placement in the management of VTE. For example, the ACCP recommends against the use of IVC filters in most patients with VTE receiving anticoagulation and does not list any prophylactic indications.^7,8 Meanwhile, the Society of Interventional Radiology lists prophylactic indications for IVC filter placement in certain patient populations, such patients with a risk of VTE and a high risk of bleeding, and notes numerous relative indications for IVC filter placement.⁸ Notably, these differences in expert opinion likely influence practice patterns, as evidenced by the increase in IVC filter placement for relative indications.^9,10

The Prevention du Risque d’Embolie Pulmonaire par Interruption Cave (PRECIP) trial is the most robust study supporting the 2016 ACCP recommendation against IVC filter use in patients that can receive anticoagulation.^7,11 This study randomized 400 patients with deep vein thrombosis (DVT) at high risk for PE to anticoagulation with or without permanent filter placement to address VTE and mortality rates associated with IVC filter placement. The trial showed that the VTE burden shifts in the presence of IVC filters. At 2-year follow-up, the group with IVC filters had nonsignificantly fewer PEs than the control group and an increased incidence of DVT. Mortality rates did not differ between groups.¹¹ At eight-year follow-up this shift in VTE burden is again seen given that the number of PEs in patients who received IVC filters decreased and the incidence of DVTs increased. Again, mortality did not differ between groups.¹² A subsequent study randomized 399 patients with DVT and acute symptomatic PE with at least one additional marker of severity to anticoagulation with or without retrievable IVC filter placement and showed no difference in recurrent PE or mortality at 3 or 6 months.¹³ These results argue against placing retrievable filters in patients receiving anticoagulation.

The identification of associated adverse events further favor the judicious use of IVC filters. A retrospective review of the long-term complications of IVC filters based on imaging data showed a 14% fracture rate, 13% IVC thrombosis rate, and a 48% perforation rate.¹⁴ Multiple studies have shown that the associated complication rates of retrievable filters are higher than those of permanent filters; such an association is concerning given that retrievable filter usage exceeds permanent filter usage.^14,15 The increase in retrievable filter usage is likely attributable to their attractive risk-benefit calculation. In theory, retrievable IVC filters should be perfect for patients who have conditions that increase VTE risk but create temporary contraindications, such as trauma or major surgery, to anticoagulation. However, anticoagulation is preferred over IVC filters in the long term because the complication rates of IVC filters increase with dwell time.¹⁶ Given the reports of adverse events and concern that IVC filters are not appropriately removed, the Food and Drug Administration recommends removing retrievable IVC filters once the risk of filters outweighs the benefits, which appears to be 29-54 days after implantation.¹⁷ However, successful retrieval rates are low, both because of the low rates of removal attempts and because of the interference of complications, such as embedded or thrombosed filters, with removal.^10,18 As an example, in a retrospective review of all patients who received an IVC filter at an academic medical center over the period of 2003-2011, nearly 25% of patients were discharged on anticoagulation after IVC filter placement.¹⁰ This suggests that their contraindication to anticoagulation and need for IVC placement have passed by the time of discharge. Nevertheless, clinicians attempted filter retrieval in only 9.6% of these patients, representing a significant missed opportunity of treatment with anticoagulation rather than IVC filters.¹⁰

Factors such as filter plan documentation, hematology involvement, patient age ≤70 years, and establishment of dedicated IVC filter clinics are correlated with improved rates of filter removal; these correlations emphasize the importance of a clear follow-up plan in the timely removal of these devices.^18,19

IVC filter placement is inappropriate in the vast majority of patients with VTE who can be anticoagulated. However the ACCP does acknowledge that a small subset of patients – specifically, those with severe or massive PE – may fall outside this guideline.⁷ Clinicians fear that these patients have low cardiopulmonary reserve and may experience hemodynamic collapse and death with another “hit” from a recurrent PE. This recommendation is consistent with the evidence that in unstable patients with PE, IVC filter placement is associated with decreased in-hospital mortality.²⁰ Data remain limited for this situation, and the decision to place an IVC filter in anticoagulated but unstable patients is an individualized one.

In stable patients admitted to the medical service with VTE and who can be anticoagulated, there is little evidence that placement of an IVC filter will improve short- or long-term mortality. Hospitalists should anticoagulate these patients with a vitamin-K antagonist, heparin product, or novel oral anticoagulants.

• Anticoagulate hemodynamically stable patients who are admitted to the medical service with VTE and who do not have a contraindication to anticoagulation. Do not place a permanent or retrievable IVC filter.
• IVC filter placement may benefit unstable patients who may experience hemodynamic collapse with an increased PE burden. IVC filter placement should be discussed with a multidisciplinary team.
• When discharging a patient with an IVC filter, hospitalists should improve retrieval rates by scheduling subsequent removal. The discharge summary should contain information about the IVC filter, as well as clear instructions regarding the plan for removal. The instructions should include radiology follow-up information and the designation of responsible physicians in case of questions.

Although IVC filter use is increasing, the evidence does not support their use in hemodynamically stable patients who can be anticoagulated. The patient described in the initial case has no contraindication to systemic anticoagulation. Therefore, she should be started on anticoagulation, and an IVC filter should not be placed.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing[email protected].

Disclosures

The authors do not have any conflicts of interest to disclose

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

A nticoagulation is the cornerstone of acute venous thromboembolism (VTE) management. Nonetheless, the use of inferior vena cava (IVC) filters in addition to anticoagulation is increasing, with wide variation in practice patterns and a growing recognition of filter-related complications. Rigorous randomized controlled data demonstrating that IVC filters, particularly the increasingly commonly placed retrievable filters, provide a mortality benefit are sparse. Given our review of IVC filter use and the lack of evidence demonstrating that IVC filters provide a mortality benefit, we recommend using anticoagulation alone for stable medical service patients admitted with acute VTE. In nuanced cases, hospitalists should engage in multidisciplinary care to develop individualized treatment options.

A 65-year-old woman with a history of diabetes mellitus, metastatic breast cancer, and peptic ulcer disease presents to the Emergency Department for the evaluation of right thigh swelling, chest pain, and dyspnea after a transcontinental flight. Physical examination is notable for a pulse of 114 beats per minute, blood pressure of 136/93 mm Hg, respiratory rate of 14 breaths per minute, oxygen saturation of 95% on room air, and swelling of the right thigh. Computerized tomography imaging demonstrates multiple bilateral pulmonary emboli. Emergency department physicians begin anticoagulation and inform you that they have ordered the placement of a retrievable inferior vena cava (IVC) filter.

Acute venous thromboembolism (VTE) accounts for more than 500,000 hospitalizations in the United States each year.¹ Although the management of VTE centers around anticoagulation, the concurrent use of IVC filters has increased over the past several decades.² Several observational studies have attempted to quantify IVC filter usage and have shown that overall filter placement has increased at an impressive rate. Within two decades, the number of patients undergoing IVC filter placement has increased nearly 25 times from 2,000 in 1979 to 49,000 in 1999.² Recent Medicare data show that claims for IVC filter placement procedures have increased from 30,756 in 1999 to 65,041 in 2008.³ IVC filter placement rates are higher in the US than in other developed countries; one review projected that in 2012, the IVC filter placement rate in a given population in the US is 25 times higher than that in a similar population in Europe.⁴

The guidelines for IVC filter usage are largely based on expert opinion, and solid data regarding this intervention are lacking. This combination is problematic, especially because the practice is becoming commonplace, and filter-related complications are increasingly recognized. Additionally, the appropriateness of filter use varies among providers, as evidenced by a retrospective study in which three VTE experts reviewed medical records to determine the appropriateness of filter placement. They unanimously agreed that filter use was appropriate in 51% of the cases, unanimously agreed that filter use was inappropriate in 26% of the cases, and lacked consensus on the appropriateness of filter use in 23% of the cases.⁵ The striking lack of consensus among experts underscores the wide range of opinion regarding the appropriateness of IVC filter placement on a case-by-case basis. Moreover, evidence suggests that physician adherence to guidelines for appropriate IVC filter use is suboptimal. One single-center study showed that only 43.5% of filters placed by interventional radiology practitioners met the guidelines established by the American College of Chest Physicians (ACCP), with a slightly increased percentage of filter placement meeting guidelines if the requesting provider is an IM-trained physician.⁶

In theory, the concept of IVC filters makes intuitive sense—filters block the ascent of any thrombus from the lower extremities to prevent the feared complication of a pulmonary embolism (PE). Unfortunately, rigorous data are limited, and consensus guidelines vary between different specialty organizations, further obfuscating the role of IVC filter placement in the management of VTE. For example, the ACCP recommends against the use of IVC filters in most patients with VTE receiving anticoagulation and does not list any prophylactic indications.^7,8 Meanwhile, the Society of Interventional Radiology lists prophylactic indications for IVC filter placement in certain patient populations, such patients with a risk of VTE and a high risk of bleeding, and notes numerous relative indications for IVC filter placement.⁸ Notably, these differences in expert opinion likely influence practice patterns, as evidenced by the increase in IVC filter placement for relative indications.^9,10

The Prevention du Risque d’Embolie Pulmonaire par Interruption Cave (PRECIP) trial is the most robust study supporting the 2016 ACCP recommendation against IVC filter use in patients that can receive anticoagulation.^7,11 This study randomized 400 patients with deep vein thrombosis (DVT) at high risk for PE to anticoagulation with or without permanent filter placement to address VTE and mortality rates associated with IVC filter placement. The trial showed that the VTE burden shifts in the presence of IVC filters. At 2-year follow-up, the group with IVC filters had nonsignificantly fewer PEs than the control group and an increased incidence of DVT. Mortality rates did not differ between groups.¹¹ At eight-year follow-up this shift in VTE burden is again seen given that the number of PEs in patients who received IVC filters decreased and the incidence of DVTs increased. Again, mortality did not differ between groups.¹² A subsequent study randomized 399 patients with DVT and acute symptomatic PE with at least one additional marker of severity to anticoagulation with or without retrievable IVC filter placement and showed no difference in recurrent PE or mortality at 3 or 6 months.¹³ These results argue against placing retrievable filters in patients receiving anticoagulation.

The identification of associated adverse events further favor the judicious use of IVC filters. A retrospective review of the long-term complications of IVC filters based on imaging data showed a 14% fracture rate, 13% IVC thrombosis rate, and a 48% perforation rate.¹⁴ Multiple studies have shown that the associated complication rates of retrievable filters are higher than those of permanent filters; such an association is concerning given that retrievable filter usage exceeds permanent filter usage.^14,15 The increase in retrievable filter usage is likely attributable to their attractive risk-benefit calculation. In theory, retrievable IVC filters should be perfect for patients who have conditions that increase VTE risk but create temporary contraindications, such as trauma or major surgery, to anticoagulation. However, anticoagulation is preferred over IVC filters in the long term because the complication rates of IVC filters increase with dwell time.¹⁶ Given the reports of adverse events and concern that IVC filters are not appropriately removed, the Food and Drug Administration recommends removing retrievable IVC filters once the risk of filters outweighs the benefits, which appears to be 29-54 days after implantation.¹⁷ However, successful retrieval rates are low, both because of the low rates of removal attempts and because of the interference of complications, such as embedded or thrombosed filters, with removal.^10,18 As an example, in a retrospective review of all patients who received an IVC filter at an academic medical center over the period of 2003-2011, nearly 25% of patients were discharged on anticoagulation after IVC filter placement.¹⁰ This suggests that their contraindication to anticoagulation and need for IVC placement have passed by the time of discharge. Nevertheless, clinicians attempted filter retrieval in only 9.6% of these patients, representing a significant missed opportunity of treatment with anticoagulation rather than IVC filters.¹⁰

Factors such as filter plan documentation, hematology involvement, patient age ≤70 years, and establishment of dedicated IVC filter clinics are correlated with improved rates of filter removal; these correlations emphasize the importance of a clear follow-up plan in the timely removal of these devices.^18,19

IVC filter placement is inappropriate in the vast majority of patients with VTE who can be anticoagulated. However the ACCP does acknowledge that a small subset of patients – specifically, those with severe or massive PE – may fall outside this guideline.⁷ Clinicians fear that these patients have low cardiopulmonary reserve and may experience hemodynamic collapse and death with another “hit” from a recurrent PE. This recommendation is consistent with the evidence that in unstable patients with PE, IVC filter placement is associated with decreased in-hospital mortality.²⁰ Data remain limited for this situation, and the decision to place an IVC filter in anticoagulated but unstable patients is an individualized one.

In stable patients admitted to the medical service with VTE and who can be anticoagulated, there is little evidence that placement of an IVC filter will improve short- or long-term mortality. Hospitalists should anticoagulate these patients with a vitamin-K antagonist, heparin product, or novel oral anticoagulants.

• Anticoagulate hemodynamically stable patients who are admitted to the medical service with VTE and who do not have a contraindication to anticoagulation. Do not place a permanent or retrievable IVC filter.
• IVC filter placement may benefit unstable patients who may experience hemodynamic collapse with an increased PE burden. IVC filter placement should be discussed with a multidisciplinary team.
• When discharging a patient with an IVC filter, hospitalists should improve retrieval rates by scheduling subsequent removal. The discharge summary should contain information about the IVC filter, as well as clear instructions regarding the plan for removal. The instructions should include radiology follow-up information and the designation of responsible physicians in case of questions.

Although IVC filter use is increasing, the evidence does not support their use in hemodynamically stable patients who can be anticoagulated. The patient described in the initial case has no contraindication to systemic anticoagulation. Therefore, she should be started on anticoagulation, and an IVC filter should not be placed.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing[email protected].

Disclosures

The authors do not have any conflicts of interest to disclose

Recent events, domestically and globally, have highlighted the numerous complex challenges that disasters and mass casualty incidents (MCIs) impose on hospitals. Mass casualty events result from natural phenomena (eg, hurricanes, tornadoes, and wildfires), accidents (eg, plane crashes, building collapses, and toxic waste spills), or man-made crises (eg, terrorism).^1-4 These events feature the potential to cause an acute surge of patients, which can overwhelm available hospital resources and personnel. Mass effect incidents are sustained crises, which often occur due to loss of infrastructure, epidemic infectious diseases, or need for hospital evacuations, and can completely overtax local and regional resources, thus requiring federal and state coordination.⁵

Hospital disaster response plans have traditionally centered on responses by the emergency department (ED) and associated surgical services to mass trauma-type events, without commensurate involvement of other hospital departments in either incident management operations or the planning process for mass effect incidents.^6,7 In particular, the role of hospitalists in the leadership structure of various hospital disaster command structures remains undefined.⁸ However, recent disasters suggest that hospitalist involvement will highly benefit hospital emergency preparedness.⁹ Hospitalists possess specialized expertise in patient triage and disposition; medical comanagement with surgical services; coordination of complex medical care (usually with continuous 24/7 in-house coverage); integration with the full spectrum of affiliated services, such as case management or patient rehabilitation; and quality improvement research.^10-12 At our institution, hospitalists are involved in the direct care of over 60% of the patients admitted across all medical and surgical services. Thus, we believe that hospitalists are uniquely qualified to offer leadership in disaster preparation, response, and recovery if integrated into hospitals’ incident command architectures. For example, although numerous acute patient surges are due to trauma MCIs, hospitalists may nevertheless act as the primary care providers in disasters that are medical in nature or that require rapid hospital evacuation and patient transfer (Table 1).

Effective participation in disaster response and planning requires a basic understanding of the organizational structures for incident management.^18,19 Overall disaster response within the United States is guided by the National Response Framework, a national-level strategy that directs coordination between military and civilian response efforts, the latter of which are structured by the National Incident Management System (NIMS).²⁰ NIMS organizes emergency management across all government levels (federal, state, and local) and the private sector under a common operational language and command structure. Both systems grew out of analyses of the September 11, 2001 attacks and Hurricane Katrina, indicating the need for a wider systemic organization to response efforts.¹ State-level efforts are designed to mobilize resources to assist in community-level operations. Incident management always falls to the local authorities. At this local level, discrete hospitals often take part in healthcare coalitions that act in conjunction with other health entities, local public health departments, and emergency medical services, forming a multiagency coordination system.⁵ This healthcare coalition (emergency support function #8 health and medical), in support of emergency managers of city and county governments, forms the core of the medical response. One commonality to all emergency management is the concept of an “all-hazards” approach, which aims to develop a broad and flexible strategy for efficient management of nearly any type of incident. This “all-hazards” approach allows effective management through each of the four phases of incident management: preparation, response, recovery, and ongoing mitigation.

Direct supervision over incident management is achieved through an Incident Command System (ICS), a hierarchical organization of positions involved in response. The top supervisory structure of ICS (Incident Command and General Staff) is the same regardless of the locale in which it operates, allowing coherent interoperability with other agencies. Incidents of any size are managed with a scalable approach; subordinate ICS positions, which are tailored according to specific needs, can be activated as needed. Healthcare implementation of the ICS structure led to the development of the Hospital Incident Command System (HICS), which now serves as the national standard for hospital-based incident management and facilitates the capacity of individual hospitals to coordinate with other resources regionally and is a part of NIMS for emergency response (Figure 1).²¹ The success of HICS-led regulatory agencies (namely the Centers for Medicare and Medicaid Services and the Joint Commission) to require ICS/HICS in-hospital incident response plans.²² The most recent HICS (Version V) excludes physician involvement in the overall management chart. However, we demonstrate how the inherent flexibility in ICS can adapt to involve hospitalists. Although HICS serves as a backbone that requires institutionally specific modifications, other institutions, such as ours, commonly have entire branches or positions renamed, reapportioned, or created to fill their specific needs. Specialized training in ICS, NIMS, and other aspects of hospital emergency response is beyond the scope of this article but is available for free through the Department of Homeland Security and FEMA.²³

Although incidents vary in terms of their severity, acuity of onset, duration, and composition of patients, a defining feature of MCIs is the rapid surge of patients with acute needs. Many MCIs are easily absorbed by local facilities. However, smaller hospitals or hospitals receiving patients from larger-scale incidents may become overwhelmed, in which larger incidents may result in an acute surge of over 20% of hospital capacity.¹³ Moreover, hospital surge capabilities have markedly diminished over the past decade due to overcrowding of emergency rooms, in part by admitted patients occupying the room space within the ED (“boarding”), further decreasing the hospitals’ capacities to accept new patients.²⁵

Our proposed framework for hospitalist involvement in MCI disaster response focuses on such a situation, with emphasis on augmentation of hospital surge capacity and facilitation of patient throughput and discharge. Notably, these goals are modified from the standard HICS architecture (Figures 1-2 and Table 2). In this framework, hospitalists can play a critical role in decompressing the emergency room through admitting medical patients as rapidly as possible (even if preliminary workup is still pending), facilitating rapid discharge of patients to allow newer admissions to reach the floor, and prioritizing patients that could be transferred to other facilities or services and thus opening additional beds for admission (eg, accepting patients from the ICU or surgical floors to increase capacities on those services). Additionally, hospitalists can comanage surgical patients while surgeons are operating, assist intensivists with medical issues, and facilitate care of patients with minor injuries.

Using the HICS framework, each of those domains would be handled by a Strike Team led by one Team Leader whose goal is to operationalize various assets into a cohesive team specializing in those goals. Table 2 summarizes these goals, as presented in the context of patient examples.

To keep up with the ICS fundamentals, Hospitalist Unit Leaders may address a large MCI with all four strike teams or may only activate the strike teams needed for a less intensive MCI. For example, a bombing may result in a patient surge of 30% more than normal operations and thus demand a full response that includes all the strike teams noted above. By contrast, a bus accident with 20 injured patients may only require a Hospitalist Unit Leader to activate the “Admissions and Internal Transfers In” Strike Team to help offload a busy emergency room.

Emergency management is comprised of four phases: preparation, response, recovery, and mitigation. The latter two phases are beyond the scope of this paper. Although most of our review has focused on modeling disaster response, hospitalist leadership remains critical in preparing for disasters. A disaster often psychologically overwhelms care providers, who feel compelled to help but are uncertain where to begin. To aid the members of a disaster response team, a state-of-the-art hospitalist group creates Job Action Sheets (JASs) for each position in their HICS organizational chart; these sheets codify how to respond and what roles are needed. These formal, protocolized sheets provide individuals assigned to these positions a description of their roles and responsibilities, including to whom they report and over whom they supervise, and include detailed checklists to aid in reaching critical milestones during the response phase. For example, the “Surgical Comanagement and Consulting” Strike Team Leader JAS would likely include the expectations of surgeons for assisting in patient management (ie, auto-consulting on all postoperative patients) and whether nursing phone calls on surgical patients would be temporarily routed to the Strike Team during periods of OR surge.

Hospitalists are well suited as leaders in disaster preparation given their ability to coordinate care among a large spectrum of stakeholders. For example, case managers and social workers are essential members of a well-structured Discharge Strike Team. Their input is critical to ensure that disaster tactics – such as care coordination contracts with local skilled nursing facilities willing to expedite discharge in emergencies to their facilities – are in-place before a real MCI. During Hurricane Sandy, mass evacuation of affected hospitals was effective through the Healthcare Facility Evacuation Center (a healthcare coalition of the New York Hospital Association) but nevertheless plagued with issues regarding situational awareness, poor communication between facilities, and difficulty bundling patients with medical records to receiving facilities – items which can be identified, anticipated, and thoroughly vetted by hospitalists well in advance of a real-world evacuation.^{26, 27}

As the Joint Commission mandates regular exercises of the emergency plan, protocols must be drilled regularly to uncover deficiencies and areas for improvement.¹⁸ The most common failure patterns in Emergency Operation Plans (EOPs) include unrealistic and ineffective expectations and poor communication between different personnel and groups, resulting in confusion and obfuscation.^28-30 Therefore, EOPs need to be both comprehensive and realistic – characteristics that can only be tested through repeated drills. These characteristics can be tested during tabletop exercises, where hospitalists assume the role of a part of the ICS structure and with JAS in hand, attempt to reason how to respond to a given scenario.³¹ Our experience is that small-scale drills conducted more frequently than the bare minimum mandated by the Joint Commission are far more effective for success in real-life situations.

Although no hospital EOP can anticipate every contingency, hospitalists can proactively practice contingency planning for sustained system-wide mass effect incidents, in which hospitals are unable to maintain normal operations and shift from standard to crisis conventions of care. For example, mass effect incidents (ie, hospital damage from an earthquake or a massive and persistent regional power failure), require planning for how a hospital-wide mass evacuation would unfold and how efforts from multiple ancillary hospital services (engineering, nursing, security, and patient transport) would be integrated. As of 2015, over 90% of hospitals have adopted an electronic health record, but only two-thirds of hospitals feature EOPs for information technology failures.^32,33 Given the large footprint of hospitalists in clinical practice, HICS principles appear ripe for application in IT outages and through development of ICS positions structured specifically to this type of contingency.³⁴

Disasters unfold rapidly with marked patient surges and the potential to strain healthcare systems over an extended period. However, in both instances, hospitalists are possibly some of the most qualified clinicians to prepare for and respond to such events. Hospitalists need to assume a leadership role in emergency preparedness to integrate seamlessly into hospital incident command structures and to shape the interdepartmental relationships vital to success – skills at which hospitalists excel. Although no plan can address all possible disasters, familiarity with HICS and well-prepared and well-written JASs should help groups respond and succeed in almost all hazards.

Disclosures

None of the authors have any conflicts of interest to report.

Recent events, domestically and globally, have highlighted the numerous complex challenges that disasters and mass casualty incidents (MCIs) impose on hospitals. Mass casualty events result from natural phenomena (eg, hurricanes, tornadoes, and wildfires), accidents (eg, plane crashes, building collapses, and toxic waste spills), or man-made crises (eg, terrorism).^1-4 These events feature the potential to cause an acute surge of patients, which can overwhelm available hospital resources and personnel. Mass effect incidents are sustained crises, which often occur due to loss of infrastructure, epidemic infectious diseases, or need for hospital evacuations, and can completely overtax local and regional resources, thus requiring federal and state coordination.⁵

Hospital disaster response plans have traditionally centered on responses by the emergency department (ED) and associated surgical services to mass trauma-type events, without commensurate involvement of other hospital departments in either incident management operations or the planning process for mass effect incidents.^6,7 In particular, the role of hospitalists in the leadership structure of various hospital disaster command structures remains undefined.⁸ However, recent disasters suggest that hospitalist involvement will highly benefit hospital emergency preparedness.⁹ Hospitalists possess specialized expertise in patient triage and disposition; medical comanagement with surgical services; coordination of complex medical care (usually with continuous 24/7 in-house coverage); integration with the full spectrum of affiliated services, such as case management or patient rehabilitation; and quality improvement research.^10-12 At our institution, hospitalists are involved in the direct care of over 60% of the patients admitted across all medical and surgical services. Thus, we believe that hospitalists are uniquely qualified to offer leadership in disaster preparation, response, and recovery if integrated into hospitals’ incident command architectures. For example, although numerous acute patient surges are due to trauma MCIs, hospitalists may nevertheless act as the primary care providers in disasters that are medical in nature or that require rapid hospital evacuation and patient transfer (Table 1).

Effective participation in disaster response and planning requires a basic understanding of the organizational structures for incident management.^18,19 Overall disaster response within the United States is guided by the National Response Framework, a national-level strategy that directs coordination between military and civilian response efforts, the latter of which are structured by the National Incident Management System (NIMS).²⁰ NIMS organizes emergency management across all government levels (federal, state, and local) and the private sector under a common operational language and command structure. Both systems grew out of analyses of the September 11, 2001 attacks and Hurricane Katrina, indicating the need for a wider systemic organization to response efforts.¹ State-level efforts are designed to mobilize resources to assist in community-level operations. Incident management always falls to the local authorities. At this local level, discrete hospitals often take part in healthcare coalitions that act in conjunction with other health entities, local public health departments, and emergency medical services, forming a multiagency coordination system.⁵ This healthcare coalition (emergency support function #8 health and medical), in support of emergency managers of city and county governments, forms the core of the medical response. One commonality to all emergency management is the concept of an “all-hazards” approach, which aims to develop a broad and flexible strategy for efficient management of nearly any type of incident. This “all-hazards” approach allows effective management through each of the four phases of incident management: preparation, response, recovery, and ongoing mitigation.

Direct supervision over incident management is achieved through an Incident Command System (ICS), a hierarchical organization of positions involved in response. The top supervisory structure of ICS (Incident Command and General Staff) is the same regardless of the locale in which it operates, allowing coherent interoperability with other agencies. Incidents of any size are managed with a scalable approach; subordinate ICS positions, which are tailored according to specific needs, can be activated as needed. Healthcare implementation of the ICS structure led to the development of the Hospital Incident Command System (HICS), which now serves as the national standard for hospital-based incident management and facilitates the capacity of individual hospitals to coordinate with other resources regionally and is a part of NIMS for emergency response (Figure 1).²¹ The success of HICS-led regulatory agencies (namely the Centers for Medicare and Medicaid Services and the Joint Commission) to require ICS/HICS in-hospital incident response plans.²² The most recent HICS (Version V) excludes physician involvement in the overall management chart. However, we demonstrate how the inherent flexibility in ICS can adapt to involve hospitalists. Although HICS serves as a backbone that requires institutionally specific modifications, other institutions, such as ours, commonly have entire branches or positions renamed, reapportioned, or created to fill their specific needs. Specialized training in ICS, NIMS, and other aspects of hospital emergency response is beyond the scope of this article but is available for free through the Department of Homeland Security and FEMA.²³

Although incidents vary in terms of their severity, acuity of onset, duration, and composition of patients, a defining feature of MCIs is the rapid surge of patients with acute needs. Many MCIs are easily absorbed by local facilities. However, smaller hospitals or hospitals receiving patients from larger-scale incidents may become overwhelmed, in which larger incidents may result in an acute surge of over 20% of hospital capacity.¹³ Moreover, hospital surge capabilities have markedly diminished over the past decade due to overcrowding of emergency rooms, in part by admitted patients occupying the room space within the ED (“boarding”), further decreasing the hospitals’ capacities to accept new patients.²⁵

Our proposed framework for hospitalist involvement in MCI disaster response focuses on such a situation, with emphasis on augmentation of hospital surge capacity and facilitation of patient throughput and discharge. Notably, these goals are modified from the standard HICS architecture (Figures 1-2 and Table 2). In this framework, hospitalists can play a critical role in decompressing the emergency room through admitting medical patients as rapidly as possible (even if preliminary workup is still pending), facilitating rapid discharge of patients to allow newer admissions to reach the floor, and prioritizing patients that could be transferred to other facilities or services and thus opening additional beds for admission (eg, accepting patients from the ICU or surgical floors to increase capacities on those services). Additionally, hospitalists can comanage surgical patients while surgeons are operating, assist intensivists with medical issues, and facilitate care of patients with minor injuries.

Using the HICS framework, each of those domains would be handled by a Strike Team led by one Team Leader whose goal is to operationalize various assets into a cohesive team specializing in those goals. Table 2 summarizes these goals, as presented in the context of patient examples.

To keep up with the ICS fundamentals, Hospitalist Unit Leaders may address a large MCI with all four strike teams or may only activate the strike teams needed for a less intensive MCI. For example, a bombing may result in a patient surge of 30% more than normal operations and thus demand a full response that includes all the strike teams noted above. By contrast, a bus accident with 20 injured patients may only require a Hospitalist Unit Leader to activate the “Admissions and Internal Transfers In” Strike Team to help offload a busy emergency room.

Emergency management is comprised of four phases: preparation, response, recovery, and mitigation. The latter two phases are beyond the scope of this paper. Although most of our review has focused on modeling disaster response, hospitalist leadership remains critical in preparing for disasters. A disaster often psychologically overwhelms care providers, who feel compelled to help but are uncertain where to begin. To aid the members of a disaster response team, a state-of-the-art hospitalist group creates Job Action Sheets (JASs) for each position in their HICS organizational chart; these sheets codify how to respond and what roles are needed. These formal, protocolized sheets provide individuals assigned to these positions a description of their roles and responsibilities, including to whom they report and over whom they supervise, and include detailed checklists to aid in reaching critical milestones during the response phase. For example, the “Surgical Comanagement and Consulting” Strike Team Leader JAS would likely include the expectations of surgeons for assisting in patient management (ie, auto-consulting on all postoperative patients) and whether nursing phone calls on surgical patients would be temporarily routed to the Strike Team during periods of OR surge.

Hospitalists are well suited as leaders in disaster preparation given their ability to coordinate care among a large spectrum of stakeholders. For example, case managers and social workers are essential members of a well-structured Discharge Strike Team. Their input is critical to ensure that disaster tactics – such as care coordination contracts with local skilled nursing facilities willing to expedite discharge in emergencies to their facilities – are in-place before a real MCI. During Hurricane Sandy, mass evacuation of affected hospitals was effective through the Healthcare Facility Evacuation Center (a healthcare coalition of the New York Hospital Association) but nevertheless plagued with issues regarding situational awareness, poor communication between facilities, and difficulty bundling patients with medical records to receiving facilities – items which can be identified, anticipated, and thoroughly vetted by hospitalists well in advance of a real-world evacuation.^{26, 27}

As the Joint Commission mandates regular exercises of the emergency plan, protocols must be drilled regularly to uncover deficiencies and areas for improvement.¹⁸ The most common failure patterns in Emergency Operation Plans (EOPs) include unrealistic and ineffective expectations and poor communication between different personnel and groups, resulting in confusion and obfuscation.^28-30 Therefore, EOPs need to be both comprehensive and realistic – characteristics that can only be tested through repeated drills. These characteristics can be tested during tabletop exercises, where hospitalists assume the role of a part of the ICS structure and with JAS in hand, attempt to reason how to respond to a given scenario.³¹ Our experience is that small-scale drills conducted more frequently than the bare minimum mandated by the Joint Commission are far more effective for success in real-life situations.

Although no hospital EOP can anticipate every contingency, hospitalists can proactively practice contingency planning for sustained system-wide mass effect incidents, in which hospitals are unable to maintain normal operations and shift from standard to crisis conventions of care. For example, mass effect incidents (ie, hospital damage from an earthquake or a massive and persistent regional power failure), require planning for how a hospital-wide mass evacuation would unfold and how efforts from multiple ancillary hospital services (engineering, nursing, security, and patient transport) would be integrated. As of 2015, over 90% of hospitals have adopted an electronic health record, but only two-thirds of hospitals feature EOPs for information technology failures.^32,33 Given the large footprint of hospitalists in clinical practice, HICS principles appear ripe for application in IT outages and through development of ICS positions structured specifically to this type of contingency.³⁴

Disasters unfold rapidly with marked patient surges and the potential to strain healthcare systems over an extended period. However, in both instances, hospitalists are possibly some of the most qualified clinicians to prepare for and respond to such events. Hospitalists need to assume a leadership role in emergency preparedness to integrate seamlessly into hospital incident command structures and to shape the interdepartmental relationships vital to success – skills at which hospitalists excel. Although no plan can address all possible disasters, familiarity with HICS and well-prepared and well-written JASs should help groups respond and succeed in almost all hazards.

Disclosures

None of the authors have any conflicts of interest to report.

Recent events, domestically and globally, have highlighted the numerous complex challenges that disasters and mass casualty incidents (MCIs) impose on hospitals. Mass casualty events result from natural phenomena (eg, hurricanes, tornadoes, and wildfires), accidents (eg, plane crashes, building collapses, and toxic waste spills), or man-made crises (eg, terrorism).^1-4 These events feature the potential to cause an acute surge of patients, which can overwhelm available hospital resources and personnel. Mass effect incidents are sustained crises, which often occur due to loss of infrastructure, epidemic infectious diseases, or need for hospital evacuations, and can completely overtax local and regional resources, thus requiring federal and state coordination.⁵

Hospital disaster response plans have traditionally centered on responses by the emergency department (ED) and associated surgical services to mass trauma-type events, without commensurate involvement of other hospital departments in either incident management operations or the planning process for mass effect incidents.^6,7 In particular, the role of hospitalists in the leadership structure of various hospital disaster command structures remains undefined.⁸ However, recent disasters suggest that hospitalist involvement will highly benefit hospital emergency preparedness.⁹ Hospitalists possess specialized expertise in patient triage and disposition; medical comanagement with surgical services; coordination of complex medical care (usually with continuous 24/7 in-house coverage); integration with the full spectrum of affiliated services, such as case management or patient rehabilitation; and quality improvement research.^10-12 At our institution, hospitalists are involved in the direct care of over 60% of the patients admitted across all medical and surgical services. Thus, we believe that hospitalists are uniquely qualified to offer leadership in disaster preparation, response, and recovery if integrated into hospitals’ incident command architectures. For example, although numerous acute patient surges are due to trauma MCIs, hospitalists may nevertheless act as the primary care providers in disasters that are medical in nature or that require rapid hospital evacuation and patient transfer (Table 1).

Effective participation in disaster response and planning requires a basic understanding of the organizational structures for incident management.^18,19 Overall disaster response within the United States is guided by the National Response Framework, a national-level strategy that directs coordination between military and civilian response efforts, the latter of which are structured by the National Incident Management System (NIMS).²⁰ NIMS organizes emergency management across all government levels (federal, state, and local) and the private sector under a common operational language and command structure. Both systems grew out of analyses of the September 11, 2001 attacks and Hurricane Katrina, indicating the need for a wider systemic organization to response efforts.¹ State-level efforts are designed to mobilize resources to assist in community-level operations. Incident management always falls to the local authorities. At this local level, discrete hospitals often take part in healthcare coalitions that act in conjunction with other health entities, local public health departments, and emergency medical services, forming a multiagency coordination system.⁵ This healthcare coalition (emergency support function #8 health and medical), in support of emergency managers of city and county governments, forms the core of the medical response. One commonality to all emergency management is the concept of an “all-hazards” approach, which aims to develop a broad and flexible strategy for efficient management of nearly any type of incident. This “all-hazards” approach allows effective management through each of the four phases of incident management: preparation, response, recovery, and ongoing mitigation.

Direct supervision over incident management is achieved through an Incident Command System (ICS), a hierarchical organization of positions involved in response. The top supervisory structure of ICS (Incident Command and General Staff) is the same regardless of the locale in which it operates, allowing coherent interoperability with other agencies. Incidents of any size are managed with a scalable approach; subordinate ICS positions, which are tailored according to specific needs, can be activated as needed. Healthcare implementation of the ICS structure led to the development of the Hospital Incident Command System (HICS), which now serves as the national standard for hospital-based incident management and facilitates the capacity of individual hospitals to coordinate with other resources regionally and is a part of NIMS for emergency response (Figure 1).²¹ The success of HICS-led regulatory agencies (namely the Centers for Medicare and Medicaid Services and the Joint Commission) to require ICS/HICS in-hospital incident response plans.²² The most recent HICS (Version V) excludes physician involvement in the overall management chart. However, we demonstrate how the inherent flexibility in ICS can adapt to involve hospitalists. Although HICS serves as a backbone that requires institutionally specific modifications, other institutions, such as ours, commonly have entire branches or positions renamed, reapportioned, or created to fill their specific needs. Specialized training in ICS, NIMS, and other aspects of hospital emergency response is beyond the scope of this article but is available for free through the Department of Homeland Security and FEMA.²³

Although incidents vary in terms of their severity, acuity of onset, duration, and composition of patients, a defining feature of MCIs is the rapid surge of patients with acute needs. Many MCIs are easily absorbed by local facilities. However, smaller hospitals or hospitals receiving patients from larger-scale incidents may become overwhelmed, in which larger incidents may result in an acute surge of over 20% of hospital capacity.¹³ Moreover, hospital surge capabilities have markedly diminished over the past decade due to overcrowding of emergency rooms, in part by admitted patients occupying the room space within the ED (“boarding”), further decreasing the hospitals’ capacities to accept new patients.²⁵

Our proposed framework for hospitalist involvement in MCI disaster response focuses on such a situation, with emphasis on augmentation of hospital surge capacity and facilitation of patient throughput and discharge. Notably, these goals are modified from the standard HICS architecture (Figures 1-2 and Table 2). In this framework, hospitalists can play a critical role in decompressing the emergency room through admitting medical patients as rapidly as possible (even if preliminary workup is still pending), facilitating rapid discharge of patients to allow newer admissions to reach the floor, and prioritizing patients that could be transferred to other facilities or services and thus opening additional beds for admission (eg, accepting patients from the ICU or surgical floors to increase capacities on those services). Additionally, hospitalists can comanage surgical patients while surgeons are operating, assist intensivists with medical issues, and facilitate care of patients with minor injuries.

Using the HICS framework, each of those domains would be handled by a Strike Team led by one Team Leader whose goal is to operationalize various assets into a cohesive team specializing in those goals. Table 2 summarizes these goals, as presented in the context of patient examples.

To keep up with the ICS fundamentals, Hospitalist Unit Leaders may address a large MCI with all four strike teams or may only activate the strike teams needed for a less intensive MCI. For example, a bombing may result in a patient surge of 30% more than normal operations and thus demand a full response that includes all the strike teams noted above. By contrast, a bus accident with 20 injured patients may only require a Hospitalist Unit Leader to activate the “Admissions and Internal Transfers In” Strike Team to help offload a busy emergency room.

Emergency management is comprised of four phases: preparation, response, recovery, and mitigation. The latter two phases are beyond the scope of this paper. Although most of our review has focused on modeling disaster response, hospitalist leadership remains critical in preparing for disasters. A disaster often psychologically overwhelms care providers, who feel compelled to help but are uncertain where to begin. To aid the members of a disaster response team, a state-of-the-art hospitalist group creates Job Action Sheets (JASs) for each position in their HICS organizational chart; these sheets codify how to respond and what roles are needed. These formal, protocolized sheets provide individuals assigned to these positions a description of their roles and responsibilities, including to whom they report and over whom they supervise, and include detailed checklists to aid in reaching critical milestones during the response phase. For example, the “Surgical Comanagement and Consulting” Strike Team Leader JAS would likely include the expectations of surgeons for assisting in patient management (ie, auto-consulting on all postoperative patients) and whether nursing phone calls on surgical patients would be temporarily routed to the Strike Team during periods of OR surge.

Hospitalists are well suited as leaders in disaster preparation given their ability to coordinate care among a large spectrum of stakeholders. For example, case managers and social workers are essential members of a well-structured Discharge Strike Team. Their input is critical to ensure that disaster tactics – such as care coordination contracts with local skilled nursing facilities willing to expedite discharge in emergencies to their facilities – are in-place before a real MCI. During Hurricane Sandy, mass evacuation of affected hospitals was effective through the Healthcare Facility Evacuation Center (a healthcare coalition of the New York Hospital Association) but nevertheless plagued with issues regarding situational awareness, poor communication between facilities, and difficulty bundling patients with medical records to receiving facilities – items which can be identified, anticipated, and thoroughly vetted by hospitalists well in advance of a real-world evacuation.^{26, 27}

As the Joint Commission mandates regular exercises of the emergency plan, protocols must be drilled regularly to uncover deficiencies and areas for improvement.¹⁸ The most common failure patterns in Emergency Operation Plans (EOPs) include unrealistic and ineffective expectations and poor communication between different personnel and groups, resulting in confusion and obfuscation.^28-30 Therefore, EOPs need to be both comprehensive and realistic – characteristics that can only be tested through repeated drills. These characteristics can be tested during tabletop exercises, where hospitalists assume the role of a part of the ICS structure and with JAS in hand, attempt to reason how to respond to a given scenario.³¹ Our experience is that small-scale drills conducted more frequently than the bare minimum mandated by the Joint Commission are far more effective for success in real-life situations.

Although no hospital EOP can anticipate every contingency, hospitalists can proactively practice contingency planning for sustained system-wide mass effect incidents, in which hospitals are unable to maintain normal operations and shift from standard to crisis conventions of care. For example, mass effect incidents (ie, hospital damage from an earthquake or a massive and persistent regional power failure), require planning for how a hospital-wide mass evacuation would unfold and how efforts from multiple ancillary hospital services (engineering, nursing, security, and patient transport) would be integrated. As of 2015, over 90% of hospitals have adopted an electronic health record, but only two-thirds of hospitals feature EOPs for information technology failures.^32,33 Given the large footprint of hospitalists in clinical practice, HICS principles appear ripe for application in IT outages and through development of ICS positions structured specifically to this type of contingency.³⁴

Disasters unfold rapidly with marked patient surges and the potential to strain healthcare systems over an extended period. However, in both instances, hospitalists are possibly some of the most qualified clinicians to prepare for and respond to such events. Hospitalists need to assume a leadership role in emergency preparedness to integrate seamlessly into hospital incident command structures and to shape the interdepartmental relationships vital to success – skills at which hospitalists excel. Although no plan can address all possible disasters, familiarity with HICS and well-prepared and well-written JASs should help groups respond and succeed in almost all hazards.

Disclosures

None of the authors have any conflicts of interest to report.

Renin-angiotensin inhibitors, which include angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs), have demonstrated benefits in the treatment of several common cardiovascular and renal conditions. For example, they are prescribed to individuals with hypertension, heart failure with reduced ejection fraction (HFrEF), prior myocardial infarction, and chronic kidney disease with proteinuria. Perhaps unsurprisingly, many individuals presenting for surgery are already on long-term ACE inhibitor or ARB therapy. For example, such individuals comprised approximately one-third of the sample in the Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) multicenter prospective cohort study of major inpatient noncardiac surgery.¹

There is considerable controversy regarding how best to manage these cardiovascular medications during the perioperative period. The critical question pertains to whether renin-angiotensin inhibitors should be temporarily withdrawn 24 hours before surgery or continued uninterrupted up to the day of surgery. The main argument for withdrawing these medications is concern that they cause perioperative hypotension. For example, a recent systematic review of randomized controlled trials (RCTs) and cohort studies found that preoperative continuation of renin-angiotensin inhibitor therapy led to a significantly increased risk of intraoperative hypotension, albeit without associated effects on rates of death, major adverse cardiac events, or postoperative hypotension.² Notably, randomized trial evidence in this meta-analysis was limited to only five trials with a total of 774 participants. Conversely, preoperative interruption of renin-angiotensin inhibitor therapy also has risks. For example, there is a potential for unintended permanent discontinuation of medications with long-term benefits.³ Furthermore, some prior cohort studies have demonstrated that the failure to resume renin-angiotensin inhibitor therapy promptly after surgery is associated with an elevated risk of postoperative mortality.^4,5 While these studies have methodological limitations related to survivorship bias and unmeasured confounders, they still raise concerns that the abrupt withdrawal of long-term cardiovascular therapy before major surgery can have adverse effects. While ACE inhibitor withdrawal has not shown adverse physiological effects in the perioperative setting, it has led to rebound myocardial ischemia in patients with prior myocardial infarction.⁶

Given this controversy, there is variation across hospitals¹ and practice guidelines with respect to perioperative management of renin-angiotensin inhibitors. For example, the 2017 Canadian Cardiovascular Society guidelines recommend that renin-angiotensin inhibitors be stopped temporarily 24 hours before major inpatient surgery,⁷ and the 2014 European guidelines recommend continuing therapy in patients with HFrEF but temporarily interrupting therapy in patients with hypertension.⁸ The 2014 American Heart Association and American College of Cardiology guidelines suggest that either continuation or interruption are reasonable options, but any interrupted therapy should be restarted postoperatively as soon as clinically feasible.⁹

In this issue of the Journal of Hospital Medicine, Shiffermiller and colleagues present a single-center RCT that provides additional high-quality data to improve our understanding of this important clinical issue.¹⁰ In a sample of 275 patients undergoing nonvascular inpatient noncardiac surgery, omission of the final dose of preoperative ACE inhibitor therapy reduced the risk of intraoperative hypotension across multiple definitions, including any episode of systolic blood pressure less than 80 mmHg (number needed to treat: 8), any episode of a systolic blood pressure less than 80 mmHg necessitating vasopressor therapy (number needed to treat: 6), and total cumulative duration of intraoperative systolic blood pressure less than 80 mmHg. In addition, the investigators found that preoperative interruption of ACE inhibitor therapy reduced the risk of postoperative hypotension (number needed to treat: 9), increased the risk of severe postoperative hypertension (number needed to harm: 9), and had no effect on clinical outcomes (eg, acute kidney injury, major adverse cardiac events). In conjunction with a recent systematic review,² these new data demonstrate that temporary preoperative discontinuation of renin-angiotensin inhibitors leads to reduced risks of intraoperative and postoperative hypotension, with the only major identified risk being episodes of postoperative hypertension.

This current evidence base suggests that, in most cases, perioperative physicians should temporarily interrupt renin-angiotensin inhibitor therapy before inpatient noncardiac surgery, provided that protocols are in place to resume treatment postoperatively as soon as clinically feasible. Nonetheless, clinicians must also be cognizant of the key limitations to current data, namely that hypotension, be it intraoperative or postoperative, remains essentially a surrogate endpoint.^11,12 Stated otherwise, the clinical importance of perioperative hypotension is largely predicated on its close association with clinically important or patient-relevant outcomes such as cardiovascular complications, acute kidney injury, and death.^13–16 There is an implicit assumption that a reduction in the risk of hypotension will necessarily lead to reduced rates of clinical adverse events. This assumption is unlikely to be true, especially since many different underlying mechanisms lead to hypotension in the dynamic perioperative environment, including decreased cardiac contractility, decreased heart rate, decreased intravascular volume status, and vasodilation. Consistent with this possibility, different perioperative interventions with similar effects on hypotension have shown quite different effects on clinical outcomes. For example, epidural analgesia invariably reduces perioperative blood pressure, yet it does not appear to increase the risk of postoperative complications.¹⁷ Similarly, both beta-blockers and clonidine increase the risk of significant perioperative hypotension and bradycardia, yet only beta-blockers appear to lead to increased rates of mortality after noncardiac surgery.^18,19 Thus, the relationship between perioperative hypotension and outcomes is clearly complex. Unless a RCT demonstrates that a hypotension-reduction strategy leads to an improvement in clinical outcomes,²⁰ perioperative physicians should not assume that prevention of hypotension will always lead to improvements in patient-relevant clinical outcomes. Similar assumptions about other surrogate endpoints in cardiovascular medicine have sometimes been spectacularly incorrect.^12,21 To more definitively address this important clinical issue, RCTs must be specifically designed to compare the effects of renin-angiotensin inhibitor therapy withdrawal versus continuation on patient-relevant and clinically important outcomes, such as death, myocardial infarction, and stroke. Fortunately, some ongoing trials will address this question, either directly (ClinicalTrials.gov NCT03374449) or as a component of a hypotension-avoidance strategy (ClinicalTrials.gov NCT03505723).

Overall, perioperative physicians should now adopt the standard approach of temporarily withdrawing renin-angiotensin inhibitor therapy 24 hours before major inpatient noncardiac surgery. Nonetheless, they should do so cautiously, recognizing that the data underpinning this strategy remain weak. As with many aspects of perioperative medicine, more research remains needed.

Disclosures

The authors have nothing to report.Funding: DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research, and a Merit Award from the Department of Anesthesia at the University of Toronto.

Renin-angiotensin inhibitors, which include angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs), have demonstrated benefits in the treatment of several common cardiovascular and renal conditions. For example, they are prescribed to individuals with hypertension, heart failure with reduced ejection fraction (HFrEF), prior myocardial infarction, and chronic kidney disease with proteinuria. Perhaps unsurprisingly, many individuals presenting for surgery are already on long-term ACE inhibitor or ARB therapy. For example, such individuals comprised approximately one-third of the sample in the Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) multicenter prospective cohort study of major inpatient noncardiac surgery.¹

There is considerable controversy regarding how best to manage these cardiovascular medications during the perioperative period. The critical question pertains to whether renin-angiotensin inhibitors should be temporarily withdrawn 24 hours before surgery or continued uninterrupted up to the day of surgery. The main argument for withdrawing these medications is concern that they cause perioperative hypotension. For example, a recent systematic review of randomized controlled trials (RCTs) and cohort studies found that preoperative continuation of renin-angiotensin inhibitor therapy led to a significantly increased risk of intraoperative hypotension, albeit without associated effects on rates of death, major adverse cardiac events, or postoperative hypotension.² Notably, randomized trial evidence in this meta-analysis was limited to only five trials with a total of 774 participants. Conversely, preoperative interruption of renin-angiotensin inhibitor therapy also has risks. For example, there is a potential for unintended permanent discontinuation of medications with long-term benefits.³ Furthermore, some prior cohort studies have demonstrated that the failure to resume renin-angiotensin inhibitor therapy promptly after surgery is associated with an elevated risk of postoperative mortality.^4,5 While these studies have methodological limitations related to survivorship bias and unmeasured confounders, they still raise concerns that the abrupt withdrawal of long-term cardiovascular therapy before major surgery can have adverse effects. While ACE inhibitor withdrawal has not shown adverse physiological effects in the perioperative setting, it has led to rebound myocardial ischemia in patients with prior myocardial infarction.⁶

Given this controversy, there is variation across hospitals¹ and practice guidelines with respect to perioperative management of renin-angiotensin inhibitors. For example, the 2017 Canadian Cardiovascular Society guidelines recommend that renin-angiotensin inhibitors be stopped temporarily 24 hours before major inpatient surgery,⁷ and the 2014 European guidelines recommend continuing therapy in patients with HFrEF but temporarily interrupting therapy in patients with hypertension.⁸ The 2014 American Heart Association and American College of Cardiology guidelines suggest that either continuation or interruption are reasonable options, but any interrupted therapy should be restarted postoperatively as soon as clinically feasible.⁹

In this issue of the Journal of Hospital Medicine, Shiffermiller and colleagues present a single-center RCT that provides additional high-quality data to improve our understanding of this important clinical issue.¹⁰ In a sample of 275 patients undergoing nonvascular inpatient noncardiac surgery, omission of the final dose of preoperative ACE inhibitor therapy reduced the risk of intraoperative hypotension across multiple definitions, including any episode of systolic blood pressure less than 80 mmHg (number needed to treat: 8), any episode of a systolic blood pressure less than 80 mmHg necessitating vasopressor therapy (number needed to treat: 6), and total cumulative duration of intraoperative systolic blood pressure less than 80 mmHg. In addition, the investigators found that preoperative interruption of ACE inhibitor therapy reduced the risk of postoperative hypotension (number needed to treat: 9), increased the risk of severe postoperative hypertension (number needed to harm: 9), and had no effect on clinical outcomes (eg, acute kidney injury, major adverse cardiac events). In conjunction with a recent systematic review,² these new data demonstrate that temporary preoperative discontinuation of renin-angiotensin inhibitors leads to reduced risks of intraoperative and postoperative hypotension, with the only major identified risk being episodes of postoperative hypertension.

This current evidence base suggests that, in most cases, perioperative physicians should temporarily interrupt renin-angiotensin inhibitor therapy before inpatient noncardiac surgery, provided that protocols are in place to resume treatment postoperatively as soon as clinically feasible. Nonetheless, clinicians must also be cognizant of the key limitations to current data, namely that hypotension, be it intraoperative or postoperative, remains essentially a surrogate endpoint.^11,12 Stated otherwise, the clinical importance of perioperative hypotension is largely predicated on its close association with clinically important or patient-relevant outcomes such as cardiovascular complications, acute kidney injury, and death.^13–16 There is an implicit assumption that a reduction in the risk of hypotension will necessarily lead to reduced rates of clinical adverse events. This assumption is unlikely to be true, especially since many different underlying mechanisms lead to hypotension in the dynamic perioperative environment, including decreased cardiac contractility, decreased heart rate, decreased intravascular volume status, and vasodilation. Consistent with this possibility, different perioperative interventions with similar effects on hypotension have shown quite different effects on clinical outcomes. For example, epidural analgesia invariably reduces perioperative blood pressure, yet it does not appear to increase the risk of postoperative complications.¹⁷ Similarly, both beta-blockers and clonidine increase the risk of significant perioperative hypotension and bradycardia, yet only beta-blockers appear to lead to increased rates of mortality after noncardiac surgery.^18,19 Thus, the relationship between perioperative hypotension and outcomes is clearly complex. Unless a RCT demonstrates that a hypotension-reduction strategy leads to an improvement in clinical outcomes,²⁰ perioperative physicians should not assume that prevention of hypotension will always lead to improvements in patient-relevant clinical outcomes. Similar assumptions about other surrogate endpoints in cardiovascular medicine have sometimes been spectacularly incorrect.^12,21 To more definitively address this important clinical issue, RCTs must be specifically designed to compare the effects of renin-angiotensin inhibitor therapy withdrawal versus continuation on patient-relevant and clinically important outcomes, such as death, myocardial infarction, and stroke. Fortunately, some ongoing trials will address this question, either directly (ClinicalTrials.gov NCT03374449) or as a component of a hypotension-avoidance strategy (ClinicalTrials.gov NCT03505723).

Overall, perioperative physicians should now adopt the standard approach of temporarily withdrawing renin-angiotensin inhibitor therapy 24 hours before major inpatient noncardiac surgery. Nonetheless, they should do so cautiously, recognizing that the data underpinning this strategy remain weak. As with many aspects of perioperative medicine, more research remains needed.

Disclosures

The authors have nothing to report.Funding: DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research, and a Merit Award from the Department of Anesthesia at the University of Toronto.

Renin-angiotensin inhibitors, which include angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs), have demonstrated benefits in the treatment of several common cardiovascular and renal conditions. For example, they are prescribed to individuals with hypertension, heart failure with reduced ejection fraction (HFrEF), prior myocardial infarction, and chronic kidney disease with proteinuria. Perhaps unsurprisingly, many individuals presenting for surgery are already on long-term ACE inhibitor or ARB therapy. For example, such individuals comprised approximately one-third of the sample in the Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) multicenter prospective cohort study of major inpatient noncardiac surgery.¹

There is considerable controversy regarding how best to manage these cardiovascular medications during the perioperative period. The critical question pertains to whether renin-angiotensin inhibitors should be temporarily withdrawn 24 hours before surgery or continued uninterrupted up to the day of surgery. The main argument for withdrawing these medications is concern that they cause perioperative hypotension. For example, a recent systematic review of randomized controlled trials (RCTs) and cohort studies found that preoperative continuation of renin-angiotensin inhibitor therapy led to a significantly increased risk of intraoperative hypotension, albeit without associated effects on rates of death, major adverse cardiac events, or postoperative hypotension.² Notably, randomized trial evidence in this meta-analysis was limited to only five trials with a total of 774 participants. Conversely, preoperative interruption of renin-angiotensin inhibitor therapy also has risks. For example, there is a potential for unintended permanent discontinuation of medications with long-term benefits.³ Furthermore, some prior cohort studies have demonstrated that the failure to resume renin-angiotensin inhibitor therapy promptly after surgery is associated with an elevated risk of postoperative mortality.^4,5 While these studies have methodological limitations related to survivorship bias and unmeasured confounders, they still raise concerns that the abrupt withdrawal of long-term cardiovascular therapy before major surgery can have adverse effects. While ACE inhibitor withdrawal has not shown adverse physiological effects in the perioperative setting, it has led to rebound myocardial ischemia in patients with prior myocardial infarction.⁶

Given this controversy, there is variation across hospitals¹ and practice guidelines with respect to perioperative management of renin-angiotensin inhibitors. For example, the 2017 Canadian Cardiovascular Society guidelines recommend that renin-angiotensin inhibitors be stopped temporarily 24 hours before major inpatient surgery,⁷ and the 2014 European guidelines recommend continuing therapy in patients with HFrEF but temporarily interrupting therapy in patients with hypertension.⁸ The 2014 American Heart Association and American College of Cardiology guidelines suggest that either continuation or interruption are reasonable options, but any interrupted therapy should be restarted postoperatively as soon as clinically feasible.⁹

In this issue of the Journal of Hospital Medicine, Shiffermiller and colleagues present a single-center RCT that provides additional high-quality data to improve our understanding of this important clinical issue.¹⁰ In a sample of 275 patients undergoing nonvascular inpatient noncardiac surgery, omission of the final dose of preoperative ACE inhibitor therapy reduced the risk of intraoperative hypotension across multiple definitions, including any episode of systolic blood pressure less than 80 mmHg (number needed to treat: 8), any episode of a systolic blood pressure less than 80 mmHg necessitating vasopressor therapy (number needed to treat: 6), and total cumulative duration of intraoperative systolic blood pressure less than 80 mmHg. In addition, the investigators found that preoperative interruption of ACE inhibitor therapy reduced the risk of postoperative hypotension (number needed to treat: 9), increased the risk of severe postoperative hypertension (number needed to harm: 9), and had no effect on clinical outcomes (eg, acute kidney injury, major adverse cardiac events). In conjunction with a recent systematic review,² these new data demonstrate that temporary preoperative discontinuation of renin-angiotensin inhibitors leads to reduced risks of intraoperative and postoperative hypotension, with the only major identified risk being episodes of postoperative hypertension.

This current evidence base suggests that, in most cases, perioperative physicians should temporarily interrupt renin-angiotensin inhibitor therapy before inpatient noncardiac surgery, provided that protocols are in place to resume treatment postoperatively as soon as clinically feasible. Nonetheless, clinicians must also be cognizant of the key limitations to current data, namely that hypotension, be it intraoperative or postoperative, remains essentially a surrogate endpoint.^11,12 Stated otherwise, the clinical importance of perioperative hypotension is largely predicated on its close association with clinically important or patient-relevant outcomes such as cardiovascular complications, acute kidney injury, and death.^13–16 There is an implicit assumption that a reduction in the risk of hypotension will necessarily lead to reduced rates of clinical adverse events. This assumption is unlikely to be true, especially since many different underlying mechanisms lead to hypotension in the dynamic perioperative environment, including decreased cardiac contractility, decreased heart rate, decreased intravascular volume status, and vasodilation. Consistent with this possibility, different perioperative interventions with similar effects on hypotension have shown quite different effects on clinical outcomes. For example, epidural analgesia invariably reduces perioperative blood pressure, yet it does not appear to increase the risk of postoperative complications.¹⁷ Similarly, both beta-blockers and clonidine increase the risk of significant perioperative hypotension and bradycardia, yet only beta-blockers appear to lead to increased rates of mortality after noncardiac surgery.^18,19 Thus, the relationship between perioperative hypotension and outcomes is clearly complex. Unless a RCT demonstrates that a hypotension-reduction strategy leads to an improvement in clinical outcomes,²⁰ perioperative physicians should not assume that prevention of hypotension will always lead to improvements in patient-relevant clinical outcomes. Similar assumptions about other surrogate endpoints in cardiovascular medicine have sometimes been spectacularly incorrect.^12,21 To more definitively address this important clinical issue, RCTs must be specifically designed to compare the effects of renin-angiotensin inhibitor therapy withdrawal versus continuation on patient-relevant and clinically important outcomes, such as death, myocardial infarction, and stroke. Fortunately, some ongoing trials will address this question, either directly (ClinicalTrials.gov NCT03374449) or as a component of a hypotension-avoidance strategy (ClinicalTrials.gov NCT03505723).

Overall, perioperative physicians should now adopt the standard approach of temporarily withdrawing renin-angiotensin inhibitor therapy 24 hours before major inpatient noncardiac surgery. Nonetheless, they should do so cautiously, recognizing that the data underpinning this strategy remain weak. As with many aspects of perioperative medicine, more research remains needed.

Disclosures

The authors have nothing to report.Funding: DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research, and a Merit Award from the Department of Anesthesia at the University of Toronto.

Patient experience and 30-day readmission are important measures of quality of care for hospitalized patients. Performance on both of these measures impact hospitals financially. Performance on the Hospital Consumer Assessment of Healthcare Systems and Providers (HCAHPS) survey is linked to 25% of the incentive payment under Value Based Purchasing (VBP) Program.¹ Starting in 2012, the Centers for Medicare and Medicaid Services (CMS) introduced the Readmission Reduction Program, penalizing hospitals financially for excessive readmissions.²

A relationship between patient experience and readmissions has been explored at the hospital level. Studies have mostly found that higher patient experience scores are associated with lower 30-day readmission rates. In a study of the relationship between 30-day risk-standardized readmission rates for three medical conditions (acute myocardial infarction, heart failure, and pneumonia) and patient experience, the authors noted that higher experience scores for overall care and discharge planning were associated with lower readmission rates for these conditions. They also concluded that patient experience scores were more predictive of 30-day readmission than clinical performance measures. Additionally, the authors predicted that if a hospital increased its total experience scores from the 25th percentile to the 75th percentile, there would be an associated decrease in readmissions by at least 2.3% for each of these conditions.³ Practice management companies and the media have cited this finding to conclude that higher patient experience drives clinical outcomes such as 30-day readmission and that patients are often the best judges of the quality of care delivered.^4,5

Other hospital-level studies have found that high 30-day readmission rates are associated with lower overall experience scores in a mixed surgical patient population; worse reports of pain control and overall care in the colorectal surgery population; lower experience scores with discharge preparedness in vascular surgery patients; and lower experience scores with physician communication, nurse communication, and discharge preparedness.^6-9 A patient-level study noted higher readmissions are associated with worse experience with physician and nursing communication along with a paradoxically better experience with discharge information.¹⁰

Because these studies used an observational design, they demonstrated associations rather than causality. An alternative hypothesis is that readmitted patients complete their patient experience survey after readmission and the low experience is the result, rather than the cause, of their readmission. For patients who are readmitted, it is unclear whether there is an opportunity to complete the survey prior to readmission and whether being readmitted may impact patient perception of quality of care. Using patient-level data, we sought to assess HCAHPS patient-experience responses linked to the index admission of the patients who were readmitted in 30 days and compare it with those patients who were not readmitted during this time period. We paid particular attention to when the surveys were returned.

Study Design

We conducted a retrospective analysis of prospectively collected 10-year HCAHPS and Press Ganey patient survey data for a single tertiary care academic hospital.

Participants

All adult patients discharged from the hospital and who responded to the routinely sent patient-experience survey were included. Surveys were sent to a random sample of 50% of the discharged patients.

The exposure group was comprised of patients who responded to the survey and were readmitted within 30 days of discharge. After subtracting 5 days from the survey receipt date for expected delays related to mail delivery time and processing time, survey response date was calculated. The exposure group was further divided into patients who responded to the survey prior to their 30-day readmission (“Pre-readmission responders”) and those that responded to the survey after their readmission (“Postreadmission responders”). A sensitivity analysis was performed by changing the number of days subtracted from the survey receipt date by 2 days in either direction. This approach did not result in any significant changes in the results.

The control group comprised patients who were not readmitted to the hospital within 30 days of discharge and who did not have an admission in the previous 30 days as well (“Not readmitted” group). An additional comparison group for exploratory analysis included patients who had experienced an admission in the prior 30 days but were not readmitted after the admission linked to the survey. These patients responded to the patient-experience surveys that were linked to their second admission in 30 days (“2^nd-admission responders” group; Figure).

Time Periods

All survey responders from the third quarter of 2006 to the first quarter of 2016 were included in the study. Additionally, administrative data on non-responders were available from 7/2006 to 8/2012. These data were used to estimate response rates. Patient level experience and administrative data were obtained in a linked fashion for these time periods.

Instruments

Press Ganey and HCAHPS surveys were sent via mail in the same envelope. Fifty percent of the discharged patients were randomized to receive the surveys. The Press Ganey survey contained 33 items encompassing several subdomains, including room, meal, nursing, physician, ancillary staff, visitor, discharge, and overall experience.

The HCAHPS survey contained 29 CMS-mandated items, of which 21 are related to patient experience. The development, testing, and methods for administration and reporting of the HCAHPS survey have been previously described and studies using this instrument have been reported in the literature.¹¹ Press Ganey patient satisfaction survey results have also been reported in the literature.¹²

Outcome Variables and Covariates

HCAHPS and Press Ganey experience survey individual item responses were the primary outcome variables of this study. Age, self-reported health status, education, primary language spoken, service line, and time taken to respond to the surveys served as the covariates. These variables are used by CMS for patient-mix adjustment and are collected on the HCAHPS survey. Additionally, the number of days to respond to the survey were included in all regression analysis to adjust for early responder effect.^13-15

Statistical Analysis

“Percent top-box” scores were calculated for each survey item for patients in each group. The percent top-box scores were calculated as the percent of patients who responded “very good” for a given item on Press Ganey survey items and “always” or “definitely yes” or “yes” or “9” or “10” on HCAHPS survey items. CMS utilizes “percent top-box scores” to calculate payments under the VBP program and to report the results publicly. Numerous studies have also reported percent top-box scores for HCAHPS survey results.¹²

We hypothesized that whether patients complete the HCAHPS survey before or after the readmission influences their reporting of experience. To test this hypothesis, HCAHPS and Press Ganey item top-box scores of “Pre-readmission responders” and “Postreadmission responders” were compared with those of the control group using multivariate logistic regression. “Pre-readmission responders” were also compared with “Postreadmission responders”.

“2nd-admission responders” were similarly compared with the control group for an exploratory analysis. Finally, “Postreadmission responders” and “2nd-admission responders” were compared in another exploratory analysis since both these groups responded to the survey after being exposed to the readmission, even though the “Postreadmission responders” group is administratively linked to the index admission.

The Johns Hopkins Institutional Review Board approved this study.

There were 43,737 survey responders, among whom 4,707 were subsequently readmitted within 30 days of discharge. Among the readmitted patients who responded to the surveys linked to their index admission, only 15.8% returned the survey before readmission (pre-readmission responders’) and 84.2% returned the survey after readmission (postreadmission responders). Additionally, 1,663 patients responded to experience surveys linked to their readmission. There were 37,365 patients in the control arm (ie, patients who responded to the survey and were not readmitted within 30 days of discharge or in the prior 30 days; Figure 1). The readmission rate among survey responders was 10.6%. Among the readmitted patients, the median number of days to readmission was 10 days while the median number of days to respond to the survey for this group was 33 days. Among the nonreadmitted patients, the median number of days to return the survey was 29 days.

In this retrospective analysis of prospectively collected Press Ganey and HCAHPS patient-experience survey data, we found that the overwhelming majority of patients readmitted within 30 days of discharge respond to HCAHPS surveys after readmission even though the survey is sent linked to the first admission. This is not unexpected since the median time to survey response is 33 days for this group, while median time to readmission is 10 days. The dissatisfaction pattern of Postreadmission responders was similar to those who responded to the survey linked to the readmission. When a patient is readmitted prior to completing the survey, their responses appear to reflect the cumulative experience of the index admission and the readmission. The lower scores of those who respond to the survey after their readmission appear to be a driver for lower patient-experience scores related to readmissions. Overall, readmission was associated with lower scores on items in five of the nine domains used to calculate patient experience related payments under VBP.¹⁶

These findings have important implications in inferring the direction of potential causal relationship between readmissions and patient experience at the hospital level. Additionally, these patients show broad dissatisfaction with areas beyond physician communication and discharge planning. These include staff responsiveness, phlebotomy, meals, hospital cleanliness, and noise level. This pattern of dissatisfaction may represent impatience and frustration with spending additional time in the hospital environment.

Our results are consistent with findings of many of the earlier studies, but our study goes a step further by using patient-level data and incorporating survey response time in our analysis.^3,7,9,10 By separating out the readmitted patients who responded to the survey prior to admission, we attempted to address the ability of patients’ perception of care to predict future readmissions. Our results do not support this idea, since pre-readmission responders had similar experience scores to non-readmitted patients. However, because of the low numbers of pre-readmission responders, the comparison lacks precision. Current HCAHPS and Press Ganey questions may lack the ability to predict future readmissions because of the timing of the survey (postdischarge) or the questions themselves.

Overall, postreadmission responders are dissatisfied with multiple domains of hospital care. Many of these survey responses may simply be related to general frustration. Alternatively, they may represent a patient population with a high degree of needs that are not as easily met by a hospital’s routine processes of care. Even though the readmission rates were 10.6% among survey responders, 14.6% of the survey responses were associated with readmissions after accounting for those who respond to surveys linked to readmission. These patients could have significant impact on cumulative experience scores.

Our study has a few limitations. First, it involves a single tertiary care academic center study, and our results may not be generalizable. Second, we did not adjust for some of the patient characteristics associated with readmissions. Patients who were admitted within 30 days are different than those not readmitted based on payor, race, length of stay, and severity of illness, and we did not adjust for these factors in our analysis. This was intentional, however. Our goal was to better understand the relationship between 30-day readmission and patient experience scores as they are used for hospital-level studies, VBP, and public reporting. For these purposes, the scores are not adjusted for factors, such as payor and length of stay. We did adjust for patient-mix adjustment factors used by CMS. Third, the response rates to the HCAHPS were low and may have biased the scores. However, HCAHPS is widely used for comparisons between hospitals has been validated, and our study results have implications with regard to comparing hospital-level performance. HCAHPS results are relevant to policy and have financial consequences.¹⁷ Fourth, our study did not directly compare whether the relationship between patient experience for the postreadmission group and nonreadmitted group was different from the relationship between the pre-readmission group and postreadmission group. It is possible that there is no difference in relationship between the groups. However, despite the small number of pre-readmission responders, these patients tended to have more favorable experience responses than those who responded after being readmitted, even after adjusting for response time. Although the P values are nonsignificant for many comparisons, the directionality of the effect is relatively consistent. Also, the vast majority of the patients fall in the postreadmission group, and these patients appear to drive the overall experience related to readmissions. Finally, since relatively few patients turned in surveys prior to readmission, we had limited power to detect a significant difference between these pre-readmission responders and nonreadmitted patients.

Our study has implications for policy makers, researchers, and providers. The HCAHPS scores of patients who are readmitted and completed the survey after being readmitted reflects their experience of both the index admission and the readmission. We did not find evidence to support that HCAHPS survey responses predict future readmissions at the patient level. Our findings do support the concept that lower readmissions rates (whether due to the patient population or processes of care that decrease readmission rates) may improve HCAHPS scores. We suggest caution in assuming that improving patient experience is likely to reduce readmission rates.

Disclosures

The authors declare no conflicts of interest.

Patient experience and 30-day readmission are important measures of quality of care for hospitalized patients. Performance on both of these measures impact hospitals financially. Performance on the Hospital Consumer Assessment of Healthcare Systems and Providers (HCAHPS) survey is linked to 25% of the incentive payment under Value Based Purchasing (VBP) Program.¹ Starting in 2012, the Centers for Medicare and Medicaid Services (CMS) introduced the Readmission Reduction Program, penalizing hospitals financially for excessive readmissions.²

A relationship between patient experience and readmissions has been explored at the hospital level. Studies have mostly found that higher patient experience scores are associated with lower 30-day readmission rates. In a study of the relationship between 30-day risk-standardized readmission rates for three medical conditions (acute myocardial infarction, heart failure, and pneumonia) and patient experience, the authors noted that higher experience scores for overall care and discharge planning were associated with lower readmission rates for these conditions. They also concluded that patient experience scores were more predictive of 30-day readmission than clinical performance measures. Additionally, the authors predicted that if a hospital increased its total experience scores from the 25th percentile to the 75th percentile, there would be an associated decrease in readmissions by at least 2.3% for each of these conditions.³ Practice management companies and the media have cited this finding to conclude that higher patient experience drives clinical outcomes such as 30-day readmission and that patients are often the best judges of the quality of care delivered.^4,5

Other hospital-level studies have found that high 30-day readmission rates are associated with lower overall experience scores in a mixed surgical patient population; worse reports of pain control and overall care in the colorectal surgery population; lower experience scores with discharge preparedness in vascular surgery patients; and lower experience scores with physician communication, nurse communication, and discharge preparedness.^6-9 A patient-level study noted higher readmissions are associated with worse experience with physician and nursing communication along with a paradoxically better experience with discharge information.¹⁰

Because these studies used an observational design, they demonstrated associations rather than causality. An alternative hypothesis is that readmitted patients complete their patient experience survey after readmission and the low experience is the result, rather than the cause, of their readmission. For patients who are readmitted, it is unclear whether there is an opportunity to complete the survey prior to readmission and whether being readmitted may impact patient perception of quality of care. Using patient-level data, we sought to assess HCAHPS patient-experience responses linked to the index admission of the patients who were readmitted in 30 days and compare it with those patients who were not readmitted during this time period. We paid particular attention to when the surveys were returned.

Study Design

We conducted a retrospective analysis of prospectively collected 10-year HCAHPS and Press Ganey patient survey data for a single tertiary care academic hospital.

Participants

All adult patients discharged from the hospital and who responded to the routinely sent patient-experience survey were included. Surveys were sent to a random sample of 50% of the discharged patients.

The exposure group was comprised of patients who responded to the survey and were readmitted within 30 days of discharge. After subtracting 5 days from the survey receipt date for expected delays related to mail delivery time and processing time, survey response date was calculated. The exposure group was further divided into patients who responded to the survey prior to their 30-day readmission (“Pre-readmission responders”) and those that responded to the survey after their readmission (“Postreadmission responders”). A sensitivity analysis was performed by changing the number of days subtracted from the survey receipt date by 2 days in either direction. This approach did not result in any significant changes in the results.

The control group comprised patients who were not readmitted to the hospital within 30 days of discharge and who did not have an admission in the previous 30 days as well (“Not readmitted” group). An additional comparison group for exploratory analysis included patients who had experienced an admission in the prior 30 days but were not readmitted after the admission linked to the survey. These patients responded to the patient-experience surveys that were linked to their second admission in 30 days (“2^nd-admission responders” group; Figure).

Time Periods

All survey responders from the third quarter of 2006 to the first quarter of 2016 were included in the study. Additionally, administrative data on non-responders were available from 7/2006 to 8/2012. These data were used to estimate response rates. Patient level experience and administrative data were obtained in a linked fashion for these time periods.

Instruments

Press Ganey and HCAHPS surveys were sent via mail in the same envelope. Fifty percent of the discharged patients were randomized to receive the surveys. The Press Ganey survey contained 33 items encompassing several subdomains, including room, meal, nursing, physician, ancillary staff, visitor, discharge, and overall experience.

The HCAHPS survey contained 29 CMS-mandated items, of which 21 are related to patient experience. The development, testing, and methods for administration and reporting of the HCAHPS survey have been previously described and studies using this instrument have been reported in the literature.¹¹ Press Ganey patient satisfaction survey results have also been reported in the literature.¹²

Outcome Variables and Covariates

HCAHPS and Press Ganey experience survey individual item responses were the primary outcome variables of this study. Age, self-reported health status, education, primary language spoken, service line, and time taken to respond to the surveys served as the covariates. These variables are used by CMS for patient-mix adjustment and are collected on the HCAHPS survey. Additionally, the number of days to respond to the survey were included in all regression analysis to adjust for early responder effect.^13-15

Statistical Analysis

“Percent top-box” scores were calculated for each survey item for patients in each group. The percent top-box scores were calculated as the percent of patients who responded “very good” for a given item on Press Ganey survey items and “always” or “definitely yes” or “yes” or “9” or “10” on HCAHPS survey items. CMS utilizes “percent top-box scores” to calculate payments under the VBP program and to report the results publicly. Numerous studies have also reported percent top-box scores for HCAHPS survey results.¹²

We hypothesized that whether patients complete the HCAHPS survey before or after the readmission influences their reporting of experience. To test this hypothesis, HCAHPS and Press Ganey item top-box scores of “Pre-readmission responders” and “Postreadmission responders” were compared with those of the control group using multivariate logistic regression. “Pre-readmission responders” were also compared with “Postreadmission responders”.

“2nd-admission responders” were similarly compared with the control group for an exploratory analysis. Finally, “Postreadmission responders” and “2nd-admission responders” were compared in another exploratory analysis since both these groups responded to the survey after being exposed to the readmission, even though the “Postreadmission responders” group is administratively linked to the index admission.

The Johns Hopkins Institutional Review Board approved this study.

There were 43,737 survey responders, among whom 4,707 were subsequently readmitted within 30 days of discharge. Among the readmitted patients who responded to the surveys linked to their index admission, only 15.8% returned the survey before readmission (pre-readmission responders’) and 84.2% returned the survey after readmission (postreadmission responders). Additionally, 1,663 patients responded to experience surveys linked to their readmission. There were 37,365 patients in the control arm (ie, patients who responded to the survey and were not readmitted within 30 days of discharge or in the prior 30 days; Figure 1). The readmission rate among survey responders was 10.6%. Among the readmitted patients, the median number of days to readmission was 10 days while the median number of days to respond to the survey for this group was 33 days. Among the nonreadmitted patients, the median number of days to return the survey was 29 days.

In this retrospective analysis of prospectively collected Press Ganey and HCAHPS patient-experience survey data, we found that the overwhelming majority of patients readmitted within 30 days of discharge respond to HCAHPS surveys after readmission even though the survey is sent linked to the first admission. This is not unexpected since the median time to survey response is 33 days for this group, while median time to readmission is 10 days. The dissatisfaction pattern of Postreadmission responders was similar to those who responded to the survey linked to the readmission. When a patient is readmitted prior to completing the survey, their responses appear to reflect the cumulative experience of the index admission and the readmission. The lower scores of those who respond to the survey after their readmission appear to be a driver for lower patient-experience scores related to readmissions. Overall, readmission was associated with lower scores on items in five of the nine domains used to calculate patient experience related payments under VBP.¹⁶

These findings have important implications in inferring the direction of potential causal relationship between readmissions and patient experience at the hospital level. Additionally, these patients show broad dissatisfaction with areas beyond physician communication and discharge planning. These include staff responsiveness, phlebotomy, meals, hospital cleanliness, and noise level. This pattern of dissatisfaction may represent impatience and frustration with spending additional time in the hospital environment.

Our results are consistent with findings of many of the earlier studies, but our study goes a step further by using patient-level data and incorporating survey response time in our analysis.^3,7,9,10 By separating out the readmitted patients who responded to the survey prior to admission, we attempted to address the ability of patients’ perception of care to predict future readmissions. Our results do not support this idea, since pre-readmission responders had similar experience scores to non-readmitted patients. However, because of the low numbers of pre-readmission responders, the comparison lacks precision. Current HCAHPS and Press Ganey questions may lack the ability to predict future readmissions because of the timing of the survey (postdischarge) or the questions themselves.

Overall, postreadmission responders are dissatisfied with multiple domains of hospital care. Many of these survey responses may simply be related to general frustration. Alternatively, they may represent a patient population with a high degree of needs that are not as easily met by a hospital’s routine processes of care. Even though the readmission rates were 10.6% among survey responders, 14.6% of the survey responses were associated with readmissions after accounting for those who respond to surveys linked to readmission. These patients could have significant impact on cumulative experience scores.

Our study has a few limitations. First, it involves a single tertiary care academic center study, and our results may not be generalizable. Second, we did not adjust for some of the patient characteristics associated with readmissions. Patients who were admitted within 30 days are different than those not readmitted based on payor, race, length of stay, and severity of illness, and we did not adjust for these factors in our analysis. This was intentional, however. Our goal was to better understand the relationship between 30-day readmission and patient experience scores as they are used for hospital-level studies, VBP, and public reporting. For these purposes, the scores are not adjusted for factors, such as payor and length of stay. We did adjust for patient-mix adjustment factors used by CMS. Third, the response rates to the HCAHPS were low and may have biased the scores. However, HCAHPS is widely used for comparisons between hospitals has been validated, and our study results have implications with regard to comparing hospital-level performance. HCAHPS results are relevant to policy and have financial consequences.¹⁷ Fourth, our study did not directly compare whether the relationship between patient experience for the postreadmission group and nonreadmitted group was different from the relationship between the pre-readmission group and postreadmission group. It is possible that there is no difference in relationship between the groups. However, despite the small number of pre-readmission responders, these patients tended to have more favorable experience responses than those who responded after being readmitted, even after adjusting for response time. Although the P values are nonsignificant for many comparisons, the directionality of the effect is relatively consistent. Also, the vast majority of the patients fall in the postreadmission group, and these patients appear to drive the overall experience related to readmissions. Finally, since relatively few patients turned in surveys prior to readmission, we had limited power to detect a significant difference between these pre-readmission responders and nonreadmitted patients.

Our study has implications for policy makers, researchers, and providers. The HCAHPS scores of patients who are readmitted and completed the survey after being readmitted reflects their experience of both the index admission and the readmission. We did not find evidence to support that HCAHPS survey responses predict future readmissions at the patient level. Our findings do support the concept that lower readmissions rates (whether due to the patient population or processes of care that decrease readmission rates) may improve HCAHPS scores. We suggest caution in assuming that improving patient experience is likely to reduce readmission rates.

Disclosures

The authors declare no conflicts of interest.

Patient experience and 30-day readmission are important measures of quality of care for hospitalized patients. Performance on both of these measures impact hospitals financially. Performance on the Hospital Consumer Assessment of Healthcare Systems and Providers (HCAHPS) survey is linked to 25% of the incentive payment under Value Based Purchasing (VBP) Program.¹ Starting in 2012, the Centers for Medicare and Medicaid Services (CMS) introduced the Readmission Reduction Program, penalizing hospitals financially for excessive readmissions.²

A relationship between patient experience and readmissions has been explored at the hospital level. Studies have mostly found that higher patient experience scores are associated with lower 30-day readmission rates. In a study of the relationship between 30-day risk-standardized readmission rates for three medical conditions (acute myocardial infarction, heart failure, and pneumonia) and patient experience, the authors noted that higher experience scores for overall care and discharge planning were associated with lower readmission rates for these conditions. They also concluded that patient experience scores were more predictive of 30-day readmission than clinical performance measures. Additionally, the authors predicted that if a hospital increased its total experience scores from the 25th percentile to the 75th percentile, there would be an associated decrease in readmissions by at least 2.3% for each of these conditions.³ Practice management companies and the media have cited this finding to conclude that higher patient experience drives clinical outcomes such as 30-day readmission and that patients are often the best judges of the quality of care delivered.^4,5

Other hospital-level studies have found that high 30-day readmission rates are associated with lower overall experience scores in a mixed surgical patient population; worse reports of pain control and overall care in the colorectal surgery population; lower experience scores with discharge preparedness in vascular surgery patients; and lower experience scores with physician communication, nurse communication, and discharge preparedness.^6-9 A patient-level study noted higher readmissions are associated with worse experience with physician and nursing communication along with a paradoxically better experience with discharge information.¹⁰

Because these studies used an observational design, they demonstrated associations rather than causality. An alternative hypothesis is that readmitted patients complete their patient experience survey after readmission and the low experience is the result, rather than the cause, of their readmission. For patients who are readmitted, it is unclear whether there is an opportunity to complete the survey prior to readmission and whether being readmitted may impact patient perception of quality of care. Using patient-level data, we sought to assess HCAHPS patient-experience responses linked to the index admission of the patients who were readmitted in 30 days and compare it with those patients who were not readmitted during this time period. We paid particular attention to when the surveys were returned.

Study Design

We conducted a retrospective analysis of prospectively collected 10-year HCAHPS and Press Ganey patient survey data for a single tertiary care academic hospital.

Participants

All adult patients discharged from the hospital and who responded to the routinely sent patient-experience survey were included. Surveys were sent to a random sample of 50% of the discharged patients.

The exposure group was comprised of patients who responded to the survey and were readmitted within 30 days of discharge. After subtracting 5 days from the survey receipt date for expected delays related to mail delivery time and processing time, survey response date was calculated. The exposure group was further divided into patients who responded to the survey prior to their 30-day readmission (“Pre-readmission responders”) and those that responded to the survey after their readmission (“Postreadmission responders”). A sensitivity analysis was performed by changing the number of days subtracted from the survey receipt date by 2 days in either direction. This approach did not result in any significant changes in the results.

The control group comprised patients who were not readmitted to the hospital within 30 days of discharge and who did not have an admission in the previous 30 days as well (“Not readmitted” group). An additional comparison group for exploratory analysis included patients who had experienced an admission in the prior 30 days but were not readmitted after the admission linked to the survey. These patients responded to the patient-experience surveys that were linked to their second admission in 30 days (“2^nd-admission responders” group; Figure).

Time Periods

All survey responders from the third quarter of 2006 to the first quarter of 2016 were included in the study. Additionally, administrative data on non-responders were available from 7/2006 to 8/2012. These data were used to estimate response rates. Patient level experience and administrative data were obtained in a linked fashion for these time periods.

Instruments

Press Ganey and HCAHPS surveys were sent via mail in the same envelope. Fifty percent of the discharged patients were randomized to receive the surveys. The Press Ganey survey contained 33 items encompassing several subdomains, including room, meal, nursing, physician, ancillary staff, visitor, discharge, and overall experience.

The HCAHPS survey contained 29 CMS-mandated items, of which 21 are related to patient experience. The development, testing, and methods for administration and reporting of the HCAHPS survey have been previously described and studies using this instrument have been reported in the literature.¹¹ Press Ganey patient satisfaction survey results have also been reported in the literature.¹²

Outcome Variables and Covariates

HCAHPS and Press Ganey experience survey individual item responses were the primary outcome variables of this study. Age, self-reported health status, education, primary language spoken, service line, and time taken to respond to the surveys served as the covariates. These variables are used by CMS for patient-mix adjustment and are collected on the HCAHPS survey. Additionally, the number of days to respond to the survey were included in all regression analysis to adjust for early responder effect.^13-15

Statistical Analysis

“Percent top-box” scores were calculated for each survey item for patients in each group. The percent top-box scores were calculated as the percent of patients who responded “very good” for a given item on Press Ganey survey items and “always” or “definitely yes” or “yes” or “9” or “10” on HCAHPS survey items. CMS utilizes “percent top-box scores” to calculate payments under the VBP program and to report the results publicly. Numerous studies have also reported percent top-box scores for HCAHPS survey results.¹²

We hypothesized that whether patients complete the HCAHPS survey before or after the readmission influences their reporting of experience. To test this hypothesis, HCAHPS and Press Ganey item top-box scores of “Pre-readmission responders” and “Postreadmission responders” were compared with those of the control group using multivariate logistic regression. “Pre-readmission responders” were also compared with “Postreadmission responders”.

“2nd-admission responders” were similarly compared with the control group for an exploratory analysis. Finally, “Postreadmission responders” and “2nd-admission responders” were compared in another exploratory analysis since both these groups responded to the survey after being exposed to the readmission, even though the “Postreadmission responders” group is administratively linked to the index admission.

The Johns Hopkins Institutional Review Board approved this study.

There were 43,737 survey responders, among whom 4,707 were subsequently readmitted within 30 days of discharge. Among the readmitted patients who responded to the surveys linked to their index admission, only 15.8% returned the survey before readmission (pre-readmission responders’) and 84.2% returned the survey after readmission (postreadmission responders). Additionally, 1,663 patients responded to experience surveys linked to their readmission. There were 37,365 patients in the control arm (ie, patients who responded to the survey and were not readmitted within 30 days of discharge or in the prior 30 days; Figure 1). The readmission rate among survey responders was 10.6%. Among the readmitted patients, the median number of days to readmission was 10 days while the median number of days to respond to the survey for this group was 33 days. Among the nonreadmitted patients, the median number of days to return the survey was 29 days.

In this retrospective analysis of prospectively collected Press Ganey and HCAHPS patient-experience survey data, we found that the overwhelming majority of patients readmitted within 30 days of discharge respond to HCAHPS surveys after readmission even though the survey is sent linked to the first admission. This is not unexpected since the median time to survey response is 33 days for this group, while median time to readmission is 10 days. The dissatisfaction pattern of Postreadmission responders was similar to those who responded to the survey linked to the readmission. When a patient is readmitted prior to completing the survey, their responses appear to reflect the cumulative experience of the index admission and the readmission. The lower scores of those who respond to the survey after their readmission appear to be a driver for lower patient-experience scores related to readmissions. Overall, readmission was associated with lower scores on items in five of the nine domains used to calculate patient experience related payments under VBP.¹⁶

These findings have important implications in inferring the direction of potential causal relationship between readmissions and patient experience at the hospital level. Additionally, these patients show broad dissatisfaction with areas beyond physician communication and discharge planning. These include staff responsiveness, phlebotomy, meals, hospital cleanliness, and noise level. This pattern of dissatisfaction may represent impatience and frustration with spending additional time in the hospital environment.

Our results are consistent with findings of many of the earlier studies, but our study goes a step further by using patient-level data and incorporating survey response time in our analysis.^3,7,9,10 By separating out the readmitted patients who responded to the survey prior to admission, we attempted to address the ability of patients’ perception of care to predict future readmissions. Our results do not support this idea, since pre-readmission responders had similar experience scores to non-readmitted patients. However, because of the low numbers of pre-readmission responders, the comparison lacks precision. Current HCAHPS and Press Ganey questions may lack the ability to predict future readmissions because of the timing of the survey (postdischarge) or the questions themselves.

Overall, postreadmission responders are dissatisfied with multiple domains of hospital care. Many of these survey responses may simply be related to general frustration. Alternatively, they may represent a patient population with a high degree of needs that are not as easily met by a hospital’s routine processes of care. Even though the readmission rates were 10.6% among survey responders, 14.6% of the survey responses were associated with readmissions after accounting for those who respond to surveys linked to readmission. These patients could have significant impact on cumulative experience scores.

Our study has a few limitations. First, it involves a single tertiary care academic center study, and our results may not be generalizable. Second, we did not adjust for some of the patient characteristics associated with readmissions. Patients who were admitted within 30 days are different than those not readmitted based on payor, race, length of stay, and severity of illness, and we did not adjust for these factors in our analysis. This was intentional, however. Our goal was to better understand the relationship between 30-day readmission and patient experience scores as they are used for hospital-level studies, VBP, and public reporting. For these purposes, the scores are not adjusted for factors, such as payor and length of stay. We did adjust for patient-mix adjustment factors used by CMS. Third, the response rates to the HCAHPS were low and may have biased the scores. However, HCAHPS is widely used for comparisons between hospitals has been validated, and our study results have implications with regard to comparing hospital-level performance. HCAHPS results are relevant to policy and have financial consequences.¹⁷ Fourth, our study did not directly compare whether the relationship between patient experience for the postreadmission group and nonreadmitted group was different from the relationship between the pre-readmission group and postreadmission group. It is possible that there is no difference in relationship between the groups. However, despite the small number of pre-readmission responders, these patients tended to have more favorable experience responses than those who responded after being readmitted, even after adjusting for response time. Although the P values are nonsignificant for many comparisons, the directionality of the effect is relatively consistent. Also, the vast majority of the patients fall in the postreadmission group, and these patients appear to drive the overall experience related to readmissions. Finally, since relatively few patients turned in surveys prior to readmission, we had limited power to detect a significant difference between these pre-readmission responders and nonreadmitted patients.

Our study has implications for policy makers, researchers, and providers. The HCAHPS scores of patients who are readmitted and completed the survey after being readmitted reflects their experience of both the index admission and the readmission. We did not find evidence to support that HCAHPS survey responses predict future readmissions at the patient level. Our findings do support the concept that lower readmissions rates (whether due to the patient population or processes of care that decrease readmission rates) may improve HCAHPS scores. We suggest caution in assuming that improving patient experience is likely to reduce readmission rates.

Disclosures

The authors declare no conflicts of interest.

Over 7 million surgeries are performed in United States hospitals each year. Among these surgeries, approximately 85% are noncardiac, nonvascular (NCNV) procedures.^1,2 Although the preoperative use of an angiotensin-converting enzyme inhibitor (ACEI) can be expected in as many as 13% of these surgeries,³ the optimal preoperative ACEI management strategy for patients undergoing NCNV surgeries is poorly understood.

High-quality evidence suggests that renin–angiotensin–aldosterone system (RAAS) inhibitors are associated with intraoperative hypotension among patients undergoing cardiac or vascular surgeries.^4-6 Intraoperative hypotension increases the risk of 30-day mortality,⁷ and the duration of intraoperative hypotension increases the risk of end organ damage.^8,9 This body of evidence suggests that withholding ACEIs prior to cardiac and vascular surgeries is safer than continuing ACEIs without interruption.

The evidence concerning perioperative management of ACEIs is inconclusive for patients undergoing NCNV procedures. Some studies comparing patients taking or not taking a RAAS inhibitor preoperatively describe negligible differences in the frequency of intraoperative hypotensive episodes or complications.^3,10 Others have found an increased risk of intraoperative hypotension and associated postoperative adverse events in patients continuing RAAS inhibitors preoperatively.^11,12 Current guideline discrepancies reflect the uncertainty of the evidence. The guidelines set by the American College of Cardiology and American Heart Association (ACC/AHA) suggest the uninterrupted perioperative continuation of RAAS inhibitors.¹³ The guidelines provided by the European Society of Cardiology and European Society of Anaesthesiology also suggest the continuation of RAAS inhibitors throughout the perioperative period for patients with systolic heart failure but recommend transient discontinuation for patients with hypertension.¹⁴

This randomized study aimed to compare the effect of two practical strategies for preoperative ACEI management on the perioperative blood pressure of patients undergoing NCNV surgery. The two strategies studied were the omission of the final preoperative ACEI dose and the uninterrupted continuation of ACEI therapy. We hypothesized that patients randomized to ACEI omission would experience intraoperative hypotensive episodes less frequently than those randomized to ACEI continuation.

Study Design and Setting

We performed a prospective randomized controlled trial (ClinicalTrials.gov: NCT01669434). The study was carried out in a preoperative evaluation clinic and its affiliated 489-bed academic medical center. Anesthesiologists and internal medicine physicians work collaboratively in the clinic to assess more than 5,000 patients annually (one-third of the institution’s elective surgeries). Patients were randomized 1:1 in block sizes of 5 and 10 and stratified by age < 65 and ≥ 65 years to the omission or continuation of the final preoperative ACEI dose (whether that dose was scheduled for the morning of surgery or the night prior). Preoperative clinicians enrolled patients and subsequently assigned them to intervention groups on the basis of a sequentially numbered list. Patients and healthcare providers were not blinded to allocation status. Intraoperative and postoperative management was provided in accordance with usual care as decided by treatment team.

Participants

Patients who presented to the preoperative evaluation clinic between May 2015 and November 2016 and who had been taking an ACEI for at least 6 weeks were eligible for inclusion. Patients taking angiotensin receptor blockers were excluded. Enrollment was limited to patients planning NCNV surgery. Patients planning intrathoracic, major vascular, organ transplant, and oncologic surgery were excluded. Patients undergoing outpatient procedures not requiring an overnight stay in the hospital were also excluded. Patients with preoperative clinic systolic blood pressure (SBP) <90 or ≥160 or diastolic blood pressure (DBP) <60 or ≥ 95 were excluded. Patients with moderate to severe or clinically decompensated heart failure (left ventricular ejection fraction < 40% or New York Heart Association class III or IV) and those with end-stage renal disease requiring dialysis were also excluded. Patients presenting more than once during the accrual period were eligible for the initial surgery only. All participating patients provided written informed consent. This project was approved by the University of Nebraska Medical Center Institutional Review Board.

Data Collection

Baseline characteristics were recorded by study personnel at the time of enrollment. We measured serum creatinine level at the preoperative visit and on postoperative day 1. An automated anesthesia information management system was used to measure intraoperative blood pressures every three minutes. Postoperative blood pressures through discharge were measured by hospital staff per usual care. During postoperative hospitalization, we queried patients about preoperative adherence to allocation. The digital abstraction of data from the electronic medical record was supplemented by chart review when necessary.

Outcomes

The primary outcome was intraoperative hypotension defined as any SBP < 80 mm Hg occurring from the administration of the first induction agent through transfer to the postanesthesia care unit (PACU). We also examined hypotension during anesthesia induction, which we defined as the 20-minute period following the administration of the first anesthesia induction agent. Episodes of SBP < 80 were defined as being associated with vasopressor administration when any vasopressor was administered during or within 10 min of the episode.

Secondary analyses included postoperative acute kidney injury (AKI), postoperative hypotensive and hypertensive episodes, cardiac events, and mortality. When comparing postoperative day 1 creatinine levels to preoperative creatinine levels, we used the Acute Kidney Injury Network definition of AKI as an increase in creatinine of 0.3 mg/dl or 50%.¹⁵ Postoperative hypotension was defined as any SBP < 90 mm Hg and postoperative hypertension as any SBP > 180 mm Hg occurring after arrival in the PACU. Major adverse cardiac events (MACE) were defined as a composite of acute coronary syndrome, acute heart failure, or new-onset arrhythmia. Discharge from the hospital served as the study endpoint for each patient.

Analysis

Fisher’s exact test was used to compare categorical outcomes between groups. The independent sample t-test or Wilcoxon rank–sum test, as appropriate, was used to compare continuous measures. We selected Fisher’s exact test over χ²-test to produce conservative estimates. Patients were maintained in their allocated group as randomized for analytical purposes regardless of adherence to allocation. We performed all analyses using SAS version 9.4 for Windows (SAS institute, Cary, North Carolina).

We estimated that a sample size of 300 patients would achieve 80% power to detect a difference of 0.17 between the group proportions of 0.33 and 0.50 at a significance level (ɑ) of 0.05 by using a two-sided z-test with continuity correction, assuming 15% loss to follow-up. This estimate allowed for 1 interim analysis using the O’Brien-Fleming spending function truncated at three standard deviations to determine the test boundaries. The monitoring boundary P values associated with the interim analysis were .003, and the threshold P value for the final analysis was .049.

Study Flow

A total of 453 patients were screened for eligibility. Among these patients, 162 were excluded, and the remaining 291 patients were randomized (Figure 1). Surgery was cancelled in six patients allocated to omission and in four patients allocated to continuation arms, respectively. Moreover, three patients in the omission arm were excluded from the analysis following randomization. Specifically, one was excluded because of early discharge without overnight stay, one was excluded because of withdrawal of consent, and one was excluded because of missing primary outcome data. In addition, three cases in the continuation arm were excluded following randomization because of the preoperative (permanent) discontinuation of ACEI therapy in two cases and discharge without an overnight stay in one case. Finally, 275 patients were included in the analysis: 137 in the ACEI omission group and 138 in the ACEI continuation group. Adherence to allocation was 88% and 92% in the omission and continuation groups, respectively.

Baseline Characteristics

The demographic data of patients allocated to ACEI omission and those allocated to ACEI continuation were similar (Table 1). A large majority of patients in both groups took the ACEI lisinopril. Overall, 187 of 275 (68%) patients were taking at least 1 antihypertensive agent, most commonly a diuretic, in addition to an ACEI. SBP measured during the preoperative clinic visit averaged 136.5 mm Hg and did not differ significantly between groups (P = .84).

Surgical Variables

General anesthesia was the most commonly utilized technique, although spinal and regional anesthesia were also represented (Table 1). The majority of cases in both groups were planning for orthopedic and spinal surgery. The method of anesthesia or type of surgery between patients allocated to ACEI omission and those allocated to continuation did not differ (P = .61 and P = .45 respectively).

Episodes of Intraoperative Hypotension

Intraoperative SBPs are displayed in Figure 2, and hemodynamic outcomes are summarized in Table 2. Episodes of SBP < 80 mm Hg during anesthesia induction were numerically less frequent in the omission group than in the continuation group; the difference between groups, however, was not statistically significant (24 of 137 [18%] vs 38 of 138 [28%], RR: 0.64, 95% CI: 0.40 to 1.00, P = .06).

Duration of Intraoperative Hypotension

The median cumulative duration of intraoperative SBP < 80 was two minutes (range 0-41) in patients allocated to the ACEI omission group compared with seven minutes (range 0-214) in those allocated to the continuation group (P < .01). The median cumulative duration of mean arterial pressure < 55 mm Hg was also shorter in the omission group (median 0 min [range 0-39] vs 3 min [range 0-122], P < .01) than in the continuation group. The duration of surgery did not differ between groups (median 141 min [range 77-554] vs 142 min [range 57-665], P = .97).

Postoperative Outcomes

RAAS inhibitor therapy was resumed within 48 h after surgery in 122 of 137 (89%) patients allocated to the omission group and in 128 of 138 (93%) patients allocated to the continuation group (RR: 0.96, 95% CI: 0.89-1.03, P = .30).

Patients allocated to the omission group were significantly less likely to experience postoperative hypotension (15 of 137 [11%] vs 31 of 138 [22%], RR: 0.49, 95% CI: 0.28 to 0.86, P = .02) and significantly more likely to experience severe postoperative hypertension (33 of 137 [24%] vs 17 of 138 [12%], RR: 1.95, 95% CI: 1.14 to 3.34, P = .01) than those allocated to the continuation group. The occurrences of postoperative AKI (RR: 0.60, 95% CI: 0.23 to 1.60, P = .44) or MACE (RR: 4.03, 95% CI: 0.46 to 35.59, P = .21) in the omission group did not differ from the continuation group. The two groups exhibited similar PACU recovery time (mean 97.2 min) and overall hospital length of stay (mean 3.0 days) (P = .49 and P = .56 ). No episodes of inpatient mortality in either group were observed.

The omission of the final preoperative ACEI dose was associated with a significant reduction in the risk of intraoperative hypotension in patients undergoing NCNV surgery. This result confirmed our hypothesis. Coupled with the knowledge that intraoperative hypotension is associated with an increased risk of complications and mortality,^7-9,16 this study favors the omission of the final preoperative ACEI dose prior to NCNV surgeries.

Our findings are in agreement with those of previous randomized studies that explored this question^4,5 and help extend results from cardiac and vascular surgeries to NCNV surgeries. Previous studies on the use of RAAS inhibitors in NCNV surgeries did not employ randomization and yielded mixed results.^3,10-12,17 A large single-institution study (n = 18,056) noted no difference in intraoperative blood pressure between patients taking ACEIs and a matched group of non-ACEI users.³ More recently, a subgroup analysis of the international VISION study showed that omitting RAAS inhibitors on the day of surgery reduced the risk of intraoperative hypotension.¹¹ In that analysis, however, only a small amount of the variability in preoperative RAAS inhibitor management was explainable by modeling known factors, thus allowing for the possibility of unmeasured confounding. Our study, which minimized confounding through randomization, is the first to prospectively compare protocols for patients undergoing NCNV surgery. In contrast to previous studies, the present study was able to report the lack of difference in postoperative RAAS inhibitor administration between study groups. Postoperative RAAS inhibitor management affects complications and mortality.^18,19

Our present finding that preoperative ACEI management affects postoperative hypotensive and hypertensive events conflicts with some previous findings.^11,20 However, recent evidence has revealed that postoperative hypotensive episodes are associated with vascular events and mortality.^11,21 In the context of that evidence, our study lends further support to the omission of the final preoperative ACEI dose. However, we did not detect any decrease in AKI, MACE, or mortality in the ACEI omission group.

This study should be considered in light of its limitations. The pragmatic nature of the study allowed for certain potential biases. Although adherence to allocation was high, the specific ACEI agent taken and the exact timing of the final dose in relation to surgery were not controlled. Anesthetic and postoperative management decisions were made by the treatment team and may have systematically varied given that the treatment team was not blinded to allocation. Furthermore, all outcome data were collected as part of routine care and may not have captured events with great fidelity. Generalizability is limited by the execution of the study at a single academic institution, the preponderance of orthopedic and spine surgeries, and by the negligible representation of ethnicities other than Caucasian. Additionally, recruitment from the preoperative evaluation clinic likely resulted in a patient group with greater comorbidity than the overall population of patients undergoing NCNV surgery. This study was powered for intraoperative hypotension and not postoperative outcomes. Our primary outcome, intraoperative hypotension, is an intermediate measure but one that has well-established associations with adverse outcomes, including mortality. One study showed that sustaining an intraoperative SBP below 70 mm Hg for longer than 5 min increased the risk of mortality from less than 1% to nearly 6%.¹⁶ A large study detected an increase in mortality associated with SBP sustained below 80 mm Hg for 10 min or longer.⁷ Intraoperative hypotension has also been associated with postoperative AKI and myocardial injury.^8,9,12

Many of the limitations of the current study could be addressed by a large randomized controlled trial of ACEI management prior to NCNV surgeries that examines clinically important endpoints beyond intraoperative hypotension. Several specific aspects of perioperative RAAS inhibitor management also deserve further investigation. Our findings may not be generalizable to patients taking ARBs or to patients with congestive heart failure. The preoperative management of ARBs and the preoperative management of RAAS inhibitors in those with congestive heart failure are important areas of focus for future research. Lastly, our finding that preoperative ACEI management decisions can affect postoperative hypotensive and hypertensive events should be substantiated by future research, and any negative consequences of those events should be further explored.

Nonetheless, our study is the largest randomized study of preoperative RAAS inhibition published to date. More than twice as many patients were randomized in this study than in all previous randomized studies combined.^4-6 To the best of our knowledge, this is also the first randomized study evaluating NCNV surgeries. Finally, our use of a practical ACEI omission protocol based on known pharmacokinetics allows for direct application to clinical practice.

Hypertension is among the most common chronic conditions encountered in patients planning surgery, and ACEIs are among the most frequently prescribed antihypertensive medications. This study showed that ACEI continuation is associated with an increased frequency and cumulative duration of intraoperative hypotension. These findings, while at odds with current ACC/AHA guidelines, align with the findings of a meta-analysis on this subject and with recent literature.^3,11-13,22

Acknowledgments

The authors wish to thank Miranda M Fricke, MS, PA-C; Tiffany K Hillyard, APRN-FNP; and Barbara Sink, MPAS, PA-C who assisted in the design and conduct of patient enrollment and randomization procedures.

Disclosures

The authors have no relevant financial conflicts of interest to report.

Funding

This study was subsidized by a grant from the University of Nebraska Medical Center Research Support Fund. The funding source had no role in the design, conduct, analysis, or reporting of the study.

Over 7 million surgeries are performed in United States hospitals each year. Among these surgeries, approximately 85% are noncardiac, nonvascular (NCNV) procedures.^1,2 Although the preoperative use of an angiotensin-converting enzyme inhibitor (ACEI) can be expected in as many as 13% of these surgeries,³ the optimal preoperative ACEI management strategy for patients undergoing NCNV surgeries is poorly understood.

High-quality evidence suggests that renin–angiotensin–aldosterone system (RAAS) inhibitors are associated with intraoperative hypotension among patients undergoing cardiac or vascular surgeries.^4-6 Intraoperative hypotension increases the risk of 30-day mortality,⁷ and the duration of intraoperative hypotension increases the risk of end organ damage.^8,9 This body of evidence suggests that withholding ACEIs prior to cardiac and vascular surgeries is safer than continuing ACEIs without interruption.

The evidence concerning perioperative management of ACEIs is inconclusive for patients undergoing NCNV procedures. Some studies comparing patients taking or not taking a RAAS inhibitor preoperatively describe negligible differences in the frequency of intraoperative hypotensive episodes or complications.^3,10 Others have found an increased risk of intraoperative hypotension and associated postoperative adverse events in patients continuing RAAS inhibitors preoperatively.^11,12 Current guideline discrepancies reflect the uncertainty of the evidence. The guidelines set by the American College of Cardiology and American Heart Association (ACC/AHA) suggest the uninterrupted perioperative continuation of RAAS inhibitors.¹³ The guidelines provided by the European Society of Cardiology and European Society of Anaesthesiology also suggest the continuation of RAAS inhibitors throughout the perioperative period for patients with systolic heart failure but recommend transient discontinuation for patients with hypertension.¹⁴

This randomized study aimed to compare the effect of two practical strategies for preoperative ACEI management on the perioperative blood pressure of patients undergoing NCNV surgery. The two strategies studied were the omission of the final preoperative ACEI dose and the uninterrupted continuation of ACEI therapy. We hypothesized that patients randomized to ACEI omission would experience intraoperative hypotensive episodes less frequently than those randomized to ACEI continuation.

Study Design and Setting

We performed a prospective randomized controlled trial (ClinicalTrials.gov: NCT01669434). The study was carried out in a preoperative evaluation clinic and its affiliated 489-bed academic medical center. Anesthesiologists and internal medicine physicians work collaboratively in the clinic to assess more than 5,000 patients annually (one-third of the institution’s elective surgeries). Patients were randomized 1:1 in block sizes of 5 and 10 and stratified by age < 65 and ≥ 65 years to the omission or continuation of the final preoperative ACEI dose (whether that dose was scheduled for the morning of surgery or the night prior). Preoperative clinicians enrolled patients and subsequently assigned them to intervention groups on the basis of a sequentially numbered list. Patients and healthcare providers were not blinded to allocation status. Intraoperative and postoperative management was provided in accordance with usual care as decided by treatment team.

Participants

Patients who presented to the preoperative evaluation clinic between May 2015 and November 2016 and who had been taking an ACEI for at least 6 weeks were eligible for inclusion. Patients taking angiotensin receptor blockers were excluded. Enrollment was limited to patients planning NCNV surgery. Patients planning intrathoracic, major vascular, organ transplant, and oncologic surgery were excluded. Patients undergoing outpatient procedures not requiring an overnight stay in the hospital were also excluded. Patients with preoperative clinic systolic blood pressure (SBP) <90 or ≥160 or diastolic blood pressure (DBP) <60 or ≥ 95 were excluded. Patients with moderate to severe or clinically decompensated heart failure (left ventricular ejection fraction < 40% or New York Heart Association class III or IV) and those with end-stage renal disease requiring dialysis were also excluded. Patients presenting more than once during the accrual period were eligible for the initial surgery only. All participating patients provided written informed consent. This project was approved by the University of Nebraska Medical Center Institutional Review Board.

Data Collection

Baseline characteristics were recorded by study personnel at the time of enrollment. We measured serum creatinine level at the preoperative visit and on postoperative day 1. An automated anesthesia information management system was used to measure intraoperative blood pressures every three minutes. Postoperative blood pressures through discharge were measured by hospital staff per usual care. During postoperative hospitalization, we queried patients about preoperative adherence to allocation. The digital abstraction of data from the electronic medical record was supplemented by chart review when necessary.

Outcomes

The primary outcome was intraoperative hypotension defined as any SBP < 80 mm Hg occurring from the administration of the first induction agent through transfer to the postanesthesia care unit (PACU). We also examined hypotension during anesthesia induction, which we defined as the 20-minute period following the administration of the first anesthesia induction agent. Episodes of SBP < 80 were defined as being associated with vasopressor administration when any vasopressor was administered during or within 10 min of the episode.

Secondary analyses included postoperative acute kidney injury (AKI), postoperative hypotensive and hypertensive episodes, cardiac events, and mortality. When comparing postoperative day 1 creatinine levels to preoperative creatinine levels, we used the Acute Kidney Injury Network definition of AKI as an increase in creatinine of 0.3 mg/dl or 50%.¹⁵ Postoperative hypotension was defined as any SBP < 90 mm Hg and postoperative hypertension as any SBP > 180 mm Hg occurring after arrival in the PACU. Major adverse cardiac events (MACE) were defined as a composite of acute coronary syndrome, acute heart failure, or new-onset arrhythmia. Discharge from the hospital served as the study endpoint for each patient.

Analysis

Fisher’s exact test was used to compare categorical outcomes between groups. The independent sample t-test or Wilcoxon rank–sum test, as appropriate, was used to compare continuous measures. We selected Fisher’s exact test over χ²-test to produce conservative estimates. Patients were maintained in their allocated group as randomized for analytical purposes regardless of adherence to allocation. We performed all analyses using SAS version 9.4 for Windows (SAS institute, Cary, North Carolina).

We estimated that a sample size of 300 patients would achieve 80% power to detect a difference of 0.17 between the group proportions of 0.33 and 0.50 at a significance level (ɑ) of 0.05 by using a two-sided z-test with continuity correction, assuming 15% loss to follow-up. This estimate allowed for 1 interim analysis using the O’Brien-Fleming spending function truncated at three standard deviations to determine the test boundaries. The monitoring boundary P values associated with the interim analysis were .003, and the threshold P value for the final analysis was .049.

Study Flow

A total of 453 patients were screened for eligibility. Among these patients, 162 were excluded, and the remaining 291 patients were randomized (Figure 1). Surgery was cancelled in six patients allocated to omission and in four patients allocated to continuation arms, respectively. Moreover, three patients in the omission arm were excluded from the analysis following randomization. Specifically, one was excluded because of early discharge without overnight stay, one was excluded because of withdrawal of consent, and one was excluded because of missing primary outcome data. In addition, three cases in the continuation arm were excluded following randomization because of the preoperative (permanent) discontinuation of ACEI therapy in two cases and discharge without an overnight stay in one case. Finally, 275 patients were included in the analysis: 137 in the ACEI omission group and 138 in the ACEI continuation group. Adherence to allocation was 88% and 92% in the omission and continuation groups, respectively.

Baseline Characteristics

The demographic data of patients allocated to ACEI omission and those allocated to ACEI continuation were similar (Table 1). A large majority of patients in both groups took the ACEI lisinopril. Overall, 187 of 275 (68%) patients were taking at least 1 antihypertensive agent, most commonly a diuretic, in addition to an ACEI. SBP measured during the preoperative clinic visit averaged 136.5 mm Hg and did not differ significantly between groups (P = .84).

Surgical Variables

General anesthesia was the most commonly utilized technique, although spinal and regional anesthesia were also represented (Table 1). The majority of cases in both groups were planning for orthopedic and spinal surgery. The method of anesthesia or type of surgery between patients allocated to ACEI omission and those allocated to continuation did not differ (P = .61 and P = .45 respectively).

Episodes of Intraoperative Hypotension

Intraoperative SBPs are displayed in Figure 2, and hemodynamic outcomes are summarized in Table 2. Episodes of SBP < 80 mm Hg during anesthesia induction were numerically less frequent in the omission group than in the continuation group; the difference between groups, however, was not statistically significant (24 of 137 [18%] vs 38 of 138 [28%], RR: 0.64, 95% CI: 0.40 to 1.00, P = .06).

Duration of Intraoperative Hypotension

The median cumulative duration of intraoperative SBP < 80 was two minutes (range 0-41) in patients allocated to the ACEI omission group compared with seven minutes (range 0-214) in those allocated to the continuation group (P < .01). The median cumulative duration of mean arterial pressure < 55 mm Hg was also shorter in the omission group (median 0 min [range 0-39] vs 3 min [range 0-122], P < .01) than in the continuation group. The duration of surgery did not differ between groups (median 141 min [range 77-554] vs 142 min [range 57-665], P = .97).

Postoperative Outcomes

RAAS inhibitor therapy was resumed within 48 h after surgery in 122 of 137 (89%) patients allocated to the omission group and in 128 of 138 (93%) patients allocated to the continuation group (RR: 0.96, 95% CI: 0.89-1.03, P = .30).

Patients allocated to the omission group were significantly less likely to experience postoperative hypotension (15 of 137 [11%] vs 31 of 138 [22%], RR: 0.49, 95% CI: 0.28 to 0.86, P = .02) and significantly more likely to experience severe postoperative hypertension (33 of 137 [24%] vs 17 of 138 [12%], RR: 1.95, 95% CI: 1.14 to 3.34, P = .01) than those allocated to the continuation group. The occurrences of postoperative AKI (RR: 0.60, 95% CI: 0.23 to 1.60, P = .44) or MACE (RR: 4.03, 95% CI: 0.46 to 35.59, P = .21) in the omission group did not differ from the continuation group. The two groups exhibited similar PACU recovery time (mean 97.2 min) and overall hospital length of stay (mean 3.0 days) (P = .49 and P = .56 ). No episodes of inpatient mortality in either group were observed.

The omission of the final preoperative ACEI dose was associated with a significant reduction in the risk of intraoperative hypotension in patients undergoing NCNV surgery. This result confirmed our hypothesis. Coupled with the knowledge that intraoperative hypotension is associated with an increased risk of complications and mortality,^7-9,16 this study favors the omission of the final preoperative ACEI dose prior to NCNV surgeries.

Our findings are in agreement with those of previous randomized studies that explored this question^4,5 and help extend results from cardiac and vascular surgeries to NCNV surgeries. Previous studies on the use of RAAS inhibitors in NCNV surgeries did not employ randomization and yielded mixed results.^3,10-12,17 A large single-institution study (n = 18,056) noted no difference in intraoperative blood pressure between patients taking ACEIs and a matched group of non-ACEI users.³ More recently, a subgroup analysis of the international VISION study showed that omitting RAAS inhibitors on the day of surgery reduced the risk of intraoperative hypotension.¹¹ In that analysis, however, only a small amount of the variability in preoperative RAAS inhibitor management was explainable by modeling known factors, thus allowing for the possibility of unmeasured confounding. Our study, which minimized confounding through randomization, is the first to prospectively compare protocols for patients undergoing NCNV surgery. In contrast to previous studies, the present study was able to report the lack of difference in postoperative RAAS inhibitor administration between study groups. Postoperative RAAS inhibitor management affects complications and mortality.^18,19

Our present finding that preoperative ACEI management affects postoperative hypotensive and hypertensive events conflicts with some previous findings.^11,20 However, recent evidence has revealed that postoperative hypotensive episodes are associated with vascular events and mortality.^11,21 In the context of that evidence, our study lends further support to the omission of the final preoperative ACEI dose. However, we did not detect any decrease in AKI, MACE, or mortality in the ACEI omission group.

This study should be considered in light of its limitations. The pragmatic nature of the study allowed for certain potential biases. Although adherence to allocation was high, the specific ACEI agent taken and the exact timing of the final dose in relation to surgery were not controlled. Anesthetic and postoperative management decisions were made by the treatment team and may have systematically varied given that the treatment team was not blinded to allocation. Furthermore, all outcome data were collected as part of routine care and may not have captured events with great fidelity. Generalizability is limited by the execution of the study at a single academic institution, the preponderance of orthopedic and spine surgeries, and by the negligible representation of ethnicities other than Caucasian. Additionally, recruitment from the preoperative evaluation clinic likely resulted in a patient group with greater comorbidity than the overall population of patients undergoing NCNV surgery. This study was powered for intraoperative hypotension and not postoperative outcomes. Our primary outcome, intraoperative hypotension, is an intermediate measure but one that has well-established associations with adverse outcomes, including mortality. One study showed that sustaining an intraoperative SBP below 70 mm Hg for longer than 5 min increased the risk of mortality from less than 1% to nearly 6%.¹⁶ A large study detected an increase in mortality associated with SBP sustained below 80 mm Hg for 10 min or longer.⁷ Intraoperative hypotension has also been associated with postoperative AKI and myocardial injury.^8,9,12

Many of the limitations of the current study could be addressed by a large randomized controlled trial of ACEI management prior to NCNV surgeries that examines clinically important endpoints beyond intraoperative hypotension. Several specific aspects of perioperative RAAS inhibitor management also deserve further investigation. Our findings may not be generalizable to patients taking ARBs or to patients with congestive heart failure. The preoperative management of ARBs and the preoperative management of RAAS inhibitors in those with congestive heart failure are important areas of focus for future research. Lastly, our finding that preoperative ACEI management decisions can affect postoperative hypotensive and hypertensive events should be substantiated by future research, and any negative consequences of those events should be further explored.

Nonetheless, our study is the largest randomized study of preoperative RAAS inhibition published to date. More than twice as many patients were randomized in this study than in all previous randomized studies combined.^4-6 To the best of our knowledge, this is also the first randomized study evaluating NCNV surgeries. Finally, our use of a practical ACEI omission protocol based on known pharmacokinetics allows for direct application to clinical practice.

Hypertension is among the most common chronic conditions encountered in patients planning surgery, and ACEIs are among the most frequently prescribed antihypertensive medications. This study showed that ACEI continuation is associated with an increased frequency and cumulative duration of intraoperative hypotension. These findings, while at odds with current ACC/AHA guidelines, align with the findings of a meta-analysis on this subject and with recent literature.^3,11-13,22

Acknowledgments

The authors wish to thank Miranda M Fricke, MS, PA-C; Tiffany K Hillyard, APRN-FNP; and Barbara Sink, MPAS, PA-C who assisted in the design and conduct of patient enrollment and randomization procedures.

Disclosures

The authors have no relevant financial conflicts of interest to report.

Funding

This study was subsidized by a grant from the University of Nebraska Medical Center Research Support Fund. The funding source had no role in the design, conduct, analysis, or reporting of the study.

Over 7 million surgeries are performed in United States hospitals each year. Among these surgeries, approximately 85% are noncardiac, nonvascular (NCNV) procedures.^1,2 Although the preoperative use of an angiotensin-converting enzyme inhibitor (ACEI) can be expected in as many as 13% of these surgeries,³ the optimal preoperative ACEI management strategy for patients undergoing NCNV surgeries is poorly understood.

High-quality evidence suggests that renin–angiotensin–aldosterone system (RAAS) inhibitors are associated with intraoperative hypotension among patients undergoing cardiac or vascular surgeries.^4-6 Intraoperative hypotension increases the risk of 30-day mortality,⁷ and the duration of intraoperative hypotension increases the risk of end organ damage.^8,9 This body of evidence suggests that withholding ACEIs prior to cardiac and vascular surgeries is safer than continuing ACEIs without interruption.

The evidence concerning perioperative management of ACEIs is inconclusive for patients undergoing NCNV procedures. Some studies comparing patients taking or not taking a RAAS inhibitor preoperatively describe negligible differences in the frequency of intraoperative hypotensive episodes or complications.^3,10 Others have found an increased risk of intraoperative hypotension and associated postoperative adverse events in patients continuing RAAS inhibitors preoperatively.^11,12 Current guideline discrepancies reflect the uncertainty of the evidence. The guidelines set by the American College of Cardiology and American Heart Association (ACC/AHA) suggest the uninterrupted perioperative continuation of RAAS inhibitors.¹³ The guidelines provided by the European Society of Cardiology and European Society of Anaesthesiology also suggest the continuation of RAAS inhibitors throughout the perioperative period for patients with systolic heart failure but recommend transient discontinuation for patients with hypertension.¹⁴

This randomized study aimed to compare the effect of two practical strategies for preoperative ACEI management on the perioperative blood pressure of patients undergoing NCNV surgery. The two strategies studied were the omission of the final preoperative ACEI dose and the uninterrupted continuation of ACEI therapy. We hypothesized that patients randomized to ACEI omission would experience intraoperative hypotensive episodes less frequently than those randomized to ACEI continuation.

Study Design and Setting

We performed a prospective randomized controlled trial (ClinicalTrials.gov: NCT01669434). The study was carried out in a preoperative evaluation clinic and its affiliated 489-bed academic medical center. Anesthesiologists and internal medicine physicians work collaboratively in the clinic to assess more than 5,000 patients annually (one-third of the institution’s elective surgeries). Patients were randomized 1:1 in block sizes of 5 and 10 and stratified by age < 65 and ≥ 65 years to the omission or continuation of the final preoperative ACEI dose (whether that dose was scheduled for the morning of surgery or the night prior). Preoperative clinicians enrolled patients and subsequently assigned them to intervention groups on the basis of a sequentially numbered list. Patients and healthcare providers were not blinded to allocation status. Intraoperative and postoperative management was provided in accordance with usual care as decided by treatment team.

Participants

Patients who presented to the preoperative evaluation clinic between May 2015 and November 2016 and who had been taking an ACEI for at least 6 weeks were eligible for inclusion. Patients taking angiotensin receptor blockers were excluded. Enrollment was limited to patients planning NCNV surgery. Patients planning intrathoracic, major vascular, organ transplant, and oncologic surgery were excluded. Patients undergoing outpatient procedures not requiring an overnight stay in the hospital were also excluded. Patients with preoperative clinic systolic blood pressure (SBP) <90 or ≥160 or diastolic blood pressure (DBP) <60 or ≥ 95 were excluded. Patients with moderate to severe or clinically decompensated heart failure (left ventricular ejection fraction < 40% or New York Heart Association class III or IV) and those with end-stage renal disease requiring dialysis were also excluded. Patients presenting more than once during the accrual period were eligible for the initial surgery only. All participating patients provided written informed consent. This project was approved by the University of Nebraska Medical Center Institutional Review Board.

Data Collection

Baseline characteristics were recorded by study personnel at the time of enrollment. We measured serum creatinine level at the preoperative visit and on postoperative day 1. An automated anesthesia information management system was used to measure intraoperative blood pressures every three minutes. Postoperative blood pressures through discharge were measured by hospital staff per usual care. During postoperative hospitalization, we queried patients about preoperative adherence to allocation. The digital abstraction of data from the electronic medical record was supplemented by chart review when necessary.

Outcomes

The primary outcome was intraoperative hypotension defined as any SBP < 80 mm Hg occurring from the administration of the first induction agent through transfer to the postanesthesia care unit (PACU). We also examined hypotension during anesthesia induction, which we defined as the 20-minute period following the administration of the first anesthesia induction agent. Episodes of SBP < 80 were defined as being associated with vasopressor administration when any vasopressor was administered during or within 10 min of the episode.

Secondary analyses included postoperative acute kidney injury (AKI), postoperative hypotensive and hypertensive episodes, cardiac events, and mortality. When comparing postoperative day 1 creatinine levels to preoperative creatinine levels, we used the Acute Kidney Injury Network definition of AKI as an increase in creatinine of 0.3 mg/dl or 50%.¹⁵ Postoperative hypotension was defined as any SBP < 90 mm Hg and postoperative hypertension as any SBP > 180 mm Hg occurring after arrival in the PACU. Major adverse cardiac events (MACE) were defined as a composite of acute coronary syndrome, acute heart failure, or new-onset arrhythmia. Discharge from the hospital served as the study endpoint for each patient.

Analysis

Fisher’s exact test was used to compare categorical outcomes between groups. The independent sample t-test or Wilcoxon rank–sum test, as appropriate, was used to compare continuous measures. We selected Fisher’s exact test over χ²-test to produce conservative estimates. Patients were maintained in their allocated group as randomized for analytical purposes regardless of adherence to allocation. We performed all analyses using SAS version 9.4 for Windows (SAS institute, Cary, North Carolina).

We estimated that a sample size of 300 patients would achieve 80% power to detect a difference of 0.17 between the group proportions of 0.33 and 0.50 at a significance level (ɑ) of 0.05 by using a two-sided z-test with continuity correction, assuming 15% loss to follow-up. This estimate allowed for 1 interim analysis using the O’Brien-Fleming spending function truncated at three standard deviations to determine the test boundaries. The monitoring boundary P values associated with the interim analysis were .003, and the threshold P value for the final analysis was .049.

Study Flow

A total of 453 patients were screened for eligibility. Among these patients, 162 were excluded, and the remaining 291 patients were randomized (Figure 1). Surgery was cancelled in six patients allocated to omission and in four patients allocated to continuation arms, respectively. Moreover, three patients in the omission arm were excluded from the analysis following randomization. Specifically, one was excluded because of early discharge without overnight stay, one was excluded because of withdrawal of consent, and one was excluded because of missing primary outcome data. In addition, three cases in the continuation arm were excluded following randomization because of the preoperative (permanent) discontinuation of ACEI therapy in two cases and discharge without an overnight stay in one case. Finally, 275 patients were included in the analysis: 137 in the ACEI omission group and 138 in the ACEI continuation group. Adherence to allocation was 88% and 92% in the omission and continuation groups, respectively.

Baseline Characteristics

The demographic data of patients allocated to ACEI omission and those allocated to ACEI continuation were similar (Table 1). A large majority of patients in both groups took the ACEI lisinopril. Overall, 187 of 275 (68%) patients were taking at least 1 antihypertensive agent, most commonly a diuretic, in addition to an ACEI. SBP measured during the preoperative clinic visit averaged 136.5 mm Hg and did not differ significantly between groups (P = .84).

Surgical Variables

General anesthesia was the most commonly utilized technique, although spinal and regional anesthesia were also represented (Table 1). The majority of cases in both groups were planning for orthopedic and spinal surgery. The method of anesthesia or type of surgery between patients allocated to ACEI omission and those allocated to continuation did not differ (P = .61 and P = .45 respectively).

Episodes of Intraoperative Hypotension

Intraoperative SBPs are displayed in Figure 2, and hemodynamic outcomes are summarized in Table 2. Episodes of SBP < 80 mm Hg during anesthesia induction were numerically less frequent in the omission group than in the continuation group; the difference between groups, however, was not statistically significant (24 of 137 [18%] vs 38 of 138 [28%], RR: 0.64, 95% CI: 0.40 to 1.00, P = .06).

Duration of Intraoperative Hypotension

The median cumulative duration of intraoperative SBP < 80 was two minutes (range 0-41) in patients allocated to the ACEI omission group compared with seven minutes (range 0-214) in those allocated to the continuation group (P < .01). The median cumulative duration of mean arterial pressure < 55 mm Hg was also shorter in the omission group (median 0 min [range 0-39] vs 3 min [range 0-122], P < .01) than in the continuation group. The duration of surgery did not differ between groups (median 141 min [range 77-554] vs 142 min [range 57-665], P = .97).

Postoperative Outcomes

RAAS inhibitor therapy was resumed within 48 h after surgery in 122 of 137 (89%) patients allocated to the omission group and in 128 of 138 (93%) patients allocated to the continuation group (RR: 0.96, 95% CI: 0.89-1.03, P = .30).

Patients allocated to the omission group were significantly less likely to experience postoperative hypotension (15 of 137 [11%] vs 31 of 138 [22%], RR: 0.49, 95% CI: 0.28 to 0.86, P = .02) and significantly more likely to experience severe postoperative hypertension (33 of 137 [24%] vs 17 of 138 [12%], RR: 1.95, 95% CI: 1.14 to 3.34, P = .01) than those allocated to the continuation group. The occurrences of postoperative AKI (RR: 0.60, 95% CI: 0.23 to 1.60, P = .44) or MACE (RR: 4.03, 95% CI: 0.46 to 35.59, P = .21) in the omission group did not differ from the continuation group. The two groups exhibited similar PACU recovery time (mean 97.2 min) and overall hospital length of stay (mean 3.0 days) (P = .49 and P = .56 ). No episodes of inpatient mortality in either group were observed.

The omission of the final preoperative ACEI dose was associated with a significant reduction in the risk of intraoperative hypotension in patients undergoing NCNV surgery. This result confirmed our hypothesis. Coupled with the knowledge that intraoperative hypotension is associated with an increased risk of complications and mortality,^7-9,16 this study favors the omission of the final preoperative ACEI dose prior to NCNV surgeries.

Our findings are in agreement with those of previous randomized studies that explored this question^4,5 and help extend results from cardiac and vascular surgeries to NCNV surgeries. Previous studies on the use of RAAS inhibitors in NCNV surgeries did not employ randomization and yielded mixed results.^3,10-12,17 A large single-institution study (n = 18,056) noted no difference in intraoperative blood pressure between patients taking ACEIs and a matched group of non-ACEI users.³ More recently, a subgroup analysis of the international VISION study showed that omitting RAAS inhibitors on the day of surgery reduced the risk of intraoperative hypotension.¹¹ In that analysis, however, only a small amount of the variability in preoperative RAAS inhibitor management was explainable by modeling known factors, thus allowing for the possibility of unmeasured confounding. Our study, which minimized confounding through randomization, is the first to prospectively compare protocols for patients undergoing NCNV surgery. In contrast to previous studies, the present study was able to report the lack of difference in postoperative RAAS inhibitor administration between study groups. Postoperative RAAS inhibitor management affects complications and mortality.^18,19

Our present finding that preoperative ACEI management affects postoperative hypotensive and hypertensive events conflicts with some previous findings.^11,20 However, recent evidence has revealed that postoperative hypotensive episodes are associated with vascular events and mortality.^11,21 In the context of that evidence, our study lends further support to the omission of the final preoperative ACEI dose. However, we did not detect any decrease in AKI, MACE, or mortality in the ACEI omission group.

This study should be considered in light of its limitations. The pragmatic nature of the study allowed for certain potential biases. Although adherence to allocation was high, the specific ACEI agent taken and the exact timing of the final dose in relation to surgery were not controlled. Anesthetic and postoperative management decisions were made by the treatment team and may have systematically varied given that the treatment team was not blinded to allocation. Furthermore, all outcome data were collected as part of routine care and may not have captured events with great fidelity. Generalizability is limited by the execution of the study at a single academic institution, the preponderance of orthopedic and spine surgeries, and by the negligible representation of ethnicities other than Caucasian. Additionally, recruitment from the preoperative evaluation clinic likely resulted in a patient group with greater comorbidity than the overall population of patients undergoing NCNV surgery. This study was powered for intraoperative hypotension and not postoperative outcomes. Our primary outcome, intraoperative hypotension, is an intermediate measure but one that has well-established associations with adverse outcomes, including mortality. One study showed that sustaining an intraoperative SBP below 70 mm Hg for longer than 5 min increased the risk of mortality from less than 1% to nearly 6%.¹⁶ A large study detected an increase in mortality associated with SBP sustained below 80 mm Hg for 10 min or longer.⁷ Intraoperative hypotension has also been associated with postoperative AKI and myocardial injury.^8,9,12

Many of the limitations of the current study could be addressed by a large randomized controlled trial of ACEI management prior to NCNV surgeries that examines clinically important endpoints beyond intraoperative hypotension. Several specific aspects of perioperative RAAS inhibitor management also deserve further investigation. Our findings may not be generalizable to patients taking ARBs or to patients with congestive heart failure. The preoperative management of ARBs and the preoperative management of RAAS inhibitors in those with congestive heart failure are important areas of focus for future research. Lastly, our finding that preoperative ACEI management decisions can affect postoperative hypotensive and hypertensive events should be substantiated by future research, and any negative consequences of those events should be further explored.

Nonetheless, our study is the largest randomized study of preoperative RAAS inhibition published to date. More than twice as many patients were randomized in this study than in all previous randomized studies combined.^4-6 To the best of our knowledge, this is also the first randomized study evaluating NCNV surgeries. Finally, our use of a practical ACEI omission protocol based on known pharmacokinetics allows for direct application to clinical practice.

Hypertension is among the most common chronic conditions encountered in patients planning surgery, and ACEIs are among the most frequently prescribed antihypertensive medications. This study showed that ACEI continuation is associated with an increased frequency and cumulative duration of intraoperative hypotension. These findings, while at odds with current ACC/AHA guidelines, align with the findings of a meta-analysis on this subject and with recent literature.^3,11-13,22

Acknowledgments

The authors wish to thank Miranda M Fricke, MS, PA-C; Tiffany K Hillyard, APRN-FNP; and Barbara Sink, MPAS, PA-C who assisted in the design and conduct of patient enrollment and randomization procedures.

Disclosures

The authors have no relevant financial conflicts of interest to report.

Funding

This study was subsidized by a grant from the University of Nebraska Medical Center Research Support Fund. The funding source had no role in the design, conduct, analysis, or reporting of the study.

High hospital occupancy is a fundamental challenge faced by healthcare systems in the United States.^1-3 However, few studies have examined the effect of high occupancy on outcomes in the inpatient setting,^4-9 and these showed mixed results. Hospital-acquired conditions (HACs), such as Clostridium difficile infection (CDI), are quality indicators for inpatient care and part of the Centers for Medicare and Medicaid Services’ Hospital-Acquired Conditions Reductions Program.^10-12 However, few studies—largely conducted outside of the US—have evaluated the association between inpatient occupancy and HACs. These studies showed increasing hospital-acquired infection rates with increasing occupancy.^13-15 Past studies of hospital occupancy have relied on annual average licensed bed counts, which are not a reliable measure of available and staffed beds and do not account for variations in patient volume and bed supply.¹⁶ Using a novel measure of inpatient occupancy, we tested the hypothesis that increasing inpatient occupancy is associated with a greater likelihood of CDI.

We performed a retrospective analysis of administrative data from non-federal, acute care hospitals in California during 2008–2012 using the Office of Statewide Health Planning and Development (OSHPD) Patient Discharge Data set, a complete census of all CA licensed general acute care hospital discharge records. This study was approved by the OSHPD Committee for the Protection of Human Subjects and was deemed exempt by our institution’s Institutional Review Board.

Selection of Participants

The study population consisted of fee-for-service Medicare enrollees ≥65 years admitted through the emergency department (ED) with a hospital length of stay (HLOS) <50 days and a primary discharge diagnosis of acute myocardial infarction (MI), pneumonia (PNA), or heart failure (HF; [identified through the respective Clinical Classification Software [CCS]).

The sample was restricted to discharges with a HLOS of <50 days, because those with longer HLOS (0.01% of study sample) were likely different in ways that may bias our findings (eg, they will likely be sicker). We limited our study to admissions through the ED to reduce potential selection bias by excluding elective admissions and hospital-to-hospital transfers, which are likely dependent on occupancy. MI, HF, and PNA diagnoses were selected because they are prevalent and have high inpatient mortality, allowing us to examine the effect of occupancy on some of the sickest inpatients.¹⁷

Hospital-acquired cases of CDI were identified as discharges (using ICD-9 code 008.45 for CDI) that were not marked as present-on-admission (POA) using the method described by Zhan et al.¹⁸ To avoid small facility outlying effects, we included hospitals that had 100 or more MI, HF, and PNA discharges that met the inclusion criteria over the study years.

OSHPD inpatient data were combined with OSHPD hospital annual financial data that contain hospital-level variables including ownership (City/County, District, Investor, and Non-Profit), geography (based on health services area), teaching status, urbanicity, and size based on the number of average annual licensed beds. If characteristics were not available for a given hospital for 1 or more years, the information from the closest available year was used for that hospital (replacement required for 10,504 (1.5%) cases; 4,856 otherwise eligible cases (0.7%) were dropped because the hospital was not included in the annual financial data for any year. Approximately 0.2% of records had invalid values for disposition, payer, or admission route, and were therefore dropped. Patient residence zip code-level socioeconomic status was measured using the percentage of families living below the poverty line, median family income, and the percentage of individuals with less than a high school degree among those aged ≥ 25 years¹⁹; these measures were divided into 3 groups (bottom quartile, top quartile, and middle 50%) for analysis.

Measure of Occupancy

Calculating Daily Census and Bed Capacity

We calculated the daily census using admission date and HLOS for each observation in our dataset. We approximated the bed capacity as the maximum daily census in the 121-day window (+/- 60 days) around each census day in each hospital. The 121-day window was chosen to increase the likelihood of capturing changes in bed availability (eg, due to unit closures) and seasonal variability. Our daily census does not include patients admitted with psychiatric and obstetrics diagnoses and long-term care/rehabilitation stays (identified through CCS categories and excluded) because these patients are not likely to compete for the same hospital resources as those receiving care for MI, HF, and PNA. See Appendix Table 1 for definition of the occupancy terms.

Calculating Relative Daily Occupancy

We developed a raw hospital-specific occupancy measure by dividing the daily census by the maximum census in each 121-day window for each hospital. We converted these raw measures to percentiles within the 121-day window to create a daily relative occupancy measure. For example, median level occupancy day would correspond to an occupancy of 0.5; a minimum or maximum occupancy day would correspond to 0 or 1, respectively. We preferred a relative occupancy measure because it assumes that what constitutes “high occupancy” likely depends on the usual occupancy level of the facility.

Measuring Admission Day Occupancy and Average Occupancy over Hospitalization

Using the relative daily occupancy values, we constructed patient-level variables representing occupancy on admission day and average occupancy during hospitalization.

Data Analysis

First, we estimated descriptive statistics of the sample for occupancy, patient-level (eg, age, race, gender, and severity of illness), hospital-level (eg, size, teaching status, and urbanicity), and incident-level (day-of-the-week and season) variables. Next, we used logistic regression with cluster standard errors to estimate the adjusted and unadjusted association of occupancy with CDI. For this analysis, occupancy was broken into 4 groups: 0.00-0.25 (low occupancy); 0.26-0.50; 0.51-0.75; and 0.76-1.00 (high occupancy), with the 0.0-0.25 group treated as the reference level. We fit separate models for admission and average occupancy and re-ran the latter model including HLOS as a sensitivity analysis.

Study Population and Hospitals

Across 327 hospitals, 558,829 discharges (including deaths) met our inclusion criteria and there were 2045 admissions with CDI. The hospital and discharge characteristics are reported in Appendix Table 2.

Relationship of Occupancy with CDI

With regard to admission occupancy, the 0.26-0.50 group did not have a significantly higher rate of CDI than the low occupancy group. Both the 0.51-0.75 and the 0.76-1.00 occupancy groups had 15% lower odds of CDI compared to the low occupancy group (Table). The adjusted results were similar, although the comparison between the low and high occupancy groups was marginally nonsignificant.

With regard to average occupancy, intermediate levels of occupancy (ie, 0.26-0.50 and 0.51-0.75 groups) had over 3-fold increased odds of CDI relative to the low occupancy group; the high occupancy group did not have significantly different odds of CDI compared to the low occupancy group (Table 1). The adjusted results were similar with no changes in statistical significance. Including HLOS tempered the adjusted odds of CDI to 1.6 for intermediate levels of occupancy, but these remained significantly higher than high or low occupancy.

Hospital occupancy is related to CDI. However, contrary to expectation, we found that higher admission and average occupancy over hospitalization were not related to more hospital-acquired CDI. CDI rates were highest for intermediate levels of average occupancy with lower CDI rates at high and low occupancy. CDI had an inverse relationship with admission occupancy.

These findings suggest that an exploration of the processes associated with hospitals accommodating higher occupancy might elucidate measures to reduce CDI. How do staffing, implementation of policies, and routine procedures vary when hospitals are busy or quiet? What aspects of care delivery that function well during high and low occupancy periods breakdown during intermediate occupancy? Hospital policies, practices, and procedures during different phases of occupancy might inform best practices. These data suggest that hospital occupancy level should be a routinely collected data element by infection control officers and that this should be linked with protocols triggered or modified with high or low occupancy that might affect HACs.

Previous studies in Europe found increasing hospital-acquired infection rates with increasing occupancy.^13-15 The authors postulated that increasing occupancy may limit available resources and increase nursing workloads, negatively impacting adherence to hand hygiene and cleaning protocols .⁸ However, these studies did not account for infections that were POA. In addition, our study examined hospitals in California after the 2006 implementation of the minimum nurse staffing policy, which means that staff to patient ratios could not fall below fixed thresholds that were typically higher than pre-policy ratios.¹⁹

This study had limitations pertaining to coded administrative data, including quality of coding and data validity. However, OSHPD has strict data reporting processes.²⁰ This study focused on 1 state; however, California is large with a demographically diverse population and hospital types, characteristics that would help generalize findings. Furthermore, when using the average occupancy measure, we could not determine whether the complication was acquired during the high occupancy period of the hospitalization.

Higher admission day occupancy was associated with lower likelihood of CDI, and CDI rates were lower at high and low average occupancy. These findings should prompt exploration of how hospitals react to occupancy changes and how those care processes translate into HACs in order to inform best practices for hospital care.

Acknowledgments

The authors would like to thank Ms. Amanda Kogowski, MPH and Mr. Rekar Taymour, MS for their editorial assistance with drafting the manuscript.

Disclosures

The authors have no conflicts to disclose.

Funding 

This study was funded by the National Institute on Aging.

High hospital occupancy is a fundamental challenge faced by healthcare systems in the United States.^1-3 However, few studies have examined the effect of high occupancy on outcomes in the inpatient setting,^4-9 and these showed mixed results. Hospital-acquired conditions (HACs), such as Clostridium difficile infection (CDI), are quality indicators for inpatient care and part of the Centers for Medicare and Medicaid Services’ Hospital-Acquired Conditions Reductions Program.^10-12 However, few studies—largely conducted outside of the US—have evaluated the association between inpatient occupancy and HACs. These studies showed increasing hospital-acquired infection rates with increasing occupancy.^13-15 Past studies of hospital occupancy have relied on annual average licensed bed counts, which are not a reliable measure of available and staffed beds and do not account for variations in patient volume and bed supply.¹⁶ Using a novel measure of inpatient occupancy, we tested the hypothesis that increasing inpatient occupancy is associated with a greater likelihood of CDI.

We performed a retrospective analysis of administrative data from non-federal, acute care hospitals in California during 2008–2012 using the Office of Statewide Health Planning and Development (OSHPD) Patient Discharge Data set, a complete census of all CA licensed general acute care hospital discharge records. This study was approved by the OSHPD Committee for the Protection of Human Subjects and was deemed exempt by our institution’s Institutional Review Board.

Selection of Participants

The study population consisted of fee-for-service Medicare enrollees ≥65 years admitted through the emergency department (ED) with a hospital length of stay (HLOS) <50 days and a primary discharge diagnosis of acute myocardial infarction (MI), pneumonia (PNA), or heart failure (HF; [identified through the respective Clinical Classification Software [CCS]).

The sample was restricted to discharges with a HLOS of <50 days, because those with longer HLOS (0.01% of study sample) were likely different in ways that may bias our findings (eg, they will likely be sicker). We limited our study to admissions through the ED to reduce potential selection bias by excluding elective admissions and hospital-to-hospital transfers, which are likely dependent on occupancy. MI, HF, and PNA diagnoses were selected because they are prevalent and have high inpatient mortality, allowing us to examine the effect of occupancy on some of the sickest inpatients.¹⁷

Hospital-acquired cases of CDI were identified as discharges (using ICD-9 code 008.45 for CDI) that were not marked as present-on-admission (POA) using the method described by Zhan et al.¹⁸ To avoid small facility outlying effects, we included hospitals that had 100 or more MI, HF, and PNA discharges that met the inclusion criteria over the study years.

OSHPD inpatient data were combined with OSHPD hospital annual financial data that contain hospital-level variables including ownership (City/County, District, Investor, and Non-Profit), geography (based on health services area), teaching status, urbanicity, and size based on the number of average annual licensed beds. If characteristics were not available for a given hospital for 1 or more years, the information from the closest available year was used for that hospital (replacement required for 10,504 (1.5%) cases; 4,856 otherwise eligible cases (0.7%) were dropped because the hospital was not included in the annual financial data for any year. Approximately 0.2% of records had invalid values for disposition, payer, or admission route, and were therefore dropped. Patient residence zip code-level socioeconomic status was measured using the percentage of families living below the poverty line, median family income, and the percentage of individuals with less than a high school degree among those aged ≥ 25 years¹⁹; these measures were divided into 3 groups (bottom quartile, top quartile, and middle 50%) for analysis.

Measure of Occupancy

Calculating Daily Census and Bed Capacity

We calculated the daily census using admission date and HLOS for each observation in our dataset. We approximated the bed capacity as the maximum daily census in the 121-day window (+/- 60 days) around each census day in each hospital. The 121-day window was chosen to increase the likelihood of capturing changes in bed availability (eg, due to unit closures) and seasonal variability. Our daily census does not include patients admitted with psychiatric and obstetrics diagnoses and long-term care/rehabilitation stays (identified through CCS categories and excluded) because these patients are not likely to compete for the same hospital resources as those receiving care for MI, HF, and PNA. See Appendix Table 1 for definition of the occupancy terms.

Calculating Relative Daily Occupancy

We developed a raw hospital-specific occupancy measure by dividing the daily census by the maximum census in each 121-day window for each hospital. We converted these raw measures to percentiles within the 121-day window to create a daily relative occupancy measure. For example, median level occupancy day would correspond to an occupancy of 0.5; a minimum or maximum occupancy day would correspond to 0 or 1, respectively. We preferred a relative occupancy measure because it assumes that what constitutes “high occupancy” likely depends on the usual occupancy level of the facility.

Measuring Admission Day Occupancy and Average Occupancy over Hospitalization

Using the relative daily occupancy values, we constructed patient-level variables representing occupancy on admission day and average occupancy during hospitalization.

Data Analysis

First, we estimated descriptive statistics of the sample for occupancy, patient-level (eg, age, race, gender, and severity of illness), hospital-level (eg, size, teaching status, and urbanicity), and incident-level (day-of-the-week and season) variables. Next, we used logistic regression with cluster standard errors to estimate the adjusted and unadjusted association of occupancy with CDI. For this analysis, occupancy was broken into 4 groups: 0.00-0.25 (low occupancy); 0.26-0.50; 0.51-0.75; and 0.76-1.00 (high occupancy), with the 0.0-0.25 group treated as the reference level. We fit separate models for admission and average occupancy and re-ran the latter model including HLOS as a sensitivity analysis.

Study Population and Hospitals

Across 327 hospitals, 558,829 discharges (including deaths) met our inclusion criteria and there were 2045 admissions with CDI. The hospital and discharge characteristics are reported in Appendix Table 2.

Relationship of Occupancy with CDI

With regard to admission occupancy, the 0.26-0.50 group did not have a significantly higher rate of CDI than the low occupancy group. Both the 0.51-0.75 and the 0.76-1.00 occupancy groups had 15% lower odds of CDI compared to the low occupancy group (Table). The adjusted results were similar, although the comparison between the low and high occupancy groups was marginally nonsignificant.

With regard to average occupancy, intermediate levels of occupancy (ie, 0.26-0.50 and 0.51-0.75 groups) had over 3-fold increased odds of CDI relative to the low occupancy group; the high occupancy group did not have significantly different odds of CDI compared to the low occupancy group (Table 1). The adjusted results were similar with no changes in statistical significance. Including HLOS tempered the adjusted odds of CDI to 1.6 for intermediate levels of occupancy, but these remained significantly higher than high or low occupancy.

Hospital occupancy is related to CDI. However, contrary to expectation, we found that higher admission and average occupancy over hospitalization were not related to more hospital-acquired CDI. CDI rates were highest for intermediate levels of average occupancy with lower CDI rates at high and low occupancy. CDI had an inverse relationship with admission occupancy.

These findings suggest that an exploration of the processes associated with hospitals accommodating higher occupancy might elucidate measures to reduce CDI. How do staffing, implementation of policies, and routine procedures vary when hospitals are busy or quiet? What aspects of care delivery that function well during high and low occupancy periods breakdown during intermediate occupancy? Hospital policies, practices, and procedures during different phases of occupancy might inform best practices. These data suggest that hospital occupancy level should be a routinely collected data element by infection control officers and that this should be linked with protocols triggered or modified with high or low occupancy that might affect HACs.

Previous studies in Europe found increasing hospital-acquired infection rates with increasing occupancy.^13-15 The authors postulated that increasing occupancy may limit available resources and increase nursing workloads, negatively impacting adherence to hand hygiene and cleaning protocols .⁸ However, these studies did not account for infections that were POA. In addition, our study examined hospitals in California after the 2006 implementation of the minimum nurse staffing policy, which means that staff to patient ratios could not fall below fixed thresholds that were typically higher than pre-policy ratios.¹⁹

This study had limitations pertaining to coded administrative data, including quality of coding and data validity. However, OSHPD has strict data reporting processes.²⁰ This study focused on 1 state; however, California is large with a demographically diverse population and hospital types, characteristics that would help generalize findings. Furthermore, when using the average occupancy measure, we could not determine whether the complication was acquired during the high occupancy period of the hospitalization.

Higher admission day occupancy was associated with lower likelihood of CDI, and CDI rates were lower at high and low average occupancy. These findings should prompt exploration of how hospitals react to occupancy changes and how those care processes translate into HACs in order to inform best practices for hospital care.

Acknowledgments

The authors would like to thank Ms. Amanda Kogowski, MPH and Mr. Rekar Taymour, MS for their editorial assistance with drafting the manuscript.

Disclosures

The authors have no conflicts to disclose.

Funding 

This study was funded by the National Institute on Aging.

High hospital occupancy is a fundamental challenge faced by healthcare systems in the United States.^1-3 However, few studies have examined the effect of high occupancy on outcomes in the inpatient setting,^4-9 and these showed mixed results. Hospital-acquired conditions (HACs), such as Clostridium difficile infection (CDI), are quality indicators for inpatient care and part of the Centers for Medicare and Medicaid Services’ Hospital-Acquired Conditions Reductions Program.^10-12 However, few studies—largely conducted outside of the US—have evaluated the association between inpatient occupancy and HACs. These studies showed increasing hospital-acquired infection rates with increasing occupancy.^13-15 Past studies of hospital occupancy have relied on annual average licensed bed counts, which are not a reliable measure of available and staffed beds and do not account for variations in patient volume and bed supply.¹⁶ Using a novel measure of inpatient occupancy, we tested the hypothesis that increasing inpatient occupancy is associated with a greater likelihood of CDI.

We performed a retrospective analysis of administrative data from non-federal, acute care hospitals in California during 2008–2012 using the Office of Statewide Health Planning and Development (OSHPD) Patient Discharge Data set, a complete census of all CA licensed general acute care hospital discharge records. This study was approved by the OSHPD Committee for the Protection of Human Subjects and was deemed exempt by our institution’s Institutional Review Board.

Selection of Participants

The study population consisted of fee-for-service Medicare enrollees ≥65 years admitted through the emergency department (ED) with a hospital length of stay (HLOS) <50 days and a primary discharge diagnosis of acute myocardial infarction (MI), pneumonia (PNA), or heart failure (HF; [identified through the respective Clinical Classification Software [CCS]).

The sample was restricted to discharges with a HLOS of <50 days, because those with longer HLOS (0.01% of study sample) were likely different in ways that may bias our findings (eg, they will likely be sicker). We limited our study to admissions through the ED to reduce potential selection bias by excluding elective admissions and hospital-to-hospital transfers, which are likely dependent on occupancy. MI, HF, and PNA diagnoses were selected because they are prevalent and have high inpatient mortality, allowing us to examine the effect of occupancy on some of the sickest inpatients.¹⁷

Hospital-acquired cases of CDI were identified as discharges (using ICD-9 code 008.45 for CDI) that were not marked as present-on-admission (POA) using the method described by Zhan et al.¹⁸ To avoid small facility outlying effects, we included hospitals that had 100 or more MI, HF, and PNA discharges that met the inclusion criteria over the study years.

OSHPD inpatient data were combined with OSHPD hospital annual financial data that contain hospital-level variables including ownership (City/County, District, Investor, and Non-Profit), geography (based on health services area), teaching status, urbanicity, and size based on the number of average annual licensed beds. If characteristics were not available for a given hospital for 1 or more years, the information from the closest available year was used for that hospital (replacement required for 10,504 (1.5%) cases; 4,856 otherwise eligible cases (0.7%) were dropped because the hospital was not included in the annual financial data for any year. Approximately 0.2% of records had invalid values for disposition, payer, or admission route, and were therefore dropped. Patient residence zip code-level socioeconomic status was measured using the percentage of families living below the poverty line, median family income, and the percentage of individuals with less than a high school degree among those aged ≥ 25 years¹⁹; these measures were divided into 3 groups (bottom quartile, top quartile, and middle 50%) for analysis.

Measure of Occupancy

Calculating Daily Census and Bed Capacity

We calculated the daily census using admission date and HLOS for each observation in our dataset. We approximated the bed capacity as the maximum daily census in the 121-day window (+/- 60 days) around each census day in each hospital. The 121-day window was chosen to increase the likelihood of capturing changes in bed availability (eg, due to unit closures) and seasonal variability. Our daily census does not include patients admitted with psychiatric and obstetrics diagnoses and long-term care/rehabilitation stays (identified through CCS categories and excluded) because these patients are not likely to compete for the same hospital resources as those receiving care for MI, HF, and PNA. See Appendix Table 1 for definition of the occupancy terms.

Calculating Relative Daily Occupancy

We developed a raw hospital-specific occupancy measure by dividing the daily census by the maximum census in each 121-day window for each hospital. We converted these raw measures to percentiles within the 121-day window to create a daily relative occupancy measure. For example, median level occupancy day would correspond to an occupancy of 0.5; a minimum or maximum occupancy day would correspond to 0 or 1, respectively. We preferred a relative occupancy measure because it assumes that what constitutes “high occupancy” likely depends on the usual occupancy level of the facility.

Measuring Admission Day Occupancy and Average Occupancy over Hospitalization

Using the relative daily occupancy values, we constructed patient-level variables representing occupancy on admission day and average occupancy during hospitalization.

Data Analysis

First, we estimated descriptive statistics of the sample for occupancy, patient-level (eg, age, race, gender, and severity of illness), hospital-level (eg, size, teaching status, and urbanicity), and incident-level (day-of-the-week and season) variables. Next, we used logistic regression with cluster standard errors to estimate the adjusted and unadjusted association of occupancy with CDI. For this analysis, occupancy was broken into 4 groups: 0.00-0.25 (low occupancy); 0.26-0.50; 0.51-0.75; and 0.76-1.00 (high occupancy), with the 0.0-0.25 group treated as the reference level. We fit separate models for admission and average occupancy and re-ran the latter model including HLOS as a sensitivity analysis.

Study Population and Hospitals

Across 327 hospitals, 558,829 discharges (including deaths) met our inclusion criteria and there were 2045 admissions with CDI. The hospital and discharge characteristics are reported in Appendix Table 2.

Relationship of Occupancy with CDI

With regard to admission occupancy, the 0.26-0.50 group did not have a significantly higher rate of CDI than the low occupancy group. Both the 0.51-0.75 and the 0.76-1.00 occupancy groups had 15% lower odds of CDI compared to the low occupancy group (Table). The adjusted results were similar, although the comparison between the low and high occupancy groups was marginally nonsignificant.

With regard to average occupancy, intermediate levels of occupancy (ie, 0.26-0.50 and 0.51-0.75 groups) had over 3-fold increased odds of CDI relative to the low occupancy group; the high occupancy group did not have significantly different odds of CDI compared to the low occupancy group (Table 1). The adjusted results were similar with no changes in statistical significance. Including HLOS tempered the adjusted odds of CDI to 1.6 for intermediate levels of occupancy, but these remained significantly higher than high or low occupancy.

Hospital occupancy is related to CDI. However, contrary to expectation, we found that higher admission and average occupancy over hospitalization were not related to more hospital-acquired CDI. CDI rates were highest for intermediate levels of average occupancy with lower CDI rates at high and low occupancy. CDI had an inverse relationship with admission occupancy.

These findings suggest that an exploration of the processes associated with hospitals accommodating higher occupancy might elucidate measures to reduce CDI. How do staffing, implementation of policies, and routine procedures vary when hospitals are busy or quiet? What aspects of care delivery that function well during high and low occupancy periods breakdown during intermediate occupancy? Hospital policies, practices, and procedures during different phases of occupancy might inform best practices. These data suggest that hospital occupancy level should be a routinely collected data element by infection control officers and that this should be linked with protocols triggered or modified with high or low occupancy that might affect HACs.

Previous studies in Europe found increasing hospital-acquired infection rates with increasing occupancy.^13-15 The authors postulated that increasing occupancy may limit available resources and increase nursing workloads, negatively impacting adherence to hand hygiene and cleaning protocols .⁸ However, these studies did not account for infections that were POA. In addition, our study examined hospitals in California after the 2006 implementation of the minimum nurse staffing policy, which means that staff to patient ratios could not fall below fixed thresholds that were typically higher than pre-policy ratios.¹⁹

This study had limitations pertaining to coded administrative data, including quality of coding and data validity. However, OSHPD has strict data reporting processes.²⁰ This study focused on 1 state; however, California is large with a demographically diverse population and hospital types, characteristics that would help generalize findings. Furthermore, when using the average occupancy measure, we could not determine whether the complication was acquired during the high occupancy period of the hospitalization.

Higher admission day occupancy was associated with lower likelihood of CDI, and CDI rates were lower at high and low average occupancy. These findings should prompt exploration of how hospitals react to occupancy changes and how those care processes translate into HACs in order to inform best practices for hospital care.

Acknowledgments

The authors would like to thank Ms. Amanda Kogowski, MPH and Mr. Rekar Taymour, MS for their editorial assistance with drafting the manuscript.

Disclosures

The authors have no conflicts to disclose.

Funding 

This study was funded by the National Institute on Aging.