Cardiopulmonary resuscitation (CPR) is a potentially lifesaving intervention associated with intense resource utilization and poor outcomes.[1, 2, 3] CPR is the default intervention for hospitalized patients in cardiopulmonary arrest in the United States. The most common measure of successful in‐hospital CPR reported in the literature is survival to (hospital) discharge, with most estimates between 13% and 37%.[3, 4, 5, 6] Poor rates of survival to discharge may be explained by use of CPR in patients for whom it was not originally intended, such as the very elderly with multiple illnesses or the terminally ill.[7, 8] Use of CPR in patients unlikely to benefit may be due to a physician's inability to estimate the probability of survival, desire to offer hope to patients, fear of litigation, and poor communication with patients about goals of care.[7, 8, 9, 10]

The general public has overly optimistic expectations about CPR; surveys have reported perceived survival after CPR of up to 90%.[11, 12, 13] Although objective information substantially affects patient preferences for resuscitation,[14] prognosis is rarely discussed during code status encounters[15, 16]; physician estimates of prognosis also are often inaccurate.[9, 17] With a scarcity of data describing the characteristics of patients undergoing multiple CPR attempts, and their outcomes, patients and their families could have false expectations about the likely outcomes from multiple CPR attempts, because physician counsel is not well‐informed.

In this study, we examine the epidemiology of in‐hospital CPR recipients stratified by the number of occurrences of CPR during a single hospitalization, along with their outcomes. We hypothesize that recipients of multiple CPR during a single hospitalization are an epidemiologically distinct group compared with those who receive CPR once during their hospitalization, and that their outcomes are worse.

METHODS

RESULTS

Of a total of 65,308,185 adults hospitalized between the years 2000 and 2009, there were 166,519 CPR recipients, yielding a CPR incidence of 2.5 per 1000 hospitalizations. Among CPR recipients, 96.6% (n=166,899) had 1 CPR and 3.4% (n=5620) had multiple CPR during their hospitalization (range, 111 CPR). When further stratified, 3% had 2 CPR attempts (n=4949) and 0.4% (n=671) had 3 CPR attempts.

Compared with patients who had 1 CPR, those who had multiple CPR were more often younger (median age, 71 vs 67 years), nonwhite, and in a low‐income quartile (all P<0.001; Table 1). Rates of admission from a nursing facility (3.3% for the 1‐CPR group vs 3.1% for the multiple‐CPR group, P=0.65) or as a trauma (0.3% for the 1‐CPR group and 0.4% for the multiple‐CPR group, P=0.34) were similar.

Patients who had multiple CPR had slightly higher mean CCI scores (2.7 vs 2.6, P=0.02). They had higher rates of neurologic compromise and aggressive interventions; they were also more commonly treated in nonteaching hospitals, and in the western region of the United States (Table 2). After multivariate analysis, several patient, clinical, and hospital factors were independently associated with occurrence of multiple CPR (Figure 1).

Figure 1

In bivariate analysis of survival, patients who had multiple CPR had lower rates of survival to discharge (11.3% vs 23.4%, P<0.001). Results were similar (11.6% for multiple CPR vs 22.5% for 1 CPR, P<0.001) when all patients who had CPR but did not have valid timing data were excluded in sensitivity analyses. Further stratification showed that survival to discharge decreased by >40% for each increase in CPR attempt (23.4%, 11.9%, and 6.7% for 1, 2, and 3 CPR attempts, respectively, P<0.001; Figure 2). After adjustment, multiple CPR versus 1 CPR during a hospitalization was independently associated with a lower likelihood of survival to discharge (adjusted OR: 0.41, 95% CI: 0.37‐0.44, P<0.001; Table 3).

Figure 2

Survivors with multiple CPR were less likely to be discharged home compared with survivors with 1 CPR (19.3% vs 29.9%, respectively, P<0.001); 1 in 15 survivors of multiple CPR were discharged to a hospice (6.8%) versus 1 in 23 1‐CPR survivors (4.3%; P=0.002). Mean length of stay was 5.8 versus 5.5 days for patients who had multiple CPR versus 1 CPR, respectively (P<0.001), and 16.0 versus 10.5 days for discharged survivors of multiple CPR versus 1 CPR (P<0.001). The average cost per day of hospitalization was higher for recipients of multiple CPR versus 1 CPR ($4484.60 vs $3581.40, P<0.001). The aggregate cost of hospitalization for 1‐time CPR recipients doubled between the years 2001 and 2009 (from $1.3 billion to $2.9 billion); that of recipients of multiple CPR attempts quadrupled in the same time frame (from $38.6 million to $160.7 million).

DISCUSSION

A number of studies have investigated the epidemiology of patients in whom CPR is attempted.[2, 3, 5, 20, 23, 24] Several pre‐, intra‐, and post‐resuscitation factors have been shown to affect the survival of resuscitated patients.[6, 7, 25, 26] To our knowledge, neither the epidemiology of hospitalized patients in whom resuscitation is attempted multiple times nor the prognostic value of multiple CPR attempts has been investigated. In this study, we found that multiple resuscitations are more commonly performed on younger, generally sicker patients; their outcomes are significantly compromised compared with patients who are resuscitated once during their hospitalization.

There was a steep decline in survival based on the number of resuscitation events. In multivariate analysis, patients who had multiple CPR were 2.5‐fold less likely to survive their hospitalization; survivors of multiple CPR also were more likely to be discharged to a hospice. Overall, this is indicative of clinical deterioration and prolongation of dying should a patient suffer multiple cardiopulmonary arrests during a hospitalization. The robust inverse relationship between multiple CPR and survival to discharge has implications for the development of prognostic models of outcomes following CPR, as previously designed prediction models of CPR outcomes such as the Cardiac Arrest Survival Post‐Resuscitation In‐hospital (CASPRI) score,[25] Pre‐Arrest Morbidity (PAM) score,[27] and Prognosis After Resuscitation (PAR) score[28] do not include multiple resuscitations as a variable of interest.

In‐hospital factors were found to be more important than patient factors, such as comorbidities or race, in determining the likelihood of multiple CPR attempts. Hospital teaching status and region remained significantly associated with likelihood of multiple CPR attempts. This is in agreement with studies that have described demographic and regional variation in utilization of do‐not‐resuscitate orders.[29, 30] These findings suggest substantial heterogeneity in the clinical culture and hospital practices across the United States regarding preemptive discussions about resuscitation. This means that where a patient receives care is a significant determinant of their probability of undergoing multiple CPR.

It is known that older patients are more likely to have advance directive orders[30, 31] and possibly document their wishes with regard to further resuscitation efforts. There also may be an inclination toward more aggressive care for younger adults compared with those of an advanced age. Uncertainty about a patient's goals of care likely feeds into an increased possibility of multiple resuscitation attempts; this may explain why neurologic compromise and being on ventilator support were independently associated with likelihood of multiple CPR, as these patients often have lost their ability to actively participate in decision‐making. The results of this study highlight the importance of engaging patients with a plausible risk of cardiopulmonary arrest about their goals for care and advance directives in a timely manner, regardless of age.

We found that the care of patients who undergo multiple resuscitations is associated with a higher cost of hospitalization than for patients in whom resuscitation is attempted once during their hospitalization. In addition, there was an exponential increase in aggregate cost over time for multiple CPR recipients compared with 1‐time CPR recipients. In a prior study, Ebell and Kruse showed an exponential inverse relationship between cost per surviving patient and rate of survival to discharge.[32] Considering that 93.3% of patients who had 3 resuscitation attempts died during their hospitalization, and that hospital‐level factors appear to play a significant role in likelihood of multiple CPR, consensus guidelines regarding the appropriateness of 3 resuscitation attempts during a single hospitalization may be relevant to aid the care of these patients.

Although the NIS is well‐validated,[18] there are some limitations. Whereas CPR incidence in this study (2.5 per 1000 hospitalizations) is within estimates (15 arrests per 1000 hospitalizations) reported in previous studies,[3, 5] potential undercoding of multiple CPR may explain why the multiple‐CPR rate in this study is lower than re‐arrest estimates provided in published studies.[2, 33] Indeed, accurate calculation of re‐arrest rates requires data on do‐not‐resuscitate orders instituted after successful resuscitation, which are not provided in the NIS. Information on patient‐provider discussions about CPR or prognosis is not included. Data regarding the underlying cause and type of arrest rhythm, rates of return to spontaneous circulation, length of code, patient location, critical‐care resources and length of critical‐care stay, availability of rapid‐response/code teams, time to defibrillation, use of therapeutic hypothermia, adherence to resuscitation guidelines, quality of CPR, and long‐term follow‐up are not included in the database. Presenting rhythms were not assessed, as there are no ICD‐9 codes for asystole and pulseless electrical activity. The NIS is de‐identified; therefore, chart review to assess the validity of codes is impossible. However, our sensitivity analyses indicate the reliability of using the number of occurrences of the CPR code as a marker of multiple CPR. The strength of our study lies in the use of data that provide a population‐level insight into the epidemiology of patients resuscitated multiple times during their hospitalization, and their outcomes.

Decision‐making about CPR is at the center of a complex debate that incorporates often divergent clinical, economic, ethical, and personal issues. As debate continues regarding when to not resuscitate,[34, 35, 36, 37] studies that explore the public perspective of survival thresholds for the provision of multiple resuscitations will be crucial. As competition for finite healthcare dollars escalates, stratified analyses of the cost implications of resuscitation care are essential. Studies are needed to examine the impact of a history of successful resuscitation in a previous hospitalization on outcomes following CPR in a subsequent hospitalization. Overall, our study fills an important knowledge gap in resuscitation practice and outcomes in the United States and highlights the importance of discussing resuscitation options between a patient and his or her family on hospital admission and, if needed, again after the first successful resuscitation attempt.

Cardiopulmonary resuscitation (CPR) is a potentially lifesaving intervention associated with intense resource utilization and poor outcomes.[1, 2, 3] CPR is the default intervention for hospitalized patients in cardiopulmonary arrest in the United States. The most common measure of successful in‐hospital CPR reported in the literature is survival to (hospital) discharge, with most estimates between 13% and 37%.[3, 4, 5, 6] Poor rates of survival to discharge may be explained by use of CPR in patients for whom it was not originally intended, such as the very elderly with multiple illnesses or the terminally ill.[7, 8] Use of CPR in patients unlikely to benefit may be due to a physician's inability to estimate the probability of survival, desire to offer hope to patients, fear of litigation, and poor communication with patients about goals of care.[7, 8, 9, 10]

The general public has overly optimistic expectations about CPR; surveys have reported perceived survival after CPR of up to 90%.[11, 12, 13] Although objective information substantially affects patient preferences for resuscitation,[14] prognosis is rarely discussed during code status encounters[15, 16]; physician estimates of prognosis also are often inaccurate.[9, 17] With a scarcity of data describing the characteristics of patients undergoing multiple CPR attempts, and their outcomes, patients and their families could have false expectations about the likely outcomes from multiple CPR attempts, because physician counsel is not well‐informed.

In this study, we examine the epidemiology of in‐hospital CPR recipients stratified by the number of occurrences of CPR during a single hospitalization, along with their outcomes. We hypothesize that recipients of multiple CPR during a single hospitalization are an epidemiologically distinct group compared with those who receive CPR once during their hospitalization, and that their outcomes are worse.

METHODS

RESULTS

Of a total of 65,308,185 adults hospitalized between the years 2000 and 2009, there were 166,519 CPR recipients, yielding a CPR incidence of 2.5 per 1000 hospitalizations. Among CPR recipients, 96.6% (n=166,899) had 1 CPR and 3.4% (n=5620) had multiple CPR during their hospitalization (range, 111 CPR). When further stratified, 3% had 2 CPR attempts (n=4949) and 0.4% (n=671) had 3 CPR attempts.

Compared with patients who had 1 CPR, those who had multiple CPR were more often younger (median age, 71 vs 67 years), nonwhite, and in a low‐income quartile (all P<0.001; Table 1). Rates of admission from a nursing facility (3.3% for the 1‐CPR group vs 3.1% for the multiple‐CPR group, P=0.65) or as a trauma (0.3% for the 1‐CPR group and 0.4% for the multiple‐CPR group, P=0.34) were similar.

Patients who had multiple CPR had slightly higher mean CCI scores (2.7 vs 2.6, P=0.02). They had higher rates of neurologic compromise and aggressive interventions; they were also more commonly treated in nonteaching hospitals, and in the western region of the United States (Table 2). After multivariate analysis, several patient, clinical, and hospital factors were independently associated with occurrence of multiple CPR (Figure 1).

Figure 1

In bivariate analysis of survival, patients who had multiple CPR had lower rates of survival to discharge (11.3% vs 23.4%, P<0.001). Results were similar (11.6% for multiple CPR vs 22.5% for 1 CPR, P<0.001) when all patients who had CPR but did not have valid timing data were excluded in sensitivity analyses. Further stratification showed that survival to discharge decreased by >40% for each increase in CPR attempt (23.4%, 11.9%, and 6.7% for 1, 2, and 3 CPR attempts, respectively, P<0.001; Figure 2). After adjustment, multiple CPR versus 1 CPR during a hospitalization was independently associated with a lower likelihood of survival to discharge (adjusted OR: 0.41, 95% CI: 0.37‐0.44, P<0.001; Table 3).

Figure 2

Survivors with multiple CPR were less likely to be discharged home compared with survivors with 1 CPR (19.3% vs 29.9%, respectively, P<0.001); 1 in 15 survivors of multiple CPR were discharged to a hospice (6.8%) versus 1 in 23 1‐CPR survivors (4.3%; P=0.002). Mean length of stay was 5.8 versus 5.5 days for patients who had multiple CPR versus 1 CPR, respectively (P<0.001), and 16.0 versus 10.5 days for discharged survivors of multiple CPR versus 1 CPR (P<0.001). The average cost per day of hospitalization was higher for recipients of multiple CPR versus 1 CPR ($4484.60 vs $3581.40, P<0.001). The aggregate cost of hospitalization for 1‐time CPR recipients doubled between the years 2001 and 2009 (from $1.3 billion to $2.9 billion); that of recipients of multiple CPR attempts quadrupled in the same time frame (from $38.6 million to $160.7 million).

DISCUSSION

A number of studies have investigated the epidemiology of patients in whom CPR is attempted.[2, 3, 5, 20, 23, 24] Several pre‐, intra‐, and post‐resuscitation factors have been shown to affect the survival of resuscitated patients.[6, 7, 25, 26] To our knowledge, neither the epidemiology of hospitalized patients in whom resuscitation is attempted multiple times nor the prognostic value of multiple CPR attempts has been investigated. In this study, we found that multiple resuscitations are more commonly performed on younger, generally sicker patients; their outcomes are significantly compromised compared with patients who are resuscitated once during their hospitalization.

There was a steep decline in survival based on the number of resuscitation events. In multivariate analysis, patients who had multiple CPR were 2.5‐fold less likely to survive their hospitalization; survivors of multiple CPR also were more likely to be discharged to a hospice. Overall, this is indicative of clinical deterioration and prolongation of dying should a patient suffer multiple cardiopulmonary arrests during a hospitalization. The robust inverse relationship between multiple CPR and survival to discharge has implications for the development of prognostic models of outcomes following CPR, as previously designed prediction models of CPR outcomes such as the Cardiac Arrest Survival Post‐Resuscitation In‐hospital (CASPRI) score,[25] Pre‐Arrest Morbidity (PAM) score,[27] and Prognosis After Resuscitation (PAR) score[28] do not include multiple resuscitations as a variable of interest.

In‐hospital factors were found to be more important than patient factors, such as comorbidities or race, in determining the likelihood of multiple CPR attempts. Hospital teaching status and region remained significantly associated with likelihood of multiple CPR attempts. This is in agreement with studies that have described demographic and regional variation in utilization of do‐not‐resuscitate orders.[29, 30] These findings suggest substantial heterogeneity in the clinical culture and hospital practices across the United States regarding preemptive discussions about resuscitation. This means that where a patient receives care is a significant determinant of their probability of undergoing multiple CPR.

It is known that older patients are more likely to have advance directive orders[30, 31] and possibly document their wishes with regard to further resuscitation efforts. There also may be an inclination toward more aggressive care for younger adults compared with those of an advanced age. Uncertainty about a patient's goals of care likely feeds into an increased possibility of multiple resuscitation attempts; this may explain why neurologic compromise and being on ventilator support were independently associated with likelihood of multiple CPR, as these patients often have lost their ability to actively participate in decision‐making. The results of this study highlight the importance of engaging patients with a plausible risk of cardiopulmonary arrest about their goals for care and advance directives in a timely manner, regardless of age.

We found that the care of patients who undergo multiple resuscitations is associated with a higher cost of hospitalization than for patients in whom resuscitation is attempted once during their hospitalization. In addition, there was an exponential increase in aggregate cost over time for multiple CPR recipients compared with 1‐time CPR recipients. In a prior study, Ebell and Kruse showed an exponential inverse relationship between cost per surviving patient and rate of survival to discharge.[32] Considering that 93.3% of patients who had 3 resuscitation attempts died during their hospitalization, and that hospital‐level factors appear to play a significant role in likelihood of multiple CPR, consensus guidelines regarding the appropriateness of 3 resuscitation attempts during a single hospitalization may be relevant to aid the care of these patients.

Although the NIS is well‐validated,[18] there are some limitations. Whereas CPR incidence in this study (2.5 per 1000 hospitalizations) is within estimates (15 arrests per 1000 hospitalizations) reported in previous studies,[3, 5] potential undercoding of multiple CPR may explain why the multiple‐CPR rate in this study is lower than re‐arrest estimates provided in published studies.[2, 33] Indeed, accurate calculation of re‐arrest rates requires data on do‐not‐resuscitate orders instituted after successful resuscitation, which are not provided in the NIS. Information on patient‐provider discussions about CPR or prognosis is not included. Data regarding the underlying cause and type of arrest rhythm, rates of return to spontaneous circulation, length of code, patient location, critical‐care resources and length of critical‐care stay, availability of rapid‐response/code teams, time to defibrillation, use of therapeutic hypothermia, adherence to resuscitation guidelines, quality of CPR, and long‐term follow‐up are not included in the database. Presenting rhythms were not assessed, as there are no ICD‐9 codes for asystole and pulseless electrical activity. The NIS is de‐identified; therefore, chart review to assess the validity of codes is impossible. However, our sensitivity analyses indicate the reliability of using the number of occurrences of the CPR code as a marker of multiple CPR. The strength of our study lies in the use of data that provide a population‐level insight into the epidemiology of patients resuscitated multiple times during their hospitalization, and their outcomes.

Decision‐making about CPR is at the center of a complex debate that incorporates often divergent clinical, economic, ethical, and personal issues. As debate continues regarding when to not resuscitate,[34, 35, 36, 37] studies that explore the public perspective of survival thresholds for the provision of multiple resuscitations will be crucial. As competition for finite healthcare dollars escalates, stratified analyses of the cost implications of resuscitation care are essential. Studies are needed to examine the impact of a history of successful resuscitation in a previous hospitalization on outcomes following CPR in a subsequent hospitalization. Overall, our study fills an important knowledge gap in resuscitation practice and outcomes in the United States and highlights the importance of discussing resuscitation options between a patient and his or her family on hospital admission and, if needed, again after the first successful resuscitation attempt.

Cardiopulmonary resuscitation (CPR) is a potentially lifesaving intervention associated with intense resource utilization and poor outcomes.[1, 2, 3] CPR is the default intervention for hospitalized patients in cardiopulmonary arrest in the United States. The most common measure of successful in‐hospital CPR reported in the literature is survival to (hospital) discharge, with most estimates between 13% and 37%.[3, 4, 5, 6] Poor rates of survival to discharge may be explained by use of CPR in patients for whom it was not originally intended, such as the very elderly with multiple illnesses or the terminally ill.[7, 8] Use of CPR in patients unlikely to benefit may be due to a physician's inability to estimate the probability of survival, desire to offer hope to patients, fear of litigation, and poor communication with patients about goals of care.[7, 8, 9, 10]

The general public has overly optimistic expectations about CPR; surveys have reported perceived survival after CPR of up to 90%.[11, 12, 13] Although objective information substantially affects patient preferences for resuscitation,[14] prognosis is rarely discussed during code status encounters[15, 16]; physician estimates of prognosis also are often inaccurate.[9, 17] With a scarcity of data describing the characteristics of patients undergoing multiple CPR attempts, and their outcomes, patients and their families could have false expectations about the likely outcomes from multiple CPR attempts, because physician counsel is not well‐informed.

In this study, we examine the epidemiology of in‐hospital CPR recipients stratified by the number of occurrences of CPR during a single hospitalization, along with their outcomes. We hypothesize that recipients of multiple CPR during a single hospitalization are an epidemiologically distinct group compared with those who receive CPR once during their hospitalization, and that their outcomes are worse.

METHODS

RESULTS

Of a total of 65,308,185 adults hospitalized between the years 2000 and 2009, there were 166,519 CPR recipients, yielding a CPR incidence of 2.5 per 1000 hospitalizations. Among CPR recipients, 96.6% (n=166,899) had 1 CPR and 3.4% (n=5620) had multiple CPR during their hospitalization (range, 111 CPR). When further stratified, 3% had 2 CPR attempts (n=4949) and 0.4% (n=671) had 3 CPR attempts.

Compared with patients who had 1 CPR, those who had multiple CPR were more often younger (median age, 71 vs 67 years), nonwhite, and in a low‐income quartile (all P<0.001; Table 1). Rates of admission from a nursing facility (3.3% for the 1‐CPR group vs 3.1% for the multiple‐CPR group, P=0.65) or as a trauma (0.3% for the 1‐CPR group and 0.4% for the multiple‐CPR group, P=0.34) were similar.

Patients who had multiple CPR had slightly higher mean CCI scores (2.7 vs 2.6, P=0.02). They had higher rates of neurologic compromise and aggressive interventions; they were also more commonly treated in nonteaching hospitals, and in the western region of the United States (Table 2). After multivariate analysis, several patient, clinical, and hospital factors were independently associated with occurrence of multiple CPR (Figure 1).

Figure 1

In bivariate analysis of survival, patients who had multiple CPR had lower rates of survival to discharge (11.3% vs 23.4%, P<0.001). Results were similar (11.6% for multiple CPR vs 22.5% for 1 CPR, P<0.001) when all patients who had CPR but did not have valid timing data were excluded in sensitivity analyses. Further stratification showed that survival to discharge decreased by >40% for each increase in CPR attempt (23.4%, 11.9%, and 6.7% for 1, 2, and 3 CPR attempts, respectively, P<0.001; Figure 2). After adjustment, multiple CPR versus 1 CPR during a hospitalization was independently associated with a lower likelihood of survival to discharge (adjusted OR: 0.41, 95% CI: 0.37‐0.44, P<0.001; Table 3).

Figure 2

Survivors with multiple CPR were less likely to be discharged home compared with survivors with 1 CPR (19.3% vs 29.9%, respectively, P<0.001); 1 in 15 survivors of multiple CPR were discharged to a hospice (6.8%) versus 1 in 23 1‐CPR survivors (4.3%; P=0.002). Mean length of stay was 5.8 versus 5.5 days for patients who had multiple CPR versus 1 CPR, respectively (P<0.001), and 16.0 versus 10.5 days for discharged survivors of multiple CPR versus 1 CPR (P<0.001). The average cost per day of hospitalization was higher for recipients of multiple CPR versus 1 CPR ($4484.60 vs $3581.40, P<0.001). The aggregate cost of hospitalization for 1‐time CPR recipients doubled between the years 2001 and 2009 (from $1.3 billion to $2.9 billion); that of recipients of multiple CPR attempts quadrupled in the same time frame (from $38.6 million to $160.7 million).

DISCUSSION

A number of studies have investigated the epidemiology of patients in whom CPR is attempted.[2, 3, 5, 20, 23, 24] Several pre‐, intra‐, and post‐resuscitation factors have been shown to affect the survival of resuscitated patients.[6, 7, 25, 26] To our knowledge, neither the epidemiology of hospitalized patients in whom resuscitation is attempted multiple times nor the prognostic value of multiple CPR attempts has been investigated. In this study, we found that multiple resuscitations are more commonly performed on younger, generally sicker patients; their outcomes are significantly compromised compared with patients who are resuscitated once during their hospitalization.

There was a steep decline in survival based on the number of resuscitation events. In multivariate analysis, patients who had multiple CPR were 2.5‐fold less likely to survive their hospitalization; survivors of multiple CPR also were more likely to be discharged to a hospice. Overall, this is indicative of clinical deterioration and prolongation of dying should a patient suffer multiple cardiopulmonary arrests during a hospitalization. The robust inverse relationship between multiple CPR and survival to discharge has implications for the development of prognostic models of outcomes following CPR, as previously designed prediction models of CPR outcomes such as the Cardiac Arrest Survival Post‐Resuscitation In‐hospital (CASPRI) score,[25] Pre‐Arrest Morbidity (PAM) score,[27] and Prognosis After Resuscitation (PAR) score[28] do not include multiple resuscitations as a variable of interest.

In‐hospital factors were found to be more important than patient factors, such as comorbidities or race, in determining the likelihood of multiple CPR attempts. Hospital teaching status and region remained significantly associated with likelihood of multiple CPR attempts. This is in agreement with studies that have described demographic and regional variation in utilization of do‐not‐resuscitate orders.[29, 30] These findings suggest substantial heterogeneity in the clinical culture and hospital practices across the United States regarding preemptive discussions about resuscitation. This means that where a patient receives care is a significant determinant of their probability of undergoing multiple CPR.

It is known that older patients are more likely to have advance directive orders[30, 31] and possibly document their wishes with regard to further resuscitation efforts. There also may be an inclination toward more aggressive care for younger adults compared with those of an advanced age. Uncertainty about a patient's goals of care likely feeds into an increased possibility of multiple resuscitation attempts; this may explain why neurologic compromise and being on ventilator support were independently associated with likelihood of multiple CPR, as these patients often have lost their ability to actively participate in decision‐making. The results of this study highlight the importance of engaging patients with a plausible risk of cardiopulmonary arrest about their goals for care and advance directives in a timely manner, regardless of age.

We found that the care of patients who undergo multiple resuscitations is associated with a higher cost of hospitalization than for patients in whom resuscitation is attempted once during their hospitalization. In addition, there was an exponential increase in aggregate cost over time for multiple CPR recipients compared with 1‐time CPR recipients. In a prior study, Ebell and Kruse showed an exponential inverse relationship between cost per surviving patient and rate of survival to discharge.[32] Considering that 93.3% of patients who had 3 resuscitation attempts died during their hospitalization, and that hospital‐level factors appear to play a significant role in likelihood of multiple CPR, consensus guidelines regarding the appropriateness of 3 resuscitation attempts during a single hospitalization may be relevant to aid the care of these patients.

Although the NIS is well‐validated,[18] there are some limitations. Whereas CPR incidence in this study (2.5 per 1000 hospitalizations) is within estimates (15 arrests per 1000 hospitalizations) reported in previous studies,[3, 5] potential undercoding of multiple CPR may explain why the multiple‐CPR rate in this study is lower than re‐arrest estimates provided in published studies.[2, 33] Indeed, accurate calculation of re‐arrest rates requires data on do‐not‐resuscitate orders instituted after successful resuscitation, which are not provided in the NIS. Information on patient‐provider discussions about CPR or prognosis is not included. Data regarding the underlying cause and type of arrest rhythm, rates of return to spontaneous circulation, length of code, patient location, critical‐care resources and length of critical‐care stay, availability of rapid‐response/code teams, time to defibrillation, use of therapeutic hypothermia, adherence to resuscitation guidelines, quality of CPR, and long‐term follow‐up are not included in the database. Presenting rhythms were not assessed, as there are no ICD‐9 codes for asystole and pulseless electrical activity. The NIS is de‐identified; therefore, chart review to assess the validity of codes is impossible. However, our sensitivity analyses indicate the reliability of using the number of occurrences of the CPR code as a marker of multiple CPR. The strength of our study lies in the use of data that provide a population‐level insight into the epidemiology of patients resuscitated multiple times during their hospitalization, and their outcomes.

Decision‐making about CPR is at the center of a complex debate that incorporates often divergent clinical, economic, ethical, and personal issues. As debate continues regarding when to not resuscitate,[34, 35, 36, 37] studies that explore the public perspective of survival thresholds for the provision of multiple resuscitations will be crucial. As competition for finite healthcare dollars escalates, stratified analyses of the cost implications of resuscitation care are essential. Studies are needed to examine the impact of a history of successful resuscitation in a previous hospitalization on outcomes following CPR in a subsequent hospitalization. Overall, our study fills an important knowledge gap in resuscitation practice and outcomes in the United States and highlights the importance of discussing resuscitation options between a patient and his or her family on hospital admission and, if needed, again after the first successful resuscitation attempt.

At the core of a good physician is mastery of critical communication skills. Good communication establishes rapport and can also heal patients. As communication is an essential ingredient of good physicianship, the recipe starts with a fundamental staplethe physician introduction. The physician introduction is step 2 of Kahn's etiquette‐based medicine checklist to promote good doctoring.[1] Although such rudimentary communication skills are cemented in kindergarten, sadly, more training is needed for doctors. In a recent Journal of Hospital Medicine study, interns failed to introduce themselves in 3 out of 5 inpatient encounters.[2]

Despite waning introductions, increasing importance is being placed on hospitalized patient's knowledge of their treating physician's name and role for patient safety. The Transitions of Care Consensus Policy Statement endorsed by 6 medical societies, including the Society of Hospital Medicine, recommend patients know who their treating physician is while caring for them at every step across the continuum, including hospitalization.[3] The Accreditation Council for Graduate Medical Education requires that patients be informed of who the supervising physician is and understand the roles of any trainees in their care.[4] Last, the death of young Lewis Blackman in South Carolina resulted in state legislation requiring clear identification of physicians and their roles for patients.[5] Given these recommendations, tools to remind physicians to introduce themselves and explain their role to patients are worth consideration. In this issue of the Journal of Hospital Medicine, the effectiveness of 2 interventions using physician photo tools is described.[6, 7]

Even though both studies advance our knowledge on the effectiveness of such interventions, nonrandom variable uptake by physicians represents a major common hurdle. Physician workload, competing priorities, and time pressures prevent physicians from distributing such tools. Consistent adopters of the cards likely already introduce themselves regularly. Interestingly, physicians likely withhold the cards from patients they perceive as unsatisfied, who ironically have the most to gain. System changes, such as increasing handoffs and transient coverage with resident duty hours, can also hamper tool effectiveness through the introduction of more physicians to remember, inherently decreasing the ability of patients to identify their treating physicians.[8]

Patient factors also affect the success of such interventions. Interestingly, patients' baseline ability to identify their physician ranged from 11% to 51% in these studies. Such differences can be readily attributed to previous disparities noted by age, race, gender, and education level in patient recall of their physician.[8] Future work should target interventions for these subgroups, while also accounting for the high prevalence of low health literacy, memory impairment, sleep loss, and poor vision among inpatients, all of which can hamper such interventions.[9, 10]

Although neither intervention improved overall patient satisfaction, patient satisfaction is influenced by a variety of factors unrelated to physician care, such as nursing or the environment. Given the inherent ceiling effect in patient satisfaction metrics, both studies were underpowered to show minor differences. It is also worth noting that complex social interventions depend on their context. Although some patients may enjoy receiving the cards, others may feel that it is not critical to their patient satisfaction. Using a realist evaluation would ask patients what they thought of the cards and why.[11] Like one of the authors, we noted that patients do like the cards, suggesting the problem is not the cards but the metrics of evaluation.[12]

In addition to robust evaluation metrics, future interventions should incorporate patient‐centered approaches to empower patients to ask their doctors about their name and role. With the request coming from patients, doctors are much more likely to comply. Using lessons from marketing and advertising, the hospital is full of artifacts, such as white boards, wristbands, remote controls, and monitors, that can be repurposed to advertise the doctor's name to the patient. Future advances can exploit new mobile technologies and repurpose old ones, such as the hospital television, to remind patients of their care team and other critical information. Regardless of what the future may bring, let's face itintroducing yourself properly to your patients is always good medicine.

At the core of a good physician is mastery of critical communication skills. Good communication establishes rapport and can also heal patients. As communication is an essential ingredient of good physicianship, the recipe starts with a fundamental staplethe physician introduction. The physician introduction is step 2 of Kahn's etiquette‐based medicine checklist to promote good doctoring.[1] Although such rudimentary communication skills are cemented in kindergarten, sadly, more training is needed for doctors. In a recent Journal of Hospital Medicine study, interns failed to introduce themselves in 3 out of 5 inpatient encounters.[2]

Despite waning introductions, increasing importance is being placed on hospitalized patient's knowledge of their treating physician's name and role for patient safety. The Transitions of Care Consensus Policy Statement endorsed by 6 medical societies, including the Society of Hospital Medicine, recommend patients know who their treating physician is while caring for them at every step across the continuum, including hospitalization.[3] The Accreditation Council for Graduate Medical Education requires that patients be informed of who the supervising physician is and understand the roles of any trainees in their care.[4] Last, the death of young Lewis Blackman in South Carolina resulted in state legislation requiring clear identification of physicians and their roles for patients.[5] Given these recommendations, tools to remind physicians to introduce themselves and explain their role to patients are worth consideration. In this issue of the Journal of Hospital Medicine, the effectiveness of 2 interventions using physician photo tools is described.[6, 7]

Even though both studies advance our knowledge on the effectiveness of such interventions, nonrandom variable uptake by physicians represents a major common hurdle. Physician workload, competing priorities, and time pressures prevent physicians from distributing such tools. Consistent adopters of the cards likely already introduce themselves regularly. Interestingly, physicians likely withhold the cards from patients they perceive as unsatisfied, who ironically have the most to gain. System changes, such as increasing handoffs and transient coverage with resident duty hours, can also hamper tool effectiveness through the introduction of more physicians to remember, inherently decreasing the ability of patients to identify their treating physicians.[8]

Patient factors also affect the success of such interventions. Interestingly, patients' baseline ability to identify their physician ranged from 11% to 51% in these studies. Such differences can be readily attributed to previous disparities noted by age, race, gender, and education level in patient recall of their physician.[8] Future work should target interventions for these subgroups, while also accounting for the high prevalence of low health literacy, memory impairment, sleep loss, and poor vision among inpatients, all of which can hamper such interventions.[9, 10]

Although neither intervention improved overall patient satisfaction, patient satisfaction is influenced by a variety of factors unrelated to physician care, such as nursing or the environment. Given the inherent ceiling effect in patient satisfaction metrics, both studies were underpowered to show minor differences. It is also worth noting that complex social interventions depend on their context. Although some patients may enjoy receiving the cards, others may feel that it is not critical to their patient satisfaction. Using a realist evaluation would ask patients what they thought of the cards and why.[11] Like one of the authors, we noted that patients do like the cards, suggesting the problem is not the cards but the metrics of evaluation.[12]

In addition to robust evaluation metrics, future interventions should incorporate patient‐centered approaches to empower patients to ask their doctors about their name and role. With the request coming from patients, doctors are much more likely to comply. Using lessons from marketing and advertising, the hospital is full of artifacts, such as white boards, wristbands, remote controls, and monitors, that can be repurposed to advertise the doctor's name to the patient. Future advances can exploit new mobile technologies and repurpose old ones, such as the hospital television, to remind patients of their care team and other critical information. Regardless of what the future may bring, let's face itintroducing yourself properly to your patients is always good medicine.

At the core of a good physician is mastery of critical communication skills. Good communication establishes rapport and can also heal patients. As communication is an essential ingredient of good physicianship, the recipe starts with a fundamental staplethe physician introduction. The physician introduction is step 2 of Kahn's etiquette‐based medicine checklist to promote good doctoring.[1] Although such rudimentary communication skills are cemented in kindergarten, sadly, more training is needed for doctors. In a recent Journal of Hospital Medicine study, interns failed to introduce themselves in 3 out of 5 inpatient encounters.[2]

Despite waning introductions, increasing importance is being placed on hospitalized patient's knowledge of their treating physician's name and role for patient safety. The Transitions of Care Consensus Policy Statement endorsed by 6 medical societies, including the Society of Hospital Medicine, recommend patients know who their treating physician is while caring for them at every step across the continuum, including hospitalization.[3] The Accreditation Council for Graduate Medical Education requires that patients be informed of who the supervising physician is and understand the roles of any trainees in their care.[4] Last, the death of young Lewis Blackman in South Carolina resulted in state legislation requiring clear identification of physicians and their roles for patients.[5] Given these recommendations, tools to remind physicians to introduce themselves and explain their role to patients are worth consideration. In this issue of the Journal of Hospital Medicine, the effectiveness of 2 interventions using physician photo tools is described.[6, 7]

Even though both studies advance our knowledge on the effectiveness of such interventions, nonrandom variable uptake by physicians represents a major common hurdle. Physician workload, competing priorities, and time pressures prevent physicians from distributing such tools. Consistent adopters of the cards likely already introduce themselves regularly. Interestingly, physicians likely withhold the cards from patients they perceive as unsatisfied, who ironically have the most to gain. System changes, such as increasing handoffs and transient coverage with resident duty hours, can also hamper tool effectiveness through the introduction of more physicians to remember, inherently decreasing the ability of patients to identify their treating physicians.[8]

Patient factors also affect the success of such interventions. Interestingly, patients' baseline ability to identify their physician ranged from 11% to 51% in these studies. Such differences can be readily attributed to previous disparities noted by age, race, gender, and education level in patient recall of their physician.[8] Future work should target interventions for these subgroups, while also accounting for the high prevalence of low health literacy, memory impairment, sleep loss, and poor vision among inpatients, all of which can hamper such interventions.[9, 10]

Although neither intervention improved overall patient satisfaction, patient satisfaction is influenced by a variety of factors unrelated to physician care, such as nursing or the environment. Given the inherent ceiling effect in patient satisfaction metrics, both studies were underpowered to show minor differences. It is also worth noting that complex social interventions depend on their context. Although some patients may enjoy receiving the cards, others may feel that it is not critical to their patient satisfaction. Using a realist evaluation would ask patients what they thought of the cards and why.[11] Like one of the authors, we noted that patients do like the cards, suggesting the problem is not the cards but the metrics of evaluation.[12]

In addition to robust evaluation metrics, future interventions should incorporate patient‐centered approaches to empower patients to ask their doctors about their name and role. With the request coming from patients, doctors are much more likely to comply. Using lessons from marketing and advertising, the hospital is full of artifacts, such as white boards, wristbands, remote controls, and monitors, that can be repurposed to advertise the doctor's name to the patient. Future advances can exploit new mobile technologies and repurpose old ones, such as the hospital television, to remind patients of their care team and other critical information. Regardless of what the future may bring, let's face itintroducing yourself properly to your patients is always good medicine.

Recent trends show a marked increase in outpatient use of chronic opioid therapy (COT) for chronic noncancer pain (CNCP)[1, 2] without decreases in reported CNCP,[3] raising concerns about the efficacy and risk‐to‐benefit ratio of opioids in this population.[4, 5, 6, 7, 8] Increasing rates of outpatient use likely are accompanied by increasing rates of opioid exposure among patients admitted to the hospital. To our knowledge there are no published data regarding the prevalence of COT during the months preceding hospitalization.

Opioid use has been linked to increased emergency room utilization[9, 10] and emergency hospitalization,[11] but associations between opioid use and inpatient metrics (eg, mortality, readmission) have not been explored. Furthermore, lack of knowledge about the prevalence of opioid use prior to hospitalization may impede efforts to improve inpatient pain management and satisfaction with care. Although there is reason to expect that strategies to safely and effectively treat acute pain during the inpatient stay differ between opioid‐nave patients and opioid‐exposed patients, evidence regarding treatment strategies is limited.[12, 13, 14] Opioid pain medications are associated with hospital adverse events, with both prior opioid exposure and lack of opioid use as proposed risk factors.[15] A better understanding of the prevalence and characteristics of hospitalized COT patients is fundamental to future work to achieve safer and more effective inpatient pain management.

The primary purpose of this study was to determine the prevalence of prior COT among hospitalized medical patients. Additionally, we aimed to characterize inpatients with occasional and chronic opioid therapy prior to admission in comparison to opioid‐nave inpatients, as differences between these groups may suggest directions for further investigation into the distinct needs or challenges of hospitalized opioid‐exposed patients.

METHODS

We used inpatient and outpatient administrative data from the Department of Veterans Affairs (VA) Healthcare System. The primary data source to identify acute medical admissions was the VA Patient Treatment File, a national administrative database of all inpatient admissions, including patient demographic characteristics, primary and secondary diagnoses (using International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM], codes), and hospitalization characteristics. Outpatient pharmacy data were from the VA Pharmacy Prescription Data Files. The VA Vital Status Files provided dates of death.

We identified all first acute medical admissions to 129 VA hospitals during fiscal years (FYs) 2009 to 2011 (October 2009September 2011). We defined first admissions as the initial medical hospitalization occurring following a minimum 365‐day hospitalization‐free period. Patients were required to demonstrate pharmacy use by receipt of any outpatient medication from the VA on 2 separate occasions within 270 days preceding the first admission, to avoid misclassification of patients who routinely obtained medications only from a non‐VA provider. Patients admitted from extended care facilities were excluded.

We grouped patients by opioid‐use status based on outpatient prescription records: (1) no opioid use, defined as no opioid prescriptions in the 6 months prior to hospitalization; (2) occasional opioid use, defined as patients who received any opioid prescription during the 6 months prior but did not meet definition of chronic use; and (3) chronic opioid therapy, defined as 90 or more days' supply of opioids received within 6 months preceding hospitalization. We did not specify continuous prescribing. Opioids included in the definition were codeine, dihydrocodeine, fentanyl (mucosal and topical), hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol.[16, 17]

We compared groups by demographic variables including age, sex, race, income, rural vs urban residence (determined from Rural‐Urban Commuting Area codes), region based on hospital location; overall comorbidity using the Charlson Comorbidity Index (CCI);[18] and 10 selected conditions to characterize comorbidity (see Supporting Information, Appendix A, in the online version of this article). These 10 conditions were chosen based on probable associations with chronic opioid use or high prevalence among hospitalized veterans.[9, 19, 20]

We used a CNCP definition based on ICD‐9‐CM codes.[9] This definition did not include episodic conditions such as migraine[2] or a measure of pain intensity.[21] All conditions were determined from diagnoses coded during any encounter in the year prior to hospitalization, exclusive of the first (ie, index) admission. We also determined the frequency of palliative care use, defined as presence of ICD‐9‐CM code V667 during index hospitalization or within the past year. Patients with palliative care use (n=3070) were excluded from further analyses.

We compared opioid use groups by baseline characteristics using the [2] statistic to determine if the distribution was nonrandom. We used analysis of variance to compare hospital length of stay between groups. We used the [2] statistic to compare rates of 4 outcomes of interest: intensive care unit (ICU) admission during the index hospitalization, discharge disposition other than home, 30‐day readmission rate, and in‐hospital or 30‐day mortality.

To assess the association between opioid‐use status and the 4 outcomes of interest, we constructed 2 multivariable regression models; the first was adjusted only for admission diagnosis using the Clinical Classification Software (CCS),[22] and the second was adjusted for demographics, CCI, and the 10 selected comorbidities in addition to admission diagnosis.

The authors had full access to and take full responsibility for the integrity of the data. All analyses were conducted using SAS statistical software version 9.2 (SAS Institute, Cary, NC). The study was approved by the University of Iowa institutional review board and the Iowa City VA Health Care System Research and Development Committee.

RESULTS

DISCUSSION

This observational study is, to our knowledge, the first to report prevalence of and characteristics associated with prior opioid use among hospitalized medical patients. The prevalence of any opioid use and of COT was substantially higher in this hospitalized cohort than reported in outpatient settings. The prevalence of any opioid use during 1 year (FY 2009) among all veterans with VA primary care use was 26.1%.[23] A study of incident prescribing rates among veterans with new diagnoses of noncancer‐related pain demonstrated 11% received an opioid prescription within 1 year.[24] Using a definition of 90 consecutive prescription days to define COT, Dobscha et al.[25] found that 5% of veterans with persistent elevated pain intensity and no previous opioid prescriptions subsequently received COT within 12 months. The high prevalence we found likely reflects cumulative effects of incident use as well as an increased symptom burden in a population defined by need for medical hospitalization.

Although a veteran population may not be generalizable to a nonveteran setting, we do note prior studies reporting prevalence of any opioid use in outpatient cohorts (in 2000 and 2005) of between 18% and 30%, with higher rates among women and patients over 65 years of age.[1, 2]

Our work was purposefully inclusive of cancer patients so that we might assess the degree to which cancer diagnoses accounted for prior opioid use in hospitalized patients. Surprisingly, the rate of COT for patients with cancer was lower than that for patients with CNCP, perhaps reflecting that a cancer condition defined in administrative data may not constitute a pain‐causing disease.

Recognition of the prevalence of opioid therapy is important as we work to understand and improve safety, satisfaction, utilization, and long‐term health outcomes associated with hospitalization. Our finding that over half of medical inpatients have preexisting CNCP diagnoses, and a not entirely overlapping proportion has prior opioid exposure, implies a need for future work to refine expectations and strategies for inpatient management, potentially tailored to prior opioid use and presence of CNCP.

A recent Joint Commission sentinel event alert[26] highlights opioid adverse events in the hospital and identifies both lack of previous opioid therapy and prior opioid therapy as factors increasing risk. ICU admission during the hospital stay may reflect adverse events such as opioid‐induced respiratory depression; in our study, patients with no opioid use prior to admission were more likely to have an ICU stay, although the effect was small. One might speculate that clinicians, accustomed to treating pain in opioid‐exposed patients, are using inappropriately large starting dosages of narcotics for inpatients without first assessing prior opioid exposure. Another possible explanation is that patients on COT are admitted to the hospital with less severe illness, potentially reflecting functional, social, or access limitations that compromise ability to manage illness in the outpatient setting. More detailed comparison of illness severity is beyond the scope of the present work.

Patient satisfaction with pain management is reflected in 2 of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) questions, and is publically reported.[27] HCAHPS results also figure in the formula for the Centers for Medicare and Medicaid Services value‐based purchasing.[28] Preadmission pain is predictive of postoperative pain[29, 30] and may shape patient expectations; how preadmission opioid use modulates nonsurgical pain and satisfaction with management in the medical inpatient remains to be studied. The high prevalence of prior COT underscores the importance of understanding characteristics of patients on COT, and potential differences and disparities in pain management, when designing interventions to augment patient satisfaction with pain management.

Although the age distribution and patterns of comorbidities differed between the opioid‐use groups, opioid therapy remained a small but significant predictor of hospital readmission; this association was independent of CNCP diagnosis. Functional outcomes are recognized as important measures of efficacy of outpatient pain management strategies,[31] with some evidence that opioids are associated with worse functioning.[32, 33] Functional limitations, as well as inadequately or inappropriately treated pain, may drive both admissions and readmissions. Alternately, COT may be a marker for unmeasured factors that increase a patient's risk of returning to the hospital. Further work is needed to elucidate the relationship between COT and healthcare utilization associated with the inpatient stay.

Our finding that patients on COT have an increased mortality risk is concerning, given the rapid expansion in use of these medications. Although pain is increasingly prevalent toward end of life,[34] we did not observe an association between either CNCP (data not shown) or occasional opioid use and mortality. COT may complicate chronic disease through adverse drug effects including respiratory depression, apnea, or endocrine or immune alteration. Complex chronically ill patients with conditions such as COPD, HF, or diabetes may be particularly susceptible to these effects. Incident use of morphine is associated with increased mortality in acute coronary syndrome and HF[35, 36]: we are not aware of any work describing the relationship between prior opioid use and incident use during hospitalization in medical patients.

CONCLUSION

Nearly 1 in 4 hospitalized veterans has current or recent COT at the time of hospital admission for nonsurgical conditions; nearly half have been prescribed any opioids. Practitioners designing interventions to improve pain management in the inpatient setting should account for prior opioid use. Patients who are on COT prior to hospitalization differ in age and comorbidities from their counterparts who are not on COT. Further elucidation of differences between opioid‐use groups may help providers address care needs during the transition to posthospitalization care. CNCP diagnoses and chronic opioid exposure are different entities and cannot serve as proxies in administrative data. Additional work on utilization and outcomes in specific patient populations may improve our understanding of the long‐term health effects of chronic opioid therapy.

Disclosures: Dr. Mosher is supported by the Veterans Administration (VA) Quality Scholars Fellowship, Office of Academic Affiliations, Department of Veterans Affairs. Dr. Cram is supported by a K24 award from NIAMS (AR062133) at the National Institutes of Health. The preliminary results of this article were presented at the Society of General Internal Medicine Annual Meeting in Denver, Colordao, April 2013. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Data are available to researchers with VA accreditation, the statistical code and the protocol are available to interested readers by contacting Dr. Mosher. The authors report no conflict of interest in regard to this study.

Recent trends show a marked increase in outpatient use of chronic opioid therapy (COT) for chronic noncancer pain (CNCP)[1, 2] without decreases in reported CNCP,[3] raising concerns about the efficacy and risk‐to‐benefit ratio of opioids in this population.[4, 5, 6, 7, 8] Increasing rates of outpatient use likely are accompanied by increasing rates of opioid exposure among patients admitted to the hospital. To our knowledge there are no published data regarding the prevalence of COT during the months preceding hospitalization.

Opioid use has been linked to increased emergency room utilization[9, 10] and emergency hospitalization,[11] but associations between opioid use and inpatient metrics (eg, mortality, readmission) have not been explored. Furthermore, lack of knowledge about the prevalence of opioid use prior to hospitalization may impede efforts to improve inpatient pain management and satisfaction with care. Although there is reason to expect that strategies to safely and effectively treat acute pain during the inpatient stay differ between opioid‐nave patients and opioid‐exposed patients, evidence regarding treatment strategies is limited.[12, 13, 14] Opioid pain medications are associated with hospital adverse events, with both prior opioid exposure and lack of opioid use as proposed risk factors.[15] A better understanding of the prevalence and characteristics of hospitalized COT patients is fundamental to future work to achieve safer and more effective inpatient pain management.

The primary purpose of this study was to determine the prevalence of prior COT among hospitalized medical patients. Additionally, we aimed to characterize inpatients with occasional and chronic opioid therapy prior to admission in comparison to opioid‐nave inpatients, as differences between these groups may suggest directions for further investigation into the distinct needs or challenges of hospitalized opioid‐exposed patients.

METHODS

We used inpatient and outpatient administrative data from the Department of Veterans Affairs (VA) Healthcare System. The primary data source to identify acute medical admissions was the VA Patient Treatment File, a national administrative database of all inpatient admissions, including patient demographic characteristics, primary and secondary diagnoses (using International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM], codes), and hospitalization characteristics. Outpatient pharmacy data were from the VA Pharmacy Prescription Data Files. The VA Vital Status Files provided dates of death.

We identified all first acute medical admissions to 129 VA hospitals during fiscal years (FYs) 2009 to 2011 (October 2009September 2011). We defined first admissions as the initial medical hospitalization occurring following a minimum 365‐day hospitalization‐free period. Patients were required to demonstrate pharmacy use by receipt of any outpatient medication from the VA on 2 separate occasions within 270 days preceding the first admission, to avoid misclassification of patients who routinely obtained medications only from a non‐VA provider. Patients admitted from extended care facilities were excluded.

We grouped patients by opioid‐use status based on outpatient prescription records: (1) no opioid use, defined as no opioid prescriptions in the 6 months prior to hospitalization; (2) occasional opioid use, defined as patients who received any opioid prescription during the 6 months prior but did not meet definition of chronic use; and (3) chronic opioid therapy, defined as 90 or more days' supply of opioids received within 6 months preceding hospitalization. We did not specify continuous prescribing. Opioids included in the definition were codeine, dihydrocodeine, fentanyl (mucosal and topical), hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol.[16, 17]

We compared groups by demographic variables including age, sex, race, income, rural vs urban residence (determined from Rural‐Urban Commuting Area codes), region based on hospital location; overall comorbidity using the Charlson Comorbidity Index (CCI);[18] and 10 selected conditions to characterize comorbidity (see Supporting Information, Appendix A, in the online version of this article). These 10 conditions were chosen based on probable associations with chronic opioid use or high prevalence among hospitalized veterans.[9, 19, 20]

We used a CNCP definition based on ICD‐9‐CM codes.[9] This definition did not include episodic conditions such as migraine[2] or a measure of pain intensity.[21] All conditions were determined from diagnoses coded during any encounter in the year prior to hospitalization, exclusive of the first (ie, index) admission. We also determined the frequency of palliative care use, defined as presence of ICD‐9‐CM code V667 during index hospitalization or within the past year. Patients with palliative care use (n=3070) were excluded from further analyses.

We compared opioid use groups by baseline characteristics using the [2] statistic to determine if the distribution was nonrandom. We used analysis of variance to compare hospital length of stay between groups. We used the [2] statistic to compare rates of 4 outcomes of interest: intensive care unit (ICU) admission during the index hospitalization, discharge disposition other than home, 30‐day readmission rate, and in‐hospital or 30‐day mortality.

To assess the association between opioid‐use status and the 4 outcomes of interest, we constructed 2 multivariable regression models; the first was adjusted only for admission diagnosis using the Clinical Classification Software (CCS),[22] and the second was adjusted for demographics, CCI, and the 10 selected comorbidities in addition to admission diagnosis.

The authors had full access to and take full responsibility for the integrity of the data. All analyses were conducted using SAS statistical software version 9.2 (SAS Institute, Cary, NC). The study was approved by the University of Iowa institutional review board and the Iowa City VA Health Care System Research and Development Committee.

RESULTS

DISCUSSION

This observational study is, to our knowledge, the first to report prevalence of and characteristics associated with prior opioid use among hospitalized medical patients. The prevalence of any opioid use and of COT was substantially higher in this hospitalized cohort than reported in outpatient settings. The prevalence of any opioid use during 1 year (FY 2009) among all veterans with VA primary care use was 26.1%.[23] A study of incident prescribing rates among veterans with new diagnoses of noncancer‐related pain demonstrated 11% received an opioid prescription within 1 year.[24] Using a definition of 90 consecutive prescription days to define COT, Dobscha et al.[25] found that 5% of veterans with persistent elevated pain intensity and no previous opioid prescriptions subsequently received COT within 12 months. The high prevalence we found likely reflects cumulative effects of incident use as well as an increased symptom burden in a population defined by need for medical hospitalization.

Although a veteran population may not be generalizable to a nonveteran setting, we do note prior studies reporting prevalence of any opioid use in outpatient cohorts (in 2000 and 2005) of between 18% and 30%, with higher rates among women and patients over 65 years of age.[1, 2]

Our work was purposefully inclusive of cancer patients so that we might assess the degree to which cancer diagnoses accounted for prior opioid use in hospitalized patients. Surprisingly, the rate of COT for patients with cancer was lower than that for patients with CNCP, perhaps reflecting that a cancer condition defined in administrative data may not constitute a pain‐causing disease.

Recognition of the prevalence of opioid therapy is important as we work to understand and improve safety, satisfaction, utilization, and long‐term health outcomes associated with hospitalization. Our finding that over half of medical inpatients have preexisting CNCP diagnoses, and a not entirely overlapping proportion has prior opioid exposure, implies a need for future work to refine expectations and strategies for inpatient management, potentially tailored to prior opioid use and presence of CNCP.

A recent Joint Commission sentinel event alert[26] highlights opioid adverse events in the hospital and identifies both lack of previous opioid therapy and prior opioid therapy as factors increasing risk. ICU admission during the hospital stay may reflect adverse events such as opioid‐induced respiratory depression; in our study, patients with no opioid use prior to admission were more likely to have an ICU stay, although the effect was small. One might speculate that clinicians, accustomed to treating pain in opioid‐exposed patients, are using inappropriately large starting dosages of narcotics for inpatients without first assessing prior opioid exposure. Another possible explanation is that patients on COT are admitted to the hospital with less severe illness, potentially reflecting functional, social, or access limitations that compromise ability to manage illness in the outpatient setting. More detailed comparison of illness severity is beyond the scope of the present work.

Patient satisfaction with pain management is reflected in 2 of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) questions, and is publically reported.[27] HCAHPS results also figure in the formula for the Centers for Medicare and Medicaid Services value‐based purchasing.[28] Preadmission pain is predictive of postoperative pain[29, 30] and may shape patient expectations; how preadmission opioid use modulates nonsurgical pain and satisfaction with management in the medical inpatient remains to be studied. The high prevalence of prior COT underscores the importance of understanding characteristics of patients on COT, and potential differences and disparities in pain management, when designing interventions to augment patient satisfaction with pain management.

Although the age distribution and patterns of comorbidities differed between the opioid‐use groups, opioid therapy remained a small but significant predictor of hospital readmission; this association was independent of CNCP diagnosis. Functional outcomes are recognized as important measures of efficacy of outpatient pain management strategies,[31] with some evidence that opioids are associated with worse functioning.[32, 33] Functional limitations, as well as inadequately or inappropriately treated pain, may drive both admissions and readmissions. Alternately, COT may be a marker for unmeasured factors that increase a patient's risk of returning to the hospital. Further work is needed to elucidate the relationship between COT and healthcare utilization associated with the inpatient stay.

Our finding that patients on COT have an increased mortality risk is concerning, given the rapid expansion in use of these medications. Although pain is increasingly prevalent toward end of life,[34] we did not observe an association between either CNCP (data not shown) or occasional opioid use and mortality. COT may complicate chronic disease through adverse drug effects including respiratory depression, apnea, or endocrine or immune alteration. Complex chronically ill patients with conditions such as COPD, HF, or diabetes may be particularly susceptible to these effects. Incident use of morphine is associated with increased mortality in acute coronary syndrome and HF[35, 36]: we are not aware of any work describing the relationship between prior opioid use and incident use during hospitalization in medical patients.

CONCLUSION

Nearly 1 in 4 hospitalized veterans has current or recent COT at the time of hospital admission for nonsurgical conditions; nearly half have been prescribed any opioids. Practitioners designing interventions to improve pain management in the inpatient setting should account for prior opioid use. Patients who are on COT prior to hospitalization differ in age and comorbidities from their counterparts who are not on COT. Further elucidation of differences between opioid‐use groups may help providers address care needs during the transition to posthospitalization care. CNCP diagnoses and chronic opioid exposure are different entities and cannot serve as proxies in administrative data. Additional work on utilization and outcomes in specific patient populations may improve our understanding of the long‐term health effects of chronic opioid therapy.

Disclosures: Dr. Mosher is supported by the Veterans Administration (VA) Quality Scholars Fellowship, Office of Academic Affiliations, Department of Veterans Affairs. Dr. Cram is supported by a K24 award from NIAMS (AR062133) at the National Institutes of Health. The preliminary results of this article were presented at the Society of General Internal Medicine Annual Meeting in Denver, Colordao, April 2013. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Data are available to researchers with VA accreditation, the statistical code and the protocol are available to interested readers by contacting Dr. Mosher. The authors report no conflict of interest in regard to this study.

Recent trends show a marked increase in outpatient use of chronic opioid therapy (COT) for chronic noncancer pain (CNCP)[1, 2] without decreases in reported CNCP,[3] raising concerns about the efficacy and risk‐to‐benefit ratio of opioids in this population.[4, 5, 6, 7, 8] Increasing rates of outpatient use likely are accompanied by increasing rates of opioid exposure among patients admitted to the hospital. To our knowledge there are no published data regarding the prevalence of COT during the months preceding hospitalization.

Opioid use has been linked to increased emergency room utilization[9, 10] and emergency hospitalization,[11] but associations between opioid use and inpatient metrics (eg, mortality, readmission) have not been explored. Furthermore, lack of knowledge about the prevalence of opioid use prior to hospitalization may impede efforts to improve inpatient pain management and satisfaction with care. Although there is reason to expect that strategies to safely and effectively treat acute pain during the inpatient stay differ between opioid‐nave patients and opioid‐exposed patients, evidence regarding treatment strategies is limited.[12, 13, 14] Opioid pain medications are associated with hospital adverse events, with both prior opioid exposure and lack of opioid use as proposed risk factors.[15] A better understanding of the prevalence and characteristics of hospitalized COT patients is fundamental to future work to achieve safer and more effective inpatient pain management.

The primary purpose of this study was to determine the prevalence of prior COT among hospitalized medical patients. Additionally, we aimed to characterize inpatients with occasional and chronic opioid therapy prior to admission in comparison to opioid‐nave inpatients, as differences between these groups may suggest directions for further investigation into the distinct needs or challenges of hospitalized opioid‐exposed patients.

METHODS

We used inpatient and outpatient administrative data from the Department of Veterans Affairs (VA) Healthcare System. The primary data source to identify acute medical admissions was the VA Patient Treatment File, a national administrative database of all inpatient admissions, including patient demographic characteristics, primary and secondary diagnoses (using International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM], codes), and hospitalization characteristics. Outpatient pharmacy data were from the VA Pharmacy Prescription Data Files. The VA Vital Status Files provided dates of death.

We identified all first acute medical admissions to 129 VA hospitals during fiscal years (FYs) 2009 to 2011 (October 2009September 2011). We defined first admissions as the initial medical hospitalization occurring following a minimum 365‐day hospitalization‐free period. Patients were required to demonstrate pharmacy use by receipt of any outpatient medication from the VA on 2 separate occasions within 270 days preceding the first admission, to avoid misclassification of patients who routinely obtained medications only from a non‐VA provider. Patients admitted from extended care facilities were excluded.

We grouped patients by opioid‐use status based on outpatient prescription records: (1) no opioid use, defined as no opioid prescriptions in the 6 months prior to hospitalization; (2) occasional opioid use, defined as patients who received any opioid prescription during the 6 months prior but did not meet definition of chronic use; and (3) chronic opioid therapy, defined as 90 or more days' supply of opioids received within 6 months preceding hospitalization. We did not specify continuous prescribing. Opioids included in the definition were codeine, dihydrocodeine, fentanyl (mucosal and topical), hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol.[16, 17]

We compared groups by demographic variables including age, sex, race, income, rural vs urban residence (determined from Rural‐Urban Commuting Area codes), region based on hospital location; overall comorbidity using the Charlson Comorbidity Index (CCI);[18] and 10 selected conditions to characterize comorbidity (see Supporting Information, Appendix A, in the online version of this article). These 10 conditions were chosen based on probable associations with chronic opioid use or high prevalence among hospitalized veterans.[9, 19, 20]

We used a CNCP definition based on ICD‐9‐CM codes.[9] This definition did not include episodic conditions such as migraine[2] or a measure of pain intensity.[21] All conditions were determined from diagnoses coded during any encounter in the year prior to hospitalization, exclusive of the first (ie, index) admission. We also determined the frequency of palliative care use, defined as presence of ICD‐9‐CM code V667 during index hospitalization or within the past year. Patients with palliative care use (n=3070) were excluded from further analyses.

We compared opioid use groups by baseline characteristics using the [2] statistic to determine if the distribution was nonrandom. We used analysis of variance to compare hospital length of stay between groups. We used the [2] statistic to compare rates of 4 outcomes of interest: intensive care unit (ICU) admission during the index hospitalization, discharge disposition other than home, 30‐day readmission rate, and in‐hospital or 30‐day mortality.

To assess the association between opioid‐use status and the 4 outcomes of interest, we constructed 2 multivariable regression models; the first was adjusted only for admission diagnosis using the Clinical Classification Software (CCS),[22] and the second was adjusted for demographics, CCI, and the 10 selected comorbidities in addition to admission diagnosis.

The authors had full access to and take full responsibility for the integrity of the data. All analyses were conducted using SAS statistical software version 9.2 (SAS Institute, Cary, NC). The study was approved by the University of Iowa institutional review board and the Iowa City VA Health Care System Research and Development Committee.

RESULTS

DISCUSSION

This observational study is, to our knowledge, the first to report prevalence of and characteristics associated with prior opioid use among hospitalized medical patients. The prevalence of any opioid use and of COT was substantially higher in this hospitalized cohort than reported in outpatient settings. The prevalence of any opioid use during 1 year (FY 2009) among all veterans with VA primary care use was 26.1%.[23] A study of incident prescribing rates among veterans with new diagnoses of noncancer‐related pain demonstrated 11% received an opioid prescription within 1 year.[24] Using a definition of 90 consecutive prescription days to define COT, Dobscha et al.[25] found that 5% of veterans with persistent elevated pain intensity and no previous opioid prescriptions subsequently received COT within 12 months. The high prevalence we found likely reflects cumulative effects of incident use as well as an increased symptom burden in a population defined by need for medical hospitalization.

Although a veteran population may not be generalizable to a nonveteran setting, we do note prior studies reporting prevalence of any opioid use in outpatient cohorts (in 2000 and 2005) of between 18% and 30%, with higher rates among women and patients over 65 years of age.[1, 2]

Our work was purposefully inclusive of cancer patients so that we might assess the degree to which cancer diagnoses accounted for prior opioid use in hospitalized patients. Surprisingly, the rate of COT for patients with cancer was lower than that for patients with CNCP, perhaps reflecting that a cancer condition defined in administrative data may not constitute a pain‐causing disease.

Recognition of the prevalence of opioid therapy is important as we work to understand and improve safety, satisfaction, utilization, and long‐term health outcomes associated with hospitalization. Our finding that over half of medical inpatients have preexisting CNCP diagnoses, and a not entirely overlapping proportion has prior opioid exposure, implies a need for future work to refine expectations and strategies for inpatient management, potentially tailored to prior opioid use and presence of CNCP.

A recent Joint Commission sentinel event alert[26] highlights opioid adverse events in the hospital and identifies both lack of previous opioid therapy and prior opioid therapy as factors increasing risk. ICU admission during the hospital stay may reflect adverse events such as opioid‐induced respiratory depression; in our study, patients with no opioid use prior to admission were more likely to have an ICU stay, although the effect was small. One might speculate that clinicians, accustomed to treating pain in opioid‐exposed patients, are using inappropriately large starting dosages of narcotics for inpatients without first assessing prior opioid exposure. Another possible explanation is that patients on COT are admitted to the hospital with less severe illness, potentially reflecting functional, social, or access limitations that compromise ability to manage illness in the outpatient setting. More detailed comparison of illness severity is beyond the scope of the present work.

Patient satisfaction with pain management is reflected in 2 of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) questions, and is publically reported.[27] HCAHPS results also figure in the formula for the Centers for Medicare and Medicaid Services value‐based purchasing.[28] Preadmission pain is predictive of postoperative pain[29, 30] and may shape patient expectations; how preadmission opioid use modulates nonsurgical pain and satisfaction with management in the medical inpatient remains to be studied. The high prevalence of prior COT underscores the importance of understanding characteristics of patients on COT, and potential differences and disparities in pain management, when designing interventions to augment patient satisfaction with pain management.

Although the age distribution and patterns of comorbidities differed between the opioid‐use groups, opioid therapy remained a small but significant predictor of hospital readmission; this association was independent of CNCP diagnosis. Functional outcomes are recognized as important measures of efficacy of outpatient pain management strategies,[31] with some evidence that opioids are associated with worse functioning.[32, 33] Functional limitations, as well as inadequately or inappropriately treated pain, may drive both admissions and readmissions. Alternately, COT may be a marker for unmeasured factors that increase a patient's risk of returning to the hospital. Further work is needed to elucidate the relationship between COT and healthcare utilization associated with the inpatient stay.

Our finding that patients on COT have an increased mortality risk is concerning, given the rapid expansion in use of these medications. Although pain is increasingly prevalent toward end of life,[34] we did not observe an association between either CNCP (data not shown) or occasional opioid use and mortality. COT may complicate chronic disease through adverse drug effects including respiratory depression, apnea, or endocrine or immune alteration. Complex chronically ill patients with conditions such as COPD, HF, or diabetes may be particularly susceptible to these effects. Incident use of morphine is associated with increased mortality in acute coronary syndrome and HF[35, 36]: we are not aware of any work describing the relationship between prior opioid use and incident use during hospitalization in medical patients.

CONCLUSION

Nearly 1 in 4 hospitalized veterans has current or recent COT at the time of hospital admission for nonsurgical conditions; nearly half have been prescribed any opioids. Practitioners designing interventions to improve pain management in the inpatient setting should account for prior opioid use. Patients who are on COT prior to hospitalization differ in age and comorbidities from their counterparts who are not on COT. Further elucidation of differences between opioid‐use groups may help providers address care needs during the transition to posthospitalization care. CNCP diagnoses and chronic opioid exposure are different entities and cannot serve as proxies in administrative data. Additional work on utilization and outcomes in specific patient populations may improve our understanding of the long‐term health effects of chronic opioid therapy.

Disclosures: Dr. Mosher is supported by the Veterans Administration (VA) Quality Scholars Fellowship, Office of Academic Affiliations, Department of Veterans Affairs. Dr. Cram is supported by a K24 award from NIAMS (AR062133) at the National Institutes of Health. The preliminary results of this article were presented at the Society of General Internal Medicine Annual Meeting in Denver, Colordao, April 2013. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Data are available to researchers with VA accreditation, the statistical code and the protocol are available to interested readers by contacting Dr. Mosher. The authors report no conflict of interest in regard to this study.