Andrew D. Auerbach, MD, MPH

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

After seven years at the helm of the Journal of Hospital Medicine, I am both pleased to hand over the reins and sad to let them go. My time as Editor in Chief has been wonderful, challenging, and fulfilling.

When I began my tenure, JHM managed approximately 350 papers annually, and published 10 times per year. We had no social media presence, a developing editorial sense (and developing Editor in Chief), and a pool of hard-working and passionate Editors. As of this year, we have handled more than 700 papers and are publishing content monthly, online only, and online first. Our dedicated team is deeply passionate about making every paper better through interaction with the authors—whether we accept it for publication or not.

JHM has added a presence on Facebook and Twitter, launched a Twitter Journal Club as a regular offering (#JHMChat), added visual abstracts to our Tweets and Facebook postings, and researched how these novel approaches increase not only the Journal’s social media presence but also its public face. Our efforts in social media were trendsetting in peer-reviewed literature, and the Editors who lead those efforts—Vineet Arora and Charlie Wray—are asked to consult for other journals regularly.

We launched two new series— Choosing Wisely®: Next Steps, and Choosing Wisely®: Things We Do For No Reason—with help from the ABIM Foundation and visionary Editors, Andy Masica, Ann Sheehy, and Lenny Feldman. These papers have pushed Hospitalists and Hospital Medicine to think carefully about the simple things we do every day, to think broadly about how to move past the initial ‘low-hanging fruit’ of value improvement, and point us towards policy and intervention approaches that are disruptive rather than incremental.

A special thank you to Som Mookherjee, Brian Harte, Dan Hunt, and Read Pierce who ably developed the Clinical Care Conundrums and Review series. They are assisted by teams of national correspondents and many contributors who’ve submitted work for those series.

I have been blessed by a team of more than a dozen Associate Editors who have ably, expeditiously, and collegially managed more than 2,000 papers. These Editors work out of a sense of altruism and commitment to Hospital Medicine and have made huge individual contributions to JHM through their reviewing expertise and ensuring that the editorial sense for JHM is as broad and innovative as our field.

Finally, I must thank my core team of Senior Deputy Editors who have shouldered the majority of editorial work, mentored Editors (including me) and Peer Reviewers, and provided strategic guidance.

How peer-reviewed journals are published is changing rapidly. Setting aside the questions of how we consume our medical literature and the transition from paper to digital, old financial models depending on subscriptions and advertising are either dying or evolving into something very different. The challenge is that the new model is very unclear and the old model based on ads and subscriptions is clearly nonviable but is the primary way to support the work of producing a journal. Moving from the current model to one based on clicks, views, or downloads will come down to who will derive benefit from those clicks/downloads, who will be willing to pay to read and learn from the work of authors, or who views that activity as being worthy enough advertise somewhere in that process or to monetize the data garnered from readers’ activities. In addition, many journals, including JHM, are supported by professional societies. While professional societies have a goal to serve their members, the goal of the peer-reviewed journal is to independently and broadly represent the field. One must reflect the other, but space between the two will always be required.

The speed with which research takes place is too slow, and the process of getting evidence into print (much less adopted) is even slower. But, this too is changing; the role of peer review and the publication process is evolving. In order to speed the potential discovery of new innovations, prepublication repositories (such as BioRxViv) are gaining popularity; well-publicized scandals around peer reviewing rings ¹ have not gone unnoticed, and have produced greater interest in using prepublication comments and online discussions as early forms of review. As a result, the disintermediation between scientist and ‘evidence’ is paralleling the disintermediation between events and messengers elsewhere. One need only review Twitter for a moment to get a sense for how crowdsourcing can lead to evidence (or news) generation for good or ill. I agree that while the end of journals (as we understand them now) is upon us, these are also opportunities for JHM as it enters its new phase and a place for leadership. ²

I am proud of what we have done at JHM in the last seven years. We have grown substantially. We have innovated and provided great service to our authors and the field of Hospital Medicine. Our growth and forward-looking approaches to social media and our digital footprint put the journal on a great path towards adapting to the trends in Hospital Medicine research and peer-reviewed publishing. Our focus on being doctors who care for patients and our teams—not just doctors who care for hospitals—is supporting the field and our practice. I look forward to seeing where JHM goes next.

After seven years at the helm of the Journal of Hospital Medicine, I am both pleased to hand over the reins and sad to let them go. My time as Editor in Chief has been wonderful, challenging, and fulfilling.

When I began my tenure, JHM managed approximately 350 papers annually, and published 10 times per year. We had no social media presence, a developing editorial sense (and developing Editor in Chief), and a pool of hard-working and passionate Editors. As of this year, we have handled more than 700 papers and are publishing content monthly, online only, and online first. Our dedicated team is deeply passionate about making every paper better through interaction with the authors—whether we accept it for publication or not.

JHM has added a presence on Facebook and Twitter, launched a Twitter Journal Club as a regular offering (#JHMChat), added visual abstracts to our Tweets and Facebook postings, and researched how these novel approaches increase not only the Journal’s social media presence but also its public face. Our efforts in social media were trendsetting in peer-reviewed literature, and the Editors who lead those efforts—Vineet Arora and Charlie Wray—are asked to consult for other journals regularly.

We launched two new series— Choosing Wisely®: Next Steps, and Choosing Wisely®: Things We Do For No Reason—with help from the ABIM Foundation and visionary Editors, Andy Masica, Ann Sheehy, and Lenny Feldman. These papers have pushed Hospitalists and Hospital Medicine to think carefully about the simple things we do every day, to think broadly about how to move past the initial ‘low-hanging fruit’ of value improvement, and point us towards policy and intervention approaches that are disruptive rather than incremental.

A special thank you to Som Mookherjee, Brian Harte, Dan Hunt, and Read Pierce who ably developed the Clinical Care Conundrums and Review series. They are assisted by teams of national correspondents and many contributors who’ve submitted work for those series.

I have been blessed by a team of more than a dozen Associate Editors who have ably, expeditiously, and collegially managed more than 2,000 papers. These Editors work out of a sense of altruism and commitment to Hospital Medicine and have made huge individual contributions to JHM through their reviewing expertise and ensuring that the editorial sense for JHM is as broad and innovative as our field.

Finally, I must thank my core team of Senior Deputy Editors who have shouldered the majority of editorial work, mentored Editors (including me) and Peer Reviewers, and provided strategic guidance.

How peer-reviewed journals are published is changing rapidly. Setting aside the questions of how we consume our medical literature and the transition from paper to digital, old financial models depending on subscriptions and advertising are either dying or evolving into something very different. The challenge is that the new model is very unclear and the old model based on ads and subscriptions is clearly nonviable but is the primary way to support the work of producing a journal. Moving from the current model to one based on clicks, views, or downloads will come down to who will derive benefit from those clicks/downloads, who will be willing to pay to read and learn from the work of authors, or who views that activity as being worthy enough advertise somewhere in that process or to monetize the data garnered from readers’ activities. In addition, many journals, including JHM, are supported by professional societies. While professional societies have a goal to serve their members, the goal of the peer-reviewed journal is to independently and broadly represent the field. One must reflect the other, but space between the two will always be required.

The speed with which research takes place is too slow, and the process of getting evidence into print (much less adopted) is even slower. But, this too is changing; the role of peer review and the publication process is evolving. In order to speed the potential discovery of new innovations, prepublication repositories (such as BioRxViv) are gaining popularity; well-publicized scandals around peer reviewing rings ¹ have not gone unnoticed, and have produced greater interest in using prepublication comments and online discussions as early forms of review. As a result, the disintermediation between scientist and ‘evidence’ is paralleling the disintermediation between events and messengers elsewhere. One need only review Twitter for a moment to get a sense for how crowdsourcing can lead to evidence (or news) generation for good or ill. I agree that while the end of journals (as we understand them now) is upon us, these are also opportunities for JHM as it enters its new phase and a place for leadership. ²

I am proud of what we have done at JHM in the last seven years. We have grown substantially. We have innovated and provided great service to our authors and the field of Hospital Medicine. Our growth and forward-looking approaches to social media and our digital footprint put the journal on a great path towards adapting to the trends in Hospital Medicine research and peer-reviewed publishing. Our focus on being doctors who care for patients and our teams—not just doctors who care for hospitals—is supporting the field and our practice. I look forward to seeing where JHM goes next.

After seven years at the helm of the Journal of Hospital Medicine, I am both pleased to hand over the reins and sad to let them go. My time as Editor in Chief has been wonderful, challenging, and fulfilling.

When I began my tenure, JHM managed approximately 350 papers annually, and published 10 times per year. We had no social media presence, a developing editorial sense (and developing Editor in Chief), and a pool of hard-working and passionate Editors. As of this year, we have handled more than 700 papers and are publishing content monthly, online only, and online first. Our dedicated team is deeply passionate about making every paper better through interaction with the authors—whether we accept it for publication or not.

JHM has added a presence on Facebook and Twitter, launched a Twitter Journal Club as a regular offering (#JHMChat), added visual abstracts to our Tweets and Facebook postings, and researched how these novel approaches increase not only the Journal’s social media presence but also its public face. Our efforts in social media were trendsetting in peer-reviewed literature, and the Editors who lead those efforts—Vineet Arora and Charlie Wray—are asked to consult for other journals regularly.

We launched two new series— Choosing Wisely®: Next Steps, and Choosing Wisely®: Things We Do For No Reason—with help from the ABIM Foundation and visionary Editors, Andy Masica, Ann Sheehy, and Lenny Feldman. These papers have pushed Hospitalists and Hospital Medicine to think carefully about the simple things we do every day, to think broadly about how to move past the initial ‘low-hanging fruit’ of value improvement, and point us towards policy and intervention approaches that are disruptive rather than incremental.

A special thank you to Som Mookherjee, Brian Harte, Dan Hunt, and Read Pierce who ably developed the Clinical Care Conundrums and Review series. They are assisted by teams of national correspondents and many contributors who’ve submitted work for those series.

I have been blessed by a team of more than a dozen Associate Editors who have ably, expeditiously, and collegially managed more than 2,000 papers. These Editors work out of a sense of altruism and commitment to Hospital Medicine and have made huge individual contributions to JHM through their reviewing expertise and ensuring that the editorial sense for JHM is as broad and innovative as our field.

Finally, I must thank my core team of Senior Deputy Editors who have shouldered the majority of editorial work, mentored Editors (including me) and Peer Reviewers, and provided strategic guidance.

How peer-reviewed journals are published is changing rapidly. Setting aside the questions of how we consume our medical literature and the transition from paper to digital, old financial models depending on subscriptions and advertising are either dying or evolving into something very different. The challenge is that the new model is very unclear and the old model based on ads and subscriptions is clearly nonviable but is the primary way to support the work of producing a journal. Moving from the current model to one based on clicks, views, or downloads will come down to who will derive benefit from those clicks/downloads, who will be willing to pay to read and learn from the work of authors, or who views that activity as being worthy enough advertise somewhere in that process or to monetize the data garnered from readers’ activities. In addition, many journals, including JHM, are supported by professional societies. While professional societies have a goal to serve their members, the goal of the peer-reviewed journal is to independently and broadly represent the field. One must reflect the other, but space between the two will always be required.

The speed with which research takes place is too slow, and the process of getting evidence into print (much less adopted) is even slower. But, this too is changing; the role of peer review and the publication process is evolving. In order to speed the potential discovery of new innovations, prepublication repositories (such as BioRxViv) are gaining popularity; well-publicized scandals around peer reviewing rings ¹ have not gone unnoticed, and have produced greater interest in using prepublication comments and online discussions as early forms of review. As a result, the disintermediation between scientist and ‘evidence’ is paralleling the disintermediation between events and messengers elsewhere. One need only review Twitter for a moment to get a sense for how crowdsourcing can lead to evidence (or news) generation for good or ill. I agree that while the end of journals (as we understand them now) is upon us, these are also opportunities for JHM as it enters its new phase and a place for leadership. ²

I am proud of what we have done at JHM in the last seven years. We have grown substantially. We have innovated and provided great service to our authors and the field of Hospital Medicine. Our growth and forward-looking approaches to social media and our digital footprint put the journal on a great path towards adapting to the trends in Hospital Medicine research and peer-reviewed publishing. Our focus on being doctors who care for patients and our teams—not just doctors who care for hospitals—is supporting the field and our practice. I look forward to seeing where JHM goes next.

Cardiac complications account for at least one-third of perioperative deaths, and lead to substantial morbidity and cost.^1-4 Current guidelines recommend that patients undergo assessment of cardiac risk and functional status prior to noncardiac surgery.⁵ Preoperative cardiac stress testing is recommended for patients whose predicted cardiac risk exceeds 1%, whose functional status is limited, and for whom testing may change management.⁵

However, patients are not specifically selected according to risk of coronary artery disease (CAD) in current guidelines. The pretest probability of CAD may vary widely in this patient population, and the resultant accuracy of cardiac stress testing in making the diagnosis of CAD may vary as well.⁵ Meanwhile, CAD is a clear risk factor for perioperative cardiac events.^6-8

Because the pretest probability of CAD is heterogeneous, the optimal modality of cardiac stress testing in this population is unclear. False-positive results would likely lead to inflated estimates of operative risk, expensive and high-risk downstream testing, and potentially cancellation of otherwise beneficial surgeries. Meanwhile, false-negative results would lead to overly optimistic estimates of surgical risk and potentially to surgical intervention at higher levels of risk than would be desirable. Current guidelines leave the selection of either dobutamine stress echocardiography (DSE) or pharmacological stress myocardial perfusion imaging to the clinician.⁵ To inform decisions regarding the selection of cardiac stress testing modality prior to noncardiac surgery, we conducted this study to estimate the diagnostic accuracy of DSE and single-photon emission computed tomography (SPECT) among this patient population.

Surgical Cohort

The American College of Surgeons’ National Surgical Quality Improvement Program (NSQIP) samples patients undergoing surgery at participating hospitals and collects standardized clinical data on preoperative risk factors and postoperative complications.⁹ We acquired public use data from the 2009 NSQIP cohort, which included more than 336,000 surgical cases from 237 hospitals (principally in the United States). We excluded from our analysis patients undergoing cardiac surgery, patients with a prior diagnosis of CAD, and patients undergoing experimental surgeries. This left a sample of 300,462 for analysis.

Prediction of Dyslipidemia

The model we used to predict the presence of obstructive CAD required the presence or absence of dyslipidemia. A number of variables are common to both NSQIP and the National Health and Nutrition Examination Survey (NHANES), including age, weight, sex, tobacco use, diabetes, and prior stroke.¹⁰ Using those common variables, we developed a logistic regression to predict a diagnosis of dyslipidemia, applied that regression to the NSQIP cohort, and dichotomized. To assess the potential impact of misclassification, we performed separate sensitivity analyses in which either no patients or all patients had dyslipidemia.

Prediction of Obstructive CAD

To estimate the probability of obstructive CAD, we applied the risk prediction tool currently recommended by the European Society of Cardiology.¹¹ The clinical version of this tool relies on age; sex; diagnoses of diabetes, hypertension, and dyslipidemia; active tobacco use; and chest pain characteristics to predict the probability of obstructive CAD on coronary angiography. We assumed that all patients in our cohort had nonspecific chest pain, the referent in the calculator.

Prediction of Perioperative Event Risk

To predict the probability of a perioperative cardiac event, we used the Myocardial Infarction or Cardiac Arrest (MICA) calculator, which was derived from an earlier cohort of NSQIP.¹² All variables required for this prediction tool were included in the 2009 NSQIP cohort; our categorization of surgeries is included as an online appendix. MICA is one of three prediction tools included in the current American College of Cardiology/American Heart Association (ACC/AHA) guidelines.⁵

Prediction of Test Accuracy

We searched the MEDLINE database for estimates of the test characteristics of DSE and SPECT that adjusted for workup bias.¹³ (Also known as sequential-ordering bias, here we refer to the phenomenon whereby further workup is based on the results of diagnostic testing, resulting in underdiagnosis among patients with negative tests and falsely high estimates of sensitivity.¹⁴) Although other modalities of myocardial perfusion imaging exist, SPECT appears to be the most widely available, utilized, and studied modality of MPI.¹⁵ Our search strategy paired (“Sensitivity and Specificity” [MeSH Terms] AND “Coronary Disease/diagnostic imaging” [MAJR] AND “bias” [TIAB]) with (“Tomography, Emission-Computed, Single-Photon” [MAJR] OR “Echocardiography, Stress” [MAJR]). We reviewed the results for sensitivity and specificity estimates that corrected for workup bias. For each of SPECT and DSE, we drew the sensitivity and specificity from normal distributions based on literature estimates (see Table). We then calculated the expected accuracy of each modality for each patient in our dataset. All analyses were performed in Stata (version 14, College Station, Texas).

The median predicted probability of obstructive CAD was 5.1% (IQR: 1.8%-13.9%). Among patients with a predicted risk of a perioperative event of 1% or greater, the median probability of obstructive CAD was 18.1% (IQR: 9.6%-32.3%). The correlation between the predicted probabilities of CAD and a perioperative event was low (0.32), but highly statistically significant (P < .001).

Both accuracy and PPV were higher for DSE than for SPECT. The predicted accuracy of DSE was greater than that of SPECT in 73.5% of cases overall and in 60.5% of cases with a predicted operative cardiac risk greater than 1%. The mean PPV of DSE was 32.9% (median: 26.7%), while the equivalent PPVs for SPECT were 14.1% and 8.2%, respectively. Among cases with a predicted operative cardiac risk greater than 1%, the mean PPV of DSE was 57.5% (median: 60.2%), while the equivalent PPVs for SPECT were 29.8% and 26.7%, respectively.

DSE had a mean predicted accuracy of 93.0% (median: 96.2%), while SPECT had a mean accuracy of 92.6% (median: 95.6%). The predicted accuracies of DSE and SPECT are shown in the Figure, stratified by predicted perioperative risk across the 1% risk threshold currently used by ACC/AHA guidelines.

In this study, we demonstrated that the expected accuracy of DSE in the diagnosis of obstructive CAD among patients undergoing noncardiac surgery is higher than that of SPECT. This finding was true in both unselected patients and those selected by a perioperative risk of greater than 1%. The use of SPECT, compared with DSE, would likely result in greater numbers of false-positive tests in this patient population and less accurate results overall.

Cardiac stress testing, as with any diagnostic test, is most useful at intermediate probabilities. Insofar as stress testing offers diagnostic value, our analysis suggests that, in the range of the predicted risk of CAD found in patients undergoing noncardiac surgery, DSE is a more efficient testing strategy. To the extent that making a diagnosis of CAD informs the decision to proceed to surgery, a more accurate test would be preferable. The lower cost of DSE, the lack of ionizing radiation, and the information provided by echocardiography regarding diagnoses other than CAD, if considered, would further amplify that preference.

However, it is important to note that both modalities have limited positive predictive value. In the median patient who meets the currently recommended 1% perioperative event risk threshold, SPECT would lead to 2.74 false positive results for every true positive result. DSE would produce approximately two false positive results for every three true positive results. If lower rates of false-positive testing are desired, different patient selection criteria are required.

A few key limitations of this work warrant discussion. First, our results likely overestimate the probability of obstructive CAD in this population. We assumed that all patients have nonspecific chest pain at the time of the preoperative evaluation, though many patients do not, in fact, have chest pain. Tools to estimate the pretest probability of CAD (such as the ESC tool that we used or the older Diamond-Forrester prediction) are intended to stratify higher-risk patients than are seen in a preoperative setting. If asymptomatic patients seen in preoperative risk assessment clinics have lower risk of CAD than what we have predicted, we will have understated the case for DSE. Moreover, cases sampled from hospitals participating in NSQIP are not representative of the national surgical population. This likely further inflates our estimates of CAD risk compared with the “true” surgical population. Finally, we cannot comment on current practice from these data. Current guidelines recommend preoperative cardiac stress testing for patients whose risk of a perioperative cardiac event exceeds 1%, whose functional status is poor or unknown, and only if said testing will change management.⁵ Using these data, we cannot determine the pretest probability of patients referred for stress testing before noncardiac surgery.

Still, this analysis suggests that, among patients undergoing noncardiac surgery, selecting patients according to the risk of perioperative events would result in a population at an overall comparatively low risk of CAD, and that in this population, DSE would be more accurate than SPECT for making the diagnosis of CAD. If a diagnosis of CAD would change the decision to proceed to surgery, DSE is likely to be a more efficient test than SPECT.

Acknowledgements

The authors would like to thank Wael Jaber, MD, for his thoughtful comments on a draft of this manuscript.

Disclosures

The authors have nothing to disclose.

Funding

The authors received no specific funding for this work.

Cardiac complications account for at least one-third of perioperative deaths, and lead to substantial morbidity and cost.^1-4 Current guidelines recommend that patients undergo assessment of cardiac risk and functional status prior to noncardiac surgery.⁵ Preoperative cardiac stress testing is recommended for patients whose predicted cardiac risk exceeds 1%, whose functional status is limited, and for whom testing may change management.⁵

However, patients are not specifically selected according to risk of coronary artery disease (CAD) in current guidelines. The pretest probability of CAD may vary widely in this patient population, and the resultant accuracy of cardiac stress testing in making the diagnosis of CAD may vary as well.⁵ Meanwhile, CAD is a clear risk factor for perioperative cardiac events.^6-8

Because the pretest probability of CAD is heterogeneous, the optimal modality of cardiac stress testing in this population is unclear. False-positive results would likely lead to inflated estimates of operative risk, expensive and high-risk downstream testing, and potentially cancellation of otherwise beneficial surgeries. Meanwhile, false-negative results would lead to overly optimistic estimates of surgical risk and potentially to surgical intervention at higher levels of risk than would be desirable. Current guidelines leave the selection of either dobutamine stress echocardiography (DSE) or pharmacological stress myocardial perfusion imaging to the clinician.⁵ To inform decisions regarding the selection of cardiac stress testing modality prior to noncardiac surgery, we conducted this study to estimate the diagnostic accuracy of DSE and single-photon emission computed tomography (SPECT) among this patient population.

Surgical Cohort

The American College of Surgeons’ National Surgical Quality Improvement Program (NSQIP) samples patients undergoing surgery at participating hospitals and collects standardized clinical data on preoperative risk factors and postoperative complications.⁹ We acquired public use data from the 2009 NSQIP cohort, which included more than 336,000 surgical cases from 237 hospitals (principally in the United States). We excluded from our analysis patients undergoing cardiac surgery, patients with a prior diagnosis of CAD, and patients undergoing experimental surgeries. This left a sample of 300,462 for analysis.

Prediction of Dyslipidemia

The model we used to predict the presence of obstructive CAD required the presence or absence of dyslipidemia. A number of variables are common to both NSQIP and the National Health and Nutrition Examination Survey (NHANES), including age, weight, sex, tobacco use, diabetes, and prior stroke.¹⁰ Using those common variables, we developed a logistic regression to predict a diagnosis of dyslipidemia, applied that regression to the NSQIP cohort, and dichotomized. To assess the potential impact of misclassification, we performed separate sensitivity analyses in which either no patients or all patients had dyslipidemia.

Prediction of Obstructive CAD

To estimate the probability of obstructive CAD, we applied the risk prediction tool currently recommended by the European Society of Cardiology.¹¹ The clinical version of this tool relies on age; sex; diagnoses of diabetes, hypertension, and dyslipidemia; active tobacco use; and chest pain characteristics to predict the probability of obstructive CAD on coronary angiography. We assumed that all patients in our cohort had nonspecific chest pain, the referent in the calculator.

Prediction of Perioperative Event Risk

To predict the probability of a perioperative cardiac event, we used the Myocardial Infarction or Cardiac Arrest (MICA) calculator, which was derived from an earlier cohort of NSQIP.¹² All variables required for this prediction tool were included in the 2009 NSQIP cohort; our categorization of surgeries is included as an online appendix. MICA is one of three prediction tools included in the current American College of Cardiology/American Heart Association (ACC/AHA) guidelines.⁵

Prediction of Test Accuracy

We searched the MEDLINE database for estimates of the test characteristics of DSE and SPECT that adjusted for workup bias.¹³ (Also known as sequential-ordering bias, here we refer to the phenomenon whereby further workup is based on the results of diagnostic testing, resulting in underdiagnosis among patients with negative tests and falsely high estimates of sensitivity.¹⁴) Although other modalities of myocardial perfusion imaging exist, SPECT appears to be the most widely available, utilized, and studied modality of MPI.¹⁵ Our search strategy paired (“Sensitivity and Specificity” [MeSH Terms] AND “Coronary Disease/diagnostic imaging” [MAJR] AND “bias” [TIAB]) with (“Tomography, Emission-Computed, Single-Photon” [MAJR] OR “Echocardiography, Stress” [MAJR]). We reviewed the results for sensitivity and specificity estimates that corrected for workup bias. For each of SPECT and DSE, we drew the sensitivity and specificity from normal distributions based on literature estimates (see Table). We then calculated the expected accuracy of each modality for each patient in our dataset. All analyses were performed in Stata (version 14, College Station, Texas).

The median predicted probability of obstructive CAD was 5.1% (IQR: 1.8%-13.9%). Among patients with a predicted risk of a perioperative event of 1% or greater, the median probability of obstructive CAD was 18.1% (IQR: 9.6%-32.3%). The correlation between the predicted probabilities of CAD and a perioperative event was low (0.32), but highly statistically significant (P < .001).

Both accuracy and PPV were higher for DSE than for SPECT. The predicted accuracy of DSE was greater than that of SPECT in 73.5% of cases overall and in 60.5% of cases with a predicted operative cardiac risk greater than 1%. The mean PPV of DSE was 32.9% (median: 26.7%), while the equivalent PPVs for SPECT were 14.1% and 8.2%, respectively. Among cases with a predicted operative cardiac risk greater than 1%, the mean PPV of DSE was 57.5% (median: 60.2%), while the equivalent PPVs for SPECT were 29.8% and 26.7%, respectively.

DSE had a mean predicted accuracy of 93.0% (median: 96.2%), while SPECT had a mean accuracy of 92.6% (median: 95.6%). The predicted accuracies of DSE and SPECT are shown in the Figure, stratified by predicted perioperative risk across the 1% risk threshold currently used by ACC/AHA guidelines.

In this study, we demonstrated that the expected accuracy of DSE in the diagnosis of obstructive CAD among patients undergoing noncardiac surgery is higher than that of SPECT. This finding was true in both unselected patients and those selected by a perioperative risk of greater than 1%. The use of SPECT, compared with DSE, would likely result in greater numbers of false-positive tests in this patient population and less accurate results overall.

Cardiac stress testing, as with any diagnostic test, is most useful at intermediate probabilities. Insofar as stress testing offers diagnostic value, our analysis suggests that, in the range of the predicted risk of CAD found in patients undergoing noncardiac surgery, DSE is a more efficient testing strategy. To the extent that making a diagnosis of CAD informs the decision to proceed to surgery, a more accurate test would be preferable. The lower cost of DSE, the lack of ionizing radiation, and the information provided by echocardiography regarding diagnoses other than CAD, if considered, would further amplify that preference.

However, it is important to note that both modalities have limited positive predictive value. In the median patient who meets the currently recommended 1% perioperative event risk threshold, SPECT would lead to 2.74 false positive results for every true positive result. DSE would produce approximately two false positive results for every three true positive results. If lower rates of false-positive testing are desired, different patient selection criteria are required.

A few key limitations of this work warrant discussion. First, our results likely overestimate the probability of obstructive CAD in this population. We assumed that all patients have nonspecific chest pain at the time of the preoperative evaluation, though many patients do not, in fact, have chest pain. Tools to estimate the pretest probability of CAD (such as the ESC tool that we used or the older Diamond-Forrester prediction) are intended to stratify higher-risk patients than are seen in a preoperative setting. If asymptomatic patients seen in preoperative risk assessment clinics have lower risk of CAD than what we have predicted, we will have understated the case for DSE. Moreover, cases sampled from hospitals participating in NSQIP are not representative of the national surgical population. This likely further inflates our estimates of CAD risk compared with the “true” surgical population. Finally, we cannot comment on current practice from these data. Current guidelines recommend preoperative cardiac stress testing for patients whose risk of a perioperative cardiac event exceeds 1%, whose functional status is poor or unknown, and only if said testing will change management.⁵ Using these data, we cannot determine the pretest probability of patients referred for stress testing before noncardiac surgery.

Still, this analysis suggests that, among patients undergoing noncardiac surgery, selecting patients according to the risk of perioperative events would result in a population at an overall comparatively low risk of CAD, and that in this population, DSE would be more accurate than SPECT for making the diagnosis of CAD. If a diagnosis of CAD would change the decision to proceed to surgery, DSE is likely to be a more efficient test than SPECT.

Acknowledgements

The authors would like to thank Wael Jaber, MD, for his thoughtful comments on a draft of this manuscript.

Disclosures

The authors have nothing to disclose.

Funding

The authors received no specific funding for this work.

Cardiac complications account for at least one-third of perioperative deaths, and lead to substantial morbidity and cost.^1-4 Current guidelines recommend that patients undergo assessment of cardiac risk and functional status prior to noncardiac surgery.⁵ Preoperative cardiac stress testing is recommended for patients whose predicted cardiac risk exceeds 1%, whose functional status is limited, and for whom testing may change management.⁵

However, patients are not specifically selected according to risk of coronary artery disease (CAD) in current guidelines. The pretest probability of CAD may vary widely in this patient population, and the resultant accuracy of cardiac stress testing in making the diagnosis of CAD may vary as well.⁵ Meanwhile, CAD is a clear risk factor for perioperative cardiac events.^6-8

Because the pretest probability of CAD is heterogeneous, the optimal modality of cardiac stress testing in this population is unclear. False-positive results would likely lead to inflated estimates of operative risk, expensive and high-risk downstream testing, and potentially cancellation of otherwise beneficial surgeries. Meanwhile, false-negative results would lead to overly optimistic estimates of surgical risk and potentially to surgical intervention at higher levels of risk than would be desirable. Current guidelines leave the selection of either dobutamine stress echocardiography (DSE) or pharmacological stress myocardial perfusion imaging to the clinician.⁵ To inform decisions regarding the selection of cardiac stress testing modality prior to noncardiac surgery, we conducted this study to estimate the diagnostic accuracy of DSE and single-photon emission computed tomography (SPECT) among this patient population.

Surgical Cohort

The American College of Surgeons’ National Surgical Quality Improvement Program (NSQIP) samples patients undergoing surgery at participating hospitals and collects standardized clinical data on preoperative risk factors and postoperative complications.⁹ We acquired public use data from the 2009 NSQIP cohort, which included more than 336,000 surgical cases from 237 hospitals (principally in the United States). We excluded from our analysis patients undergoing cardiac surgery, patients with a prior diagnosis of CAD, and patients undergoing experimental surgeries. This left a sample of 300,462 for analysis.

Prediction of Dyslipidemia

The model we used to predict the presence of obstructive CAD required the presence or absence of dyslipidemia. A number of variables are common to both NSQIP and the National Health and Nutrition Examination Survey (NHANES), including age, weight, sex, tobacco use, diabetes, and prior stroke.¹⁰ Using those common variables, we developed a logistic regression to predict a diagnosis of dyslipidemia, applied that regression to the NSQIP cohort, and dichotomized. To assess the potential impact of misclassification, we performed separate sensitivity analyses in which either no patients or all patients had dyslipidemia.

Prediction of Obstructive CAD

To estimate the probability of obstructive CAD, we applied the risk prediction tool currently recommended by the European Society of Cardiology.¹¹ The clinical version of this tool relies on age; sex; diagnoses of diabetes, hypertension, and dyslipidemia; active tobacco use; and chest pain characteristics to predict the probability of obstructive CAD on coronary angiography. We assumed that all patients in our cohort had nonspecific chest pain, the referent in the calculator.

Prediction of Perioperative Event Risk

To predict the probability of a perioperative cardiac event, we used the Myocardial Infarction or Cardiac Arrest (MICA) calculator, which was derived from an earlier cohort of NSQIP.¹² All variables required for this prediction tool were included in the 2009 NSQIP cohort; our categorization of surgeries is included as an online appendix. MICA is one of three prediction tools included in the current American College of Cardiology/American Heart Association (ACC/AHA) guidelines.⁵

Prediction of Test Accuracy

We searched the MEDLINE database for estimates of the test characteristics of DSE and SPECT that adjusted for workup bias.¹³ (Also known as sequential-ordering bias, here we refer to the phenomenon whereby further workup is based on the results of diagnostic testing, resulting in underdiagnosis among patients with negative tests and falsely high estimates of sensitivity.¹⁴) Although other modalities of myocardial perfusion imaging exist, SPECT appears to be the most widely available, utilized, and studied modality of MPI.¹⁵ Our search strategy paired (“Sensitivity and Specificity” [MeSH Terms] AND “Coronary Disease/diagnostic imaging” [MAJR] AND “bias” [TIAB]) with (“Tomography, Emission-Computed, Single-Photon” [MAJR] OR “Echocardiography, Stress” [MAJR]). We reviewed the results for sensitivity and specificity estimates that corrected for workup bias. For each of SPECT and DSE, we drew the sensitivity and specificity from normal distributions based on literature estimates (see Table). We then calculated the expected accuracy of each modality for each patient in our dataset. All analyses were performed in Stata (version 14, College Station, Texas).

The median predicted probability of obstructive CAD was 5.1% (IQR: 1.8%-13.9%). Among patients with a predicted risk of a perioperative event of 1% or greater, the median probability of obstructive CAD was 18.1% (IQR: 9.6%-32.3%). The correlation between the predicted probabilities of CAD and a perioperative event was low (0.32), but highly statistically significant (P < .001).

Both accuracy and PPV were higher for DSE than for SPECT. The predicted accuracy of DSE was greater than that of SPECT in 73.5% of cases overall and in 60.5% of cases with a predicted operative cardiac risk greater than 1%. The mean PPV of DSE was 32.9% (median: 26.7%), while the equivalent PPVs for SPECT were 14.1% and 8.2%, respectively. Among cases with a predicted operative cardiac risk greater than 1%, the mean PPV of DSE was 57.5% (median: 60.2%), while the equivalent PPVs for SPECT were 29.8% and 26.7%, respectively.

DSE had a mean predicted accuracy of 93.0% (median: 96.2%), while SPECT had a mean accuracy of 92.6% (median: 95.6%). The predicted accuracies of DSE and SPECT are shown in the Figure, stratified by predicted perioperative risk across the 1% risk threshold currently used by ACC/AHA guidelines.

In this study, we demonstrated that the expected accuracy of DSE in the diagnosis of obstructive CAD among patients undergoing noncardiac surgery is higher than that of SPECT. This finding was true in both unselected patients and those selected by a perioperative risk of greater than 1%. The use of SPECT, compared with DSE, would likely result in greater numbers of false-positive tests in this patient population and less accurate results overall.

Cardiac stress testing, as with any diagnostic test, is most useful at intermediate probabilities. Insofar as stress testing offers diagnostic value, our analysis suggests that, in the range of the predicted risk of CAD found in patients undergoing noncardiac surgery, DSE is a more efficient testing strategy. To the extent that making a diagnosis of CAD informs the decision to proceed to surgery, a more accurate test would be preferable. The lower cost of DSE, the lack of ionizing radiation, and the information provided by echocardiography regarding diagnoses other than CAD, if considered, would further amplify that preference.

However, it is important to note that both modalities have limited positive predictive value. In the median patient who meets the currently recommended 1% perioperative event risk threshold, SPECT would lead to 2.74 false positive results for every true positive result. DSE would produce approximately two false positive results for every three true positive results. If lower rates of false-positive testing are desired, different patient selection criteria are required.

A few key limitations of this work warrant discussion. First, our results likely overestimate the probability of obstructive CAD in this population. We assumed that all patients have nonspecific chest pain at the time of the preoperative evaluation, though many patients do not, in fact, have chest pain. Tools to estimate the pretest probability of CAD (such as the ESC tool that we used or the older Diamond-Forrester prediction) are intended to stratify higher-risk patients than are seen in a preoperative setting. If asymptomatic patients seen in preoperative risk assessment clinics have lower risk of CAD than what we have predicted, we will have understated the case for DSE. Moreover, cases sampled from hospitals participating in NSQIP are not representative of the national surgical population. This likely further inflates our estimates of CAD risk compared with the “true” surgical population. Finally, we cannot comment on current practice from these data. Current guidelines recommend preoperative cardiac stress testing for patients whose risk of a perioperative cardiac event exceeds 1%, whose functional status is poor or unknown, and only if said testing will change management.⁵ Using these data, we cannot determine the pretest probability of patients referred for stress testing before noncardiac surgery.

Still, this analysis suggests that, among patients undergoing noncardiac surgery, selecting patients according to the risk of perioperative events would result in a population at an overall comparatively low risk of CAD, and that in this population, DSE would be more accurate than SPECT for making the diagnosis of CAD. If a diagnosis of CAD would change the decision to proceed to surgery, DSE is likely to be a more efficient test than SPECT.

Acknowledgements

The authors would like to thank Wael Jaber, MD, for his thoughtful comments on a draft of this manuscript.

Disclosures

The authors have nothing to disclose.

Funding

The authors received no specific funding for this work.

Clinicians, educators, and medical journals are increasingly using the social media outlet, Twitter, as a medium to connect and engage with their colleagues. In particular, online journal clubs have created a space for the timely discussion of research, creation of online communities, and dissemination of research.

Social media-based journal clubs are thought to be one way in which journals can leverage the power of social networks so that researchers can engage with a diverse range of end users⁴ (including bedside clinicians, administrators, and patients). Several examples of these models exist. For example, #GeriMedJC acts as a complimentary, synchronous chat that takes place at the same time as a live, in-person journal club. #NephJC offers multiple 1-hour chats per month and provides an in-depth summary and analysis of each article, while #UroJC is an asynchronous discussion that takes place over 48 hours. Few data exist to describe whether any of these programs produce measurable improvements in indicators of engagement or dissemination of results.

In 2015, the Journal of Hospital Medicine (JHM) began producing a Twitter-based journal club as a means to connect and engage the Hospital Medicine community and allow for discussion and rapid exchange of information and opinions around a specific clinical topic. This study aims to describe the implementation of the first Journal-sponsored, Twitter-based online journal club and ascertain its impact on both Twitter and journal metrics.

#JHMChat was launched in October 2015, and was initially held every 2-3 months until January 2017, when chats began to take place monthly. Each 1-hour chat focused on a recently published article in JHM, was moderated by a JHM social media editor (C.M.W., V.M.A.), and included at least 1 study author or guest expert. Articles were chosen by the social media editors based on the following criteria: (1) attractiveness to possible participants, (2) providing topic variety within the journal club series, and (3) sustainability and topic conduciveness to the online chat model. Chats were held at 9 PM EST in order to engage hospitalists across all US time zones and on different days to accommodate authors’ availability. All sessions were framed by 3-4 questions intended to encourage discussion and presented to chat participants at spaced intervals so as to stimulate a current of responses.

Chats were promoted by way of the JHM (@JHospMedicine, 3400 followers) and Society of Hospital Medicine (SHM; @SHMLive, 5800 followers) Twitter feeds beginning 1 month prior to each session. Visual Abstracts^5,6 were used to publicize the sessions, also via Twitter, starting in February 2017.

Continuing Medical Education (CME) credits were offered through the SHM to registered participants, starting in July 2016.⁷ All sessions were cosponsored by the American Board of Internal Medicine (ABIM) Foundation and the Costs of Care Organization, a non-profit organization aimed at improving healthcare value.

Twitter Metrics

After each session, the following Twitter-based engagement metrics were obtained using the Symplur^© Healthcare Hashtag project;⁸ total number of participants and tweets, tweets/participant, and total impressions (calculated as the number of tweets from each participant multiplied by the number of followers that participant currently had then summed up for all participants). Simply put, impressions can also be thought of as the number of times a single Tweet makes it into someone else’s Twitter feed. So as to avoid artificially inflated metrics, all were obtained 2 hours after the end of the journal club. Participants were defined as anyone who posted an original tweet or retweeted during the session and were encouraged to tag their tweets with the hashtag #JHMChat for post-discussion indexing and measurement. Because authors’ or guests’ popularity on Twitter may influence participation rates, we also assessed the number of followers for each participating author. Spearman’s rank correlation was calculated (Microsoft ExcelTM) where appropriate.

Altmetrics and Page Views

As a means to measure exposure and dissemination external to Twitter, we assessed the change (“Delta”) in the each article’s Altmetric score⁹, a digital-based metric that quantifies the attention received by a scientific publication on various online platforms including news, blogs, and social media. Delta Altmetric scores were calculated as the difference between the scores on the day of the session and 2 weeks after the respective session, with higher scores indicating greater global online discussion. By measuring the Altmetric score on the day of the discussion, we established a baseline score for comparison purposes. Additionally, this allowed us to better attribute any changes that may have occurred to the discussion itself.

Additionally, using information provided by the journal publisher (John Wiley & Sons Publishing) in 2016, we assessed the effect of #JHMChat on the number of article page views on the JHM website relative to the release of the electronic Table of Contents (eTOC). The eTOC release was chosen as it is historically associated with a high number of page views. In order to isolate the effect of #JHMChat, we only reviewed months in which #JHMChat was not held within 3 days of the eTOC release. Because JHM changed publishers in January 2017, we only assessed page view data on 2016 sessions, as the new publisher lacked enhanced search optimization to obtain these data.

Thematic Analysis

In addition to the above measurements, a thematic analysis of each article was conducted to assess any common themes that would influence our chosen metrics. Themes were assessed and ascribed by one author (C.M.W.) and verified by another (V.M.A.).

Participant and Author Experience

To assess the participant experience, responses to a post-session CME questionnaire that assessed (1) overall quality, (2) comprehensiveness of the discussion, (3) whether the participant would recommend the chat to a colleague, and (4) whether participation would lead to practice-changing measures were reviewed. Registration of each session for CME was also quantified. Finally, each participating author was asked to fill out an electronic post-chat survey (SurveyMonkey^®) meant to assess the authors’ experience with the journal club (Appendix).

Between October 2015 and November 2017, a total of 15 sessions were held with a mean of 2.17 (±0.583) million impressions/session, 499 (±129) total tweets/session, and 73 (±24) participants/session (compared to a range of 21-58 participants/session from other online journal clubs, where reported) with 7.2 (±2.0) tweets/participant (Table 1). The total number of participants for all sessions was 1096. Participating authors had on average 1389 (±2714) followers, ranging from a low of 37 to a high of 10,376 (Appendix). No correlation between author following and number of participants (r = 0.19), impressions (r = 0.05), or change in Altmetric score (r = 0.17) was seen.

Thematic analysis revealed 3 predominant themes among the chosen articles: Value-based care (VBC), Quality and Patient Safety (QPS), and Medical Education (ME). Articles focused on VBC had the greatest number of impressions (mean ±SD: 2.61 ± 0.55 million) and participants (mean ±SD: 90 ± 12), while QPS articles had the fewest impressions (mean ±SD: 1.71 ± 0.59 million) and number of participants (mean ±SD: 47 ± 16). The mean increase in the Altmetric score among all discussed articles was 14 (±12), from an average baseline of 30 (±37). Medical Education-themed articles appeared to garner the greatest increase in Altmetric scores, averaging an increase of 32 points, compared with an average baseline score of 31 (±32). In contrast, VBC and QPS articles averaged an increase of 8.6 and 8.4 points, from average baselines of 55 (±53) and 17 (±13), respectively. A 2-month analysis of JHM articles not included in these discussions, in which Altmetric scores were measured in the same way as those from the discussion, revealed a baseline Altmetric score of 27 (±24) with an average increase of 8 (±6) 2 weeks following the chat.

Four articles met the inclusion criteria for page view analysis and suggested that article page views increased to similar levels as the eTOC release (mean: 2668 vs. 2998, respectively; P = .35) (Figure). These increases equate to a 33% and 50% increase in average daily page views (2002) for the chat and eTOC release, respectively.

The Twitter-based journal club #JHMChat appears to increase social media awareness and dissemination of journal articles and was considered a useful engagement platform by both authors and participants.

Articles with a focus on VBC and ME had the greatest impact on dissemination metrics, particularly, total impressions and Altmetric scores, respectively. Given the strong presence and interest in these topics within Twitter and social media, these findings are not surprising.^10,11 For example, over the past several years, the VBC movement has taken shape and grown alongside the expansion of social media, thus giving a space for this community to grow and engage. Of note, the cosponsorship relationship with the ABIM Foundation (which works closely with the Choosing Wisely™ campaign) and the Costs of Care Organization could have influenced the participation and dissemination rates of VBC articles. Medical education articles were also popular and appeared to have increased uptake after chats, based on their Altmetric scores. This may be explained by the fact that medical educators have long utilized social media as a means to connect and engage within their community.^12–14 It is also possible that the use of Twitter by trainees (residents, students) may have driven some of the dissemination of ME articles, as this group may not be regular subscribers to JHM.

Online journal clubs offer distinct advantages over traditional in-person journal clubs. First, online journal clubs allow for increased connectivity among online communities, bringing together participants from different geographic areas with diverse training and clinical experiences. Subsequently, this allows for the rapid exchange of both personal and organizational approaches to the topic of discussion.^15–17 Second, online journal clubs allow for continual access to the discussion material. For example, while the metrics used in this study only assessed active, synchronous participation, anecdotal evidence and feedback to the authors suggests that many individuals passively engaged by following along or reviewed the chat feed post hoc at their convenience. This asynchronous access is a quality not found in more traditional journal club formats. Finally, because online journal clubs commonly operate with a flattened hierarchy,¹⁸ they can break down access barriers to both the researchers who performed the study and thought leaders who commonly participate.¹⁷

Several insightful lessons were gleaned in the production and management of this online journal club. On the implementation side, promotion, preparation, and continued organization of an online journal club requires a fair amount of work. In this case, the required time and resources were provided by 2 social media editors in addition to administrative assistance from the SHM. The high attrition rate of online journal clubs over the years attests to these difficulties.²⁴ Additionally, finding incentives to attract and sustain participation can be difficult, as we noted that neither CME nor author popularity (based on their Twitter following) appeared to influence engagement metrics (number of participants, total tweets, and tweets/participant). We also found that partnering with other journal club communities, in particular #NephJC, lead to greater participation rates and impressions. Thus, leveraging connections and topics that span clinical domains may be one way to improve and broaden engagement within these forums. Finally, feedback from participants revealed that the timing of the journal club and the inability to have in-depth discussions, a characteristic commonly associated with traditional journal clubs, were problematic.

This study has several limitations. First, the metrics used to assess social media engagement and dissemination can be easily skewed. For instance, the activity of 1 or 2 individuals with large followings can dramatically influence the number of impressions, giving a falsely elevated sense of broad dissemination. Conversely, there may have been some participants who did not use the #JHMChat hashtag, thus leading to an underestimation in these metrics. Second, while we report total impressions as a measure of dissemination, this metric represents possible interactions and does not guarantee interaction or visualization of that tweet. Additionally, we were unable to characterize our participants and their participation rates over time, as this information is not made available through Symplur© analytics. Third, our page view assessment was limited to 2016 sessions only; therefore, these data may not be an accurate reflection of the impact of #JHMChat on this metric. Fourth, given the marginal response rate to our CME questionnaire, a selection bias could have occurred. Finally, whether social media discussions such as online journal clubs act as leading indicators for future citations remains unclear, as some research has shown an association between increased Altmetric scores and increased citation rates,^19-21 while others have not.^22,23 Our study was not equipped to assess this correlation.

Online journal clubs create new opportunities to connect, engage, and disseminate medical research. These developing forums provide journal editors, researchers, patients, and clinicians with a means to connect and discuss research in ways that were not previously possible. In order to continue to evolve and grow, future research in online journal clubs should explore the downstream effects on citation rates, clinical uptake, and participant knowledge after the sessions.

Acknowledgments

The authors would like to thank Felicia Steele for her assistance in organizing and promoting the chats. Additionally, the authors would like to thank all the authors, guests and participants who took time from their families, work, and daily lives to participate in these activities. Your time and presence were truly appreciated.

Disclosures

The authors of this article operate as the Social Media Editors (C.M.W., V.M.A.) and the Editor-in-Chief (A.A.) for the Journal of Hospital Medicine. Dr. Wray had full access to all the data in the project, takes responsibility for the integrity of the data, and the accuracy of the data analysis.

Clinicians, educators, and medical journals are increasingly using the social media outlet, Twitter, as a medium to connect and engage with their colleagues. In particular, online journal clubs have created a space for the timely discussion of research, creation of online communities, and dissemination of research.

Social media-based journal clubs are thought to be one way in which journals can leverage the power of social networks so that researchers can engage with a diverse range of end users⁴ (including bedside clinicians, administrators, and patients). Several examples of these models exist. For example, #GeriMedJC acts as a complimentary, synchronous chat that takes place at the same time as a live, in-person journal club. #NephJC offers multiple 1-hour chats per month and provides an in-depth summary and analysis of each article, while #UroJC is an asynchronous discussion that takes place over 48 hours. Few data exist to describe whether any of these programs produce measurable improvements in indicators of engagement or dissemination of results.

In 2015, the Journal of Hospital Medicine (JHM) began producing a Twitter-based journal club as a means to connect and engage the Hospital Medicine community and allow for discussion and rapid exchange of information and opinions around a specific clinical topic. This study aims to describe the implementation of the first Journal-sponsored, Twitter-based online journal club and ascertain its impact on both Twitter and journal metrics.

#JHMChat was launched in October 2015, and was initially held every 2-3 months until January 2017, when chats began to take place monthly. Each 1-hour chat focused on a recently published article in JHM, was moderated by a JHM social media editor (C.M.W., V.M.A.), and included at least 1 study author or guest expert. Articles were chosen by the social media editors based on the following criteria: (1) attractiveness to possible participants, (2) providing topic variety within the journal club series, and (3) sustainability and topic conduciveness to the online chat model. Chats were held at 9 PM EST in order to engage hospitalists across all US time zones and on different days to accommodate authors’ availability. All sessions were framed by 3-4 questions intended to encourage discussion and presented to chat participants at spaced intervals so as to stimulate a current of responses.

Chats were promoted by way of the JHM (@JHospMedicine, 3400 followers) and Society of Hospital Medicine (SHM; @SHMLive, 5800 followers) Twitter feeds beginning 1 month prior to each session. Visual Abstracts^5,6 were used to publicize the sessions, also via Twitter, starting in February 2017.

Continuing Medical Education (CME) credits were offered through the SHM to registered participants, starting in July 2016.⁷ All sessions were cosponsored by the American Board of Internal Medicine (ABIM) Foundation and the Costs of Care Organization, a non-profit organization aimed at improving healthcare value.

Twitter Metrics

After each session, the following Twitter-based engagement metrics were obtained using the Symplur^© Healthcare Hashtag project;⁸ total number of participants and tweets, tweets/participant, and total impressions (calculated as the number of tweets from each participant multiplied by the number of followers that participant currently had then summed up for all participants). Simply put, impressions can also be thought of as the number of times a single Tweet makes it into someone else’s Twitter feed. So as to avoid artificially inflated metrics, all were obtained 2 hours after the end of the journal club. Participants were defined as anyone who posted an original tweet or retweeted during the session and were encouraged to tag their tweets with the hashtag #JHMChat for post-discussion indexing and measurement. Because authors’ or guests’ popularity on Twitter may influence participation rates, we also assessed the number of followers for each participating author. Spearman’s rank correlation was calculated (Microsoft ExcelTM) where appropriate.

Altmetrics and Page Views

As a means to measure exposure and dissemination external to Twitter, we assessed the change (“Delta”) in the each article’s Altmetric score⁹, a digital-based metric that quantifies the attention received by a scientific publication on various online platforms including news, blogs, and social media. Delta Altmetric scores were calculated as the difference between the scores on the day of the session and 2 weeks after the respective session, with higher scores indicating greater global online discussion. By measuring the Altmetric score on the day of the discussion, we established a baseline score for comparison purposes. Additionally, this allowed us to better attribute any changes that may have occurred to the discussion itself.

Additionally, using information provided by the journal publisher (John Wiley & Sons Publishing) in 2016, we assessed the effect of #JHMChat on the number of article page views on the JHM website relative to the release of the electronic Table of Contents (eTOC). The eTOC release was chosen as it is historically associated with a high number of page views. In order to isolate the effect of #JHMChat, we only reviewed months in which #JHMChat was not held within 3 days of the eTOC release. Because JHM changed publishers in January 2017, we only assessed page view data on 2016 sessions, as the new publisher lacked enhanced search optimization to obtain these data.

Thematic Analysis

In addition to the above measurements, a thematic analysis of each article was conducted to assess any common themes that would influence our chosen metrics. Themes were assessed and ascribed by one author (C.M.W.) and verified by another (V.M.A.).

Participant and Author Experience

To assess the participant experience, responses to a post-session CME questionnaire that assessed (1) overall quality, (2) comprehensiveness of the discussion, (3) whether the participant would recommend the chat to a colleague, and (4) whether participation would lead to practice-changing measures were reviewed. Registration of each session for CME was also quantified. Finally, each participating author was asked to fill out an electronic post-chat survey (SurveyMonkey^®) meant to assess the authors’ experience with the journal club (Appendix).

Between October 2015 and November 2017, a total of 15 sessions were held with a mean of 2.17 (±0.583) million impressions/session, 499 (±129) total tweets/session, and 73 (±24) participants/session (compared to a range of 21-58 participants/session from other online journal clubs, where reported) with 7.2 (±2.0) tweets/participant (Table 1). The total number of participants for all sessions was 1096. Participating authors had on average 1389 (±2714) followers, ranging from a low of 37 to a high of 10,376 (Appendix). No correlation between author following and number of participants (r = 0.19), impressions (r = 0.05), or change in Altmetric score (r = 0.17) was seen.

Thematic analysis revealed 3 predominant themes among the chosen articles: Value-based care (VBC), Quality and Patient Safety (QPS), and Medical Education (ME). Articles focused on VBC had the greatest number of impressions (mean ±SD: 2.61 ± 0.55 million) and participants (mean ±SD: 90 ± 12), while QPS articles had the fewest impressions (mean ±SD: 1.71 ± 0.59 million) and number of participants (mean ±SD: 47 ± 16). The mean increase in the Altmetric score among all discussed articles was 14 (±12), from an average baseline of 30 (±37). Medical Education-themed articles appeared to garner the greatest increase in Altmetric scores, averaging an increase of 32 points, compared with an average baseline score of 31 (±32). In contrast, VBC and QPS articles averaged an increase of 8.6 and 8.4 points, from average baselines of 55 (±53) and 17 (±13), respectively. A 2-month analysis of JHM articles not included in these discussions, in which Altmetric scores were measured in the same way as those from the discussion, revealed a baseline Altmetric score of 27 (±24) with an average increase of 8 (±6) 2 weeks following the chat.

Four articles met the inclusion criteria for page view analysis and suggested that article page views increased to similar levels as the eTOC release (mean: 2668 vs. 2998, respectively; P = .35) (Figure). These increases equate to a 33% and 50% increase in average daily page views (2002) for the chat and eTOC release, respectively.

The Twitter-based journal club #JHMChat appears to increase social media awareness and dissemination of journal articles and was considered a useful engagement platform by both authors and participants.

Articles with a focus on VBC and ME had the greatest impact on dissemination metrics, particularly, total impressions and Altmetric scores, respectively. Given the strong presence and interest in these topics within Twitter and social media, these findings are not surprising.^10,11 For example, over the past several years, the VBC movement has taken shape and grown alongside the expansion of social media, thus giving a space for this community to grow and engage. Of note, the cosponsorship relationship with the ABIM Foundation (which works closely with the Choosing Wisely™ campaign) and the Costs of Care Organization could have influenced the participation and dissemination rates of VBC articles. Medical education articles were also popular and appeared to have increased uptake after chats, based on their Altmetric scores. This may be explained by the fact that medical educators have long utilized social media as a means to connect and engage within their community.^12–14 It is also possible that the use of Twitter by trainees (residents, students) may have driven some of the dissemination of ME articles, as this group may not be regular subscribers to JHM.

Online journal clubs offer distinct advantages over traditional in-person journal clubs. First, online journal clubs allow for increased connectivity among online communities, bringing together participants from different geographic areas with diverse training and clinical experiences. Subsequently, this allows for the rapid exchange of both personal and organizational approaches to the topic of discussion.^15–17 Second, online journal clubs allow for continual access to the discussion material. For example, while the metrics used in this study only assessed active, synchronous participation, anecdotal evidence and feedback to the authors suggests that many individuals passively engaged by following along or reviewed the chat feed post hoc at their convenience. This asynchronous access is a quality not found in more traditional journal club formats. Finally, because online journal clubs commonly operate with a flattened hierarchy,¹⁸ they can break down access barriers to both the researchers who performed the study and thought leaders who commonly participate.¹⁷

Several insightful lessons were gleaned in the production and management of this online journal club. On the implementation side, promotion, preparation, and continued organization of an online journal club requires a fair amount of work. In this case, the required time and resources were provided by 2 social media editors in addition to administrative assistance from the SHM. The high attrition rate of online journal clubs over the years attests to these difficulties.²⁴ Additionally, finding incentives to attract and sustain participation can be difficult, as we noted that neither CME nor author popularity (based on their Twitter following) appeared to influence engagement metrics (number of participants, total tweets, and tweets/participant). We also found that partnering with other journal club communities, in particular #NephJC, lead to greater participation rates and impressions. Thus, leveraging connections and topics that span clinical domains may be one way to improve and broaden engagement within these forums. Finally, feedback from participants revealed that the timing of the journal club and the inability to have in-depth discussions, a characteristic commonly associated with traditional journal clubs, were problematic.

This study has several limitations. First, the metrics used to assess social media engagement and dissemination can be easily skewed. For instance, the activity of 1 or 2 individuals with large followings can dramatically influence the number of impressions, giving a falsely elevated sense of broad dissemination. Conversely, there may have been some participants who did not use the #JHMChat hashtag, thus leading to an underestimation in these metrics. Second, while we report total impressions as a measure of dissemination, this metric represents possible interactions and does not guarantee interaction or visualization of that tweet. Additionally, we were unable to characterize our participants and their participation rates over time, as this information is not made available through Symplur© analytics. Third, our page view assessment was limited to 2016 sessions only; therefore, these data may not be an accurate reflection of the impact of #JHMChat on this metric. Fourth, given the marginal response rate to our CME questionnaire, a selection bias could have occurred. Finally, whether social media discussions such as online journal clubs act as leading indicators for future citations remains unclear, as some research has shown an association between increased Altmetric scores and increased citation rates,^19-21 while others have not.^22,23 Our study was not equipped to assess this correlation.

Online journal clubs create new opportunities to connect, engage, and disseminate medical research. These developing forums provide journal editors, researchers, patients, and clinicians with a means to connect and discuss research in ways that were not previously possible. In order to continue to evolve and grow, future research in online journal clubs should explore the downstream effects on citation rates, clinical uptake, and participant knowledge after the sessions.

Acknowledgments

The authors would like to thank Felicia Steele for her assistance in organizing and promoting the chats. Additionally, the authors would like to thank all the authors, guests and participants who took time from their families, work, and daily lives to participate in these activities. Your time and presence were truly appreciated.

Disclosures

The authors of this article operate as the Social Media Editors (C.M.W., V.M.A.) and the Editor-in-Chief (A.A.) for the Journal of Hospital Medicine. Dr. Wray had full access to all the data in the project, takes responsibility for the integrity of the data, and the accuracy of the data analysis.

Clinicians, educators, and medical journals are increasingly using the social media outlet, Twitter, as a medium to connect and engage with their colleagues. In particular, online journal clubs have created a space for the timely discussion of research, creation of online communities, and dissemination of research.

Social media-based journal clubs are thought to be one way in which journals can leverage the power of social networks so that researchers can engage with a diverse range of end users⁴ (including bedside clinicians, administrators, and patients). Several examples of these models exist. For example, #GeriMedJC acts as a complimentary, synchronous chat that takes place at the same time as a live, in-person journal club. #NephJC offers multiple 1-hour chats per month and provides an in-depth summary and analysis of each article, while #UroJC is an asynchronous discussion that takes place over 48 hours. Few data exist to describe whether any of these programs produce measurable improvements in indicators of engagement or dissemination of results.

In 2015, the Journal of Hospital Medicine (JHM) began producing a Twitter-based journal club as a means to connect and engage the Hospital Medicine community and allow for discussion and rapid exchange of information and opinions around a specific clinical topic. This study aims to describe the implementation of the first Journal-sponsored, Twitter-based online journal club and ascertain its impact on both Twitter and journal metrics.

#JHMChat was launched in October 2015, and was initially held every 2-3 months until January 2017, when chats began to take place monthly. Each 1-hour chat focused on a recently published article in JHM, was moderated by a JHM social media editor (C.M.W., V.M.A.), and included at least 1 study author or guest expert. Articles were chosen by the social media editors based on the following criteria: (1) attractiveness to possible participants, (2) providing topic variety within the journal club series, and (3) sustainability and topic conduciveness to the online chat model. Chats were held at 9 PM EST in order to engage hospitalists across all US time zones and on different days to accommodate authors’ availability. All sessions were framed by 3-4 questions intended to encourage discussion and presented to chat participants at spaced intervals so as to stimulate a current of responses.

Chats were promoted by way of the JHM (@JHospMedicine, 3400 followers) and Society of Hospital Medicine (SHM; @SHMLive, 5800 followers) Twitter feeds beginning 1 month prior to each session. Visual Abstracts^5,6 were used to publicize the sessions, also via Twitter, starting in February 2017.

Continuing Medical Education (CME) credits were offered through the SHM to registered participants, starting in July 2016.⁷ All sessions were cosponsored by the American Board of Internal Medicine (ABIM) Foundation and the Costs of Care Organization, a non-profit organization aimed at improving healthcare value.

Twitter Metrics

After each session, the following Twitter-based engagement metrics were obtained using the Symplur^© Healthcare Hashtag project;⁸ total number of participants and tweets, tweets/participant, and total impressions (calculated as the number of tweets from each participant multiplied by the number of followers that participant currently had then summed up for all participants). Simply put, impressions can also be thought of as the number of times a single Tweet makes it into someone else’s Twitter feed. So as to avoid artificially inflated metrics, all were obtained 2 hours after the end of the journal club. Participants were defined as anyone who posted an original tweet or retweeted during the session and were encouraged to tag their tweets with the hashtag #JHMChat for post-discussion indexing and measurement. Because authors’ or guests’ popularity on Twitter may influence participation rates, we also assessed the number of followers for each participating author. Spearman’s rank correlation was calculated (Microsoft ExcelTM) where appropriate.

Altmetrics and Page Views

As a means to measure exposure and dissemination external to Twitter, we assessed the change (“Delta”) in the each article’s Altmetric score⁹, a digital-based metric that quantifies the attention received by a scientific publication on various online platforms including news, blogs, and social media. Delta Altmetric scores were calculated as the difference between the scores on the day of the session and 2 weeks after the respective session, with higher scores indicating greater global online discussion. By measuring the Altmetric score on the day of the discussion, we established a baseline score for comparison purposes. Additionally, this allowed us to better attribute any changes that may have occurred to the discussion itself.

Additionally, using information provided by the journal publisher (John Wiley & Sons Publishing) in 2016, we assessed the effect of #JHMChat on the number of article page views on the JHM website relative to the release of the electronic Table of Contents (eTOC). The eTOC release was chosen as it is historically associated with a high number of page views. In order to isolate the effect of #JHMChat, we only reviewed months in which #JHMChat was not held within 3 days of the eTOC release. Because JHM changed publishers in January 2017, we only assessed page view data on 2016 sessions, as the new publisher lacked enhanced search optimization to obtain these data.

Thematic Analysis

In addition to the above measurements, a thematic analysis of each article was conducted to assess any common themes that would influence our chosen metrics. Themes were assessed and ascribed by one author (C.M.W.) and verified by another (V.M.A.).

Participant and Author Experience

To assess the participant experience, responses to a post-session CME questionnaire that assessed (1) overall quality, (2) comprehensiveness of the discussion, (3) whether the participant would recommend the chat to a colleague, and (4) whether participation would lead to practice-changing measures were reviewed. Registration of each session for CME was also quantified. Finally, each participating author was asked to fill out an electronic post-chat survey (SurveyMonkey^®) meant to assess the authors’ experience with the journal club (Appendix).

Between October 2015 and November 2017, a total of 15 sessions were held with a mean of 2.17 (±0.583) million impressions/session, 499 (±129) total tweets/session, and 73 (±24) participants/session (compared to a range of 21-58 participants/session from other online journal clubs, where reported) with 7.2 (±2.0) tweets/participant (Table 1). The total number of participants for all sessions was 1096. Participating authors had on average 1389 (±2714) followers, ranging from a low of 37 to a high of 10,376 (Appendix). No correlation between author following and number of participants (r = 0.19), impressions (r = 0.05), or change in Altmetric score (r = 0.17) was seen.

Thematic analysis revealed 3 predominant themes among the chosen articles: Value-based care (VBC), Quality and Patient Safety (QPS), and Medical Education (ME). Articles focused on VBC had the greatest number of impressions (mean ±SD: 2.61 ± 0.55 million) and participants (mean ±SD: 90 ± 12), while QPS articles had the fewest impressions (mean ±SD: 1.71 ± 0.59 million) and number of participants (mean ±SD: 47 ± 16). The mean increase in the Altmetric score among all discussed articles was 14 (±12), from an average baseline of 30 (±37). Medical Education-themed articles appeared to garner the greatest increase in Altmetric scores, averaging an increase of 32 points, compared with an average baseline score of 31 (±32). In contrast, VBC and QPS articles averaged an increase of 8.6 and 8.4 points, from average baselines of 55 (±53) and 17 (±13), respectively. A 2-month analysis of JHM articles not included in these discussions, in which Altmetric scores were measured in the same way as those from the discussion, revealed a baseline Altmetric score of 27 (±24) with an average increase of 8 (±6) 2 weeks following the chat.

Four articles met the inclusion criteria for page view analysis and suggested that article page views increased to similar levels as the eTOC release (mean: 2668 vs. 2998, respectively; P = .35) (Figure). These increases equate to a 33% and 50% increase in average daily page views (2002) for the chat and eTOC release, respectively.

The Twitter-based journal club #JHMChat appears to increase social media awareness and dissemination of journal articles and was considered a useful engagement platform by both authors and participants.

Articles with a focus on VBC and ME had the greatest impact on dissemination metrics, particularly, total impressions and Altmetric scores, respectively. Given the strong presence and interest in these topics within Twitter and social media, these findings are not surprising.^10,11 For example, over the past several years, the VBC movement has taken shape and grown alongside the expansion of social media, thus giving a space for this community to grow and engage. Of note, the cosponsorship relationship with the ABIM Foundation (which works closely with the Choosing Wisely™ campaign) and the Costs of Care Organization could have influenced the participation and dissemination rates of VBC articles. Medical education articles were also popular and appeared to have increased uptake after chats, based on their Altmetric scores. This may be explained by the fact that medical educators have long utilized social media as a means to connect and engage within their community.^12–14 It is also possible that the use of Twitter by trainees (residents, students) may have driven some of the dissemination of ME articles, as this group may not be regular subscribers to JHM.

Online journal clubs offer distinct advantages over traditional in-person journal clubs. First, online journal clubs allow for increased connectivity among online communities, bringing together participants from different geographic areas with diverse training and clinical experiences. Subsequently, this allows for the rapid exchange of both personal and organizational approaches to the topic of discussion.^15–17 Second, online journal clubs allow for continual access to the discussion material. For example, while the metrics used in this study only assessed active, synchronous participation, anecdotal evidence and feedback to the authors suggests that many individuals passively engaged by following along or reviewed the chat feed post hoc at their convenience. This asynchronous access is a quality not found in more traditional journal club formats. Finally, because online journal clubs commonly operate with a flattened hierarchy,¹⁸ they can break down access barriers to both the researchers who performed the study and thought leaders who commonly participate.¹⁷

Several insightful lessons were gleaned in the production and management of this online journal club. On the implementation side, promotion, preparation, and continued organization of an online journal club requires a fair amount of work. In this case, the required time and resources were provided by 2 social media editors in addition to administrative assistance from the SHM. The high attrition rate of online journal clubs over the years attests to these difficulties.²⁴ Additionally, finding incentives to attract and sustain participation can be difficult, as we noted that neither CME nor author popularity (based on their Twitter following) appeared to influence engagement metrics (number of participants, total tweets, and tweets/participant). We also found that partnering with other journal club communities, in particular #NephJC, lead to greater participation rates and impressions. Thus, leveraging connections and topics that span clinical domains may be one way to improve and broaden engagement within these forums. Finally, feedback from participants revealed that the timing of the journal club and the inability to have in-depth discussions, a characteristic commonly associated with traditional journal clubs, were problematic.

This study has several limitations. First, the metrics used to assess social media engagement and dissemination can be easily skewed. For instance, the activity of 1 or 2 individuals with large followings can dramatically influence the number of impressions, giving a falsely elevated sense of broad dissemination. Conversely, there may have been some participants who did not use the #JHMChat hashtag, thus leading to an underestimation in these metrics. Second, while we report total impressions as a measure of dissemination, this metric represents possible interactions and does not guarantee interaction or visualization of that tweet. Additionally, we were unable to characterize our participants and their participation rates over time, as this information is not made available through Symplur© analytics. Third, our page view assessment was limited to 2016 sessions only; therefore, these data may not be an accurate reflection of the impact of #JHMChat on this metric. Fourth, given the marginal response rate to our CME questionnaire, a selection bias could have occurred. Finally, whether social media discussions such as online journal clubs act as leading indicators for future citations remains unclear, as some research has shown an association between increased Altmetric scores and increased citation rates,^19-21 while others have not.^22,23 Our study was not equipped to assess this correlation.

Online journal clubs create new opportunities to connect, engage, and disseminate medical research. These developing forums provide journal editors, researchers, patients, and clinicians with a means to connect and discuss research in ways that were not previously possible. In order to continue to evolve and grow, future research in online journal clubs should explore the downstream effects on citation rates, clinical uptake, and participant knowledge after the sessions.

Acknowledgments

The authors would like to thank Felicia Steele for her assistance in organizing and promoting the chats. Additionally, the authors would like to thank all the authors, guests and participants who took time from their families, work, and daily lives to participate in these activities. Your time and presence were truly appreciated.

Disclosures

The authors of this article operate as the Social Media Editors (C.M.W., V.M.A.) and the Editor-in-Chief (A.A.) for the Journal of Hospital Medicine. Dr. Wray had full access to all the data in the project, takes responsibility for the integrity of the data, and the accuracy of the data analysis.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.