Andrew D. Auerbach

2014 ACC/AHA Guideline on Perioperative Cardiovascular Evaluation and Management of Patients Undergoing Noncardiac Surgery A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines

Jeffrey L. Anderson, MD, FACC, FAHA, Chair Jonathan L. Halperin, MD, FACC, FAHA, Chair-Elect Nancy M. Albert, PhD, RN, FAHA Biykem Bozkurt, MD, PhD, FACC, FAHA Ralph G. Brindis, MD, MPH, MACC Lesley H. Curtis, PhD, FAHA David DeMets, PhD[¶¶][1] Lee A. Fleisher, MD, FACC, FAHA Samuel

Findings of the First Consensus Conference on Medical Emergency Teams

Background:Studies have established that physiologic instability and services mismatching precede adverse events in hospitalized patients. In response to these considerations, the concept of a Rapid Response System (RRS) has emerged. The responding team is commonly known as a medical emergency team (MET), rapid response team (RRT), or critical care outreach (CCO). Studies show that an RRS may improve outcome, but questions remain regarding the benefit, design elements, and advisability of implementing a MET system. Methods:In June 2005 an International Conference on Medical Emergency Teams (ICMET) included experts in patient safety, hospital medicine, critical care medicine, and METs. Seven of 25 had no experience with an RRS, and the remainder had experience with one of the three major forms of RRS. After preconference telephone and e-mail conversations by the panelists in which questions to be discussed were characterized, literature reviewed, and preliminary answers created, the panelists convened for 2 days to create a consensus document. Four major content areas were addressed: What is a MET response? Is there a MET syndrome? What are barriers to METS? How should outcome be measured? Panelists considered whether all hospitals should implement an RRS. Results:Patients needing an RRS intervention are suddenly critically ill and have a mismatch of resources to needs. Hospitals should implement an RRS, which consists of four elements: an afferent, “crisis detection” and “response triggering” mechanism; an efferent, predetermined rapid response team; a governance/administrative structure to supply and organize resources; and a mechanism to evaluate crisis antecedents and promote hospital process improvement to prevent future events.

Cardiac Disease Evaluation and Management Among Kidney and Liver Transplantation Candidates A Scientific Statement From the American Heart Association and the American College of Cardiology Foundation

Endorsed by the American Society of Transplant Surgeons, American Society of Transplantation, and National Kidney Foundation Krista L. Lentine, MD, MS, Co-Chair; Salvatore P. Costa, MD, Co-Chair; Matthew R. Weir, MD, FAHA; John F. Robb, MD, FAHA; Lee A. Fleisher, MD, FAHA; Bertram L. Kasiske, MD; Robert L. Carithers, MD; Michael Ragosta, MD; Kline Bolton, MD; Andrew D. Auerbach, MD; Kim A. Eagle, MD, FAHA, Chair; on behalf of the American Heart Published by Elsevier Inc. http://dx.doi.org/10.1016/j.jacc.2012.05.008

Implementation of a Voluntary Hospitalist Service at a Community Teaching Hospital: Improved Clinical Efficiency and Patient Outcomes

Context Many studies suggest that hospitalists reduce average length of stay and costs but have little or no effect on patient survival. Contribution This 2-year cohort study from a community-based urban teaching hospital found that patients cared for by faculty hospitalists rather than community physicians had shorter lengths of stay, lower costs, and better in-hospital and 1- and 2-month survival rates. Implications Length of stay and cost benefits were apparent only in year 2 of the study, which suggests that experience is an important aspect of successful care by hospitalists. Cautions The study was retrospective, was done in a single site, and involved only five hospitalists. The Editors The organization of inpatient services has been transformed with the development of the hospitalist (1). Traditionally, primary care physicians have cared for their own inpatients. In the hospitalist model, a hospitalist becomes the patients attending physician during hospitalization and the outpatient physician resumes supervision of the patient after discharge (2). Several studies have demonstrated improved clinical efficiency in the hospitalist model, but these studies have focused largely on academic centers or health maintenance organizations, or have not used concurrent controls or reported longer periods of follow-up (3-7). One published study examining a hospitalist system at a community-based teaching hospital suggested improvement in clinical efficiency and a reduction in readmissions (8). However, analytic limitations open these findings to many interpretations. To examine the effects of implementation of a hospitalist service on resource utilization and patient outcomes over time, we studied 5308 consecutive patients admitted to an urban community teaching hospital in San Francisco, California. Methods Study Site Mount Zion Hospital (San Francisco, California) was a 280-bed community-based teaching hospital affiliated with University of California, San Francisco. Mount Zions inpatient facilities were closed in November 1999 because of financial pressures. During the year before closure, all physicians were aware of the hospitals financial difficulties, but no individual or group was made a focus of efforts to improve clinical efficiency. Discussions about possible closure began 1 month after this study ended, and the hospital closed 5 months later. Medical patients at Mount Zion Hospital were admitted to one of four medical teams composed of a resident, one to two interns, and zero to three medical students. Mount Zion medical teams cared for common inpatient diagnoses, as well as specialty-associated diagnoses such as cancer, acute myocardial infarction, and cerebrovascular accidents. Housestaff wrote all orders and provided 24-hour coverage to inpatients. Each team had a ward attending physician who before 1 July 1997 was a full-time faculty member serving in this role for 1 month each year. Community-based physicians remained the physician of record for most patients and worked with house officers in the care of their hospitalized patients. On 1 July 1997, Mount Zion implemented a voluntary hospitalist service. Hospitalists, who were University of California, San Francisco, faculty based at Mount Zion, served as ward attendings 6 to 8 months per year and spent their remaining time in ambulatory practice or teaching. Hospitalists cared for patients without primary physicians, patients with faculty or house officer primary care physicians, and patients whose community-based physician chose to use the hospitalist service. Rotating nonhospitalist faculty continued to provide some inpatient care after implementation of the hospitalist service. Patients were admitted to rotating faculty according to the same criteria used for hospitalist services. There were no differences in other inpatient care systems available to community, rotating, or hospitalist physicians (for example, level of housestaff coverage, computer systems, case managers, social workers, or nursing staff). Patients Between 1 July 1997 and 30 June 1999, 5907 patients 18 years of age or older were admitted to the medical service at Mount Zion Hospital. We excluded patients who were admitted for chemotherapy or as part of a research protocol (n = 167) and those for whom some data on primary diagnosis were missing (n = 30). The resulting cohort was composed of 5710 patients, of whom 3693 (65%) were cared for by community-based physicians, 1615 (28%) were cared for by hospitalists, and 402 (7%) were cared for by rotating faculty. Data Management At Mount Zion Hospital, data were drawn from TSI (Transition Systems, Inc., Boston, Massachusetts) administrative databases, a cost-accounting system that collects data abstracted from patient charts at discharge. These databases contain information on sociodemographic characteristics, principal diagnosis (in the form of International Classification of Diseases, 9th revision, codes), diagnosis-related group, length of stay, costs, number of consultations, and whether the patient was in an intensive care unit during hospitalization. Data were manually screened for validity of physician designation as hospitalist or community physician by using previously published definitions of hospitalist physician characteristics (1, 2). Discharge summaries of patients who died during hospitalization were examined to validate deaths. An additional 200 discharge summaries of randomly selected patients discharged alive were also reviewed, revealing no errors. Information regarding physician characteristics and board certification was obtained from hospital credentialing databases. Patient survival to points in time after hospitalization was determined by using data from the California State Death Index (for patients admitted before 1 January 1999) and Social Security death indexes (for patients admitted on or after 1 January 1999 and for those who did not reside in California). Statistical Analysis To satisfy normality requirements and stabilize variance of residuals, we explored two methods of transforming skewed data on cost and length of stay: logarithmic conversion and truncation at the mean + 3 SDs. Since both techniques yielded similar results, we chose to present results by using truncation, as has been done in previous studies of inpatient costs and utilization (4, 9-11). All costs were adjusted to 1999 U.S. dollars by using an annual inflation rate of 3% (4). Primary analyses compared 5308 patients cared for by community or hospital-based physicians; we excluded the few patients cared for by rotating physicians from core analyses. This method was chosen to maximize our ability to discern differences in rare outcomes (such as death or readmission), to determine trends in frequent outcomes (such as length of stay), and to maintain focus on our primary question: hospitalist-directed versus community physiciandirected inpatient care. For bivariable comparisons, we used the Fisher exact test or the Wilcoxon rank-sum test. Unadjusted survival rates were estimated by using KaplanMeier product-limit methods. We then used multivariable models to determine the independent effect of hospitalist care on patient outcomes. Using automated forward and stepwise selection techniques along with manually entered variables, we fit multivariable linear regression models to determine the independent association of hospitalist care with length of stay and costs. Items were selected on the basis of the statistical significance of their association with the outcome or on observed confounding with other independent variables, or to maintain face validity of the model. Similar methods were used in fitting logistic models of readmission; use of consultations; and Cox proportional-hazards models of survival to discharge, 30 days, and 60 days. All analyses were performed by using SAS software, version 8.0 for Windows (SAS Institute, Inc., Cary, North Carolina). Multivariable models contained adjustment for patient age, sex, ethnicity, insurance type, source of admission (for example, emergency department), site of discharge, whether a cardiovascular procedure was performed during hospitalization, whether the patient received care in an intensive care unit during hospitalization, and case-mix measures. For case-mix measures, specific diagnoses were defined by using International Classification of Diseases, 9th revision, codes for pneumonia, asthma, congestive heart failure, acute myocardial infarction, angina, unstable angina, chest pain, cancer, gastrointestinal hemorrhage, HIV infection, and cerebrovascular accident. Models also contained a variable indexed to admission date to adjust for secular trends. Trends in adjusted outcomes were tested by using variables dummy-coded to indicate service and year of admission. Because patients were not randomly assigned to hospitalists or community physicians, we performed secondary analyses using a propensity score (12, 13). In our analyses, the propensity score represents the likelihood that any given patient would be admitted to a hospitalist attending physician. The propensity score was calculated in a logistic regression model with attending designation [that is, hospitalist vs. community physician] as the dependent variable. The model contained all covariates in core models, as well as variables found to contribute to nonrandom allocation of patients to specialty care at a P value less than or equal to 0.20. The propensity score was then used in analyses of cost, length of stay, and mortality in two ways: 1) multivariable analyses stratified within tertiles of propensity score and 2) multivariable analyses using the score as a continuous adjustment variable. Results Physician Characteristics One hundred thirteen community physicians, 20 rotating physicians, and 5 hospitalist physicians admitted patients to Mount Zion Hospital during the 2 years of this study. The mean age was 34 years for hospital

2014 ACC/AHA Guideline on Perioperative Cardiovascular Evaluation and Management of Patients Undergoing Noncardiac Surgery: Executive Summary

Preamble 2216 1. Introduction 2217 2. Clinical Risk Factors: Recommendations 2220 3. Approach to Perioperative Cardiac Testing 2221 4. Supplemental Preoperative Evaluation: Recommendations 2221 5. Perioperative Therapy: Recommendations 2224 6. Anesthetic Consideration and Intraoperative Management: Recommendations 2228 7. Surveillance and Management for Perioperative MI: Recommendations 2229 8. Future Research Directions 2230 Appendix 1. Author Relationships With Industry and Other Entities (Relevant) 2237 Appendix 2. Reviewer Relationships With Industry and Other Entities (Relevant) 2239 Appendix 3. Related Recommendations From Other CPGs 2244 References 2230 The American College of Cardiology (ACC) and the American Heart Association (AHA) are committed to the prevention and management of cardiovascular diseases through professional education and research for clinicians, providers, and patients. Since 1980, the ACC and AHA have shared a responsibility to translate scientific evidence into clinical practice guidelines (CPGs) with recommendations to standardize and improve cardiovascular health. These CPGs, based on systematic methods to evaluate and classify evidence, provide a cornerstone of quality …

Redefining Readmission Risk Factors for General Medicine Patients

BACKGROUND Readmissions are costly both financially for our healthcare system and emotionally for our patients. Identifying factors that increase risk for readmissions may be helpful to focus resources to optimize the discharge process and reduce avoidable readmissions. OBJECTIVE To identify factors associated with readmission within 30 days for general medicine patients. METHODS We performed a retrospective observational study of an administrative database at an urban 550-bed tertiary care academic medical center. Cohort patients were discharged from the general medicine service over a 2-year period from June 1, 2006, to May 31, 2008. Clinical, operational, and sociodemographic factors were evaluated for association with readmission. RESULTS Our cohort included 10,359 consecutive admissions (6805 patients) discharged from the general medicine service. The 30-day readmission rate was 17.0%. In multivariate analysis, factors associated with readmission included black race (odds ratio [OR], 1.43; 95% confidence interval [CI], 1.24-1.65), inpatient use of narcotics (1.33; 1.16-1.53) and corticosteroids (1.24; 1.09-1.42), and the disease states of cancer (with metastasis 1.61; 1.33-1.95; without metastasis 1.95; 1.54-2.47), renal failure (1.19; 1.05-1.36), congestive heart failure (1.30; 1.09-1.56), and weight loss (1.26; 1.09-1.47). Medicaid payer status (1.15; 0.97-1.36) had a trend toward readmission. CONCLUSION Readmission of general medicine patients within 30 days is common and associated with several easily identifiable clinical and nonclinical factors. Identification of these risk factors can allow providers to target interventions to reduce potentially avoidable readmissions.

“July Effect”: Impact of the Academic Year-End Changeover on Patient Outcomes: A Systematic Review

BACKGROUND It is commonly believed that the quality of health care decreases during trainee changeovers at the end of the academic year. PURPOSE To systematically review studies describing the effects of trainee changeover on patient outcomes. DATA SOURCES Electronic literature search of PubMed, Educational Research Information Center (ERIC), EMBASE, and the Cochrane Library for English-language studies published between 1989 and July 2010. STUDY SELECTION Title and abstract review followed by full-text review to identify studies that assessed the effect of the changeover on patient outcomes and that used a control group or period as a comparator. DATA EXTRACTION Using a standardized form, 2 authors independently abstracted data on outcomes, study setting and design, and statistical methods. Differences between reviewers were reconciled by consensus. Studies were then categorized according to methodological quality, sample size, and outcomes reported. DATA SYNTHESIS Of the 39 included studies, 27 (69%) reported mortality, 19 (49%) reported efficiency (length of stay, duration of procedure, hospital charges), 23 (59%) reported morbidity, and 6 (15%) reported medical error outcomes; all studies focused on inpatient settings. Most studies were conducted in the United States. Thirteen (33%) were of higher quality. Studies with higher-quality designs and larger sample sizes more often showed increased mortality and decreased efficiency at time of changeover. Studies examining morbidity and medical error outcomes were of lower quality and produced inconsistent results. LIMITATIONS The review was limited to English-language reports. No study focused on the effect of changeovers in ambulatory care settings. The definition of changeover, resident role in patient care, and supervision structure varied considerably among studies. Most studies did not control for time trends or level of supervision or use methods appropriate for hierarchical data. CONCLUSION Mortality increases and efficiency decreases in hospitals because of year-end changeovers, although heterogeneity in the existing literature does not permit firm conclusions about the degree of risk posed, how changeover affects morbidity and rates of medical errors, or whether particular models are more or less problematic. PRIMARY FUNDING SOURCE National Heart, Lung, and Blood Institute.

The Influence of Procedure Volumes and Standardization of Care on Quality and Efficiency in Total Joint Replacement Surgery

BACKGROUND The relationship between surgeon and hospital procedure volumes and clinical outcomes in total joint arthroplasty has long fueled a debate over regionalization of care. At the same time, numerous policy initiatives are focusing on improving quality by incentivizing surgeons to adhere to evidence-based processes of care. The purpose of this study was to evaluate the independent contributions of surgeon procedure volume, hospital procedure volume, and standardization of care on short-term postoperative outcomes and resource utilization in lower-extremity total joint arthroplasty. METHODS An analysis of 182,146 consecutive patients who underwent primary total joint arthroplasty was performed with use of data entered into the Perspective database by 3421 physicians from 312 hospitals over a two-year period. Adherence to evidence-based processes of care was defined by administration of appropriate perioperative antibiotic prophylaxis, beta-blockade, and venous thromboembolism prophylaxis. Patient outcomes included mortality, length of hospital stay, discharge disposition, surgical complications, readmissions, and reoperations within the first thirty days after discharge. Hierarchical models were used to estimate the effects of hospital and surgeon procedure volume and process standardization on individual and combined surgical outcomes and length of stay. RESULTS After adjustment in multivariate models, higher surgeon volume was associated with lower risk of complications, lower rates of readmission and reoperation, shorter length of hospital stay, and higher likelihood of being discharged home. Higher hospital volume was associated with lower risk of mortality, lower risk of readmission, and higher likelihood of being discharged home. The impact of process standardization was substantial; maximizing adherence to evidence-based processes of care resulted in improved clinical outcomes and shorter length of hospital stay, independent of hospital or surgeon procedure volume. CONCLUSIONS Although surgeon and hospital procedure volumes are unquestionably correlated with patient outcomes in total joint arthroplasty, process standardization is also strongly associated with improved quality and efficiency of care. The exact relationship between individual processes of care and patient outcomes has not been established; however, our findings suggest that process standardization could help providers optimize quality and efficiency in total joint arthroplasty, independent of hospital or surgeon volume.

Helical CT pulmonary angiography predictors of in-hospital morbidity and mortality in patients with acute pulmonary embolism.

Purpose To determine if CT variables predict in-hospital morbidity and mortality in patients with pulmonary embolism (PE). Materials and Methods CT scans and charts of 173 patients with CT scans positive for PE were reviewed. CT scans were reviewed for leftward ventricular septal bowing, increased right ventricle (RV) to left ventricle (LV) diameter ratio, clot burden, increased pulmonary artery to aorta diameter ratio, and oligemia. Charts were reviewed for severe morbidity and mortality outcomes: death from pulmonary emboli or any cause, and cardiac arrest. Charts were also reviewed for milder morbidity outcomes: intubation, vasopressor use, or admission to an intensive care unit (ICU) and for multiple comorbidities. Results No CT predictor was significantly associated with severe morbidity or mortality outcomes. Ventricular septal bowing and increased RV/LV diameter ratio were both associated with subsequent admission to an ICU (P = 0.004 and P = 0.025, respectively). Oligemia (either lung) was associated with subsequent intubation; right lung oligemia was associated with the subsequent use of vasopressors. After controlling for history of congestive heart failure, ischemic heart disease, and pulmonary disease, both septal bowing and an increased RV/LV diameter ratio remained associated with admission to an ICU. Conclusion No CT variables predicted severe in-hospital morbidity and mortality (death from pulmonary embolism, death from any cause, or cardiac arrest) in patients with PE. However, ventricular septal bowing and increased RV/LV diameter ratio were both strongly predictive of less severe morbidity, namely, subsequent ICU admission, and oligemia was associated with subsequent intubation and vasopressor use.

Assessing and Reducing the Cardiac Risk of Noncardiac Surgery

Accurate estimation of a patient’s risk for postoperative cardiac events (eg, myocardial infarction, unstable angina, ventricular tachycardia, pulmonary edema, and death) after noncardiac surgery can guide allocation of clinical resources, use of preventive therapies, and priorities for future research. This review addresses selected issues in clinical risk assessment, approaches to using diagnostic tests, choices among preventive interventions, and postoperative monitoring. Although we have not used a formal systematic review protocol, we emphasize evidence published after the American College of Cardiology/American Heart Association (ACC/AHA) 1 and American College of Physicians (ACP) 2 guidelines, outline the limitations of the evidence, and suggest clinical approaches. A summary of our review of the evidence is presented in Table 1, and suggested approaches using these data are presented in Table 2 and Figures 1 and 2.