Faculty Bibliography

OBJECTIVE: To determine the clinical significance of tachycardia in the postoperative period. PATIENTS AND METHODS: Individuals 18 years or older undergoing hip and knee arthroplasty were included in the study. Two data sets were collected from different time periods: development data set from January 1, 2011, through December 31, 2011, and validation data set from December 1, 2012, through September 1, 2014. We used the development data set to identify the optimal definition of tachycardia with the strongest association with the vascular composite outcome (pulmonary embolism and myocardial necrosis and infarction). The predictive value of this definition was assessed in the validation data set for each outcome of interest, pulmonary embolism, myocardial necrosis and infarction, and infection using multiple logistic regression to control for known risk factors. RESULTS: In 1755 patients in the development data set, a maximum heart rate (HR) greater than 110 beats/min was found to be the best cutoff as a correlate of the composite vascular outcome. Of the 4621 patients who underwent arthroplasty in the validation data set, 40 (0.9%) had pulmonary embolism. The maximum HR greater than 110 beats/min had an odds ratio (OR) of 9.39 (95% CI, 4.67-18.87; sensitivity, 72.5%; specificity, 78.0%; positive predictive value, 2.8%; negative predictive value, 99.7%) for pulmonary embolism. Ninety-seven patients (2.1%) had myocardial necrosis (elevated troponin). The maximum HR greater than 110 beats/min had an OR of 4.71 (95% CI, 3.06-7.24; sensitivity, 47.4%; specificity, 78.1%; positive predictive value, 4.4%; negative predictive value, 98.6%) for this outcome. Thirteen (.3%) patients had myocardial infarction according to our predetermined definition, and the maximum HR greater than 110 beats/min had an OR of 1.72 (95% CI, 0.47-6.27). CONCLUSION: Postoperative tachycardia within the first 4 days of surgery should not be dismissed as a postoperative variation in HR, but may precede clinically significant adverse outcomes.

Readmission rates declined after announcement of the Hospital Readmission Reduction Program (HRRP), which penalizes hospitals for excess readmissions for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. To compare trends in readmission rates for target and nontarget conditions, stratified by hospital penalty status. Retrospective cohort study of Medicare fee-for-service beneficiaries older than 64 years discharged between January 1, 2008, and June 30, 2015, from 2214 penalty hospitals and 1283 nonpenalty hospitals. Difference-interrupted time-series models were used to compare trends in readmission rates by condition and penalty status. Hospital penalty status or target condition under the HRRP. Thirty-day risk adjusted, all-cause unplanned readmission rates for target and nontarget conditions. The study included 48137102 hospitalizations of 20351161 Medicare beneficiaries. In January 2008, the mean readmission rates for AMI, HF, pneumonia, and nontarget conditions were 21.9%, 27.5%, 20.1%, and 18.4%, respectively, at hospitals later subject to financial penalties and 18.7%, 24.2%, 17.4%, and 15.7% at hospitals not subject to penalties. Between January 2008 and March 2010, prior to HRRP announcement, readmission rates were stable across hospitals (except AMI at nonpenalty hospitals). Following announcement of HRRP (March 2010), readmission rates for both target and nontarget conditions declined significantly faster for patients at hospitals later subject to financial penalties compared with those at nonpenalized hospitals (for AMI, additional decrease of -1.24 [95% CI, -1.84 to -0.65] percentage points per year relative to nonpenalty discharges; for HF, -1.25 [95% CI, -1.64 to -0.86]; for pneumonia, -1.37 [95% CI, -1.80 to -0.95]; and for nontarget conditions, -0.27 [95% CI, -0.38 to -0.17]; P < .001 for all). For penalty hospitals, readmission rates for target conditions declined significantly faster compared with nontarget conditions (for AMI, additional decline of -0.49 [95% CI, -0.81 to -0.16] percentage points per year relative to nontarget conditions [P = .004]; for HF, -0.90 [95% CI, -1.18 to -0.62; P < .001]; and for pneumonia, -0.57 [95% CI, -0.92 to -0.23; P < .001]). In contrast, among nonpenalty hospitals, readmissions for target conditions declined similarly or more slowly compared with nontarget conditions (for AMI, additional increase of 0.48 [95% CI, 0.01-0.95] percentage points per year [P = .05]; for HF, 0.08 [95% CI, -0.30 to 0.46; P = .67]; for pneumonia, 0.53 [95% CI, 0.13-0.93; P = .01]). After HRRP implementation in October 2012, the rate of change for readmission rates plateaued (P < .05 for all except pneumonia at nonpenalty hospitals), with the greatest relative change observed among hospitals subject to financial penalty. Medicare fee-for-service patients at hospitals subject to penalties under the HRRP had greater reductions in readmission rates compared with those at nonpenalized hospitals. Changes were greater for target vs nontarget conditions for patients at the penalized hospitals but not at the other hospitals.

Importance: Shift-to-shift transitions in care among house staff are associated with adverse events. However, the association between end-of-rotation transition (in which care of the patient is transferred) and adverse events is uncertain. Objective: To examine the association of end-of-rotation house staff transitions with mortality among hospitalized patients. Design, Setting, and Participants: Retrospective multicenter cohort study of patients admitted to internal medicine services (N = 230701) at 10 university-affiliated US Veterans Health Administration hospitals (2008-2014). Exposures: Transition patients (defined as those admitted prior to an end-of-rotation transition who died or were discharged within 7 days following transition) were stratified by type of transition (intern only, resident only, or intern + resident) and compared with all other discharges (control). An alternative analysis comparing admissions within 2 days before transition with admissions on the same 2 days 2 weeks later was also conducted. Main Outcomes and Measures: The primary outcome was in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and readmission rates. A difference-in-difference analysis assessed whether outcomes changed after the 2011 Accreditation Council for Graduate Medical Education (ACGME) duty hour regulations. Adjustments included age, sex, race/ethnicity, month, year, length of stay, comorbidities, and hospital. Results: Among 230701 patient discharges (mean age, 65.6 years; men, 95.8%; median length of stay, 3.0 days), 25938 intern-only, 26456 resident-only, and 11517 intern + resident end-of-rotation transitions occurred. Overall mortality was 2.18% in-hospital, 9.45% at 30 days, and 14.43% at 90 days. Adjusted hospital mortality was significantly greater in transition vs control patients for the intern-only group (3.5% vs 2.0%; odds ratio [OR], 1.12 [95% CI, 1.03-1.21]) and the intern + resident group (4.0% vs 2.1%; OR, 1.18 [95% CI, 1.06-1.33]), but not for the resident-only group (3.3% vs 2.0%; OR, 1.07 [95% CI, 0.99-1.16]). Adjusted 30-day and 90-day mortality rates were greater in all transition vs control comparisons (30-day mortality: intern-only group, 14.5% vs 8.8%, OR, 1.17 [95% CI, 1.13-1.22]; resident-only group, 13.8% vs 8.9%, OR, 1.11 [95% CI, 1.04-1.18]; intern + resident group, 15.5% vs 9.1%, OR, 1.21 [95% CI, 1.12-1.31]; 90-day mortality: intern-only group, 21.5% vs 13.5%, OR, 1.14 [95% CI, 1.10-1.19]; resident-only group, 20.9% vs 13.6%, OR, 1.10 [95% CI, 1.05-1.16]; intern + resident group, 22.8% vs 14.0%, OR, 1.17 [95% CI, 1.11-1.23]). Duty hour changes were associated with greater adjusted hospital mortality for transition patients in the intern-only group and intern + resident group than for controls (intern-only: OR, 1.11 [95% CI, 1.02-1.21]; intern + resident: OR, 1.17 [95% CI, 1.02-1.34]). The alternative analyses did not demonstrate any significant differences in mortality between transition and control groups. Conclusions and Relevance: Among patients admitted to internal medicine services in 10 Veterans Affairs hospitals, end-of-rotation transition in care was associated with significantly higher in-hospital mortality in an unrestricted analysis that included most patients, but not in an alternative restricted analysis. The association was stronger following institution of ACGME duty hour regulations.

Importance: Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation. Objective: To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital. Design, Setting, and Participants: We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data. Main Outcomes and Measures: Heart failure diagnosis based on discharge diagnosis and physician review of sampled medical records. Results: A total of 47119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively. Conclusions and Relevance: The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

There is an active public debate about whether patients' socioeconomic status should be included in the readmission measures used to determine penalties in Medicare's Hospital Readmissions Reduction Program (HRRP). Using the current Centers for Medicare and Medicaid Services methodology, we compared risk-standardized readmission rates for hospitals caring for high and low proportions of patients of low socioeconomic status (as defined by their Medicaid status or neighborhood income). We then calculated risk-standardized readmission rates after additionally adjusting for patients' socioeconomic status. Our results demonstrate that hospitals caring for large proportions of patients of low socioeconomic status have readmission rates similar to those of other hospitals. Moreover, readmission rates calculated with and without adjustment for patients' socioeconomic status are highly correlated. Readmission rates of hospitals caring for patients of low socioeconomic status changed by approximately 0.1 percent with adjustment for patients' socioeconomic status, and only 3-4 percent fewer such hospitals reached the threshold for payment penalty in Medicare's HRRP. Overall, adjustment for socioeconomic status does not change hospital results in meaningful ways.

Purpose: To characterize the utility of shoulder imaging in the outpatient setting; Define predictor variables for useful shoulder imaging in terms of guiding the selection of the primary diagnosis and treatment Materials and Methods: We conducted a retrospective review of adult patients over a 32 month period evaluated and imaged for a primary complaint of shoulder pain in one of three outpatient settings: (1) orthopaedics, (2) emergency department [ED], and (3) internal medicine [IM]. Our sample population was chosen through a review of electronic medical records, using shoulder related ICD-9 codes and physician names. The main outcome variable for this study was imaging utility. A useful imaging examination was defined as a study that satisfied at least one of the following 4 criteria: changed the clinical diagnosis, guided a change in treatment selection, provided a final diagnosis, or guided definitive treatment. A utility score was assigned to each study based on the number of criteria satisfied (range 0-4) with a score of 0 defined as no utility, 1 low utility, 2 moderate utility, and score of > 3 high utility. For patients receiving multiple sequential imaging studies during their workup, each study was included and scored separately. The potential predictor variables evaluated for useful imaging included age, gender, trauma history, symptom chronicity, and injury setting (sports vs. non-sports). Statistical analysis included 95 % confidence intervals and binary logistic regression. Results: A total of 122 patients (70 female/52 male; mean age 47 years (range 18-84)) underwent a total of 171 imaging studies (109 radiographs/57 MRIs/3 CT/2 ultrasound) as part of their initial workup. 106 studies were ordered from orthopaedics, 64 from ED, and 1 from IM. CT and ultrasound utility were not assessed due to low number of cases. Overall, 95.9 % of the imaging studies met the minimum criteria for utility, most commonly helping guide the selection of a definitive treatment (71.9 %). 30.4 % of the studies were categorized as moderately useful, while 12.9 % were classified as highly useful. 95.4 % of radiographs met the criteria for utility, the majority of which were categorized as low utility (78.9 %). Both sports related injury history and trauma were predictive of at least moderate utility for radiographs (p = 0.039, p = 0.004). Younger age was a significant predictor of at least moderate utility for radiographs, most commonly in patients under 32 (p=0.003, AUC 0.748). 96.5 % of MRIs met the criteria for utility, the majority of which were categorized as moderate or high utility (84.2 %). None of the variables investigated were found to significantly predict MRI utility. For patients undergoing radiographs and MRI, MRI was found more useful than radiographs in 53 % of patients with an average utility score of 2.1. Equal utility was found in 42 % of cases, while radiographs were found most useful in 5 % of patients. Conclusion: Our study suggests that both radiographs and MRI have utility in the outpatient evaluation of shoulder pain. This serves as a potential first step towards the development of evidence based imaging algorithms that can be used and tested in future studies