Statistical and Clinical Aspects of Hospital Outcomes Profiling

Hospital profiling involves a comparison of a health care provider's structure, processes of care, or outcomes to a standard, often in the form of a report card. Given the ubiquity of report cards and similar consumer ratings in contemporary American culture, it is notable that these are a relatively recent phenomenon in health care. Prior to the 1986 release of Medicare hospital outcome data, little such information was publicly available. We review the historical evolution of hospital profiling with special emphasis on outcomes; present a detailed history of cardiac surgery report cards, the paradigm for modern provider profiling; discuss the potential unintended negative consequences of public report cards; and describe various statistical methodologies for quantifying the relative performance of cardiac surgery programs. Outstanding statistical issues are also described.Comment: Published in at http://dx.doi.org/10.1214/088342307000000096 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Composite Metric for Benchmarking Site Performance in TAVR: Results from the STS/ACC TVT Registry

Background: Transcatheter aortic valve replacement (TAVR) is a transformative therapy for aortic stenosis. Despite rapid improvements in technology and techniques, serious complications remain relatively common and are not well described by single outcome measures. The purpose of this study was to determine if there is site-level variation in TAVR outcomes in the United States using a novel 30-day composite measure. Methods: We performed a retrospective cohort study using data from the STS/ACC TVT Registry to develop a novel ranked composite performance measure that incorporates mortality and serious complications. The selection and rank order of the complications for the composite was determined by their adjusted association with 1-year outcomes. Sites whose risk-adjusted outcomes were significantly more or less frequent than the national average based on a 95% probability interval were classified as performing worse or better than expected. Results: The development cohort consisted of 52,561 patients who underwent TAVR between January 1, 2015 and December 31, 2017. Based on the associations with 1-year risk-adjusted mortality and health status, we identified four periprocedural complications to include in the composite risk model in addition to mortality. Ranked empirically according to severity, these included stroke, major, life-threatening or disabling bleeding, stage III acute kidney injury, and moderate or severe peri-valvular regurgitation. Based on these ranked outcomes, we found that there was significant site-level variation in quality of care in TAVR in the United States. Overall, better than expected site performance was observed in 25/301 (8%) of sites; performance as expected was observed in 242/301 sites (80%); and worse than expected performance was observed in 34/301 (11%) of sites. Thirty-day mortality, stroke, major, life-threatening or disabling bleeding, and moderate or severe peri-valvular leak were each substantially more common in sites with worse than expected performance as compared with other sites. There was good aggregate reliability of the model. Conclusions: There are substantial variations in the quality of TAVR care received in the United States, and 11% of sites were identified as providing care below the average level of performance. Further study is necessary to determine structural, process-related, and technical factors associated with high- and low-performing sites

Risk Prediction in Aortic Valve Replacement: Incremental Value of the Preoperative Echocardiogram

Background: Risk prediction is a critical step in patient selection for aortic valve replacement (AVR), yet existing risk scores incorporate very few echocardiographic parameters. We sought to evaluate the incremental predictive value of a complete echocardiogram to identify high‐risk surgical candidates before AVR. Methods and Results: A cohort of patients with severe aortic stenosis undergoing surgical AVR with or without coronary bypass was assembled at 2 tertiary centers. Preoperative echocardiograms were reviewed by independent observers to quantify chamber size/function and valve function. Patient databases were queried to extract clinical data. The cohort consisted of 432 patients with a mean age of 73.5 years and 38.7% females. Multivariable logistic regression revealed 3 echocardiographic predictors of in‐hospital mortality or major morbidity: E/e’ ratio reflective of elevated left ventricular (LV) filling pressure; myocardial performance index reflective of right ventricular (RV) dysfunction; and small LV end‐diastolic cavity size. Addition of these echocardiographic parameters to the STS risk score led to an integrated discrimination improvement of 4.1% (P<0.0001). After a median follow‐up of 2 years, Cox regression revealed 5 echocardiographic predictors of all‐cause mortality: small LV end‐diastolic cavity size; LV mass index; mitral regurgitation grade; right atrial area index; and mean aortic gradient <40 mm Hg. Conclusions: Echocardiographic measures of LV diastolic dysfunction and RV performance add incremental value to the STS risk score and should be integrated in prediction when evaluating the risk of AVR. In addition, findings of small hypertrophied LV cavities and/or low mean aortic gradients confer a higher risk of 2‐year mortality

Extension of adrenocortical carcinoma into the right atrium – echocardiographic diagnosis: A case report

BACKGROUND: Adrenocortical carcinoma is a rare, highly malignant tumor. Cardiac involvement of the tumor is very rare. Echocardiography facilitates the evaluation of the cardiac involvement of the tumor. CASE PRESENTATION: We describe a patient with an adrenal tumor. Transthoracic echo showed its extension into the right atrium. Accordingly, a combined abdominal and cardiac operation was performed, monitored by transesophageal echocardiography. CONCLUSION: This case highlights the importance of echocardiography in revealing the cardiac involvement by this tumor and in planning the operative procedure

Artificial Neural Networks Versus Multiple Logistic Regression to Predict 30-Day Mortality After Operations For Type A Ascending Aortic Dissection§

There are few comparative reports on the overall accuracy of neural networks (NN), assessed only versus multiple logistic regression (LR), to predict events in cardiovascular surgery studies and none has been performed among acute aortic dissection (AAD) Type A patients. OBJECTIVES: We aimed at investigating the predictive potential of 30-day mortality by a large series of risk factors in AAD Type A patients comparing the overall performance of NN versus LR. METHODS: We investigated 121 plus 87 AAD Type A patients consecutively operated during 7 years in two Centres. Forced and stepwise NN and LR solutions were obtained and compared, using receiver operating characteristic area under the curve (AUC) and their 95% confidence intervals (CI) and Gini's coefficients. Both NN and LR models were re-applied to data from the second Centre to adhere to a methodological imperative with NN. RESULTS: Forced LR solutions provided AUC 87.9+/-4.1% (CI: 80.7 to 93.2%) and 85.7+/-5.2% (CI: 78.5 to 91.1%) in the first and second Centre, respectively. Stepwise NN solution of the first Centre had AUC 90.5+/-3.7% (CI: 83.8 to 95.1%). The Gini's coefficients for LR and NN stepwise solutions of the first Centre were 0.712 and 0.816, respectively. When the LR and NN stepwise solutions were re-applied to the second Centre data, Gini's coefficients were, respectively, 0.761 and 0.850. Few predictors were selected in common by LR and NN models: the presence of pre-operative shock, intubation and neurological symptoms, immediate post-operative presence of dialysis in continuous and the quantity of post-operative bleeding in the first 24 h. The length of extracorporeal circulation, post-operative chronic renal failure and the year of surgery were specifically detected by NN. CONCLUSIONS: Different from the International Registry of AAD, operative and immediate post-operative factors were seen as potential predictors of short-term mortality. We report a higher overall predictive accuracy with NN than with LR. However, the list of potential risk factors to predict 30-day mortality after AAD Type A by NN model is not enlarged significantly