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Objective: To establish a case-adjusted hospital-specific performance evaluation tool using machine learning methodology for cesarean delivery. Data Sources: Secondary data were collected from patients between January 1, 2015 and February 28, 2018 using a hospital's 'Electronic Data Warehouse' database from Illinois, USA. Study Design: The machine learning methodology of optimal classification trees (OCTs) was used to predict cesarean delivery rate by physician group, thereby establishing the case-adjusted benchmarking standards in comparison to the overall hospital cesarean delivery rate. Outcomes of specific patient populations of each participating practice were predicted, as if each were treated in the overall hospital environment. The resulting OCTs estimate physician group expected cesarean delivery outcomes, both aggregate and in specific clinical situations. Data Collection/Extraction Methods: Twelve thousand eight hunderd and forty one singleton, vertex, term deliveries, cared for by practices with [greater than or equal to]50 births. Principal Findings: The overall rate of cesarean delivery was 18.6% (n = 2384), with a range of 13.3%-33.7% amongst 22 physician practices. An optimal decision tree was used to create a prediction model for the hospital overall, which defined 23 patient cohorts divided by 46 nodes. The model's performance for prediction of cesarean delivery is as follows: area under the curve 0.73, sensitivity 98.4%, specificity 16.1%, positive predictive value 83.7%, negative predictive value 70.6%. Comparisons with the overall hospital's specific-case adjusted benchmark groups revealed that several groups outperformed the overall hospital benchmark, and some practice groups underperformed in comparison to the overall hospital benchmark. Conclusions: OCT benchmarking can assess physician practice-specific case-adjusted performance, both overall and clinical situation-specific, and can serve as a valuable tool for hospital self-assessment and quality improvement. KEYWORDS cesarean birth, cesarean delivery, cesarean section, database, machine learning, optimal classification trees, risk analysis/modeling, statistics What is known on this topic * The cesarean delivery rate in the United States for low-risk births is higher than the World Health Organization's target rate and associated with increased maternal morbidity and mortality. * Criticisms of prior benchmarking models are the inclusion of limited patient-related factors, reporting of a single summary measure of hospital performance, and outdated analytical methods to predict risk based only on binary risk factors. * Crude comparison of cesarean delivery rates between hospitals or practice groups may not be appropriate given variation in case mix. What this study adds * Optimal classification trees (OCTs) benchmarking analysis is a novel and nonlinear technology that can assess physician practice-specific case-adjusted performance for cesarean delivery with high sensitivity of 98.4% in this study. * OCT benchmarking analysis can be used to compare practice group performance in cesarean delivery with an area under the curve of 0.73. * OCT benchmarking analysis can compare practice groups and reveal differences in performance in comparison to the overall hospital's performance., 1 | INTRODUCTION The current cesarean delivery (CD) rate in the United States for the most recent data from 2018 was 31.9%, with a low-risk CD rate (nulliparous, term, singleton, [...]

Background:We have previously shown that the machine learning methodology of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery (CHS). We have now applied this methodology to define benchmarking standards after CHS, permitting case-adjusted hospital-specific performance evaluation. Methods:The European Congenital Heart Surgeons Association Congenital Database data subset (31 792 patients) who had undergone any of the 10 “benchmark procedure group” primary procedures were analyzed. OCT models were built predicting hospital mortality (HM), and prolonged postoperative mechanical ventilatory support time (MVST) or length of hospital stay (LOS), thereby establishing case-adjusted benchmarking standards reflecting the overall performance of all participating hospitals, designated as the “virtual hospital.” These models were then used to predict individual hospitals’ expected outcomes (both aggregate and, importantly, for risk-matched patient cohorts) for their own specific cases and case-mix, based on OCT analysis of aggregate data from the “virtual hospital.” Results:The raw average rates were HM = 4.4%, MVST = 15.3%, and LOS = 15.5%. Of 64 participating centers, in comparison with each hospital's specific case-adjusted benchmark, 17.0% statistically (under 90% confidence intervals) overperformed and 26.4% underperformed with respect to the predicted outcomes for their own specific cases and case-mix. For MVST and LOS, overperformers were 34.0% and 26.4%, and underperformers were 28.3% and 43.4%, respectively. OCT analyses reveal hospital-specific patient cohorts of either overperformance or underperformance. Conclusions:OCT benchmarking analysis can assess hospital-specific case-adjusted performance after CHS, both overall and patient cohort-specific, serving as a tool for hospital self-assessment and quality improvement.

AbstractBackground:Venous thromboembolism (VTE), comprised of deep vein thrombosis (DVT) and pulmonary embolism (PE), is commonly treated with a low-molecular-weight heparin such as enoxaparin plus a vitamin K antagonist (VKA) to prevent recurrence. Administration of enoxaparin  VKA is hampered by complexities of laboratory monitoring and frequent dose adjustments. Rivaroxaban, an orally administered anticoagulant, has been compared with enoxaparin  VKA in the EINSTEIN trials. The objective was to evaluate the cost-effectiveness of rivaroxaban compared with enoxaparin  VKA as anticoagulation treatment for acute, symptomatic, objectively-confirmed DVT or PE.Methods:A Markov model was built to evaluate the costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios associated with rivaroxaban compared to enoxaparin  VKA in adult patients treated for acute DVT or PE. All patients entered the model in the ‘on-treatment’ state upon commencement of oral rivaroxaban or enoxaparin  VKA for 3, 6, or 12 months. Transition probabilities were obtained from the EINSTEIN trials during treatment and published literature after treatment. A 3-month cycle length, US payer perspective (2012), 5-year time horizon and a 3 annual discount rate were used.Results:Treatment with rivaroxaban cost 2,448 per-patient less and was associated with 0.0058 more QALYs compared with enoxaparin  VKA, making it a dominant economic strategy. Upon one-way sensitivity analysis, the model’s results were sensitive to the reduction in index VTE hospitalization length-of-stay associated with rivaroxaban compared with enoxaparin  VKA. At a willingness-to-pay threshold of 50,000/QALY, probabilistic sensitivity analysis showed rivaroxaban to be cost-effective compared with enoxaparin  VKA approximately 76 of the time.Limitations:The model did not account for the benefits associated with an oral and minimally invasive administration of rivaroxaban. ‘Real-world’ applicability is limited because data from the EINSTEIN trials were used in the model. Also, resource utilization and costs were based on the US healthcare system.Conclusion:Rivaroxaban is a cost-effective option for anticoagulation treatment of acute VTE patients.