Faculty Bibliography

Introduction: Clostridium difficile infection (CDI) is one of the most common hospital-acquired infections leading to prolonged hospitalization and significant morbidity. Only a few prior studies have developed predictive risk models for CDI and all but one have utilized logistic regression (LR) models to identify risk factors. Automated machine learning (AutoML) programs consistently outperform standard LR models in non-medical contexts. This study aims to investigate the utility of AutoML methods in developing a model for post-operative CDI prediction.
Method(s): We used an AutoML system developed by Amazon, Autogluon v0.3.1, to evaluate the prediction accuracy of post-surgical CDI using the 2016-2018 ACS NSQIP database. A total of A total of 3,049,617 patients and 79 pre-operative features were included in the model. Post-operative CDI was defined as CDI within 30 days of surgery. Models were trained for 4 hours to optimize performance on the Brier score, with lower being better. Validation of all performance metrics was done using the 2019 NSQIP database.
Result(s): 0.36% of the patients (n = 11,001) developed post-operative CDI. Brier scores were calculated for each model with the top performing model being an ensembled neural net model having a Brier score of 0.0027 on the test set. The corresponding AUROC and AUC-PR was 0.840 and 0.015 respectively (Figure).
Conclusion(s): The models generated via AutoML to predict post-operative CDI had discriminatory characteristics greater than or equal to those models reported in the literature. Future post-operative CDI models may benefit from automated machine learning techniques

Background: Risk calculators to predict perioperative major adverse cardiovascular and cerebrovascular events (MACCE) often rely on logistic regression (LR) analysis. Automated machine learning (AutoML) processes regularly outperform regular machine learning (ML) and LR methods for predictive accuracy. Commercial AutoML systems have not yet been applied to predict perioperative MACCE after non-cardiac and cardiac surgeries.
Method(s): We used a commercial AutoML system, Google AutoML Tables (GAMLT), to predict perioperative MACCE in the 2019 ACS NSQIP database. MACCE was defined as death, myocardial infarction, cardiac arrest, or stroke. Default AutoML settings were used, with 80% of cases randomly selected for training, 10% for validation, and 10% for testing. Global feature importance was determined through the Shapley method. Two models were generated: Model 1 included 81 pre-operative features; Model 2 included the top 21 features from Model 1 and was independently validated with 2016-2018 NSQIP data.
Result(s): Model 1 yielded an area under the receiver operating characteristic (AUROC) of 0.934 in the 2019 ACS NSQIP dataset. Model 2, an ensemble of 25 feedforward neural net models, yielded an AUROC of 0.914-0.920 for MACCE in 2016-2019 (Figure).
Conclusion(s): Compared to existing risk calculators, GAMLT-derived models offered novel feature detection and comparable predictive performance for MACCE. AutoML analyses should be considered for risk estimation of perioperative MACCE. [Formula presented]
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BACKGROUND:Guidelines recommend moderate to high-intensity statins and antithrombotic agents in patients with atherosclerotic cardiovascular disease (ASCVD). However, guideline-directed medical therapy (GDMT) remains suboptimal. METHODS:In this quality initiative, best practice alerts (BPA) in the electronic health record (EHR) were utilized to alert providers to prescribe to GDMT upon hospital discharge in ASCVD patients. Rates of GDMT were compared for 5 months pre- and post-BPA implementation. Multivariable regression was used to identify predictors of GDMT. RESULTS:In 5985 pre- and 5568 post-BPA patients, the average age was 69.1 ± 12.8 years and 58.5% were male. There was a 4.0% increase in statin use from 67.3% to 71.3% and a 3.1% increase in antithrombotic use from 75.3% to 78.4% in the post-BPA cohort. CONCLUSIONS:This simple EHR-based initiative was associated with a modest increase in ASCVD patients being discharged on GDMT. Leveraging clinical decision support tools provides an opportunity to influence provider behavior and improve care for ASCVD patients, and warrants further investigation.