IMPORTANCE Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.OBJECTIVE To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.

BackgroundThe current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI.Methods and findingsWe used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749–0.754) versus 0.711 (95% CI 0.708–0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615–0.0618) versus 0.0636 (95% CI 0.0634–0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988–1.028) versus 1.036 (95% CI 1.015–1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015–March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors.Conclusio...

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Key Points Question Can machine learning techniques, bolstered by better selection of variables, improve prediction of major bleeding after percutaneous coronary intervention (PCI)? Findings In this comparative effectiveness study that modeled more than 3 million PCI procedures, machine learning techniques improved the prediction of post-PCI major bleeding to a C statistic of 0.82 compared with a C statistic of 0.78 from the existing model. Machine learning techniques improved the identification of an additional 3.7% of bleeding cases and 1.0% of nonbleeding cases. Meaning By leveraging more complex, raw variables, machine learning techniques are better able to identify patients at risk for major bleeding and who can benefit from bleeding avoidance therapies.

BackgroundCharacterizing and assessing the prevalence, awareness, and treatment patterns of patients with isolated diastolic hypertension (IDH) can generate new knowledge and highlight opportunities to improve their care.Methods and ResultsWe used data from the China PEACE (Patient‐centered Evaluative Assessment of Cardiac Events) Million Persons Project, which screened 2 351 035 participants aged 35 to 75 years between 2014 and 2018. IDH was defined as systolic and diastolic blood pressure of <140 and ≥90 mm Hg; awareness as self‐reported diagnosis of hypertension; and treatment as current use of antihypertensive medications. Of the 2 310 184 participants included (mean age 55.7 years; 59.5% women); 73 279 (3.2%) had IDH, of whom 63 112 (86.1%) were untreated, and only 6512 (10.3%) of the untreated were aware of having hypertension. When compared with normotensives, participants who were <60 years, men, at least college educated, had body mass index of >28 kg/m2, consumed alcohol, had diabetes mellitus, and prior cardiovascular events were more likely to have IDH (all P<0.01). Among those with IDH, higher likelihood of awareness was associated with increased age, women, college education, body mass index of >28 kg/m2, higher income, diabetes mellitus, prior cardiovascular events, and Central or Eastern region (all P<0.05). Most treated participants with IDH reported taking only 1 class of antihypertensive medication.Conclusions IDH affects a substantial number of people in China, however, few are aware of having hypertension and most treated participants are poorly managed, which suggests the need to improve the diagnosis and treatment of people with IDH.