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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IMPORTANCE Identifying modifiable risk factors, such as stress, that could inform the design of peripheral artery disease (PAD) management strategies is critical for reducing the risk of mortality. Few studies have examined the association of self-perceived stress with outcomes in patients with PAD. OBJECTIVE To examine the association of high levels of self-perceived stress with mortality in patients with PAD. DESIGN, SETTING, AND PARTICIPANTS This cohort study analyzed data from the registry of the Patient-Centered Outcomes Related to Treatment Practices in Peripheral Arterial Disease: Investigating Trajectories (PORTRAIT) study, a multicenter study that enrolled patients with new or worsening symptoms of PAD who presented to 16 subspecialty clinics across the US, the Netherlands, and Australia from June 2, 2011, to December 3, 2015. However, the present study included only patients in the US sites because assessments of mortality for patients in the Netherlands and Australia were not available. Data analysis was conducted from July 2019 to March 2020. EXPOSURE Self-perceived stress was quantified using the 4-item Perceived Stress Scale (PSS-4), with a score range of 0 to 16. A score of 6 or higher indicated high stress in this cohort. Missing scores were imputed using multiple imputation by chained equations with predictive mean matching. Stress was assessed at baseline and at 3-, 6-, and 12-month follow-up. Patients who reported high levels of stress at 2 or more follow-up assessments were categorized as having chronic stress. MAIN OUTCOMES AND MEASURES All-cause mortality was the primary study outcome. Such data for the subsequent 4 years after the 12-month follow-up were obtained from the National Death Index. RESULTS The final cohort included 765 patients, with a mean (SD) age of 68.4 (9.7) years. Of these patients, 57.8% were men and 71.6% were white individuals. High stress levels were reported in 65% of patients at baseline and in 20% at the 12-month follow-up. In an adjusted Cox proportional hazards regression model accounting for demographics, comorbidities, disease severity, treatment type, and socioeconomic status, exposure to chronic stress during the 12 months of follow-up was independently associated with increased risk of all-cause mortality in the subsequent 4 years (hazard ratio, 2.12; 95% CI, 1.14-3.94; P = .02). CONCLUSIONS AND RELEVANCE In thie cohort study of patients with PAD, higher stress levels in the year after diagnosis appeared to be associated with greater long-term mortality risk, even after adjustment for confounding factors. These findings suggest that, given that stress is a modifiable risk (continued) Key Points Question Is there an association between chronic stress and mortality risk in patients with peripheral artery disease? Findings In this cohort study of 765 patients with new symptoms of peripheral artery disease, higher stress levels in the year after diagnosis were independently associated with higher risk of mortality in the subsequent 4 years. Meaning The findings o...

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Background Despite advancements from balloon angioplasty to drug-eluting stents, primary patency rates after endovascular revascularization of peripheral artery disease have remained inferior compared to surgery. Endovascular revascularization has been limited by restenosis and mechanical stent failure. Thus, there is increased research into other nonstent-based local drug delivery modalities, which can provide an active drug to inhibit restenosis focally and avoid the risk of systemic adverse effects.