Using Artificial Intelligence in Predicting Ischemic Stroke Events After Percutaneous Coronary Intervention.

BACKGROUND: Ischemic stroke (IS) is an uncommon but severe complication in patients undergoing percutaneous coronary intervention (PCI). Despite significant morbidity and economic cost associated with post PCI IS, a validated risk prediction model is not currently available. AIMS: We aim to develop a machine learning model that predicts IS after PCI. METHODS: We analyzed data from Mayo Clinic CathPCI registry from 2003 to 2018. Baseline clinical and demographic data, electrocardiography (ECG), intra/post-procedural data, and echocardiographic variables were abstracted. A random forest (RF) machine learning model and a logistic regression (LR) model were developed. The receiver operator characteristic (ROC) analysis was used to assess model performance in predicting IS at 6-month, 1-, 2-, and 5-years post-PCI. RESULTS: A total of 17,356 patients were included in the final analysis. The mean age of this cohort was 66.9 ± 12.5 years, and 70.7% were male. Post-PCI IS was noted in 109 patients (.6%) at 6 months, 132 patients (.8%) at 1 year, 175 patients (1%) at 2 years, and 264 patients (1.5%) at 5 years. The area under the curve of the RF model was superior to the LR model in predicting ischemic stroke at 6 months, 1-, 2-, and 5-years. Periprocedural stroke was the strongest predictor of IS post discharge. CONCLUSIONS: The RF model accurately predicts short- and long-term risk of IS and outperforms logistic regression analysis in patients undergoing PCI. Patients with periprocedural stroke may benefit from aggressive management to reduce the future risk of IS.

Artificial Intelligence Trumps TAVI2-SCORE and CoreValve Score in Predicting 1-Year Mortality Post-Transcatheter Aortic Valve Replacement.

BACKGROUND/PURPOSE: Machine learning has been used to predict procedural risk in patients undergoing various medical interventions and procedures. One-year mortality in patients after Transcatheter Aortic Valve Replacement (TAVR) has a wide range (from 8.5 to 24% in various studies). We sought to apply machine learning to determine predictors of one year mortality in patients undergoing TAVR. METHODS/MATERIALS: A retrospective study of 1055 patients who underwent TAVR (Jan 2014-June 2017) with one-year follow up was completed. Baseline demographics, clinical, electrocardiography (ECG), Computed Tomography (CT) and echocardiography data were abstracted. Variables with near zero variance or ≥50% missing data were excluded. The Gradient Boosting Machine learning (GBM) prediction model included 163 variables and was optimized using 5-fold cross-validation repeated 10-times. The receiver operator characteristic (ROC) for the GBM model was calculated to predict one-year mortality post TAVR, and then compared to the TAVI2-SCORE and CoreValve score. RESULTS: Among 1055 TAVR patients (mean age 80.9 ± 7.9 years, 42% female), 14.02% died at one year. 78% had balloon expandable valves placed. Based on GBM, the ten most predictive variables for one-year survival were cardiac power index, hemoglobin, systolic blood pressure, INR, diastolic blood pressure, body mass index, valve calcium score, serum creatinine, aortic annulus area, and albumin. The area under ROC to predict survival for the GBM model vs TAVI2-SCORE and CoreValve Score was 0.72 (95% CI 0.68-0.78) vs 0.56 (95%CI 0.51-0.62) and 0.53 (95% CI 0.47-0.59) respectively with p < 0.0001. CONCLUSION: The GBM model outperforms TAVI2-SCORE and CoreValve Score in predicting mortality one-year post TAVR.

Development of advanced conduction disturbances following balloon-expandable transcatheter aortic valve replacement leads to poorer clinical outcomes.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) is a reliable method of treating patients with severe aortic stenosis, but is associated with postprocedure conduction defects. OBJECTIVE: The purpose of this study was to compare clinical outcomes in patients who developed advanced conduction defects post-TAVR to those who did not. METHODS: We conducted a retrospective chart review of 243 patients who underwent balloon-expandable TAVR with the Edwards Sapien valve to determine the incidence of advanced conduction defects in our cohort. We compared clinical outcomes including overall mortality, improvement in symptomatology, and improvement in left ventricular ejection fraction. RESULTS: Among the 243 patients included in the study, 9.1% (22/243) required permanent pacemaker (PPM); 19.8% (48/243) developed left bundle branch block (LBBB), and 71.2% (173/243) did not develop any permanent advanced conduction defects. Overall 1-year mortality was similar across all three groups. There was significant improvement in New York Heart Association functional capacity of all groups post-TAVR, but this was much less in the PPM group (45.5% vs 68.8%, P = .04). Postprocedure from TAVR, patients with LBBB or PM were less likely to have improvement in their ejection fraction (net loss of -0.7% for LBBB and -5.7% for PPM compared to a net gain of 2.3% for no-LBBB/PM (P = .02). CONCLUSION: Patients who develop LBBB or require PM post-TAVR with Edwards Sapien valves are less likely to improve New York Heart Association functional capacity and more likely to have no improvement or deterioration of their pre-TAVR left ventricular ejection fraction.