Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions

• Published in: European heart journal Vol. 45; no. 8; pp. 601 - 609
• Main Authors: Hamilton, David E, Albright, Jeremy, Seth, Milan, Painter, Ian, Maynard, Charles, Hira, Ravi S, Sukul, Devraj, Gurm, Hitinder S
• Format: Journal Article
• Language: English
• Published: England 21.02.2024
• Subjects: Acute Kidney Injury - etiology; Hemorrhage - etiology; Humans; Machine Learning; Patient Preference; Percutaneous Coronary Intervention - methods; Renal Dialysis; Risk Assessment - methods; Risk Factors; Stroke - etiology; Treatment Outcome; Risk prediction; Percutaneous coronary intervention; Machine learning
• DOI: 10.1093/eurheartj/ehad836
• ISSN: 0195-668X; 1522-9645

Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural variables to accurately predict common post-PCI complications. A group of 66 adults underwent a semiquantitative survey assessing a preferred list of outcomes and model display. The machine learning cohort included 107 793 patients undergoing PCI procedures performed at 48 hospitals in Michigan between 1 April 2018 and 31 December 2021 in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) registry separated into training and validation cohorts. External validation was conducted in the Cardiac Care Outcomes Assessment Program database of 56 583 procedures in 33 hospitals in Washington. Overall rate of in-hospital mortality was 1.85% (n = 1999), acute kidney injury 2.51% (n = 2519), new-onset dialysis 0.44% (n = 462), stroke 0.41% (n = 447), major bleeding 0.89% (n = 942), and transfusion 2.41% (n = 2592). The model demonstrated robust discrimination and calibration for mortality {area under the receiver-operating characteristic curve [AUC]: 0.930 [95% confidence interval (CI) 0.920-0.940]}, acute kidney injury [AUC: 0.893 (95% CI 0.883-0.903)], dialysis [AUC: 0.951 (95% CI 0.939-0.964)], stroke [AUC: 0.751 (95%CI 0.714-0.787)], transfusion [AUC: 0.917 (95% CI 0.907-0.925)], and major bleeding [AUC: 0.887 (95% CI 0.870-0.905)]. Similar discrimination was noted in the external validation population. Survey subjects preferred a comprehensive list of individually reported post-procedure outcomes. Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions.