Precision phenotyping from routine laboratory parameters for out of hospital survival prediction in an all comers prospective PCI registry

• Published in: Scientific reports 2024-10, Vol.14 (1), p.24837-12, Article 24837
• Main Authors: Călburean, Paul-Adrian, Harpa, Marius, Scurtu, Anda-Cristina, Grebenișan, Paul, Nistor, Ioana-Andreea, Vacariu, Victor, Drincal, Reka-Katalin, Şulea, Ioana Paula, Oltean, Tiberiu, Mesaroş, Petru-Vasile, Hadadi, László
• Format: Article
• Language: English
• Subjects: 692/4019; 692/4019/2776; Aged; Cardiovascular disease; Cardiovascular diseases; Coronary artery disease; Coronary Artery Disease - blood; Coronary Artery Disease - mortality; Creatinine; Datasets; Female; Heart diseases; Hemoglobin; Humanities and Social Sciences; Humans; Laboratories; Laboratory tests; Lymphocytes; Machine Learning; Male; Middle Aged; Monocytes; Mortality; multidisciplinary; Patients; Percutaneous Coronary Intervention; Phenotype; Phenotypes; Phenotyping; Population studies; Predictions; Prospective Studies; Registries; Renal function; ROC Curve; Science; Science (multidisciplinary); Survival; Survival analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-76936-3

Out-of-hospital mortality in coronary artery disease (CAD) is particularly high and established adverse event prediction tools are yet to be available. Our study aimed to investigate whether precision phenotyping can be performed using routine laboratory parameters for the prediction of out-of-hospital survival in a CAD population treated by percutaneous coronary intervention (PCI). All patients treated by PCI and discharged alive in a tertiary center between January 2016 – December 2022 that have been included prospectively in the local registry were analyzed. 115 parameters from the PCI registry and 266 parameters derived from routine laboratory testing were used. An extreme gradient-boosted decision tree machine learning (ML) algorithm was trained and used to predict all-cause and cardiovascular-cause survival. A total of 4027 patients with 4981 PCI hospitalizations were randomly included in the 70% training dataset and 1729 patients with 2160 PCI hospitalizations were randomly included in the 30% validation dataset. All-cause and cardiovascular cause mortality was 17.5% and 12.2%. The integrated area under the receiver operator characteristic curve for prediction of all-cause and cardiovascular cause mortality by the ML on the validation dataset was 0.844 and 0.837, respectively (all p