NSTEMI-CARE: A Calibrated Multilayer Decision-Support Tool for Early Level-of-Care Assignment in Non-ST-Segment Elevation Myocardial Infarction

Background: Patients with non-ST-segment elevation myocardial infarction (NSTEMI) are frequently admitted to intensive care or monitored settings despite heterogeneous risk of ICU-requiring complications. We developed NSTEMI-CARE, a calibrated multilayer decision-support algorithm for early hospital level-of-care assignment. Methods: We conducted a retrospective cohort study of consecutive patients with NSTEMI and angiographically confirmed obstructive coronary artery disease admitted between 1 July 2017 and 31 December 2020 to a cardiovascular referral center in Bogotá, Colombia. The primary outcome was a composite of ICU-requiring complications. Candidate machine-learning models were evaluated for discrimination and calibration. The selected probabilistic model was integrated with predefined clinical triggers and a Gray-Zone decision rule to generate a binary recommendation for general ward versus ICU/monitored care. Results: Among 981 patients, 155 (15.8%) experienced the primary outcome. The isotonic-calibrated HistGradientBoosting model achieved an AUC of 0.821, average precision of 0.618, and Brier score of 0.092 during five-fold out-of-fold internal validation. The final algorithm assigned 464 patients (47.3%) to general ward care and 517 (52.7%) to ICU/monitored care. The observed event rate among ward-assigned patients was 3.9%. Sensitivity was 88.4%, specificity 54.0%, positive predictive value 26.5%, and negative predictive value 96.1%. In secondary comparative analysis, NSTEMI-CARE showed greater discrimination than ACTION-ICU (AUC 0.821 vs. 0.645; p < 0.001). Conclusions: NSTEMI-CARE identified a substantial subgroup potentially suitable for general ward management while maintaining a high negative predictive value. By integrating calibrated risk estimation with explicit clinical decision rules, the algorithm provides a clinically interpretable framework for early level-of-care assignment in NSTEMI. External and prospective validation is required before clinical implementation.

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Journal
Journal of Personalized Medicine
Published
2026-10-22
DOI
https://doi.org/10.3390/jpm16110539
Primary Topic
Acute Myocardial Infarction Research
Type
article
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article

NSTEMI-CARE: A Calibrated Multilayer Decision-Support Tool for Early Level-of-Care Assignment in Non-ST-Segment Elevation Myocardial Infarction

Daniel Isaza, Nicolás Ariza Ordoñez, David Gabriel David-Pardo, R. Reyes et al.
Journal of Personalized Medicine
Acute Myocardial Infarction Research
article

NSTEMI-CARE: A Calibrated Multilayer Decision-Support Tool for Early Level-of-Care Assignment in Non-ST-Segment Elevation Myocardial Infarction

Daniel Isaza, Nicolás Ariza Ordoñez, David Gabriel David-Pardo, R. Reyes, Karen Lorena Rangel-Rivera, Juan Felipe Vásquez, José Jovany Puentes-Castrillón, Carolina Idrovo-Turbay, Juan Pablo Estrada-Maya, Ramon Medina-Mur, Valeria Ramirez-Lovera
article en

Abstract

Background: Patients with non-ST-segment elevation myocardial infarction (NSTEMI) are frequently admitted to intensive care or monitored settings despite heterogeneous risk of ICU-requiring complications. We developed NSTEMI-CARE, a calibrated multilayer decision-support algorithm for early hospital level-of-care assignment. Methods: We conducted a retrospective cohort study of consecutive patients with NSTEMI and angiographically confirmed obstructive coronary artery disease admitted between 1 July 2017 and 31 December 2020 to a cardiovascular referral center in Bogotá, Colombia. The primary outcome was a composite of ICU-requiring complications. Candidate machine-learning models were evaluated for discrimination and calibration. The selected probabilistic model was integrated with predefined clinical triggers and a Gray-Zone decision rule to generate a binary recommendation for general ward versus ICU/monitored care. Results: Among 981 patients, 155 (15.8%) experienced the primary outcome. The isotonic-calibrated HistGradientBoosting model achieved an AUC of 0.821, average precision of 0.618, and Brier score of 0.092 during five-fold out-of-fold internal validation. The final algorithm assigned 464 patients (47.3%) to general ward care and 517 (52.7%) to ICU/monitored care. The observed event rate among ward-assigned patients was 3.9%. Sensitivity was 88.4%, specificity 54.0%, positive predictive value 26.5%, and negative predictive value 96.1%. In secondary comparative analysis, NSTEMI-CARE showed greater discrimination than ACTION-ICU (AUC 0.821 vs. 0.645; p < 0.001). Conclusions: NSTEMI-CARE identified a substantial subgroup potentially suitable for general ward management while maintaining a high negative predictive value. By integrating calibrated risk estimation with explicit clinical decision rules, the algorithm provides a clinically interpretable framework for early level-of-care assignment in NSTEMI. External and prospective validation is required before clinical implementation.

Journal of Personalized MedicineVol. 16(11)
Universidad Industrial de Santander (CO), Universidad El Bosque (CO), Military University Nueva Granada (CO), Institute of Cardiology (CO), Universidad del Rosario (CO)
Openalex Percentile: Top 12%
Acute Myocardial Infarction Research
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