Machine learning score to predict in-hospital outcomes in patients hospitalized in cardiac intensive care unit
Authors: Weizman Orianne , Hamzi Kenza , Henry Patrick , Schurtz Guillaume , Hauguel-Moreau Marie , Trimaille Antonin , Bedossa Marc , Dib Jean Claude , Attou Sabir , Boukertouta Tanissia , Boccara Franck , Pommier Thibaut , Lim Pascal , Bochaton Thomas , Millischer Damien , Merat Benoit , Picard Fabien , Grinberg Nissim , Sulman David , Pasdeloup Bastien , El Ouahidi Yassine , Gonçalves Treçy , Vicaut Eric , Dillinger Jean-Guillaume , Toupin Solenn , Pezel Théo ,   ADDICT-ICCU Investigators, Aboyans Victor , Albert Emeric , Albert Franck , Alvain Sean , Amri Nabil , Andrieu Stéphane , Attou Sabir , Auvray Simon , Azzakani Sonia , Azencot Ruben , Bedossa Marc , Boccara Franck , Boccara Albert , Bochaton Thomas , Bonnefoy-Cudraz Eric , Bonnet Guillaume , Bonnet Guillaume , Bouali Nabil , Bouchot Océane , Bouleti Claire , Boukertouta Tanissia , Brette Jean Baptiste , Canu Marjorie , Chaib Aures , Charbonnel Clement , Chaussade Anne Solene , Coppens Alexandre , Cottin Yves , Darmon Arthur , De ANGELIS Elena , Delmas Clément , Delsarte Laura , Deney Antoine , Dib Jean Claude , Dillinger Jean-Guillaume , Docq Clemence , Dupasquier Valentin , Elbaz Meyer , El Hadad Antony , El Ouahidi Amine , Ezzouhairi Nacim , Fabre Julien , Fard Damien , Fauvel Charles , Gerbaud Édouard , Gilard Martine , Goralski Marc , Grinberg Nissim , Grentzinger Alain , Hauguel-Moreau Marie , Henry Patrick , Huet Fabien , Landemaine Thomas , Lattuca Benoit , Lemarchand Léo , Levasseur Thomas , Lim Pascal , Maitre Ballesteros Laura , Mansencal Nicolas , Marie Benjamin , Martinez David , Merat Benoit , Meune Christophe , Millischer Damien , Moine Thomas , Nhan Pascal , Noirclerc Nathalie , Ohlmann Patrick , Pezel Théo , Picard Fabien , Piliero Nicolas , Pommier Thibaut , Puymirat Etienne , Ramonatxo Arthur , Rossanaly Vasram Reza , Roubille François , Roule Vincent , Schurtz Guillaume , Stevenard Mathilde , Sulman David , Swedsky Fédérico , Tea Victoria , Thevenet Eugénie , Thuaire Christophe , Trimaille Antonin , Tron Christophe , Viboud Guillaume , Yomi Dominique , Zakine Cyril 
Published: December 2024

Abstract

Aims
Although some scores based on traditional statistical methods are available for risk stratification in patients hospitalized in cardiac intensive care units (CICUs), the interest of machine learning (ML) methods for risk stratification in this field is not well established. We aimed to build an ML model to predict in-hospital major adverse events (MAE) in patients hospitalized in CICU.

Methods and results
In April 2021, a French national prospective multicentre study involving 39 centres included all consecutive patients admitted to CICU. The primary outcome was in-hospital MAE, including death, resuscitated cardiac arrest, or cardiogenic shock. Using 31 randomly assigned centres as an index cohort (divided into training and testing sets), several ML models were evaluated to predict in-hospital MAE. The eight remaining centres were used as an external validation cohort. Among 1499 consecutive patients included (aged 64 ± 15 years, 70% male), 67 had in-hospital MAE (4.3%). Out of 28 clinical, biological, ECG, and echocardiographic variables, seven were selected to predict MAE in the training set (n = 844). Boosted cost-sensitive C5.0 technique showed the best performance compared with other ML methods [receiver operating characteristic area under the curve (AUROC) = 0.90, precision–recall AUC = 0.57, F1 score = 0.5]. Our ML score showed a better performance than existing scores (AUROC: ML score = 0.90 vs. Thrombolysis In Myocardial Infarction (TIMI) score: 0.56, Global Registry of Acute Coronary Events (GRACE) score: 0.52, Acute Heart Failure (ACUTE-HF) score: 0.65; all P &lt; 0.05). Machine learning score also showed excellent performance in the external cohort (AUROC = 0.88).

Conclusion
This new ML score is the first to demonstrate improved performance in predicting in-hospital outcomes over existing scores in patients admitted to the intensive care unit based on seven simple and rapid clinical and echocardiographic variables.

Trial Registration
ClinicalTrials.gov Identifier: NCT05063097.