| Autor: |
Yamga E; Department of Medicine Centre Hospitalier de l'Université de Montréal (CHUM) Montreal QC Canada., Mantena S; Harvard Medical School Boston MA USA., Rosen D; Johns Hopkins School of Medicine Baltimore MD USA., Bucholz EM; University of Colorado School of Medicine Aurora CO USA.; Heart Institute, Children's Hospital of Colorado Aurora CO USA., Yeh RW; Harvard Medical School Boston MA USA.; Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology Beth Israel Deaconess Medical Center Boston MA USA., Celi LA; Harvard Medical School Boston MA USA.; Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology Cambridge MA USA.; Department of Biostatistics, Harvard T.H. Chan School of Public Health Boston MA USA., Ustun B; Halıcıoğlu Data Science Institute University of California San Diego CA USA., Butala NM; University of Colorado School of Medicine Aurora CO USA.; Rocky Mountain Regional VA Medical Center Aurora CO USA. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of the American Heart Association [J Am Heart Assoc] 2023 Jul 04; Vol. 12 (13), pp. e029232. Date of Electronic Publication: 2023 Jun 22. |
| DOI: |
10.1161/JAHA.122.029232 |
| Abstrakt: |
Background Mortality prediction in critically ill patients with cardiogenic shock can guide triage and selection of potentially high-risk treatment options. Methods and Results We developed and externally validated a checklist risk score to predict in-hospital mortality among adults admitted to the cardiac intensive care unit with Society for Cardiovascular Angiography & Interventions Shock Stage C or greater cardiogenic shock using 2 real-world data sets and Risk-Calibrated Super-sparse Linear Integer Modeling (RiskSLIM). We compared this model to those developed using conventional penalized logistic regression and published cardiogenic shock and intensive care unit mortality prediction models. There were 8815 patients in our training cohort (in-hospital mortality 13.4%) and 2237 patients in our validation cohort (in-hospital mortality 22.8%), and there were 39 candidate predictor variables. The final risk score (termed BOS,MA 2 ) included maximum blood urea nitrogen ≥25 mg/dL, minimum oxygen saturation <88%, minimum systolic blood pressure <80 mm Hg, use of mechanical ventilation, age ≥60 years, and maximum anion gap ≥14 mmol/L, based on values recorded during the first 24 hours of intensive care unit stay. Predicted in-hospital mortality ranged from 0.5% for a score of 0 to 70.2% for a score of 6. The area under the receiver operating curve was 0.83 (0.82-0.84) in training and 0.76 (0.73-0.78) in validation, and the expected calibration error was 0.9% in training and 2.6% in validation. Conclusions Developed using a novel machine learning method and the largest cardiogenic shock cohorts among published models, BOS,MA 2 is a simple, clinically interpretable risk score that has improved performance compared with existing cardiogenic-shock risk scores and better calibration than general intensive care unit risk scores. |
| Databáze: |
MEDLINE |
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