• Eduardo A Trujillo Rivera, James M Chamberlain, Anita K Patel, Hiroki Morizono, Julia A Heneghan, and Murray M Pollack.
• George Washington University School of Medicine and Health Sciences, Washington, DC.
• Pediatr Crit Care Me. 2022 May 1; 23 (5): 344352344-352.

ObjectivesAssess a machine learning method of serially updated mortality risk.DesignRetrospective analysis of a national database (Health Facts; Cerner Corporation, Kansas City, MO).SettingHospitals caring for children in ICUs.PatientsA total of 27,354 admissions cared for in ICUs from 2009 to 2018.InterventionsNone.Main OutcomeHospital mortality risk estimates determined at 6-hour time periods during care in the ICU. Models were truncated at 180 hours due to decreased sample size secondary to discharges and deaths.Measurements And Main ResultsThe Criticality Index, based on physiology, therapy, and care intensity, was computed for each admission for each time period and calibrated to hospital mortality risk (Criticality Index-Mortality [CI-M]) at each of 29 time periods (initial assessment: 6 hr; last assessment: 180 hr). Performance metrics and clinical validity were determined from the held-out test sample (n = 3,453, 13%). Discrimination assessed with the area under the receiver operating characteristic curve was 0.852 (95% CI, 0.843-0.861) overall and greater than or equal to 0.80 for all individual time periods. Calibration assessed by the Hosmer-Lemeshow goodness-of-fit test showed good fit overall (p = 0.196) and was statistically not significant for 28 of the 29 time periods. Calibration plots for all models revealed the intercept ranged from--0.002 to 0.009, the slope ranged from 0.867 to 1.415, and the R2 ranged from 0.862 to 0.989. Clinical validity assessed using population trajectories and changes in the risk status of admissions (clinical volatility) revealed clinical trajectories consistent with clinical expectations and greater clinical volatility in deaths than survivors (p < 0.001).ConclusionsMachine learning models incorporating physiology, therapy, and care intensity can track changes in hospital mortality risk during intensive care. The CI-M's framework and modeling method are potentially applicable to monitoring clinical improvement and deterioration in real time.Copyright © 2022 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.

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