The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU

Authors

Anita K. Patel, Department of Pediatrics, Division of Critical Care Medicine, Children's National Health System, George Washington University School of Medicine and Health Sciences, Washington, DC, United States.
Eduardo Trujillo-Rivera, Department of Bio-Informatics, Children's National Health System, George Washington University School of Medicine and Health Sciences, Washington, DC, United States.
Hiroki Morizono, Department of Pediatrics, Children's National Research Institute, George Washington University School of Medicine and Health Sciences, Washington, DC, United States.
Murray M. Pollack, Department of Pediatrics, Division of Critical Care Medicine, Children's National Health System, George Washington University School of Medicine and Health Sciences, Washington, DC, United States.

Document Type

Journal Article

Publication Date

1-1-2022

Journal

Frontiers in pediatrics

Volume

10

DOI

10.3389/fped.2022.1023539

Keywords

criticality index; dynamic outcome prediction; machine learning; mortality prediction; prediction algorithm

Abstract

BACKGROUND: The Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change. OBJECTIVES: To assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions. POPULATION: There were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing. MODEL: A neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression. RESULTS: Discrimination assessed across all time periods found an AUROC of 0.851 (0.841-0.862) and an AUPRC was 0.443 (0.417-0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689-0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058-0.328) and a maximum value of 0.499 (0.229-0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed clinical expectations about the trajectories of death and survivors. CONCLUSIONS: The Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.

APA Citation

Patel, Anita K.; Trujillo-Rivera, Eduardo; Morizono, Hiroki; and Pollack, Murray M., "The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU" (2022). GW Authored Works. Paper 2155.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/2155

Department

Pediatrics