Development and External Validation of a Multimodal Artificial Intelligence Mortality Prediction Model of Critically Ill Patients Using Multicenter Data
4 affiliations
- 1Department of Biomedical Informatics, University of California, San Diego Health, La Jolla, California; Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, California.
- 2Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, California.
- 3Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, California; Division of Critical Care Medicine, Department of Anesthesiology, University of California, San Diego, La Jolla, California.
- 4Division of Critical Care Medicine, Department of Anesthesiology, University of California, San Diego, La Jolla, California.
Abstract
Background:: Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep-learning model, using structured and unstructured clinical data, to predict in-hospital mortality risk among critically ill patients after their initial 24 h intensive care unit (ICU) admission.
Methods:: This study used data from Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, the Electronic Intensive Care Unit Collaborative Research Database (eICU), and the high time-resolution ICU data set (HiRID). A multimodal model was developed on the MIMIC data sets, featuring time series components occurring within the first 24 h of ICU admission and predicting risk of subsequent inpatient mortality. Inputs included time-invariant variables, time-variant variables, clinical notes, and chest x-ray images. External validation occurred in a temporally separated MIMIC population, the high time-resolution ICU data set, and the eICU data set. Area under the receiver operating characteristics (AUROC), area under the precision–recall curves (AUPRC), and Brier scores were reported.
Results:: A total of 203,434 ICU admissions from more than 200 hospitals between 2001 to 2022 were included, in which mortality rate ranged from 5.2 to 7.9% across the four data sets. The model integrating structured data points had AUROC, AUPRC, and Brier scores of 0.92 (95% CI, 0.90 to 0.93), 0.53 (95% CI, 0.49 to 0.57), and 0.19 (95% CI, 0.18 to 0.20), respectively. The model was externally validated on eight different institutions within the eICU data set, demonstrating AUROCs ranging from 0.84 to 0.92. When including only patients with available clinical notes and imaging data, the AUROC, AUPRC, and Brier score improved from 0.87 (95% CI, 0.85 to 0.89) to 0.89 (95% CI, 0.87 to 0.91), 0.43 [(95% CI, 0.36 to 0.51) to 0.48 (95% CI, 0.40 to 0.56), and 0.37 (95% CI, 0.36 to 0.39) to 0.17 (95% CI, 0.16 to 0.19), respectively. The difference in AUROC was statistically significant based on a DeLong test (P = 0.0167).
Conclusions:: These findings highlight the importance of incorporating multiple sources of patient information for mortality prediction and the importance of external validation.
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BibTeX
@article{mamandipoor2026development,
title = {{Development and External Validation of a Multimodal Artificial Intelligence Mortality Prediction Model of Critically Ill Patients Using Multicenter Data}},
author = {Behrooz Mamandipoor and Chun-Nan Hsu and Martin Krause and Ulrich H. Schmidt and Rodney A. Gabriel},
journal = {Anesthesiology},
year = {2026},
month = aug,
doi = {10.1097/aln.0000000000006294},
url = {https://doi.org/10.1097/aln.0000000000006294},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Development and External Validation of a Multimodal Artificial Intelligence Mortality Prediction Model of Critically Ill Patients Using Multicenter Data
AU - Behrooz Mamandipoor
AU - Chun-Nan Hsu
AU - Martin Krause
AU - Ulrich H. Schmidt
AU - Rodney A. Gabriel
PY - 2026
DA - 2026/08/04
JO - Anesthesiology
DO - 10.1097/aln.0000000000006294
UR - https://doi.org/10.1097/aln.0000000000006294
AB - Background:: Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep-learning model, using structured and unstructured clinical data, to predict in-hospital mortality risk among critically ill patients after their initial 24 h intensive care unit (ICU) admission. Methods:: This study used data from Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, the Electronic Intensive Care Unit Collaborative Research Database (eICU), and the high time-resolution ICU data set (HiRID). A multimodal model was developed on the MIMIC data sets, featuring time series components occurring within the first 24 h of ICU admission and predicting risk of subsequent inpatient mortality. Inputs included time-invariant variables, time-variant variables, clinical notes, and chest x-ray images. External validation occurred in a temporally separated MIMIC population, the high time-resolution ICU data set, and the eICU data set. Area under the receiver operating characteristics (AUROC), area under the precision–recall curves (AUPRC), and Brier scores were reported. Results:: A total of 203,434 ICU admissions from more than 200 hospitals between 2001 to 2022 were included, in which mortality rate ranged from 5.2 to 7.9% across the four data sets. The model integrating structured data points had AUROC, AUPRC, and Brier scores of 0.92 (95% CI, 0.90 to 0.93), 0.53 (95% CI, 0.49 to 0.57), and 0.19 (95% CI, 0.18 to 0.20), respectively. The model was externally validated on eight different institutions within the eICU data set, demonstrating AUROCs ranging from 0.84 to 0.92. When including only patients with available clinical notes and imaging data, the AUROC, AUPRC, and Brier score improved from 0.87 (95% CI, 0.85 to 0.89) to 0.89 (95% CI, 0.87 to 0.91), 0.43 [(95% CI, 0.36 to 0.51) to 0.48 (95% CI, 0.40 to 0.56), and 0.37 (95% CI, 0.36 to 0.39) to 0.17 (95% CI, 0.16 to 0.19), respectively. The difference in AUROC was statistically significant based on a DeLong test (P = 0.0167). Conclusions:: These findings highlight the importance of incorporating multiple sources of patient information for mortality prediction and the importance of external validation.
SN - 0003-3022
SN - 1528-1175
ER -