期刊
JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY
卷 164, 期 1, 页码 211-+出版社
MOSBY-ELSEVIER
DOI: 10.1016/j.jtcvs.2021.10.060
关键词
machine learning; electronic health records; univentricular heart; extracorporeal membrane oxygenation; cardiopulmonary resuscitation; intubation; intratracheal
资金
- Children's Hospital of Philadelphia (CHOP)
- Richard King Mellon Foundation [MWRIF 3659]
- National Institutes of Health/National Heart, Lung, and Blood Institute Small Business Innovation Research Program [2R44HL117340-03A1/04/05]
This study developed and evaluated a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration using routinely collected electronic health record (EHR) data. The model accurately predicted deterioration events up to 8 hours in advance.
Objectives: To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data. Materials and Methods: In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease <6 months old were admitted to the cardiac intensive care unit before stage 2 palliation between 2014 and 2019. Using machine-learning techniques, we developed the Intensive care Warning Index (I-WIN), which systematically assessed 1028 regularly collected EHR variables (vital signs, medications, laboratory tests, and diagnoses) to identify patients in the cardiac intensive care unit at elevated risk of clinical deterioration. An ensemble of 5 extreme gradient boosting models was developed and validated on 203 cases (130 emergent endotracheal intubations, 34 cardiac arrests requiring cardiopulmonary resuscitation, 10 extracorporeal membrane oxygenation cannulations, and 29 cardiac arrests requiring cardiopulmonary resuscitation onto extracorporeal membrane oxygenation) and 378 control periods from 446 patients. Results: At 4 hours before deterioration, the model achieved an area under the receiver operating characteristic curve of 0.92 (95% confidence interval, 0.84-0.98), 0.881 sensitivity, 0.776 positive predictive value, 0.862 specificity, and 0.571 Brier skill score. Performance remained high at 8 hours before deterioration with 0.815 (0.688-0.921) area under the receiver operating characteristic curve. Conclusions: I-WIN accurately predicted deterioration events in critically-ill infants with high-risk congenital heart disease up to 8 hours before deterioration, potentially allowing clinicians to target interventions. We propose a paradigm shift from conventional expert consensus-based selection of risk factors to a data-driven, machine-learning methodology for risk prediction. With the increased availability of data capture in EHRs, I-WIN can be extended to broader applications in data-rich environments in critical care.
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