Prediction of In-Hospital Cardiac Arrest in the Intensive CareUnit: Machine Learning-Based Multimodal Approach

Background: Early identification of impending in-hospital cardiac arrest (IHCA) improves clinical outcomes but remains elusive for practicing clinicians. Objective: We aimed to develop a multimodal machine learning algorithm based on ensemble techniques to predict the occurrence of IHCA. Methods: Our model was developed by the Multiparameter Intelligent Monitoring of Intensive Care (MIMIC)-IV databaseand validated in the Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Baseline features consistingof patient demographics, presenting illness, and comorbidities were collected to train a random forest model. Next, vital signswere extracted to train a long short-term memory model. A support vector machine algorithm then stacked the results to formthe final prediction model. Results: Of 23,909 patients in the MIMIC-IV database and 10,049 patients in the eICU-CRD database, 452 and 85 patients,respectively, had IHCA. At 13 hours in advance of an IHCA event, our algorithm had already demonstrated an area underthe receiver operating characteristic curve of 0.85 (95% CI 0.815-0.885) in the MIMIC-IV database. External validation withthe eICU-CRD and National Taiwan University Hospital databases also presented satisfactory results, showing area under thereceiver operating characteristic curve values of 0.81 (95% CI 0.763-0.851) and 0.945 (95% CI 0.934-0.956), respectively. Conclusions: Using only vital signs and information available in the electronic medical record, our model demonstrates it is possible to detect a trajectory of clinical deterioration up to 13 hours in advance. This predictive tool, which has under goneexternal validation, could forewarn and help clinicians identify patients in need of assessment to improve their overall prognosis.