Deep‐learning artificial intelligence analysis of clinical variables predicts mortality in COVID‐19 patients

Zhu, Jocelyn S; Ge, Peilin; Jiang, Chunguo; Zhang, Yong; Li, Xiaoran; Zhao, Zirun; Zhang, Liming; Duong, Tim Q

STUDY OBJECTIVE: The large number of clinical variables associated with coronavirus disease 2019 (COVID‐19) infection makes it challenging for frontline physicians to effectively triage COVID‐19 patients during the pandemic. This study aimed to develop an efficient deep‐learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID‐19 patients. METHODS: This retrospective study consisted of 181 hospitalized patients with confirmed COVID‐19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep‐learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance were compared with those using COVID‐19 severity score, CURB‐65 score and pneumonia severity index (PSI). RESULTS: Of the 181 COVID‐19 patients, 39 expired and 142 survived. Five top predictors of mortality were D‐dimer, O(2) Index, neutrophil:lymphocyte ratio, C‐reactive protein, and lactate dehydrogenase. The top 5 predictors and the resultant risk score yielded, respectively, an area under curve (AUC) of 0.968 ([95% CI:0.87‐1.0]) and 0.954 ([95% CI:0.80‐0.99]) for the testing dataset. Our models outperformed COVID‐19 severity score (AUC = 0.756), CURB‐65 score (AUC = 0.671), and PSI (AUC = 0.838). The mortality rates for our risk stratification scores (0‐5) were 0, 0, 6.7, 18.2, 67.7, and 83.3%, respectively. CONCLUSIONS AND RELEVANCE: Deep‐learning prediction model and the resultant risk stratification score may prove useful in clinical decision‐making under time‐sensitive and resource‐constrained environment. This article is protected by copyright. All rights reserved