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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.
  • Zhu JS; Departments of Radiology, Renaissance School of Medicine Stony Brook University Stony Brook New York USA.
  • Ge P; Departments of Radiology, Renaissance School of Medicine Stony Brook University Stony Brook New York USA.
  • Jiang C; Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang Hospital Capital Medical University Beijing China.
  • Zhang Y; Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China.
  • Li X; Departments of Radiology, Renaissance School of Medicine Stony Brook University Stony Brook New York USA.
  • Zhao Z; Departments of Radiology, Renaissance School of Medicine Stony Brook University Stony Brook New York USA.
  • Zhang L; Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang Hospital Capital Medical University Beijing China.
  • Duong TQ; Departments of Radiology, Renaissance School of Medicine Stony Brook University Stony Brook New York USA.
J Am Coll Emerg Physicians Open ; 1(6): 1364-1373, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1898687
ABSTRACT

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 was 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, O2 Index, neutrophillymphocyte 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:

Deep-learning prediction model and the resultant risk stratification score may prove useful in clinical decisionmaking under time-sensitive and resource-constrained environment.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Journal: J Am Coll Emerg Physicians Open Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Journal: J Am Coll Emerg Physicians Open Year: 2020 Document Type: Article