A predictive model for respiratory distress in patients with COVID-19: a retrospective study

Xin Zhang; Wei Wang; Cheng Wan; Gong Cheng; Yuechuchu Yin; Kaidi Cao; Xiaoliang Zhang; Zhongmin Wang; Shumei Miao; Yun Yu; Jie Hu; Ruochen Huang; Yun Ge; Ying Chen; Yun Liu

doi:10.21037/atm-20-4977

A predictive model for respiratory distress in patients with COVID-19: a retrospective study

Ann Transl Med. 2020 Dec;8(23):1585. doi: 10.21037/atm-20-4977.

Authors

Xin Zhang^{1

2

3

4}, Wei Wang⁵, Cheng Wan^{1

3}, Gong Cheng⁶, Yuechuchu Yin^{1

2

3}, Kaidi Cao^{1

2

3}, Xiaoliang Zhang^{1

2

3}, Zhongmin Wang^{1

2

3}, Shumei Miao^{1

2

3}, Yun Yu^{1

3}, Jie Hu^{1

3}, Ruochen Huang^{1

2

3}, Yun Ge⁴, Ying Chen⁴, Yun Liu¹

Affiliations

¹ Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
² Department of Information, the First Affiliated Hospital, Nanjing Medical University, Nanjing, China.
³ Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, China.
⁴ School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
⁵ Network Information Center, Wuhan No. 1 Hospital, Wuhan, China.
⁶ State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.

Abstract

Background: Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis. We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19.

Methods: We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission. These variables were analyzed using Kaplan-Meier and Cox proportional hazards (PHs) analyses. Hazard ratios (HRs) and performance of the final model were determined.

Results: Neutrophil count >6.3×10⁹/L, D-dimer level ≥1.00 mg/L, and temperature ≥37.3 °C at admission showed significant positive association with the outcome of respiratory distress in the final model. Complement C3 (C3) of 0.9-1.8 g/L, platelet count >350×10⁹/L, and platelet count of 125-350×10⁹/L showed a significant negative association with outcomes of respiratory distress in the final model. The final model had a C statistic of 0.891 (0.867-0.915), an Akaike's information criterion (AIC) of 567.65, and a bootstrap confidence interval (CI) of 0.866 (0.842-0.89). This five-factor model could help in early allocation of medical resources.

Conclusions: The predictive model based on the five factors obtained at admission can be applied for calculating the risk of respiratory distress and classifying patients at an early stage. Accordingly, high-risk patients can receive timely and effective treatment, and health resources can be allocated effectively.

Keywords: Coronavirus disease 2019 (COVID-19); coronavirus; predictive model; respiratory distress; severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).