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

[tetano]

Ann Transl Med


. 2020 Dec;8(23):1585.
doi: 10.21037/atm-20-4977.
A predictive model for respiratory distress in patients with COVID-19: a retrospective study


Xin Zhang ^{1 2 3 4} , Wei Wang ⁵ , Cheng Wan ^{1 3} , Gong Cheng ⁶ , 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 Ge ⁴ , Ying Chen ⁴ , Yun Liu ¹



Affiliations

• PMID: 33437784
• PMCID: PMC7791231
• DOI: 10.21037/atm-20-4977

Free PMC article

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).