A Predicting Nomogram for Mortality in Patients With COVID-19 - Pan, Deng; Cheng, Dandan; Cao, Yiwei; Hu, Chuan; Zou, Fenglin; Yu, Wencheng; Xu, Tao (Frontiers in Public Health, 2020)
Backgroud: The global COVID-19 epidemic remains severe, with the cumulative global death toll reaching more than 207,170 as of 2 May 2020[1]. Purpose: To develop a reliable nomogram to predict the mortality for patients with COVID-19, which can help us distinguish between patients who are at high risk of death and need close attention. Patients and Methods: For the single-center retrospective study, we collected 21 cases of patients who died in the critical illness area of the Optical Valley Branch of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from February 9 to March 10. Meanwhile, we selected 99 patients discharged during this period for analysis. Multivariable logistic regression analysis was used to develop the predicting model, the nomogram incorporated the demographics, clinical characteristics, and laboratory results. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Results: Predictors contained in the individualized prediction nomogram included the c-reactive protein, PaO2/FiO2 and cTnI. The model showed good calibration and discrimination [AUC,0.988; 95% confidence interval (CI), 0.972–1.000]. Decision curve analysis demonstrated that the nomogram was clinically useful possibly. Conclusion: This study presents a nomogram that incorporates the c-reactive protein, PaO2/FiO2 and cTnI, which can be conveniently used to facilitate the individualized prediction of mortality in patients with COVID-19. Our study have some limitations, the study was a single-center retrospective study with small sample, and the next step is to include as many cases as possible in multiple centers to gain a more complete understanding of the mortality in COVID-19 patients.