Details

Autor(en) / Beteiligte

• Dong, Yi-Min
• Sun, Jia
• Li, Yi-Xin
• Chen, Qian
• Liu, Qing-Quan
• Sun, Zhou
• Pang, Ran
• Chen, Fei
• Xu, Bing-Yang
• Manyande, Anne
• Clark, Taane G
• Li, Jin-Ping
• Orhan, Ilkay Erdogan
• Tian, Yu-Ke
• Wang, Tao
• Wu, Wei
• Ye, Da-Wei

Titel

Development and Validation of a Nomogram for Assessing Survival in Patients With COVID-19 Pneumonia

Ist Teil von

• Clinical infectious diseases, 2021-02, Vol.72 (4), p.652-660

Ort / Verlag

US: Oxford University Press

Erscheinungsjahr

2021

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• Abstract Background The outbreak of coronavirus disease 2019 (COVID-19) has spread worldwide and continues to threaten peoples’ health as well as put pressure on the accessibility of medical systems. Early prediction of survival of hospitalized patients will help in the clinical management of COVID-19, but a prediction model that is reliable and valid is still lacking. Methods We retrospectively enrolled 628 confirmed cases of COVID-19 using positive RT-PCR tests for SARS-CoV-2 in Tongji Hospital, Wuhan, China. These patients were randomly grouped into a training (60%) and a validation (40%) cohort. In the training cohort, LASSO regression analysis and multivariate Cox regression analysis were utilized to identify prognostic factors for in-hospital survival of patients with COVID-19. A nomogram based on the 3 variables was built for clinical use. AUCs, concordance indexes (C-index), and calibration curves were used to evaluate the efficiency of the nomogram in both training and validation cohorts. Results Hypertension, higher neutrophil-to-lymphocyte ratio, and increased NT-proBNP values were found to be significantly associated with poorer prognosis in hospitalized patients with COVID-19. The 3 predictors were further used to build a prediction nomogram. The C-indexes of the nomogram in the training and validation cohorts were 0.901 and 0.892, respectively. The AUC in the training cohort was 0.922 for 14-day and 0.919 for 21-day probability of in-hospital survival, while in the validation cohort this was 0.922 and 0.881, respectively. Moreover, the calibration curve for 14- and 21-day survival also showed high coherence between the predicted and actual probability of survival. Conclusions We built a predictive model and constructed a nomogram for predicting in-hospital survival of patients with COVID-19. This model has good performance and might be utilized clinically in management of COVID-19. A nomogram consisting of hypertension, neutrophil-lymphocyte ratio, and NT-proBNP is an effective tool for predicting in-hospital survival probability of patients with coronavirus disease 2019 (COVID-19). This nomogram could be used to optimize the clinical management of COVID-19.