TY - JOUR
T1 - Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China
T2 - the PLANS (platelet lymphocyte age neutrophil sex) model
AU - Li, Jiong
AU - Chen, Yuntao
AU - Chen, Shujing
AU - Wang, Sihua
AU - Zhang, Dingyu
AU - Wang, Junfeng
AU - Postmus, Douwe
AU - Zeng, Hesong
AU - Qin, Guoyou
AU - Shen, Yin
AU - Jiang, Jinjun
AU - Yu, Yongfu
N1 - Funding Information:
This study was funded by the National Nature Science Foundation of China (81870062 to Jinjun Jiang, 81900038 to Shujing Chen, 82073570 to Jiong Li, and 11871164 to Guoyou Qin), COVID-19 Emergency Response Project of Wuhan Science and Technology Department (2020020201010018 to Yin Shen), and National Key R&D Program of China (2017YFE0103400 to Yin Shen). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Funding Information:
We would like to show our respect and gratitude to all the health workers who are at the front line of the outbreak response and fighting against COVID-19 in China. The study was approved by Jinyintan Hospital Ethics Committee (KY-2020-06.01), Union Hospital Ethics Committee (2020–0039), and the Institutional Review Board of Tongji Hospital, China (No. TJ-C20200140). Written informed consent was waived by these three Ethics Committees due to the emergence of this infectious diseases. Administrative permissions to access the raw data were granted by Jinyintan Hospital Ethics Committee, Union Hospital Ethics Committee, and the Institutional Review Board of Tongji Hospital. Data were anonymized for this study.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/17
Y1 - 2020/12/17
N2 - BACKGROUND: Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions.METHODS: Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031).RESULTS: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles.CONCLUSIONS: The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.
AB - BACKGROUND: Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions.METHODS: Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031).RESULTS: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles.CONCLUSIONS: The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.
KW - COVID-19
KW - In-hospital mortality
KW - PLANS
KW - Prognostic model
UR - https://www.scopus.com/pages/publications/85097685092
U2 - 10.1186/s12879-020-05688-y
DO - 10.1186/s12879-020-05688-y
M3 - Article
C2 - 33334318
SN - 1471-2334
VL - 20
SP - 1
EP - 10
JO - BMC Infectious Diseases
JF - BMC Infectious Diseases
IS - 1
M1 - 959
ER -