Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (platelet lymphocyte age neutrophil sex) model.
Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (platelet lymphocyte age neutrophil sex) model.
复制标题
用于预测中国武汉市 COVID-19 住院患者院内死亡率的预后模型的推导和验证：PLANS（血小板淋巴细胞年龄中性粒细胞性别）模型。
DOI：
10.1186/s12879-020-05688-y
复制
发表时间：
2020-12-17
影响因子：
3.7
通讯作者：
Yu Y
中科院分区：
文献类型：
--
作者：
Li J;Chen Y;Chen S;Wang S;Zhang D;Wang J;Postmus D;Zeng H;Qin G;Shen Y;Jiang J;Yu Y
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. 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). 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. 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.
登录
查看更多内容
影响因子：
2
作者：
Austin, Peter C.;Lee, Douglas S.;D'Agostino, Ralph B.;Fine, Jason P.
通讯作者：
Fine, Jason P.
DOI：
10.15557/pimr.2020.0003
发表时间：
2020-01-01
影响因子：
3.6
作者：
通讯作者：
--
影响因子：
5.4
作者：
Hainmueller, Jens
通讯作者：
Hainmueller, Jens
影响因子：
56.9
作者：
Kissler, Stephen M.;Tedijanto, Christine;Lipsitch, Marc
通讯作者：
Lipsitch, Marc
影响因子：
24.3
作者：
Guan, Wei-jie;Liang, Wen-hua;He, Jian-xing
通讯作者：
He, Jian-xing