Selected article for: "good model and model evaluate"

Author: Wang, Ruoran; He, Min; Yin, Wanhong; Liao, Xuelian; Wang, Bo; Jin, Xiaodong; Ma, Yao; Yue, Jirong; Bai, Lang; Liu, Dan; Zhu, Ting; Huang, Zhixin; Kang, Yan
Title: The Prognostic Nutritional Index is associated with mortality of COVID‐19 patients in Wuhan, China
  • Cord-id: r0pd2o26
  • Document date: 2020_9_11
  • ID: r0pd2o26
    Snippet: BACKGROUND: Declared as pandemic by WHO, the coronavirus disease 2019 (COVID‐19) pneumonia has brought great damage to human health. The uncontrollable spread and poor progression of COVID‐19 have attracted much attention from all over the world. We designed this study to develop a prognostic nomogram incorporating Prognostic nutritional index (PNI) in COVID‐19 patients. METHODS: Patients confirmed with COVID‐19 and treated in Renmin Hospital of Wuhan University from January to February
    Document: BACKGROUND: Declared as pandemic by WHO, the coronavirus disease 2019 (COVID‐19) pneumonia has brought great damage to human health. The uncontrollable spread and poor progression of COVID‐19 have attracted much attention from all over the world. We designed this study to develop a prognostic nomogram incorporating Prognostic nutritional index (PNI) in COVID‐19 patients. METHODS: Patients confirmed with COVID‐19 and treated in Renmin Hospital of Wuhan University from January to February 2020 were included in this study. We used logistic regression analysis to find risk factors of mortality in these patients. A prognostic nomogram was constructed and receiver operating characteristics (ROC) curve was drawn to evaluate the predictive value of PNI and this prognostic model. RESULTS: Comparison of baseline characteristics showed non‐survivors had higher age (P < .001), male ratio (P = .038), neutrophil‐to‐lymphocyte ratio (NLR) (P < .001), platelet‐to‐lymphocyte ratio (PLR) (P < .001), and PNI (P < .001) than survivors. In the multivariate logistic regression analysis, independent risk factors of mortality in COVID‐19 patients included white blood cell (WBC) (OR 1.285, P = .039), PNI (OR 0.790, P = .029), LDH (OR 1.011, P < .015). These three factors were combined to build the prognostic model. Area under the ROC curve (AUC) of only PNI and the prognostic model was 0.849 (95%Cl 0.811‐0.888) and 0.950 (95%Cl 0.922‐0.978), respectively. And calibration plot showed good stability of the prognostic model. CONCLUSION: This research indicates PNI is independently associated with the mortality of COVID‐19 patients. Prognostic model incorporating PNI is beneficial for clinicians to evaluate progression and strengthen monitoring for COVID‐19 patients.

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