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
Abstract OBJECTIVE To develop and validate a prognostic model for in-hospital mortality in COVID-19 patients using routinely collected demographic and clinical characteristics.DESIGN Multicenter, retrospective cohort study.SETTING Jinyintan Hospital, Union Hospital, and Tongji Hosptial in Wuhan, China.PARTICIPANTS A pooled derivation cohort of 1008 COVID-19 patients from Jinyintan Hospital, Union Hospital in Wuhan and an external validation cohort of 1031 patients from Tongji Hospital in Wuhan, China.MAIN OUTCOME MEASURES Outcome of interest was in-hospital mortality, treating discharged alive from hospital as the competing event. Fine-Gray models, using backward elimination for inclusion of predictor variables and allowing non-linear effects of continuous variables, were used to derive a prognostic model for predicting in-hospital mortality among COVID-19 patients. Internal validation was implemented to check model overfitting using bootstrap approach. External validation to a separate hospital was implemented to evaluate the generalizability of the model.RESULTS The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (n=1008, 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 (n=1031, 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 survival curves were close to the observed survival curves across patients with different risk profiles.CONCLUSIONS The PLANS model based on the five routinely collected demographic and clinical characteristics (platelet count, lymphocyte count, age, neutrophil count, and sex) showed excellent discriminative and calibration accuracy in predicting in-hospital mortality in COVID-19 patients. This prognostic model would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality..
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Preprint| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
bioRxiv.org - (2021) vom: 14. Jan. Zur Gesamtaufnahme - year:2021 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Jiong [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2020.05.13.20100370 |
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| PPN (Katalog-ID): |
XBI017915686 |
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| 520 | |a Abstract OBJECTIVE To develop and validate a prognostic model for in-hospital mortality in COVID-19 patients using routinely collected demographic and clinical characteristics.DESIGN Multicenter, retrospective cohort study.SETTING Jinyintan Hospital, Union Hospital, and Tongji Hosptial in Wuhan, China.PARTICIPANTS A pooled derivation cohort of 1008 COVID-19 patients from Jinyintan Hospital, Union Hospital in Wuhan and an external validation cohort of 1031 patients from Tongji Hospital in Wuhan, China.MAIN OUTCOME MEASURES Outcome of interest was in-hospital mortality, treating discharged alive from hospital as the competing event. Fine-Gray models, using backward elimination for inclusion of predictor variables and allowing non-linear effects of continuous variables, were used to derive a prognostic model for predicting in-hospital mortality among COVID-19 patients. Internal validation was implemented to check model overfitting using bootstrap approach. External validation to a separate hospital was implemented to evaluate the generalizability of the model.RESULTS The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (n=1008, 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 (n=1031, 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 survival curves were close to the observed survival curves across patients with different risk profiles.CONCLUSIONS The PLANS model based on the five routinely collected demographic and clinical characteristics (platelet count, lymphocyte count, age, neutrophil count, and sex) showed excellent discriminative and calibration accuracy in predicting in-hospital mortality in COVID-19 patients. This prognostic model would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality. | ||
| 700 | 1 | |a Chen, Yuntao |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Shujing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Sihua |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Dingyu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Junfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Postmus, Douwe |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Hesong |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Guoyou |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Yin |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Jinjun |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Yongfu |e verfasserin |4 aut | |
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