Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
| العنوان: | Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study |
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| المؤلفون: | Qiao Li Wang, Yanjie Fan, Qijian Chen, Chang Li, Jiang Yue, Zhuheng Zhang, Shuwei Tian, Jin Mei, Bin Li, Weihua Hu, Qifa Ye, Zaishu Chen |
| المصدر: | BMJ Open BMJ Open, Vol 10, Iss 12 (2020) |
| بيانات النشر: | BMJ Publishing Group, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | medicine.medical_specialty, China, Calibration (statistics), Epidemiology, infectious diseases, Risk Assessment, Risk Factors, Medicine, Humans, Pandemics, Retrospective Studies, Receiver operating characteristic, business.industry, SARS-CoV-2, COVID-19, Retrospective cohort study, General Medicine, medicine.disease, Prognosis, Hospitalization, Survival Rate, Respiratory failure, ROC Curve, Heart failure, Cohort, business, Algorithm, Predictive modelling, Algorithms, Follow-Up Studies |
| الوصف: | ObjectiveThis study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.DesignRetrospective cohort study.SettingFive designated tertiary hospitals for COVID-19 in Hubei province, China.ParticipantsWe routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.MethodsThe model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measuresThe primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.ResultsThe full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.ConclusionThe prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification. |
| اللغة: | English |
| تدمد: | 2044-6055 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b794e305329adf901a4b4d6595e41cf4Test http://europepmc.org/articles/PMC7768618Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b794e305329adf901a4b4d6595e41cf4 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20446055 |
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