Derivation and external validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19
| العنوان: | Derivation and external validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19 |
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| المؤلفون: | Mann Cz, Chelsea Abshire, Johann A. Gagnon-Bartsch, Scott Kaatz, Scott A. Flanders, Hallie C. Prescott, Lakshmi Swaminathan, Monica L Yost |
| بيانات النشر: | Cold Spring Harbor Laboratory, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | medicine.medical_specialty, Framingham Risk Score, medicine.diagnostic_test, Coronavirus disease 2019 (COVID-19), business.industry, Mortality rate, Triage, Pulse oximetry, Emergency medicine, Medicine, Observational study, In patient, Derivation, business |
| الوصف: | BackgroundAs SARS-CoV-2 continues to spread, and hospitals are treating a large number of patients with COVID-19, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage.ObjectiveAs SARS-CoV-2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with COVID-19 that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals.MethodsIn this observational study we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for SARS-CoV-2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model.ResultsOur final model includes the patient’s age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital’s COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The AUC for the derivation and validation sets were .796 and .829 respectively.ConclusionsRisk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter. |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::f2349c787876b9117f82ee6bc9f7b328 https://doi.org/10.1101/2021.05.04.21256599 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........f2349c787876b9117f82ee6bc9f7b328 |
| قاعدة البيانات: | OpenAIRE |
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