Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 1 de 1
Filter
Add filters

Database
Language
Document Type
Year range
1.
Medicine (Baltimore) ; 100(40): e27422, 2021 Oct 08.
Article in English | MEDLINE | ID: covidwho-1462561

ABSTRACT

ABSTRACT: As severe acute respiratory syndrome coronavirus 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 2019 novel coronavirus (COVID-19) that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals.In this retrospective 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 severe acute respiratory syndrome coronavirus 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.Two thousand one hundred and ninety-three patients in the Mi-COVID19 registry met our inclusion criteria. The derivation and validation sets ultimately included 1690 and 398 patients, respectively, with mortality rates of 19.6% and 18.6%, respectively. The average age of participants in the study after exclusions was 64 years old, and the participants were 48% female, 49% Black, and 87% non-Hispanic. Our 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 area under the receiver operating characteristics curve for the derivation and validation sets were .796 (95% confidence interval, .767-.826) and .829 (95% confidence interval, .782-.876) respectively.We conclude that the risk 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.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Age Factors , Aged , Aged, 80 and over , Body Mass Index , Comorbidity , Creatinine/blood , Female , Health Behavior , Humans , Logistic Models , Male , Michigan/epidemiology , Middle Aged , Oximetry , Prognosis , ROC Curve , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Sex Factors , Socioeconomic Factors
SELECTION OF CITATIONS
SEARCH DETAIL