Derivation and external validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19: A multicenter retrospective cohort study.

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.

Protocol - Evaluating the Effect of ACA Medicaid Expansion on Mortality During the COVID-19 Pandemic Using County-level Matching.

States are able to choose whether to expand Medicaid as part of the Affordable Care Act (ACA); thus it is of interest to understand the impact of this policy choice. In this protocol, we outline a study on the impact of Medicaid expansion as part of the ACA on mortality during the COVID-19 pandemic in the United States. County-level matching using full, optimal matching with a propensity score model is used to estimate causal effects in this observational study. Due to the provisional nature of mortality data in 2020 as reported by the CDC, we outline a modified aligned rank test to account for censored data as well as reporting lags for different states. We aim to make connections between statistical and ethnographic methodologies by particularly examining adjacent counties and similar counties that are in the same region of the US and in vastly different regions of the US. Finally, we aim to add to the growing literature about the effect of ACA Medicaid expansion on mortality by calculating effects, disaggregating by race.