Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs

Abstract

The sudden emergence of COVID-19 has brought significant challenges to the care of Veterans. An improved ability to predict a patient's clinical course would facilitate optimal care decisions, resource allocation, family counseling, and strategies for safely easing distancing restrictions. The Care Assessment Need (CAN) score is an existing risk assessment tool within the Veterans Health Administration (VA), and produces a score from 0 to 99, with a higher score correlating to a greater risk. The model was originally designed for the nonacute outpatient setting and is automatically calculated from structured data variables in the electronic health record. This multisite retrospective study of 6591 Veterans diagnosed with COVID-19 from March 2, 2020 to May 26, 2020 was designed to assess the utility of repurposing the CAN score as objective and automated risk assessment tool to promptly enhance clinical decision making for Veterans diagnosed with COVID-19. We performed bivariate analyses on the dichotomized CAN 1-year mortality score (high vs. low risk) and each patient outcome using Chi-square tests of independence. Logistic regression models using the continuous CAN score were fit to assess its predictive power for outcomes of interest. Results demonstrated that a CAN score greater than 50 was significantly associated with the following outcomes after positive COVID-19 test: hospital admission (OR 4.6), prolonged hospital stay (OR 4.5), ICU admission (3.1), prolonged ICU stay (OR 2.9), mechanical ventilation (OR 2.6), and mortality (OR 7.2). Repurposing the CAN score offers an efficient way to risk-stratify COVID-19 Veterans. As a result of the compelling statistical results, and automation, this tool is well positioned for broad use across the VA to enhance clinical decision-making.

Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2–Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization

“…The CAN score was recently shown to be a predictor of COVID-19-related mortality. 18 The value of each predictor was ascertained before the beginning of the observation period on May 21, 2020. We identified comorbid conditions recorded at any time before May 21, 2020 (eMethods in the Supplement).…”

Development of a Real-Time Risk Model (RTRM) for Predicting In-Hospital COVID-19 Mortality

“…Finally, evaluation of effect size and significance of all predictors in the RTRM multivariable regression model not only corroborates most of the current key predictors of mortality in COVID-19 such as age, WHO PS, male sex, comorbidities, and markers of inflammation and organ dysfunction, but also shows clear dose responses for critical laboratory measures. 13 Our study has several limitations, some of which is inherent to real-world EHR data. In particular, because ICU care status was based on EHR location data, ICU misclassification may have occurred due to creation of COVID-19 isolation wards within ICUs, regardless of the necessity for ICUlevel care.…”

Identification of drugs associated with reduced severity of COVID-19: A case-control study in a large population

“…Several other research groups have recognized the potential for EHRs to enable large-scale studies in COVID-19 and the challenges of this sort of retrospective research are reviewed in (Dagliati et al, 2021; Sudat et al, 2021). To give a few examples, EHRs have also been used to predict: i) COVID-19 mortality based on pre-existing conditions (Estiri et al, 2021; Osborne et al, 2020), ii) early diagnosis of COVID-19 based on clinical notes (Wagner et al, 2020) and iii) eligibility of COVID-19 patients for clinical trials by matching trial criteria with patient records (Kim et al, 2021).…”