Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19

Abstract

Background: Accurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality. Methods: All consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality. Results: 22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n=16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n=6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9). Conclusion: A prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

Real-world Effectiveness and Tolerability of Monoclonal Antibody Therapy for Ambulatory Patients With Early COVID-19

“…To address resource scarcity, the Scarce Medications Allocation Subcommittee of the Utah Crisis Standards of Care (CSC) Workgroup was convened with the dual aims of targeting available MAb infusions to the patients most likely to benefit and ensuring equity in delivery [ 8 ]. A simple clinical prediction score for predicting severe COVID-19 resulting in hospitalization or mortality among ambulatory patients was validated in a large cohort of Utah patients and adopted for use in MAb allocation [ 9 ]. The score weights age, gender, shortness of breath, comorbidities, and non-White race or Hispanic/Latinx ethnicity to address recognized disparities in poor COVID-19 outcomes in these populations.…”

The MAb Squad: Delivering Covid-19 Monoclonal Antibody Therapy Across a Large Geographic Region

“…To do this, the committee evaluated available risk stratification models and decided upon a simple scoring tool validated in a large, representative local population and based on accurate discrimination for both hospitalization and mortality. 5 An eligibility threshold was then chosen that would match the available capacity to the number of new cases identified at that threshold, creating an adaptive method of continuous supply-demand matching. The committee then coordinated site selection to strategically ensure that locations were distributed equitably across the state.…”