ABSTRACT
BACKGROUND: We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. METHODS: We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including > 40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 hours. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. RESULTS: Overall, 3,706 patients (2,587 in the development and 1,119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79 - 0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. CONCLUSIONS: Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors. This article is protected by copyright. All rights reserved.
ABSTRACT
BACKGROUND: Treatment decisions for Coronavirus Disease 2019 (COVID-19) depend on disease severity, but the prescribing pattern by severity and drivers of therapeutic choices remain unclear. OBJECTIVES: The objectives of the study were to evaluate pharmacological treatment patterns by COVID-19 severity and identify the determinants of prescribing for COVID-19. METHODS: Using electronic health record data from a large Massachusetts-based healthcare system, we identified all patients aged ≥ 18 years hospitalized with laboratory-confirmed COVID-19 from 1 March to 24 May, 2020. We defined five levels of COVID-19 severity at hospital admission: (1) hospitalized but not requiring supplemental oxygen; (2-4) hospitalized and requiring oxygen ≤ 2, 3-4, and ≥ 5 L per minute, respectively; and (5) intubated or admitted to an intensive care unit. We assessed the medications used to treat COVID-19 or as supportive care during hospitalization. RESULTS: Among 2821 patients hospitalized for COVID-19, we found inpatient mortality increased by severity from 5% for level 1 to 23% for level 5. As compared to patients with severity level 1, those with severity level 5 were 3.53 times (95% confidence interval 2.73-4.57) more likely to receive a medication used to treat COVID-19. Other predictors of treatment were fever, low oxygen saturation, presence of co-morbidities, and elevated inflammatory biomarkers. The use of most COVID-19 relevant medications has dropped substantially while the use of remdesivir and therapeutic anticoagulants has increased over the study period. CONCLUSIONS: Careful consideration of disease severity and other determinants of COVID-19 drug use is necessary for appropriate conduct and interpretation of non-randomized studies evaluating outcomes of COVID-19 treatments.