Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network

Abstract

Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Predictors of all‐cause mortality among patients hospitalized with influenza, respiratory syncytial virus, or SARS‐CoV‐2

“… 2 This is particularly useful in the context of a new and emerging respiratory virus where information and resources are scarce. 2 , 3 …”

“…2 This is particularly useful in the context of a new and emerging respiratory virus where information and resources are scarce. 2,3 Several studies have compared shared and divergent predictors of severe disease among patients with influenza and respiratory syncytial virus (RSV), [4][5][6][7][8][9] two respiratory viruses with high seasonal prevalence prior to the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, few papers have compared predictors of severity across influenza, RSV, and SARS-CoV-2.…”

“…Knowing who is at highest risk of severe disease from respiratory viruses may support proactive clinical decision‐making and help distribute resources to healthcare settings with high prevalence of risk factors 2 . This is particularly useful in the context of a new and emerging respiratory virus where information and resources are scarce 2,3 …”

Predictors of all-cause mortality among patients hospitalized with influenza, respiratory syncytial virus, or SARS-CoV-2

“…Knowing who is at highest risk of severe disease from respiratory viruses may support proactive clinical decision-making, and help distribute resources to healthcare settings with high prevalence of risk factors [3,4]. This is particularly useful in the context of a new and emerging respiratory virus where information and resources are scarce [3,5].…”

“…2 This is particularly useful in the context of a new and emerging respiratory virus where information and resources are scarce. 2,3 Several studies have compared shared and divergent predictors of severe disease among patients with influenza and respiratory syncytial virus (RSV) [4][5][6][7][8][9] , two respiratory viruses with high seasonal prevalence prior to the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, few papers have compared predictors of severity across influenza, RSV, and SARS-CoV-2.…”