Predicting individual risk for COVID19 complications using EMR data

BackgroundThe global pandemic of COVID-19 has challenged healthcare organizations and caused numerous deaths and hospitalizations worldwide. The need for data-based decision support tools for many aspects of controlling and treating the disease is evident but has been hampered by the scarcity of real-world reliable data. Here we describe two approaches: a. the use of an existing EMR-based model for predicting complications due to influenza combined with available epidemiological data to create a model that identifies individuals at high risk to develop complications due to COVID-19 and b. a preliminary model that is trained using existing real world COVID-19 data. MethodsWe have utilized the computerized data of Maccabi Healthcare Services a 2.3 million member state-mandated health organization in Israel. The age and sex matched matrix used for training the XGBoost ILI-based model included, circa 690,000 rows and 900 features. The available dataset for COVID-based model included a total 2137 SARS-CoV-2 positive individuals who were either not hospitalized (n = 1658), or hospitalized and marked as mild (n = 332), or as having moderate (n = 83) or severe (n = 64) complications. FindingsThe AUC of our models and the priors on the 2137 COVID-19 patients for predicting moderate and severe complications as cases and all other as controls, the AUC for the ILI-based model was 0.852[0.824-0.879] for the COVID19-based model - 0.872[0.847-0.879]. InterpretationThese models can effectively identify patients at high-risk for complication, thus allowing optimization of resources and more focused follow up and early triage these patients if once symptoms worsen. FundingThere was no funding for this study Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe have search PubMed for coronavirus[MeSH Major Topic] AND the following MeSH terms: risk score, predictive analytics, algorithm, predictive analytics. Only few studies were found on predictive analytics for developing COVID19 complications using real-world data. Many of the relevant works were based on self-reported information and are therefore difficult to implement at large scale and without patient or physician participation. Added value of this studyWe have described two models for assessing risk of COVID-19 complications and mortality, based on EMR data. One model was derived by combining a machine-learning model for influenza-complications with epidemiological data for age and sex dependent mortality rates due to COVID-19. The other was directly derived from initial COVID-19 complications data. Implications of all the available evidenceThe developed models may effectively identify patients at high-risk for developing COVID19 complications. Implementing such models into operational data systems may support COVID-19 care workflows and assist in triaging patients.