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A score-based risk model for predicting severe COVID-19 infection as a key component of lockdown exit strategy

By Noa Dagan, Noam Barda, Dan Riesel, Itamar Grotto, Siegal Sadetzki, Ran D. Balicer

Posted 23 May 2020
medRxiv DOI: 10.1101/2020.05.20.20108571

Background: As many countries consider and employ various lockdown exit strategies, health authorities seek tools to provide differential targeted advice for social distancing based on personal risk for severe COVID-19. However, striking a balance between a scientifically precise multivariable risk prediction model, and a model which can easily be used by the general public, remains a challenge. A list of risk criteria, as defined by the CDC for example, provides a simple solution, but may be too inclusive by classifying a substantial portion of the population at high risk. Score-based risk classification tools may provide a good compromise between accuracy and simplicity. Objective: To create a score-based risk classification tool for severe COVID-19. Methods: The outcome was defined as a composite of being labeled severe during hospitalization or dying due to COVID-19. The risk classification tool was developed using retrospective data from all COVID-19 patients that were diagnosed until April 1st, 2020 in a large healthcare organization ("training set"). The developed tool combines 10 risk factors using simple summation, and defines three risk levels according to the patient's age and number of accumulated risk points - basic risk, high risk and very-high risk (the last two levels are also considered together as the elevated risk group). The tool's performance in accurately identifying individuals at risk was evaluated using a "temporal test set" of COVID-19 patients diagnosed between April 2nd and April 22nd, 2020, later than those used for model development. The tool's performance was also compared to that of the CDC's criteria. The healthcare organization's general population was used to evaluate the proportion of patients that would be classified to each of the model's risk levels and as elevated risk by the CDC criteria. Results: A total of 2,421, 2,624 and 4,631,168 individuals were included in the training, test, and general population cohorts, respectively. The outcome rate in the training and test sets was 5%. Overall, 18% of the general population would be classified at elevated risk by the model, with a resulting sensitivity of 92%, compared to 35% that would be defined as elevated risk by the CDC criteria, with a resulting sensitivity of 96%. Within the model's elevated risk groups, the high and very-high risk groups comprised 15% and 3% of the general population, with an incidence rate (PPV) of 15% and 33%, respectively. Discussion: A simple to communicate score-based risk classification tool classifies at elevated risk about half of the population that is considered to have an elevated risk by the CDC risk criteria, with only a 4% reduction in sensitivity. The model's ability to further divide the elevated risk population into two markedly different subgroups allows providing more refined recommendations to the general public and limiting the restrictions of social distancing to a smaller and more manageable subset of the population. This model was adopted by the Israeli ministry of health as its risk classification tool for COVID-19 lab tests prioritization and for targeting its instructions on risk management during the lockdown exit strategy.

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