Dataset Information

An Algorithm for Classifying Patients Most Likely to Develop Severe Coronavirus Disease 2019 Illness

ABSTRACT: Supplemental Digital Content is available in the text.

Objectives:

To develop an algorithm that predicts an individualized risk of severe coronavirus disease 2019 illness (i.e., ICU admission or death) upon testing positive for coronavirus disease 2019.

Design:

A retrospective cohort study.

Setting:

Cleveland Clinic Health System.

Patients:

Those hospitalized with coronavirus disease 2019 between March 8, 2020, and July 13, 2020.

Interventions:

A temporal coronavirus disease 2019 test positive cut point of June 1 was used to separate the development from validation cohorts. Fine and Gray competing risk regression modeling was performed.

Measurements and Main Results:

The development set contained 4,520 patients who tested positive for coronavirus disease 2019 between March 8, 2020, and May 31, 2020. The validation set contained 3,150 patients who tested positive between June 1 and July 13. Approximately 9% of patients were admitted to the ICU or died of coronavirus disease 2019 within 2 weeks of testing positive. A prediction cut point of 15% was proposed. Those who exceed the cutoff have a 21% chance of future severe coronavirus disease 2019, whereas those who do not have a 96% chance of avoiding the severe coronavirus disease 2019. In addition, application of this decision rule identifies 89% of the population at the very low risk of severe coronavirus disease 2019 (< 4%).

Conclusions:

We have developed and internally validated an algorithm to assess whether someone is at high risk of admission to the ICU or dying from coronavirus disease 2019, should he or she test positive for coronavirus disease 2019. This risk should be a factor in determining resource allocation, protection from less safe working conditions, and prioritization for vaccination.

SUBMITTER: Kattan M

PROVIDER: S-EPMC7746202 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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