Michigan Medicine's disease prediction tool outperformed Epic's: 3 details

To manage scant resources, University of Michigan researchers developed a machine learning model to predict which COVID-19 patients are likely to become severely ill. The model outperformed Epic's deterioration index tool, according to a study published Feb. 17 in The BMJ.

Three details:

  1. The tool was tested on 33,119 adult COVID-19 patients. It was tested at first at Ann Arbor-based Michigan Medicine and then 12 other hospitals.

  2. The model was designed to predict clinical deterioration within the first five days of hospital admission, defined as death or requiring mechanical ventilation, intravenous vasopressors or heated high-flow nasal cannula.

  3. To calculate its prediction, the tool analyzes nine personal and clinical characteristics from patients' EHR: age, respiratory rate, oxygen saturation, oxygen flow rate, pulse oximetry type, head-of-bed position, position during blood pressure measurement, venous blood gas pH and partial pressure of carbon dioxide in arterial blood.

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