Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods.
Brandon C CummingsSardar AnsariJonathan R MotykaGuan WangRichard P MedlinSteven L KronickKarandeep S SinghPauline K ParkLena M NapolitanoRobert P DicksonMichael R MathisMichael W SjodingAndrew J AdmonRoss BlankJakob I McSparronKevin R WardChristopher E GilliesPublished in: JMIR medical informatics (2021)
The PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation.