Fig 1.
Image-augmented deterioration model.
The image-augmented deterioration index combines outputs from an image model, an EHR deterioration index, and two time dependent variables encoding the time since the chest x-ray was performed and the time since the patient was admitted. Both the EHR deterioration index and image model can be varied. We provide additional details of chest x-ray preprocessing in the S1 Fig.
Fig 2.
Prediction timeline during a hospitalization.
A hospitalization timeline is shown for a hypothetical patient illustrating typical data availability and when predictions can occur. Image model predictions are made only during the time at which image studies are taken. The image-augmented deterioration index makes predictions for all windows after the image has been taken, using the deterioration index prediction prior to the first radiograph.
Table 1.
Demographic and clinical characteristics of the patients analyzed.
Fig 3.
Model predictions for COVID19 positive patients within the first 48 hours of admission, shown with exponential weight moving average and 95% CIs.
Each plot shows the number of patients flagged as low-risk by lowest aggregated prediction and the resulting accuracy for that fraction of patients. The top plot compares the EDI augmented model to the EDI model. The bottom plot compares the MCURES augmented model to the MCURES model.
Fig 4.
The fraction of patients correctly identified by each of the models as low-risk, shown over hours after hospital admission.
Note that the MCURES and MCURES-augmented models generally have better performance over the EDI and EDI-augmented models and so we select 0.9 NPV as an appropriate cutoff for EDI and 0.95 NPV for MCURES.
Table 2.
Reported area under the receiver operating characteristic (AUROC) and 95% CIs for each deterioration index for identifying high-risk patients within the first five days of admission.