Abstract
This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients will need escalated care early on using deep-learning neural networks. Analysis was performed on hospitalized COVID-19 patients between February 7, 2020 and May 4, 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs, and blood gases were collected. We compared data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: i) COVID-19 patients admitted to the general floor (N=1203) versus those directly admitted to ICU (N=104), and ii) patients not upgraded to ICU (N=979) versus those upgraded to the ICU (N=224) from the general floor. A deep neural network algorithm was used to predict ICU admission, with 80% training and 20% testing. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis (ROC). We found that C-reactive protein, lactate dehydrogenase, creatinine, white-blood cell count, D-dimer, and lymphocyte count showed temporal divergence between patients were upgraded to ICU compared to those were not. The deep learning predictive model ranked essentially the same set of laboratory variables to be important predictors of needing ICU care. The AUC for predicting ICU admission was 0.782±0.013 for the test dataset. Adding vital sign and blood-gas data improved AUC (0.861±0.018). This study identified a few laboratory tests that were predictive of escalated care. This work could help frontline physicians to anticipate downstream ICU needs to more effectively allocate healthcare resources.
Peer Review #2 of "Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables (v0.1)"
