Predictive analytics that reflect disease burden – the cumulative COMET score

Abstract

Predictive analytics tools variably take into account data from the electronic medical record, lab tests, nursing charted vital signs and continuous cardiorespiratory monitoring data to deliver an instantaneous score that indicates patient risk or instability. Few, if any, of these tools reflect the risk to a patient accumulated over the course of an entire hospital stay. This approach fails to best utilize all of the collated data regarding the risk or instability sustained by the patient, and hence fails to fully characterize this to optimize the ability of treating clinicians to maximize the chances of a favorable outcome. We have built on our instantaneous CoMET predictive analytics score to generate the cumulative CoMET score (cCOMET), which sums all of the instantaneous CoMET scores throughout a hospital admission relative to a baseline expected risk unique to that patient. We have shown that higher cCOMET scores predict mortality, but not length of stay, and that higher baseline CoMET scores predict higher cCoMET scores at discharge/death. cCoMET scores were higher in males in our cohort, and added information to the final CoMET when it came to the prediction of death. In summary, if one is going to go to the trouble and expense of performing repeated measures when performing predictive analytics calculations, we have shown that including all of these measures in a cumulative way adds data to instantaneous predictive analytics, and could improve the ability of clinicians to predict deterioration, and improve patient outcomes in so doing.