**4.2 Cedars Sinai deterioration index (CS-DI)**

After evaluating the accuracy of the EDI model, and other early warning systems, a decision was made at our organization to create a Cedars-Sinai deterioration index (CS-DI) machine learning algorithm that uses data from the patient's electronic medical record and calculates a percentage value that predicts the likelihood of a patient deterioration with an escalation of care. The predefined intervention point would automatically be activated if the calculated deterioration percentage value is reached and generate an alert notifying care providers to intervene sooner and possibly prevent further deterioration. Once trained, the CS-DI was deployed as a clinical decision support application to identify patients at risk for sepsis in real-time. Seventy percent of the cohort was used as the training set for the model while the other 30% was used as the test set. We used the CS-DI percentage value calculated to predict a composite outcome of further deterioration, intensive care unit-level care, mechanical ventilation, or hospital death.

Among the cohort examined, we found patients who met or exceeded the set threshold of 68.8 had an 87% probability of a composite outcome during their hospitalization with sensitivity of 39% and a median lead time of 24 hours from when the threshold was first exceeded. Among the patients hospitalized for at least 48 hours who had not experienced a composite outcome, 13% never exceeded 37.9 with a negative predictive value of 90% and a sensitivity above the threshold of 92%. When run against the MEWS early warning system, NEWS early warning system, and the EDI, the CS-DI predicted deterioration on average a full hour ahead of the other deterioration index models.
