Our algorithm looked at tens of thousands of patients, and computationally phenotyped them through every minute of data using the international consensus definition of septic shock, and based on early warning times, found clinical ground truth. We also discovered that the Sepsis 2 definition had a property that was temporarily unstable. This is to say that the state of a patient with sepsis as defined by Sepsis 2, was changing all the time, and it was not possible to predict ground truth. With found the Sepsis 3 definitions to be temporarily stable with few state transitions. The major factor was that the criteria in Sepsis 2 had included a diagnosis of SIRS before sepsis was considered as a diagnosis, and it was removed from 3. We believe that SIRS was causing frequent state changes, as an ambiguous diagnosis.

We are able to predict those patients with sepsis who will transition to shock many hours before they go into shock. We are also able to identify distinct temporal patterns of the risk score corresponding to patient populations with high (up to 60%) versus low (10-20%) mortality. For each of these groups, we looked at comorbidities, diagnoses such as kidney failure and cancer, but we do not know what the relationship is or what is different about these patient groups and the fact that they are in the 60% mortality pool. We know their physiology is saying they are in the mortality pool, but not why. What this means is how these patients are being treated could be the issue (physicians with different levels of training, and other factors involved in treatment decisions). In our work, patients were classified into high and low risk. We found that patients in the low risk received vasopressors and adequate fluid resuscitation and for patients in the high-risk pool, fewer had received vasopressors or fluids. The question is, why are these patients not getting these things. Our algorithm to predict the transition to septic shock can positively influence treatment decisions made by many physicians, to confirm the value of treatment and prevent the development of septic shock. We’ve also identified and know the time to look for proteomic and genomic biomarkers for the early predictive shock signature that could correlate with this high risk/these measures are not routinely done clinically, and this line of work could be very helpful in understanding the fundamental biology of the very rapid change in patient state when they cross the risk score threshold.