Continuous predictive monitoring has been employed successfully to predict subclinical adverse events. Should low values on these models, however, reassure us that a patient will not have an adverse outcome? Negative predictive values of such models could help predict safe patient discharge. The goal of this study was to validate the negative predictive value of an ensemble model for critical illness (using previously developed models for respiratory instability, hemorrhage, and sepsis) based on bedside monitoring data in the intensive care units and intermediate care unit.Methods.
We calculated the relative risk of 3 critical illnesses for all patients every 15 minutes (n = 124,588) for 2,924 patients downgraded from the surgical intensive care units and intermediate care unit between May 2014 to May 2016. We constructed an ensemble model to estimate at the time of intensive care units or intermediate care unit discharge the probability of favorable outcome after downgrade.Results.
Outputs form the ensemble model stratified patients by risk of favorable and bad outcomes in both intensive care units/intermediate care unit; area under the receiver operating characteristic curve = .639/.629 respectively for favorable outcomes and .645/.641 for adverse events. These performance characteristics are commensurate with published models for predicting readmission. The ensemble model remained a statistically significant predictor after adjusting for hospital duration of stay and admitting service. The rate of favorable outcome in the highest and lowest deciles in the intensive care units were 76.2% and 27.3% (2.8-fold decrease) and 88.3% and 33.2% in the intermediate care unit (2.7-fold decrease), respectively.Conclusion.
An ensemble model for critical illness predicts favorable outcome after downgrade and safe patient discharge (hospital stay <7 days, no readmission, upgrade, or death).