Objective: Clinical trials of therapies for sepsis have been mostly unsuccessful in impacting mortality. This may be partly due to the use of insensitive mortality end points. We explored whether modeling survival was more sensitive than traditional end points in detecting mortality differences in cohorts of patients with sepsis.
Design: Patients were stratified into seven a priori defined paired subgroups that reflected high and low mortality risk according to known clinical risk factors. We fitted an exponential survival model to the high- and low-risk cohort of each subgroup, providing estimates of the rate of dying, long-term survival, and excess day 1 mortality. Mortality in the high- and low-risk cohorts in each subgroup was compared using model parameters, fixed-point mortality, and Kaplan-Meier survival analysis.
Setting: Eight intensive care units within a university teaching institution.
Patients: One hundred thirty patients with severe sepsis or suspected Gram-negative bacteremia.
Interventions: None.
Measurements and main results: Overall mortality of the cohort was 58.5% at 28 days. The survival of the entire cohort was well described by an exponential model (r2 =.99). Modeling identified differences in high- and low-risk cohorts in five of the seven paired subgroups, while conventional end-points only detected differences in 2.
Conclusions: Modeling survival was more sensitive than conventional end-points in identifying survival differences between high- and low-risk subgroups. We encourage further evaluation of modeling in the search for more sensitive mortality end points.