LAUSR

Abstract Predicting long-term outcomes after sepsis is important when caring for patients with this condition. The purpose of the present study was to develop models predicting long-term mortality of patients with sepsis, including septic shock. Retrospective data from 446 patients with sepsis (60.8% men; median age, 71 years) treated at a single university-affiliated tertiary care hospital over 3 years were reviewed. Binary logistic regression was used to identify factors predicting mortality at 180 and 365 days after arrival at the emergency department. Long-term prognosis scores for the 180- and 365-day models were calculated by assigning points to variables according to their β coefficients. The 180- and 365-day mortality rates were 40.6% and 47.8%, respectively. Multivariate analysis identified the following factors for inclusion in the 180- and 365-day models: age ≥65 years, body mass index ≤18.5 kg/m2, hemato-oncologic diseases as comorbidities, and ventilator care. Patients with scores of 0 to ≥3 had 180-day survival rates of 83.8%, 70.8%, 42.3%, and 25.0%, respectively, and 365-day survival rates of 72.1%, 64.6%, 36.2%, and 15.9%, respectively (all differences P < .001; log-rank test). The areas under the receiver operating characteristic curves of the 180- and 365-day models were 0.713 (95% confidence interval [CI] 0.668–0.756, P < .001) and 0.697 (95% CI 0.650–0.740, P < .001), respectively. These long-term prognosis models based on baseline patient characteristics and treatments are useful for predicting the 6- and 12-month mortality rates of patients with sepsis.