Predictors of 1-year mortality following discharge from the surgical intensive care unit after sepsis

Surgery. 2024 Oct 9:S0039-6060(24)00672-X. doi: 10.1016/j.surg.2024.08.037. Online ahead of print.

Abstract

Background: Postsepsis syndrome is associated with significant long-term mortality. The objective of this study was to determine predictors of mortality within 1 year of discharge from the surgical intensive care unit.

Methods: We retrospectively reviewed patients admitted to a surgical intensive care unit with sepsis (sequential organ failure assessment score ≥2, 2011-2022). Those who died within 1 year from discharge (n = 171) were compared to survivors (n = 639). Baseline characteristics, sepsis presentation, and hospitalization data were compared. A multiple logistic regression was performed to determine predictors of 1-year mortality after discharge.

Results: Compared with survivors, those who died were older, less likely to be transferred from another institution (35% vs 46%, P = .003), had more metastatic cancer (9% vs 1%, P < .01), or stage III + chronic kidney disease (16% vs 7%, P < .01). Admission sequential organ failure assessment score, lactate, and vasopressor use were comparable. The 1-year mortality cohort exhibited increased respiratory (15% vs 9%) and abdominal (66% vs 54%) infections (P < .01), median length of stay (29 vs 19, P < .005), renal failure (14% vs 9%, P = .048), and dependent discharge. Adjusted predictors of death included age (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.02-1.05), metastatic cancer (OR 8.0, 95% CI 2.6-25), chronic kidney disease (OR 2.8, 95% CI 1.4-5.6), length of stay (OR 1.02, 95% CI 1.0-1.03), and dependent discharge. A length of stay in the top quartile (>32 days) was associated with a 3-fold increase in postdischarge mortality compared with the lowest quartile (<10 days).

Conclusion: We identified independent predictors of postdischarge mortality following sepsis, including age, length of stay, dependent discharge, and stage III + chronic kidney disease. These data can identify at-risk patients who can be targeted for closer follow-up.