Predictors of Cumulative 90-D Mortality for Septic Patients Undergoing Abdominal Surgery.

INTRODUCTION

Septic surgical patients undergoing emergency general surgery represent a distinct population with unique challenges. This study aimed to identify predictors of cumulative 90-d mortality, including clinical and socioeconomic factors, and to analyze causes of death in this cohort.

METHODS

A retrospective analysis was conducted on patients admitted to a surgical intensive care unit from 2011 to 2019 with sepsis (sequential organ failure assessment score ≥2) undergoing emergency intra-abdominal surgery (n = 498). Demographics, comorbidities, sepsis presentation, and socioeconomic metrics, including the area deprivation index (ADI), were analyzed. Independent predictors of mortality were identified using multiple logistic regression. The causes of death were categorized and analyzed.

RESULTS

Among 498 patients, 46% (n = 229) died within 90 d. Nonsurvivors were older (65 ± 13.7 versus 61.2 ± 13.5 y, P < 0.01), more often transferred from external facilities (59% versus 46%, P < 0.01), and had higher rates of liver disease, chronic kidney disease, metastatic cancer, obesity, and higher Charlson comorbidity index scores (P < 0.01 for all). Independent predictors of 90-d mortality included admission sequential organ failure assessment scores, serum lactate, obesity, ADI, Charlson comorbidity index, and transfer status. ADI remained a significant predictor, while the distressed communities index did not. Of the deaths, 76.9% were in-hospital deaths, with intra-abdominal catastrophes (35.4%), multisystem organ failure (25.2%), and pulmonary causes (16.4%) as the most common causes.

CONCLUSIONS

Intra-abdominal catastrophes, multiorgan failure, and pulmonary complications are leading causes of death in septic emergency general surgery patients. ADI is a robust socioeconomic predictor of mortality, underscoring the need for integrating social determinants into risk assessment and tailored care strategies. Developing comprehensive risk models may enhance prognostication and guide clinical decision-making in this critical population.