AI Summary & Key Takeaways
AI InsightIts simplicity and reliance on routinely available data support its use as a practical tool for perioperative risk stratification and critical care planning.
📌 3 Key Takeaways
Abstract
Aims: Emergency surgery encompasses a broad spectrum of clinical presentations, often characterized by acute physiological derangement and limited opportunities for preoperative optimization. Reliable preoperative tools that anticipate postoperative deterioration are therefore essential, particularly for planning intensive care unit (ICU) utilization. Although the Emergency Surgery Score (ESS) has been validated for outcome prediction, its role in forecasting postoperative ICU admission and length of hospital stay warrants further clarification. Methods: This retrospective observational study evaluated adult patients who underwent emergency surgery for acute abdominal conditions at a tertiary care center. Preoperative ESS was calculated for each patient using established variables. Postoperative ICU admission constituted the primary outcome, while length of hospital stay was defined as the secondary outcome. Comparative analyses were performed between patients requiring ICU care and those managed on the surgical ward. The independent predictive value of ESS was assessed using binary logistic regression, and its relationship with length of hospital stay was examined through regression modeling. Results: Among 88 patients included in the analysis, 8 (9.1%) required postoperative ICU admission. ESS values were significantly higher in patients admitted to the ICU compared with those treated on the general ward. Logistic regression demonstrated that ESS independently predicted postoperative ICU admission, with each one-point increase in ESS being associated with a significantly higher likelihood of ICU admission (OR 5.02, 95% CI 1.79-14.07). Model calibration and discrimination were both strong. In parallel, higher ESS values were associated with a marked prolongation of hospital stay. Conclusion: ESS is a robust preoperative predictor of postoperative ICU admission and hospital length of stay in emergency surgical patients. Its simplicity and reliance on routinely available data support its use as a practical tool for perioperative risk stratification and critical care planning.