Regression analysis for intensive care unit (ICU) admission prediction in elderly patients with acute appendicitis

Acute appendicitis in the elderly often presents with atypical symptoms and is frequently complicated at diagnosis, potentially requiring intensive care unit (ICU) admission. Early risk identification is essential for effective triage. Hematologic biomarkers may provide rapid, accessible tools for predicting disease severity. The objective of this study was to evaluate the predictive value of hematologic parameters-particularly lymphocyte count, neutrophil-to-lymphocyte ratio (NLR), and immature granulocyte (IG) indices-for ICU admission and complicated appendicitis in elderly patients.

This retrospective observational study included patients aged ≥65 years who underwent appendectomy for suspected acute appendicitis between 2018-2021 and in 2024 at a tertiary emergency department. Patients were grouped by ICU admission status and histopathological diagnosis (complicated vs. non-complicated appendicitis). Preoperative clinical and laboratory data were analyzed using logistic regression and receiver operating characteristic (ROC) analysis. A post hoc power analysis was also performed.

Of 143 patients included, 33 (23.1%) required ICU admission. These patients were older and had higher rates of diabetes and coronary artery disease. Lymphocyte counts were significantly lower and NLR values higher in the ICU group. In multivariate analysis, only lymphocyte count independently predicted ICU admission. Complicated appendicitis was associated with age, diabetes, low lymphocyte count, high IG%, and elevated NLR in univariate analysis; however, only age, diabetes, and lymphocyte count remained significant in multivariate modeling. ROC analysis showed moderate diagnostic performance for lymphocyte count (area under the curve=0.669) in identifying complicated cases.

Lymphopenia is an independent predictor of ICU admission and complicated appendicitis in elderly patients. Routine blood parameters may support early clinical risk stratification in emergency settings.