Lasso-Enhanced Logistic Regression for Early Prediction of Pulmonary Infection in Critically Ill Post-Abdominal Surgery Patients.

Background and Objectives: To identify predictors of pulmonary infection in critically ill patients after abdominal surgery and to develop an early postoperative risk stratification model. Materials and Methods: Medical records of ICU patients after abdominal surgery (January 2016-June 2024) with Acute Physiology and Chronic Health Evaluation II (APACHE II) scores ≥10 were retrospectively analyzed. Patients were categorized according to the presence or absence of pulmonary infection. Candidate variables were screened using LASSO regression, followed by multivariate logistic regression to identify independent predictors. A nomogram-based prediction model was constructed and internally validated. Results: Among 4852 patients, 390 (8.0%) developed pulmonary infections. Overall, 8 independent predictors were identified: Male sex (vs. female) (OR 1.509, 95% CI: 1.091-2.087, p = 0.013), chronic obstructive pulmonary disease (OR 4.139, 95% CI: 2.872-5.966, p < 0.001), atrial fibrillation (OR 2.320, 95% CI: 1.366-3.939, p = 0.002), hypertension (OR 1.869, 95% CI: 1.372-2.539, p < 0.001), chronic renal insufficiency (OR 2.412, 95% CI: 1.143-5.091, p = 0.021), preoperative total bilirubin (OR 1.003, 95% CI: 1.001-1.004, p = 0.002), rectal surgery (OR 0.354, 95% CI: 0.151-0.830, p = 0.017), and invasive mechanical ventilation duration > 6 h (OR 2.206, 95% CI: 1.628-2.990, p < 0.001). The nomogram demonstrated good discrimination (AUC: 0.734 95% CI: 0.698-0.770) and calibration. Conclusions: This study identified 8 independent predictors of pulmonary infection and developed an internally validated early postoperative risk stratification model with satisfactory performance. The model may assist clinicians in identifying high-risk patients and guiding timely preventive strategies in ICU practice.