The Predictive Factors of Mechanical Ventilation Duration in Intensive Care Unit Patients: A Cross-Sectional Study in Southern Iran

Background and Objective: Duration of mechanical ventilation is a key prognostic factor in Intensive Care Unit (ICU) patients, closely linked to mortality and healthcare costs. Given the limited and context-specific data available in Iran, this 2025 study aimed to identify predictors of ventilation duration among ICU patients in southern Iran. Methods: This cross-sectional study was conducted from June to October 2025 in the Mega ICU of Golestan Hospital, affiliated with Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. A total of 248 patients who received invasive mechanical ventilation for at least 48 hours were selected through convenience sampling. Daily data were collected using standardized tools (GCS, APACHE II, CAM-ICU, and CPIS), along with outcomes such as sepsis, successful weaning, sinus tachycardia, and ICU stay. Independent predictors of Mechanical ventilation duration were identified using multivariable linear regression in SPSS version 22. Results: In this study, 64.5% of the participants were male, and their mean age was 43.48 ± 13.87 years. Trauma was the most common reason for hospitalization (58.9%). The mean duration of mechanical ventilation was 15.20 ± 7.46 days, and 55.2% of the patients received prolonged mechanical ventilation (PMV), defined as more than 14 days. The multiple regression model (including 15 variables) explained 15.9% of the variance in ventilation duration and showed that ventilator-associated pneumonia (VAP) was the strongest predictor of PMV (B = 5.801, p < 0.001). In addition, delirium (B = 2.903, p = 0.047), trauma diagnosis (B = 2.306, p = 0.049), and age (B = 0.087, p = 0.026) were identified as independent predictors of increased mechanical ventilation duration. Conclusion: The study shows that VAP, delirium, trauma as the primary diagnosis, and older age are key contributors to prolonged mechanical ventilation in ICU patients. These findings underscore the need for effective VAP prevention, delirium management, and focused care for trauma and elderly patients. Given the model’s moderate predictive strength, future research should incorporate additional variables, such as real-time ventilator parameters and other dynamic clinical factors, to better clarify determinants of ventilation duration.