Assessment of RSBI, IWI and Dead Space/Tidal Volume by Respiratory Failure Type in Successfully Weaned Emergency Department Patients: A Cross-Sectional Study

Background and Aim: Mechanical ventilation is essential in conditions such as acute respiratory failure. However, prolonged use may lead to complications; therefore, there is a need for timely liberation. Special indicators are important for this purpose, including the Rapid Shallow Breathing Index (RSBI), the dead space to tidal volume ratio (VD/VT), and the Integrative Weaning Index (IWI), which offers a composite evaluation of ventilatory mechanics. This study aimed to compare these indices in Type 1 and Type 2 respiratory failure to examine their feasibility in assessing the likelihood of successful extubation. Methods The present cross-sectional study involved 35 participants (23 with Type 1 respiratory failure and 12 with Type 2 respiratory failure) successfully weaned from mechanical ventilation in the Emergency Department of a tertiary care hospital between 2022 and 2024. RSBI, VD/VT, IWI, and arterial blood gas values were evaluated. Data were analyzed descriptively and statistically, with statistical significance determined at p < 0.05. Results: Type 2 respiratory failure patients had a higher VD/VT ratio (0.37±0.04 vs. 0.29±0.13, p = 0.046) and a lower PaO₂/FiO₂ ratio (169±49.6 vs. 244±95.6, p = 0.017) than Type 1 patients. RSBI values did not differ significantly between the groups (p = 1.00). IWI values showed a trend toward lower scores in Type 2 patients, reflecting reduced weaning readiness, although the difference was not statistically significant. These results demonstrate that patients with hypercapnia have more significant impairments in gas exchange. Conclusion: RSBI was found to be less discriminative in identifying weaning failure, whereas VD/VT proved to be a more reliable predictor in hypercapnic respiratory failure. Additionally, IWI may serve as a useful complementary tool to improve weaning accuracy by integrating multiple physiological parameters into a single predictive measure.