Factors Associated with Outcomes of Non-Invasive Ventilation in Acute Hypercapnic Respiratory Failure with Acidosis: A Study from a Tertiary Care Hospital in Pakistan

TL;DR

This study identifies factors affecting non-invasive ventilation outcomes in patients with respiratory failure in a resource-limited hospital in Pakistan.

Contribution

The study provides locally relevant predictors of NIV success and mortality in a resource-limited ICU setting.

Findings

Older age, higher one-hour PCO2, FiO2, and respiratory rate were associated with increased mortality.

Higher WBC counts predicted the need for mechanical ventilation and correlated with late physiological response.

Locally generated data can inform timely escalation to mechanical ventilation and patient selection for NIV.

Abstract

Background: This study aimed to identify factors influencing non-invasive ventilation (NIV) outcomes in patients with hypercapnic respiratory failure due to various respiratory conditions in a resource-limited intensive care unit (ICU) setting. These predictors can guide us in the prompt decision of ventilation, resulting in better outcomes. Methods: Patients requiring NIV for hypercapnic respiratory failure of any cause were included. Arterial blood gases were measured at 1 and 24 h, and an improvement in pH ≥ 7.35 was taken as a cut-off for early and late physiological responses, respectively. Binary regression analysis was used to identify predictors of physiological response, need for mechanical ventilation, and mortality. Results: Among 226 patients (139 males), the underlying causes were obstructive (71%), restrictive (25%), and infective disorders (4%). Older age, higher one-hour PCO2, FiO2, and respiratory rate were associated with increased mortality. Late physiological response correlated with higher IPAP and WBC counts, while higher WBC counts also predicted need for mechanical ventilation on binary logistic regression. Conclusions: Higher one-hour PCO2, older age, higher FiO2, respiratory rate, WBC count, and IPAP predicted an unfavorable outcome of NIV in acute hypercapnoic respiratory failure. Locally generated data can support timely escalation to mechanical ventilation and inform patient selection for initial NIV therapy in resource-limited settings.

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