Shazia Rizwan

Dr. Shazia Rizwan


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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24 February 2026 in Journal of Clinical Medicine
Verified
Journal Article
Open access
PDF file
AKAsifa Karamat
Asifa Karamat
SRShazia Rizwan
Shazia Rizwan
HBHuma Batool
Huma Batool
AAAtiqa Ambreen
Atiqa Ambreen
TMTehmina Mustafa
Tehmina Mustafa
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.