Prognosticating pleural infection based on RAPID score in a tertiary-care hospital

Background: Pleural infection remains a significant cause of morbidity and mortality worldwide. The study aims to determine the mortality risk in patients with pleural infection using the renal function, Age, Purulence of pleural fluid, infection source, and dietary status (RAPID) score. Material and Methods: A cross-sectional study was conducted at the Chest Unit-1, Ojha Institute of Chest Diseases, Dow University of Health Sciences. A total of 171 patients aged 18–80 years with confirmed pleural infection were enrolled through non-probability consecutive sampling. Demographic, clinical, laboratory, and pleural fluid data were collected, and the RAPID score was calculated for each patient. Data were analyzed using SPSS V.25, Quantitative variables were reported as mean ± SD or median (IQR) based on distribution, and categorical variables as frequencies and percentages. Associations were tested using chi-square or Fisher’s exact test. Results: Of the 171 patients, 75 (43.9 %) were male, and the mean age was 55.1 ± 13.05 years. RAPID score distribution was low risk in 78 (45.6%), moderate in 58 (33.9%), and high in 35 (20.5%). Mortality rates were 3.5% in the low-risk group, 15.5% in the moderate group, and 46.4% in the high-risk group (p < 0.001). High RAPID scores were also associated with longer hospital stay median 12 (IQR 9–15) vs. 8 (IQR 6–11) days, p = 0.002. Conclusion: The RAPID score reliably stratifies pleural infection patients into mortality risk categories, enabling early identification of high-risk cases and timely intervention. Keywords: Mortality, Pleural infection, Prognosis, RAPID score, Tertiary care.

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Journal
Infectious Diseases Journal of Pakistan
Published
2025-09-30
DOI
https://doi.org/10.61529/idjp.v34i3.451
Primary Topic
COVID-19 diagnosis using AI
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article
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article

Prognosticating pleural infection based on RAPID score in a tertiary-care hospital

Faisal Faiyaz Zuberi, Saeideh Salari, Farrukh Raza, AEM Masharul Islam
Infectious Diseases Journal of Pakistan
COVID-19 diagnosis using AI
article

Prognosticating pleural infection based on RAPID score in a tertiary-care hospital

Faisal Faiyaz Zuberi, Saeideh Salari, Farrukh Raza, AEM Masharul Islam
article en

Abstract

Background: Pleural infection remains a significant cause of morbidity and mortality worldwide. The study aims to determine the mortality risk in patients with pleural infection using the renal function, Age, Purulence of pleural fluid, infection source, and dietary status (RAPID) score. Material and Methods: A cross-sectional study was conducted at the Chest Unit-1, Ojha Institute of Chest Diseases, Dow University of Health Sciences. A total of 171 patients aged 18–80 years with confirmed pleural infection were enrolled through non-probability consecutive sampling. Demographic, clinical, laboratory, and pleural fluid data were collected, and the RAPID score was calculated for each patient. Data were analyzed using SPSS V.25, Quantitative variables were reported as mean ± SD or median (IQR) based on distribution, and categorical variables as frequencies and percentages. Associations were tested using chi-square or Fisher’s exact test. Results: Of the 171 patients, 75 (43.9 %) were male, and the mean age was 55.1 ± 13.05 years. RAPID score distribution was low risk in 78 (45.6%), moderate in 58 (33.9%), and high in 35 (20.5%). Mortality rates were 3.5% in the low-risk group, 15.5% in the moderate group, and 46.4% in the high-risk group (p < 0.001). High RAPID scores were also associated with longer hospital stay median 12 (IQR 9–15) vs. 8 (IQR 6–11) days, p = 0.002. Conclusion: The RAPID score reliably stratifies pleural infection patients into mortality risk categories, enabling early identification of high-risk cases and timely intervention. Keywords: Mortality, Pleural infection, Prognosis, RAPID score, Tertiary care.

Infectious Diseases Journal of PakistanVol. 34(3)
Dow University of Health Sciences (PK)
Openalex Percentile: Top 59%
COVID-19 diagnosis using AI
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