Stage-driven mortality and clinical risk stratification among lung cancer patients in Dhaka, Bangladesh: A multi-hospital secondary analysis
- 1Comilla Medical College, Cumilla, Bangladesh
- 2Tbilisi State Medical University, Tbilisi, Georgia
Abstract
Introduction: Lung cancer is a major cause of cancer mortality, and patient-level outcome data from Bangladesh remain limited. Locally relevant, interpretable risk-stratification models may help identify patients with higher recorded mortality risk using routinely available clinical variables. This study evaluated mortality-associated factors and the hospital-wise internal-external validation performance of a clinical risk-stratification model among lung cancer patients in Dhaka, Bangladesh. Methods: We performed a retrospective secondary analysis of a publicly available, de-identified multi-hospital dataset comprising 1,782 patients. The primary endpoint was recorded binary mortality status, defined from the source variable survived (No = mortality event; Yes = no recorded mortality event). Multivariable logistic regression was used for association analysis. A hospital-agnostic logistic prediction model was evaluated using leave-one-hospital-out internal-external validation. Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC), calibration by Brier score, calibration intercept and slope, and predicted-risk deciles, and potential clinical utility by decision curve analysis. Results: Among 1,782 patients, 770 (43.2%) were recorded as survived = No. Recorded mortality increased across cancer stages: 16.3% in Stage I, 32.5% in Stage II, 55.6% in Stage III, and 79.2% in Stage IV. Compared with Stage I, adjusted odds ratios were 2.54 (95% CI 1.86 to 3.46) for Stage II, 6.66 (95% CI 4.86 to 9.14) for Stage III, and 20.77 (95% CI 14.46 to 29.83) for Stage IV. Surgery was associated with lower adjusted odds than chemotherapy (adjusted OR 0.65, 95% CI 0.48 to 0.89). Apparent AUROC was 0.769; pooled leave-one-hospital-out AUROC was 0.753 with a Brier score of 0.198. Pooled calibration yielded an intercept of −0.031 and slope of 0.911. At the evaluated 0.05 increments, the model had greater net benefit than treat-all and treat-none strategies from approximately 0.20 to 0.75. Conclusion: Recorded mortality in this multi-hospital Bangladeshi lung cancer dataset was strongly stage-driven. An interpretable logistic risk model showed moderate discrimination and reasonable calibration across hospital-wise internal-external validation. These findings support further evaluation of routinely available clinical risk stratification, but independent external and prospective validation are required before clinical implementation.
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BibTeX
@article{das2026stagedriven,
title = {{Stage-driven mortality and clinical risk stratification among lung cancer patients in Dhaka, Bangladesh: A multi-hospital secondary analysis}},
author = {Sourov Das and Sushmita Kundu and Salma Jahan Tanny},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359406},
url = {https://doi.org/10.1371/journal.pone.0359406},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Stage-driven mortality and clinical risk stratification among lung cancer patients in Dhaka, Bangladesh: A multi-hospital secondary analysis
AU - Sourov Das
AU - Sushmita Kundu
AU - Salma Jahan Tanny
PY - 2026
DA - 2026/10/15
JO - PLOS One
DO - 10.1371/journal.pone.0359406
UR - https://doi.org/10.1371/journal.pone.0359406
AB - Introduction: Lung cancer is a major cause of cancer mortality, and patient-level outcome data from Bangladesh remain limited. Locally relevant, interpretable risk-stratification models may help identify patients with higher recorded mortality risk using routinely available clinical variables. This study evaluated mortality-associated factors and the hospital-wise internal-external validation performance of a clinical risk-stratification model among lung cancer patients in Dhaka, Bangladesh. Methods: We performed a retrospective secondary analysis of a publicly available, de-identified multi-hospital dataset comprising 1,782 patients. The primary endpoint was recorded binary mortality status, defined from the source variable survived (No = mortality event; Yes = no recorded mortality event). Multivariable logistic regression was used for association analysis. A hospital-agnostic logistic prediction model was evaluated using leave-one-hospital-out internal-external validation. Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC), calibration by Brier score, calibration intercept and slope, and predicted-risk deciles, and potential clinical utility by decision curve analysis. Results: Among 1,782 patients, 770 (43.2%) were recorded as survived = No. Recorded mortality increased across cancer stages: 16.3% in Stage I, 32.5% in Stage II, 55.6% in Stage III, and 79.2% in Stage IV. Compared with Stage I, adjusted odds ratios were 2.54 (95% CI 1.86 to 3.46) for Stage II, 6.66 (95% CI 4.86 to 9.14) for Stage III, and 20.77 (95% CI 14.46 to 29.83) for Stage IV. Surgery was associated with lower adjusted odds than chemotherapy (adjusted OR 0.65, 95% CI 0.48 to 0.89). Apparent AUROC was 0.769; pooled leave-one-hospital-out AUROC was 0.753 with a Brier score of 0.198. Pooled calibration yielded an intercept of −0.031 and slope of 0.911. At the evaluated 0.05 increments, the model had greater net benefit than treat-all and treat-none strategies from approximately 0.20 to 0.75. Conclusion: Recorded mortality in this multi-hospital Bangladeshi lung cancer dataset was strongly stage-driven. An interpretable logistic risk model showed moderate discrimination and reasonable calibration across hospital-wise internal-external validation. These findings support further evaluation of routinely available clinical risk stratification, but independent external and prospective validation are required before clinical implementation.
SN - 1932-6203
ER -