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Background <p>Accurate discrimination between tuberculous (TPE) and malignant pleural effusion (MPE) is a major clinical challenge. Most existing models rely on non-routine laboratory tests and lack rigorous multicenter external validation.</p> Objective <p>To develop and validate a clinical prediction model integrating the pleural fluid adenosine deaminase to lactate dehydrogenase ratio (ADA/LDH) and routine indicators for TPE vs. MPE differentiation.</p> Methods <p>In this multicenter retrospective study conducted between January 2023 and December 2025, patients from five hospitals in Anhui Province, China, were divided into a training cohort (<i>n</i> = 290), an internal validation cohort (<i>n</i> = 72), and an external validation cohort (<i>n</i> = 93). Predictors were screened via univariable analysis and backward stepwise regression based on the Akaike Information Criterion (AIC). The optimal ADA/LDH cutoff was identified as 5.83% using restricted cubic splines (RCS) and simplified to 6.0% for clinical practicability without compromising model performance. A Firth penalized logistic regression model was constructed to mitigate data separation caused by the strong predictive effect of the ADA/LDH ratio.</p> Results <p>The final model included three statistically significant variables: pleural fluid ADA/LDH ratio (≥ 6.0% vs. &lt; 6.0%), age, and sex. An ADA/LDH ratio ≥ 6.0% was the strongest independent predictor (OR = 13.32, 95% CI 6.51–27.28, <i>P</i> &lt; 0.001). The model demonstrated excellent and stable discriminative ability with AUCs of 0.901 (training cohort), 0.893 (internal validation cohort), and 0.916 (external validation cohort). Calibration was good across all cohorts (Brier scores: 0.1235, 0.1249, 0.1159, respectively). Decision curve analysis demonstrated that the model provided numerically higher net benefit than the “treat all” and “treat none” strategies across the clinically relevant threshold range of 0%–90%.</p> Conclusion <p>This multicenter study developed and validated a robust Firth penalized prediction model centered on the pleural fluid ADA/LDH ratio. The model demonstrates excellent discriminative ability, good calibration, and potential clinical utility for differentiating TPE from MPE in TB-endemic regions of China.</p>

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Derivation and validation of a clinical prediction model incorporating the pleural fluid ADA-to-LDH ratio for differentiating tuberculous from malignant pleural effusions: a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Zhiyu Pan,
  • Sha Tao,
  • Haoyu Sheng,
  • Xiuliang Xu,
  • Zhixin Lin,
  • Fen Huang,
  • Fang Liu,
  • Yajuan Wang,
  • Yihui Sun,
  • Yingchun Sun,
  • Wei Qian,
  • Jianghua Yang

摘要

Background

Accurate discrimination between tuberculous (TPE) and malignant pleural effusion (MPE) is a major clinical challenge. Most existing models rely on non-routine laboratory tests and lack rigorous multicenter external validation.

Objective

To develop and validate a clinical prediction model integrating the pleural fluid adenosine deaminase to lactate dehydrogenase ratio (ADA/LDH) and routine indicators for TPE vs. MPE differentiation.

Methods

In this multicenter retrospective study conducted between January 2023 and December 2025, patients from five hospitals in Anhui Province, China, were divided into a training cohort (n = 290), an internal validation cohort (n = 72), and an external validation cohort (n = 93). Predictors were screened via univariable analysis and backward stepwise regression based on the Akaike Information Criterion (AIC). The optimal ADA/LDH cutoff was identified as 5.83% using restricted cubic splines (RCS) and simplified to 6.0% for clinical practicability without compromising model performance. A Firth penalized logistic regression model was constructed to mitigate data separation caused by the strong predictive effect of the ADA/LDH ratio.

Results

The final model included three statistically significant variables: pleural fluid ADA/LDH ratio (≥ 6.0% vs. < 6.0%), age, and sex. An ADA/LDH ratio ≥ 6.0% was the strongest independent predictor (OR = 13.32, 95% CI 6.51–27.28, P < 0.001). The model demonstrated excellent and stable discriminative ability with AUCs of 0.901 (training cohort), 0.893 (internal validation cohort), and 0.916 (external validation cohort). Calibration was good across all cohorts (Brier scores: 0.1235, 0.1249, 0.1159, respectively). Decision curve analysis demonstrated that the model provided numerically higher net benefit than the “treat all” and “treat none” strategies across the clinically relevant threshold range of 0%–90%.

Conclusion

This multicenter study developed and validated a robust Firth penalized prediction model centered on the pleural fluid ADA/LDH ratio. The model demonstrates excellent discriminative ability, good calibration, and potential clinical utility for differentiating TPE from MPE in TB-endemic regions of China.