ArticleFrontiers in medicine2026
Development and validation of a prediction model for tuberculous peritoneal effusion.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: The aim of this study was to develop and validate a nomogram for diagnosing tuberculous peritoneal effusion (TPE). Patients and methods: This was a retrospective, single-center study. We collected data on basic characteristics, laboratory tests, and clinical information from consecutive patients with TPE and non-tuberculous peritoneal effusion (non-TPE) using an electronic medical record database. The data were randomly divided into a training group and a validation group at a 7:3 ratio. Variables were selected using LASSO regression, and a predictive model was established using multivariable stepwise logistic regression to construct the diagnostic nomogram. The diagnostic performance of the nomogram was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Results: A total of 351 patients were included in the study, with 245 in the training group and 106 in the validation group. Age, fever, ascites adenosine deaminase (ADA), ascites carcinoembryonic antigen (CEA), ascites total protein (TP), serum CEA, and serum creatinine (Cr) were identified as independent predictors. Based on these seven variables, we developed a diagnostic nomogram. The area under the curve (AUC) of the nomogram model was 0.974 for the training group and 0.955 for the validation group. The calibration curves showed good agreement, and DCA indicated that the model provides the greatest net benefit within the threshold probability range for both the training and validation cohorts. The analysis of the model's probability rationality demonstrated that the diagnostic performance of the nomogram was superior to that of any single indicator. Conclusion: We developed and validated a nomogram model for diagnosing TPE based on seven clinical parameters. This model demonstrates excellent diagnostic performance in distinguishing TPE from non-TPE cases and provides a solid foundation for clinicians to formulate diagnostic strategies.
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