ArticlePediatric surgery international2024
Clinical prediction model for pediatric lymphadenopathy: enhancing diagnostic precision and treatment decision making.
Article in Pediatric surgery international, 2024. 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
introductionLymph node enlargement is common in children, with 90% of physiologically palpable lymph nodes. This study aimed to develop a predictive model based on clinical characteristics to enhance the diagnosis of pediatric lymphadenopathy and provide insights into biopsy outcomes. MATERIALS AND
methodsA clinical prediction rule was developed using a retrospective, cross-sectional design for patients under 15 years who underwent lymph node biopsy from 2012 to 2022. Multivariable risk regression was used to analyze benign and malignant lesions, presenting results through risk difference and AUROC for each group. Predicted probabilities were applied in a logistic regression equation to classify patients' lymphadenopathy as reactive hyperplasia, benign, or malignant.
resultsOf 188 children, 70 (37.2%) had benign lymphadenopathy beyond reactive hyperplasia, and 27 (14.4%) had malignant lymphadenopathy. The predictive model included 12 characteristics such as size, location, duration, associated symptoms, and lymph node examination. Predictive accuracy was 92.2% for benign cases (AUROC = 0.92; 95% CI 0.87-0.96) and 98.6% for malignancy (AUROC = 0.98; 95% CI 0.94-0.99). Overall accuracy for predicting both benign and malignant tumors was 68.3%.
conclusionThe model demonstrated reasonably accurate predictions for the clinical characteristics of pediatric lymphadenopathy. It tended to overestimate malignancy but did not miss diagnoses, aiding in reducing unnecessary lymph node biopsies in benign cases.
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