Abstract
Background: The Traditional Chinese Medicine (TCM) Inheritance Computing Platform (V3.0) provides advanced data-mining capabilities for analyzing large-scale clinical data, enabling systematic extraction of prescription patterns and therapeutic principles. Objectives: This systematic review applied the TCM Inheritance Computing Platform to summarize clinical practice patterns in rUTIs, focusing on the clinical experience of Professor Qu Wei and evidence from clinical trials conducted between 2022 and 2025. Methods: Clinical trials were systematically retrieved from international registries (ClinicalTrials.gov, WHO ICTRP, ChiCTR, PubMed). Variables, including demographics, syndrome classification, interventions and outcomes, were standardized and encoded for analysis. Frequency analysis, association rule mining and hierarchical clustering were conducted to identify high-frequency symptoms, core prescriptions, herb compatibility patterns and therapeutic clusters. Results: Thirty-eight clinical trials (3,462 patients) were analyzed, predominantly female (72.4%), aged 28–57 years, with 3–6 recurrent episodes annually. Damp-heat in the lower jiao (62.1%) was the most frequent TCM syndrome, followed by spleen–kidney qi deficiency (38.6%) and yin deficiency with heat (21.3%). Key herbs included Huang Bai (71%), Fu Ling (69%), Che Qian Zi (65%) and Bai Zhu (58%). Association rule mining highlighted core herb pairs such as Huang Bai–Fu Ling (support 61%, confidence 0.82). Cluster analysis revealed three therapeutic groups: (A) damp-heat clearing formulas, (B) damp-heat with qi stagnation and (C) kidney-tonifying prescriptions. Interventions reduced recurrence by 30–45%, improved symptoms, quality of life and showed immunomodulatory and microbiota-regulating effects. Conclusion: The TCM Inheritance Computing Platform effectively summarized rUTI practice patterns, validating damp-heat clearing and kidney-tonifying as core strategies. Herb pair associations strengthen evidence-based integration of TCM, offering a replicable, data-driven model for recurrent infection management.