RESUMEN
Background: With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods: We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings: From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation: Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding: This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).
RESUMEN
Background: The recurrence of colorectal adenomas (CRAs) after endoscopy predisposes patients to a risk of colorectal cancer. Guided by the traditional Chinese medicine (TCM), patients with colorectal diseases usually manifest with spleen deficiency syndrome (SDS) and are treated with Sijunzi decoction (SJZD). Therefore, this trial aims to explore the efficacy and safety of SJZD in the prevention and treatment of CRAs recurrence. Methods: SJZD on prevention and treatment of CRAs recurrence after resection: a multicenter, randomized, double-blind, placebo-controlled trial was designed. Patients who undergo polypectomy of CRAs will be recruited and randomized into a SJZD group and a placebo group in a 1:1 ratio. The intervention phase will be 12 months. The follow-up period will last 24 months. The primary outcome is the CRA recurrence rate after intervention. The secondary outcomes include the CRA recurrence rate at the second year post-polypectomy, the pathological type of adenoma and the alterations in SDS scores after intervention. Discussion: Previous clinical practice has observed the sound effect of SJZD in the context of gastrointestinal diseases. A number of experiments have also validated the active components in SJZD. This trial aims to provide tangible evidence for the usage of SJZD, hoping to reduce the recurrence of CRAs.