Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model.

Background: Primary biliary cholangitis (PBC) is a rare autoimmune liver disease with few effective treatments and a poor prognosis, and its incidence is on the rise. There is an urgent need for more targeted treatment strategies to accurately identify high-risk patients. The use of stochastic survival forest models in machine learning is an innovative approach to constructing a prognostic model for PBC that can improve the prognosis by identifying high-risk patients for targeted treatment. Method: Based on the inclusion and exclusion criteria, the clinical data and follow-up data of patients diagnosed with PBC-associated cirrhosis between January 2011 and December 2021 at Taizhou Hospital of Zhejiang Province were retrospectively collected and analyzed. Data analyses and random survival forest model construction were based on the R language. Result: Through a Cox univariate regression analysis of 90 included samples and 46 variables, 17 variables with p-values <0.1 were selected for initial model construction. The out-of-bag (OOB) performance error was 0.2094, and K-fold cross-validation yielded an internal validation C-index of 0.8182. Through model selection, cholinesterase, bile acid, the white blood cell count, total bilirubin, and albumin were chosen for the final predictive model, with a final OOB performance error of 0.2002 and C-index of 0.7805. Using the final model, patients were stratified into high- and low-risk groups, which showed significant differences with a P value <0.0001. The area under the curve was used to evaluate the predictive ability for patients in the first, third, and fifth years, with respective results of 0.9595, 0.8898, and 0.9088. Conclusion: The present study constructed a prognostic model for PBC-associated cirrhosis patients using a random survival forest model, which accurately stratified patients into low- and high-risk groups. Treatment strategies can thus be more targeted, leading to improved outcomes for high-risk patients.

Hybrid argon plasma coagulation for the treatment of Barrett's esophagus: A prospective, multicenter study.

BACKGROUND: The incidence of Barrett's esophagus (BE) in China is lower compared to the Western populations. Hence, studies conducted in the Chinese population has been limited. The current treatment options available for BE treatment includes argon plasma coagulation (APC), radiofrequency ablation and cryoablation, all with varying degrees of success. AIM: To determine the efficacy and safety of HybridAPC in the treatment of BE. METHODS: The study cohort consisted of patients with BE who underwent HybridAPC ablation treatment. These procedures were performed by seven endoscopists from different tertiary hospitals. The duration of the procedure, curative rate, complications and recurrent rate by 1-year follow-up were recorded. RESULTS: Eighty individuals were enrolled for treatment from July 2017 to June 2020, comprising of 39 males and 41 females with a median age of 54 years (range, 30 to 83 years). The technical success rate of HybridAPC was 100% and the overall curative rate was 98.15%. No severe complications occurred during the operation. BE cases were classified as short-segment BE and long-segment BE. Patients with short-segment BE were all considered cured without complications. Thirty-six patients completed the one-year follow-up without recurrence. Twenty-four percent had mild dysplasia which were all resolved with one post-procedural treatment. The mean duration of the procedure was 10.94 ± 6.52 min. CONCLUSION: Treatment of BE with HybridAPC was found to be a simple and quick procedure that is safe and effective during the short-term follow-up, especially in cases of short-segment BE. This technique could be considered as a feasible alternative ablation therapy for BE.

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.

Does Hospital Ownership Influence Hand Hygiene Compliance? / 华中科技大学学报（医学）（英德文版）

The issue as to whether hospital ownership has an impact on the quality of care has long been a serious concern.Hand hygiene (HH) compliance is regarded as an important indicator of the quality of care in the control of hospital-acquired infections.However,little information is available on whether hospital ownership influences HH compliance.In this study,of 229 hospitals selected from Hubei province in China,152 were public and 77 were private hospitals.A total of 23 652 healthcare workers (HCWs) were surveyed,using a convenience sampling.HH compliance,the WHO's "My Five Moments for hand hygiene" (5MHH),among HCWs,together with the factors of hospital ownership,training frequency,bed occupancy rates,etc.were collected.Univariate analysis and ordinal logistic regression analysis were used to analyze factors affecting HH compliance.Overall,HH compliance rates were 67％ and 79％ for public and private hospitals,respectively.The HH compliance rates of HCWs and 5MHH were between 55％ and 95％,and influenced by hospital ownership (P＜0.05),excluding compliance rate at the moment after body fluid exposure,and other influence factors included training frequency and bed occupancy rate (P＜0.05).HH compliance is better in private than in public hospitals.Hospital ownership is a significant factor affecting HH compliance,in addition to training frequency and bed occupancy rate.