Objective. This systematic review and meta-analysis were performed to investigate the efficacy and safety of Chinese herbal medicine (CHM) in the treatment of knee osteoarthritis (KOA). Methods. An electronic search was conducted in eight databases (PubMed, EMBASE, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese VIP Database, and Wanfang Database) from inception until December 2019. The risk of bias assessment of the included RCTs was evaluated by Cochrane collaboration’s tool. The inclusion criteria were RCTs that investigated the efficacy and safety of CHM in the treatment of KOA, with no restrictions on publication status or language. The exclusion criteria included nonrandomized or quasi-RCTs, no clear KOA diagnostic approach, combined Chinese medicinal herbs with other traditional Chinese medicine treatment modalities, and published using repeated data and missing data. We computed the relative risk (RR) and the standard mean difference (SMD) for dichotomous outcomes and continuous outcomes, respectively. When heterogeneity was detected or there was significant statistical heterogeneity ( P < 0.05 or I 2 > 50 % ), a random-effects model was employed, followed by further subgroup analysis and metaregression estimations to ascertain the origins of heterogeneity. Otherwise, we used a fixed-effects model ( P ≥ 0.05 or I 2 ≤ 50 % ). The primary outcome measures were visual analog score (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Lysholm score, and Lequesne index. Secondary outcome measures were the total clinical effective rate and adverse events. The meta-analysis was performed using the Stata 14.0 software. Results. A total of 56 RCTs comprising 5350 patients met the inclusion criteria. This meta-analysis showed that application of CHM as adjuvant therapy or monotherapy for KOA can significantly decrease VAS, WOMAC, and the Lequesne index and improve the Lysholm score as well as the total effective rate. In addition, this treatment has fewer adverse effects, suggesting that CHM is generally safe and well tolerated among patients with KOA. Conclusion. Our study offers supportive evidence that CHM, either adjuvant therapy or monotherapy, reduces the VAS, WOMAC, and Lequesne index and improves the Lysholm score and overall effective rate in patients with KOA. Additionally, CHM was well tolerated and safe in KOA patients. We found frequently used CHMs that might contribute to the formulation of a herbal formula that could be considered for further clinical use. However, given the heterogeneity and limited sample size in this study, larger multicenter and high-quality RCTs are needed to validate the benefits of CHM in the treatment of KOA.
Background and Aim. It is of importance to predict the risk of gastric cancer (GC) for endoscopists because early detection of GC determines the determines the selection of best treatment strategy and the prognosis of patients. The aim of the study was to evaluate the utility of a predictive nomogram based on Kyoto classification of gastritis for GC. Methods. It was a retrospective study that included 2639 patients who received esophagogastroduodenoscopy and serum pepsinogen (PG) assay from January 2020 to November 2020 at the Endoscopy Center of the Department of Gastroenterology, Wenzhou Central Hospital. Routine biopsy was conducted to determine the benign and malignant lesions pathologically. All cases were randomly divided into the training set (70%) and the validation set (30%) by using bootstrap method. A nomogram was formulated according to multivariate analysis of training set. The predictive accuracy and discriminative ability of the nomogram were assessed by concordance index (C-index), area under the curve (AUC) of receiver operating characteristic curve (ROC) as well as calibration curve and were validated by validation set.Results. Multivariate analysis indicated that age, sex, PG I/II ratio and Kyoto classification scores were independent predictive variables for GC. The C-index of the nomogram of the training set was 0.79 (95% CI: 0.74 to 0.84) and the AUC of ROC is 0.79. The calibration curve of the nomogram demonstrated an optimal agreement between predicted probability and observed probability of the risk of GC. In the validation set, the C-index was 0.86 (95% CI: 0.79 to 0.94) with a calibration curve of better concurrence.Conclusion. The nomogram formulated was proven to be of high predictive value for GC.
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