Further validation of the Health Scale of Traditional Chinese Medicine (HSTCM)

<p>Abstract</p> <p>Background</p> <p>Few health measurement scales are based on Chinese medicine theory. The Health Scale of Traditional Chinese Medicine (HSTCM) was developed to fill this gap. The aim of this study is to validate the HSTCM.</p> <p>Methods</p> <p>A convenience sample of 630 participants was recruited in 11 settings. All participants were asked to complete the HSTCM and World Health Organization Quality of Life Measure-Abbreviated Version (WHOQOL-BREF).</p> <p>Results</p> <p>Properties of the HSTCM were tested. Intra-class correlation coefficient representing the inter-interviewer reliability was 0.99 (95%CI) for the overall instrument. Spearman-Brown correlation coefficient and Cronbach's coefficient alpha were 0.81 and 0.94 respectively, indicating satisfactory internal reliability and inter-interviewer reliability. Spearman's rho correlation coefficient between the HSTCM and WHOQOL-BREFF was -0.67. A receiver operating characteristic (ROC) curve analysis was performed to test the discriminate validation. Areas under the ROC curve analysis for the HSTCM and its domains ranged 0.71–0.87 and all the lower levels of 95%CI were greater than 0.50.</p> <p>Conclusion</p> <p>The HSTCM was validated as a generic health scale and may complement existing health measurement scales in Chinese medicine health care.</p

Does Tai Chi relieve fatigue? A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND:Fatigue is not only a familiar symptom in our daily lives, but also a common ailment that affects all of our bodily systems. Several randomized controlled trials (RCTs) have proven Tai Chi to be beneficial for patients suffering from fatigue, however conclusive evidence is still lacking. A systematic review and meta-analysis was performed on all RCTs reporting the effects of Tai Chi for fatigue. METHODS:In the end of April 2016, seven electronic databases were searched for RCTs involving Tai Chi for fatigue. The search terms mainly included Tai Chi, Tai-ji, Taiji, fatigue, tiredness, weary, weak, and the search was conducted without language restrictions. Methodological quality was assessed using the Cochrane Risk of Bias tool. RevMan 5.3 software was used for meta-analysis. Publication bias was estimated with a funnel plot and Egger's test. We also assessed the quality of evidence with the GRADE system. RESULTS:Ten trials (n = 689) were included, and there was a high risk of bias in the blinding. Two trials were determined to have had low methodological quality. Tai Chi was found to have improved fatigue more than conventional therapy (standardized mean difference (SMD): -0.45, 95% confidence interval (CI): -0.70, -0.20) overall, and have positive effects in cancer-related fatigue (SMD:-0.38, 95% CI: -0.65, -0.11). Tai Chi was also more effective on vitality (SMD: 0.63, 95% CI: 0.20, 1.07), sleep (SMD: -0.32, 95% CI: -0.61, -0.04) and depression (SMD: -0.58, 95% CI: -1.04, -0.11). However, no significant difference was found in multiple sclerosis-related fatigue (SMD: -0.77, 95% CI: -1.76, 0.22) and age-related fatigue (SMD: -0.77, 95% CI: -1.78, 0.24). No adverse events were reported among the included studies. The quality of evidence was moderate in the GRADE system. CONCLUSIONS:The results suggest that Tai Chi could be an effective alternative and /or complementary approach to existing therapies for people with fatigue. However, the quality of the evidence was only moderate and may have the potential for bias. There is still absence of adverse events data to evaluate the safety of Tai Chi. Further multi-center RCTs with large sample sizes and high methodological quality, especially carefully blinded design, should be conducted in future research. REGISTRATION NUMBER:PROSPERO CRD42016033066

Outcome Reporting Variability in Trials of Chinese Medicine for Hyperlipidemia: A Systematic Review for Developing a Core Outcome Set

Introduction. Hyperlipidemia is an underlying process behind cardiovascular disease. Chinese medicine (CM) may be effective in treating hyperlipidemia, but there is a lack of studies with high methodological quality. A major reason for this is heterogeneity in outcome reporting. Therefore, this study explores the degree of outcome reporting variation in CM trials for hyperlipidemia. It then generates a list of potentially important outcomes for developing a core outcome set (COS). Methods. A systematic review of literature focusing on studies of CM for hyperlipidemia was conducted. Outcomes were listed verbatim and grouped into 8 domains. Outcome frequency and definition uniformity were analyzed. Results. 3,702 studies and 452 individual outcomes were identified. These outcomes were reported 27,328 times, of which 1.6% were reported as primary outcomes, and 13.3% were defined. The most frequent outcome was total triglyceride, represented in 86.7% of the studies, followed by total cholesterol (86.0%), total effective rate (75.1%), high-density lipoprotein cholesterol (73.2%), and low-density lipoprotein cholesterol (60.5%). However, 43.6% of outcomes were reported only once. The largest outcome domain was “pathological or pathophysiological outcomes,” which included 67.0% of outcomes. Of the “response rate related outcomes” domain, total effective rate was the most frequently reported outcome (n = 2,780), and 95.3% of the studies gave a clear definition. However, these definitions were often contradictory. Only 10 papers reported cardiovascular events, 3 of which referred to them as primary outcomes. Moreover, ten patient-reported outcomes were reported in the retrieved literature 19 times in total. The majority of the outcomes did not report measurement instruments (MIs) (269/453, 59.4%). MIs of the surrogate outcomes were reported more frequently. Conclusion. Outcome reporting in CM trials for hyperlipidemia is inconsistent and ill-defined, creating barriers to data synthesis and comparison. Thus, we propose and are developing a COS for CM trials for hyperlipidemia

Epidemiology and Outcomes of Complicated Skin and Soft Tissue Infections among Inpatients in Southern China from 2008 to 2013.

Complicated skin and soft tissue infections (cSSTI) are some of the most commonly treated infections in hospitals, and place heavy economic burdens on patients and society. Here we report the findings from an analysis of cSSTI based on a retrospective study which was conducted within the Chinese inpatient population. We focused our research on the analysis of the patient population, antibiotic treatment, clinical outcome and economic burden. The study population comprised 527 selected patients hospitalized between 2008 and 2013. Among the hospitalizations with microbiological diagnoses, 61.41% (n = 113) were diagnosed as infected with Gram-positive bacteria, while 46.20% (n = 85) were infected with Gram-negative bacteria. The most commonly found Gram-positive bacteria was Staphylococcus aureus (40.76%, n = 75), and the most common Gram-negative bacteria was Escherichia coli (14.13%, n = 26). About 20% of the Staphylococcus aureus were methicillin-resistant. The resistance rate of isolated Staphylococcus aureus or Escherichia coli to penicillin was around 90%; in contrast, the resistance rate to vancomycin, linezolid or imipenem was low (<20%). A large percentage of patients were treated with cephalosporins and fluoroquinolones, while vancomycin and imipenem were also included to treat drug-resistant pathogens. Over half of the hospitalizations (58.43%, n = 336) experienced treatment modifications. The cost to patients with antibiotic modifications was relatively higher than to those without. In conclusion, our study offers an analysis of the disease characteristics, microbiological diagnoses, treatment patterns and clinical outcomes of cSSTI in four hospitals in Guangdong Province, and sheds lights on the current clinical management of cSSTI in China

Development a nomogram prognostic model for survival in heart failure patients based on the HF-ACTION data

Abstract Background The risk assessment for survival in heart failure (HF) remains one of the key focuses of research. This study aims to develop a simple and feasible nomogram model for survival in HF based on the Heart Failure-A Controlled Trial Investigating Outcomes of Exercise TraiNing (HF-ACTION) to support clinical decision-making. Methods The HF patients were extracted from the HF-ACTION database and randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Multivariate Cox regression was used to identify and integrate significant prognostic factors to form a nomogram, which was displayed in the form of a static nomogram. Bootstrap resampling (resampling = 1000) and cross-validation was used to internally validate the model. The prognostic performance of the model was measured by the concordance index (C-index), calibration curve, and the decision curve analysis. Results There were 1394 patients with HF in the overall analysis. Seven prognostic factors, which included age, body mass index (BMI), sex, diastolic blood pressure (DBP), exercise duration, peak exercise oxygen consumption (peak VO2), and loop diuretic, were identified and applied to the nomogram construction based on the training cohort. The C-index of this model in the training cohort was 0.715 (95% confidence interval (CI): 0.700, 0.766) and 0.662 (95% CI: 0.646, 0.752) in the validation cohort. The area under the ROC curve (AUC) value of 365- and 730-day survival is (0.731, 0.734) and (0.640, 0.693) respectively in the training cohort and validation cohort. The calibration curve showed good consistency between nomogram-predicted survival and actual observed survival. The decision curve analysis (DCA) revealed net benefit is higher than the reference line in a narrow range of cutoff probabilities and the result of cross-validation indicates that the model performance is relatively robust. Conclusions This study created a nomogram prognostic model for survival in HF based on a large American population, which can provide additional decision information for the risk prediction of HF