The impact of exercise intervention for patients undergoing hemodialysis on fatigue and quality of life: A protocol for systematic review and meta-analysis.
- 作者列表："Zhang F","Bai Y","Zhao X","Huang L","Zhang Y","Zhang H
BACKGROUND:This study aims to determine the therapeutic efficacy of exercise interventions for patients undergoing hemodialysis (HD) on fatigue and health-related quality of life (HRQoL). METHODS:This review will only include randomized controlled trials (RCTs). The search strategy will be performed in 4 English databases, 4 Chinese databases, Clinical Trials.gov, and the Chinese Clinical Trial Registry. All English or Chinese RCTs, published from inception to May 31, 2020, will be sought. Two reviewers will screen, select studies, extract data, and assess quality independently. Primary outcome is fatigue assessed by questionnaire. The methodological quality including the risk of bias of the included studies will be evaluated using the Physiotherapy Evidence Database scale. Stata 12.0 software will be used for heterogeneity assessment, generating funnel-plots, data synthesis, subgroup analysis, and sensitivity analysis. RESULTS:We will provide some more practical and targeted results investigating the effect of exercise interventions for patients undergoing HD on fatigue and HRQoL in the current meta-analysis, and point out the main limitation of previous studies. CONCLUSION:The study will provide recent evidence for evaluating the therapeutic efficacy of exercise interventions for patients undergoing HD on fatigue and HRQoL. REGISTRATION NUMBER:INPLASY202050071 (DOI: 10.37766/inplasy2020.5.0071).
背景: 本研究旨在确定治疗运动干预患者血液透析 (HD) 对乏力和健康相关生活质量 (HRQoL). 方法: 本综述仅包括随机对照试验 (rct)。检索策略将在4个英文数据库、4个中文数据库、Clinical trials.Gov和中文临床试验注册中心进行。所有英文或中文rct，从一开始到2020年5月31日，将被寻找。两名评价者将独立筛选、选择研究、提取数据和评估质量。主要终点是乏力的问卷调查.包括纳入研究的偏倚风险在内的方法学质量将使用物理治疗证据数据库量表进行评估。Stata 12.0软件将用于异质性评估、生成漏斗图、数据综合、亚组分析和敏感性分析。 结果: 我们将在目前的荟萃分析中提供一些更实际的、有针对性的结果，调查对接受HD的患者的运动干预乏力和HRQoL的影响，并指出以往研究的主要局限性。 结论: 本研究将为评价运动干预对维持性血液透析乏力和HRQoL的疗效提供最新证据。 注册号: INPLASY202050071 (DOI: 10.37766/inplasy2020.5.0071)。
METHODS:AIM:Clinical interpretation of B-type natriuretic peptide (BNP) levels in haemodialysis (HD) patients for fluid management remains elusive. METHODS:We conducted a retrospective observational monocentric study. We built a mathematical model to predict BNP levels, using multiple linear regressions. Fifteen clinical/biological characteristics associated with BNP variation were selected. A first cohort of 150 prevalent HD (from September 2015 to March 2016) was used to build several models. The best model proposed was internally validated in an independent cohort of 75 incidents HD (from March 2016 to December 2017). RESULTS:In cohort 1, mean BNP level was 630 ± 717 ng/mL. Cardiac disease (CD - stable coronary artery disease and/or atrial fibrillation) was present in 45% of patients. The final model includes age, systolic blood pressure, albumin, CD, normo-hydrated weight (NHW) and the fluid overload (FO) assessed by bio-impedancemetry. The correlation between the measured and the predicted log-BNP was 0.567 and 0.543 in cohorts 1 and 2, respectively. Age (β = 3.175e-2 , P < 0.001), CD (β = 5.243e-1 , P < 0.001) and FO (β = 1.227e-1 , P < 0.001) contribute most significantly to the BNP level, respectively, but within a certain range. We observed a logistic relationship between BNP and age between 30 and 60 years, after which this relationship was lost. BNP level was inversely correlated with NHW independently of CD. Finally, our model allows us to predict the BNP level according to the FO. CONCLUSION:We developed a mathematical model capable of predicting the BNP level in HD. Our results show the complex contribution of age, CD and FO on BNP level.
METHODS:AIM:The removal of cysteine during a dialysis procedure may affect glutathione (GSH) concentration, allowing haemodialysis (HD) patients to become more susceptible to oxidative damage. This study was performed to determine whether the change of GSH/glutathione disulfide (GSSG) redox state and GSH redox potential were linked with the change of cysteine or oxidative stress in patients receiving HD treatment. METHODS:Sixty-seven HD patients who had received regular HD treatment were recruited. Plasma GSH, GSSG, cysteine and malondialdehyde (MDA) were measured at both pre- and post-HD. RESULTS:Plasma cysteine, GSH and GSSG levels significantly decreased after the completion of HD, compared to the levels at pre-HD. Plasma MDA concentration, GSH/GSSG ratio and GSH redox potential remained constant during the dialysis session. Plasma GSH and GSSG were positively associated with plasma MDA at post-HD, while GSH redox potential was negatively associated with plasma MDA at post-HD. However, plasma GSH, GSSG, GSH/GSSG ratio and GSH redox potential were not associated with plasma cysteine at either pre- or post-HD. CONCLUSION:The GSH and GSSG levels were significantly utilized during a HD session, and their levels were significantly associated with increased oxidative stress. HD patients may require higher GSH demands to cope with increased oxidative stress during an HD session.
METHODS:OBJECTIVE:Although there is no consensus on how to use an electrocardiogram (ECG) in patients with hyperkalemia, physicians often obtain it in the acute setting when diagnosing and treating hyperkalemia. The objective of this study is to evaluate if physicians are able to detect hyperkalemia based on the ECG. METHODS:The study was conducted at a large county hospital with a population of end stage renal disease (ESRD) patients who received hemodialysis (HD) solely on an emergent basis. Five hundred twenty eight ECGs from ESRD patients were evaluated. The prevalence of hyperkalemia was approximately 60% in this cohort, with at least half of them in the severe hyperkalemia range (K ≥ 6.5 mEq/L). RESULTS:The mean sensitivity and specificity of the emergency physicians detecting hyperkalemia were 0.19 (± 0.16) and 0.97(± 0.04) respectively. The mean positive predictive value of evaluators for detecting hyperkalemia was 0.92 (±0.13) and the mean negative predictive value was 0.46 (± 0.05). In severe hyperkalemia (K ≥ 6.5 mEq/L), the mean sensitivity improved to 0.29 (± 0.20), while specificity decreased to 0.95 (±0.07). CONCLUSION:An ECG is not a sensitive method of detecting hyperkalemia and should not be relied upon to rule it out. However, the ECG has a high specificity for detecting hyperkalemia and could be used as a rule in test.