Background: The aim of this systematic review is to describe the different types of anchors and statistical methods used in estimating the Minimal Clinically Important Difference (MCID) for Health-Related Quality of Life (HRQoL) instruments. Methods: PubMed and Google scholar were searched for English and French language studies published from 2010 to 2018 using selected keywords. We included original articles (reviews, meta-analysis, commentaries and research letters were not considered) that described anchors and statistical methods used to estimate the MCID in HRQoL instruments. Results: Forty-seven papers satisfied the inclusion criteria. The MCID was estimated for 6 generic and 18 diseasespecific instruments. Most studies in our review used anchor-based methods (n = 41), either alone or in combination with distribution-based methods. The most common applied anchors were non-clinical, from the viewpoint of patients. Different statistical methods for anchor-based methods were applied and the Change Difference (CD) was the most used one. Most distributional methods included 0.2 standard deviations (SD), 0.3 SD, 0.5 SD and 1 standard error of measurement (SEM). MCID values were very variable depending on methods applied, and also on clinical context of the study. Conclusion: Multiple anchors and methods were applied in the included studies, which lead to different estimations of MCID. Using several methods enables to assess the robustness of the results. This corresponds to a sensitivity analysis of the methods. Close collaboration between statisticians and clinicians is recommended to integrate an agreement regarding the appropriate method to determine MCID for a specific context.

BackgroundHealth-Related Quality of Life (HRQoL) assessment after kidney transplantation has become an important tool in evaluating outcomes. This study aims to identify the associated factors with HRQoL among a representative sample size of Kidney Transplant Recipients (KTR) at the time of their inclusion in the study.MethodsData of this cross-sectional design is retrieved from a longitudinal study conducted in five French kidney transplant centers in 2011, and included KTR aged 18 years with a functioning graft for at least 1 year. Measures include demographic, psycho-social and clinical characteristics. To evaluate HRQoL, the Short Form-36 Health Survey (SF-36) and a HRQoL instrument for KTR (ReTransQol) were administered. Multivariate linear regression models were performed.ResultsA total of 1424 patients were included, with 61.4% males, and a mean age of 55.7 years (±13.1). Demographic and clinical characteristics were associated with low HRQoL scores for both questionnaires. New variables were found in our study: perceived poor social support and being treated by antidepressants were associated with low scores of Quality of Life (QoL), while internet access was associated with high QoL scores.ConclusionThe originality of our study’s findings was that psycho-social variables, particularly KTR treated by antidepressants and having felt unmet needs for any social support, have a negative effect on their QoL. It may be useful to organize a psychological support specifically adapted for patients after kidney transplantation.

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BackgroundThe main reason for anemia in renal failure patients is the insufficient erythropoietin production by the kidneys. Beside erythropoietin deficiency, in vitro studies have incriminated uremic toxins in the pathophysiology of anemia but clinical data are sparse. In order to assess if indole 3-acetic acid (IAA), indoxyl sulfate (IS), and paracresyl sulfate (PCS) -three protein bound uremic toxins- are clinically implicated in end-stage renal disease anemia we studied the correlation between IAA, IS and PCS plasmatic concentrations with hemoglobin and Erythropoietin Stimulating Agents (ESA) use in hemodialysis patients.MethodsBetween June and July 2014, we conducted an observational cross sectional study in two hemodialysis center. Three statistical approaches were conducted. First, we compared patients treated with ESA and those not treated. Second, we performed linear regression models between IAA, IS, and PCS plasma concentrations and hemoglobin, the ESA dose over hemoglobin ratio (ESA/Hemoglobin) or the ESA resistance index (ERI). Third, we used a polytomous logistic regression model to compare groups of patients with no/low/high ESA dose and low/high hemoglobin statuses.ResultsOverall, 240 patients were included in the study. Mean age ± SD was 67.6 ± 16.0 years, 55.4% were men and 42.5% had diabetes mellitus.When compared with ESA treated patients, patients with no ESA had higher hemoglobin (mean 11.4 ± 1.1 versus 10.6 ± 1.2 g/dL; p <0.001), higher transferrin saturation (TSAT, 31.1 ± 16.3% versus 23.1 ± 11.5%; p < 0.001), less frequently an IV iron prescription (52.1 versus 65.7%, p = 0.04) and were more frequently treated with hemodiafiltration (53.5 versus 36.7%). In univariate analysis, IAA, IS or PCS plasma concentrations did not differ between the two groups.In the linear model, IAA plasma concentration was not associated with hemoglobin, but was negatively associated with ESA/Hb (p = 0.02; R = 0.18) and with the ERI (p = 0.03; R = 0.17). IS was associated with none of the three anemia parameters. PCS was positively associated with hemoglobin (p = 0.03; R = 0.14), but negatively with ESA/Hb (p = 0.03; R = 0.17) and the ERI (p = 0.02; R = 0.19). In multivariate analysis, the association of IAA concentration with ESA/Hb or ERI was not statistically significant, neither was the association of PCS with ESA/Hb or ERI. Identically, in the subgroup of 76 patients with no inflammation (CRP <5 mg/L) and no iron deficiency (TSAT >20%) linear regression between IAA, IS or PCS and any anemia parameter did not reach significance.In the third model, univariate analysis showed no intergroup significant differences for IAA and IS. Regarding PCS, the Low Hb/High ESA group had lower concentrations. However, when we compared PCS with the other significant characteristics of the five groups to the Low Hb/high ESA (our reference group), the polytomous logistic regression model didn’t show any significant difference for PCS.ConclusionsIn our study, using three different statistical models, we were unable to show any c...

Background: The use of the Internet for searching and sharing health information and for health care interactions may have a great potential for Renal Transplant Recipients (RTR). This study aims to determine the characteristics associated with Internet and social network use in a representative sample of RTR at the time of their inclusion in the study. Methods: Data of this cross-sectional design is retrieved from a longitudinal study conducted in five French kidney transplant centers in 2011, and included Renal Transplant Recipients aged 18 years with a functioning graft for at least 1 year. Measures include demographic characteristics (age, gender, level of education, employment status, living arrangement, having children, invalidity and monthly incomes in the household), psycho-social characteristics measured by the perceived social support questionnaire, and medical characteristics (previous dialysis treatment, duration since transplantation, graft rejection episodes, chronic graft dysfunction, health status and comorbidities: neoplasia for the current transplant, hypertension, diabetes mellitus, smoking status, BMI > 30 kg/m 2 and Charlson Comorbidity Index (CCI)). Polytomous linear regression analysis was performed to describe the Internet and social network users' profiles, using lack of Internet access as the comparison category. Results: Among the 1416 RTR participating in the study, 20.1% had no Internet access in the household, 29.4% connected to social networks and 50.5% were not connected to social networks. Patients who connected the most to the Internet and social networks were younger, male, without children, employed, with high monthly incomes in the household, without hypertension and having felt a need for an informative or an esteem support. Conclusion: In our study, the majority of RTR were actively using Internet and social networks. Renal transplant units should develop flexible and Web-based sources related to transplant information, which will allow a rapid adaptation to changes in prevalent practice, improve the health of the patients and reflect their preferences.