Development and Evaluation of a Peer-to-Peer Intervention to Increase Self-Management among Adult In-Center Hemodialysis Patients

Background: Peer-to-peer (P2P) support programs have the potential to assist ESRD patients in managing their disease and improve outcomes. Yet, there is little research examining P2P programs’ impact on psychosocial outcomes and disease management behaviors. Methods: A 4-month P2P mentoring intervention was designed and piloted in a facility serving 249 in-center hemodialysis patients in Lynchburg, Virginia. Preceded by a social marketing effort, which included a program naming contest and participant recruitment, the intervention included: (1) mentor training, (2) pairing of mentees and mentors, (3) kick-off social mixers, (4) ongoing meetings between mentees and mentors, (5) mentor training booster, and (6) a final celebration. A single arm quasi-experimental study with repeated measurements at three time points was used with data collection over four months. The hypotheses that the intervention would result in improvements for both mentees and mentors (i.e., self-efficacy, knowledge, perceived social support, dialysis social support (i.e., support from peers within the dialysis setting), and self-management behaviors) were tested using repeated measures ANOVA or the Friedman’s test for nonparametric data. Results: Mentees experienced increases in self-efficacy, F(2,22)=8.15, p\u3c.01; knowledge, F(2,44)=6.62, p\u3c.01; perceived social support, F(2,22)=7.30, p\u3c.01; and dialysis social support, F(2,44)=4.79, p=.01. Mentors experienced increases in knowledge, F(2,22)=11.88, p\u3c.01; dialysis social support, F(2,42)=3.19, p=.05; and dialysis self-management, χ2(2) = 7.65, p =.02. Conclusion: A P2P mentoring program for in-center hemodialysis patients can be beneficial for both mentees and mentors. Future research should focus on larger groups of patients using more rigorous research designs

Nomenclature for kidney function and disease: report of a Kidney Disease:Improving Global Outcomes (KDIGO) Consensus Conference

The worldwide burden of kidney disease is rising, but public awareness remains limited, underscoring the need for more effective communication by stakeholders in the kidney health community. Despite this need for clarity, the nomenclature for describing kidney function and disease lacks uniformity. In June 2019, Kidney Disease: Improving Global Outcomes (KDIGO) convened a Consensus Conference with the goal of standardizing and refining the nomenclature used in the English language to describe kidney function and disease, and of developing a glossary that could be used in scientific publications. Guiding principles of the conference were that the revised nomenclature should be patient-centered, precise, and consistent with nomenclature used in the KDIGO guidelines. Conference attendees reached general consensus on the following recommendations: (i) to use "kidney" rather than "renal" or "nephro-" when referring to kidney disease and kidney function; (ii) to use "kidney failure" with appropriate descriptions of presence or absence of symptoms, signs, and treatment, rather than "end-stage kidney disease"; (iii) to use the KDIGO definition and classification of acute kidney diseases and disorders (AKD) and acute kidney injury (AKI), rather than alternative descriptions, to define and classify severity of AKD and AKI; (iv) to use the KDIGO definition and classification of chronic kidney disease (CKD) rather than alternative descriptions to define and classify severity of CKD; and (v) to use specific kidney measures, such as albuminuria or decreased glomerular filtration rate (GFR), rather than "abnormal" or "reduced" kidney function to describe alterations in kidney structure and function. A proposed 5-part glossary contains specific items for which there was general agreement. Conference attendees acknowledged limitations of the recommendations and glossary, but they considered standardization of scientific nomenclature to be essential for improving communication

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

A. Palotie on työryhmän Schizophrenia Working Grp Psychiat jäsen.We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P = 1 x 10(-4)) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P = 8.4 x 10(-7)). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.Peer reviewe

Peer mentorship to improve outcomes in patients on hemodialysis (PEER-HD): a randomized controlled trial protocol

Abstract Background Patients receiving in-center hemodialysis experience disproportionate morbidity and incur high healthcare-related costs. Much of this cost stems from potentially avoidable hospitalizations. Peer mentorship has been used effectively to improve outcomes for patients with complex chronic diseases. We propose testing the efficacy of peer mentorship on hospitalization rates among patients receiving hemodialysis. Methods This is a multicenter parallel group randomized controlled pragmatic trial of patients treated at hemodialysis facilities in Bronx, NY and Nashville, TN. The study has two phases. Phase 1 will enroll and train 16 hemodialysis patients (10 in Bronx, NY and 6 in Nashville TN) to be mentors using a program focused on enhancing self-efficacy, dialysis self-management and autonomy-supportive communication skills. Phase 2 will enroll 200 high risk adults receiving hemodialysis (140 in Bronx, NY and 60 in Nashville, TN), half of whom will be randomized to intervention and half to usual care. Intervention participants are assigned to weekly telephone calls with trained mentors (see Phase 1) for a 3-month period. The primary outcome of Phase 1 will be engagement of mentors with training and change in knowledge scores and autonomy skills from pre- to post-training. The primary outcome of Phase 2 will be the composite count of ED visits and hospitalizations at the end of study follow-up in patient participants assigned to intervention as compared to those assigned to usual care. Secondary outcomes for Phase 2 include the change over the trial period in validated survey scores measuring perception of social support and self-efficacy, and dialysis adherence metrics, among intervention participants as compared to usual care participants. Discussion The PEER-HD study will test the feasibility and efficacy of a pragmatic peer-mentorship program designed for patients receiving hemodialysis on ED visit and hospitalization rates. If effective, peer-mentorship holds promise as a scalable patient-centered intervention to decrease hospital resource utilization, and by extension morbidity and cost, for patients receiving maintenance in-center hemodialysis. Trial registration Clinicaltrials.gov identifier: NCT03595748 ; 7/23/2018. Trial sponsor National Institutes of Diabetes, Digestive and Kidney Disease (NIDDK) 5R18DK118471. Funding Funding for this study was provided by the National Institutes of Diabetes, Digestive and Kidney Disease: R18DK118471. Study status This is an ongoing study and not complete. We are still collecting data for observational follow-up on participants. Related articles No related articles for this study have been submitted to any journal. The study sponsor and funders had no role in the design, analysis or interpretation of this data. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.http://deepblue.lib.umich.edu/bitstream/2027.42/173578/1/12882_2022_Article_2701.pd

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

<p>Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 +/- 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 +/- 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 +/- 0.06 s.e.), and ADHD and major depressive disorder (0.32 +/- 0.07 s.e.), low between schizophrenia and ASD (0.16 +/- 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.</p>