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Diabetes is the leading cause of end-stage renal disease (ESRD) and contributes to heightened morbidity and mortality in dialysis patients. Given that ESRD patients are susceptible to hypoglycemia and hyperglycemia via multiple pathways, adequate glycemic monitoring and control is a cornerstone in diabetic kidney disease management. In ESRD, existing glycemic metrics such as glycated hemoglobin, self-monitored blood glucose, fructosamine, and glycated albumin have limitations in accuracy, convenience, and accessibility. In contrast, continuous glucose monitoring (CGM) provides automated, less invasive glucose measurements and more comprehensive glycemic data versus conventional metrics. Here, we report a 48-year-old male with ESRD due to diabetes receiving thrice-weekly hemodialysis who experienced decreased patient-burden, greater glucose monitoring adherence, improved glycemic parameters, and reduction in hypoglycemia after transitioning to CGM. Through this case, we discuss how CGM is a practical, convenient patient-centered tool that may improve metabolic outcomes and quality of life in ESRD patients with diabetes.

ObjectivesAmong hemodialysis patients, clinical practice guidelines recommend dietary potassium restriction given concerns about potential hyperkalemia leading to malignant arrhythmias and mortality. However, there are sparse data informing recommendations for dietary potassium intake in this population. We thus sought to examine the relationship between dietary potassium intake and death risk in a prospective cohort of hemodialysis patients.Design and methodsAmong 415 hemodialysis patients from the prospective "Malnutrition, Diet, and Racial Disparities in Chronic Kidney Disease" cohort recruited across 16 outpatient dialysis clinics, information regarding dietary potassium intake was obtained using Food Frequency Questionnaires administered over October 2011 to March 2015. We first examined associations of baseline dietary potassium intake categorized as tertiles with mortality risk using Cox regression. We then examined clinical characteristics associated with low dietary potassium intake (defined as the lowest tertile) using logistic regression.ResultsIn expanded case-mix Cox analyses, patients whose dietary potassium intake was in the lowest tertile had higher mortality (ref: highest tertile) (adjusted hazard ratio 1.74, 95% confidence interval 1.14-2.66). These associations had even greater magnitude of risk following adjustment for laboratory and nutritional covariates (adjusted hazard ratio 2.65, 95% confidence interval 1.40-5.04). In expanded case-mix restricted cubic spline analyses, there was a monotonic increase in mortality risk with incrementally lower dietary potassium intake. In expanded case-mix logistic regression models, female sex; higher serum bicarbonate; and lower dietary energy, protein, and fiber intake were associated with low dietary potassium intake.ConclusionsIn a prospective cohort of hemodialysis patients, lower dietary potassium intake was associated with higher mortality risk. These findings suggest that excessive dietary potassium restriction may be deleterious in hemodialysis patients, and further studies are needed to determine the optimal dietary potassium intake in this population.

BackgroundHigh-protein diets (e.g., Paleo, Atkins, South Beach, ketogenic) have gained popularity as a means to promote weight loss and avoid excess carbohydrate consumption. Yet in chronic kidney disease (CKD) patients, evidence suggests low dietary protein intake (DPI) leads to attenuation of kidney function decline, although concerns remain for risk of protein-energy wasting.ObjectivesTo examine associations of DPI with mortality in a nationally representative cohort of US adults, stratified by kidney function.MethodsWe examined the association between daily DPI scaled to actual body weight (ABW), ascertained by 24-h dietary recall, with all-cause mortality among 27,604 continuous NHANES adult participants (1999-2010), stratified according to impaired versus normal kidney function (estimated glomerular filtration rates

End-stage renal disease patients on dialysis experience frequent hospitalizations. In addition to known temporal patterns of hospitalizations over the life span on dialysis, where poor outcomes are typically exacerbated during the first year on dialysis, variations in hospitalizations among dialysis facilities across the US contribute to spatial variation. Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities are considered as spatially nested functional data (FD) with longitudinal hospitalizations nested in dialysis facilities and dialysis facilities nested in geographic regions. A multilevel Karhunen-Loéve expansion is utilized to model the two-level (facility and region) FD, where spatial correlations are induced among region-specific principal component scores accounting for regional variation. A new efficient algorithm based on functional principal component analysis and Markov Chain Monte Carlo is proposed for estimation and inference. We report a novel application using USRDS data to characterize spatiotemporal patterns of hospitalization rates for over 400 health service areas across the US and over the posttransition time on dialysis. Finite sample performance of the proposed method is studied through simulations.

Profiling or evaluation of health care providers involves the application of statistical models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission, adjusted for patient case-mix characteristics. The nationally adopted method is based on random effects (RE) hierarchical logistic regression models. Although RE models are sensible for modeling hierarchical data, novel high dimensional fixed effects (FE) models have been proposed which may be well-suited for the objective of identifying sub-standard performance. However, there are limited comparative studies. Thus, we examine their relative performance, including the impact of inadequate case-mix adjustment.

Background/objectivesOlder adults are prone to functional decline during prolonged hospitalization. Although rehabilitation therapy is critical to preserving function, little is known about rehabilitation duration (RD) in this population. We sought to determine the extent of rehabilitation therapy provided to older adults during prolonged hospitalization, and whether this differs by sociodemographic and clinical characteristics.DesignRetrospective cohort.SettingSingle-site safety-net hospital.ParticipantsOlder adults (≥65 years) hospitalized for ≥14 days between 2016 and 2017.MeasurementsThe primary outcome was RD, defined as the average number of minutes of physical and occupational therapy per week. We used a multivariable generalized linear model to assess for differences in RD by sociodemographic and clinical characteristics. For a sub-cohort of hospitalizations with a baseline mobility assessment, we repeated analyses including mobility limitation as a covariate.ResultsAmong 1,031 hospitalizations by 925 unique patients (median age 72, 49% female, 79% non-white, 40% non-English speaking), the median RD was 61.3 minutes/week (interquartile range = 16.5-127.3). Covariates associated with lesser RD included black (57.2 fewer minutes/week; 95% confidence interval (CI) = 22.9-91.4) and Hispanic (75.6 fewer minutes/week; 95% CI = 33.8-117.4) race/ethnicity, speaking a language other than English or Spanish (51.7 fewer minutes/week; 95% CI = 21.3-82.0), prolonged mechanical ventilation (30.0 fewer minutes/week; 95% CI = 6.6-53.3), and do-not-resuscitate code status (36.0 fewer minutes/week; 95% CI = 17.1-54.8). The inclusion of mobility limitation among the sub-cohort (n = 350) did not meaningfully change the associations.ConclusionWe found large disparities in RD for racial/ethnic and language minorities and clinically vulnerable older adults (mechanical ventilation and do-not-resuscitate code status), independent of clinical severity and functional and cognitive impairment. Greater RD for these groups may improve functional outcomes and narrow the disparity gap.

The study aims to analyze Big Five personality traits affecting various work outcomes of public servants working at the Public Non-Business Units under the People’s Committee of Hanoi City. Relying on the Big Five Theory, previous studies, and in-depth interviews with experts, the research model has been developed and tested in Public Non-Business Units. The authors carried out data collected from 420 public servants working at the Public Non-Business Units using a survey questionnaire by convenient sampling method to ascertain the impact of personality traits on diversity work outcomes (organizational commitment, job satisfaction, and performance). The descriptive statistics, Cronbach’s Alpha test, EFA, CFA, and SEM were employed for data analysis. The findings highlighted this study showed a positive correlation between Big 5 Personality Traits and organizational commitment, job performance, and satisfaction of public servants at the Public Non-Business Units. The results brought some theoretical and practical implications for leaders of the Public Non-Business Units to realize the importance of personality traits to the organizational commitment, job performance, and satisfaction of public servants as well as to add to the existing knowledge of work outcomes in the literature. There are solutions to increase organizational commitment, job performance, and satisfaction of public servants at the Public Non-Business Units in the future.

Profiling or evaluation of health care providers, including hospitals or dialysis facilities, involves the application of hierarchical regression models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission. This is achieved by comparing a specific provider's estimate of unplanned readmission rate, adjusted for patient case-mix, to a normative standard, typically defined as an "average" national readmission rate across all providers. Profiling is of national importance in the United States because the Centers for Medicare and Medicaid Services (CMS) policy for payment to providers is dependent on providers' performance, which is part of a national strategy to improve delivery and quality of patient care. Novel high dimensional fixed effects (FE) models have been proposed for profiling dialysis facilities and are more focused towards inference on the tail of the distribution of provider outcomes, which is well-suited for the objective of identifying sub-standard ("extreme") performance. However, the extent to which estimation and inference procedures for FE profiling models are effective when the outcome is sparse and/or when there are relatively few patients within a provider, referred to as the "low information" context, have not been examined. This scenario is common in practice when the patient outcome of interest is cause-specific 30-day readmissions, such as 30-day readmission due to infections in patients on dialysis, which is only about ~ 8% compared to the > 30% for all-cause 30-day readmission. Thus, we examine the feasibility and effectiveness of profiling models under the low information context in simulation studies and propose a novel correction method to FE profiling models to better handle sparse outcome data.

Attention to technology integration in teaching and learning has created a big change in the education landscape. There is no limit to the application of information technology in education in general and in mathematics education in particular. Although there have been many studies on the opportunities and applications of ICT in Mathematics education in the world, many aspects remain unexplored. In this study, the authors apply bibliometric analysis for the purpose of evaluating the scientific results of publications involving in the use of ICT in Mathematics education, which were indexed in the Scopus database. 205 publications were cited to analyze growth trends, contributions of authors, research institutions, co-authors in the research field and the development of research direction. The analysis results show that publications that mainly appear in recent years are published on sources with low citation index; Indonesia, Malaysia and Australia are the most influential countries in studies on this topic; Research cooperation in this field is not strong; The main research topics and trends are online teaching, realistic mathematics education, and computer-aided teaching. From the research results obtained, the authors also furnish some recommendations in the article for future research in this field.

For patients on dialysis, hospitalizations remain a major risk factor for mortality and morbidity. We use data from a large national database, United States Renal Data System, to model time-varying effects of hospitalization risk factors as functions of time since initiation of dialysis. To account for the three-level hierarchical structure in the data where hospitalizations are nested in patients and patients are nested in dialysis facilities, we propose a multilevel mixed effects varying coefficient model (MME-VCM) where multilevel (patient- and facility-level) random effects are used to model the dependence structure of the data. The proposed MME-VCM also includes multilevel covariates, where baseline demographics and comorbidities are among the patient-level factors, and staffing composition and facility size are among the facility-level risk factors. To address the challenge of high-dimensional integrals due to the hierarchical structure of the random effects, we propose a novel two-step approximate EM algorithm based on the fully exponential Laplace approximation. Inference for the varying coefficient functions and variance components is achieved via derivation of the standard errors using score contributions. The finite sample performance of the proposed estimation procedure is studied through simulations.

Profiling analysis aims to evaluate health care providers by modeling each provider's performance with respect to a patient outcome, such as unplanned hospital readmission. High-dimensional regression models are used in profiling to risk-adjust for patient case-mix covariates. Case-mix covariates typically ascertained from administrative databases are inherently error-prone. We examine the impact of case-mix measurement error (ME) on profiling models. The results show that even though the models' coefficient estimates are biased, this does not affect the estimation of standardized readmission ratio (SRR). However, ME leads to increased variation in SRR estimates and degrades the ability to identify under-performing providers.

Chronic kidney disease (CKD) affects >10% of the adult population. Each year, approximately 120,000 Americans develop end-stage kidney disease and initiate dialysis, which is costly and associated with functional impairments, worse health-related quality of life, and high early-mortality rates, exceeding 20% in the first year. Recent declarations by the World Kidney Day and the U.S. Government Executive Order seek to implement strategies that reduce the burden of kidney failure by slowing CKD progression and controlling uremia without dialysis. Pragmatic dietary interventions may have a role in improving CKD outcomes and preventing or delaying dialysis initiation. Evidence suggests that a patient-centered plant-dominant low-protein diet (PLADO) of 0.6–0.8 g/kg/day composed of >50% plant-based sources, administered by dietitians trained in non-dialysis CKD care, is promising and consistent with the precision nutrition. The scientific premise of the PLADO stems from the observations that high protein diets with high meat intake not only result in higher cardiovascular disease risk but also higher CKD incidence and faster CKD progression due to increased intraglomerular pressure and glomerular hyperfiltration. Meat intake increases production of nitrogenous end-products, worsens uremia, and may increase the risk of constipation with resulting hyperkalemia from the typical low fiber intake. A plant-dominant, fiber-rich, low-protein diet may lead to favorable alterations in the gut microbiome, which can modulate uremic toxin generation and slow CKD progression, along with reducing cardiovascular risk. PLADO is a heart-healthy, safe, flexible, and feasible diet that could be the centerpiece of a conservative and preservative CKD-management strategy that challenges the prevailing dialysis-centered paradigm.

Introduction:Although residual urine output (UOP) is associated with better survival and quality of life in dialysis patients, frequent measurement by 24-hour urine collection is burdensome. We thus sought to examine the association of patients' self-reported residual UOP, as an alternative proxy of measured residual UOP, with mortality risk in a prospective hemodialysis cohort study. Methods:Among 670 hemodialysis patients from the prospective multicenter Malnutrition, Diet, and Racial Disparities in Kidney Disease study, we examined associations of residual UOP, ascertained by patient self-report, with all-cause mortality. Patients underwent protocolized surveys assessing presence and frequency of UOP (absent, every 1-3 days, >1 time per day) every 6 months from 2011 to 2015. We examined associations of baseline and time-varying UOP with mortality using Cox regression. Results:In analyses of baseline UOP, absence of UOP was associated with higher mortality in expanded case-mix adjusted Cox models (ref: presence of UOP): hazard ratio (HR), 1.78 (95% confidence interval [CI], 1.16-2.72). In analyses examining baseline frequency of UOP, point estimates suggested a graded association between lower frequency of UOP and higher mortality, although estimates for UOP every 1 to 3 days did not reach statistical significance (reference: UOP >1 time per day): HR, 1.29 (95% CI, 0.82-2.05) and HR, 1.97 (95% CI, 1.24-3.12) for UOP every 1 to 3 days and absence of UOP, respectively. Similar findings were observed in analyses of time-varying UOP. Conclusion:In hemodialysis patients, there is a graded association between lower frequency of self-reported UOP and higher mortality. Further studies are needed to determine the clinical impact of more frequent assessment of residual UOP using self-reported methods.

Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Previous profiling methods have considered binary outcomes, such as 30-day hospital readmission or mortality. For the unique population of dialysis patients, regular blood works are required to evaluate effectiveness of treatment and avoid adverse events, including dialysis inadequacy, imbalance mineral levels, and anemia among others. For example, anemic events (when hemoglobin levels exceed normative range) are recurrent and common for patients on dialysis. Thus, we propose high-dimensional Poisson and negative binomial regression models for rate/count outcomes and introduce a standardized event ratio measure to compare the event rate at a specific facility relative to a chosen normative standard, typically defined as an average national rate across all facilities. Our proposed estimation and inference procedures overcome the challenge of high-dimensional parameters for thousands of dialysis facilities. Also, we investigate how overdispersion affects inference in the context of profiling analysis. The proposed methods are illustrated with profiling dialysis facilities for recurrent anemia events.

BackgroundPopulation-based studies show there is a high prevalence of chronic kidney disease (CKD) patients suffering from chronic pain. While opiates are frequently prescribed in non-dialysis-dependent CKD (NDD-CKD) patients, there may be toxic accumulation of metabolites, particularly among those progressing to end-stage renal disease (ESRD). We examined the association of opiate versus other analgesic use during the pre-ESRD period with post-ESRD mortality among NDD-CKD patients transitioning to dialysis.MethodsWe examined a national cohort of US Veterans with NDD-CKD who transitioned to dialysis over 2007-14. Among patients who received ≥1 prescription(s) in the Veterans Affairs (VA) Healthcare System within 1 year of transitioning to dialysis, we examined associations of pre-ESRD analgesic status, defined as opiate, gabapentin/pregabalin, other non-opiate analgesic, versus no analgesic use, with post-ESRD mortality using multivariable Cox models.ResultsAmong 57,764 patients who met eligibility criteria, pre-ESRD opiate and gabapentin/pregabalin use were each associated with higher post-ESRD mortality (ref: no analgesic use), whereas non-opiate analgesic use was not associated with higher mortality in expanded case-mix analyses: HRs (95% CIs) 1.07 (1.05-1.10), 1.07 (1.01-1.13), and 1.00 (0.94-1.06), respectively. In secondary analyses, increasing frequency of opiate prescriptions exceeding 1 opiate prescription in the 1-year pre-ESRD period was associated with incrementally higher post-ESRD mortality (ref: no analgesic use).ConclusionsIn NDD-CKD patients transitioning to dialysis, pre-ESRD opiate and gabapentin/pregabalin use were associated with higher post-ESRD mortality, whereas non-opiate analgesic use was not associated with death. There was a graded association between increasing frequency of pre-ESRD opiate use and incrementally higher mortality.

Purpose of reviewDialysis has been the prevailing treatment paradigm in advanced chronic kidney disease (CKD) for patients ineligible for or unlikely to receive kidney transplantation. As dialysis may neither offer survival benefit nor improved quality of life in certain groups, there has been increasing interest in conservative management as an alternative approach.Recent findingsExperts and workgroups suggest the main goals of conservative management are to optimize quality of life, treat symptoms of end-stage renal disease without dialysis or transplant, and improve survival and cardiovascular health. Given the implications of preserved kidney function on clinical outcomes, preservative management has been proposed as an integral component of conservative management. Growing evidence suggests the survival benefit of dialysis vs. conservative management without dialysis is marginal or even reversed in certain subpopulations (elderly, multimorbid, cardiovascular disease). Limited data suggest that conservative and preservative management is associated with equivalent to more favorable trajectories of health-related quality of life and symptom burden over time as opposed to dialysis.SummaryWhereas existing data suggest conservative management is a viable patient-centered treatment strategy, further research is needed to determine the comparative effectiveness of preservative kidney management vs. dialysis or palliative management, as well as which patient subgroups will most benefit from these treatment strategies.

BackgroundAdvanced chronic kidney disease (CKD) patients, including those receiving dialysis, have a high prevalence of thyroid dysfunction. Although hypothyroidism is associated with higher death risk in end-stage renal disease (ESRD) patients, no studies have examined whether thyroid status in the pre-ESRD period impacts mortality after dialysis initiation.MethodsAmong US veterans with CKD identified from the national Veterans Affairs database that transitioned to dialysis over the period from October 2007 to September 2011, we examined the association of pre-ESRD serum thyrotropin (TSH) levels averaged over the 1-year pre-dialysis ('prelude') period with all-cause mortality in the first year following dialysis initiation.ResultsAmong 15 335 patients in the 1-year prelude cohort, TSH levels >5.0 mIU/L were associated with higher mortality in expanded case-mix Cox models (reference: TSH 0.5-5.0 mIU/L): adjusted hazard ratio (aHR) [95% confidence interval (CI) 1.20 (1.07-1.33). Similar findings were observed for TSH >5.0 mIU/L and mortality in the 2- and 5-year cohorts: aHRs (95% CI) 1.11 (1.02-1.21) and 1.15 (1.07-1.24), respectively. Analyses of finer gradations of TSH in the 1-year prelude cohort demonstrated that incrementally higher levels >5.0 mIU/L were associated with increasingly higher mortality in expanded case-mix models (reference: TSH 0.5-3.0 mIU/L): aHRs (95% CI) 1.18 (1.04-1.33) and 1.28 (1.03-1.59) for TSH levels >5.0-10.0 mIU/L and >10.0 mIU/L, respectively. In the 2- and 5-year cohorts, mortality associations persisted most strongly for those with TSH >10.0 mIU/L, particularly after laboratory covariate adjustment.ConclusionsAmong new ESRD patients, there is a dose-dependent relationship between higher pre-ESRD TSH levels >5.0 mIU/L and post-ESRD mortality. Further studies are needed to determine the impact of TSH reduction with thyroid hormone supplementation in this population.

Rationale & objectiveHealth-related quality of life (HRQoL) has been recognized as a strong predictor of mortality among hemodialysis patients. However, differences in the association of HRQoL with survival across diverse racial/ethnic groups have not been well studied in this population.Study designObservational cohort study.Setting & participantsWe examined the relationship between HRQoL and mortality in a prospective cohort of racially/ethnically diverse hemodialysis patients recruited from 18 outpatient dialysis units during 2011 to 2016.ExposureUsing the 36-Item Short Form Health Survey (SF-36) administered every 6 months, HRQoL was ascertained by 36 questions summarized as 2 Physical and Mental Component and 8 subscale scores.OutcomeAll-cause mortality.Analytical approachAssociations of time-varying SF-36 scores with mortality were estimated using Cox models in the overall cohort and within racial/ethnic subgroups.ResultsAmong 753 hemodialysis patients who met eligibility criteria, expanded case-mix analyses showed that the lowest quartiles of time-varying Physical and Mental Component scores were associated with higher mortality in the overall cohort (reference: highest quartile): adjusted HRs, 2.30 (95% CI, 1.53-3.47) and 1.54 (95% CI, 1.05-2.25), respectively. In analyses stratified by race/ethnicity, the lowest quartile of Physical Component scores was significantly associated with higher mortality across all groups: adjusted HRs, 2.64 (95% CI, 1.31-5.29), 1.84 (95% CI, 1.01-3.38), and 3.18 (95% CI, 1.13-8.91) for Hispanic, African American, and other race/ethnicity patients, respectively. The lowest quartile of time-varying physical functioning, role limitations due to physical health, role limitations due to emotional problems, social functioning, and pain subscale scores were associated with higher mortality in the overall cohort and particularly in Hispanics and blacks.LimitationsResidual confounding cannot be excluded.ConclusionsLower SF-36 Physical Component and subscale scores were associated with higher mortality in hemodialysis patients, including those of minority background. Further studies are needed to determine whether interventions that augment physical health might improve the survival of these diverse populations.

IntroductionGiven the high mortality rate within the first year of dialysis initiation, an accurate estimation of postdialysis mortality could help patients and clinicians in decision making about initiation of dialysis. We aimed to use machine learning (ML) by incorporating complex information from electronic health records to predict patients at risk for postdialysis short-term mortality.MethodsThis study was carried out on a contemporary cohort of 27,615 US veterans with incident end-stage renal disease (ESRD). We implemented a random forest method on 49 variables obtained before dialysis transition to predict outcomes of 30-, 90-, 180-, and 365-day all-cause mortality after dialysis initiation.ResultsThe mean (±SD) age of our cohort was 68.7 ± 11.2 years, 98.1% of patients were men, 29.4% were African American, and 71.4% were diabetic. The final random forest model provided C-statistics (95% confidence intervals) of 0.7185 (0.6994-0.7377), 0.7446 (0.7346-0.7546), 0.7504 (0.7425-0.7583), and 0.7488 (0.7421-0.7554) for predicting risk of death within the 4 different time windows. The models showed good internal validity and replicated well in patients with various demographic and clinical characteristics and provided similar or better performance compared with other ML algorithms. Results may not be generalizable to non-veterans. Use of predictors available in electronic medical records has limited the assessment of number of predictors.ConclusionWe implemented and ML-based method to accurately predict short-term postdialysis mortality in patients with incident ESRD. Our models could aid patients and clinicians in better decision making about the best course of action in patients approaching ESRD.

Although the mortality of infants with congenital diaphragmatic hernia (CDH) has been improving since the late 1990s, this observation has not been paralleled among the CDH cohort receiving extracorporeal membrane oxygenation (ECMO). We sought to elucidate why the mortality rate in the CDH-ECMO population has remained at approximately 50% despite consistent progress in the field by examining the baseline risk profile/characteristics of neonates with CDH before ECMO (pre-ECMO). Neonates with a diagnosis of CDH were identified in the Extracorporeal Life Support Organization (ELSO) Registry from 1992 to 2015. Individual pre-ECMO risk score (RS) for mortality was categorized to pre-ECMO risk-stratified cohorts. Temporal trends based on individual-level mortality by risk cohorts were assessed by logistic regression. We identified 6,696 neonates with CDH. The mortality rates during this time period were approximately 50%. The average baseline pre-ECMO RS increased during this period: mean increase of 0.35 (95% confidence interval [CI]: 0.324-0.380). In the low-risk cohort, the likelihood of mortality increased over time: each 5 year change was associated with a 7.3% increased likelihood of mortality (odds ratio [OR]: 1.0726; 95% CI: 1.0060-1.1437). For the moderate-risk cohort, the likelihood of mortality decreased by 7.05% (OR: 0.9295; 95% CI: 0.8822-0.9793). There was no change in the odds of mortality for the high-risk cohort (OR: 0.9650; 95% CI: 0.8915-1.0446). Although the overall mortality rate remained approximately constant over time, the individual likelihood of death has declined over time in the moderate-risk cohort, increased in the low-risk cohort, and remained unchanged in the high-risk cohort.

BACKGROUND: Millions of traumatized refugees worldwide have resettled in the United States. For one of the largest, the Cambodian community, having their mental health needs met has been a continuing challenge. A multicomponent health information technology screening tool was designed to aid provider recognition and treatment of major depressive disorder and posttraumatic stress disorder (PTSD) in the primary care setting. METHODS: In a clustered randomized controlled trial, 18 primary care providers were randomized to receive access to a multicomponent health information technology mental health screening intervention, or to a minimal intervention control group; 390 Cambodian American patients empaneled to participating providers were assigned to the providers randomized group. RESULTS: Electronic screening revealed that 65% of patients screened positive for depression and 34% screened positive for PTSD. Multilevel mixed effects logistic models, accounting for clustering structure, indicated that providers in the intervention were more likely to diagnose depression [odds ratio (OR), 6.5; 95% confidence interval (CI), 1.48-28.79; P=0.013] and PTSD (OR, 23.3; 95% CI, 2.99-151.62; P=0.002) among those diagnosed during screening, relative to the control group. Providers in the intervention were more likely to provide evidence-based guideline (OR, 4.02; 95% CI, 1.01-16.06; P=0.049) and trauma-informed (OR, 15.8; 95% CI, 3.47-71.6; P

Background:Unplanned hospital readmissions are a major source of morbidity among dialysis patients, in whom the risk of hospital readmission is exceptionally high. The contribution of dialysis facility staffing to hospital readmission has been largely overlooked. Methods:Using annual data of dialysis patients from the United States Renal Data System from 2010 to 2013, we assessed dialysis facilities with a significantly worse (SW) and facilities with a nonsignificant (NS) standardized readmission ratio (SRR). SRR estimates were risk adjusted for patient factors, past year comorbidities, and index hospitalization characteristics. Facility staffing variables were compared between 2 exposure groups: facilities with SW and NS SRRs. Four measures of staffing, including patient-to-staffing ratio, were compared between SW and matched NS facilities. Results:About 136,000-148,000 dialysis patients with 269,000-319,000 index hospital discharges were used to identify facilities with SW and facilities with NS SRR annually. Approximately 3-4% of facilities were identified as having SW SRR among > 5,000 facilities annually. The percent of nurses-to-total staff was significantly lower in 2010 for SW facilities than in matched NS facilities (42.5 vs. 45.6%, p = 0.012), but this disparity was attenuated by 2013 (44.8 vs. 44.7%, p = 0.949). There was a higher patient-to-nurse ratio for SW facilities than for NS facilities (mean 16.4 vs. 15.2, p = 0.038) in 2010 as well, and the disparity was reduced by 2013. The trends were similar for patient-to-total staff and patient-to-registered nurse, but not statistically significant. Conclusions:This study found that dialysis facilities with SW 30-day readmission rates had lower proportions of nurses-to-total staff and higher patient-to-nurse ratios, but this disparity improved in recent years. Additional research is warranted focusing on how evidence-based staffing at dialysis facilities can contribute to reduction of hospital readmission, and this knowledge is needed to inform clinical practice guidelines and policy decisions regarding optimal dialysis patient staffing.

BackgroundIndividuals with fragile X syndrome (FXS) typically demonstrate profound executive function (EF) deficits that interfere with learning, socialization, and emotion regulation. We completed the first large, non-pharmacological controlled trial for FXS, designed to evaluate the efficacy of Cogmed, a computer/tablet-based working memory (WM) training program.MethodsThe study was a randomized, blinded, parallel two-arm controlled trial in 100 children and adolescents with FXS (63 male, 37 female; 15.28 ± 3.36 yrs.). Participants were randomized equally to adaptive (difficulty level adjusted to performance) or non-adaptive (control) Cogmed training. Participants were assessed at home using objective measures of WM (primary outcome) and EF at baseline, following 20-25 caregiver-supported sessions over 5-6 weeks, and at follow-up 3 months after cessation of training. Parents and teachers provided ratings of WM, attention, and EF.ResultsThe WM composite and selective domains of EF (distractibility, cognitive flexibility), as well as parent- and teacher-reported attention and EF, significantly improved across the full study sample, with many changes maintained at follow-up. However, comparisons of improvement between adaptive and non-adaptive control conditions did not differ, showing that progressively challenging the WM system by expanding span length did not provide added benefit overall.ConclusionsFurther experimental comparisons are needed before Cogmed working memory training can be considered empirically validated for children with FXS, forming the basis of treatment recommendation. However, given that prior studies show no significant changes on these measures in FXS without treatment, that improvements were maintained for 3 months, and that blinded teachers reported improvements in the classroom, the modest benefits seen in both adaptive and non-adaptive groups overall are unlikely to be attributable to placebo or practice effects alone. Future analyses examining inter-individual differences (e.g., baseline capacity, training efficiency, co-morbidity, training environment, characteristics of training aide) may help to link this intervention to outcomes and potential transfer effects.Trial registrationUS National Institutes of Health (ClinicalTrials.gov), NCT02747394 .

ObjectiveThe impact of glycemic control in diabetic patients with chronic kidney disease (CKD) who may or may not transition to dialysis remains uncertain, given recent interest in the conservative management of advanced CKD without dialysis therapy, which may benefit from alternative glycemic control strategies.Design and methodsAmong a national cohort of US Veterans, we examined the association of glycemic status, defined by averaged random blood glucose and hemoglobin A1c (HbA1c), with mortality after transitioning to dialysis over 2007-2011 (Transition Cohort) compared with patients in a one-to-one matched cohort of CKD patients with diabetes who did not transition to dialysis (Nontransition Cohort).ResultsAmong 17,121 patients in the Transition Cohort, averaged random glucose ≥200 mg/dL was associated with higher mortality in expanded case-mix analyses (reference: 100-

Profiling or evaluation of health care providers involves the application of statistical models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission, adjusted for patient case-mix characteristics. The nationally adopted method is based on random effects (RE) hierarchical logistic regression models. Although RE models are sensible for modeling hierarchical data, novel high dimensional fixed effects (FE) models have been proposed which may be well-suited for the objective of identifying sub-standard performance. However, there are limited comparative studies. Thus, we examine their relative performance, including the impact of inadequate case-mix adjustment.

Objective: Selective serotonin reuptake inhibitors like sertraline have been shown in observational studies and anecdotal reports to improve language development in young children with fragile X syndrome (FXS). A previous controlled trial of sertraline in young children with FXS found significant improvement in expressive language development as measured by the Mullen Scales of Early Learning (MSEL) among those with comorbid autism spectrum disorder (ASD) in post hoc analysis, prompting the authors to probe whether sertraline is also indicated in nonsyndromic ASD. Methods: The authors evaluated the efficacy of 6 months of treatment with low-dose sertraline in a randomized, double-blind, placebo-controlled trial in 58 children with ASD aged 24 to 72 months. Results: 179 subjects were screened for eligibility, and 58 were randomized to sertraline (32) or placebo (26). Eight subjects from the sertraline arm and five from the placebo arm discontinued. Intent-to-treat analysis showed no significant difference from placebo on the primary outcomes (MSEL expressive language raw score and age equivalent combined score) or secondary outcomes. Sertraline was well tolerated, with no difference in side effects between sertraline and placebo groups. No serious adverse events possibly related to study treatment occurred. Conclusion: This randomized controlled trial of sertraline treatment showed no benefit with respect to primary or secondary outcome measures. For the 6-month period, treatment in young children with ASD appears safe, although the long-term side effects of low-dose sertraline in early childhood are unknown. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT02385799.

For chronic dialysis patients, a unique population requiring continuous medical care, methodologies to monitor patient outcomes, such as hospitalizations, over time, after initiation of dialysis, are of particular interest. Contributing to patient hospitalizations is a number of multilevel covariates such as demographics and comorbidities at the patient level and staffing composition at the dialysis facility level. We propose a varying coefficient model for multilevel risk factors (VCM-MR) to study the time-varying effects of covariates on patient hospitalization risk as a function of time on dialysis. The proposed VCM-MR also includes subject-specific random effects to account for within-subject correlation and dialysis facility-specific fixed effect varying coefficient functions to allow for the modeling of flexible time-varying facility-specific risk trajectories. An approximate EM algorithm and an iterative Newton-Raphson approach are proposed to address the challenge of estimation of high-dimensional parameters (varying coefficient functions) for thousands of dialysis facilities in the United States. The proposed modeling allows for comparisons between time-varying effects of multilevel risk factors as well as testing of facility-specific fixed effects. The method is applied to model hospitalization risk using the rich hierarchical data available on dialysis patients initiating dialysis between January 1, 2006 and December 31, 2008 from the United States Renal Data System, a large national database, where 331 443 hospitalizations over time are nested within patients, and 89 889 patients are nested within 2201 dialysis facilities. Patients are followed-up until December 31, 2013, where the follow-up time is truncated five years after the initiation of dialysis. Finite sample properties are studied through extensive simulations.