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RESULTSAmong 146,967 incident HD patients, median survival was 1106 days for those initiating with a CVC compared with 1290 days for patients with an AVA, corresponding to a 184-day difference and an 88% restricted mean survival time (RMST) ratio. In the sustained access analysis, median survival was 448 days for CVC-only vs 1226 days for AVA-only patients (RMST difference = 778 days, RMST ratio = 52%). After inverse probability treatment weighting, AVA initiation was associated with a 25% lower mortality risk (hazard ratio: 0.75, 95% confidence interval: 0.73-0.76) and sustained AVA use with a 62% lower risk (hazard ratio: 0.38, 95% confidence interval: 0.36-0.40). Differences in infection-related deaths between the groups were small (8.6%-10.6% of deaths in all comparison groups).CONCLUSIONSCVC use was associated with higher mortality compared with AVA. Although AVA use remained linked with better survival across analyses, the precise magnitude of any access-related benefit cannot be determined within the constraints of observational data. There are strong indications that the excess risk at least partially reflects differences in baseline health and patient selection rather than a direct causal effect.OBJECTIVECentral venous catheters (CVCs) are commonly linked with higher mortality in hemodialysis (HD) patients compared with arteriovenous accesses (AVAs). However, patients with CVCs often have greater comorbidities, complicating causal interpretation. This study aimed to assess the association between vascular access type and survival adjusting for relevant confounders.METHODSIn this retrospective cohort study, data from 146,967 incident HD patients treated between 2016 and 2019 at a large North American dialysis organization (Fresenius Medical Care North America) were analyzed. Multiple analytic strategies were conducted including inverse probability treatment weighted and time-dependent survival analyses.

RESULTSAmong 417 metabolomic features, 10 showed significant changes between baseline and PIP. Two metabolites, α-guanidinoglutaric acid and N-acetylneuraminic acid, were identified through library matching, while the remainder were characterized by mass and retention time. Temporal analysis revealed both transient metabolic shifts, which returned to baseline, and persistent changes, which remained altered post-COVID.CONCLUSIONSThese findings suggest that early metabolic changes before COVID-19 diagnosis may be detected in routine serum samples, offering opportunities to develop predictive models for early detection. Identifying these unique metabolomics fingerprints could improve personalized surveillance strategies and enhance understanding of COVID-19's impact on hemodialysis patients.BACKGROUNDMaintenance hemodialysis patients experience higher morbidity and mortality from COVID-19, partly due to comorbidities like diabetes and cardiovascular disease. However, kidney disease-related metabolic processes may also contribute.METHODSIn this prospective, multi-center, observational study, we analyzed 201 routine serum samples from 30 hemodialysis patients (average age 59.2 ± 13.3 years, 57% male) with confirmed COVID-19, collected from 60 days before and 60 days after diagnosis. Untargeted liquid chromatography/mass spectrometry was used to profile metabolites. Linear and semi-parametric mixed-effects models were applied to assess changes across four phases: baseline (-60 to -15 days), putative incubation period (PIP; -14-0 days), acute (1-14 days), and post-COVID (15-60 days). Because infection and symptoms may vary across individuals, -14-0 days were used as an approximate pre-diagnosis window rather than a precise incubation interval.

RESULTSA total of 324 patients participated in the study. HRSN was common with 56% of participants reporting at least one HRSN. Food insecurity (35%) and housing instability (24%) was most common. All QoL subscores were significantly lower in patients who had at least one HRSN. In regression models, housing and transportation insecurity most frequently emerged as significant variables associated with lower QoL subscores even after adjusting for patient demographics. Burden scores showed the largest effect sizes (housing instability β =-17.90, P < 0.001, transportation problems β =-14.03, P = 0.001).KEY POINTSHealth-related social needs are common in patients on in-center hemodialysis. All quality of life subscores are significantly lower in patients with at least one unmet health-related social needs.CONCLUSIONHRSN is significantly associated with lower QoL scores, with largest effect sizes seen with housing instability and transportation problems. Increased screening and intervention for HRSN may improve QoL among people on hemodialysis.BACKGROUNDPeople on hemodialysis often report lower quality of life (QoL) compared with people not on hemodialysis. People with kidney disease have a high prevalence of health-related social needs (HRSN). The association of HRSN and QoL in people on hemodialysis remains understudied. Although some groups of patients treated with hemodialysis tend to have lower QoL, there exists minimal research investigating the mechanism by which this occurs.METHODSWe surveyed people receiving hemodialysis at five urban dialysis units using the Kidney Disease Quality of Life and the Accountable Health Communities Health-Related Social Needs Screening Tool to assess their housing, food, transportation, utilities, and perceived safety. We calculated physical and mental component scores as well as subscores measuring burden, symptoms, and effect of kidney disease. We analyzed scores using Python packages. We used the Shapiro-Wilk test to assess normality. For analysis we used the Wilcoxon rank-sum test and univariate, multivariate, and least absolute shrinkage and selection operator regressions.

RESULTSFive decommissioning strategies were identified: disposal, donation, reuse, sale and recycling/trade-in. Substantial variability and limited formalization in these strategies were observed across and within hospitals. Economic consequences included repair costs, depreciation and resale value. Social consequences were important, yet typically secondary. Environmental consequences were recognized but rarely formalized, although indirect environmental benefits from economically driven repair activities were acknowledged.CONCLUSIONSDecommissioning strategies for hemodialysis machines in Dutch hospitals do not use formalized guidelines and are still predominantly shaped by economic drivers. The recognition that each decommissioning strategy entails distinct economic, social and environmental consequences highlights the need for more balanced decision-making. By embedding sustainability principles into hospital policies and standardizing decommissioning procedures, hospitals can move toward more circular and responsible dialysis care.BACKGROUNDThe decommissioning of hemodialysis machines, particularly in the context of transitioning from hemodialysis to hemodiafiltration, remains understudied despite its importance for sustainable healthcare. This study evaluates decommissioning strategies for hemodialysis machines used by Dutch hospitals, analyzing the economic, social and environmental consequences.METHODSA qualitative, exploratory study was conducted through semi-structured interviews with 15 professionals from 11 Dutch hospitals that retired hemodialysis machines. The analysis focused on understanding decommissioning strategies and their economic, social and environmental consequences.

RESULTSThe cumulative lag 0-3 risk of hospitalization associated with heat exposure was highest in the West (rate ratio [RR]: 1.099; 95% confidence interval [CI]: 1.041, 1.160), whereas the highest risk of mortality was observed in the Northwest region (RR: 1.097; 95% CI: 1.007, 1.195). We observed significant increases in the risk of hospitalization at the low- and mid-latitude bands and a significant increase in the risk of mortality in the mid-latitude band.CONCLUSIONWe observed spatial heterogeneity across US climate regions. The strongest effects of heat exposure were observed in the Ohio Valley, South, and West regions for hospitalization and the Upper Midwest, Southeast, and Northwest regions for mortality. Findings may be used to inform targeted interventions to patients with ESKD residing in areas with higher risks of adverse health outcomes following heat exposure.BACKGROUNDThe impact of heat exposure on patients with end-stage kidney disease (ESKD) is of growing concern in the context of climate change. In this study, we investigated the association of heat exposure with hospitalization and mortality, and how the risk of these adverse health outcomes varied by climate region in the US.METHODSWe obtained hospitalization and mortality data for patients with ESKD receiving in-center hemodialysis treatment between 2012 and 2018 at Fresenius Kidney Care facilities located within the contiguous US. We used the treatment facility location to assign heat exposure using maximum universal thermal climate index temperature data. We conducted a space-time-stratified case-crossover study using conditional Poisson regression with distributed lag nonlinear models to examine the effects of heat exposure at the 95th percentile of the region-specific temperature distribution for lags of three days. Stratified analyses were run to assess differences in associations across nine climate regions and three latitude bands.

RESULTSWe utilized data from 693 patients who contributed 42 656 hemodialysis sessions and 355 693 intradialytic SBP measurements. IDH occurred in 16.2% of hemodialysis treatments. Our model predicted IDH 15-75 min in advance with an AUROC of 0.89. Top IDH predictors were the most recent intradialytic SBP and IDH rate, as well as mean nadir SBP of the previous 10 dialysis sessions.CONCLUSIONSReal-time prediction of IDH during an ongoing hemodialysis session is feasible and has a clinically actionable predictive performance. If and to what degree this predictive information facilitates the timely deployment of preventive interventions and translates into lower IDH rates and improved patient outcomes warrants prospective studies.BACKGROUNDIn maintenance hemodialysis patients, intradialytic hypotension (IDH) is a frequent complication that has been associated with poor clinical outcomes. Prediction of IDH may facilitate timely interventions and eventually reduce IDH rates.METHODSWe developed a machine learning model to predict IDH in in-center hemodialysis patients 15-75 min in advance. IDH was defined as systolic blood pressure (SBP) <90 mmHg. Demographic, clinical, treatment-related and laboratory data were retrieved from electronic health records and merged with intradialytic machine data that were sent in real-time to the cloud. For model development, dialysis sessions were randomly split into training (80%) and testing (20%) sets. The area under the receiver operating characteristic curve (AUROC) was used as a measure of the model's predictive performance.

RESULTSFifteen subjects (age 59±15 years, eight men) were studied during a total of 63 treatments. The controller functioned as intended and issued a total of 1037 recommendations. Compared with standard-of-care treatments, its use was associated with a higher probability of RBV target range attainment (69% versus 47%) and lower nadir systolic (106 versus 111 mm Hg) and diastolic (55 versus 59 mm Hg) BP.KEY POINTSThe ultrafiltration rate feedback controller functioned as intended, improving relative blood volume target attainment over standard care. Predialytic, postdialytic, and mean intradialytic BPs were not statistically different between treatments with versus without controller usage. Intradialytic nadir BP was on average slightly lower with use of the controller (106 versus 111 mm Hg systolic).CONCLUSIONSThe UFR feedback controller operated as intended, and its use led to a substantial increase in the rate of RBV target range attainment. This technology holds promise for improving fluid management in chronic hemodialysis patients.BACKGROUNDRelative blood volume (RBV) monitors are increasingly being used during hemodialysis. Manual ultrafiltration rate (UFR) adjustments to establish a favorable RBV trajectory are not feasible in routine practice. The goal of this study was to characterize the behavior of a new UFR feedback controller in vivo.METHODSIn this pilot trial, chronic hemodialysis patients were prospectively studied during up to six successful study dialysis treatments each. During each study visit, the feedback controller generated UFR recommendations designed to guide the subject's RBV curve toward a predefined target trajectory. Each recommendation was evaluated by licensed health care staff and then either implemented or disregarded. The results were compared with standard-of-care treatments in the same subjects.

RESULTSWe analyzed 4,075 consecutive 5-minute segments from 89 hemodialysis treatments in 22 hemodialysis patients. While 891 (21.9%) segments showed saw-tooth pattern, 3,184 (78.1%) did not. In the test data set, the rate of correct SaO2 pattern classification was 96% with an area under the receiver operating curve of 0.995 (95% CI: 0.993 to 0.998).CONCLUSIONOur 1D-CNN algorithm accurately classifies SaO2 saw-tooth pattern. The SaO2 pattern classification can be performed in real time during an ongoing hemodialysis treatment, provide timely alert in the event of respiratory instability or sleep apnea, and trigger further diagnostic and therapeutic interventions.BACKGROUNDMaintenance hemodialysis patients experience high morbidity and mortality, primarily from cardiovascular and infectious diseases. It was discovered recently that low arterial oxygen saturation (SaO2) is associated with a pro-inflammatory phenotype and poor patient outcomes. Sleep apnea is highly prevalent in maintenance hemodialysis patients and may contribute to intradialytic hypoxemia. In sleep apnea, normal respiration patterns are disrupted by episodes of apnea because of either disturbed respiratory control (i.e., central sleep apnea) or upper airway obstruction (i.e., obstructive sleep apnea). Intermittent SaO2 saw-tooth patterns are a hallmark of sleep apnea. Continuous intradialytic measurements of SaO2 provide an opportunity to follow the temporal evolution of SaO2 during hemodialysis. Using artificial intelligence, we aimed to automatically identify patients with repetitive episodes of intermittent SaO2 saw-tooth patterns.METHODSThe analysis utilized intradialytic SaO2 measurements by the Crit-Line device (Fresenius Medical Care, Waltham, MA). In patients with an arterio-venous fistula as vascular access, this FDA approved device records 150 SaO2 measurements per second in the extracorporeal blood circuit of the hemodialysis system. The average SaO2 of a 10-second segment is computed and streamed to the cloud. Periods comprising thirty 10-second segments (i.e., 300 s or five minutes) were independently adjudicated by two researchers for the presence or absence of SaO2 saw-tooth pattern. We built one-dimensional convolutional neural networks (1D-CNN), a state-of-the-art deep learning method, for SaO2 pattern classification and randomly assigned SaO2 time series segments to either a training (80%) or a test (20%) set.

RESULTSThe highest daily wildfire-related PM2.5 concentration observed (251.1 μg/m3) far exceeded the National Ambient Air Quality Standard (35 μg/m3). The presence of wildfire smoke plume was associated with an 18% increase in risk of same day (lag0) all-cause mortality (rate ratio [RR]:1.18; 95% confidence interval [CI], 1.13-1.24) and a 3% increase in risk of all-cause hospitalization (RR:1.03; 95% CI: 1.00-1.07). A 10 μg/m3 increase in wildfire-related PM2.5 was associated with a 139% increase in same day all-cause mortality (RR: 2.39; 95% CI: 1.79-3.18), and a 33% increase in all-cause hospitalization (RR:1.33; 95% CI: 1.10-1.62).CONCLUSIONOur findings suggest that air pollution from the 2023 Canadian wildfires resulted in increased risk of mortality and hospitalization among hemodialysis patients in Eastern and Midwestern USA.INTRODUCTIONSmoke plumes from the 2023 Canadian wildfires severely impacted air quality across the Eastern and Midwestern USA. However, a comprehensive health impact assessment is lacking in this large region. We investigated the association between wildfire-related air pollutants and the risk of mortality and hospitalization among hemodialysis patients in 22 heavily impacted states in the Eastern and Midwestern USA.METHODSWe conducted a retrospective observational study using a time-stratified case-crossover analysis with a conditional quasi-Poisson model. The study included 52,995 patients with end-stage kidney disease (ESKD) receiving hemodialysis at Fresenius Kidney Care clinics during June and July 2023. The presence of wildfire smoke and fine particulate matter (with aerodynamic diameter < 2.5 microns, PM2.5) concentrations were assessed using satellite-derived smoke polygons and ground-based monitors. Daily number of all-cause deaths, all-cause hospitalizations, respiratory disease hospitalizations, and cardiovascular disease hospitalizations were counted for each hemodialysis clinic.

Large language models (LLMs) such as ChatGPT are increasingly positioned to be integrated into various aspects of daily life, with promising applications in healthcare, including personalized nutritional guidance for patients with chronic kidney disease (CKD). However, for LLM-powered nutrition support tools to reach their full potential, active collaboration of healthcare professionals, patients, caregivers and LLM experts is crucial. We conducted a comprehensive review of the literature on the use of LLMs as tools to enhance nutrition recommendations for patients with CKD, curated by our expertise in the field. Additionally, we considered relevant findings from adjacent fields, including diabetes and obesity management. Currently, the application of LLMs for CKD-specific nutrition support remains limited and has room for improvement. Although LLMs can generate recipe ideas, their nutritional analyses often underestimate critical food components such as electrolytes and calories. Anticipated advancements in LLMs and other generative artificial intelligence (AI) technologies are expected to enhance these capabilities, potentially enabling accurate nutritional analysis, the generation of visual aids for cooking and identification of kidney-healthy options in restaurants. While LLM-based nutritional support for patients with CKD is still in its early stages, rapid advancements are expected in the near future. Engagement from the CKD community, including healthcare professionals, patients and caregivers, will be essential to harness AI-driven improvements in nutritional care with a balanced perspective that is both critical and optimistic.

COVID-19 has a higher rate of morbidity and mortality among dialysis patients than the general population. Identifying infected patients early with the support of predictive models helps dialysis centers implement concerted procedures (e.g., temperature screenings, universal masking, isolation treatments) to control the spread of SARS-CoV-2 and mitigate outbreaks. We collect data from multiple sources, including demographics, clinical, treatment, laboratory, vaccination, socioeconomic status, and COVID-19 surveillance. Previous early prediction models, such as logistic regression, SVM, and XGBoost, require sophisticated feature engineering and need improved prediction performance. We create deep learning models, including Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to predict SARS-CoV-2 infections during incubation. Our study shows deep learning models with minimal feature engineering can identify those infected patients more accurately than previously built models. Our Long Short-Term Memory (LSTM) model consistently performed well, with an AUC exceeding 0.80, peaking at 0.91 in August 2021. The CNN model also demonstrated strong results with an AUC above 0.75. Both models outperformed previous best XGBoost models by over 0.10 in AUC. Prediction accuracy declined as the pandemic evolved, dropping to approximately 0.75 between September 2021 and January 2022. Maintaining a 20% false positive rate, our LSTM and CNN models identified 66% and 64% of positive cases among patients, significantly outperforming XGBoost models at 42%. We also identify key features for dialysis patients by calculating the gradient of the output with respect to the input features. By closely monitoring these factors, dialysis patients can receive earlier diagnoses and care, leading to less severe outcomes. Our research highlights the effectiveness of deep neural networks in analyzing longitudinal data, especially in predicting COVID-19 infections during the crucial incubation period. These deep network approaches surpass traditional methods relying on aggregated variable means, significantly improving the accurate identification of SARS-CoV-2 infections.

RESULTSThe QIP group comprised 44 patients (59 ± 17 years), the concurrent control group 48 patients (59 ± 16 years), the historic control group 57 patients (58 ± 15 years). Six-month post-AVF creation, the fraction of non-censored patients with catheter in place was 21% in the QIP cohort, 67% in the concurrent control group, and 68% in the historic control group. In unadjusted and adjusted analysis, catheter residence time post-fistula creation was shorter in QIP patients compared to either control groups (p < 0.001).CONCLUSIONScvO2-based assessment of fistula maturation is associated with shorter catheter residence post-AVF creation.INTRODUCTIONArteriovenous fistula (AVF) maturation assessment is essential to reduce venous catheter residence. We introduced central venous oxygen saturation (ScvO2) and estimated upper body blood flow (eUBBF) to monitor newly created fistula maturation and recorded catheter time in patients with and without ScvO2-based fistula maturation.METHODSFrom 2017 to 2019, we conducted a multicenter quality improvement project (QIP) in hemodialysis patients with the explicit goal to shorten catheter residence time post-AVF creation through ScvO2-based maturation monitoring. In patients with a catheter as vascular access, we tracked ScvO2 and eUBBF pre- and post-AVF creation. The primary outcome was catheter residence time post-AVF creation. We compared catheter residence time post-AVF creation between QIP patients and controls. One control group comprised concurrent patients; a second control group comprised historic controls (2014-2016). We conducted Kaplan-Meier analysis and constructed a Cox proportional hazards model with variables adjustment to assess time-to-catheter removal.

OBJECTIVESThe rising diversity of food preferences and the desire to provide better personalized care provide challenges to renal dietitians working in dialysis clinics. To address this situation, we explored the use of a large language model, specifically, ChatGPT using the GPT-4 model (openai.com), to support nutritional advice given to dialysis patients.RESULTSChatGPT generated a daily menu with five recipes. The renal dietitian rated the recipes at 3 (3, 3) [median (Q1, Q3)], the cooking instructions at 5 (5,5), and the nutritional analysis at 2 (2, 2) on the five-point Likert scale. ChatGPT's nutritional analysis underestimated calories by 36% (95% CI: 44-88%), protein by 28% (25-167%), fat 48% (29-81%), phosphorus 54% (15-102%), potassium 49% (40-68%), and sodium 53% (14-139%). The nutritional analysis of online available recipes differed only by 0 to 35%. The translations were rated as reliable by native speakers (4 on the five-point Likert scale).CONCLUSIONWhile ChatGPT-4 shows promise in providing personalized nutritional guidance for diverse dialysis patients, improvements are necessary. This study highlights the importance of thorough qualitative and quantitative evaluation of artificial intelligence-generated content, especially regarding medical use cases.METHODSWe tasked ChatGPT-4 with generating a personalized daily meal plan, including nutritional information. Virtual "patients" were generated through Monte Carlo simulation; data from a randomly selected virtual patient were presented to ChatGPT. We provided to ChatGPT patient demographics, food preferences, laboratory data, clinical characteristics, and available budget, to generate a one-day sample menu with recipes and nutritional analyses. The resulting daily recipe recommendations, cooking instructions, and nutritional analyses were reviewed and rated on a five-point Likert scale by an experienced renal dietitian. In addition, the generated content was rated by a renal dietitian and compared with a U. S. Department of Agriculture-approved nutrient analysis software. ChatGPT also analyzed nutrition information of two recipes published online. We also requested a translation of the output into Spanish, Mandarin, Hungarian, German, and Dutch.

Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose a flexible nonparametric mixed-effects mixture model that identifies risk factors and classifies patients with biological changes. We model the latent probability of biological changes using a logistic regression model and trajectories in the latent groups using smoothing splines. We developed an EM algorithm to maximize the penalized likelihood for estimating all parameters and mean functions. We evaluate our methods by simulations and apply the proposed model to investigate changes in temperature in a cohort of COVID-19-infected hemodialysis patients.

RESULTSThe intervention group showed an improved median percentage of hemoglobin measurements within target at 47% (interquartile range, 39–58), with a 10% point median difference between the two groups (95% confidence interval, 3 to 16; P = 0.008). The odds ratio of being within the hemoglobin target in the standard-of-care group compared with the group receiving the personalized ESA recommendations was 0.68 (95% confidence interval, 0.51 to 0.92). The variability of hemoglobin levels decreased in the intervention group, with the percentage of patients experiencing fluctuating hemoglobin levels being 45% versus 82% in the standard-of-care group. ESA usage was reduced by approximately 25% in the intervention group.KEY POINTSWe conducted a randomized controlled pilot trial in patients on hemodialysis using a physiology-based individualized anemia therapy assistance software. Patients in the group receiving erythropoiesis-stimulating agent dose recommendations from the novel software showed improvement in hemoglobin stability and erythropoiesis-stimulating agent utilization.CONCLUSIONSOur results demonstrated an improved hemoglobin target attainment and variability by using personalized ESA recommendations using the physiology-based anemia therapy assistance software.CLINICAL TRIAL REGISTRATION NUMBER:NCT04360902.BACKGROUNDAnemia is common among patients on hemodialysis. Maintaining stable hemoglobin levels within predefined target levels can be challenging, particularly in patients with frequent hemoglobin fluctuations both above and below the desired targets. We conducted a multicenter, randomized controlled trial comparing our anemia therapy assistance software against a standard population-based anemia treatment protocol. We hypothesized that personalized dosing of erythropoiesis-stimulating agents (ESAs) improves hemoglobin target attainment.METHODSNinety-six patients undergoing hemodialysis and receiving methoxy polyethylene glycol-epoetin beta were randomized 1:1 to the intervention group (personalized ESA dose recommendations computed by the software) or the standard-of-care group for 26 weeks. The therapy assistance software combined a physiology-based mathematical model and a model predictive controller designed to stabilize hemoglobin levels within a tight target range (10–11 g/dl). The primary outcome measure was the percentage of hemoglobin measurements within the target. Secondary outcome measures included measures of hemoglobin variability and ESA utilization.

RESULTSIn total, 38 151 patients (52.2%) had complete data and made up the main cohort. Sensitivity analyses were conducted in 67 421 patients (92.3%) after eliminating variables with a high proportion of missing data points. Selected features diverged between datasets and workflows. A previously failed arteriovenous access appeared to be the most stable predictor for subsequent failure. Prediction of re-conversion based on the demographic and clinical information resulted in an area under the receiver operating characteristic curve (ROCAUC) between 0.541 and 0.571, whereas models predicting all cause mortality performed considerably better (ROCAUC 0.662 - 0.683).OBJECTIVEThe decision to convert from catheter to arteriovenous access is difficult yet very important. The ability to accurately predict fistula survival prior to surgery would significantly improve the decision making process. Many previously investigated demographic and clinical features have been associated with fistula failure. However, it is not conclusively understood how reliable predictions based on these parameters are at an individual level. The aim of this study was to investigate the probability of arteriovenous fistula maturation and survival after conversion using machine learning workflows.CONCLUSIONWhile group level depiction of major adverse outcomes after catheter to arteriovenous fistula or graft conversion is possible using the included variables, patient level predictions are associated with limited performance. Factors during and after fistula creation as well as biomolecular and genetic biomarkers might be more relevant predictors of fistula survival than baseline clinical conditions.METHODSA retrospective cohort study on multicentre data from a large North American dialysis organisation was conducted. The study population comprised 73 031 chronic in centre haemodialysis patients. The dataset included 49 variables including demographic and clinical features. Two distinct feature selection and prediction pipelines were used: LASSO regression and Boruta followed by a random forest classifier. Predictions were facilitated for re-conversion to catheter within one year. Additionally, all cause mortality predictions were conducted to serve as a comparator.

RESULTSWe analysed data of 848 patients (814 starting on CAPD and 34 starting on APD). The SBP decreased by 4 (SD 22) mmHg when transitioning from CAPD to APD (p < 0.001) and increased by 4 (SD 21) mmHg when transitioning from APD to CAPD (p = 0.38); consistent findings were seen for DBP. There was no significant change in the number of antihypertensive drugs prescribed before and after transition.CONCLUSIONSTransition between PD modalities seems to directly impact on BP levels. Further studies are needed to confirm if switching to APD could be an effective treatment for uncontrolled hypertension among CAPD patients.BACKGROUNDHypertension is a leading cause of kidney failure, affects most dialysis patients and associates with adverse outcomes. Hypertension can be difficult to control with dialysis modalities having differential effects on sodium and water removal. There are two main types of peritoneal dialysis (PD), automated peritoneal dialysis (APD) and continuous ambulatory peritoneal dialysis (CAPD). It is unknown whether one is superior to the other in controlling blood pressure (BP). Therefore, the aim of our study was to analyse the impact of switching between these two PD modalities on BP levels in a nationally representative cohort.METHODSThis was a cohort study of patients on PD from 122 dialysis centres in Brazil (BRAZPD II study). Clinical and laboratory data were collected monthly throughout the study duration. We selected all patients who remained on PD at least 6 months and 3 months on each modality at minimum. We compared the changes in mean systolic/diastolic blood pressures (SBP/DBP) before and after modality transition using a multilevel mixed-model where patients were at first level and their clinics at the second level.

DISCUSSIONConcurrent combined monitoring of intradialytic ScvO2 and RBV change may provide additional insights into a patient's circulatory status. Patients with low ScvO2 and small changes in RBV may represent a specifically vulnerable group of patients at particularly high risk for adverse outcomes, possibly related to poor cardiac reserve and fluid overload.INTRODUCTIONIn maintenance hemodialysis (HD) patients, low central venous oxygen saturation (ScvO2 ) and small decline in relative blood volume (RBV) have been associated with adverse outcomes. Here we explore the joint association between ScvO2 and RBV change in relation to all-cause mortality.FINDINGSBaseline comprised 5231 dialysis sessions in 216 patients. The median RBV change was -5.5% and median ScvO2 was 58.8%. During follow-up, 44 patients (20.4%) died. In the adjusted model, all-cause mortality was highest in patients with ScvO2 below median and RBV change above median (HR 6.32; 95% confidence interval [CI] 1.37-29.06), followed by patients with ScvO2 below median and RBV change below median (HR 5.04; 95% CI 1.14-22.35), and ScvO2 above median and RBV change above median (HR 4.52; 95% CI 0.95-21.36).METHODSWe conducted a retrospective study in maintenance HD patients with central venous catheters as vascular access. During a 6-month baseline period, Crit-Line (Fresenius Medical Care, Waltham, MA) was used to measure continuously intradialytic ScvO2 and hematocrit-based RBV. We defined four groups per median change of RBV and median ScvO2 . Patients with ScvO2 above median and RBV change below median were defined as reference. Follow-up period was 3 years. We constructed Cox proportional hazards model with adjustment for age, diabetes, and dialysis vintage to assess the association between ScvO2 and RBV and all-cause mortality during follow-up.

CONCLUSIONAs found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.BACKGROUNDThe coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective.METHODSWe developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors.RESULTFrom April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination.

RESULTSWe observed positive associations between inclement weather and missed appointment (rainfall, hurricane and tropical storm, snowfall, snow depth, and wind advisory) when compared with noninclement weather days. The risk of missed appointments was most pronounced during the day of inclement weather (lag 0) for rainfall (incidence rate ratio [RR], 1.03 per 10-mm rainfall; 95% confidence interval [CI], 1.02 to 1.03) and snowfall (RR, 1.02; 95% CI, 1.01 to 1.02). Over 7 days (lag 0-6), hurricane and tropical storm exposures were associated with a 55% higher risk of missed appointments (RR, 1.55; 95% CI, 1.22 to 1.98). Similarly, 7-day cumulative exposure to sustained wind advisories was associated with 29% higher risk (RR, 1.29; 95% CI, 1.25 to 1.31), while wind gusts advisories showed a 34% higher risk (RR, 1.34; 95% CI, 1.29 to 1.39) of missed appointment.CONCLUSIONSInclement weather was associated with higher risk of missed hemodialysis appointments within the Northeastern United States. Furthermore, the association between inclement weather and missed hemodialysis appointments persisted for several days, depending on the inclement weather type.BACKGROUNDNonadherence to hemodialysis appointments could potentially result in health complications that can influence morbidity and mortality. We examined the association between different types of inclement weather and hemodialysis appointment adherence.METHODSWe analyzed health records of 60,135 patients with kidney failure who received in-center hemodialysis treatment at Fresenius Kidney Care clinics across the Northeastern US counties during 2001-2019. County-level daily meteorological data on rainfall, hurricane and tropical storm events, snowfall, snow depth, and wind speed were extracted using National Oceanic and Atmosphere Agency data sources. A time-stratified case-crossover study design with conditional Poisson regression was used to estimate the effect of inclement weather exposures within the Northeastern US region. We applied a distributed lag nonlinear model framework to evaluate the delayed effect of inclement weather for up to 1 week.

RESULTSWe included 302,613 patients. Baseline phosphate was 5.1±1.2 mg/dl, and mean DR was +0.6±3.3 mg/dl. Across different levels of phosphate, higher levels of DR of phosphate were associated with higher risk of all-cause mortality. In patients with lower levels of phosphate and serum albumin, the effect of a negative DR was most pronounced, whereas in patients with higher phosphate levels, a positive DR was related to increased mortality.KEY POINTSAn increase in serum phosphate variability is an independent risk factor of mortality. The effects of a positive directional range (DR) is most pronounced in patients with high serum phosphate levels whereas the effects of a negative DR is most pronounced in patients with low serum phosphate and/or serum albumin.CONCLUSIONSHigher variability of serum phosphate is related to mortality at all levels of phosphate, especially in lower levels with a negative DR and in low serum albumin levels. This could possibly reflect dietary intake in patients who are already inflamed or malnourished, where a further reduction in serum phosphate should prompt for nutritional evaluation.BACKGROUNDIn maintenance hemodialysis (HD) patients, previous studies have shown that serum phosphate levels have a bidirectional relation to outcome. Less is known about the relation between temporal dynamics of serum phosphate in relation to outcome. We aimed to further explore the relation between serum phosphate variability and all-cause mortality.METHODSAll adult incident HD patients treated in US Fresenius Kidney Care clinics between January 2010 and October 2018 were included. Baseline period was defined as 6 months after initiation of HD and months 7–18 as follow-up period. All-cause mortality was recorded during the follow-up period. The primary metric of variability used was directional range (DR) that is the difference between the largest and smallest values within a time period; DR was positive when the smallest value preceded the largest and negative otherwise. Cox proportional hazards models with spline terms were applied to explore the association between phosphate, DR, and all-cause mortality. In addition, tensor product smoothing splines were computed to further elucidate the interactions of phosphate, DR, and all-cause mortality.

RESULTSDuring 180,319 HD sessions among 2554 patients, PRR had high within-patient and between-patient variability. Female sex and hypoalbuminemia were associated with low PRR at multiple time points during the first hour of HD. Low starting PRR has a higher hazard of IDH, whereas high starting PRR was protective (hazard ratio [HR], 1.26, 95% confidence interval [CI], 1.18 to 1.35 versus HR, 0.79, 95% CI, 0.73 to 0.85, respectively). However, when accounting for time-varying PRR and time-varying confounders, compared with a moderate PRR, while a consistently low PRR was associated with increased risk of hypotension (odds ratio [OR], 1.09, 95% CI, 1.02 to 1.16), a consistently high PRR had a stronger association with hypotension within the next 15 minutes (OR, 1.38, 95% CI, 1.30 to 1.45).KEY POINTSDirectly studying plasma refill rate (PRR) during hemodialysis (HD) can offer insight into physiologic mechanisms that change throughout HD. PRR at the start and during HD is associated with intradialytic hypotension, independent of ultrafiltration rate. A rising PRR during HD may be an early indicator of compensatory mechanisms for impending circulatory instability.CONCLUSIONSWe present a straightforward technique to quantify plasma refill that could easily integrate with devices that monitor hematocrit during HD. Our study highlights how examining patterns of plasma refill may enhance our understanding of circulatory changes during HD, an important step to understand how current technology might be used to improve hemodynamic instability.BACKGROUNDAttaining the optimal balance between achieving adequate volume removal while preserving organ perfusion is a challenge for patients receiving maintenance hemodialysis (HD). Current strategies to guide ultrafiltration are inadequate.METHODSWe developed an approach to calculate the plasma refill rate (PRR) throughout HD using hematocrit and ultrafiltration data in a retrospective cohort of patients receiving maintenance HD at 17 dialysis units from January 2017 to October 2019. We studied whether (1) PRR is associated with traditional risk factors for hemodynamic instability using logistic regression, (2) low starting PRR is associated with intradialytic hypotension (IDH) using Cox proportional hazard regression, and (3) time-varying PRR throughout HD is associated with hypotension using marginal structural modeling.

No abstract available

Patients with renal anemia are frequently treated with erythropoiesis-stimulating agents (ESAs), which are dynamically dosed in order to stabilize blood hemoglobin levels within a specified target range. During typical ESA treatments, a fraction of patients experience hemoglobin 'cycling' periods during which hemoglobin levels periodically over- and undershoot the target range. Here we report a specific mechanism of hemoglobin cycling, whereby cycles emerge from the patient's delayed physiological response to ESAs and concurrent ESA dose adjustments. We introduce a minimal theoretical model that can explain dynamic hallmarks of observed hemoglobin cycling events in clinical time series and elucidates how physiological factors (such as red blood cell lifespan and ESA responsiveness) and treatment-related factors (such as dosing schemes) affect cycling. These results show that in general, hemoglobin cycling cannot be attributed to patient physiology or ESA treatment alone but emerges through an interplay of both, with consequences for the design of ESA treatment strategies.

MATERIALS AND METHODSWe included data HD patients who had data across a baseline period of at least 1 year and 1 day in the internationally representative Monitoring Dialysis Outcomes (MONDO) Initiative dataset. Twenty-three input parameters considered in the model were chosen in an a priori manner. The prediction model used 1 year baseline data to predict death in the following 3 years. The dataset was randomly split into 80% training data and 20% testing data for model development. Two different modeling techniques were used to build the mortality prediction model.DISCUSSIONIn the internationally representative MONDO data for HD patients, we describe the development of a ML model and a traditional statistical model that was suitable for classification of a prevalent HD patient's 3-year risk of death. While both models had a reasonably high AUROC, the ML model was able to identify levels of hematocrit (HCT) as an important risk factor in mortality. If implemented in clinical practice, such proof-of-concept models could be used to provide pre-emptive care for HD patients.INTRODUCTIONSeveral factors affect the survival of End Stage Kidney Disease (ESKD) patients on dialysis. Machine learning (ML) models may help tackle multivariable and complex, often non-linear predictors of adverse clinical events in ESKD patients. In this study, we used advanced ML method as well as a traditional statistical method to develop and compare the risk factors for mortality prediction model in hemodialysis (HD) patients.FINDINGSA total of 95,142 patients were included in the analysis sample. The area under the receiver operating curve (AUROC) of the model on the test data with XGBoost ML model was 0.84 on the training data and 0.80 on the test data. AUROC of the logistic regression model was 0.73 on training data and 0.75 on test data. Four out of the top five predictors were common to both modeling strategies.

RESULTSWe studied 18,726 incident hemodialysis patients. Their age at dialysis initiation was 71.3 ± 11.9 years; 10,802 (58%) were males. Within the first 6 months, 2068 (11%) patients died, and 12,295 patients (67%) survived >36 months (survivor cohort). Hemodialysis patients who died showed a distinct biphasic pattern of change in inflammatory markers where an initial decline of inflammation was followed by a rapid rise that was consistently evident approximately 6 months before death. This pattern was similar in all patients who died and was consistent across the survival time intervals. In contrast, in the survivor cohort, we observed initial decline of inflammation followed by sustained low levels of inflammatory biomarkers.CONCLUSIONOur international study of incident hemodialysis patients highlights a temporal relationship between serial measurements of inflammatory markers and patient survival. This finding may inform the development of prognostic models, such as the integration of dynamic changes in inflammatory markers for individual risk profiling and guiding preventive and therapeutic interventions.INTRODUCTIONInflammation is highly prevalent among patients with end-stage kidney disease and is associated with adverse outcomes. We aimed to investigate longitudinal changes in inflammatory markers in a diverse international incident hemodialysis patient population.METHODSThe MONitoring Dialysis Outcomes (MONDO) Consortium encompasses hemodialysis databases from 31 countries in Europe, North America, South America, and Asia. The MONDO database was queried for inflammatory markers (total white blood cell count [WBC], neutrophil count, lymphocyte count, serum albumin, and C-reactive protein [CRP]) and hemoglobin levels in incident hemodialysis patients. Laboratory parameters were measured every month. Patients were stratified by survival time (≤6 months, >6 to 12 months, >12 to 18 months, >18 to 24 months, >24 to 30 months, >30 to 36 months, and >36 months) following dialysis initiation. We used cubic B-spline basis function to evaluate temporal changes in inflammatory parameters in relationship with patient survival.

RESULTSIn the effectiveness analysis concerning mRNA vaccines, we observed 850 SARS-CoV-2 infections and 201 COVID-19 related deaths among the 28110 patients during a mean follow up of 44 ± 40 days. In the effectiveness analysis concerning viral-carrier vaccines, we observed 297 SARS-CoV-2 infections and 64 COVID-19 related deaths among 12888 patients during a mean follow up of 48 ± 32 days. We observed 18.5/100-patient-year and 8.5/100-patient-year fewer infections and 5.4/100-patient-year and 5.2/100-patient-year fewer COVID-19 related deaths among patients vaccinated with mRNA and viral-carrier vaccines respectively, compared to matched unvaccinated controls. Estimated vaccine effectiveness at days 15, 30, 60 and 90 after the first dose of a mRNA vaccine was: for infection, 41.3%, 54.5%, 72.6% and 83.5% and, for death, 33.1%, 55.4%, 80.1% and 91.2%. Estimated vaccine effectiveness after the first dose of a viral-carrier vaccine was: for infection, 38.3% without increasing over time and, for death, 56.6%, 75.3%, 92.0% and 97.4%.CONCLUSIONIn this large, real-world cohort of hemodialyzed patients, mRNA and viral-carrier COVID-19 vaccines were associated with reduced COVID-19 related mortality. Additionally, we observed a strong reduction of SARS-CoV-2 infection in hemodialysis patients receiving mRNA vaccines.BACKGROUNDHemodialysis patients have high-risk of severe SARS-CoV-2 infection but were unrepresented in randomized controlled trials evaluating the safety and efficacy of COVID-19 vaccines. We estimated the real-world effectiveness of COVID-19 vaccines in a large international cohort of hemodialysis patients.METHODSIn this historical, 1:1 matched cohort study, we included adult hemodialysis patients receiving treatment from December 1, 2020, to May 31, 2021. For each vaccinated patient, an unvaccinated control was selected among patients registered in the same country and attending a dialysis session around the first vaccination date. Matching was based on demographics, clinical characteristics, past COVID-19 infections and a risk score representing the local background risk of infection at vaccination dates. We estimated the effectiveness of mRNA and viral-carrier COVID-19 vaccines in preventing infection and mortality rates from a time-dependent Cox regression stratified by country.

RESULTSAmong HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0-14 days, 7.9% and 4.6% of patients died within 15-30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0-14 and 15-30 days after COVID-19, yet not mortality > 30 days after presentation.CONCLUSIONSFindings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0-14 and 15-30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods.BACKGROUNDWe developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas.METHODSWe used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0-14, 15-30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries.

In patients with kidney failure treated by hemodialysis, intradialytic arterial oxygen saturation (SaO2) time series present intermittent high-frequency high-amplitude oximetry patterns (IHHOP), which correlate with observed sleep-associated breathing disturbances. A new method for identifying such intermittent patterns is proposed. The method is based on the analysis of recurrence in the time series through the quantification of an optimal recurrence threshold ([Formula: see text]). New time series for the value of [Formula: see text] were constructed using a rolling window scheme, which allowed for real-time identification of the occurrence of IHHOPs. The results for the optimal recurrence threshold were confronted with standard metrics used in studies of obstructive sleep apnea, namely the oxygen desaturation index (ODI) and oxygen desaturation density (ODD). A high correlation between [Formula: see text] and the ODD was observed. Using the value of the ODI as a surrogate to the apnea-hypopnea index (AHI), it was shown that the value of [Formula: see text] distinguishes occurrences of sleep apnea with great accuracy. When subjected to binary classifiers, this newly proposed metric has great power for predicting the occurrences of sleep apnea-related events, as can be seen by the larger than 0.90 AUC observed in the ROC curve. Therefore, the optimal threshold [Formula: see text] from recurrence analysis can be used as a metric to quantify the occurrence of abnormal behaviors in the arterial oxygen saturation time series.

Our bones are constantly being renewed in a fine-tuned cycle of destruction and formation that helps keep them healthy and strong. However, this process can become imbalanced and lead to osteoporosis, where the bones are weakened and have a high risk of fracturing. This is particularly common post-menopause, with one in three women over the age of 50 experiencing a broken bone due to osteoporosis. There are several drug types available for treating osteoporosis, which work in different ways to strengthen bones. These drugs can be taken individually or combined, meaning that a huge number of drug combinations and treatment strategies are theoretically possible. However, it is not practical to test the effectiveness of all of these options in human trials. This could mean that patients are not getting the maximum potential benefit from the drugs available. Jörg et al. developed a mathematical model to predict how different osteoporosis drugs affect the process of bone renewal in the human body. The model could then simulate the effect of changing the order in which the therapies were taken, which showed that the sequence had a considerable impact on the efficacy of the treatment. This occurs because different drugs can interact with each other, leading to an improved outcome when they work in the right order. These results suggest that people with osteoporosis may benefit from altered treatment schemes without changing the type or amount of medication taken. The model could suggest new treatment combinations that reduce the risk of bone fracture, potentially even developing personalised plans for individual patients based on routine clinical measurements in response to different drugs.

RESULTSWe included 9,211 patients (age 65.4 ± 13.7 years, dialysis vintage 4.2 ± 3.7 years) eligible for the study. The 30-day mortality rate was 20.8%. In LR models, several potentially modifiable factors were associated with higher mortality: body mass index (BMI) 30-40 kg/m2 (OR: 1.28, CI: 1.10-1.50), single-pool Kt/V (OR off-target vs on-target: 1.19, CI: 1.02-1.38), overhydration (OR: 1.15, CI: 1.01-1.32), and both low (<2.5 mg/dl) and high (≥5.5 mg/dl) serum phosphate levels (OR: 1.52, CI: 1.07-2.16 and OR: 1.17, CI: 1.01-1.35). On-line hemodiafiltration was protective in the model using KPIs (OR: 0.86, CI: 0.76-0.97). SHapley Additive exPlanations analysis in XGBoost models shows a high influence on prediction for several modifiable factors as well, including inflammatory parameters, high BMI, and fluid overload. In both LR and XGBoost models, age, gender, and comorbidities were strongly associated with mortality.CONCLUSIONBoth conventional and machine learning techniques showed that KPIs and modifiable risk factors in different dimensions ascertained 6 months before the COVID-19 suspicion date were associated with 30-day COVID-19-related mortality. Our results suggest that adequate dialysis and achieving KPI targets remain of major importance during the COVID-19 pandemic as well.INTRODUCTIONPatients with end-stage kidney disease face a higher risk of severe outcomes from SARS-CoV-2 infection. Moreover, it is not well known to what extent potentially modifiable risk factors contribute to mortality risk. In this historical cohort study, we investigated the incidence and risk factors for 30-day mortality among hemodialysis patients with SARS-CoV-2 infection treated in the European Fresenius Medical Care NephroCare network using conventional and machine learning techniques.METHODSWe included adult hemodialysis patients with the first documented SARS-CoV-2 infection between February 1, 2020, and March 31, 2021, registered in the clinical database. The index date for the analysis was the first SARS-CoV-2 suspicion date. Patients were followed for up to 30 days until April 30, 2021. Demographics, comorbidities, and various modifiable risk factors, expressed as continuous parameters and as key performance indicators (KPIs), were considered to tap multiple dimensions including hemodynamic control, nutritional state, and mineral metabolism in the 6 months before the index date. We used logistic regression (LR) and XGBoost models to assess risk factors for 30-day mortality.

RESULTSWe used data from 4,285 PD patients (Brazil n = 1,388 and United States n = 2,897). Model estimates showed lower vitality levels within 90 days of starting PD were associated with a higher risk of mortality, which was consistent in Brazil and the United States cohorts. In the multivariate survival model, each 10-unit increase in vitality score was associated with lower risk of all-cause mortality in both cohorts (Brazil HR = 0.79 [95%CI 0.70 to 0.90] and United States HR = 0.90 [95%CI 0.88 to 0.93], pooled HR = 0.86 [95%CI 0.75 to 0.98]). Results for all models provided consistent effect estimates.CONCLUSIONSAmong patients in Brazil and the United States, lower vitality score in the initial months of PD was independently associated with all-cause mortality.BACKGROUNDWe tested if fatigue in incident Peritoneal Dialysis associated with an increased risk for mortality, independently from main confounders.METHODSWe conducted a side-by-side study from two of incident PD patients in Brazil and the United States. We used the same code to independently analyze data in both countries during 2004 to 2011. We included data from adults who completed KDQOL-SF vitality subscale within 90 days after starting PD. Vitality score was categorized in four groups: >50 (high vitality), ≥40 to ≤50 (moderate vitality), >35 to <40 (moderate fatigue), ≤35 (high fatigue; reference group). In each country's cohort, we built four distinct models to estimate the associations between vitality (exposure) and all-cause mortality (outcome): (i) Cox regression model; (ii) competing risk model accounting for technique failure events; (iii) multilevel survival model of clinic-level clusters; (iv) multivariate regression model with smoothing splines treating vitality as a continuous measure. Analyses were adjusted for age, comorbidities, PD modality, hemoglobin, and albumin. A mixed-effects meta-analysis was used to pool hazard ratios (HRs) from both cohorts to model mortality risk for each 10-unit increase in vitality.