Development and validation of AI-based triage support algorithms for prevention of intradialytic hypotension.

BACKGROUND: Intradialytic hypotension remains one of the most recurrent complications of dialysis sessions. Inadequate management can lead to adverse outcomes, highlighting the need to develop personalized approaches for the prevention of intradialytic hypotension. Here, we sought to develop and validate two AI-based risk models predicting the occurrence of symptomatic intradialytic hypotension at different time points. METHODS: The models were built using the XGBoost algorithm and they predict the occurrence of intradialytic hypotension in the next dialysis session and in the next month. The initial dataset, obtained from routinely collected data in the EuCliD® Database, was split to perform model derivation, training and validation. Model performance was evaluated by concordance statistic and calibration charts; the importance of features was assessed with the Shapley Additive Explanation (SHAP) methodology. RESULTS: The final dataset included 1,249,813 dialysis sessions, and the incidence rate of intradialytic hypotension was 10.07% (95% CI 10.02-10.13). Our models retained good discrimination (AUC around 0.8) and a suitable calibration yielding to the selection of three classification thresholds identifying four distinct risk groups. Variables providing the most significant impact on risk estimates were blood pressure dynamics and other metrics mirroring hemodynamic instability over time. CONCLUSIONS: Recurrent symptomatic intradialytic hypotension could be reliably and accurately predicted using routinely collected data during dialysis treatment and standard clinical care. Clinical application of these prediction models would allow for personalized risk-based interventions for preventing and managing intradialytic hypotension.

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The 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. Methods: We 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. Result: From 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. Conclusion: As 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.

Biphasic Dynamics of Inflammatory Markers Following Hemodialysis Initiation: Results From the International MONitoring Dialysis Outcome Initiative.

Introduction: Inflammation 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. Methods: The 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. Results: We 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. Conclusion: Our 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.

Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries.

BACKGROUND: We 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. METHODS: We 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. RESULTS: Among 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. CONCLUSIONS: Findings 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.

Fatigue in incident peritoneal dialysis and mortality: A real-world side-by-side study in Brazil and the United States.

BACKGROUND: We tested if fatigue in incident Peritoneal Dialysis associated with an increased risk for mortality, independently from main confounders. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: Among patients in Brazil and the United States, lower vitality score in the initial months of PD was independently associated with all-cause mortality.

Cephalic arch restenosis rates in hemodialysis patients with brachiocephalic fistulae: a retrospective multicenter analysis of 3301 patients.

BACKGROUND: We evaluated restenosis rates at the cephalic arch after percutaneous angioplasty and stenting procedures in patients with brachial artery to cephalic vein arteriovenous fistula (BCAVF) hemodialysis access. METHODS: We used data from adult hemodialysis patients treated at a national network of 44 outpatient interventional facilities during Oct 2011-2015. We included data from patients with BCAVF who received an exclusive angioplasty, or stent with angioplasty, for treatment of cephalic arch stenosis and had ≥1 subsequent evaluation of the cephalic arch. Median percent restenosis per month at cephalic arch and days between encounters was calculated from the 1st index to 2nd procedure, and for up to 4 subsequent encounters. Analyses were stratified by intervention and device types. RESULTS: We identified a cohort of 3301 patients (mean age 62.2 ± 13.9 years, 58.5% male, 33.2% white race) with a BCAVF who had an angioplasty, or stent, at the cephalic arch for an index and ≥ 1 follow-up procedure. Between the 1st index to 2nd procedure, patients who received an angioplasty (n = 2663) or stent (n = 933) showed a median decrease of 18.9 and 16.5% in luminal diameter per month and a median time of 93 and 91 days between encounters, respectively. Restenosis and day rates were similar for standard versus high-pressure angioplasties. Bare metal stents showed 10.1 percentage point higher restenosis rate compared to stent grafts. Restenosis rates and time to restenosis were relatively consistent across subsequent encounters. CONCLUSIONS: Findings suggest hemodialysis patients with a BCAVF who require an angioplasty or stent to treat a stenosis at the cephalic arch will have stenosis reformed at a rate of 18.9 and 16.5% per month after the first intervention, respectively. Findings suggest patients are at risk of having significant lesions at the cephalic arch within 3 months after the previous intervention.

Effectiveness of COVID-19 vaccines in a large European hemodialysis cohort.

Background: Hemodialysis 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. Methods: In 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. Results: In 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%. Conclusion: In 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.

Relationship between serum phosphate levels and survival in chronic hemodialysis patients: interactions with age, malnutrition and inflammation.

BACKGROUND: Evidence indicates that the inverse relationships between phosphate levels and mortality maybe modified by age. Furthermore, malnutrition and inflammation could strengthen the risk associated with phosphate abnormalities. This study aimed to assess the associations between phosphate levels and mortality while accounting for the interactions with age and parameters associated with malnutrition and inflammation in hemodialysis (HD) patients. METHODS: Adult HD patients (n = 245 853) treated in Fresenius Medical Care North America clinics from January 2010 to October 2018 were enrolled. Baseline was defined as Months 4-6 on dialysis, with the subsequent 12 months as the follow-up period. Univariate and multivariate Cox proportional hazard models with spline terms were applied to study the nonlinear relationships between serum phosphate levels and mortality. The interactions of phosphate levels with albumin, creatinine, normalized protein catabolic rate (nPCR) and neutrophil-lymphocyte ratio (NLR) were assessed with smoothing spline analysis of variance Cox proportional hazard models. RESULTS: Older patients tended to have lower levels of serum phosphate, albumin, creatinine and nPCR. Additionally, both low (<4.0 mg/dL) and high (>5.5 mg/dL) phosphate levels were associated with higher risk of mortality across all age strata. The U-shaped relationships between phosphate levels and outcome persisted even for patients with low or high levels of serum albumin, creatinine, nPCR and NLR, respectively. CONCLUSION: The consistent U-shaped relationships between serum phosphate and mortality across age strata and levels of inflammatory and nutritional status should prompt the search for underlying causes and potentially nutritional intervention in clinical practice.

Machine Learning for Prediction of Patients on Hemodialysis with an Undetected SARS-CoV-2 Infection.

Background: We developed a machine learning (ML) model that predicts the risk of a patient on hemodialysis (HD) having an undetected SARS-CoV-2 infection that is identified after the following ≥3 days. Methods: As part of a healthcare operations effort, we used patient data from a national network of dialysis clinics (February-September 2020) to develop an ML model (XGBoost) that uses 81 variables to predict the likelihood of an adult patient on HD having an undetected SARS-CoV-2 infection that is identified in the subsequent ≥3 days. We used a 60%:20%:20% randomized split of COVID-19-positive samples for the training, validation, and testing datasets. Results: We used a select cohort of 40,490 patients on HD to build the ML model (11,166 patients who were COVID-19 positive and 29,324 patients who were unaffected controls). The prevalence of COVID-19 in the cohort (28% COVID-19 positive) was by design higher than the HD population. The prevalence of COVID-19 was set to 10% in the testing dataset to estimate the prevalence observed in the national HD population. The threshold for classifying observations as positive or negative was set at 0.80 to minimize false positives. Precision for the model was 0.52, the recall was 0.07, and the lift was 5.3 in the testing dataset. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the model was 0.68 and 0.24 in the testing dataset, respectively. Top predictors of a patient on HD having a SARS-CoV-2 infection were the change in interdialytic weight gain from the previous month, mean pre-HD body temperature in the prior week, and the change in post-HD heart rate from the previous month. Conclusions: The developed ML model appears suitable for predicting patients on HD at risk of having COVID-19 at least 3 days before there would be a clinical suspicion of the disease.

Artificial intelligence enabled applications in kidney disease.

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

Changes in pre-dialysis blood pressure variability in the first year of dialysis associate with mortality in European hemodialysis patients: a retrospective cohort study on behalf of the MONDO Initiative.

Pre-hemodialysis systolic blood pressure variability (pre-HD SBPV) has been associated with outcomes. The association of a change in pre-HD SBPV over time with outcomes, and predictors of this change, has not yet been studied. Therefore, we studied this in a cohort of 8825 incident hemodialysis (HD) patients from the European Monitoring Dialysis Outcomes Initiative database. Patient level pre-HD SBPV was calculated as the standard deviation of the residuals of a linear regression model of systolic blood pressure (SBP) over time divided by individual mean SBP in the respective time periods. The pre-HD SBPV difference between months 1-6 and 7-12 was used as an indicator of pre-HD SBPV change. The association between pre-HD SBPV change and all-cause mortality in year 2 was analyzed by multivariate Cox models. Predictors of pre-HD SBPV change was determined by logistic regression models. We found the highest pre-HD SBPV tertile, in the first 6 months after initiation of HD, had the highest mortality rates (adjusted HR 1.44 (95% confidence intervals (95% CI): 1.15-1.79)). An increase in pre-HD SBPV between months 1-6 and 7-12 was associated with an increased risk of mortality in year 2 (adjusted HR 1.29 (95% CI: 1.05-1.58)) compared with stable pre-HD SPBV. A pre-HD SBPV increase was associated with female gender, higher mean pre-HD SBP and pulse pressure, and lower HD frequency.

Impacts of dialysis adequacy and intradialytic hypotension on changes in dialysis recovery time.

BACKGROUND: Dialysis recovery time (DRT) surveys capture the perceived time after HD to return to performing regular activities. Prior studies suggest the majority of HD patients report a DRT > 2 h. However, the profiles of and modifiable dialysis practices associated with changes in DRT relative to the start of dialysis are unknown. We hypothesized hemodialysis (HD) dose and rates of intradialytic hypotension (IDH) would associate with changes in DRT in the first years after initiating dialysis. METHODS: We analyzed data from adult HD patients who responded to a DRT survey ≤180 days from first date of dialysis (FDD) during 2014 to 2017. DRT survey was administered with annual KDQOL survey. DRT survey asks: "How long does it take you to be able to return to your normal activities after your dialysis treatment?" Answers are: < 0.5, 0.5-to-1, 1-to-2, 2-to-4, or > 4 h. An adjusted logistic regression model computed odds ratio for a change to a longer DRT (increase above DRT > 2 h) in reference to a change to a shorter DRT (decrease below DRT < 2 h, or from DRT > 4 h). Changes in DRT were calculated from incident (≤180 days FDD) to first prevalent (> 365-to- ≤ 545 days FDD) and second prevalent (> 730-to- ≤ 910 days FDD) years. RESULTS: Among 98,616 incident HD patients (age 62.6 ± 14.4 years, 57.8% male) who responded to DRT survey, a higher spKt/V in the incident period was associated with 13.5% (OR = 0.865; 95%CI 0.801-to-0.935) lower risk of a change to a longer DRT in the first-prevalent year. A higher number of HD treatments with IDH episodes per month in the incident period was associated with a 0.8% (OR = 1.008; 95%CI 1.001-to-1.015) and 1.6% (OR = 1.016; 95%CI 1.006-to-1.027) higher probability of a change to a longer DRT in the first- and second-prevalent years, respectively. Consistently, an increased in incidence of IDH episodes/months was associated to a change to a longer DRT over time. CONCLUSIONS: Incident patients who had higher spKt/V and less sessions with IDH episodes had a lower likelihood of changing to a longer DRT in first year of HD. Dose optimization strategies with cardiac stability in fluid removal should be tested.

Impact of hemodialysis and post-dialysis period on granular activity levels.

BACKGROUND: Physical activity (PA) is typically lower on hemodialysis (HD) days. Albeit intradialytic inactivity is expected, it is unknown whether recovery after HD contributes to low PA. We investigated the impact of HD and post-HD period on granular PA relative to HD timing. METHODS: We used baseline data from the HDFIT trial conducted from August 2016 to October 2017. Accelerometry measured PA over 1 week in patients who received thrice-weekly high-flux HD (vintage 3 to 24 months), were clinically stable, and had no ambulatory limitations. PA was assessed on HD days (0 to ≤24 h after start HD), first non-HD days (> 24 to ≤48 h after start HD) and second non-HD day (> 48 to ≤72 h after start HD). PA was recorded in blocks/slices: 4 h during HD, 0 to ≤2 h post-HD (30 min slices), and > 2 to ≤20 h post-HD (4.5 h slices). Blocks/slices of PA were captured at concurrent/parallel times on first/second non-HD days compared to HD days. RESULTS: Among 195 patients (mean age 53 ± 15 years, 71% male), step counts per 24-h were 3919 ± 2899 on HD days, 5308 ± 3131 on first non-HD days (p < 0.001), and 4926 ± 3413 on second non-HD days (p = 0.032). During concurrent/parallel times to HD on first and second non-HD days, patients took 1308 and 1128 more steps (both p < 0.001). Patients took 276 more steps and had highest rates of steps/hour 2-h post-HD versus same times on first non-HD days (all p < 0.05). Consistent findings were observed on second non-HD days. CONCLUSIONS: PA was higher within 2-h of HD versus same times on non-HD days. Lower PA on HD days was attributable to intradialytic inactivity. The established PA profiles are of importance to the design and development of exercise programs that aim to increase activity during and between HD treatments. TRIAL REGISTRATION: HDFIT was prospectively registered 20 April 2016 on ClinicalTrials.gov (NCT02787161).

Conversion from Intravenous Vitamin D Analogs to Oral Calcitriol in Patients Receiving Maintenance Hemodialysis.

BACKGROUND AND OBJECTIVES: In the United States, intravenous vitamin D analogs are the first-line therapy for management of secondary hyperparathyroidism in hemodialysis patients. Outside the United States, oral calcitriol (1,25-dihydroxyvitamin D3) is routinely used. We examined standard laboratory parameters of patients on in-center hemodialysis receiving intravenous vitamin D who switched to oral calcitriol. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We conducted a retrospective cohort study of adult patients treated within Fresenius Kidney Care clinics. During a 6-month period (December 2013 to May 2014), we identified patients on an intravenous vitamin D analog (doxercalciferol or paricalcitol) who switched to oral calcitriol and matched them to patients receiving an intravenous vitamin D analog. Mean serum calcium, phosphate, and intact parathyroid hormone (iPTH) concentrations were examined for up to 12 months of follow-up. We used Poisson and Cox proportional hazards regression models to examine hospitalization and survival rates. The primary analysis was conducted as intention-to-treat; secondary analyses included an as-treated evaluation. RESULTS: A total of 2280 patients who switched to oral calcitriol were matched to 2280 patients receiving intravenous vitamin D. Compared with patients on intravenous vitamin D, mean calcium and phosphate levels in the oral calcitriol group were lower after the change to oral calcitriol. In contrast, iPTH levels were higher in the oral calcitriol group. At 12 months, the percentage of patients with composite laboratories in target range (calcium <10 mg/dl, phosphate 3.0-5.5 mg/dl, and iPTH 150-600 pg/ml) were comparable between groups (45% versus 45%; P=0.96). Hospital admissions, length of hospital stay, and survival were comparable between groups. An as-treated analysis and excluding those receiving cinacalcet did not reveal significant between-group differences. CONCLUSIONS: Among patients receiving in-center hemodialysis who were switched to oral calcitriol versus those on an intravenous vitamin D analog, the aggregate of all mineral and bone laboratory parameters in range was largely similar between groups.

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated 'digital clinics'. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased 'digital literacy' for all those implicated in their care.

Association of all-cause mortality with pre-dialysis systolic blood pressure and its peridialytic change in chronic hemodialysis patients.

BACKGROUND: Pre-dialysis systolic blood pressure (pre-HD SBP) and peridialytic SBP change have been associated with morbidity and mortality among hemodialysis (HD) patients in previous studies, but the nature of their interaction is not well understood. METHODS: We analyzed pre-HD SBP and peridialytic SBP change (calculated as post-HD SBP minus pre-HD SBP) between January 2001 and December 2012 in HD patients treated in US Fresenius Medical Care facilities. The baseline period was defined as Months 4-6 after HD initiation, and all-cause mortality was noted during follow-up. Only patients who survived baseline and had no missing covariates were included. Censoring events were renal transplantation, modality change or study end. We fitted a Cox proportional hazard model with a bivariate spline functions for the primary predictors (pre-HD SBP and peridialytic SBP change) with adjustment for age, gender, race, diabetes, access-type, relative interdialytic weight gain, body mass index, albumin, equilibrated normalized protein catabolic rate and ultrafiltration rate. RESULTS: A total of 172 199 patients were included. Mean age was 62.1 years, 61.6% were white and 55% were male. During a median follow-up of 25.0 months, 73 529 patients (42.7%) died. We found that a peridialytic SBP rise combined with high pre-HD SBP was associated with higher mortality. In contrast, when concurrent with low pre-HD SBP, a peridialytic SBP rise was associated with better survival. CONCLUSION: The association of pre-HD and peridialytic SBP change with mortality is complex. Our findings call for a joint, not isolated, interpretation of pre-HD SBP and peridialytic SBP change.

Remote Treatment Monitoring on Hospitalization and Technique Failure Rates in Peritoneal Dialysis Patients.

Background: An integrated kidney disease healthcare company implemented a peritoneal dialysis (PD) remote treatment monitoring (RTM) application in 2016. We assessed if RTM utilization associates with hospitalization and technique failure rates. Methods: We used data from adult (age ≥18 years) patients on PD treated from October 2016 through May 2019 who registered online for the RTM. Patients were classified by RTM use during a 30-day baseline after registration. Groups were: nonusers (never entered data), moderate users (entered one to 15 treatments), and frequent users (entered >15 treatments). We compared hospital admission/day and sustained technique failure (required >6 consecutive weeks of hemodialysis) rates over 3, 6, 9, and 12 months of follow-up using Poisson and Cox models adjusted for patient/clinical characteristics. Results: Among 6343 patients, 65% were nonusers, 11% were moderate users, and 25% were frequent users. Incidence rate of hospital admission was 22% (incidence rate ratio [IRR]=0.78; P=0.002), 24% (IRR=0.76; P<0.001), 23% (IRR=0.77; P≤0.001), and 26% (IRR=0.74; P≤0.001) lower in frequent users after 3, 6, 9, and 12 months, respectively, versus nonusers. Incidence rate of hospital days was 38% (IRR=0.62; P=0.013), 35% (IRR=0.65; P=0.001), 34% (IRR=0.66; P≤0.001), and 32% (IRR=0.68; P<0.001) lower in frequent users after 3, 6, 9, and 12 months, respectively, versus nonusers. Sustained technique failure risk at 3, 6, 9, and 12 months was 33% (hazard ratio [HR]=0.67; P=0.020), 31% (HR=0.69; P=0.003), 31% (HR=0.69; P=0.001), and 27% (HR=0.73; P=0.001) lower, respectively, in frequent users versus nonusers. Among a subgroup of survivors of the 12-month follow-up, sustained technique failure risk was 26% (HR=0.74; P=0.023) and 21% (HR=0.79; P=0.054) lower after 9 and 12 months, respectively, in frequent users versus nonusers. Conclusions: Our findings suggest frequent use of an RTM application associates with less hospital admissions, shorter hospital length of stay, and lower technique failure rates. Adoption of RTM applications may have the potential to improve timely identification/intervention of complications.

Seasonal and Secular Trends of Cardiovascular, Nutritional, and Inflammatory Markers in Patients on Hemodialysis.

Background: All life on earth has adapted to the effects of changing seasons. The general and ESKD populations exhibit seasonal rhythms in physiology and outcomes. The ESKD population also shows secular trends over calendar time that can convolute the influences of seasonal variations. We conducted an analysis that simultaneously considered both seasonality and calendar time to isolate these trends for cardiovascular, nutrition, and inflammation markers. Methods: We used data from adult patients on hemodialysis (HD) in the United States from 2010 through 2014. An additive model accounted for variations over both calendar time and time on dialysis. Calendar time trends were decomposed into seasonal and secular trends. Bootstrap procedures and likelihood ratio methods tested if seasonal and secular variations exist. Results: We analyzed data from 354,176 patients on HD at 2436 clinics. Patients were 59±15 years old, 57% were men, and 61% had diabetes. Isolated average secular trends showed decreases in pre-HD systolic BP (pre-SBP) of 2.6 mm Hg (95% CI, 2.4 to 2.8) and interdialytic weight gain (IDWG) of 0.35 kg (95% CI, 0.33 to 0.36) yet increases in post-HD weight of 2.76 kg (95% CI, 2.58 to 2.97). We found independent seasonal variations of 3.3 mm Hg (95% CI, 3.1 to 3.5) for pre-SBP, 0.19 kg (95% CI, 0.17 to 0.20) for IDWG, and 0.62 kg (95% CI, 0.46 to 0.79) for post-HD weight as well as 0.12 L (95% CI, 0.11 to 0.14) for ultrafiltration volume, 0.41 ml/kg per hour (95% CI, 0.37 to 0.45) for ultrafiltration rates, and 3.30 (95% CI, 2.90 to 3.77) hospital days per patient year, which were higher in winter versus summer. Conclusions: Patients on HD show marked seasonal variability of key indicators. Secular trends indicate decreasing BP and IDWG and increasing post-HD weight. These methods will be of importance for independently determining seasonal and secular trends in future assessments of population health.

Prediction of Mortality and Hospitalization Risk Using Nutritional Indicators and Their Changes Over Time in a Large Prevalent Hemodialysis Cohort.

OBJECTIVE(S): Malnutrition and protein-energy wasting are associated with morbidity and mortality in hemodialysis patients. Existing nutritional scores rely primarily on cross-sectional data. Using readily available nutritional indicators, we developed models to predict the risk of mortality and hospitalization in prevalent hemodialysis patients. DESIGN AND METHODS: In this retrospective study, we constructed prediction models of 1-year mortality and hospitalization using generalized linear models, generalized additive models (GAM), classification tree, and random forest models. The models were compared using area under the receiver-operating characteristics curve (AUC) and calibration curves. Model predictors included nutritional and inflammation indicators, demographics, comorbidities, and slopes of all continuous variables over 6 months. Patients were randomly split in the ratio 2:1:1 into training, testing, and validation cohorts, respectively. We included patients with hemodialysis vintage ≥1 year from Fresenius Medical Care North America clinics from July 2011 to December 2012 (N = 21,802 in mortality analysis; N = 13,892 in hospitalization analysis).The outcome variables were 1-year mortality and hospitalization. RESULTS: For mortality prediction, GAM was the best model (AUC = 0.85, 95% confidence interval = 0.83-0.86), comprised of neutrophil-to-lymphocyte ratio slope, serum bicarbonate slope, and vintage as nonlinear predictors, and age, serum albumin, and creatinine as linear predictors. For hospitalization prediction, GAM was also the best model (AUC = 0.70, 95% confidence interval = 0.62-0.79) and included neutrophil-to-lymphocyte ratio slope, bicarbonate slope, volume of urea distribution, vintage, and phosphate slope as nonlinear predictors, in addition to albumin, congestive heart failure, age, phosphate, equilibrated normalized protein catabolic rate, and creatinine as linear predictors. Both models demonstrated good calibration, with mild overestimation of hospitalization risk at the highest risk interval. CONCLUSIONS: The GAM model can accurately predict the risk of mortality and hospitalization. Application of these prediction models could inform allocation of nutritional interventions to patients at highest nutritional risk.