The Use of Anemia Control Model Is Associated with Improved Hemoglobin Target Achievement, Lower Rates of Inappropriate Erythropoietin Stimulating Agents, and Severe Anemia among Dialysis Patients.

INTRODUCTION: The Anemia Control Model (ACM) is a certified medical device suggesting the optimal ESA and iron dosage for patients on hemodialysis. We sought to assess the effectiveness and safety of ACM in a large cohort of hemodialysis patients. METHODS: This is a retrospective study of dialysis patients treated in NephroCare centers between June 1, 2013 and December 31, 2019. We compared patients treated according to ACM suggestions and patients treated in clinics where ACM was not activated. We stratified patients belonging to the reference group by historical target achievement rates in their referral centers (tier 1: <70%; tier 2: 70-80%; tier 3: >80%). Groups were matched by propensity score. RESULTS: After matching, we obtained four groups with 85,512 patient-months each. ACM had 18% higher target achievement rate, 63% smaller inappropriate ESA administration rate, and 59% smaller severe anemia risk compared to Tier 1 centers (all p < 0.01). The corresponding risk ratios for ACM compared to Tier 2 centers were 1.08 (95% CI: 1.08-1.09), 0.49 (95% CI: 0.47-0.51), and 0.64 (95% CI: 0.61-0.68); for ACM compared to Tier 3 centers, 1.01 (95% CI: 1.01-1.02), 0.66 (95% CI: 0.63-0.69), and 0.94 (95% CI: 0.88-1.00), respectively. ACM was associated with statistically significant reductions in ESA dose administration. CONCLUSION: ACM was associated with increased hemoglobin target achievement rate, decreased inappropriate ESA usage and a decreased incidence of severe anemia among patients treated according to ACM suggestion.

Chronic kidney disease-associated pruritus (CKD-aP) is associated with worse quality of life and increased healthcare utilization among dialysis patients.

PURPOSE: Chronic pruritus significantly impairs hemodialysis patients' health status and quality of life (QOL) and it is associated with higher mortality rate, more frequent hospitalizations, poorer dialysis and medication adherence, and deteriorated mental status. However, pruritus is still underestimated, underdiagnosed, and undertreated in the real-life clinical scenario. We investigated prevalence, clinical characteristics, clinical correlates, severity as well as physical and psychological burden of chronic pruritus among adult hemodialysis patients in a large international real-world cohort. METHODS: We conducted a retrospective cross-sectional study of patients registered in 152 Fresenius Medical Care (FMC) NephroCare clinics located in Italy, France, Ireland, United Kingdom, and Spain. Demographic and medical data were retrieved from the EuCliD® (European Clinical) database, while information on pruritus and QoL were abstracted from KDQOL™-36 and 5-D Itch questionnaire scores. RESULTS: A total of 6221 patients were included, of which 1238 were from France, 163 Ireland, 1469 Italy, 2633 Spain, and 718 UK. The prevalence of mild-to-severe pruritus was 47.9% (n = 2977 patients). Increased pruritus severity was associated with increased use of antidepressants, antihistamines, and gabapentin. Patients with severe pruritus more likely suffered from diabetes, more frequently missed dialysis sessions, and underwent more hospitalizations due to infections. Both mental and physical QOL scores were progressively lower as the severity of pruritus increased; this association was robust to adjustment for potential confounders. CONCLUSION: This international real-world analysis confirms that chronic pruritus is a highly prevalent condition among dialysis patients and highlights its considerable burden on several dimensions of patients' life.

Prolonged patient survival after implementation of a continuous quality improvement programme empowered by digital transformation in a large dialysis network.

BACKGROUND: Treatment of end-stage kidney disease patients is extremely challenging given the interconnected functional derangements and comorbidities characterizing the disease. Continuous quality improvement (CQI) in healthcare is a structured clinical governance process helping physicians adhere to best clinical practices. The digitization of patient medical records and data warehousing technologies has standardized and enhanced the efficiency of the CQI's evidence generation process. There is limited evidence that ameliorating intermediate outcomes would translate into better patient-centred outcomes. We sought to evaluate the relationship between Fresenius Medical Care medical patient review CQI (MPR-CQI) implementation and patients' survival in a large historical cohort study. METHODS: We included all incident adult patients with 6-months survival on chronic dialysis registered in the Europe, Middle East and Africa region between 2011 and 2018. We compared medical key performance indicator (KPI) target achievements and 2-year mortality for patients enrolled prior to and after MPR-CQI policy onset (Cohorts A and B). We adopted a structural equation model where MPR-CQI policy was the exogenous explanatory variable, KPI target achievements was the mediator variable and survival was the outcome of interest. RESULTS: About 4270 patients (Cohort A: 2397; Cohort B: 1873) met the inclusion criteria. We observed an increase in KPI target achievements after MPR-CQI policy implementation. Mediation analysis demonstrated a significant reduction in mortality due to an indirect effect of MPR-CQI implementation through improvement in KPI target achievement occurring in the post-implementation era [odds ratio 0.70 (95% confidence interval 0.65-0.76); P < 0.0001]. CONCLUSIONS: Our study suggests that MPR-CQI achieved by standardized clinical practice and periodic structured MPR may improve patients' survival through improvement in medical KPIs.

Practice Patterns and Outcomes of Online Hemodiafiltration: A Real-World Evidence Study in a Russian Dialysis Network.

BACKGROUND: Evidence suggests that online hemodiafiltration (OL-HDF) is associated with improved survival. Whether the dose-response relationship between convective volume and mortality may be confounded by selection bias or descends from practice patterns is not clear. We sought to evaluate the role of patients' characteristics and practice patterns on OL-HDF dose and mortality in a large private dialysis network in the Republic of Russia. METHODS: In this multicenter, historical cohort study, we included adult incident patients on OL-HDF with at least 90 days of survival on renal replacement therapy in centers belonging to the Russian Federation Fresenius Medical Care network (January 1, 2011, to December 31, 2016). We evaluated predictors and outcomes (survival) of substitution volume target achievement (Qsub > 21 L/session). RESULTS: Among 1,081 enrolled patients, the average Qsub was 22.9 (±3.2) L/session; the mean ultrafiltration volume was 1.6 (±0.8) L/session. The mean age was 55.8 ± 13.2; 42% were woman. Most common comorbidities were congestive heart failure (39.7%) and peripheral vascular disease (21.7%). The average hemoglobin was 9.3 ± 1.3. The case-mix adjusted center effect accounted for 20% of variance in Qsub. The top 10 most important variables associated with higher Qsub were effective Qb, serum protein, Charlson's comorbidity index, hemoglobin, year of dialysis initiation (proxy of high Qsub treatment policy in the clinic network), predialysis heart rate, serum bicarbonate, serum phosphate, age, serum sodium, and dry body weight. In addition, we found that the association of Qb with Qsub is moderated by year of enrollment, intradialytic weight gain, and coronary artery disease, whereas higher hemoglobin concentration moderated the relationship between treatment time and Qsub. Finally, Qsub between 21 and 25 L/session was associated with longer 5-year survival. CONCLUSIONS: Both center-dependent clinical practice standards and patient clinical conditions substantially contributed to the risk of low Qsub. We confirmed previous evidence indicating better survival among patients with Qsub ≥ 21 L/session.

Long-term mortality risk associated with citric acid- and acetic acid-based bicarbonate haemodialysis: a historical cohort propensity score-matched study in a large, multicentre, population-based study.

BACKGROUND: Citric acid-based bicarbonate dialysate (CiD) is increasingly used in haemodialysis (HD) to improve haemodynamic tolerance and haemocompatibility associated with acetic acid-based bicarbonate dialysate. Safety concerns over CiD have been raised recently after a French ecological study reported higher mortality hazard in HD clinics with high CiD consumption. Therefore, we evaluated the mortality risk associated with various acidifiers (AcD, CiD) of bicarbonate dialysate. METHODS: In this multicentre, historical cohort study, we included adult incident HD patients (European, Middle-East and Africa Fresenius Medical Care network; 1 January 2014 to 31 October 2018). We recorded acidifiers of bicarbonate dialysis and dialysate composition for each dialysis session. In the primary intention-to-treat analysis, patients were assigned to the exposed group if they received CiD in >70% of sessions during the first 3 months (CiD70%), whereas the non-exposed group received no CiD at all. In the secondary analysis, exposure was assessed on a monthly basis for the whole duration of the follow-up. RESULTS: We enrolled 10 121 incident patients during the study period. Of them, 371 met the criteria for inclusion in CiD70%. After propensity score matching, mortality was 11.43 [95% confidence interval (CI) 8.86-14.75] and 12.04 (95% CI 9.44-15.35) deaths/100 person-years in the CiD0% and CiD70% groups, respectively (P = 0.80). A similar association trend was observed in the secondary analysis. CONCLUSIONS: We did not observe evidence of increased mortality among patients exposed to CiD in a large European cohort of dialysis patients despite the fact that physicians were more inclined to prescribe CiD to subjects with worse medical conditions.

Detecting high-risk chronic kidney disease-mineral bone disorder phenotypes among patients on dialysis: a historical cohort study.

BACKGROUND: The clinical management of chronic kidney disease-mineral bone disorder (CKD-MBD) remains extremely challenging, partially due to difficulties in defining high-risk phenotypes based on serum biomarkers. We evaluated the prevalence and outcomes of 27 mutually exclusive CKD-MBD phenotypes in a large, multi-national cohort of chronic dialysis patients over a 5-year follow-up study. METHODS: In this historical cohort study, we enrolled all haemodialysis patients registered in EuCliD® on 1 July 2011 across 28 Europe, the Middle East and Africa (EMEA) and South American countries. We created 27 mutually exclusive phenotypes based on combinations of serum parathyroid hormone (PTH), phosphorus (P) and calcium (Ca) 6-month averages (L, low; T, target; H, high). We tested the association between CKD-MBD phenotypes and 5-year mortality and hospitalization risk by outcome risk score-adjusted proportional hazard regression. RESULTS: We enrolled 35 721 eligible patients. Eastern European and South American countries generally achieved poorer CKD-MBD control when compared with Western European countries (prevalence ratio: 0.79; P < 0.001). There were 15 795 deaths [126.7 deaths/1000 person-years; 95% confidence interval (CI) 124.7-128.7]; 18 014 had at least one hospitalization (203.9 hospitalizations/1000 person-years; 95% CI 201.0-206.9); the incidence of the composite endpoint was 280.0 events/1000 person-years (95% CI 276.6-283.5). In the fully adjusted model, relative mortality risk ranged from hazard ratio (HR) = 1.07 (PTH/Ca/P: TLT) to HR = 1.59 (PTH/Ca/P: LTL), whereas the relative composite endpoint risk ranged from HR = 1.07 (PTH/Ca/P: TTH) to HR = 1.36 (PTH/Ca/P: LTL). CONCLUSION: We identified several CKD-MBD phenotypes associated with reduced hospitalization-free survival and increased mortality. Ranking of relative risk estimates or excess events concurs in informing healthcare priority setting.

Patients' Characteristics Affect the Survival Benefit of Warfarin Treatment for Hemodialysis Patients with Atrial Fibrillation. A Historical Cohort Study.

BACKGROUND: Stroke prevention in dialysis-dependent patients with atrial fibrillation (AF) is an unresolved clinical dilemma. Indeed, no randomized controlled trial evaluating the efficacy and safety of oral anticoagulants in this population, has been conducted so far. Observational research on the use of warfarin in patients on dialysis has shown conflicting results. This uncertainty is mirrored by the wide variations in warfarin prescription patterns across centers. We sought to evaluate the association between the use of vitamin K antagonists (VKAs) and mortality among hemodialysis patient with AF and to assess potential factors affecting the risk-benefit profile of warfarin in this population. METHODS: A total of 91,987 patients registered in the European Clinical Dialysis Database® system from January 2004 to January 2015. Of which, 9,238 patients were identified with a diagnosis of AF. After excluding ineligible patients, a 1:1 propensity score matched cohort of 1,324 warfarin users and non-users were assembled. RESULTS: VKA use was associated with both increased 90-day survival (hazard ratio, HR 0.47, p < 0.01) and 6-year survival (HR 0.76, p < 0.01); however, a trend indicated a stronger early benefit (p for time-interaction <0.01). Moderation analysis showed that patients' age and clinical history of stroke strongly influenced warfarin-related benefits on survival. CONCLUSION: VKA may provide an early survival benefit; however, this is partially offset later during the follow-up. In addition, heterogeneous risk-benefit profiles were observed among subgroups of dialysis-dependent patients with AF, further emphasizing the complexities of tailoring stroke prevention strategies in this population.

Optimal convection volume for improving patient outcomes in an international incident dialysis cohort treated with online hemodiafiltration.

Online hemodiafiltration (OL-HDF), the most efficient renal replacement therapy, enables enhanced removal of small and large uremic toxins by combining diffusive and convective solute transport. Randomized controlled trials on prevalent chronic kidney disease (CKD) patients showed improved patient survival with high-volume OL-HDF, underlining the effect of convection volume (CV). This retrospective international study was conducted in a large cohort of incident CKD patients to determine the CV threshold and range associated with survival advantage. Data were extracted from a cohort of adult CKD patients treated by post-dilution OL-HDF over a 101-month period. In total, 2293 patients with a minimum of 2 years of follow-up were analyzed using advanced statistical tools, including cubic spline analyses for determination of the CV range over which a survival increase was observed. The relative survival rate of OL-HDF patients, adjusted for age, gender, comorbidities, vascular access, albumin, C-reactive protein, and dialysis dose, was found to increase at about 55 l/week of CV and to stay increased up to about 75 l/week. Similar analysis of pre-dialysis ß2-microglobin (marker of middle-molecule uremic toxins) concentrations found a nearly linear decrease in marker concentration as CV increased from 40 to 75 l/week. Analysis of log C-reactive protein levels showed a decrease over the same CV range. Thus, a convection dose target based on convection volume should be considered and needs to be confirmed by prospective trials as a new determinant of dialysis adequacy.

High flow nasal oxygen vs. conventional oxygen therapy over respiratory oxygenation index after esophagectomy: an observational study.

Background: Postoperative pulmonary complications after esophagectomy still represent a matter of concern. High flow nasal cannula (HFNC) early after major abdominal and thoracic surgery has demonstrated some advantages over conventional oxygen therapy. Data about respiratory effect of HFNC after esophagectomy is scarce. The primary aim of this study is to investigate if the early use of HFNC after esophagectomy could enhance patients' postoperative respiratory oxygenation (ROX) index and, ultimately, reduce postoperative pneumonia. Methods: In this single center retrospective study all patients undergoing to esophagectomy for cancer from May 2020 to November 2022 were evaluated. Historical cohort (HC) received postoperative oxygen supplementation with Venturi mask or nasal goggles, and a cohort was put under HFNC (HFNC cohort). ROX index, blood gas analysis, radiological atelectasis score (RAS), post-operative complications' data and information on hospital stay have been collected and analyzed. Results: Seventy-one patients were included for the final statistical analysis, 31 in the HFNC and 40 in the HC cohort. Mean age was 64±10 years and body mass index (BMI) was 26 [24-29] kg/m2. ROX index was higher in the HFNC patients than in the HC, 20.8 [16.7-25.9] vs. 14.9 [10.8-18.2] (P<0.0001). In the HFNC cohort patients, pH was higher, 7.42 [7.40-7.44] vs. 7.39 [7.37-7.43] than HC, while PaCO2 was lower in HFNC cohort compared with HC, 39 [36-41] vs. 42 [39-45] mmHg, respectively (P=0.01). RAS was similar between the two cohorts of patients, 1.5±0.98 vs. 1.4±1.04 in the HFNC and the HC cohort, respectively (P=0.611). Lower acute respiratory failure (ARF) rate was recorded among HFNC than HC cohort, 0% vs. 13% respectively, P=0.06. No difference in pneumonia frequency between two cohorts was shown. Conclusions: HFNC improved the ROX index after esophagectomy through significant respiratory rate reduction. This tool should be considered for early respiratory support after extubation in this category of patients, not only as a rescue therapy for ARF, but also to optimize early postoperative respiratory function. Whether this will improve patients' outcomes requires further large randomized controlled trials.

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.

Modifiable Risk Factors Are Important Predictors of COVID-19-Related Mortality in Patients on Hemodialysis.

Introduction: Patients 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. Methods: We 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. Results: We 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. Conclusion: Both 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.

The Cardiovascular Literature-Based Risk Algorithm (CALIBRA): Predicting Cardiovascular Events in Patients With Non-Dialysis Dependent Chronic Kidney Disease.

Background and Objectives: Cardiovascular (CV) disease is the main cause of morbidity and mortality in patients suffering from chronic kidney disease (CKD). Although it is widely recognized that CV risk assessment represents an essential prerequisite for clinical management, existing prognostic models appear not to be entirely adequate for CKD patients. We derived a literature-based, naïve-bayes model predicting the yearly risk of CV hospitalizations among patients suffering from CKD, referred as the CArdiovascular, LIterature-Based, Risk Algorithm (CALIBRA). Methods: CALIBRA incorporates 31 variables including traditional and CKD-specific risk factors. It was validated in two independent CKD populations: the FMC NephroCare cohort (European Clinical Database, EuCliD®) and the German Chronic Kidney Disease (GCKD) study prospective cohort. CALIBRA performance was evaluated by c-statistics and calibration charts. In addition, CALIBRA discrimination was compared with that of three validated tools currently used for CV prediction in CKD, namely the Framingham Heart Study (FHS) risk score, the atherosclerotic cardiovascular disease risk score (ASCVD), and the Individual Data Analysis of Antihypertensive Intervention Trials (INDANA) calculator. Superiority was defined as a ΔAUC>0.05. Results: CALIBRA showed good discrimination in both the EuCliD® medical registry (AUC 0.79, 95%CI 0.76-0.81) and the GCKD cohort (AUC 0.73, 95%CI 0.70-0.76). CALIBRA demonstrated improved accuracy compared to the benchmark models in EuCliD® (FHS: ΔAUC=-0.22, p<0.001; ASCVD: ΔAUC=-0.17, p<0.001; INDANA: ΔAUC=-0.14, p<0.001) and GCKD (FHS: ΔAUC=-0.16, p<0.001; ASCVD: ΔAUC=-0.12, p<0.001; INDANA: ΔAUC=-0.04, p<0.001) populations. Accuracy of the CALIBRA score was stable also for patients showing missing variables. Conclusion: CALIBRA provides accurate and robust stratification of CKD patients according to CV risk and allows score calculations with improved accuracy compared to established CV risk scores also in real-world clinical cohorts with considerable missingness rates. Our results support the generalizability of CALIBRA across different CKD populations and clinical settings.

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.

Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network.

Accurate predictions of COVID-19 epidemic dynamics may enable timely organizational interventions in high-risk regions. We exploited the interconnection of the Fresenius Medical Care (FMC) European dialysis clinic network to develop a sentinel surveillance system for outbreak prediction. We developed an artificial intelligence-based model considering the information related to all clinics belonging to the European Nephrocare Network. The prediction tool provides risk scores of the occurrence of a COVID-19 outbreak in each dialysis center within a 2-week forecasting horizon. The model input variables include information related to the epidemic status and trends in clinical practice patterns of the target clinic, regional epidemic metrics, and the distance-weighted risk estimates of adjacent dialysis units. On the validation dates, there were 30 (5.09%), 39 (6.52%), and 218 (36.03%) clinics with two or more patients with COVID-19 infection during the 2-week prediction window. The performance of the model was suitable in all testing windows: AUC = 0.77, 0.80, and 0.81, respectively. The occurrence of new cases in a clinic propagates distance-weighted risk estimates to proximal dialysis units. Our machine learning sentinel surveillance system may allow for a prompt risk assessment and timely response to COVID-19 surges throughout networked European clinics.

Predicting mortality in hemodialysis patients using machine learning analysis.

BACKGROUND: Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. METHODS: Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. RESULTS: There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68-0.73] and superior to logistic regression models (ΔAUC 0.007-0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. CONCLUSIONS: Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

Development and Validation of a Machine Learning Model Predicting Arteriovenous Fistula Failure in a Large Network of Dialysis Clinics.

Background: Vascular access surveillance of dialysis patients is a challenging task for clinicians. We derived and validated an arteriovenous fistula failure model (AVF-FM) based on machine learning. Methods: The AVF-FM is an XG-Boost algorithm aimed at predicting AVF failure within three months among in-centre dialysis patients. The model was trained in the derivation set (70% of initial cohort) by exploiting the information routinely collected in the Nephrocare European Clinical Database (EuCliD®). Model performance was tested by concordance statistic and calibration charts in the remaining 30% of records. Features importance was computed using the SHAP method. Results: We included 13,369 patients, overall. The Area Under the ROC Curve (AUC-ROC) of AVF-FM was 0.80 (95% CI 0.79-0.81). Model calibration showed excellent representation of observed failure risk. Variables associated with the greatest impact on risk estimates were previous history of AVF complications, followed by access recirculation and other functional parameters including metrics describing temporal pattern of dialysis dose, blood flow, dynamic venous and arterial pressures. Conclusions: The AVF-FM achieved good discrimination and calibration properties by combining routinely collected clinical and sensor data that require no additional effort by healthcare staff. Therefore, it can potentially enable risk-based personalization of AVF surveillance strategies.

Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD).

Current equation-based risk stratification algorithms for kidney failure (KF) may have limited applicability in real world settings, where missing information may impede their computation for a large share of patients, hampering one from taking full advantage of the wealth of information collected in electronic health records. To overcome such limitations, we trained and validated the Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD), a novel algorithm predicting end-stage kidney disease (ESKD). PROGRES-CKD is a naïve Bayes classifier predicting ESKD onset within 6 and 24 months in adult, stage 3-to-5 CKD patients. PROGRES-CKD trained on 17,775 CKD patients treated in the Fresenius Medical Care (FMC) NephroCare network. The algorithm was validated in a second independent FMC cohort (n = 6760) and in the German Chronic Kidney Disease (GCKD) study cohort (n = 4058). We contrasted PROGRES-CKD accuracy against the performance of the Kidney Failure Risk Equation (KFRE). Discrimination accuracy in the validation cohorts was excellent for both short-term (stage 4-5 CKD, FMC: AUC = 0.90, 95%CI 0.88-0.91; GCKD: AUC = 0.91, 95% CI 0.86-0.97) and long-term (stage 3-5 CKD, FMC: AUC = 0.85, 95%CI 0.83-0.88; GCKD: AUC = 0.85, 95%CI 0.83-0.88) forecasting horizons. The performance of PROGRES-CKD was non-inferior to KFRE for the 24-month horizon and proved more accurate for the 6-month horizon forecast in both validation cohorts. In the real world setting captured in the FMC validation cohort, PROGRES-CKD was computable for all patients, whereas KFRE could be computed for complete cases only (i.e., 30% and 16% of the cohort in 6- and 24-month horizons). PROGRES-CKD accurately predicts KF onset among CKD patients. Contrary to equation-based scores, PROGRES-CKD extends to patients with incomplete data and allows explicit assessment of prediction robustness in case of missing values. PROGRES-CKD may efficiently assist physicians' prognostic reasoning in real-life applications.

Atrial fibrillation in dialysis patients: time to abandon warfarin?

Atrial fibrillation (AF) is a frequent clinical complication in dialysis patients, and warfarin therapy represents the most common approach for reducing the risk of stroke in this population. However, current evidence based on observational studies, offer conflicting results, whereas no randomized controlled trials have been carried out so far. Additionally, many clinicians are wary of the possible role of warfarin as vascular calcification inducer and its potential to increase the high risk of bleeding among patients on dialysis. Ideally the most promising therapy would be based on direct inhibitors of factor IIa or Xa; however, at the moment, none of these drugs can be safely prescribed in dialysis patients, because of their potentially dangerous accumulation, and the lack of sufficient experience with apixaban or rivaroxaban, two drugs showing a favorable pharmacokinetic profile in end-stage renal disease. Hence, the use of vitamin K inhibitors is currently the only pharmacological option for stroke prevention in dialysis patients with atrial fibrillation, leaving the clinicians in a management conundrum.This review discusses the trade-offs implicated in warfarin use for this population, the promises of newly developed drugs, the role of dialysis as atrial fibrillation trigger, as well as potential non-pharmacological management options suitable in selected clinical situations.