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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 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.

BackgroundCitric 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.MethodsIn 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.ResultsWe 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.ConclusionsWe 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.

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.

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.

Background and Aims Peritonitis is a common and potentially severe complication for peritoneal dialysis (PD) patients. It is associated with mortality and technique failure risk and contributes significantly to their healthcare cost. Despite several peritonitis prevention programs based on education and training have been implemented worldwide, it has been reported a large variability of efficacy across patients groups and healthcare settings. In order to avoid unnecessary treatment of low risk patients, healthcare prevention programs should be personalized based on accurate patients’ risk profiling, so that high risk patients may be addressed with intensified prevention programs. However, referral strategy (i.e. defining when risk is too much and deserves special attention) depends the availability, efficacy and cost of medical interventions. In this study, we demonstrate through a program implementation simulator, how different referral strategies to inform peritonitis prevention program among PD patients informed by AI-based risk stratification tools, produce different healthcare and health economics outcomes. In particular, the simulation considers a prevention program characterized by standard of care, which affects all patients as well as an intensive intervention for a subset of high-risk patients (e.g. special training or medical treatment). Method The Peritonitis Risk Score model was trained and validated among 9325 PD patients treated in FMC network (Model accuracy, AUC=0.86). The pharmaco-economic model simulation was performed considering a cohort of 22,900 adult PD patients, treated in Fresenius Medical Care dialysis network between January 1, 2011 and December 31, 2018, for which the Peritonitis Risk Score was computed at a given date. The occurrence of an acute peritonitis in the month after prediction has been registered. We simulated the program outcomes in terms of proportion of referrals to the intensified prevention program, false omission rate, peritonitis risk reduction, overall cost-savings, number needed to treat. We considered the following scenario based on previous cost-effectiveness analysis on peritonitis risk prevention: Results Given the action threshold selected, 5.3% of patients entered the intensified intervention program (PPV=9.5%); the false omission rate was 2.2%. Cost savings for the intensified healthcare where generated when the effect size of the intensified intervention exceeded 1.4 (figure 1A). For that effect size the number needed to treat for each prevented peritonitis was NNT=23.4. Overall, 162 peritonitis/month could be prevented in the whole network (peritonitis with no intervention=592; Peritonitis after intervention=430). When a less conservative threshold was selected, 12.2% of patients entered the intensified prevention program (PPV=7.3%), generating a false omission rate=1.9%. Cost savings were never generated (i.e. the intensified program needed investment to be sustained) but with the same effect size of 1.4 additional 24 peritonitis/months could be saved in the whole network (peritonitis with no intervention=592; Peritonitis after intervention=406). The number needed to treat for the intensified program was NTT=30.4 (figure 1B). Conclusion Cost-effectiveness simulating tool provides a rational evaluation framework for AI-based referral to peritonitis preventive programs. This tool can be easily adapted for any healthcare program based on patient risk score.

Background Innovative care models such as public-private partnerships (PPPs) may help meet the challenge of providing cost-effective high-quality care for the steadily growing and complex chronic kidney disease population since they combine the expertise and efficiency of a specialized dialysis provider with the population care approach of a public entity. We report the five-years main clinical outcomes of a population of patients treated on hemodialysis within a PPP-care model in Italy. Methods This descriptive retrospective cohort study consisted of all consecutive hemodialysis patients treated in the NephroCare-operated Nephrology and Dialysis unit of the Seriate Hospital in 2012–2016, which exercises a PPP-care model. Clinical and treatment information was obtained from the European Clinical Database. Hospitalization outcomes and cumulative all-cause mortality incidences that accounted for competing risks were calculated. Results We included 401 hemodialysis patients (197 prevalent and 204 incident patients) in our study. The mean cohort age and age-adjusted Charlson Comorbidity Index were 67.0 years and 6.7, respectively. Patients were treated with online high-volume hemodiafiltration or high-flux hemodialysis. Parameters of treatment efficiency were above the recommended targets throughout the study period. Patients in the PPP experienced benefits in terms of hospitalization (average number of hospital admissions/patient-year: 0.79 and 1.13 for prevalent and incident patients, respectively; average length of hospitalization: 8.9 days for both groups) and had low cumulative all-cause mortality rates (12 months: 10.6 and 7.8%, 5 years: 42.0 and 35.9%, for prevalent and incident patients, respectively). Conclusions Results of our descriptive study suggest that hemodialysis patients treated within a PPP-care model framework received care complying with recommended treatment targets and may benefit in terms of hospitalization and mortality outcomes. Electronic supplementary material The online version of this article (10.1186/s12882-019-1224-2) contains supplementary material, which is available to authorized users.

Introduction. In order to help facilitate the uptake of best clinical practices, improve outcomes, enhance efficiency and reduce costs, few healthcare organizations have introduced Continuous Quality Improvement (CQI) programs. Since September 2014, dialysis centers belonging to the EMEA Fresenius Medical Care (FME) network have introduced a CQI policy called Medical Peer Review (MPR) based on key performance indicators (KPI) related to patient’s clinical status. We exploit the quasi-experimental setting generated by the introduction of FMC CQI policy, to evaluate the association between improvement in intermediate outcomes (key performance indicators) and prolonged survival among dialysis patients registered in the EMEA FME network. Methods. We conducted a historical cohort study. We included adult patients on chronic dialysis with less than 90 days between renal replacement therapy (RRT) initiation date and first treatment date in FME clinics. We compared KPI target achievement (P-BSC score) and 2-year mortality for patients enrolled prior to MPR-CQI policy onset (Cohort A) and a cohort of patients enrolled after MPR-CQI policy onset (Cohort B). Structural Equation model was adopted to estimate the association of MPR-CQI policy on patients’ survival through changes in intermediate outcomes (P-BSC score). Results. The Cohort A and Cohort B consisted of 2397 and 1873 patients, respectively. We observed no difference across groups concerning the distribution of age (63.1 vs 62.8 years), gender (59% vs 60% males) and body mass index (27.6 vs 26.4 kg/m2); Cohort A showed lower Charlson’s comorbidity index (3.3±1.5 vs 3.8±1.9, p Conclusion. We showed that, after discounting for potential unmodifiable confounding factors and potential unmeasured selection/chronological bias, improvement of intermediate outcomes and performance indicators occurred after MPR-CQI policy implementation, was associated to a strong improvement in survival. Figure