Chronic Kidney Disease: Exploring Value-Based Healthcare as a Potential Viable Solution.

BACKGROUND: Increasing healthcare expenditures have triggered a trend from volume to value by linking patient outcome to costs. This concept first described as value-based healthcare (VBHC) by Michael Porter is especially applicable for chronic conditions. This article aims to explore the applicability of the VBHC framework to the chronic kidney disease (CKD) care area. METHODS: The 4 dimensions of VBHC (measure value; set and communicate value benchmarking; coordinate care; payment to reward value-add) were explored for the CKD care area. Available data was reviewed focusing on CKD initiatives in Europe to assess to what extent each of the 4 dimensions of VBHC have been applied in practice. RESULTS: Translating VBHC into value-based renal care (VBRC) seems to be initiated to a limited extent in European health systems. In most cases not all dimensions of VBHC have been utilized in the renal care initiatives. CONCLUSION: The translation of VBHC into VBRC is possible and even desirable if an optimal treatment pathway for CKD patients could be achieved. This would require an organizational change in health system set up and should include a strategy focusing on full care responsibility. The patient outcome perspective and health economic analysis need to be the centre of attention.

Development of an International Standard Set of Value-Based Outcome Measures for Patients With Chronic Kidney Disease: A Report of the International Consortium for Health Outcomes Measurement (ICHOM) CKD Working Group.

Value-based health care is increasingly promoted as a strategy for improving care quality by benchmarking outcomes that matter to patients relative to the cost of obtaining those outcomes. To support the shift toward value-based health care in chronic kidney disease (CKD), the International Consortium for Health Outcomes Measurement (ICHOM) assembled an international working group of health professionals and patient representatives to develop a standardized minimum set of patient-centered outcomes targeted for clinical use. The considered outcomes and patient-reported outcome measures were generated from systematic literature reviews. Feedback was sought from patients and health professionals. Patients with very high-risk CKD (stages G3a/A3 and G3b/A2-G5, including dialysis, kidney transplantation, and conservative care) were selected as the target population. Using an online modified Delphi process, outcomes important to all patients were selected, such as survival and hospitalization, and to treatment-specific subgroups, such as vascular access survival and kidney allograft survival. Patient-reported outcome measures were included to capture domains of health-related quality of life, which were rated as the most important outcomes by patients. Demographic and clinical variables were identified to be used as case-mix adjusters. Use of these consensus recommendations could enable institutions to monitor, compare, and improve the quality of their CKD care.

Greater fluid overload and lower interdialytic weight gain are independently associated with mortality in a large international hemodialysis population.

Background: Fluid overload and interdialytic weight gain (IDWG) are discrete components of the dynamic fluid balance in haemodialysis patients. We aimed to disentangle their relationship, and the prognostic importance of two clinically distinct, bioimpedance spectroscopy (BIS)-derived measures, pre-dialysis and post-dialysis fluid overload (FOpre and FOpost) versus IDWG. Methods: We conducted a retrospective cohort study on 38 614 incident patients with one or more BIS measurement within 90 days of haemodialysis initiation (1 October 2010 through 28 February 2015). We used fractional polynomial regression to determine the association pattern between FOpre, FOpost and IDWG, and multivariate adjusted Cox models with FO and/or IDWG as longitudinal and time-varying predictors to determine all-cause mortality risk. Results: In analyses using 1-month averages, patients in quartiles 3 and 4 (Q3 and Q4) of FO had an incrementally higher adjusted mortality risk compared with reference Q2, and patients in Q1 of IDWG had higher adjusted mortality compared with Q2. The highest adjusted mortality risk was observed for patients in Q4 of FOpre combined with Q1 of IDWG [hazard ratio (HR) = 2.66 (95% confidence interval 2.21-3.20), compared with FOpre-Q2/IDWG-Q2 (reference)]. Using longitudinal means of FO and IDWG only slightly altered all HRs. IDWG associated positively with FOpre, but negatively with FOpost, suggesting a link with post-dialysis extracellular volume depletion. Conclusions: FOpre and FOpost were consistently positive risk factors for mortality. Low IDWG was associated with short-term mortality, suggesting perhaps an effect of protein-energy wasting. FOpost reflected the volume status without IDWG, which implies that this fluid marker is clinically most intuitive and may be best suited to guide volume management in haemodialysis patients.

An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients.

Managing anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses. Our retrospective study consisted of a 12-month control phase (standard anemia care), followed by a 12-month observation phase (ACM-guided care) encompassing 752 patients undergoing hemodialysis therapy in 3 NephroCare clinics located in separate countries. The percentage of hemoglobin values on target, the median darbepoetin dose, and individual hemoglobin fluctuation (estimated from the intrapatient hemoglobin standard deviation) were deemed primary outcomes. In the observation phase, median darbepoetin consumption significantly decreased from 0.63 to 0.46 µg/kg/month, whereas on-target hemoglobin values significantly increased from 70.6% to 76.6%, reaching 83.2% when the ACM suggestions were implemented. Moreover, ACM introduction led to a significant decrease in hemoglobin fluctuation (intrapatient standard deviation decreased from 0.95 g/dl to 0.83 g/dl). Thus, ACM support helped improve anemia outcomes of hemodialysis patients, minimizing erythropoietic-stimulating agent use with the potential to reduce the cost of treatment.

Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients.

Anemia management, based on erythropoiesis stimulating agents (ESA) and iron supplementation, has become an increasingly challenging problem in hemodialysis patients. Maintaining hemodialysis patients within narrow hemoglobin targets, preventing cycling outside target, and reducing ESA dosing to prevent adverse outcomes requires considerable attention from caregivers. Anticipation of the long-term response (i.e. at 3 months) to the ESA/iron therapy would be of fundamental importance for planning a successful treatment strategy. To this end, we developed a predictive model designed to support decision-making regarding anemia management in hemodialysis (HD) patients treated in center. An Artificial Neural Network (ANN) algorithm for predicting hemoglobin concentrations three months into the future was developed and evaluated in a retrospective study on a sample population of 1558 HD patients treated with intravenous (IV) darbepoetin alfa, and IV iron (sucrose or gluconate). Model inputs were the last 90 days of patients' medical history and the subsequent 90 days of darbepoetin/iron prescription. Our model was able to predict individual variation of hemoglobin concentration 3 months in the future with a Mean Absolute Error (MAE) of 0.75 g/dL. Error analysis showed a narrow Gaussian distribution centered in 0 g/dL; a root cause analysis identified intercurrent and/or unpredictable events associated with hospitalization, blood transfusion, and laboratory error or misreported hemoglobin values as the main reasons for large discrepancy between predicted versus observed hemoglobin values. Our ANN predictive model offers a simple and reliable tool applicable in daily clinical practice for predicting the long-term response to ESA/iron therapy of HD patients.

Implementation of a quality and safety checklist for haemodialysis sessions.

BACKGROUND: Patient survival and quality of life depend on each haemodialysis session being performed without fault. Monthly assessments of dialysis dose adequacy often fall short of this. This study reports the results of a feasibility study for the achievement of improved safety and quality in a haemodialysis session with the implementation of a 15-point checklist. METHODS: Fifteen quality indicators were compiled and tested in a Portuguese dialysis clinic from 1 February 2012 to 30 June 2013. The checklist was completed by the nursing staff and comprised three parts: Pre-session Safety Checks; Session Initiation Checks and Post-session Quality Checks. The maximum score that could be reached per session was 15. RESULTS: One hundred and twenty-eight patients were distributed over 2-3 shifts. Of the 16 nurses employed, 4 were full time. The final average score was between 14 and 15. No nurse-specific and no shift-specific significant differences were detected. Four issues were identified that had a major effect on the results as a whole: delays in connection time; incompletely delivered treatment time; non-achievement of final body weight and failure to reach a Kt/V of at least 1.4. Improvements were most consistent in the Monday-Wednesday-Friday morning shifts compared with other shifts, and were temporarily compromised by the opening of a new shift. CONCLUSIONS: The implementation of checklists for haemodialysis is feasible in routine clinical practice, even in clinics where only part of the staff is employed full time. The application of such checklists enhances the overall quality and safety of the delivered treatment.

A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis.

Chronic Kidney Disease (CKD) anemia is one of the main common comorbidities in patients undergoing End Stage Renal Disease (ESRD). Iron supplement and especially Erythropoiesis Stimulating Agents (ESA) have become the treatment of choice for that anemia. However, it is very complicated to find an adequate treatment for every patient in each particular situation since dosage guidelines are based on average behaviors, and thus, they do not take into account the particular response to those drugs by different patients, although that response may vary enormously from one patient to another and even for the same patient in different stages of the anemia. This work proposes an advance with respect to previous works that have faced this problem using different methodologies (Machine Learning (ML), among others), since the diversity of the CKD population has been explicitly taken into account in order to produce a general and reliable model for the prediction of ESA/Iron therapy response. Furthermore, the ML model makes use of both human physiology and drug pharmacology to produce a model that outperforms previous approaches, yielding Mean Absolute Errors (MAE) of the Hemoglobin (Hb) prediction around or lower than 0.6 g/dl in the three countries analyzed in the study, namely, Spain, Italy and Portugal.

Body composition and survival in dialysis patients: results from an international cohort study.

BACKGROUND AND OBJECTIVES: High body mass index appears protective in hemodialysis patients, but uncertainty prevails regarding which components of body composition, fat or lean body mass, are primarily associated with survival. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data between April 2006 and December 2012 were extracted from the Fresenius Medical Care Europe subset of the international MONitoring Dialysis Outcomes initiative. Fresenius Medical Care Europe archives a unique repository of predialysis body composition measurements determined by multifrequency bioimpedance (BCM Body Composition Monitor). The BCM Body Composition Monitor reports lean tissue indices (LTIs) and fat tissue indices (FTIs), which are the respective tissue masses normalized to height squared, relative to an age- and sex-matched healthy population. The relationship between LTI and FTI and all-cause mortality was studied by Kaplan-Meier analysis, multivariate Cox regression, and smoothing spline ANOVA logistic regression. RESULTS: In 37,345 hemodialysis patients, median (25th-75th percentile) LTI and FTI were 12.2 (10.3-14.5) and 9.8 (6.6-12.4) kg/m(2), respectively. Median (25th-75th percentile) follow-up time was 266 (132-379) days; 3458 (9.2%) patients died during follow-up. Mortality was lowest with both LTI and FTI in the 10th-90th percentile (reference group) and significantly higher at the lower LTI and FTI extreme (hazard ratio [HR], 3.37; 95% confidence interval [95% CI], 2.94 to 3.87; P<0.001). Survival was best with LTI between 15 and 20 kg/m(2) and FTI between 4 and 15 kg/m(2) (probability of death during follow-up: <5%). When taking the relation between both compartments into account, the interaction was significant (P=0.01). Higher FTI appeared protective in patients with low LTI (HR, 3.37; 95% CI, 2.94 to 3.87; P<0.001 at low LTI-low FTI, decreasing to HR, 1.79; 95% CI, 1.47 to 2.17; P<0.001 at low LTI-high FTI). CONCLUSIONS: This large international study indicates best survival in patients with both LTI and FTI in the 10th-90th percentiles of a healthy population. In analyses of body composition, both lean tissue and fat tissue compartments and also their relationship should be considered.

Optimization of anemia treatment in hemodialysis patients via reinforcement learning.

OBJECTIVE: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. METHODS: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDPs). Computing optimal drug administration strategies for chronic diseases is a sequential decision-making problem in which the goal is to find the best sequence of drug doses. MDPs are particularly suitable for modeling these problems due to their ability to capture the uncertainty associated with the outcome of the treatment and the stochastic nature of the underlying process. The RL algorithm employed in the proposed methodology is fitted Q iteration, which stands out for its ability to make an efficient use of data. RESULTS: The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level. The performance of the proposed method is evaluated and compared with the well-known Q-learning algorithm and with a standard protocol. Simulation results show that the performance of Q-learning is substantially lower than FQI and the protocol. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. CONCLUSION: Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.

Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques.

Patients who suffer from chronic renal failure (CRF) tend to suffer from an associated anemia as well. Therefore, it is essential to know the hemoglobin (Hb) levels in these patients. The aim of this paper is to predict the hemoglobin (Hb) value using a database of European hemodialysis patients provided by Fresenius Medical Care (FMC) for improving the treatment of this kind of patients. For the prediction of Hb, both analytical measurements and medication dosage of patients suffering from chronic renal failure (CRF) are used. Two kinds of models were trained, global and local models. In the case of local models, clustering techniques based on hierarchical approaches and the adaptive resonance theory (ART) were used as a first step, and then, a different predictor was used for each obtained cluster. Different global models have been applied to the dataset such as Linear Models, Artificial Neural Networks (ANNs), Support Vector Machines (SVM) and Regression Trees among others. Also a relevance analysis has been carried out for each predictor model, thus finding those features that are most relevant for the given prediction.

Cannulation technique influences arteriovenous fistula and graft survival.

Hemodialysis patient survival is dependent on the availability of a reliable vascular access. In clinical practice, procedures for vascular access cannulation vary from clinic to clinic. We investigated the impact of cannulation technique on arteriovenous fistula and graft survival. Based on an April 2009 cross-sectional survey of vascular access cannulation practices in 171 dialysis units, a cohort of patients with corresponding vascular access survival information was selected for follow-up ending March 2012. Of the 10,807 patients enrolled in the original survey, access survival data were available for 7058 patients from nine countries. Of these, 90.6% had an arteriovenous fistula and 9.4% arteriovenous graft. Access needling was by area technique for 65.8%, rope-ladder for 28.2%, and buttonhole for 6%. The most common direction of puncture was antegrade with bevel up (43.1%). A Cox regression model was applied, adjusted for within-country effects, and defining as events the need for creation of a new vascular access. Area cannulation was associated with a significantly higher risk of access failure than rope-ladder or buttonhole. Retrograde direction of the arterial needle with bevel down was also associated with an increased failure risk. Patient application of pressure during cannulation appeared more favorable for vascular access longevity than not applying pressure or using a tourniquet. The higher risk of failure associated with venous pressures under 100 or over 150 mm Hg should open a discussion on limits currently considered acceptable.

Computational intelligence for the Balanced Scorecard: studying performance trends of hemodialysis clinics.

OBJECTIVES: The Balanced Scorecard (BSC) is a general, widely employed instrument for enterprise performance monitoring based on the periodic assessment of strategic Key Performance Indicators that are scored against preset targets. The BSC is currently employed as an effective management support tool within Fresenius Medical Care (FME) and is routinely analyzed via standard statistical methods. More recently, the application of computational intelligence techniques (namely, self-organizing maps) to BSC data has been proposed as a way to enhance the quantity and quality of information that can be extracted from it. In this work, additional methods are presented to analyze the evolution of clinic performance over time. METHODS: Performance evolution is studied at the single-clinic level by computing two complementary indexes that measure the proportion of time spent within performance clusters and improving/worsening trends. Self-organizing maps are used in conjunction with these indexes to identify the specific drivers of the observed performance. The performance evolution for groups of clinics is modeled under a probabilistic framework by resorting to Markov chain properties. These allow a study of the probability of transitioning between performance clusters as time progresses for the identification of the performance level that is expected to become dominant over time. RESULTS: We show the potential of the proposed methods through illustrative results derived from the analysis of BSC data of 109 FME clinics in three countries. We were able to identify the performance drivers for specific groups of clinics and to distinguish between countries whose performances are likely to improve from those where a decline in performance might be expected. According to the stationary distribution of the Markov chain, the expected trend is best in Turkey (where the highest performance cluster has the highest probability, P=0.46), followed by Portugal (where the second best performance cluster dominates, with P=0.50), and finally Italy (where the second best performance cluster has P=0.34). CONCLUSION: These results highlight the ability of the proposed methods to extract insights about performance trends that cannot be easily extrapolated using standard analyses and that are valuable in directing management strategies within a continuous quality improvement policy.

Use of Self-Organizing Maps for Balanced Scorecard analysis to monitor the performance of dialysis clinic chains.

The Balanced Scorecard (BSC) is a validated tool to monitor enterprise performances against specific objectives. Through the choice and the evaluation of strategic Key Performance Indicators (KPIs), it provides a measure of the past company's outcome and allows planning future managerial strategies. The Fresenius Medical Care (FME) BSC makes use of 30 KPIs for a continuous quality improvement strategy within its dialysis clinics. Each KPI is monthly associated to a score that summarizes the clinic efficiency for that month. Standard statistical methods are currently used to analyze the BSC data and to give a comprehensive view of the corporate improvements to the top management. We herein propose the Self-Organizing Maps (SOMs) as an innovative approach to extrapolate information from the FME BSC data and to present it in an easy-readable informative form. A SOM is a computational technique that allows projecting high-dimensional datasets to a two-dimensional space (map), thus providing a compressed representation. The SOM unsupervised (self-organizing) training procedure results in a map that preserves similarity relations existing in the original dataset; in this way, the information contained in the high-dimensional space can be more easily visualized and understood. The present work demonstrates the effectiveness of the SOM approach in extracting useful information from the 30-dimensional BSC dataset: indeed, SOMs enabled both to highlight expected relationships between the KPIs and to uncover results not predictable with traditional analyses. Hence we suggest SOMs as a reliable complementary approach to the standard methods for BSC interpretation.

Converting to a capitation system for dialysis payment--the Portuguese experience.

Due to the challenge of operating within an economically strained healthcare budget, Portuguese health authorities convened with dialysis providers and agreed on a framework to change from a fee-for-service reimbursement modality to a capitation payment system for hemodialysis. This article reviews the components of the agreed capitation package implemented in 2008 as well as the necessary preparatory work undertaken by a for-profit 34-unit dialysis network (approx. 4,200 patients) to cope with the introduction of this system. Furthermore, trends in clinical quality indicators and in resource management are reviewed for 3 years immediately following capitation introduction. Here, improvements were observed over time for the specified clinical targets. Simultaneously, costs controllable by the physician could be reduced. As more countries convert to a capitation or bundled payment system for hemodialysis services, this article offers insight into the scope of the necessary preparatory work and the possible consequences in terms of costs and treatment quality.

Adopting on-line hemodiafiltration as standard therapy in EMEA NephroCare centers.

This paper describes the historical journey that led to the adoption of on-line hemodiafiltration (HDF) as a standard therapy for the patients in the Fresenius Medical Care (FME) NephroCare dialysis network. In 1998, FME faced the tremendous challenge of consolidating a series of heterogeneous clinics under one umbrella. In 2002, the European Best Practice Guidelines (EBPG) for hemodialysis (HD) were published by the European Renal Association which FME promptly adopted within its clinic network. On the basis of this document, the strategic decision was taken to apply high-flux, biocompatible membranes throughout the entire network. To cope with the effective implementation of this step, the clinics' technical infrastructure was updated. The widespread application of high-flux therapy, together with the implementation of the required infrastructure, especially concerning water quality, opened the way to the extensive use of on-line HDF. To fully realize this ambitious goal, two further technological steps were targeted and successfully reached: introduction of the Fresenius 5008 dialysis equipment and an even stricter control of the water quality. The combined pressure from the educational activities, which brought about a preliminary cultural change, and the creation of a target based on the percentage of treatments by this technique resulted in an increasing implementation of this modality by the individual clinics. After 2004, on-line HDF continuously increased its share among the dialysis techniques prescribed in the network and currently more than 50% of patients are on this modality.

Delivering quality of care while managing the interests of all stakeholders.

National healthcare systems worldwide face growing challenges to reconcile interests of patients for high-quality medical care and of payers for sustainable and affordable funding. Advances in the provision of renal replacement therapy can only be made by developing and implementing appropriate sophisticated and state-of-the-art business models that include reimbursement schemes for comprehensive care packages. Such business models must succeed in integrating and reconciling the interests of all stakeholders. NephroCare as dialysis provider has adopted and tailored recognized management techniques, i.e. Balanced Scorecard and Kaizen, to achieve these goals. Success of the complete business model package is tangible - strategies initiated to improve treatment quality even at the cost of providers have been translated into win-win scenarios for the complete stakeholder community. Room for improvement exists: the possibility to extend the portfolio of service offerings within the comprehensive care frame, as well as the challenge for achieving a balance between the stability of targets while keeping these up to date concerning new insights.

Managing complexity at dialysis service centers across Europe.

INTRODUCTION: Dialysis is probably one of the areas of medicine with more guidelines than any other. Issues such as dialysis dose are dealt with in those guidelines, and minimum values to be reached are defined. A target has to be set and reached by using a data-driven continuous quality improvement (CQI) approach. Data collection must be programmed and structured from the beginning. METHODS: Fresenius started its activities as a dialysis provider in 1996, following the merger of its dialysis business with the leading service provider in the US, National Medical Care. Currently Fresenius Medical Care's European activities involve more than 320 dialysis centers located in 15 countries and treating more than 24,000 patients. Management is based on a bi-dimensional organization where line managers can rely on international functional departments. Under this framework, the CQI techniques are applied in conjunction with benchmarking in a system driven by quality targets. In order to combine clinical governance with management targets, the Balanced ScoreCard system was selected. The Balanced ScoreCard monitors the efficiency of each dialysis center compared to an ideal model, targeting maximum possible efficiency whilst having a unique target for patient outcomes. CONCLUSION: A clear definition of targets is fundamental and activities need to be monitored and continuously improved; scientific collection of clinical data is the key.