A comparative study of the risk profile of hemodialysis patients in a for profit network and in two regional registries of the Italian Society of Nephrology.

In 2013, the Italian Society of Nephrology joined forces with Nephrocare-Italy to create a clinical research cohort of patients on file in the data-rich clinical management system (EUCLID) of this organization for the performance of observational studies in the hemodialysis (HD) population. To see whether patients in EUCLID are representative of the HD population in Italy, we set out to compare the whole EUCLID population with patients included in the regional HD registries in Emilia-Romagna (Northern Italy) and in Calabria (Southern Italy), the sole regions in Italy which have systematically collected an enlarged clinical data set allowing comparison with the data-rich EUCLID system. An analysis of prevalent and incident patients in 2010 and 2011 showed that EUCLID patients had a lower prevalence of coronary heart disease, peripheral vascular disease, heart failure, valvular heart disease, liver disease, peptic ulcer and other comorbidities and risk factors and a higher fractional urea clearance (Kt/V) than those in the Emilia Romagna and Calabria registries. Accordingly, survival analysis showed a lower mortality risk in the EUCLID 2010 and 2011 cohorts than in the combined two regional registries in the corresponding years: for 2010, hazard ratio (HR) EUCLID vs. Regional registries: 0.80 [95% confidence interval: 0.71-0.90]; for 2011, HR: 0.76 [0.65-0.90]. However, this difference was nullified by statistical adjustment for the difference in comorbidities and risk factors, indicating that the longer survival in the EUCLID database was attributable to the lower risk profile of patients included in that database. This preliminary analysis sets the stage for future observational studies and indicates that appropriate adjustment for difference in comorbidities and risk factors is needed to generalize to the Italian HD population analyses based on the data-rich EUCLID database.

Anti -citrullinated peptide antibodies profiling in established rheumatoid arthritis.

OBJECTIVES: Anti-citrullinated protein/peptide antibodies (ACPA) represent an important tool for the diagnosis of rheumatoid arthritis (RA) and the presence of multiple ACPA specificities is highly correlated with the evolution towards RA. However, little is known about the association of single specificities with disease manifestations and response to therapy in established RA. The aim of this work is to evaluate in a retrospective study the clinico-serological association of ACPA detected using VCP1 and VCP2 (EBV-derived citrullinated peptides) and HCP1 and HCP2 (histone-H4-derived citrulinated peptides) in established RA. METHODS: In 413 RA patients, anti-VCP1, -VCP2, -HCP1, -HCP2 were measured by ELISA. Patients were evaluated for systemic involvement, disease activity/severity, ongoing and past therapies. Data were analyzed by cluster analysis (CA) and principal component analysis (PCA). RESULTS: Anti-VCP1 were detected in 44% of RA patients; anti-VCP2 in 52%; anti-HCP1 in 46% and anti-HCP2 in 63%. CA and PCA independently demonstrated that ACPA levels are associated with RF positivity, and lung involvement. Subdividing patients in 5 groups according to the number of anti-peptide antibodies, mean antibody level and RF positivity, as well as the frequency of lung involvement, progressively increase in parallel with the number of ACPA specificities. CONCLUSIONS: Higher number/levels of ACPA subtypes is associated with lung involvement but not with erosive disease. Moreover, a broader ACPA repertoire may identify patients treated with biological therapy, probably affected by a more severe disease. In conclusion, ACPA typing might be relevant for a better characterization of some disease features in established RA.

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.

Antibodies directed against endogenous and exogenous citrullinated antigens pre-date the onset of rheumatoid arthritis.

BACKGROUND: Anti-citrullinated-peptide antibodies (ACPA) have been detected in individuals with developing rheumatoid arthritis (RA) before the onset of symptom, with an initially limited spectrum of reactivities that gradually broadens. The aim was to analyze the evolution of ACPA response pre-dating symptom onset, using four selected citrullinated exogenous and endogenous antigens. METHODS: A cohort of 521 individuals sampled before symptoms of RA appeared and 272 population controls were identified from the Biobank of Northern Sweden; 241 samples from patients with early RA were also collected. ACPA were detected by ELISA on viral citrullinated peptides (VCP) derived from Epstein-Barr-virus nuclear antigen (EBNA)1 and EBNA2 (VCP1 and VCP2) and histone-4-derived citrullinated peptides (HCP1 and HCP2). RESULTS: In pre-symptomatic individuals vs. patients with early RA, anti-VCP1 antibodies were detected in 10.4 % vs. 36.1 %, anti-VCP2 in 17.1 % vs. 52.3 %, anti-HCP1 in 10.2 % vs. 37.3 %, and anti-HCP2 in 16.3 % vs. 48.5 %, respectively. Anti-VCP and anti-HCP concentrations were significantly increased in pre-symptomatic individuals vs. controls (p < 0.001) and were increased approaching symptom onset. Anti-VCP and anti-HCP appeared simultaneously (median (IQR) 5.3 (6) years before symptom onset) and in combination yielded a high-risk ratio for disease development (OR = 8.0-18.9). Anti-VCP2 and anti-HCP2 antibodies were associated with HLA-DRB1*0401 in pre-symptomatic individuals. Three peptidylarginine deiminase (PAD)I3/PADI4 single nucleotide polymorphisms (SNPs) were significantly associated with anti-HCP1. CONCLUSIONS: Anti-VCP and anti-HCP antibodies pre-date symptom onset and predict disease development, but no hierarchy of citrullinated epitopes can be identified. These results suggest that no inciting citrullinated antigen so far described is common to all patients with RA. The association between PADI3/PADI4 polymorphism and anti-HCP1 antibodies suggests a novel link between deimination and production of ACPA.

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.

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.

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.

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.

Role of α1-adrenoceptor subtypes in pupil dilation studied with gene-targeted mice.

PURPOSE: The α1A-adrenoceptor (α1A-AR) subtype was suggested to mediate contraction and trophic effects in the iris dilator muscle, and thus its pharmacological blockade may be involved in intraoperative floppy iris syndrome. We tested the hypothesis that the α1A-AR mediates pupil dilation and trophic effects in the mouse iris. METHODS: The α1-AR subtype mRNA expression was quantified in iris tissue by real-time PCR. To assess the role of individual α1-ARs for mediating pupil dilation, the α1-AR agonist phenylephrine was topically applied to the ocular surface of mice deficient in one of the three α1-AR subtypes (α1A-AR(-/-), α1B-AR(-/-), α1D-AR(-/-), respectively) and wild-type controls. Changes in pupil diameter were measured under a microscope in restrained mice. Moreover, iris and iris muscle thickness were determined in cryosections. RESULTS: Messenger RNA for all three α1-AR subtypes was detected the iris of wild-type mice with a rank order of abundance of α1A ≥ α1B > > α1D. The lack of a single α1-AR gene did not affect mRNA expression of the remaining two receptor subtypes. Phenylephrine induced pupil dilation in wild-type mice that was reduced in extent and duration in α1A-AR(-/-) and, less so, in α1B-AR(-/-) but not in α1D-AR(-/-) mice. The lack of a single α1-AR subtype had no effect on iris or iris muscle thickness. CONCLUSIONS: The α1-AR-induced mydriasis in mice is mediated mainly by the α1A-AR, with a smaller contribution of the α1B-AR, matching the relative abundance of these subtypes at the mRNA level. The lack of a single α1-AR subtype does not appear to cause atrophy in the mouse iris.

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.

Epidermal growth factor-receptor activation modulates Src-dependent resistance to lapatinib in breast cancer models.

INTRODUCTION: Src tyrosine kinase overactivation has been correlated with a poor response to human epidermal growth factor receptor 2 (HER2) inhibitors in breast cancer. To identify the mechanism by which Src overexpression sustains this resistance, we tested a panel of breast cancer cell lines either sensitive or resistant to lapatinib. METHODS: To determine the role of Src in lapatinib resistance, we evaluated the effects of Src inhibition/silencing in vitro on survival, migration, and invasion of lapatinib-resistant cells. In vivo experiments were performed in JIMT-1 lapatinib-resistant cells orthotopically implanted in nude mice. We used artificial metastasis assays to evaluate the effect of Src inhibition on the invasiveness of lapatinib-resistant cells. Src-dependent signal transduction was investigated with Western blot and ELISA analyses. RESULTS: Src activation was higher in lapatinib-resistant than in lapatinib-sensitive cells. The selective small-molecule Src inhibitor saracatinib combined with lapatinib synergistically inhibited the proliferation, migration, and invasion of lapatinib-resistant cells. Saracatinib combined with lapatinib significantly prolonged survival of JIMT-1-xenografted mice compared with saracatinib alone, and impaired the formation of lung metastases. Unexpectedly, in lapatinib-resistant cells, Src preferentially interacted with epidermal growth factor receptor (EGFR) rather than with HER2. Moreover, EGFR targeting and lapatinib synergistically inhibited survival, migration, and invasion of resistant cells, thereby counteracting Src-mediated resistance. These findings demonstrate that Src activation in lapatinib-resistant cells depends on EGFR-dependent rather than on HER2-dependent signaling. CONCLUSIONS: Complete pharmacologic EGFR/HER2 inhibition is required to reverse Src-dependent resistance to lapatinib in breast cancer.

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.

Sphingosine kinase 1 overexpression contributes to cetuximab resistance in human colorectal cancer models.

PURPOSE: Although the anti-EGF receptor (EGFR) monoclonal antibody cetuximab is an effective strategy in colorectal cancer therapy, its clinical use is limited by intrinsic or acquired resistance. Alterations in the "sphingolipid rheostat"-the balance between the proapoptotic molecule ceramide and the mitogenic factor sphingosine-1-phosphate (S1P)-due to sphingosine kinase 1 (SphK1) overactivation have been involved in resistance to anticancer-targeted agents. Moreover, cross-talks between SphK1 and EGFR-dependent signaling pathways have been described. EXPERIMENTAL DESIGN: We investigated SphK1 contribution to cetuximab resistance in colorectal cancer, in preclinical in vitro/in vivo models, and in tumor specimens from patients. RESULTS: SphK1 was found overexpressed and overactivated in colorectal cancer cells with intrinsic or acquired resistance to cetuximab. SphK1 contribution to resistance was supported by the demonstration that SphK1 inhibition by N,N-dimethyl-sphingosine or silencing via siRNA in resistant cells restores sensitivity to cetuximab, whereas exogenous SphK1 overexpression in sensitive cells confers resistance to these agents. Moreover, treatment of resistant cells with fingolimod (FTY720), a S1P receptor (S1PR) antagonist, resulted in resensitization to cetuximab both in vitro and in vivo, with inhibition of tumor growth, interference with signal transduction, induction of cancer cells apoptosis, and prolongation of mice survival. Finally, a correlation between SphK1 expression and cetuximab response was found in colorectal cancer patients.

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.

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.