Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting

BACKGROUND

Wearable devices represent a new approach for monitoring key clinical parameters, such as ECG signals, for research and health purposes. These devices could outcompete medical devices in terms of affordability and use in out-clinic settings, allowing remote monitoring. The major limitation, especially when compared to implantable devices, is the presence of artifacts. Several authors reported a relevant percentage of recording time with poor/unusable traces for ECG, potentially hampering the use of these devices for this purpose. For this reason, it is of the utmost importance to develop a simple and inexpensive system enabling the user of the wearable devices to have immediate feedback on the quality of the acquired signal, allowing for real-time correction.

METHODS

A simple algorithm that can work in real time to verify the quality of the ECG signal (acceptable and unacceptable) was validated. Based on simple statistical parameters, the algorithm was blindly tested by comparison with ECG tracings previously classified by two expert cardiologists.

RESULTS

The classifications of 7200 10s-signal samples acquired on 20 patients with a commercial wearable ECG monitor were compared. The algorithm has an overall efficiency of approximately 95%, with a sensitivity of 94.7% and a specificity of 95.3%.

CONCLUSIONS

The results demonstrate that even a simple algorithm can be used to classify signal coarseness, and this could allow real-time intervention by the subject or the technician.

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.

[Therapeutic Plasma Exchange in a Patient with Chronic Hemodialysis and a New Diagnosis of Myasthenia Gravis]

Case ReportC.S.T. (♂, 71 years old) is a patient with multiple and severe comorbidities, undergoing thrice-weekly chronic hemodialysis since 2008 due to the progression of post-lithiasic uropathy. Over the past 2 months, the patient had been experiencing progressive ptosis of the eyelids, muscle weakness, and ultimately dysphagia and dysarthria that emerged in the last few days. Urgently admitted to the Neurology department, electromyography (EMG) was performed, leading to a diagnosis of predominant cranial myasthenia gravis (with borderline anti-acetylcholine receptor antibody serology). Prompt treatment with pyridostigmine and steroids was initiated. Considering the high risk of acute myasthenic decompensation, therapeutic plasma exchange (TPE) with centrifugation technique was promptly undertaken after femoral CVC placement. TPE sessions were alternated with hemodialysis. The patient's condition complicated after the third TPE session, with septic shock caused by Methicillin-Sensitive Staphylococcus Aureus (MSSA). The patient was transferred to the Intensive Care Unit (ICU). Due to hemodynamic instability, continuous veno-venous hemodiafiltration (CVVHDF) with citrate anticoagulation was administered for 72 hours. After resolving the septic condition, intermittent treatment with Acetate-Free Biofiltration (AFB) technique was resumed. The patient completed the remaining three TPE sessions and, once the acute condition was resolved, was transferred back to Neurology. Here, the patient continued the treatment and underwent a rehabilitation program, showing significant motor and functional recovery until discharge. Conclusions. The multidisciplinary interaction among Nephrologists, Neurologists, Anesthesiologists, and experts from the Immunohematology and Transfusion Medicine Service enabled the management and treatment of a rare condition (MG) in a high-risk chronic hemodialysis patient.

[New Mutation of CYP24A1 in a Case of Idiopathic Infantile Hypercalcemia Diagnosed in Adulthood]

Mutations in the 24-hydroxylase gene CYP24A1 have been recognized as causes of childhood idiopathic hypercalcemia (IIH), a rare disease (incidence <1:1,000,000 live births) characterized by increased vitamin D sensitivity, with symptomatic severe hypercalcemia. IIH was first described in Great Britain two years after the start of a program of vitamin D supplementation in milk for the prevention of rickets, manifesting in about 200 children with severe hypercalcemia, dehydration, growth failure, weight loss, muscle hypotonia, and nephrocalcinosis. The association between the epidemic occurrence of IIH and vitamin D administration was quickly attributed to intrinsic hypersensitivity to vitamin D, and the pathogenic mechanism was recognized in the inactivation of Cytochrome P450 family 24 subfamily A member 1 (CYP24A1), which was identified as the molecular basis of the pathology. The phenotypic spectrum of CYP24A1 mutation can be variable, manifesting predominantly with childhood onset and severe symptomatology (severe hypercalcemia, growth retardation, lethargy, muscle hypotonia, dehydration), but also with juvenile-adult onset forms with nephrolithiasis, nephrocalcinosis, and alterations in phosphocalcium homeostasis. We describe the case of a patient in whom the diagnosis of IIH was made in adulthood, presenting with finding of nephrocalcinosis in childhood, and with subsequent onset of severe hypercalcemia with hypercalciuria, hypoparathyroidism, hypervitaminosis D, and recurrent renal lithiasis. Genetic investigation revealed the presence in homozygosity of the c_428_430delAAG_p.Glu143del variant in the CYP24A1 gene with autosomal recessive transmission, a mutation not reported in the literature.

European Nephrologists' Attitudes toward the Application of Artificial Intelligence in Clinical Practice: A Comprehensive Survey

INTRODUCTION

The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients' treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders.

OBJECTIVES

We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice.

METHODS

An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey.

RESULTS

The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes.

CONCLUSION

While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.

From electronic health records to clinical management systems: how the digital transformation can support healthcare services

Healthcare systems worldwide are currently undergoing significant transformations in response to increasing costs, a shortage of healthcare professionals and the growing complexity of medical needs among the population. Value-based healthcare reimbursement systems are emerging as an attempt to incentivize patient-centricity and cost containment. From a technological perspective, the transition to digitalized services is intended to support these transformations. A Health Information System (HIS) is a technological solution designed to govern the data flow generated and consumed by healthcare professionals and administrative staff during the delivery of healthcare services. However, the exponential growth of digital capabilities and applied advanced analytics has expanded their traditional functionalities and brought the promise of automating administrative procedures and simple repetitive tasks, while enhancing the efficiency and outcomes of healthcare services by incorporating decision support tools for clinical management. The future of HIS is headed towards modular architectures that can facilitate implementation and adaptation to different environments and systems, as well as the integration of various tools, such as artificial intelligence (AI) models, in a seamless way. As an example, we present the experience and future developments of the European Clinical Database (EuCliD®). EuCliD is a multilingual HIS used by 20 000 nurses and physicians on a daily basis to manage 105 000 patients treated in 1100 clinics in 43 different countries. EuCliD encompasses patients' follow-up, automatic reporting and mobile applications while enabling efficient management of clinical processes. It is also designed to incorporate multiagent systems to automate repetitive tasks, AI modules and advanced dynamic dashboards.

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.

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.

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

Algorithm for Mobile Platform-Based Real-Time QRS Detection

Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan-Tompkins (AMPT), which is a simplified version of the well-established Pan-Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan-Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5-20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection.

Effectiveness of Cold Hemodialysis (HD) for the Prevention of HD Hypotension and Mortality in the General HD Population

BACKGROUND

Cold hemodialysis (HD) prevented intra dialysis hypotension (IDH) in small, short term, randomized trials in selected patients with IDH. Whether this treatments prevents IDH and mortality in the HD population at large is unknown.

METHODS

We investigated the relationship between dialysate temperature and the risk of IDH, i.e. nadir BP <90 mmHg, (GEE model) and all cause mortality (Cox's regression) in an incident cohort of hemodialysis patients (n = 8071). To control for confounding by bias by indication and other factors we applied instrumental variables adjusting for case mix at facility level.

RESULTS

The twenty-seven % of patients in the study cohort were systematically treated with a dialysate temperature ≤ 35.5°. Over a median follow-up of 13.6 months (IQR: 5.2-26.1 months), a 0.5°C reduction of the dialysate temperature was associated with a small (-2.4%) reduction of the risk of IDH [OR: 0.976, 95% CI: 0.957-0.995, P = 0.013]. In case-mix, facility level adjusted analysis, the association became much stronger [OR: 0.67, 95% CI: 0.63-0.72, Risk reduction = 33%), P < 0.001]. In contrast, colder dialysate temperature had no effect on mortality both in the unadjusted [hazard ratio (HR) (0.5°C decrease): 1.074, 95%: 0.972-1.187, P = 0.16] and case-mix adjusted analysis at facility level [HR: 1.01, 95% CI: 0.88-1.16, P = 0.84]. Similar results were registered in additional analyses by instrumental variables applying the median dialysate temperature or the facility percentage of patients prescribed a dialysate temperature < 36°C. Further analyses restricted to patients with recurrent IDH fully confirmed these findings.

CONCLUSIONS

Cold HD was associated with IDH in the hemodialysis population but had no association with all-cause mortality.

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.

International consensus conference recommendations on ultrasound education for undergraduate medical students

OBJECTIVES

The purpose of this study is to provide expert consensus recommendations to establish a global ultrasound curriculum for undergraduate medical students.

METHODS

64 multi-disciplinary ultrasound experts from 16 countries, 50 multi-disciplinary ultrasound consultants, and 21 medical students and residents contributed to these recommendations. A modified Delphi consensus method was used that included a systematic literature search, evaluation of the quality of literature by the GRADE system, and the RAND appropriateness method for panel judgment and consensus decisions. The process included four in-person international discussion sessions and two rounds of online voting.

RESULTS

A total of 332 consensus conference statements in four curricular domains were considered: (1) curricular scope (4 statements), (2) curricular rationale (10 statements), (3) curricular characteristics (14 statements), and (4) curricular content (304 statements). Of these 332 statements, 145 were recommended, 126 were strongly recommended, and 61 were not recommended. Important aspects of an undergraduate ultrasound curriculum identified include curricular integration across the basic and clinical sciences and a competency and entrustable professional activity-based model. The curriculum should form the foundation of a life-long continuum of ultrasound education that prepares students for advanced training and patient care. In addition, the curriculum should complement and support the medical school curriculum as a whole with enhanced understanding of anatomy, physiology, pathophysiological processes and clinical practice without displacing other important undergraduate learning. The content of the curriculum should be appropriate for the medical student level of training, evidence and expert opinion based, and include ongoing collaborative research and development to ensure optimum educational value and patient care.

CONCLUSIONS

The international consensus conference has provided the first comprehensive document of recommendations for a basic ultrasound curriculum. The document reflects the opinion of a diverse and representative group of international expert ultrasound practitioners, educators, and learners. These recommendations can standardize undergraduate medical student ultrasound education while serving as a basis for additional research in medical education and the application of ultrasound in clinical practice.

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.

MO801: Use of the Anemia Control Model is Associated With Improved Hemoglobin Target Achievement, as Well as Lower Rates of Inappropriate ESA USE And Severe Anemia Among Dialysis Patients

BACKGROUND AND AIMS

Renal anemia management remains challenging for the nephrologist. Chronic inflammation, EPO resistance, uremia, malnutrition, iron deficiency, dialysis modalities, bleeding, infections, hydration status all interfere with erythropoiesis and erythrocytes half-life. Since 2013, &gt;100 clinics in the FMC Nephrocare network have been using the Anemia Control Model (ACM), an artificial-intelligence based, certified medical device, decision support tool, to assist nephrologists in optimizing the monthly ESA and iron dose based on the specific characteristics of the patient and the amount of ESA dose already taken in the last 90 days. Based on this widespread experience, we sought to evaluate the real-world effectiveness of the use of ACM to facilitate hemoglobin target achievement among dialysis patients.

METHOD

In this historical cohort study, we analyzed electronic health records (EHR) of adult patients receiving in-center hemodialysis therapy in Fresenius Medical Care European dialysis clinics between 1 January 2015 and 31 December 2018 registered in the European Clinical Database (EuCliD®). We compared the rate of target achievement between patients treated with the ESA dose, suggested by ACM, against three control groups of patients treated in clinics without ACM and stratified by different target achievement rates for hemoglobin (Hb in the range of 10–12 g/dL or &gt; 12 g/dL without ESA administration) (Fig. 1). To decrease indication bias, we matched patients belonging to the four comparison groups by a propensity-score assessing the likelihood of receiving the same dose suggested by ACM based on patients’ characteristics (i.e. demographic, comorbidities, anemia-related biomarkers and dialysis vintage). Centers where Hb target achievement &lt; 70% were classified as Tier 1 centers; centers where Hb target achievement was between 70% and 80% were classified as Tier 2 centers, and centers where Hb target achievement was &gt;80% were classified as Tier 3 centers. Performance was evaluated in the month before the index date for all the patients in any given clinic. For each matched group, the index date corresponds to the date of dose suggestion (ACM group) or to the last Hb assessment date (for all matched patients belonging to other three control groups). The study endpoints were Hb target achievement, inappropriate ESA administration (ESA administration despite Hb &gt; 12 g/dL) and severe anemia (Hb &lt; 9 g/dL). All endpoints have been evaluated in the month following the index date.

RESULTS

After matching, we obtained four groups accounting for 8 5512 patient-months each. There were negligible differences in patients’ characteristics across the four matched groups (Table 1). In the ACM group, the Hb target achievement rate was 87.52/100 patient-months 95% confidence interval (95% CI): 86.89–88.15], inappropriate ESA use rate was 4.13/100 patient-months (95% CI: 3.99–4.27) and the incidence of severe anemia was 2.14/100 patient-months (95% CI: 2.04–2.24); the Hb target achievement rate ratio (95% CI) was 1.18 (1.17–1.2), 1.08 (1.07–1.09), 1.01 (1–1.02) for ACM versus Tier 1 centers, ACM versus Tier 2 centers and ACM versus Tier 3 centers, respectively. Rate ratio for inappropriate ESA administration were 0.37 (0.36–0.39), 0.49 (0.47–0.51), 0.66 (0.64–0.69) for ACM versus Tier 1 centers, ACM versus Tier 2 centers and ACM versus Tier 3 centers, respectively. The risk ratios for severe anemia were 0.43 (0.41–0.46), 0.65 (0.62–0.7), 0.95 (0.89–1.02) for ACM versus Tier 1 centers, ACM versus Tier 2 centers and ACM versus Tier 3 centers, respectively.

CONCLUSION

In this matched historical cohort study, we observed an increased hemoglobin target achievement rate, decreased inappropriate ESA usage and a decreased incidence of severe anemia among patients treated according to ACM suggestions. Target achievement rate among patients treated according to ACM suggestions was 87%. The magnitude of the effect in favor of ACM was larger in centers with a lower target achievement rate.

FC087: Effectiveness of Covid-19 Vaccines in a Large European Haemodialysis Cohort

BACKGROUND AND AIMS

To date, no large-scale study has evaluated the effectiveness of COVID-19 vaccines in hemodialysis patients. We sought to evaluate the effectiveness of vaccines against SARS-CoV-2 infections and death in haemodialysis patients registered in the Fresenius Medical Care (FMC) Nephrocare network.

METHOD

In this historical, 1:1 matched cohort study, we analysed electronic health records (EHR) of individuals receiving in-center haemodialysis therapy in FMC European dialysis clinics from 1 December 2020, to 31 May 2021 (study period). For each vaccinated patient, an unvaccinated patient was selected among patients registered in the same country and attending a dialysis session within +/–3 days from the vaccination date. Matching without replacement was based on demographics, clinical characteristics, past COVID-19 infections and a risk score representing the local (dialysis centre) background risk of infection at each vaccination date. The infection risk score was calculated from an artificial Intelligence model predicting the risk of COVID-19 outbreak in each clinic over a 2-week prediction horizon. The infection risk score was based on trends in regional COVID-19 epidemic metrics, FMC COVID-19 reporting system and clinical practice patterns. The index date was the date of the first vaccination for the vaccinated and the matching treatment date for the unvaccinated controls. To overcome violation of the proportional hazard assumption, we estimated the effectiveness of the COVID-19 vaccines in preventing infection and mortality rates as 1—hazard ratio estimated from a time-dependent extended Cox regression stratified by country and vaccine type.

RESULTS

We included 44 458 patients, 22 229 vaccinated and matched 22 229 unvaccinated. Distribution of covariates was balanced across study arms after matching (Figure 1A). In the effectiveness analysis on mRNA vaccines, we observed 850 SARS-CoV-2 infections and 201 COVID19-related deaths among the 28 110 patients (14 055 vaccinated and 14 055 unvaccinated) during a mean follow up time of 44 ± 40 days. In the effectiveness analysis of viral-vector vaccines, we observed 297 SARS-CoV-2 infections and 64 COVID19-related deaths among 12 888 patients (6444 vaccinated and 6444 unvaccinated) during a mean a follow-up time of 48 ± 32 days (Figure 1B). 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-vector vaccines respectively, as compared to matched unvaccinated controls. The effectiveness of COVID-19 vaccines concerning both symptomatic infections and COVID-related death along the follow up period is shown in Figure 2.

CONCLUSION

In this matched, historical cohort study, we observed a strong reduction in both SARS-CoV-2 symptomatic infection and COVID-19-related death among dialysis patients receiving an mRNA vaccine. Despite seemingly less protective against symptomatic infections, we observed similar reduction in COVID-19 mortality rate among patients receiving a viral-carrier vaccine.

MO926: Development and Implementation of an Electronic Patient-Reported Outcome System to Support Health-Related Quality of Life Management in a Large, European Dialysis Network

BACKGROUND AND AIMS

Electronic patient-reported outcome measures (ePROMs) may help personalize healthcare for dialysis patients. ePROMs allow patients to self-report their symptoms and disease burden remotely and make the data available to healthcare professionals (HCP) in real-time. Such information can be used for digital phenotyping, symptoms and disease burden monitoring and to enable personalized healthcare through improved outcomes prediction, as well as Screen and Act, N-of-1 trials and treat-to-target approaches. However, routine use of ePROM in clinical practice presents several challenges, including respondent burden and a lack of clear guidance on medical and psycho-educational intervention in response to patients’ complains. We sought to implement a quality improvement programme based on ePROM management in a large network of dialysis patients.

METHOD

ePROM were collected as part of a screen and act programme launched for the first time in all centres belonging to the Fresenius Medical Care (FMC) Nephrocare network in France (FR), Italy (IT), Spain (SP), UK and Ireland (IR) starting on 20 September 2021. The screening phase ended on 4 December 2021. The action phase is still ongoing. The programme aims at screening all dialysis patients for undetected quality of life and adjustment problems and allow physicians, at their sole discretion, to provide remediation actions potentially including adjustment of treatment plan based on revision of patients’ clinical charts and administration of health education modules. The questionnaire included KDQOL-36, the 5D-Itch questionnaire and an ad hoc module on intradialytic symptoms, and its content was mapped to SONG-HD initiative endpoints. ePROM were collected at the bedside or at patients’ home with a web application available for tablet and smartphones either as self-report or assisted self-report. All ePROM data were recorded in EuCLiD®, the clinical information systems adopted by FMC Nephrocare clinics, as part of patients’ clinical chart. A dashboard displaying patients’ scores was shown to the attending healthcare professional upon survey completion. Statistics about patients’ engagement, scores and action alerts are presented in clinical reports. In the present analysis, we report a retrospective analysis of data abstracted from the EuCliD® database.

RESULTS

Overall, 152 centres (93%) joined the campaign. Participation statistics across countries are reported in Fig. 1. Among 11 537 active patients treated in the five participating countries, 1553 were ineligible because belonging to non-participating centres, temporarily transferred to other centres outside the network (i.e. vacation, rehabilitation, etc.), hospitalized, or because they were considered unable to respond to the survey (i.e. language barrier, inability to read and write or cognitive impairment). Around 23% (IT) to 30% (FR) were considered eligible with assisted administration only. Attrition rate ranged from 3% (IT) to 15% (FR). Item missingness was &lt;2% for all items throughout the survey except for the item on sexual activity (&gt;10% in all countries). KDQOL-36 scores are reported in Fig. 2; 22.5% reported moderate to very severe pruritus mostly distributed on the head/scalp, chest, abdomen, back, lower legs and thighs, forearm and upper arm (all &gt;30% prevalence). Between 10% (IT) and 20% (UK) reported any complications during their last dialysis session, and between 6% (SP) and 12% (IR) suspended or interrupted treatment because of their symptoms. Prevalence of recovery time &gt;3 h ranged from 34.8% (FR) to 46.4% (IR).

CONCLUSION

The ePROM quality improvement campaign attained wide participation and low attrition. We found no evidence of respondent burden given the low missingness rate throughout the survey items. The screening phase allowed to collect and organize a wealth of patient-reported outcomes amenable of medical or psycho-educational interventions.

MO196: Moderate-To-Severe CKD-Associated Pruritus (CKD-AP) is Independently Associated with Worse Health-Related Quality of Life (HRQOL) Among Dialysis Patients

BACKGROUND AND AIMS

Chronic kidney disease–associated pruritus (CKD-aP) is common, yet underrecognized condition among end-stage kidney disease patients. We sought to evaluate the prevalence of CKD-aP and its impact on health-related quality of life (HRQOL) among dialysis patients across five European countries.

METHOD

This is a retrospective, observational analysis of data abstracted from the EuCliD® database (European Clinical database), the clinical information system adopted in Fresenius Medical Care (FMC) outpatient dialysis facilities in several European countries. Patient-Reported Outcomes Measures (PROM) were collected as part of a continuous quality improvement program launched for the first time in all centres belonging to the Nephrocare network in France, Italy, Spain, United Kingdom and Ireland. The presence and severity of CKD-aP were documented based on information captured by both the Kidney Disease Quality of Life (KDQOL™-36) and the 5-D Itch questionnaires. HRQOL was assessed with KDQOL™-36. Clinical and demographic data have been extracted from EuCliD®. The relationship between CKD-aP and HRQOL was evaluated with generalized linear models.

RESULTS

The ePROM campaign attained a large participation rate with very low attrition. Among 9974 who were actively treated in the clinics and were able to fill in a self-reported questionnaire, 8157 responded to the survey and only 1033 (11.2%) refused to participate. We enrolled 6221 patients in the present secondary analysis. The study flowchart is reported in Figure 1. Slightly more than half of patients (52%) reported no CKD-aP, 25% mild, whereas roughly 23% reported moderate-to-severe CKD-aP. Patients reporting more severe CKD-aP had shorter dialysis vintage, higher phosphate and parathormone, and more frequently diabetes. Average KDQOL-36 scores are reported in Figure 2a. We observed a strong, graded relationship between CKD-aP severity and KDQOL-36 scores (Figure 2b). The association was robust to adjustment for potential confounders. The quality of life penalty associated with more severe pruritus was clinically significant based on distribution based minimal clinically important difference threshold for the KDQOL-36 scales.

CONCLUSION

CKD-aP was common in a large, representative sample of the dialysis population in five European countries. We observed a large, clinically important, quality of life penalty associated with CKD-aP even after adjusting for potential confounders.

FC004: Improved Prediction of Hospitalization and Mortality Risk by Addition of Patient Reported Outcome Measures (PROM) into Risk Algorithms among Dialysis Patients

BACKGROUND AND AIMS

Health-Related Quality of Life (HRQOL) is a key outcome in dialysis as it stands at the vertex of the outcomes pyramid. The Kidney Disease Quality of Life (KDQOL™-36) questionnaire has been specifically developed for patients affected by kidney disease and it combines both generic and disease-specific scales capturing unique facets of patients’ adaptation to the physical and mental burden of renal disease and dialysis treatment. We sought to evaluate the association between HRQOL scores and the prevalence of adverse outcomes such as mortality and hospitalization events among prevalent dialysis patients.

METHOD

This is a retrospective, observational analysis of patient-reported outcomes measures (PROMs) collected in NephroCare clinics in Spain in 2019. Clinical and socio-demographic patients’ information were abstracted from EuCliD® (the European Clinical Database) and merged to PROMs HRQOL was assessed by KDQOL™-36 questionnaire and its scores for physical and mental composite (PCS and MCS) as well as for specific-disease dimensions (effects, burden and symptoms) were computed. A renal factor score (RFS) was calculated through principal component analysis to summarize the score of these latter. Mortality and hospitalizations events in the year following the survey administration date were collected and the possible association of these events with HRQOL scores was then explored. Cox and logistic regression models evaluated the relationship between KDQOL™-36 scores and survival and hospitalization events, respectively. A hierarchical model building strategy was adopted to test the incremental discrimination provided by HRQOL scores. First, two outcome risk scores (ORS) were constructed to predict the likelihood of hospitalization and mortality within 12 months given traditional clinical and socio-demographic confounders. Second, KDQOL™-36 scores were added to ORS in the fully adjusted model. We assessed both individual ORS-adjusted coefficient estimates for the KDQOL-36 scales and the KDQOL-36 associated increment in model ROC-AUC.

RESULTS

We enrolled 1838 prevalent dialysis patients in the present analysis with a response rate of 80.6% (study flowchart is reported in Figure 1). Mean age was 68.8 ± 14.4, 60% were men, 65% presented arteriovenous fistula as vascular access and 66% were on HDF modality. We observed a graded relationship between almost all KDQOL™-36 scores and adverse patient-centred outcomes (average KDQOL-36™ scores are reported in Figure 2). These associations were robust to adjustment for potential confounders. ROC-AUC describe the discrimination accuracy of models predicting hospitalization and mortality events, where both improved after including KDQOL-36™ scores beyond traditional clinical and socio-demographic factors.

CONCLUSION

We observed a monotonic, clinically important association denoting increased hospitalization and mortality risk among patients with worse KDQOL-36™ scores. The association was robust to adjustment for potential confounders. The KDQOL-36™ score captures unique facets of health, which are not sufficiently accounted for by traditional clinical and socio-demographic correlates of hospitalization and survival. Careful assessment of HRQOL in routine practice can uncover important health trajectories otherwise overlooked by standard clinical care.

FC095: Development and Validation of an Ai-Based Triage Algorithm for Intensified Prevention of Intradialytic Hypotension

BACKGROUND AND AIMS

Complications during hemodialysis (HD) sessions remain one of the main challenges in the dialytic patients’ management. Intradialytic hypotension (IDH) is among the most frequent adverse events in HD treatments. However, the IDH incidence varies widely across studies from 7.5% up to 68% of HD sessions, because of the lack of a univocal IDH definition. Besides, inadequate IDH management can lead to adverse outcome including increased mortality risk, hospitalization and vascular access thrombosis. IDH can be partially prevented by improved hydration management, as well as fine tuning of dialysis and pharmacological prescriptions. Nevertheless, a personalized approach to IDH prevention is still challenging. Therefore, new, cutting-edge approaches to predict these events is of critical importance to advance dialysis practice. We sought to develop and validate an AI-based classification model to predict the risk of IDH in the next HD treatment.

METHOD

The IDH was defined as any treatments with IDH symptoms reported in the clinical chart with a fall of ≥20 mmHg in systolic blood pressure (SBP) from pre-dialysis to nadir intradialytic levels plus ≥2 responsive measures. The aim of IDH model is to predict the risk of IDH in the next HD session exploiting a high-performance machine learning classifier called XGBoost. A feature selection strategy based on a tradeoff between prediction accuracy and model complexity was performed to determine the set of input variables. All the model's variables are routinely collected in clinical practice or they are automatically recorded from dialysis machine: no additional effort is required by the clinical staff to use the risk model. The accuracy of the prediction was evaluated as the average of the Area Under the ROC Curve (AUC-ROC) on 10 different random sampling of the dataset. In each randomization 70% of the dataset has been used for training and 30% for testing. The appropriateness of the model was also assessed computing the Risk Rate Ratio of the incidence of the IDH in 4 risk classes.

RESULTS

The final dataset comprised 8217 adult patients for a total of 1.7 million of HD sessions collected in EuCliD database by Fresenius Medical Care (FMC) clinics in Portugal and Spain between January 2018 and November 2021. The IDH incidence was of 10%. Baseline patients’ characteristics are the following: male: n = 5361 (65%); age (year): 67.93 ± 14.39; dialysis vintage (days): 752.58 ± 1742.7; BMI: 27.08 ± 5.76; and 3532 (43%) patients affected by diabetes. The average of AUC-ROCs was 0.80 [95% confidence interval (95% CI) 0.79–0.81]. Variables associated with the greatest impact on risk estimates were different metrics on intra-dialytic blood pressures, fluid overload, treatment time, clinic IDH rate and use of diuretics (Figure 1). We defined three thresholds determining four distinct risk groups, ranging from low-risk patients to high-risk patients. The relative Risk Rate Ratio for each class is reported in Table 1.

CONCLUSION

The developed AI model showed a good discrimination and excellent calibration. Moreover, the definition of four risk groups together with new continuous and interactive monitoring protocols, will promote the application of new personalized digital therapeutic program to prevent IDH. The great advantage of this strategy is that the triad ‘patient-nurse-physician’ will tightly cooperate to prevent IDH achieving a concrete improvement of the patient's quality of life.

MO867: Confirmatory Factor Analysis and Computer Adaptive Testing System Prototype Based on Multidimensional Item Response Theory of KDQOL-36 Among a Large Sample of Spanish Dialysis Patients

BACKGROUND AND AIMS

The Kidney Disease Quality of Life (KDQOL™-36) is widely used to assess the quality of life of dialysis patients worldwide. It combines disease-specific and generic scales capturing unique facets of patients’ adaptation to the physical and mental burden of renal disease and dialysis treatment. the KDQOL™-36 psychometric properties and cross-national validation have been subject to intense review in latest years suggesting mixed replicability in different populations. Furthermore, its length may hamper its wider use in clinical practice as respondent burden may be substantial if used longitudinally within continuous quality improvement programs. Therefore, we sought to evaluate its measurement properties and develop an item response theory model enabling personalized survey administration while maximizing measurement efficiency.

METHOD

This is a retrospective, observational analysis of Patient-Reported Outcomes Measures (PROM) collected in Spanish NephroCare clinics during 2019 as part of a continuous quality improvement program launched by the country medical director of Spain. We used Multidimensional Item Response Theory (MIRT) models to confirm the theoretical factor structure underlying the measurement model of KDQOL™-36. MIRT extends classical IRT in that it allows complex factor structures in the multifactorial space. A critical advantage of scoring systems based on MIRT models over classical test theory models is the ability to personalize survey administration based on patient's characteristics and previous responses, reduction of measurement error, objective calibration, evaluation of test and item bias, greater accuracy in the assessment of change due to therapeutic intervention, and evaluation of model and person fit. We specified 4 competitive theoretical models for the SF-12 and the disease-specific KDQOL items (Figure 1). First, we fitted a unidimensional model, to exclude that one single factor could explain the response pattern of the questionnaire. Second, we tested a two-factor (or three-factor) orthogonal model for the SF-12 (and disease-specific KDQOL) to account for the standard scoring structure of the KDQOL™-36 questionnaire. Third, we tested the hypothesis of correlated two-factor (or three-factor) structure. Fourth, we tested a bifactor model structure, which accounts for a general HRQOL latent construct as well as lower order dimensions tapping specific dimensions of health. We expect the bifactor model would capture the pleiotropic effect of ESKD on patients’ life adjustment. We compared model fit with RMSEA, CFI and TLI statistics. For illustrative purposes, we simulated MIRT-based adaptive testing using different Delta Thetas as stopping rules to assesses measurement efficiency for the SF-12 component of the questionnaire.

RESULTS

Among patients completing the survey in the 2019 ePROM Spanish wave, 1838 (80.6%) dialysis prevalent patients met the inclusion criteria for the present analysis. Mean age was 68.8 ± 14.4,  60% were men, 65% had an arteriovenous fistula, and 66% were on HDF.

For both generic and disease-specific KDQOL-36 components, the bifactor model significantly improved empirical fit (Figure 1). Despite slightly inferior compared with the bifactor model solutions, a simpler correlated second-order factor solution showed acceptable model fit. CAT simulation of the SF-12 showed reduction in the amount of administered item while preserving measurement reliability (Figure 2).

CONCLUSION

As previously reported, a correlated two factor structure and three-factor structure for the SF-12 and disease-specific KDQOL, respectively, showed acceptable fit to the observed response pattern. However, a bifactor model structure improved model fit. CAT simulation based on delta theta rules for the SF-12 questionnaire demonstrated promising results as an item selection strategy.

MO890: Modifiable and Non-Modifiable Factors Associated With Increased COVID-19 Related Mortality Among Dialysis Patients

BACKGROUND AND AIMS

Patients with end-stage kidney disease (ESKD) face higher risk for severe outcomes from COVID-19 infection. Moreover, it is not well known to which extent potentially modifiable risk factors contribute to mortality risk. In this study, we investigated the incidence and risk factors for 30-day case-fatality of COVID-19 in haemodialysis patients treated in the European Fresenius Medical Care (FMC) Nephrocare network.

METHOD

In this historical cohort study, we included unvaccinated adult dialysis patients with a first documented SARS-CoV-2 infection between 1 February 2020 and 31 March 2021 (study period) registered in the European Clinical Database (EuCliD®). The first SARS-CoV-2 suspicion date for all documented infections was considered the index date for the analysis. Patients were followed for up to 30 days. Follow-up time was defined from the index date until the date of death, end of follow-up period or lost to follow-up, whichever occurred first. We ascertained patients’ characteristics in the 6-month period prior to index date. We used logistic regression and XGBoost to assess risk factors for 30-day mortality.

RESULTS

We included 9211 patients meeting the inclusion criteria for the study (Table 1). Age was 65.4 ± 13.7 years, dialysis vintage was 4.2 ± 3.7 years. In the follow up period, 1912 patients died within 30 days (20.8%, 95% confidence interval: 19.9%–21.6%). Correlates of COVID-19 related mortality are summarized in Table 2. Several potentially modifiable factors were associated with increased risk of death: patients on HD compared with online haemodiafiltration had shorter survival after presentation with COVID-19 as well as those who did not achieve the therapeutic targets for serum albumin, erythropoietin resistance index, protein catabolic rate, haemodynamic status, C-reactive protein, single-pool Kt/V, hydration status and serum sodium in the months before infection. The discrimination accuracy of prediction models developed with XGBoost was similar to that observed for main-effect logistic regression (AUC 0.69 and 0.71, respectively) suggesting that no major cross-interaction and non-linear effect could improve prediction accuracy.

CONCLUSION

We observed high 30-day COVID-19 related mortality among unvaccinated dialysis patients. Older patients, men and those with greater comorbidities had higher risk of death after COVID-19 infection. Derangement in potentially modifiable factors in the 6 months prior to COVID-19 infection was independently associated with increased mortality. Whether achievement of clinical therapeutic targets is associated with improved survival after COVID-19 infection is a matter of further research.

Reduced Motion External Defibrillation (RMD): Reduced Subject Motion with Equivalent Defibrillation Efficiency validated in Swine

BACKGROUND

External defibrillators are used for arrhythmia cardioversion and for defibrillating during cardiac arrest. During defibrillation, short-duration Biphasic pulses cause intense motion due to rapid chest-wall muscle contraction. A reduced-motion external defibrillator (RMD) was constructed by integrating a commercial defibrillator with a Tetanizing-waveform generator. A long-duration low-amplitude Tetanizing-waveform slowly stimulated the chest musculature prior to the Biphasic pulse, reducing muscle contraction during the shock.

OBJECTIVE

Evaluate RMD defibrillation in swine for subject-motion during defibrillation pulses and for defibrillation effectiveness. RMD defibrillation can reduce the duration of arrhythmia ablation-therapy or simplify cardioversion procedures.

METHODS

The Tetanizing unit delivered a triangular 1-kHz pulse of 0.25-2.0sec duration and 10-100Volt peak amplitude, subsequently triggering the conventional defibrillator to output standard 1-200J energy Biphasic pulses at the next R-wave. Forward-limb motion was evaluated by measuring Peak Acceleration and Limb Work during RMD (Tetanizing+Biphasic) or Biphasic-pulse-only waveforms at 10^-3sec sampling-rate. Seven swine were arrested electrically and subsequently defibrillated. Biphasic-pulse-only and RMD defibrillations were repeated 25-35 times/swine, varying Tetanizing parameters and the Biphasic-pulse energy. Defibrillation thresholds (DFTs) were established by measuring the minimum energy required to restore sinus-rhythm with Biphasic-pulse-only or RMD defibrillations.

RESULTS

Two forward-limb acceleration-peaks occurred during both the Tetanizing-waveform and Biphasic-pulse, indicating rapid and slower nociceptic (pain-sensation) nerve-fiber activation. Optimal RMD Tetanizing-parameters (25-35V, 0.25-0.75sec duration), relative to Biphasic-pulse-only defibrillations, resulted in 74+10% smaller Peak Accelerations and 85+10% reduced Limb Work. DFT energies were identical, comparing RMD to Biphasic-pulse-only defibrillations.

CONCLUSION

Relative to conventional defibrillations, RMD defibrillations maintain rhythm-restoration efficiency with drastically reduced subject-motion.

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.

How to assess the risks associated with the usage of a medical device based on predictive modeling: the case of an anemia control model certified as medical device

BACKGROUND

The successful application of Machine Learning (ML) to many clinical problems can lead to its implementation as a medical device (MD), which is important to assess the associated risks.

METHODS

An anemia control model (ACM), certified as MD, may face adverse events as a result of wrong predictions that are translated into suggestions of doses of erythropoietic stimulating agents to dialysis patients. Risks are assessed as the combination of severity and probability of a given hazard. While severities are typically assessed by clinicians, probabilities are tightly related to the performance of the predictive model.

RESULTS

A postmarketing data set formed by all adult patients registered in French, Portuguese, and Spanish clinics, belonging to an international network, was considered; 3876 patients and 11,508 suggestions were eventually included. The achieved results show that there are no statistical differences between the probabilities of adverse events that are estimated in the ACM test set (using only Spanish clinics) and those actually observed in the postmarketing cohort.

CONCLUSIONS

The risks of an ACM-MD can be accurately and robustly estimated, thus enhancing patients' safety. The proposed methodology is applicable to other clinical decisions based on predictive models since our proposal does not depend on the particular predictive model.

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.

Trajectories of clinical and laboratory characteristics associated with COVID-19 in hemodialysis patients by survival

INTRODUCTION

The clinical impact of COVID-19 has not been established in the dialysis population. We evaluated the trajectories of clinical and laboratory parameters in hemodialysis (HD) patients.

METHODS

We used data from adult HD patients treated at an integrated kidney disease company who received a reverse transcription polymerase chain reaction (RT-PCR) test to investigate suspicion of a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection between May 1 and September 1, 2020. Nonparametric smoothing splines were used to fit data for individual trajectories and estimate the mean change over time in patients testing positive or negative for SARS-CoV-2 and those who survived or died within 30 days of first suspicion or positive test date. For each clinical parameter of interest, the difference in average daily changes between COVID-19 positive versus negative group and COVID-19 survivor versus nonsurvivor group was estimated by fitting a linear mixed effects model based on measurements in the 14 days before (i.e., Day -14 to Day 0) Day 0.

RESULTS

There were 12,836 HD patients with a suspicion of COVID-19 who received RT-PCR testing (8895 SARS-CoV-2 positive). We observed significantly different trends (p < 0.05) in pre-HD systolic blood pressure (SBP), pre-HD pulse rate, body temperature, ferritin, neutrophils, lymphocytes, albumin, and interdialytic weight gain (IDWG) between COVID-19 positive and negative patients. For COVID-19 positive group, we observed significantly different clinical trends (p < 0.05) in pre-HD pulse rate, lymphocytes, neutrophils, and albumin between survivors and nonsurvivors. We also observed that, in the group of survivors, most clinical parameters returned to pre-COVID-19 levels within 60-90 days.

CONCLUSION

We observed unique temporal trends in various clinical and laboratory parameters among HD patients who tested positive versus negative for SARS-CoV-2 infection and those who survived the infection versus those who died. These trends can help to define the physiological disturbances that characterize the onset and course of COVID-19 in HD patients.

MO682TAILORING AI-BASED REFERRAL TO INTENSIFIED INTERVENTION PROGRAMS FOR PERITONITIS PREVENTION WITH COST-EFFECTIVENESS SIMULATION

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.

Leveraging digital transformation to empower clinical governance: enhancement in intermediate clinical endpoints and patients' survival after implementation of a continuous quality improvement program in a large dialysis network

BACKGROUND

Treatment of end-stage kidney disease patients is extremely challenging given the inter-connected 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 CQ&Iacute;s evidence generation process. There is limited evidence that ameliorating intermediate outcomes would translate into better patient-centered outcomes. We sought to evaluate the relationship between Fresenius Medical Care (FME) 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 EMEA region between 2011-2018. We compared medical Key Performance Indicator (KPI) target achievements and 2-year mortality for patients enrolled prior and after to MPR-CQI policy onset (Cohort A and Cohort B). We adopted a structural equation model where MPR-CQI policy was the exogenous explanatory variable, KPI target achievements the mediator variable, and survival was the outcome of interest.

RESULTS

4.270 patients (Cohort A: 2.397; Cohort B: 1.873) 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 indirect effect of MPR-CQI implementation through improvement in KPI target achievement occurring in the post-implementation era (OR: 0.70; 95%CI: 0.65-0.76; p < 0.0001).

CONCLUSIONS

Our study suggests that MPR-CQI achieved by standardized clinical practice and periodical, structured, medical patient review may improve patients' survival through improvement in medical KPIs.

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

Background

We developed a machine learning (ML) model that predicts the risk of a patient on hemodialysis (HD) having an undetected SARS-CoV-2 infection that is identified after the following ≥3 days.

Methods

As part of a healthcare operations effort, we used patient data from a national network of dialysis clinics (February-September 2020) to develop an ML model (XGBoost) that uses 81 variables to predict the likelihood of an adult patient on HD having an undetected SARS-CoV-2 infection that is identified in the subsequent ≥3 days. We used a 60%:20%:20% randomized split of COVID-19-positive samples for the training, validation, and testing datasets.

Results

We used a select cohort of 40,490 patients on HD to build the ML model (11,166 patients who were COVID-19 positive and 29,324 patients who were unaffected controls). The prevalence of COVID-19 in the cohort (28% COVID-19 positive) was by design higher than the HD population. The prevalence of COVID-19 was set to 10% in the testing dataset to estimate the prevalence observed in the national HD population. The threshold for classifying observations as positive or negative was set at 0.80 to minimize false positives. Precision for the model was 0.52, the recall was 0.07, and the lift was 5.3 in the testing dataset. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the model was 0.68 and 0.24 in the testing dataset, respectively. Top predictors of a patient on HD having a SARS-CoV-2 infection were the change in interdialytic weight gain from the previous month, mean pre-HD body temperature in the prior week, and the change in post-HD heart rate from the previous month.

Conclusions

The developed ML model appears suitable for predicting patients on HD at risk of having COVID-19 at least 3 days before there would be a clinical suspicion of the disease.

Multi-organ point-of-care ultrasound for COVID-19 (PoCUS4COVID): international expert consensus

COVID-19 has caused great devastation in the past year. Multi-organ point-of-care ultrasound (PoCUS) including lung ultrasound (LUS) and focused cardiac ultrasound (FoCUS) as a clinical adjunct has played a significant role in triaging, diagnosis and medical management of COVID-19 patients. The expert panel from 27 countries and 6 continents with considerable experience of direct application of PoCUS on COVID-19 patients presents evidence-based consensus using GRADE methodology for the quality of evidence and an expedited, modified-Delphi process for the strength of expert consensus. The use of ultrasound is suggested in many clinical situations related to respiratory, cardiovascular and thromboembolic aspects of COVID-19, comparing well with other imaging modalities. The limitations due to insufficient data are highlighted as opportunities for future research.

Rituximab in people living with HIV affected by immune-mediated renal diseases: a case-series

Over the last two decades, rituximab (RTX) has played an important role in the treatment of some lymphoproliferative malignancies and immune-mediated diseases. RTX administration is generally safe and well-tolerated, but side effects including late-onset neutropenia, hypogammaglobulinemia, hepatitis B reactivation and rare cases of progressive multifocal leukoencephalopathy have been observed after its administration. Although there are no absolute contraindications regarding its use in people living with HIV (PLWH), the prescription of this drug has been principally limited in patients with oncohematological diseases. In this report, we described the outcome of four PLWH who underwent RTX therapy after the diagnosis of immune-mediated renal disease. The main RTX-associated adverse effects were leukopenia, late-onset neutropenia and decline of CD4+ and CD8+ T-cell counts. In addition, two of the four patients experienced pneumonia requiring hospitalization within six months from the last RTX infusion. We suggest that RTX should be used with caution in PLWH until further evidence emerges on its safety profile in this vulnerable population.

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.

Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks

Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to provide suggestions when some strict requirements are not met (such as having a three month history prior to the prediction). Here, recurrent neural networks (RNNs) are used to model whole patient histories, providing predictions at every time step since the very first day. Furthermore, an unprecedented amount of data (∼110,000 patients from many different medical centers in twelve countries, without exclusion criteria) was used to train it, thus allowing it to generalize for every single patient. The resulting model outperforms state-of-the-art Hemoglobin prediction, providing excellent results even when tested on a prospective dataset. Simultaneously, it allows to bring the benefits of algorithmic anemia control to a very large group of patients.

Novel role of focused airway ultrasound in early airway assessment of suspected laryngeal trauma

Background

Upper airway injury secondary to blunt neck trauma can lead to upper airway obstruction and potentially cause a life-threatening condition. The most important aspect in the care of laryngeal trauma is to establish a secure airway. Focused airway ultrasound enables recognition of important upper airway structures, offers early opportunity to identify life-threatening upper airway injury, and allows assessment of the extent of injury. This information that can be obtained rapidly at the bedside has the potential to facilitate rapid intervention.

Case presentation

We report a case series that illustrate the diagnostic value of focused airway ultrasound in the diagnosis of laryngeal trauma in patients presenting with blunt neck injury.

Conclusion

Early recognition, appropriate triaging, accurate airway evaluation, and prompt management of such injuries are essential. In this case series, we introduce the potential role of focused airway ultrasound in suspected laryngeal trauma, and the correlation of these exam findings with that of computed tomography (CT) scanning, based on the Schaefer classification of laryngeal injury.

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 &gt;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.

P1378PROCESS NORMALIZATION AND CLINICAL OUTCOMES FOLLOWING IMPLEMENTATION OF THE ANEMIA CONTROL MODEL FOR PERSONALIZED ANEMIA MANAGEMENT

Background and Aims

Anemia management is still challenging in a large share of dialysis patients. ESA resistance show wide variations across patients as well as fluctuations along time within the same patient. The Anemia Control Model (ACM) is a software application which can be integrated in electronic health record systems. ACM is based on an artificial neural network system trained on 1 million health records which simulates patient’s personalized dose-response relationship to ESA and Iron. Based on patients’ response forecast, ACM suggests the optimal drug dose recommendations to reach or maintain the hemoglobin level within target and to minimize the drug consumption. The ACM has earned medical device certification and it is currently used in 13 countries within the EMEA FMC network. Effectiveness and safety of ACM has been demonstrated in previous real-world evidence studies. We report ACM use and its clinical outcomes among hemodialysis patients registered in the Spanish FME Network

Method

We recorded all medical prescriptions and their clinical outcome for which an ACM recommendation was computed (ACM prescription). prescription occurred in 9 clinics located in Catalunya (Spain) from September 2017 to September 2018. We report dynamics of ACM suggestion acceptance rate, ESA consumption and Hb target achievement in the study period.

Results

There were 1189 active patients per month during the study period on average. ACM suggestion acceptance rate was 73,3%. ESA monthly consumption ranged from 234:7±281:9 – 310.2 ± 310:8 UI/Kg; 86% of patients receiving ESA doses according to ACM recommendations achieved or remained within Hb target range; on the contrary, when physicians rejected ACM recommendations and prescribed a different dosage, only 77% of patients reached or maintained the Hb level on target (p&lt;0.05).

Conclusion

Our study demonstrated that ACM suggestions were frequently accepted by physicians. Acceptance of ACM suggestion was associated with higher Hb target achievement.

P1449MORTALITY RISK ASSOCIATED WITH ACETIC ACID BASED AND CITRIC ACID BASED BICARBONATE HEMODIALYSIS: A HISTORICAL COHORT STUDY

Background

Citric Acid Based Bicarbonate Dialysate (CiD) is increasingly used in hemodialysis to improve hemodynamic tolerance and hemocompatibility associated with Acetic Acid Based Bicarbonate dialysis (AcD). We sought to evaluate the mortality risk associated with different dialysate acidifiers.

Methods

We conducted a multicenter, historical cohort study; all adult incident hemodialysis patients registered in EMEA Fresenius Medical Care network since January 1st 2014 to October 31st 2018 were enrolled. We recorded dialysate acidifier composition for each dialysis session. In the primary analysis (intention-to-treat population), patients were assigned to the exposed group if they received CiD in more than 70% of sessions during the first three months (CiD70%), whereas non-exposed 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 criteria for inclusion in CiD70%. After propensity score matching mortality was 11.43 (95%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 physicians were more inclined to prescribe CiD to subjects with worse medical conditions.

Role of saccharides on thermal stability of phycocyanin in aqueous solutions

The effect of heat and equivalent thermal effect ( [Formula: see text] ) on discolouration of Spirulina algae extracts in water, sucrose and trehalose solutions at different concentration was investigated and kinetics of phycocyanin degradation evaluated by spectrophotometric and circular dichroism. At constant temperature, colour loss increased at increasing time and decreased at increasing solute concentration. Circular dichroism confirmed relation between colour loss and protein structure destabilization, and the thermostabilising effect of saccharides with sucrose performing better than trehalose. Apparent constant rate values determined by the Weibullian probabilistic model describe the corresponding phycocyanin degradation kinetics; a linear correlation between the activation energy and a_w of the solutions has been found. Origin of phycocyanin and system saccharide concentration resulted significant discriminant factors on the discolouration when [Formula: see text] was taken into account. Results may find application in product formulation and processing optimisation, thereby the use of Spirulina extracts as colouring foodstuff could be enhanced.

P1548THE VALUE OF BEING A NETWORK: CLINICAL GOVERNANCE AND DIALYSIS CLINIC PERFORMANCE MANAGEMENT IS ASSOCIATED WITH IMPROVED PATIENTS SURVIVAL

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&lt;0.01) and higher dialysis vintage (32.9±27.0 vs 21.3±22.2 days, p-value&lt;0.01) compared to Cohort B. P-BSC scores over the 6-month ascertainment period was 5.25±1.47 in the pre CQI-MPR policy cohort, while it was significantly higher (6.67±1.63) in the post CQI-MPR policy cohort. Mediation analysis demonstrated a strong indirect effect of CQI-MPR implementation on mortality trough improvement of P-BSC rating score occurring in the post-implementation era (OR=0.70, p&lt;0.001)

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:

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.

Veterinary-focused assessment with sonography for trauma-airway, breathing, circulation, disability and exposure: a prospective observational study in 64 canine trauma patients

OBJECTIVE

To describe the technique and findings of the 'veterinary focused assessment with sonography for trauma-airway, breathing, circulation, disability and exposure' protocol in dogs suffering from trauma.

MATERIALS AND METHODS

Prospective observational study on a new point-of-care ultrasound protocol on 64 dogs suffering from trauma and comparison of findings with radiology.

RESULTS

Comparison of the results of this new ultrasound protocol for trauma patients with radiography findings for pneumothorax, pleural effusion, alveolar-interstitial syndrome and abdominal effusion revealed positive agreement of 89, 83, 100 and 87% and negative agreement of 76, 83, 76 and 92%, respectively. Novel findings of the 'veterinary focused assessment with sonography for trauma-airway, breathing, circulation, disability and exposure' exam, which were not previously reported for dogs undergoing focused assessment with sonography for trauma, included alveolar-interstitial syndrome (suggestive of pulmonary contusions), diaphragmatic hernia, retroperitoneal effusion and tracheal injury. Our new technique may also help identify increased intracranial pressure via changes in optic nerve sheath diameter and haemodynamic instability through the evaluation of the caudal vena cava and cardiac function.

CLINICAL SIGNIFICANCE

The described ultrasound examination protocol can be rapidly performed on dogs suffering from trauma during resuscitation and it may detect injuries previously undetectable using other veterinary point-of-care ultrasound protocols.

Development of an Artificial Intelligence Model to Guide the Management of Blood Pressure, Fluid Volume, and Dialysis Dose in End-Stage Kidney Disease Patients: Proof of Concept and First Clinical Assessment

Background

Fluid volume and blood pressure (BP) management are crucial endpoints for end-stage kidney disease patients. BP control in clinical practice mainly relies on reducing extracellular fluid volume overload by diminishing targeted postdialysis weight. This approach exposes dialysis patients to intradialytic hypotensive episodes.

Summary

Both chronic hypertension and intradialytic hypotension lead to adverse long-term outcomes. Achieving the optimal trade-off between adequate fluid removal and the risk of intradialytic adverse events is a complex task in clinical practice given the multiple patient-related and dialysis-related factors affecting the hemodynamic response to treatment. State-of-the-art artificial intelligence has been adopted in other complex decision-making tasks for dialysis patients and may help personalize the multiple dialysis-related prescriptions affecting patients' intradialytic hemodynamics. As a proof of concept, we developed a multiple-endpoint model predicting session-specific Kt/V, fluid volume removal, heart rate, and BP based on patient characteristics, historic hemodynamic responses, and dialysis-related prescriptions.

Key Messages

The accuracy and precision of this preliminary model is extremely encouraging. Such analytic tools may be used to anticipate patients' reactions through simulation so that the best strategy can be chosen based on clinical judgment or formal utility functions.