Radiofrequency guidewire-facilitated recanalization of chronic thoracic central venous occlusions in hemodialysis patients.

PURPOSE: To assess the outcome and safety of radiofrequency (RF) wire recanalization in patients with end-stage renal disease (ESRD) and chronic central venous occlusions (CVO). MATERIALS AND METHODS: A retrospective review of ESRD patients who underwent RF-wire recanalization of symptomatic chronic thoracic CVO from January 2017 to August 2022 yielded 20 patients who underwent 21 procedures. All patients had undergone at least one prior unsuccessful attempt at central venous recanalization using conventional catheter-based techniques. Technical success was defined by the ability to cross the CVO using RF-wire recanalization enabling endovascular treatment. Access circuit patency was evaluated based on follow-up imaging and symptomatic improvement. RESULTS: Radiofrequency wire recanalization was successful in 17/21 procedures (81%) with all patients (100%) reporting resolution of arm ± facial swelling. Three major complications occurred (14%): two hemothoraces and one hemopericardium. Medial stent diameter was 13 mm (range, 9-14 mm). Mean duration of hospital stay was 2 days ± 3 days. Mean procedure time was 158 ± 46 min with a mean fluoroscopy time of 31.7 ± 16.3 min. Primary unassisted patency at 6 and 12 months was 94 ± 6% and 85 ± 10%, respectively. Additional interventions resulted in significantly increased stent graft patency (P = 0.006). CONCLUSION: Radiofrequency wire-enabled recanalization of CVO in symptomatic dialysis patients has a high rate of technical success with resolution of arm and facial swelling and resumed use of the ipsilateral dialysis access. Although a superior safety profile was seen than with needle-based techniques such as sharp recanalization, major complications were not infrequent indicating that this RF-wire procedure should be performed in centers equipped to manage central venous perforations.

Percutaneous Management of Dialysis Access Steal Syndrome: Interventions and Outcomes from a Single Institution's 20-Year Experience.

PURPOSE: To determine safety and effectiveness of percutaneous interventions performed by interventional radiologists at a single institution over 2 decades in patients with dialysis access steal syndrome (DASS). MATERIALS AND METHODS: A retrospective review of fistulograms from 2001 to 2021 (N = 11,658) was performed. In total, 286 fistulograms in 212 patients with surgically created dialysis accesses met inclusion criterion of fistulography for suspected DASS. Chart review collected data regarding patient demographics, comorbidities, access characteristics, fistulography findings, intervention(s) performed, and outcomes. Procedures with and without DASS intervention were compared. Odds ratios (ORs), adjusted for age, sex, comorbidities, access characteristics, and multiple within-patient events, were calculated using logistic regression to determine associations between steal intervention status and outcome variables: (a) major adverse events, (b) access preservation, and (c) follow-up surgery. A percutaneously treatable cause of DASS was present in 128 cases (45%). Treatment of DASS lesions was performed in 118 cases. Fifteen embolizations were also performed in patients without DASS lesions. RESULTS: Technical success of DASS interventions, defined by the Society of Interventional Radiology (SIR) reporting standards, was 94%; 54% of interventions resulted in DASS symptom improvement at a median follow-up of 15 days. Patients with steal intervention had 60% lower odds of follow-up surgery (OR, 0.4; P = .007). There was no difference in major adverse events (P = .98) or access preservation (P = .13) between groups. CONCLUSIONS: In this retrospective cohort study, approximately half of DASS fistulograms revealed a percutaneously treatable cause of steal. Over half of DASS interventions resulted in symptomatic relief. Percutaneous intervention was associated with lower odds of follow-up surgery without compromising access preservation.

Association between End-Stage Renal Disease and Major Adverse Limb Events after Peripheral Vascular Intervention.

PURPOSE: To examine the effect of end-stage renal disease (ESRD) on the likelihood of major adverse limb events (MALEs) in patients with Rutherford Category 4-6 critical limb ischemia (CLI) who underwent percutaneous vascular intervention (PVI). MATERIALS AND METHODS: Two contemporaneous cohorts of patients who underwent PVI for symptomatic CLI from 2012 to 2022, differing in ESRD status, were matched using propensity score methods. This database identified 628 patients who underwent 1,297 lower extremity revascularization procedures; propensity score matching yielded 147 patients (180 limbs, 90 limbs in each group). Kaplan-Meier and Cox proportional hazard analyses were used to assess the effect of ESRD status on MALEs, stratified into major amputation (further stratified into above-knee amputation and below-knee amputation [BKA]) and reintervention (PVI or bypass). RESULTS: After PVI, 31.3% of patients in the matched cohorts experienced a MALE (45.7% ESRD vs 18.2% non-ESRD), and 15.6% experienced a major amputation (27.1% ESRD vs 5.2% non-ESRD). Cox proportional hazards analysis revealed that ESRD was an independent predictor of MALE (hazard ratio [HR], 3.15; 95% CI, 1.58-6.29; P = .001), major amputation (HR, 7.00; 95% CI, 2.06-23.79; P = .002), and BKA (HR, 7.56; 95% CI, 1.71-33.50; P = .008). CONCLUSIONS: ESRD is strongly predictive of MALE and major amputation risk, specifically BKA, in patients undergoing PVI for Rutherford Category 4-6 CLI. These patients warrant closer follow-up, and new methods may become necessary to predict and further reduce their amputation risk.

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies.

BACKGROUND: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed. METHODS: We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk. RESULTS: Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score. CONCLUSIONS: Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine Learning.

Background: Preeclampsia is a pregnancy-specific disease characterized by new onset hypertension after 20 weeks of gestation that affects 2-8% of all pregnancies and contributes to up to 26% of maternal deaths. Despite extensive clinical research, current predictive tools fail to identify up to 66% of patients who will develop preeclampsia. We sought to develop a tool to longitudinally predict preeclampsia risk. Methods: In this retrospective model development and validation study, we examined a large cohort of patients who delivered at six community and two tertiary care hospitals in the New England region between 02/2015 and 06/2023. We used sociodemographic, clinical diagnoses, family history, laboratory, and vital signs data. We developed eight datasets at 14, 20, 24, 28, 32, 36, 39 weeks gestation and at the hospital admission for delivery. We created linear regression, random forest, xgboost, and deep neural networks to develop multiple models and compared their performance. We used Shapley values to investigate the global and local explainability of the models and the relationships between the predictive variables. Findings: Our study population (N=120,752) had an incidence of preeclampsia of 5.7% (N=6,920). The performance of the models as measured using the area under the curve, AUC, was in the range 0.73-0.91, which was externally validated. The relationships between some of the variables were complex and non-linear; in addition, the relative significance of the predictors varied over the pregnancy. Compared to the current standard of care for preeclampsia risk stratification in the first trimester, our model would allow 48.6% more at-risk patients to be identified. Interpretation: Our novel preeclampsia prediction tool would allow clinicians to identify patients at risk early and provide personalized predictions, as well as longitudinal predictions throughout pregnancy. Funding: National Institutes of Health, Anesthesia Patient Safety Foundation. RESEARCH IN CONTEXT: Evidence before this study: Current tools for the prediction of preeclampsia are lacking as they fail to identify up to 66% of the patients who develop preeclampsia. We searched PubMed, MEDLINE, and the Web of Science from database inception to May 1, 2023, using the keywords "deep learning", "machine learning", "preeclampsia", "artificial intelligence", "pregnancy complications", and "predictive models". We identified 13 studies that employed machine learning to develop prediction models for preeclampsia risk based on clinical variables. Among these studies, six included biomarkers such as serum placental growth factor, pregnancy-associated plasma protein A, and uterine artery pulsatility index, which are not routinely available in our clinical practice; two studies were in diverse cohorts of more than 100 000 patients, and two studies developed longitudinal predictions using medical records data. However, most studies have limited depth, concerns about data leakage, overfitting, or lack of generalizability.Added value of this study: We developed a comprehensive longitudinal predictive tool based on routine clinical data that can be used throughout pregnancy to predict the risk of preeclampsia. We tested multiple types of predictive models, including machine learning and deep learning models, and demonstrated high predictive power. We investigated the changes over different time points of individual and group variables and found previously known and novel relationships between variables such as red blood cell count and preeclampsia risk.Implications of all the available evidence: Longitudinal prediction of preeclampsia using machine learning can be achieved with high performance. Implementation of an accurate predictive tool within the electronic health records can aid clinical care and identify patients at heightened risk who would benefit from aspirin prophylaxis, increased surveillance, early diagnosis, and escalation in care. These results highlight the potential of using artificial intelligence in clinical decision support, with the ultimate goal of reducing iatrogenic preterm birth and improving perinatal care.

Frequent vascular access thrombosis in a patient with end stage kidney disease on hemodialysis.

Vascular access thrombosis (VAT) is common among patients receiving hemodialysis and leads to missed dialysis treatments, hospitalizations, catheter placement, and graft/fistula abandonment. This article reviews the association between hypercoagulability and VAT and the high prevalence of hypercoagulable states in end-stage kidney disease (ESKD). This article reviews the role of antithrombotic and anticoagulant medications in preventing VAT. The article concludes by reviewing the unique challenges of using vitamin K antagonists in patients with ESKD.

Comparison of 99mTc-DTPA and 51Cr-EDTA for glomerular filtration rate measurement with the continuous infusion method.

BACKGROUND: In late 2018, the production of 51Chromium-labelled ethylenediamine tetra-acetic acid (51Cr-EDTA), a validated and widely used radio-isotopic tracer for measuring glomerular filtration rate, was halted. Technetium-99m-diethylenetriaminepentaacetic acid (99mTc-DTPA) has been validated for GFR measurement with a single bolus injection, a procedure not suitable in patients with extracellular compartment hyperhydration. In such cases, a bolus followed by continuous infusion of the tracer is required. The aim of this study was to evaluate whether 99mTc-DTPA with the infusion protocol can replace 51Cr-EDTA for GFR measurement. METHODS: We conducted a prospective single centre study during February and March 2019. All patients referred for GFR measurement received both radiotracers simultaneously: 51Cr-EDTA and 99mTc-DTPA bolus and continuous infusion were administered concomitantly through the same intravenous route. Over four and a half hours, plasma and urine samples were collected to calculate urinary and plasma clearance. RESULTS: Twenty-two patients were included (mean age 63.4 ± 17.5 years; 68% men). Mean urinary clearance of 51Cr-EDTA and 99mTc-DTPA was 52.4 ± 22.5 mL/min and 52.8 ± 22.6 mL/min, respectively (p = 0.47), with a mean bias of 0.39 ± 2.50 mL/min, an accuracy within 10% of 100% (95% CI 100; 100) and a Pearson correlation coefficient of 0.994. Mean plasma clearance of 51Cr-EDTA and 99mTc-DTPA was 54.8 ± 20.9 mL/min and 54.4 ± 20.9 mL/min, respectively (p = 0.61), with a mean bias of - 0.43 ± 3.89 mL/min, an accuracy within 10% of 77% (95% CI 59; 91) and a Pearson correlation coefficient of 0.983. CONCLUSIONS: Urinary and plasma clearance of 99mTc-DTPA can be used with the infusion protocol to measure GFR.

A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research.

Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation of electronic health record data, live data, and real-time high-resolution device data can be challenging and time-consuming. The development of clinically relevant AI tools requires overcoming challenges in data acquisition, scarce hospital resources, and requirements for data governance. These bottlenecks may result in resource-heavy needs and long delays in research and development of AI systems. We present a system and methodology to accelerate data acquisition, dataset development and analysis, and AI model development. We created an interactive platform that relies on a scalable microservice architecture. This system can ingest 15,000 patient records per hour, where each record represents thousands of multimodal measurements, text notes, and high-resolution data. Collectively, these records can approach a terabyte of data. The platform can further perform cohort generation and preliminary dataset analysis in 2-5 minutes. As a result, multiple users can collaborate simultaneously to iterate on datasets and models in real time. We anticipate that this approach will accelerate clinical AI model development, and, in the long run, meaningfully improve healthcare delivery.

Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores.

Background: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20 weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed. Methods: We identified a cohort of N=1,125 pregnant individuals who delivered between 05/2015-05/2022 at Mass General Brigham hospitals with available electronic health record (EHR) data and linked genetic data. Using clinical EHR data and systolic blood pressure polygenic risk scores (SBP PRS) derived from a large genome-wide association study, we developed machine learning (xgboost) and linear regression models to predict preeclampsia risk. Results: Pregnant individuals with an SBP PRS in the top quartile had higher blood pressures throughout pregnancy compared to patients within the lowest quartile SBP PRS. In the first trimester, the most predictive model was xgboost, with an area under the curve (AUC) of 0.73. Adding the SBP PRS to the models improved the performance only of the linear regression model from AUC 0.70 to 0.71; the predictive power of other models remained unchanged. In late pregnancy, with data obtained up to the delivery admission, the best performing model was xgboost using clinical variables, which achieved an AUC of 0.91. Conclusions: Integrating clinical and genetic factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented in clinical practice to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: a Methodological Approach.

Current AI-driven research in radiology requires resources and expertise that are often inaccessible to small and resource-limited labs. The clinicians who are able to participate in AI research are frequently well-funded, well-staffed, and either have significant experience with AI and computing, or have access to colleagues or facilities that do. Current imaging data is clinician-oriented and is not easily amenable to machine learning initiatives, resulting in inefficient, time consuming, and costly efforts that rely upon a crew of data engineers and machine learning scientists, and all too often preclude radiologists from driving AI research and innovation. We present the system and methodology we have developed to address infrastructure and platform needs, while reducing the staffing and resource barriers to entry. We emphasize a data-first and modular approach that streamlines the AI development and deployment process while providing efficient and familiar interfaces for radiologists, such that they can be the drivers of new AI innovations.

Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure.

BACKGROUND: Clinical decisions are mainly driven by the ability of physicians to apply risk stratification to patients. However, this task is difficult as it requires complex integration of numerous parameters and is impacted by patient heterogeneity. We sought to evaluate the ability of transplant physicians to predict the risk of long-term allograft failure and compare them to a validated artificial intelligence (AI) prediction algorithm. METHODS: We randomly selected 400 kidney transplant recipients from a qualified dataset of 4000 patients. For each patient, 44 features routinely collected during the first-year post-transplant were compiled in an electronic health record (EHR). We enrolled 9 transplant physicians at various career stages. At 1-year post-transplant, they blindly predicted the long-term graft survival with probabilities for each patient. Their predictions were compared with those of a validated prediction system (iBox). We assessed the determinants of each physician's prediction using a random forest survival model. RESULTS: Among the 400 patients included, 84 graft failures occurred at 7 years post-evaluation. The iBox system demonstrates the best predictive performance with a discrimination of 0.79 and a median calibration error of 5.79%, while physicians tend to overestimate the risk of graft failure. Physicians' risk predictions show wide heterogeneity with a moderate intraclass correlation of 0.58. The determinants of physicians' prediction are disparate, with poor agreement regardless of their clinical experience. CONCLUSIONS: This study shows the overall limited performance and consistency of physicians to predict the risk of long-term graft failure, demonstrated by the superior performances of the iBox. This study supports the use of a companion tool to help physicians in their prognostic judgement and decision-making in clinical care.

The ability to predict the risk of a particular event is key to clinical decision-making, for example when predicting the risk of a poor outcome to help decide which patients should receive an organ transplant. Computer-based systems may help to improve risk prediction, particularly with the increasing volume and complexity of patient data available to clinicians. Here, we compare predictions of the risk of long-term kidney transplant failure made by clinicians with those made by our computer-based system (the iBox system). We observe that clinicians' overall performance in predicting individual long-term outcomes is limited compared to the iBox system, and demonstrate wide variability in clinicians' predictions, regardless of level of experience. Our findings support the use of the iBox system in the clinic to help clinicians predict outcomes and make decisions surrounding kidney transplants.

Sodium Bicarbonate Prescription and Extracellular Volume Increase: Real-world Data Results from the AlcalUN Study.

Oral alkalization with sodium bicarbonate (NaHCO3 ) or citrate is prescribed for conditions ranging from metabolic acidosis to nephrolithiasis. Although most nephrologists/urologists use this method routinely, extracellular volume (ECV) increase is the main feared adverse event reported for NaHCO3 . Thus far, no trial has specifically studied this issue in a real-world setting. AlcalUN (NCT03035812) is a multicentric, prospective, open-label cohort study with nationwide (France) enrollment in 18 (public and private) nephrology/urology units. Participants were adult outpatients requiring chronic (>1 month) oral alkalization by either NaHCO3 -containing or no-NaHCO3 -containing agents. The ECV increase (primary outcome) was judged based on body weight increase (ΔBW), blood pressure increase (ΔBP), and/or new-onset edema at the first follow-up visit (V1). From February 2017 to February 2020, 156 patients were enrolled. After a median 106 days of treatment, 91 (72%) patients reached the primary outcome. They had lower systolic (135 (125, 141) vs. 141 (130, 150), P = 0.02) and diastolic (77 (67, 85) vs. 85 (73, 90), P = 0.03) BP values, a higher plasma chloride (106.0 (105.0, 109.0) vs. 105.0 (102.0, 107.0), P = 0.02) at baseline, and a less frequent history of nephrolithiasis (32 vs. 56%, P = 0.02). Patients experienced mainly slight ΔBP (< 10 mmHg). The primary outcome was not associated (P = 0.79) with the study treatment (129 received NaHCO3 and 27 received citrate). We subsequently developed three different models of propensity score matching; each confirmed our results. Chronic oral alkalization with NaHCO3 is no longer associated with an ECV increase compared to citrate in real-life settings.

Renal Function Decline With Small Interfering RNA Silencing Aminolevulinic Acid Synthase 1 (ALAS1).

INTRODUCTION: Givosiran is an RNA interference therapeutic designed to block the synthesis of the aminolevulinic acid (ALA) synthase 1 (ALAS1) enzyme in patients with acute intermittent porphyria (AIP). Givosiran may have adverse effects on the kidney. METHODS: We performed a descriptive case series of renal function parameters of all the patients who received givosiran in France. Twenty patients receiving givosiran between March 2018 and July 2020 in France were analyzed: 7 patients in the ENVISION trial and 13 patients treated in collaboration with the Centre de Référence Maladies Rares Prophyries. RESULTS: A transient decrease in renal function was observed in all but 2 patients (90%) within the 3 months following givosiran initiation. None of the patients developed acute kidney injury or disease. Patients of the ENVISION cohort were followed for at least 30 months: 2 patients did not experience estimated glomerular filtration rate (eGFR) loss, 3 patients experienced a modest decline in renal function (-3.4 ml/min per 1.73 m2 per year in average), and 2 patients had a clearly abnormal eGFR loss (-5.8 ml/min per 1.73 m2 per year in average). None of the patients had biochemical signs of active tubular or glomerular injury. One patient's kidney was biopsied without finding any signs of an active kidney disease and with normal ALAS1 tubular expression. CONCLUSIONS: Givosiran is associated with a transient moderate increase in serum creatinine (sCr) without sign of kidney injury. A long-term deleterious impact of ALAS1 inhibition on renal function is not excluded. Because AIP promotes chronic kidney disease, it is difficult to separate the long-term effects of givosiran from the natural progression of the renal disease.

Acute Myocarditis Revealing Adult-Onset Still's Disease.

A 34-year-old man presented with fever, palpitations, maculopapular rash, pharyngitis, left cheilitis, and bilateral gonalgia. High-sensitivity troponin I concentration was 4,900 ng/l. Transthoracic echocardiogram revealed reduced global longitudinal strain. Cardiac magnetic resonance imaging showed acute myocarditis. Adult-onset Still's disease was diagnosed, and treatment with intravenous corticosteroids and tocilizumab was initiated. (Level of Difficulty: Beginner.).

Cytotoxic CD8+ T cells promote granzyme B-dependent adverse post-ischemic cardiac remodeling.

Acute myocardial infarction is a common condition responsible for heart failure and sudden death. Here, we show that following acute myocardial infarction in mice, CD8+ T lymphocytes are recruited and activated in the ischemic heart tissue and release Granzyme B, leading to cardiomyocyte apoptosis, adverse ventricular remodeling and deterioration of myocardial function. Depletion of CD8+ T lymphocytes decreases apoptosis within the ischemic myocardium, hampers inflammatory response, limits myocardial injury and improves heart function. These effects are recapitulated in mice with Granzyme B-deficient CD8+ T cells. The protective effect of CD8 depletion on heart function is confirmed by using a model of ischemia/reperfusion in pigs. Finally, we reveal that elevated circulating levels of GRANZYME B in patients with acute myocardial infarction predict increased risk of death at 1-year follow-up. Our work unravels a deleterious role of CD8+ T lymphocytes following acute ischemia, and suggests potential therapeutic strategies targeting pathogenic CD8+ T lymphocytes in the setting of acute myocardial infarction.

Cross-lingual Unified Medical Language System entity linking in online health communities.

OBJECTIVE: In Hebrew online health communities, participants commonly write medical terms that appear as transliterated forms of a source term in English. Such transliterations introduce high variability in text and challenge text-analytics methods. To reduce their variability, medical terms must be normalized, such as linking them to Unified Medical Language System (UMLS) concepts. We present a method to identify both transliterated and translated Hebrew medical terms and link them with UMLS entities. MATERIALS AND METHODS: We investigate the effect of linking terms in Camoni, a popular Israeli online health community in Hebrew. Our method, MDTEL (Medical Deep Transliteration Entity Linking), includes (1) an attention-based recurrent neural network encoder-decoder to transliterate words and mapping UMLS from English to Hebrew, (2) an unsupervised method for creating a transliteration dataset in any language without manually labeled data, and (3) an efficient way to identify and link medical entities in the Hebrew corpus to UMLS concepts, by producing a high-recall list of candidate medical terms in the corpus, and then filtering the candidates to relevant medical terms. RESULTS: We carry out experiments on 3 disease-specific communities: diabetes, multiple sclerosis, and depression. MDTEL tagging and normalizing on Camoni posts achieved 99% accuracy, 92% recall, and 87% precision. When tagging and normalizing terms in queries from the Camoni search logs, UMLS-normalized queries improved search results in 46% of the cases. CONCLUSIONS: Cross-lingual UMLS entity linking from Hebrew is possible and improves search performance across communities. Annotated datasets, annotation guidelines, and code are made available online (https://github.com/yonatanbitton/mdtel).

Comparison of Clinical Performance of VectorFlow and Palindrome Symmetric-Tip Dialysis Catheters: A Multicenter, Randomized Trial.

PURPOSE: To compare clinical performance of 2 widely used symmetric-tip hemodialysis catheters. MATERIALS AND METHODS: Patients with end-stage renal disease initiating or resuming hemodialysis were randomized to receive an Arrow-Clark VectorFlow (n = 50) or Palindrome catheter (n = 50). Primary outcome was 90-d primary unassisted catheter patency. Secondary outcomes were Kt/V ([dialyzer urea clearance × total treatment time]/total volume of urea distribution), urea reduction ratio (URR), and effective blood flow (QB). RESULTS: Primary unassisted patency rates with the VectorFlow catheter at 30, 60, and 90 d were 95.5% ± 3.3, 87.2% ± 7.3, and 80.6% ± 9.8, respectively, compared with 89.1% ± 6.2, 79.4% ± 10.0, and 71.5% ± 12.6 with the Palindrome catheter (P = .20). Patients with VectorFlow catheters had a mean Kt/V of 1.5 at 30-, 60-, and 90-day time points, significantly higher than the mean Kt/V of 1.3 among those with Palindrome catheters (P = .0003). URRs were not significantly different between catheters. Catheter QB rates exceeded National Kidney Foundation-recommended thresholds of 300 mL/min at all time points for both catheters and were similar for both catheters (median, 373 mL/min). Catheter failure, ie, poor flow rate requiring guide-wire exchange or removal, within the 90-day primary outcome occurred in 3 VectorFlow subjects and 5 Palindrome subjects (P = .72). Infection rates were similar, with 0.98 infections per 1,000 catheter days for VectorFlow catheters compared with 2.62 per 1,000 catheter days for Palindrome catheters (P = .44). CONCLUSIONS: The 90-day primary patency rates of Palindrome and VectorFlow catheters were not significantly different, and both achieved sustained high QB through 90 day follow-up. However, dialysis adequacy based on Kt/V was consistently better with the VectorFlow catheter versus the Palindrome.

Diagnosis and Treatment of Nonmaturing Fistulae for Hemodialysis Access via Transradial Approach: A Case-Control Study.

PURPOSE: To compare outcomes of transradial access for endovascular treatment of nonmaturing hemodialysis fistulae compared to brachial arteriography followed by unidirectional or bidirectional fistula access for intervention. MATERIALS AND METHODS: In this institutional review board-approved, retrospective, case-control study, 56 consecutive patients with nonmaturing arteriovenous fistulae underwent percutaneous intervention between 2015 and 2018. The transradial group (n = 28) underwent radial artery access for diagnostic fistulography and intervention. The control group (n = 28) underwent retrograde brachial artery access for fistulography followed by unidirectional/bidirectional fistula access for intervention. Both groups had similar demographics, fistula characteristics, and stenosis locations. RESULTS: Fewer punctures were required in the transradial group compared to controls (1.2 vs 2.4, P < .0001), and procedure time was shorter (64.9 vs 91.3 minutes, P = .0016). Anatomic, technical, and clinical success rates trended higher in the transradial group compared to controls (93% vs 86%, 96% vs 89%, and 82% vs 64%, respectively). Nonmaturation resulting in fistula abandonment was lower in the transradial group (3.7% vs 25%, P = .025). Primary unassisted patency at 3, 6, and 12 months was 77.1% ± 8.2%, 73.1% ± 8.7%, and 53.3% ± 10.6% in the transradial group, respectively, and 63.0% ± 9.3%, 55.6% ± 9.6%, and 48.1% ± 9.6% in the control group, respectively (P = .76). Primary assisted patency at 12 months was 92.3% ± 5.3% in the transradial group compared to 61.8% ± 9.6% at 12 months in the control group (P = .021). No major complications occurred. Minor complications were lower in the transradial group than in the control group (14% vs 39%, P = .068). CONCLUSIONS: Treatment of nonmaturing fistulae via a transradial approach was safe, improved midterm patency, and was associated with lower rates of fistula abandonment.

The Controversy of Contrast-Induced Nephropathy With Intravenous Contrast: What Is the Risk?

Contrast-induced nephropathy (CIN) has long been observed in both experimental and clinical studies. However, recent observational studies have questioned the prevalence and severity of CIN following intravenous contrast exposure. Initial studies of acute kidney injury following intravenous contrast were limited by the absence of control groups or contained control groups that did not adjust for additional acute kidney injury risk factors, including prevalent chronic kidney disease, as well as accepted prophylactic strategies. More contemporary use of propensity score-adjusted models have attempted to minimize the risk for selection bias, although bias cannot be completely eliminated without a prospective randomized trial. Based on existing data, we recommend the following CIN risk classification: patients with estimated glomerular filtration rates (eGFRs) ≥ 45mL/min/1.73m2 are at negligible risk for CIN, while patients with eGFRs<30mL/min/1.73m2 are at high risk for CIN. Patients with eGFRs between 30 and 44mL/min/1.73m2 are at an intermediate risk for CIN unless diabetes mellitus is present, which would further increase the risk. In all patients at any increased risk for CIN, the risk for CIN needs to be balanced by the risk of not performing an intravenous contrast-enhanced study.