Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear. METHODS: This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation. RESULTS: Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation. CONCLUSIONS: These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury.

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

Relationship Between Cognitive Impairment and Depression Among Middle Aged and Older Adults in Primary Care.

Objectives: To determine rates of previously undetected cognitive impairment among patients with depression in primary care. Methods: Patients ages 55 and older with no documented history of dementia or mild cognitive impairment were recruited from primary care practices in New York City, NY and Chicago, IL (n = 855). Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA) and depression with the Patient Health Questionnaire-8. Results: The mean age was 66.8 (8.0) years, 45.3% were male, 32.7% Black, and 29.2% Latinx. Cognitive impairment increased with severity of depression: 22.9% in persons with mild depression, 27.4% in moderate depression and 41.8% in severe depression (p = .0002). Severe depression was significantly associated with cognitive impairment in multivariable analysis (standardized ß = -.11, SE = 0.33, p < .0001). Discussion: Depression was strongly associated with previously undetected cognitive impairment. Primary care clinicians should consider screening, or expand their screening, for both conditions.

Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City.

BACKGROUND: Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown. METHODS: This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment. RESULTS: A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003). CONCLUSION: The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models.

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.

Background: Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). Objective: We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. Methods: We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. Results: We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. Conclusions: We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.

Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.

INTRODUCTION: End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care planning and optimizing healthcare resource allocation. METHODS: We obtained electronic health record data from patients with CKD4 in a large health system between January 1, 2006, and December 31, 2016. We developed and validated four models, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network (ANN), to predict ESKD at 3 years. We utilized area under the receiver operating characteristic curve (AUROC) to evaluate model performances and utilized Shapley additive explanation (SHAP) values and plots to define feature dependence of the best performance model. RESULTS: We included 3,160 patients with CKD4. ESKD was observed in 538 patients (21%). All approaches had similar AUROCs; ANN yielded the highest AUROC (0.77; 95%CI 0.75 to 0.79) and LASSO regression (0.77; 95%CI 0.75 to 0.79), followed by random forest (0.76; 95% CI 0.74 to 0.79), and XGBoost (0.76; 95% CI 0.74 to 0.78). CONCLUSIONS: We developed and validated several models for near-term prediction of kidney failure in CKD4. ANN, random forest, and XGBoost demonstrated similar predictive performances. Using this suite of models, interventions can be customized based on risk, and population health and resources appropriately allocated.

Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection.

Background: Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods: In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results: Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we identified 164/2635 (6.2%) of the significantly differentiated genes associated with overall decrease in long-term kidney function. The strongest associations were 'autophagy', 'renal impairment via fibrosis', and 'cardiac structure and function'. Conclusions: We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures, indicating generalizability in therapeutic approaches. SIGNIFICANCE STATEMENT: Peripheral transcriptomic findings in acute and long-term kidney dysfunction after hospitalization for SARS-CoV2 infection are unclear. We evaluated peripheral blood molecular signatures in AKI from COVID-19 (COVID-AKI) and their association with long-term kidney dysfunction using the largest hospitalized cohort with transcriptomic data. Analysis of 283 hospitalized patients of whom 37% had AKI, highlighted the contribution of mitochondrial dysfunction driven by endoplasmic reticulum stress in the acute stages. Subsequently, long-term kidney function decline exhibits significant associations with markers of cardiac structure and function and immune mediated dysregulation. There were similar biomolecular signatures in other inflammatory states, such as sepsis. This enhances the potential for repurposing and generalizability in therapeutic approaches.

Development and Validation of a Formative Assessment Tool for Nephrology Fellows' Clinical Reasoning.

BACKGROUND: Diagnostic errors are commonly driven by failures in clinical reasoning. Deficits in clinical reasoning are common among graduate medical learners, including nephrology fellows. We created and validated an instrument to assess clinical reasoning in a national cohort of nephrology fellows and established performance thresholds for remedial coaching. METHODS: Experts in nephrology education and clinical reasoning remediation designed an instrument to measure clinical reasoning through a written patient encounter note from a web-based, simulated AKI consult. The instrument measured clinical reasoning in three domains: problem representation, differential diagnosis with justification, and diagnostic plan with justification. Inter-rater reliability was established in a pilot cohort ( n =7 raters) of first-year nephrology fellows using a two-way random effects agreement intraclass correlation coefficient model. The instrument was then administered to a larger cohort of first-year fellows to establish performance standards for coaching using the Hofstee method ( n =6 raters). RESULTS: In the pilot cohort, there were 15 fellows from four training program, and in the study cohort, there were 61 fellows from 20 training programs. The intraclass correlation coefficients for problem representation, differential diagnosis, and diagnostic plan were 0.90, 0.70, and 0.50, respectively. Passing thresholds (% total points) in problem representation, differential diagnosis, and diagnostic plan were 59%, 57%, and 62%, respectively. Fifty-nine percent ( n =36) met the threshold for remedial coaching in at least one domain. CONCLUSIONS: We provide validity evidence for a simulated AKI consult for formative assessment of clinical reasoning in nephrology fellows. Most fellows met criteria for coaching in at least one of three reasoning domains, demonstrating a need for learner assessment and instruction in clinical reasoning.

Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19.

RATIONALE & OBJECTIVE: Patients hospitalized with COVID-19 are at increased risk for major adverse kidney events (MAKE). We sought to identify plasma biomarkers predictive of MAKE in patients hospitalized with COVID-19. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: A total of 576 patients hospitalized with COVID-19 between March 2020 and January 2021 across 3 academic medical centers. EXPOSURE: Twenty-six plasma biomarkers of injury, inflammation, and repair from first available blood samples collected during hospitalization. OUTCOME: MAKE, defined as KDIGO stage 3 acute kidney injury (AKI), dialysis-requiring AKI, or mortality up to 60 days. ANALYTICAL APPROACH: Cox proportional hazards regression to associate biomarker level with MAKE. We additionally applied the least absolute shrinkage and selection operator (LASSO) and random forest regression for prediction modeling and estimated model discrimination with time-varying C index. RESULTS: The median length of stay for COVID-19 hospitalization was 9 (IQR, 5-16) days. In total, 95 patients (16%) experienced MAKE. Each 1 SD increase in soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 was significantly associated with an increased risk of MAKE (adjusted HR [AHR], 2.30 [95% CI, 1.86-2.85], and AHR, 2.26 [95% CI, 1.73-2.95], respectively). The C index of sTNFR1 alone was 0.80 (95% CI, 0.78-0.84), and the C index of sTNFR2 was 0.81 (95% CI, 0.77-0.84). LASSO and random forest regression modeling using all biomarkers yielded C indexes of 0.86 (95% CI, 0.83-0.89) and 0.84 (95% CI, 0.78-0.91), respectively. LIMITATIONS: No control group of hospitalized patients without COVID-19. CONCLUSIONS: We found that sTNFR1 and sTNFR2 are independently associated with MAKE in patients hospitalized with COVID-19 and can both also serve as predictors for adverse kidney outcomes. PLAIN-LANGUAGE SUMMARY: Patients hospitalized with COVID-19 are at increased risk for long-term adverse health outcomes, but not all patients suffer long-term kidney dysfunction. Identification of patients with COVID-19 who are at high risk for adverse kidney events may have important implications in terms of nephrology follow-up and patient counseling. In this study, we found that the plasma biomarkers soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 measured in hospitalized patients with COVID-19 were associated with a greater risk of adverse kidney outcomes. Along with clinical variables previously shown to predict adverse kidney events in patients with COVID-19, both sTNFR1 and sTNFR2 are also strong predictors of adverse kidney outcomes.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

Patient-Specific Pharmacokinetics and Dasatinib Nephrotoxicity.

BACKGROUND: Dasatinib has been associated with nephrotoxicity. We sought to examine the incidence of proteinuria on dasatinib and determine potential risk factors that may increase dasatinib-associated glomerular injury. METHODS: We examined glomerular injury through urine albumin-creatinine ratio (UACR) in 82 patients with chronic myelogenous leukemia who were on tyrosine-kinase inhibitor therapy for at least 90 days. t tests were used to compare mean differences in UACR, while regression analysis was used to assess the effects of drug parameters on proteinuria development while on dasatinib. We assayed plasma dasatinib pharmacokinetics using tandem mass spectroscopy and further described a case study of a patient who experienced nephrotic-range proteinuria while on dasatinib. RESULTS: Participants treated with dasatinib ( n =32) had significantly higher UACR levels (median 28.0 mg/g; interquartile range, 11.5-119.5) than participants treated with other tyrosine-kinase inhibitors ( n =50; median 15.0 mg/g; interquartile range, 8.0-35.0; P < 0.001). In total, 10% of dasatinib users exhibited severely increased albuminuria (UACR >300 mg/g) versus zero in other tyrosine-kinase inhibitors. Average steady-state concentrations of dasatinib were positively correlated with UACR ( ρ =0.54, P = 0.03) and duration of treatment ( P = 0.003). There were no associations with elevated BP or other confounding factors. In the case study, kidney biopsy revealed global glomerular damage with diffuse foot process effacement that recovered on termination of dasatinib treatment. CONCLUSIONS: Exposure to dasatinib was associated with a significant chance of developing proteinuria compared with other similar tyrosine-kinase inhibitors. Dasatinib plasma concentration significantly correlated with higher risk of developing proteinuria while receiving dasatinib. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_09_08_CJN0000000000000219.mp3.

Dasatinib nephrotoxicity correlates with patient-specific pharmacokinetics.

Introduction: Dasatinib has been associated with nephrotoxicity. We sought to examine the incidence of proteinuria on dasatinib and determine potential risk factors that may increase dasatinib-associated glomerular injury. Methods: We examine glomerular injury via urine albumin-to-creatinine ratio (UACR) in 101 chronic myelogenous leukemia patients who were on tyrosine-kinase inhibitor (TKI) therapy for at least 90 days. We assay plasma dasatinib pharmacokinetics using tandem mass spectroscopy, and further describe a case study of a patient who experienced nephrotic-range proteinuria while on dasatinib. Results: Patients treated with dasatinib (n= 32) had significantly higher UACR levels (median 28.0 mg/g, IQR 11.5 - 119.5) than patients treated with other TKIs (n=50; median 15.0 mg/g, IQR 8.0 - 35.0; p < 0.001). In total, 10% of dasatinib users exhibited severely increased albuminuria (UACR > 300 mg/g) versus zero in other TKIs. Average steady state concentrations of dasatinib were positively correlated with UACR (ρ = 0.54, p = 0.03) as well as duration of treatment ( p =0.003). There were no associations with elevated blood pressure or other confounding factors. In the case study, kidney biopsy revealed global glomerular damage with diffuse foot process effacement that recovered upon termination of dasatinib treatment. Conclusions: Exposure to dasatinib is associated a significant chance of developing proteinuria compared to other similar TKIs. Dasatinib plasma concentration significantly correlates with increased risk of developing proteinuria while receiving dasatinib. Screening for renal dysfunction and proteinuria is strongly advised for all dasatinib patients.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury ( NGAL ) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2 , trefoil factor 3 , transmembrane emp24 domain-containing protein 10 , and cystatin-C indicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Impact of Vaccination on Major Adverse Cardiovascular Events in Patients With COVID-19 Infection.

Taken from the largest U.S. cohort of patients with SARS-CoV2, our results demonstrate the association of even partial vaccination with lower risk of MACE after SARS-CoV-2 infection.

Major cardiovascular events and subsequent risk of kidney failure with replacement therapy: a CKD Prognosis Consortium study.

AIMS: Chronic kidney disease (CKD) increases risk of cardiovascular disease (CVD). Less is known about how CVD associates with future risk of kidney failure with replacement therapy (KFRT). METHODS AND RESULTS: The study included 25 903 761 individuals from the CKD Prognosis Consortium with known baseline estimated glomerular filtration rate (eGFR) and evaluated the impact of prevalent and incident coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) events as time-varying exposures on KFRT outcomes. Mean age was 53 (standard deviation 17) years and mean eGFR was 89 mL/min/1.73 m2, 15% had diabetes and 8.4% had urinary albumin-to-creatinine ratio (ACR) available (median 13 mg/g); 9.5% had prevalent CHD, 3.2% prior stroke, 3.3% HF, and 4.4% prior AF. During follow-up, there were 269 142 CHD, 311 021 stroke, 712 556 HF, and 605 596 AF incident events and 101 044 (0.4%) patients experienced KFRT. Both prevalent and incident CVD were associated with subsequent KFRT with adjusted hazard ratios (HRs) of 3.1 [95% confidence interval (CI): 2.9-3.3], 2.0 (1.9-2.1), 4.5 (4.2-4.9), 2.8 (2.7-3.1) after incident CHD, stroke, HF and AF, respectively. HRs were highest in first 3 months post-CVD incidence declining to baseline after 3 years. Incident HF hospitalizations showed the strongest association with KFRT [HR 46 (95% CI: 43-50) within 3 months] after adjustment for other CVD subtype incidence. CONCLUSION: Incident CVD events strongly and independently associate with future KFRT risk, most notably after HF, then CHD, stroke, and AF. Optimal strategies for addressing the dramatic risk of KFRT following CVD events are needed.

Higher Symptom Frequency and Severity After the Long Interdialytic Interval in Patients on Maintenance Intermittent Hemodialysis.

Introduction: Patients on intermittent hemodialysis (HD) have a high symptom burden. Though studies report higher hospitalizations and mortality after the long interdialytic interval, whether symptoms vary based on the interdialytic interval is unclear. Methods: This is a prospective observational study of patients over the age of 18 who received in-center HD. Patients were surveyed on the presence and severity of 20 different symptoms at the end of 12 HD sessions. Wilcoxon signed-rank test was used for comparison of severity for each symptom by the interval. Multivariable generalized estimating equation with Poisson regression by repeated measure method was used to determine the association of interdialytic interval and symptom frequency while adjusting for potential confounders. Results: From the 97 patients enrolled, the most common symptoms were fatigue (60.8%), cramping (58.8%), and dry skin (52.6%). There was large variability in the frequency of symptoms, ranging 0% to 8% of treatments. The most severe symptoms were bone pain (mean severity score 2.2±0.9) and diarrhea (mean severity score 2.2±0.7). Eight of the 20 symptoms were significantly more common after the long interdialytic interval including fatigue (22% vs. 15%, P < 0.001) and cramping (21% vs. 16%, P = 0.003). The long interval had a 37% higher incidence rate for symptoms compared to the short interval even after adjustment. Results were similar across genders. Conclusion: Symptoms are more common after the long interdialytic interval. Clinical assessment and research evaluating patient symptoms need to be cognizant of when patients are surveyed or include the length of interdialytic interval as a confounding variable.

Unplanned 30-Day Readmission Rates for Autosomal Dominant Polycystic Kidney Disease: Insight from the Nationwide Readmissions Database.

INTRODUCTION: Among end-stage kidney disease (ESKD) patients on dialysis with autosomal dominant polycystic kidney disease (ADPKD), relatively little is known about the epidemiology and risk factors for 30-day readmissions in the USA. Therefore, we evaluated the 30-day unplanned readmission rates and predictors and inpatient care costs among ESKD patients with and without ADPKD using a nationally representative, all-payer database. METHODS: We utilized the Nationwide Readmissions Database from 2013 to 2018 to identify patients admitted for ESKD on dialysis with and without ADPKD using ICD-9 and ICD-10 codes. The primary outcome was a 30-day, unplanned readmission rate. Secondary outcomes were readmission reasons and timing, mortality, cost of hospitalization and rehospitalization, and adjusted predictors of readmissions. We used χ2 tests, t tests, and Wilcoxon rank-sum tests for descriptive analyses and survey logistic regression to calculate adjusted odds ratios and 95% confidence intervals for associations with readmissions adjusting for confounders. RESULTS: From 2013 to 2018, in a cohort of 1,404,144 hospitalizations with ESKD on dialysis as the primary and secondary diagnosis on index admission, there were 8,213 (0.58%) patients with ADPKD and 1,395,932 patients without ADPKD. Those who had ADPKD during index admissions had fewer 30 days readmissions (18.8 vs. 23.8%, p < 0.0001). The cost of hospitalizations and readmissions in ESKD on-dialysis patients with ADPKD was higher than non-ADPKD patients. Compared to ESKD patients without ADPKD who were readmitted, readmitted ADPKD patients were more likely to be younger with a lower Elixhauser Comorbidity Index (ECI) score; have received kidney transplant, lower source of income, elective index admissions, private insurance; and be discharged routinely, admitted in hospitals with larger bed size, in teaching hospitals, and less likely to get admitted through the emergency department. Younger age (<75 years), higher ECI score, longer length of stay, Medicare and Medicaid insurance, self-pay, discharge to a short-term hospital, specialized care, home health care, and against medical advice were associated with significantly increased odds of readmission. ADPKD patients were 31% less likely to get readmitted and 43% less likely to die during readmissions. DISCUSSION/CONCLUSION: Nationwide, ESKD on-dialysis patients with ADPKD were less likely to have 30-day readmission than patients without ADPKD. Inpatient mortality during readmissions in patients admitted with ESKD on dialysis was lower with ADPKD as compared to those without ADPKD at the cost of higher health care expenses.