Sex differences in clinical characteristics, management, and outcomes in patients admitted for ventricular tachycardia: 2016-2018.

INTRODUCTION: The concurrent data on sex disparities in VT management and outcomes have remained unclear. Therefore, our objective was to determine the impact of sex on ventricular tachycardia (VT) management and outcomes in patients admitted with VT, dervied from the US National Inpatient Sample database (NIS). METHODS: We used data from the US NIS to identify hospitalized adult patients who were admitted with VT between 2016 and 2018. Regression analysis was conducted to evaluate the impact of sex on VT management, in-hospital mortality, complications, length of stay, and hospitalization costs. RESULTS: Of the database, a total of 146 070 patients, who were primarily hospitalized for VT, were approximated. Among these, women comprised 25.5%; they were significantly younger and had fewer comorbidities. Of procedural aspects, women were less likely to receive an angiogram, mechanical support, implantable cardioverter-defibrillator implantation, and VT ablation compared to men. Notably, women were associated with higher do-not-resuscitate rates and in-hospital cardiac arrests than men. No differences in in-hospital mortality and cardiogenic shock were observed between men and women (p > .05). Length of stay was significantly longer for women, while no differences in hospital costs were observed in both sexes. CONCLUSION: Significant sex disparities in management and outcomes were observed in admitted patients with VT. Our results reflect the need for further studies to explore factors causing such diversities.

Differences between kidney retransplant recipients as identified by machine learning consensus clustering.

BACKGROUND: Our study aimed to characterize kidney retransplant recipients using an unsupervised machine-learning approach. METHODS: We performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 17 443 kidney retransplant recipients in the OPTN/UNOS database from 2010 to 2019. We identified each cluster's key characteristics using the standardized mean difference of >.3. We compared the posttransplant outcomes, including death-censored graft failure and patient death among the assigned clusters RESULTS: Consensus cluster analysis identified three distinct clusters of kidney retransplant recipients. Cluster 1 recipients were predominantly white and were less sensitized. They were most likely to receive a living donor kidney transplant and more likely to be preemptive (30%) or need ≤1 year of dialysis (32%). In contrast, cluster 2 recipients were the most sensitized (median PRA 95%). They were more likely to have been on dialysis >1 year, and receive a nationally allocated, low HLA mismatch, standard KDPI deceased donor kidney. Recipients in cluster 3 were more likely to be minorities (37% Black; 15% Hispanic). They were moderately sensitized with a median PRA of 87% and were also most likely to have been on dialysis >1 year. They received locally allocated high HLA mismatch kidneys from standard KDPI deceased donors. Thymoglobulin was the most commonly used induction agent for all three clusters. Cluster 1 had the most favorable patient and graft survival, while cluster 3 had the worst patient and graft survival. CONCLUSION: The use of an unsupervised machine learning approach characterized kidney retransplant recipients into three clinically distinct clusters with differing posttransplant outcomes. Recipients with moderate allosensitization, such as those represented in cluster 3, are perhaps more disadvantaged in the kidney retransplantation process. Potential opportunities for improvement specific to these re-transplant recipients include working to improve opportunities to improve access to living donor kidney transplantation, living donor paired exchange and identifying strategies for better HLA matching.

Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering.

BACKGROUND: Educational attainment significantly influences post-transplant outcomes in kidney transplant patients. However, research on specific attributes of lower-educated subgroups remains underexplored. This study utilized unsupervised machine learning to segment kidney transplant recipients based on education, further analyzing the relationship between these segments and post-transplant results. METHODS: Using the OPTN/UNOS 2017-2019 data, consensus clustering was applied to 20,474 kidney transplant recipients, all below a college/university educational threshold. The analysis concentrated on recipient, donor, and transplant features, aiming to discern pivotal attributes for each cluster and compare post-transplant results. RESULTS: Four distinct clusters emerged. Cluster 1 comprised younger, non-diabetic, first-time recipients from non-hypertensive younger donors. Cluster 2 predominantly included white patients receiving their first-time kidney transplant either preemptively or within three years, mainly from living donors. Cluster 3 included younger re-transplant recipients, marked by elevated PRA, fewer HLA mismatches. In contrast, Cluster 4 captured older, diabetic patients transplanted after prolonged dialysis duration, primarily from lower-grade donors. Interestingly, Cluster 2 showcased the most favorable post-transplant outcomes. Conversely, Clusters 1, 3, and 4 revealed heightened risks for graft failure and mortality in comparison. CONCLUSIONS: Through unsupervised machine learning, this study proficiently categorized kidney recipients with lesser education into four distinct clusters. Notably, the standout performance of Cluster 2 provides invaluable insights, underscoring the necessity for adept risk assessment and tailored transplant strategies, potentially elevating care standards for this patient cohort.

Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering.

Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients.

Temporal trends and in-hospital complications of catheter ablation for atrial fibrillation among patients with moderate and advanced chronic kidney diseases: 2005-2018.

INTRODUCTION: Real-world data on atrial fibrillation (AF) ablation among moderate and advanced chronic kidney disease (CKD) patients have so far remained scarce, especially in-hospital AF ablation outcomes. METHODS: We drew data from the US National Inpatient Sample to identify hospitalized patients who underwent AF ablation between 2005 and 2018, and further stratified by CKD classification. We assessed the trend of AF ablation, as well as its complications. RESULTS: A total of 152 630 patients who were primarily hospitalized for AF and underwent ablation were estimated. Among these, CKD patients were found in a total of 1509 participants, with 978, 206, and 325 under CKD3, CKD4, and CKD5/ESKD, respectively. There was a significant increment in admission rates for AF ablation in the CKD population across all CKD classifications (p < .001). All CKD patients were statistically older, with higher coexisting comorbidities, while hypertension was found substantially lower than non-CKD patients (p ≤ .001). Importantly, CKD, especially CKD3 and CKD5/ESKD, was significantly associated with an increased risk of total complications, and total bleeding, Neurological complications were found statistically lower in CKD patients (p = .029), and no mortality rates were significantly different (p = .287). CONCLUSION: Our study observed an increase in admission trends for AF ablation among moderate and advanced CKD patients from 2005 to 2018. CKD was strongly associated with higher procedure-related complications and bleeding, but neurological safety profiles and mortalities rates were nonsignificantly different.

Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes.

Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT) recipients with DGF using OPTN/UNOS data. DGF was observed in 20.9% (n = 17,073) of KT and most kidneys had a KDPI score <85%. Four distinct clusters were identified. Cluster 1 recipients were young, high PRA re-transplants. Cluster 2 recipients were older diabetics and more likely to receive higher KDPI kidneys. Cluster 3 recipients were young, black, and non-diabetic; they received lower KDPI kidneys. Cluster 4 recipients were middle-aged, had diabetes or hypertension and received well-matched standard KDPI kidneys. By cluster, one-year patient survival was 95.7%, 92.5%, 97.2% and 94.3% (p < 0.001); one-year graft survival was 89.7%, 87.1%, 91.6%, and 88.7% (p < 0.001). There were no differences between clusters after accounting for death-censored graft loss (p = 0.08). Clinically meaningful differences in recipient characteristics were noted between clusters, however, after accounting for death and return to dialysis, there were no differences in death-censored graft loss. Greater emphasis on recipient comorbidities as contributors to DGF and outcomes may help improve utilization of DGF at-risk kidneys.

Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering.

Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 ± 13 years) who received dual kidney transplant from pediatric (mean donor age 3 ± 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 ± 9 years) who received dual kidney transplant from adult (mean donor age 59 ± 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI ≥ 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage.

Urine albumin dipstick independently predicts cardiovascular and renal outcomes among rural Thai population: a 14-year retrospective cohort study.

BACKGROUND: Albuminuria is an established risk marker for both cardiovascular and renal outcomes. In this study, we expected to use portable and inexpensive test strips to detect urine albumin level for risk stratification in cardiovascular and renal outcomes among rural Thai community. OBJECTIVE: To evaluate the relationship between urine albumin dipstick and cardiovascular and renal complications in rural Thai population. METHODS: We conducted a retrospective study in 635 rural Thai adults who tested urine albuminuria by using commercial urine albumin dipstick and the Micral-albumin test II strips at baseline. The subjects were divided into normoalbuminuria (albumin < 20 mg/L), microalbuminuria (albumin 20-200 mg/L), or macroalbuminuria (Urine dipstick at least 1+ or albumin > 200 mg/L). We collected data on the incidences of primary composite outcomes including cardiovascular or renal morbidity and mortality. Incident density and cox regression were analyzed to evaluate the association between albuminuria status and primary composite outcome. RESULTS: During an average 14-year follow-up, 102 primary composite events occurred including 59 (13.1%), 32 (20.6%) and 11 (39.3%) among 452, 155, and 28 subjects with normoalbuminuria, microalbuminuria, and macroalbuminuria, respectively. Incident densities of primary composite outcome were elevated continually according to the degree of albuminuria (9.36, 17.11 and 38.12 per 1000 person-years). Compared with the subjects without albuminuria, subjects with microalbuminuria and macroalbuminuria at baseline had higher risk for primary composite outcome in univariate model. After multivariate analysis was performed, the effect of macroalbuminuria was only persisted with 3.13-fold risk (adjusted HR 3.13; 95% CI 1.40-6.96, P= 0.005). CONCLUSION: Albuminuria from semi-quantitative methods is an important factor predicting cardiovascular and renal risk among subjects in Thai rural population. Our findings support to also incorporating urine albumin dipstick into assessments of cardiovascular risk in the general population.

Inpatient burden and mortality of heatstroke in the United States.

BACKGROUND: This study aimed to assess inpatient prevalence, characteristics, outcomes, and resource utilisation of hospitalisation for heatstroke in the United States. Additionally, this study aimed to explore factors associated with in-hospital mortalities of heatstroke. METHODS: The 2003-2014 National Inpatient Sample database was used to identify hospitalised patients with a principal diagnosis of heatstroke. The inpatient prevalence, clinical characteristics, in-hospital treatments, outcomes, length of hospital stay, and hospitalisation cost were studied. Multivariable logistic regression was performed to identify independent factors associated with in-hospital mortality. RESULTS: A total of 3372 patients were primarily admitted for heatstroke, accounting for an overall inpatient prevalence of heatstroke amongst hospitalised patients of 36.3 cases per 1 000 000 admissions in the United States with an increasing trend during the study period (P < .001). Age 40-59 was the most prevalent age group. During the hospital stay, 20% required mechanical ventilation, and 2% received renal replacement therapy. Rhabdomyolysis was the most common complication. Renal failure was the most common end-organ failure, followed by neurological, respiratory, metabolic, hematologic, circulatory, and liver systems. The in-hospital mortality rate of heatstroke hospitalisation was 5% with a decreasing trend during the study period (P < .001). The presence of end-organ failure was associated with increased in-hospital mortality, whereas more recent years of hospitalisation was associated with decreased in-hospital mortality. The median length of hospital stay was 2 days. The median hospitalisation cost was $17 372. CONCLUSION: The inpatient prevalence of heatstroke in the United States increased, while the in-hospital mortality of heatstroke decreased.

Acute kidney injury among salicylate intoxication hospitalisations in the United States.

BACKGROUND: This study aimed to evaluate the risk factors and the association of acute kidney injury (AKI) with outcomes, and resource utilisation in patients hospitalised because of salicylate intoxication in the United States. METHODS: Hospitalised patients with a primary diagnosis of salicylate intoxication from 2003 to 2014 were identified in the National Inpatient Sample (NIS) database. End-stage kidney disease patients were excluded. The occurrence of AKI was identified using hospital diagnosis code. Clinical characteristics, in-hospital treatment, outcomes and resource utilisation were compared between patients with and without AKI. RESULTS: A total of 13 787 eligible hospital admissions were included in the analysis. AKI occurred in 1279 (9.3%) admissions. Older age, male sex, more recent year of hospitalisation, anaemia, hypertension, congestive heart failure, chronic kidney disease, volume depletion, sepsis and ventricular arrhythmia/cardiac arrest were significantly associated with increased risk of AKI, whereas Hispanic race was associated with decreased risk. AKI was significantly associated with increased risk of organ failure, and in-hospital mortality. In addition, the need for ventilation support, blood component transfusion, renal replacement therapy, length of hospital stay and hospitalisation cost were higher in AKI patients. CONCLUSION: Approximately one tenth of salicylate intoxication patients developed AKI during hospitalisation. AKI was associated with higher morbidity, mortality and resource utilisations.

Incidence and Risk Factors Associated with Outpatient Hypoglycemia in Patients with Type 2 Diabetes and Chronic Kidney Disease: A Nationwide Study.

BACKGROUND: Chronic kidney disease and hypoglycemia are common complications in individuals with diabetes. Currently, the association of renal function with hypoglycemic complications in type 2 diabetes mellitus (T2DM) is inconclusive. This study aims to assess the associations between estimated glomerular filtration rate (eGFR) and cumulative incidence of hypoglycemia, hypoglycemia-related hospitalizations, and incidence of outpatient hypoglycemia among T2DM patients in Thailand using a nationwide patient sample. METHODS: We conducted a nationwide retrospective cohort study based on the DM/HT study of the Medical Research Network of the Consortium of Thai Medical Schools. This study assessed adult T2DM patients from 831 public hospitals in Thailand in the year 2012-2013. eGFR was categorized into ≥90, 60-89, 30-59, 15-29, and <15 mL/min/1.73 m2. The associations between eGFR and hypoglycemia, hypoglycemia-related hospitalizations, and incidence of outpatient hypoglycemia were assessed using multivariate logistic regression and Poisson regression. RESULTS: A total of 25,056 T2DM patients with available eGFR were included in the analysis. The mean age was 60.9 ± 10.5 years. The cumulative incidence of hypoglycemia and hypoglycemia-related hospitalizations was 3.6% and 1.7%, respectively. Incidence of outpatient hypoglycemia, mild hypoglycemia, and severe hypoglycemia was 2.99 (2.59-3.43), 2.47 (2.11-2.88), and 0.52 (0.36-0.72) per 100 patient-years, respectively. Patients with eGFR of 30-59, 15-29, and <15 mL/min/1.73 m2 were significantly associated with an increased risk of hypoglycemia, hypoglycemia-related hospitalizations, and incidence of outpatient hypoglycemia when compared to patients with eGFR of ≥90 mL/min/1.73 m2. CONCLUSION: Reduced eGFR was independently associated with increased hypoglycemia, hypoglycemia-related hospitalizations, and risk of outpatient hypoglycemia. Increasing awareness of the heightened risk of hypoglycemia with declining renal function may prompt changes to diabetic management for at-risk individuals.

Impact of ANCA-Associated Vasculitis on Outcomes of Hospitalizations for Goodpasture's Syndrome in the United States: Nationwide Inpatient Sample 2003-2014.

Background and objectives: Goodpasture's syndrome (GS) is a rare, life-threatening autoimmune disease. Although the coexistence of anti-neutrophil cytoplasmic antibody (ANCA) with Goodpasture's syndrome has been recognized, the impacts of ANCA vasculitis on mortality and resource utilization among patients with GS are unclear. Materials and Methods: We used the National Inpatient Sample to identify hospitalized patients with a principal diagnosis of GS from 2003 to 2014 in the database. The predictor of interest was the presence of ANCA-associated vasculitis. We tested the differences concerning in-hospital treatment and outcomes between GS patients with and without ANCA-associated vasculitis using logistic regression analysis with adjustment for other clinical characteristics. Results: A total of 964 patients were primarily admitted to hospital for GS. Of these, 84 (8.7%) had a concurrent diagnosis of ANCA-associated vasculitis. Hemoptysis was more prevalent in GS patients with ANCA-associated vasculitis. During hospitalization, GS patients with ANCA-associated required non-significantly more mechanical ventilation and non-invasive ventilation support, but non-significantly less renal replacement therapy and plasmapheresis than those with GS alone. There was no significant difference in in-hospital outcomes, including organ failure and mortality, between GS patients with and without ANCA-associated vasculitis. Conclusions: Our study demonstrated no significant differences between resource utilization and in-hospital mortality among hospitalized patients with coexistence of ANCA vasculitis and GS, compared to those with GS alone.

Effects of Probiotics on Inflammation and Uremic Toxins Among Patients on Dialysis: A Systematic Review and Meta-Analysis.

BACKGROUND/OBJECTIVES: We performed this systematic review and meta-analysis to evaluate effects of probiotics on inflammation, uremic toxins, and gastrointestinal (GI) symptoms in end-stage renal disease (ESRD) patients. METHODS: A literature search was conducted utilizing MEDLINE, EMBASE, and Cochrane Database from inception through October 2017. We included studies that assessed assessing effects of probiotics on inflammatory markers, protein-bound uremic toxins (PBUTs), and GI symptoms in ESRD patients on dialysis. Effect estimates from the individual study were extracted and combined utilizing random effect, generic inverse variance method of DerSimonian and Laird. The protocol for this meta-analysis is registered with PROSPERO; No. CRD42017082137. RESULTS: Seven clinical trials with 178 ESRD patients were enrolled. There was a significant reduction in serum C-reactive protein (CRP) from baseline to post-probiotic course (≥ 2 months after treatment) with standardized mean difference (SMD) of - 0.42 (95% CI - 0.68 to - 0.16, p = 0.002). When compared to control, patients who received probiotics also had a significant higher degree of reduction in CRP level with SMDs of - 0.37 (95% CI - 0.72 to 0.03, p = 0.04). However, there were no significant changes in serum TNF-alpha or albumin with SMDs of - 0.32 (95% CI - 0.92 to 0.28, p = 0.29) and 0.16 (95% CI - 0.20 to 0.53, p = 0.39), respectively. After probiotic course, there were also significant decrease in PBUTs and improvement in overall GI symptoms (reduction in GI symptom scores) with SMDs of - 0.61 (95% CI - 1.16 to - 0.07, p = 0.03) and - 1.04 (95% CI - 1.70 to - 0.38, p = 0.002), respectively. CONCLUSION: Our study demonstrates potential beneficial effects of probiotics on inflammation, uremic toxins, and GI Symptoms in ESRD patients. Future large-scale clinical studies are required to assess its benefits on other important clinical outcomes including patient mortality.

Safety and efficacy of apixaban versus warfarin in patients with end-stage renal disease: Meta-analysis.

BACKGROUND: At the present, apixaban is the only nonvitamin K oral anticoagulant approved by the Food and Drug Administration for use with patients with creatinine clearance <15 mL/min or end-stage renal disease (ESRD). However, the recommendations are based on pharmacokinetic and pharmacodynamic data and there was lack of clinical trial evidence. We aimed to assess safety and efficacy of apixaban in patients with advanced chronic kidney disease (CKD) or ESRD. METHODS: Databases were searched through November 2017. Studies that reported incidence or odd ratios of bleeding complications or thromboembolic events in the use of apixaban in patients with CKD stage 4-5 or ESRD on dialysis were included. Effect estimates from the individual study were extracted and combined using random-effect, generic inverse variance method of DerSimonian and Laird. RESULTS: Five studies were included into the analysis consisting of 43,850 patients in observational cohort studies. The majority of patients (87%) used apixaban for atrial fibrillation. The pooled estimated incidence of any bleeding complications on apixaban was 17.4% (95% confidence interval [CI]: 13.0%-23.0%). Compared to warfarin, apixaban was significantly associated with reduced risk of major bleeding (pooled odds ratio [OR], 0.42; 95% CI, 0.28-0.61). In studies in ESRD patients on dialysis, the pooled OR of major bleeding was 0.27 (95% CI, 0.07-0.95). There was no significant difference in risk of thromboembolic events in advanced CKD or ESRD patients on apixaban versus vitamin K antagonists (pooled OR, 0.56; 95% CI, 0.23-1.39). CONCLUSIONS: Among patients with advanced CKD and ESRD, the use of apixaban was associated with lower risk of major bleeding compared to warfarin, and was found to be relatively effective with no excess risk of thromboembolic events.

Effect of intradialytic weight resistance training exercise in sarcopenic hemodialysis patients: A randomized controlled trial.

BACKGROUND: Sarcopenia has a high prevalence in end-stage kidney disease (ESKD). However, there is limited evidence of resistance exercise in these patients. OBJECTIVE: The study investigated the effects of resistance exercise on muscle mass, strength, and physical functioning. METHOD: Fifty-three patients were randomly assigned to resistance training exercise (n = 26) and standard exercise (n = 27) groups. All of the patients were diagnosed with sarcopenia by the Asian Working Group for Sarcopenia 2019 criteria. RESULTS: After 12 weeks, an improvement in leg muscle strength was significantly greater in the resistant exercise group compared with standard exercise (12.19 vs. 2.83 kg, p < 0.001). Appendicular skeletal muscle mass had a mean difference (1.01 vs. 1.02 kg/m2 , p = 0.96). Physical performance status had a mean difference (-2.3 vs. -18 s, p = 0.42). There were no serious adverse events. CONCLUSION: Over a 12-week follow-up, resistance exercise improved muscle strength in sarcopenic ESKD patients. Muscle mass and physical performance showed no significant change, but there is still a trend demonstrating to improve.

Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering.

Objectives: This study aimed to identify distinct clusters of very elderly kidney transplant recipients aged ≥80 and assess clinical outcomes among these unique clusters. Design: Cohort study with machine learning (ML) consensus clustering approach. Setting and participants: All very elderly (age ≥80 at time of transplant) kidney transplant recipients in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database database from 2010 to 2019. Main outcome measures: Distinct clusters of very elderly kidney transplant recipients and their post-transplant outcomes including death-censored graft failure, overall mortality and acute allograft rejection among the assigned clusters. Results: Consensus cluster analysis was performed in 419 very elderly kidney transplant and identified three distinct clusters that best represented the clinical characteristics of very elderly kidney transplant recipients. Recipients in cluster 1 received standard Kidney Donor Profile Index (KDPI) non-extended criteria donor (ECD) kidneys from deceased donors. Recipients in cluster 2 received kidneys from older, hypertensive ECD deceased donors with a KDPI score ≥85%. Kidneys for cluster 2 patients had longer cold ischaemia time and the highest use of machine perfusion. Recipients in clusters 1 and 2 were more likely to be on dialysis at the time of transplant (88.3%, 89.4%). Recipients in cluster 3 were more likely to be preemptive (39%) or had a dialysis duration less than 1 year (24%). These recipients received living donor kidney transplants. Cluster 3 had the most favourable post-transplant outcomes. Compared with cluster 3, cluster 1 had comparable survival but higher death-censored graft failure, while cluster 2 had lower patient survival, higher death-censored graft failure and more acute rejection. Conclusions: Our study used an unsupervised ML approach to cluster very elderly kidney transplant recipients into three clinically unique clusters with distinct post-transplant outcomes. These findings from an ML clustering approach provide additional understanding towards individualised medicine and opportunities to improve care for very elderly kidney transplant recipients.

Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering.

BACKGROUND: The utilization of multi-dimensional patient data to subtype hepatorenal syndrome (HRS) can individualize patient care. Machine learning (ML) consensus clustering may identify HRS subgroups with unique clinical profiles. In this study, we aim to identify clinically meaningful clusters of hospitalized patients for HRS using an unsupervised ML clustering approach. METHODS: Consensus clustering analysis was performed based on patient characteristics in 5564 patients primarily admitted for HRS in the National Inpatient Sample from 2003-2014 to identify clinically distinct HRS subgroups. We applied standardized mean difference to evaluate key subgroup features, and compared in-hospital mortality between assigned clusters. RESULTS: The algorithm revealed four best distinct HRS subgroups based on patient characteristics. Cluster 1 patients (n = 1617) were older, and more likely to have non-alcoholic fatty liver disease, cardiovascular comorbidities, hypertension, and diabetes. Cluster 2 patients (n = 1577) were younger and more likely to have hepatitis C, and less likely to have acute liver failure. Cluster 3 patients (n = 642) were younger, and more likely to have non-elective admission, acetaminophen overdose, acute liver failure, to develop in-hospital medical complications and organ system failure, and to require supporting therapies, including renal replacement therapy, and mechanical ventilation. Cluster 4 patients (n = 1728) were younger, and more likely to have alcoholic cirrhosis and to smoke. Thirty-three percent of patients died in hospital. In-hospital mortality was higher in cluster 1 (OR 1.53; 95% CI 1.31-1.79) and cluster 3 (OR 7.03; 95% CI 5.73-8.62), compared to cluster 2, while cluster 4 had comparable in-hospital mortality (OR 1.13; 95% CI 0.97-1.32). CONCLUSIONS: Consensus clustering analysis provides the pattern of clinical characteristics and clinically distinct HRS phenotypes with different outcomes.

Characteristics of Kidney Transplant Recipients with Prolonged Pre-Transplant Dialysis Duration as Identified by Machine Learning Consensus Clustering: Pathway to Personalized Care.

Longer pre-transplant dialysis duration is known to be associated with worse post-transplant outcomes. Our study aimed to cluster kidney transplant recipients with prolonged dialysis duration before transplant using an unsupervised machine learning approach to better assess heterogeneity within this cohort. We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 5092 kidney transplant recipients who had been on dialysis ≥ 10 years prior to transplant in the OPTN/UNOS database from 2010 to 2019. We characterized each assigned cluster and compared the posttransplant outcomes. Overall, the majority of patients with ≥10 years of dialysis duration were black (52%) or Hispanic (25%), with only a small number (17.6%) being moderately sensitized. Within this cohort, three clinically distinct clusters were identified. Cluster 1 patients were younger, non-diabetic and non-sensitized, had a lower body mass index (BMI) and received a kidney transplant from younger donors. Cluster 2 recipients were older, unsensitized and had a higher BMI; they received kidney transplant from older donors. Cluster 3 recipients were more likely to be female with a higher PRA. Compared to cluster 1, cluster 2 had lower 5-year death-censored graft (HR 1.40; 95% CI 1.16-1.71) and patient survival (HR 2.98; 95% CI 2.43-3.68). Clusters 1 and 3 had comparable death-censored graft and patient survival. Unsupervised machine learning was used to characterize kidney transplant recipients with prolonged pre-transplant dialysis into three clinically distinct clusters with variable but good post-transplant outcomes. Despite a dialysis duration ≥ 10 years, excellent outcomes were observed in most recipients, including those with moderate sensitization. A disproportionate number of minority recipients were observed within this cohort, suggesting multifactorial delays in accessing kidney transplantation.