Cheungpasitporn, Wisit

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.

AbstractBackgroundHigh‐power short‐duration (HPSD) atrial fibrillation (AF) ablation with a power of 40–50 W was proved to be safe and effective. Very high‐power short‐duration (vHPSD) AF ablation is a novel method using >50 W to obtain more durable AF ablation. This study aimed to evaluate the efficacy and safety of vHPSD ablation compared with HPSD ablation and conventional power ablation.MethodsA literature search for studies that reported AF ablation outcomes, including short‐term freedom from atrial arrhythmia, first‐pass isolation (FPI) rate, procedure time, and major complications, was conducted utilizing MEDLINE, EMBASE, and Cochrane databases. All relevant studies were included in this analysis. A random‐effects model of network meta‐analysis and surface under cumulative ranking curve (SUCRA) were used to rank the treatment for all outcomes.ResultsA total of 29 studies with 9721 patients were included in the analysis. According to the SUCRA analysis, HPSD ablation had the highest probability of maintaining sinus rhythm. Point estimation showed an odds ratio of 1.5 (95% confidence interval [CI]: 1.2–1.9) between HPSD ablation and conventional power ablation and an odds ratio of 1.3 (95% CI: 0.78–2.2) between vHPSD ablation and conventional power ablation. While the odds ratio of FPI between HPSD ablation and conventional power ablation was 3.6 (95% CI: 1.5–8.9), the odds ratio between vHPSD ablation and conventional power ablation was 2.2 (95% CI: 0.61–8.6). The procedure times of vHPSD and HPSD ablations were comparable and, therefore, shorter than that of conventional power ablation. Major complications were low in all techniques.ConclusionvHPSD ablation did not yield higher efficacy than HPSD ablation and conventional power ablation. With the safety concern, vHPSD ablation outcomes were comparable with those of other techniques.

AbstractBackgroundLeft bundle branch area pacing (LBBAP) has recently become a promising option for the near‐natural restoration of electrical activation. However, the clinical relevance of therapeutic effects in individuals with heart failure with reduced ejection fraction (HFrEF) and dyssynchrony remains unknown.MethodsMEDLINE, EMBASE, and Cochrane databases were searched from inception until June 2022. Data from each study was combined using a random‐effects model, the generic inverse variance method of DerSimonian and Laird, to calculate standard mean differences and pooled incidence ratio, with 95% confidence intervals (CIs).ResultsA total of 772 HFrEF patients were analyzed from 15 observational studies per protocol. The success rate of LBBAP implantation was 94.8% (95% CI 89.9–99.6, I2 = 79.4%), which was strongly correlated with shortening QRS duration after LBBAP implantation, with a mean difference of −48.10 ms (95% CI −60.16 to −36.05, I2 = 96.7%). Over a period of 6–12 months of follow‐up, pacing parameters were stable over time. There were significant improvements in left ventricular ejection fraction (LVEF), left ventricular end‐systolic volume (LVESV), left ventricular end‐diastolic diameter (LVEDD), and left ventricular end‐diastolic volume (LVEDV) with mean difference of 16.38% (95% CI 13.13–19.63, I2 = 90.2%), −46.23 ml (95% CI −63.17 to −29.29, I2 = 86.82%), −7.21 mm (95% CI −9.71 to −4.71, I2 = 84.6%), and −44.52 ml (95% CI −64.40 to −24.64, I2 = 85.9%), respectively.ConclusionsLBBAP was associated with improvements in both cardiac function and electrical synchrony. The benefits of LBBAP in individuals with HFrEF and dyssynchrony should be further validated by randomized studies.

AbstractWell‐selected patients with kidney disease and diabetes mellitus who undergo simultaneous kidney‐pancreas transplantation often experience dramatic improvements in quality of life and long‐term survival compared to those who remain on medical therapy. Over the past several years the importance of frailty in the pancreas transplant candidate and recipient populations has grown. More patients with advanced age have entered the waitlist, and complications from prolonged diabetes, even in younger patients, have created increased evidence of risk for frailty. Given these concerns, and the broad challenges facing pancreas transplantation volumes overall, we generated this review to help establish the impact and implications. We summarize the interplay of immunological factors, aging, environmental factors, diabetes mellitus, and chronic kidney disease that put these patients at risk for frailty. We discuss its measurement and recommend a combination of two instruments (both well‐validated and one entirely objective). We describe the outcomes for patients before and after pancreas transplantation who may have frailty, and what interventions can be taken to mitigate its effects. Broader investigation into frailty in the pancreas transplant population is needed to better understand how to select patients for pancreas transplantation and to how manage its consequences thereafter.

Introduction: Posttransplant anemia is a common finding after kidney transplantation. A previous meta-analysis reported an association between anemia and graft loss. However, data on cardiovascular outcomes have not yet been reported. Objective: We conducted an updated meta-analysis to examine the association between posttransplant anemia and outcomes after transplantation including cardiovascular mortality in adult kidney transplant recipients. Methods: We comprehensively searched the databases of MEDLINE and EMBASE from inception to November 2021. Data from each study were combined using the random-effects model. Generic inverse variance method of DerSimonian and Laird was employed to calculate the risk ratios and 95% CIs. Results: Seventeen studies from August 2006 to April 2019 were included (16 463 kidney transplantation recipients). Posttransplant anemia was associated with overall mortality (pooled risk ratio = 1.72 [1.39, 2.13], I 2 = 56%), graft loss (pooled risk ratio = 2.28 [1.77, 2.93], I 2 = 94%), cardiovascular death (pooled risk ratio = 2.06 [1.35, 3.16], I 2 = 0%), and cardiovascular events (pooled risk ratio = 1.33 [1.10, 1.61], I 2 = 0%). Early anemia (≤6 months), compared with late anemia (>6 months), has higher risk of overall mortality and graft loss with a pooled risk ratio of 2.63 (95% CI 1.79-3.86; I 2 = 0%) and 2.96 (95% CI 2.29-3.82; I 2 = 0%), respectively. Discussion: In addition to increased risk of graft loss, our updated meta-analysis demonstrated that posttransplant anemia was significantly associated with poor outcomes after kidney transplantation including overall mortality, graft loss, cardiovascular death, and cardiovascular events. Future studies are required to assess the effects of treatment strategies for posttransplant anemia on posttransplant outcomes including cardiovascular mortality.

Background: The Mediterranean, Dietary Approach to Stop Hypertension (DASH), and plant-based diets may provide cardiovascular benefit to the general population. However, data on their effect on end stage kidney disease (ESKD) patients are limited. This systematic review aims to assess the impact of Mediterranean, DASH, and plant-based diets on outcomes among ESKD patients. Methods: A literature review was conducted in EMBASE, MEDLINE, and Cochrane databases from inception through September 2022 to identify studies that assess the clinical outcomes of Mediterranean, DASH, or plant-based diets on ESKD patients on hemodialysis (HD) or peritoneal dialysis (PD). Effect estimates from the individual studies were derived utilizing the random-effect, generic inverse variance approach of DerSimonian and Laird. Results: Seven studies with 9400 ESKD patients (8395 HD and 1005 PD) met the eligibility criteria and were included in the data analysis. Pooled odds ratios (ORs) of mortality for ESKD patients who adhered to the Mediterranean versus plant-based diet were 0.49 (95% CI: 0.07–3.54; two studies, I2 = 67%) and 0.87 (95% CI: 0.75–1.01; two studies, I2 = 0%), respectively. Data on mortality for ESKD patients on a DASH diet were limited to one study with an OR of 1.00 (95% CI: 0.89–1.12). The pooled OR of cardiovascular mortality among ESKD patients who adhered to a plant-based diet was 0.86 (95% CI: 0.68–1.08; two studies, I2 = 0%), compared to those who did not. Data on cardiovascular mortality among those with Mediterranean and DASH diet were limited to one study with ORs of 1.14 (95% CI: 0.90–1.43) and 1.19 (95% CI: 0.99–1.43), respectively. Mediterranean diet adherence was found to be associated with reduced risk of left ventricular hypertrophy (LVH) with an OR of 0.82 (95% CI: 0.68–0.99) in a study including 127 ESKD patients. The risk of hyperkalemia was not significant among those with a plant-based diet with an OR of 1.00 (95% CI: 0.94–1.07) in a study including 150 ESKD patients. Conclusions: While our systematic review demonstrated no significant associations of Mediterranean, DASH, and plant-based diets with reduced all-cause mortality or cardiovascular mortality, there was also no evidence that suggested harmful effects of these diets to ESKD patients.

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.

Background: Our study aimed to characterize kidney transplant recipients who received high kidney donor profile index (KDPI) kidneys using unsupervised machine learning approach. Methods: We used the OPTN/UNOS database from 2010 to 2019 to perform consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 8935 kidney transplant recipients from deceased donors with KDPI ≥ 85%. We identified each cluster’s key characteristics using the standardized mean difference of >0.3. We compared the posttransplant outcomes among the assigned clusters. Results: Consensus cluster analysis identified 6 clinically distinct clusters of kidney transplant recipients from donors with high KDPI. Cluster 1 was characterized by young, black, hypertensive, non-diabetic patients who were on dialysis for more than 3 years before receiving kidney transplant from black donors; cluster 2 by elderly, white, non-diabetic patients who had preemptive kidney transplant or were on dialysis less than 3 years before receiving kidney transplant from older white donors; cluster 3 by young, non-diabetic, retransplant patients; cluster 4 by young, non-obese, non-diabetic patients who received dual kidney transplant from pediatric, black, non-hypertensive non-ECD deceased donors; cluster 5 by low number of HLA mismatch; cluster 6 by diabetes mellitus. Cluster 4 had the best patient survival, whereas cluster 3 had the worst patient survival. Cluster 2 had the best death-censored graft survival, whereas cluster 4 and cluster 3 had the worst death-censored graft survival at 1 and 5 years, respectively. Cluster 2 and cluster 4 had the best overall graft survival at 1 and 5 years, respectively, whereas cluster 3 had the worst overall graft survival. Conclusions: Unsupervised machine learning approach kidney transplant recipients from donors with high KDPI based on their pattern of clinical characteristics into 6 clinically distinct clusters.

Background: Increased incidence of cardiovascular disease, including stroke, has been consistently observed in patients with chronic inflammatory diseases, although data on antineutrophil cytoplasmic autoantibody (ANCA)-associated vasculitides (AAV) are still limited due to the relative rarity of the disease. Methods: Two investigators independently searched published studies indexed in MEDLINE and EMBASE database from inception to June 2019 using the search terms related to AAV and stroke. The eligible study must be cohort study that consisted of cohort of patients with AAV and cohort of patients without AAV. The study must follow the participants for incident stroke. The magnitude of difference in the incidence of stoke between the cohorts must be reported. Pooled effect estimates were calculated by combining the effect estimate of each eligible study using generic inverse variance method. Statistical heterogeneity was assessed using the Cochran's Q test and I 2 statistics. All analyses were conducted using RevMan 5.3 software from the Cochrane Collaboration. Results: A total of six cohort studies fulfilled the eligibility criteria and were included into the meta-analysis. Patients with AAV had a higher risk of developing incident stroke than individuals without AAV with the a pooled risk ratio of 2.02 (95% CI, 1.02–4.00; I 2 of 89%). Funnel revealed no suggestive evidence of publication bias. Conclusion: A significantly higher risk of incident stroke among patients with AAV than individuals without AAV was demonstrated by this meta-analysis.

Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset. Results: The incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI. Conclusion: We were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.