Citations

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.

Background There are limited data on the outcomes of acute myocardial infarction–cardiogenic shock (AMI-CS) in patients with concomitant cancer. Methods A retrospective cohort of adult AMI-CS admissions was identified from the National Inpatient Sample (2000–2017) and stratified by active cancer, historical cancer, and no cancer. Outcomes of interest included in-hospital mortality, use of coronary angiography, use of percutaneous coronary intervention, do-not-resuscitate status, palliative care use, hospitalization costs, and hospital length of stay. Results Of the 557,974 AMI-CS admissions during this 18-year period, active and historical cancers were noted in 14,826 (2.6%) and 27,073 (4.8%), respectively. From 2000 to 2017, there was a decline in active cancers (adjusted odds ratio, 0.70 [95% CI, 0.63–0.79]; P < .001) and an increase in historical cancer (adjusted odds ratio, 2.06 [95% CI, 1.89–2.25]; P < .001). Compared with patients with no cancer, patients with active and historical cancer received less-frequent coronary angiography (57%, 67%, and 70%, respectively) and percutaneous coronary intervention (40%, 47%, and 49%%, respectively) and had higher do-not-resuscitate status (13%, 15%, 7%%, respectively) and palliative care use (12%, 10%, 6%%, respectively) (P < .001). Compared with those without cancer, higher in-hospital mortality was found in admissions with active cancer (45.9% vs 37.0%; adjusted odds ratio, 1.29 [95% CI, 1.24–1.34]; P < .001) but not historical cancer (40.1% vs 37.0%; adjusted odds ratio, 1.01 [95% CI, 0.98–1.04]; P = .39). AMI-CS admissions with cancer had a shorter hospitalization duration and lower costs (all P < .001). Conclusion Concomitant cancer was associated with less use of guideline-directed procedures. Active, but not historical, cancer was associated with higher mortality in patients with AMI-CS.

Ketogenic diets (KDs) are highly effective in the treatment of epilepsy. However, numerous complications have been reported. During the initiation phase of the diet, common side effects include vomiting, hypoglycemia, metabolic acidosis and refusal of the diet. While on the diet, the side effects involve the following systems: gastrointestinal, hepatic, cardiovascular, renal, dermatological, hematologic and bone. Many of the common side effects can be tackled easily with careful monitoring including blood counts, liver enzymes, renal function tests, urinalysis, vitamin levels, mineral levels, lipid profiles, and serum carnitine levels. Some rare and serious side effects reported in the literature include pancreatitis, protein-losing enteropathy, prolonged QT interval, cardiomyopathy and changes in the basal ganglia. These serious complications may need more advanced work-up and immediate cessation of the diet. With appropriate monitoring and close follow-up to minimize adverse effects, KDs can be effective for patients with intractable epilepsy.

Chronic kidney disease (CKD) is a major public health problem, affecting between 8% and 16% of the population worldwide [...]

Background: There is limited evidence on the association of serum phosphate with mortality in patients receiving continuous renal replacement therapy (CRRT). Objective: To assess the association of serum phosphate with mortality in critically ill patients requiring CRRT for acute kidney injury (AKI). Design: A cohort study. Setting: A tertiary referral hospital in the United States. Patients: Acute kidney injury patients receiving CRRT from 2006 through 2015 in intensive care units. Measurements: (1) Serum phosphate before CRRT and (2) mean serum phosphate during CRRT were categorized into 3 groups; ≤2.4 (hypophosphatemia), 2.5 to 4.5 (normal serum phosphate group), and ≥4.6 (hyperphosphatemia) mg/dL. Methods: Multivariable logistic regression was used to assess the association between serum phosphate and 90-day mortality. Results: A total of 1108 patients were included in this study. Of these, 55% died within 90 days after CRRT initiation. Before CRRT, 3%, 30%, and 66% had hypophosphatemia, normophosphatemia, and hyperphosphatemia, respectively. Before CRRT, both hypophosphatemia and hyperphosphatemia were significantly associated with higher 90-day mortality with the adjusted odds ratio (OR) of 2.22 (95% confidence interval [CI]: [1.03, 4.78]) and 1.62 (95% CI: [1.21, 2.18]), respectively. During CRRT, 3%, 85%, and 12% had mean serum phosphate in hypophosphatemia, normophosphatemia, and hyperphosphatemia range. During CRRT, hyperphosphatemia was significantly associated with higher 90-day mortality with adjusted OR of 2.22 (95% CI: [1.45, 3.38]). Limitations: Single center, observational design, lack of information regarding causes of serum phosphate derangement. Conclusion: Most CRRT patients had hyperphosphatemia before CRRT initiation but maintain normal serum phosphate during CRRT. Before CRRT, hypo- and hyperphosphatemia, and during CRRT, hyperphosphatemia predicted higher mortality. Trial registration: Not registered.

Background: Left 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. Methods & Results: MEDLINE, 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 (CI). A 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 to 99.6, I 2 = 79.4%), which was strongly correlated with shortening QRS duration after LBBAP implantation, with a mean difference of −48.10 msec (95% CI −60.16 to −36.05, I 2 = 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 to 19.63 I 2 = 90.2 %), −46.23 mL (95% CI −63.17 to −29.29, I 2 = 86.82%), −7.21 mm (95% CI −9.71 to −4.71, I 2 = 84.6%), and −44.52 mL (95% CI −64.40 to −24.64, I 2 = 85.9 %), respectively . Conclusions: LBBAP 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.

Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.

Background: There have been concerns regarding increased perioperative mortality, length of hospital stay, and rates of graft loss in kidney transplant recipients with functional limitations. The application of machine learning consensus clustering approach may provide a novel understanding of unique phenotypes of functionally limited kidney transplant recipients with distinct outcomes in order to identify strategies to improve outcomes. Methods: Consensus cluster analysis was performed based on recipient-, donor-, and transplant-related characteristics in 3205 functionally limited kidney transplant recipients (Karnofsky Performance Scale (KPS) < 40% at transplant) in the OPTN/UNOS database from 2010 to 2019. Each cluster’s key characteristics were identified using the standardized mean difference. Posttransplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection were compared among the clusters Results: Consensus cluster analysis identified two distinct clusters that best represented the clinical characteristics of kidney transplant recipients with limited functional status prior to transplant. Cluster 1 patients were older in age and were more likely to receive deceased donor kidney transplant with a higher number of HLA mismatches. In contrast, cluster 2 patients were younger, had shorter dialysis duration, were more likely to be retransplants, and were more likely to receive living donor kidney transplants from HLA mismatched donors. As such, cluster 2 recipients had a higher PRA, less cold ischemia time, and lower proportion of machine-perfused kidneys. Despite having a low KPS, 5-year patient survival was 79.1 and 83.9% for clusters 1 and 2; 5-year death-censored graft survival was 86.9 and 91.9%. Cluster 1 had lower death-censored graft survival and patient survival but higher acute rejection, compared to cluster 2. Conclusion: Our study used an unsupervised machine learning approach to characterize kidney transplant recipients with limited functional status into two clinically distinct clusters with differing posttransplant outcomes.

AbstractIntroductionAtrial fibrillation (AF) ablation is increasingly performed worldwide. As comfort with AF ablation increases, the procedure is increasingly used in patients that are older and in those with more comorbidities. However, it is not well established whether AF ablation in the elderly, especially those >75 years old, has comparable safety and efficacy to younger populations.ObjectiveTo compare the efficacy and safety profiles in patients older than 75 years undergoing AF ablation with younger patients.MethodsDatabases from EMBASE, Medline, PubMed, and Cochrane, were searched from inception through September 2021. Studies that compared the success rates in AF catheter ablation and all complications rates between patients who were older vs under 75 years were included. Effect estimates from the individual studies were extracted and combined using random effect, generic inverse variance method of DerSimonian and Laird.ResultsTwenty‐seven observational studies were included in the analysis consisting of 363,542 patients who underwent AF ablation. Comparing patients older than 75 years old to younger patients, there was no difference in the success of ablation rates between elderly and younger patients (pooled OR 0.85: 95% CI:0.69–1.05, p = .131). On the other hand, AF ablation in the elderly was associated with higher complication rates (pooled OR 1.42: 95% CI:1.21–1.68, p < .001).ConclusionAs AF ablation is expanded to elderly populations, our study found that AF ablation success rates were similar in both elderly and younger patients. However, older patients experience higher rates of complications that should be considered when offering the procedure and as a means to improve outcomes with future innovations.