Less sodium, more potassium, or both: population-wide strategies to prevent hypertension.

Hypertension is among the most prevalent medical conditions globally and a major contributor to chronic kidney disease, cardiovascular disease, and death. Prevention through nonpharmacological, population-level interventions is critically needed to halt this worldwide epidemic. However, there are ongoing disagreements as to where public policy efforts should focus. Recently the Salt Substitute and Stroke Study demonstrated the efficacy of substituting table salt with potassium salt to reduce the risk of stroke, major cardiovascular events, and death. However, this sparked debate over whether sodium or potassium should be prioritized in countries where table salt substitution was less feasible. In this commentary, we summarize arguments in favor of either strategy: reduced sodium or increased potassium intake. Moreover, we discuss evidence and policy approaches related to either or combined approaches relevant to cultural context. Ultimately, there is an urgent need for policies that both reduce sodium and increase potassium intake; however, identifying a strategy that fits cultural context will be key to improve population-wide blood pressures.

The current therapeutic approach for anaemia after kidney transplant.

PURPOSE OF REVIEW: Anaemia after kidney transplantation is a common finding with no uniform management guideline. Most approaches are derived from the chronic kidney disease (CKD) population. Recent advances for the treatment of anaemia in patients with CKD/End stage renal disease include hypoxia-inducible factor-prolyl hydroxylase inhibitor (HIF-PHi), a novel class of oral erythropoietin-stimulating agents (ESAs). We present relevant studies of HIF-PHi in the transplant population and its implications on the management of posttransplant anaemia. RECENT FINDINGS: Data on HIF-PHi use in the kidney transplant population are promising. Limited data demonstrate a significant increase in haemoglobin, with a comparable safety profile to epoetin. Reported adverse effects include overcorrection and low iron stores. SUMMARY: Current therapeutic approaches to anaemia in the kidney transplant population is mostly derived from the CKD population. More studies are needed on HIF-Phi, a novel class of ESAs that has thus far demonstrated promise in the kidney transplant population.

Renal manifestations of hepatitis E among immunocompetent and solid organ transplant recipients.

Hepatitis E virus (HEV) infections are generally self-limited. Rare cases of hepatitis E induced fulminant liver failure requiring liver transplantation are reported in the literature. Even though HEV infection is generally encountered among developing countries, a recent uptrend is reported in developed countries. Consumption of unprocessed meat and zoonosis are considered to be the likely transmission modalities in developed countries. Renal involvement of HEV generally holds a benign and self-limited course. Although rare cases of cryoglobulinemia are reported in immunocompetent patients, glomerular manifestations of HEV infection are frequently encountered in immunocompromised and solid organ transplant recipients. The spectrum of renal manifestations of HEV infection include pre-renal failure, glomerular disorders, tubular and interstitial injury. Kidney biopsy is the gold standard diagnostic test that confirms the pattern of injury. Management predominantly includes conservative approach. Reduction of immunosuppressive medications and ribavirin (for 3-6 mo) is considered among patients with solid organ transplants. Here we review the clinical course, pathogenesis, renal manifestations, and management of HEV among immunocompetent and solid organ transplant recipients.

Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival.

BACKGROUND: The objective of this study was to characterize hypernatremia patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters. METHODS: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 922 hospitalized adult patients with admission serum sodium of > 145 mEq/L. We calculated the standardized difference of each variable to identify each cluster's key features. We assessed the association of each hypernatremia cluster with hospital and 1-year mortality. RESULTS: There were three distinct clusters of patients with hypernatremia on admission: 318 (34%) patients in cluster 1, 339 (37%) patients in cluster 2, and 265 (29%) patients in cluster 3. Cluster 1 consisted of more critically ill patients with more severe hypernatremia and hypokalemic hyperchloremic metabolic acidosis. Cluster 2 consisted of older patients with more comorbidity burden, body mass index, and metabolic alkalosis. Cluster 3 consisted of younger patients with less comorbidity burden, higher baseline eGFR, hemoglobin, and serum albumin. Compared to cluster 3, odds ratios for hospital mortality were 15.74 (95% CI 3.75-66.18) for cluster 1, and 6.51 (95% CI 1.48-28.59) for cluster 2, whereas hazard ratios for 1-year mortality were 6.25 (95% CI 3.69-11.46) for cluster 1 and 4.66 (95% CI 2.73-8.59) for cluster 2. CONCLUSION: Our cluster analysis identified three clinically distinct phenotypes with differing mortality risk in patients hospitalized with hypernatremia.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.

BACKGROUND: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. METHODS: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster's key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. RESULTS: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. CONCLUSION: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements.

BACKGROUND: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. METHODS: We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster's key features. We assessed the association of the clusters with mortality. RESULTS: In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. CONCLUSION: Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.

BACKGROUND: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. METHODS: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster's key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. RESULTS: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). CONCLUSION: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.

Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering.

Background and Objectives: Despite the association between hyperchloremia and adverse outcomes, mortality risks among patients with hyperchloremia have not consistently been observed among all studies with different patient populations with hyperchloremia. The objective of this study was to characterize hyperchloremic patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters. Materials and Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,394 hospitalized adult patients with admission serum chloride of >108 mEq/L. We calculated the standardized mean difference of each variable to identify each cluster's key features. We assessed the association of each hyperchloremia cluster with hospital and one-year mortality. Results: There were three distinct clusters of patients with admission hyperchloremia: 3237 (28%), 4059 (36%), and 4098 (36%) patients in clusters 1 through 3, respectively. Cluster 1 was characterized by higher serum chloride but lower serum sodium, bicarbonate, hemoglobin, and albumin. Cluster 2 was characterized by younger age, lower comorbidity score, lower serum chloride, and higher estimated glomerular filtration (eGFR), hemoglobin, and albumin. Cluster 3 was characterized by older age, higher comorbidity score, higher serum sodium, potassium, and lower eGFR. Compared with cluster 2, odds ratios for hospital mortality were 3.60 (95% CI 2.33-5.56) for cluster 1, and 4.83 (95% CI 3.21-7.28) for cluster 3, whereas hazard ratios for one-year mortality were 4.49 (95% CI 3.53-5.70) for cluster 1 and 6.96 (95% CI 5.56-8.72) for cluster 3. Conclusions: Our cluster analysis identified three clinically distinct phenotypes with differing mortality risks in hospitalized patients with admission hyperchloremia.

Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia.

BACKGROUND: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. METHODS: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster's key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. RESULTS: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. CONCLUSION: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.

Comparison of Efficacy of Systemic Therapies in Advanced Hepatocellular Carcinoma: Updated Systematic Review and Frequentist Network Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Several systemic agents have been approved for use in advanced hepatocellular carcinoma (aHCC). However, it is unclear which treatment is superior in either the first- or second-line settings due to the paucity of head-to-head comparative trials. Therefore, we have conducted a systematic review and network meta-analysis for the indirect comparison of the systemic agents in the first line and second line settings. METHODS: Randomized clinical trials evaluating systemic agents in first and second line settings in aHCC from inception to April 2020 were identified by searching PubMed, EMBASE, and Cochrane Databases and the annual ASCO and ESMO conferences from 2017 to 2020. Studies in English reporting clinical outcomes including overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) were included. The primary outcomes of interest were pooled hazard ratios (HR) of OS and pooled odds ratios (OR) of ORR in first line studies and pooled HR of PFS and OR of ORR for second line studies. Additionally, OS for second line agents were reported in the qualitative analysis. RESULTS: Overall, first line studies comprised 8335 patients (13 studies) and second line studies comprised 4612 patients (11 studies). In the first line setting, atezolizumab plus bevacizumab was associated with the highest OS benefit over sorafenib (HR 0.58, 95% CI, 0.42-0.80; P-score 0.993). Additionally, lenvatinib was associated with the greatest ORR benefit (OR 3.34, 95% CI, 2.17-5.14; P-score 0.080) in the first line setting. In the second line setting, cabozantinib was associated with the highest PFS benefit over placebo (HR 0.44, 95% CI, 0.29-0.66; P-score 0.854) as well as the highest ORR benefit (OR 9.40, 95% CI, 1.25-70.83, P-score, 0.266). CONCLUSION: Atezolizumab plus bevacizumab appears to have superior efficacy among first line agents whereas cabozantinib appears to be superior in the second line setting. Further studies are warranted to determine whether the type of prior therapy received affects the efficacy of subsequent second line therapy.

Urine biomarkers informative of human kidney allograft rejection and tolerance.

We developed urinary cell messenger RNA (mRNA) profiling to monitor in vivo status of human kidney allografts based on our conceptualization that the kidney allograft may function as an in vivo flow cell sorter allowing access of graft infiltrating cells to the glomerular ultrafiltrate and that interrogation of urinary cells is informative of allograft status. For the profiling urinary cells, we developed a two-step preamplification enhanced real-time quantitative PCR (RT-QPCR) assays with a customized amplicon; preamplification compensating for the low RNA yield from urine and the customized amplicon facilitating absolute quantification of mRNA and overcoming the inherent limitations of relative quantification widely used in RT-QPCR assays. Herein, we review our discovery and validation of urinary cell mRNAs as noninvasive biomarkers prognostic and diagnostic of acute cellular rejection (ACR) in kidney allografts. We summarize our results reflecting the utility of urinary cell mRNA profiling for predicting reversal of ACR with anti-rejection therapy; differential diagnosis of kidney allograft dysfunction; and noninvasive diagnosis and prognosis of BK virus nephropathy. Messenger RNA profiles associated with human kidney allograft tolerance are also summarized in this review. Altogether, data supporting the idea that urinary cell mRNA profiles are informative of kidney allograft status and tolerance are reviewed in this report.

Prevalence and Risk Factors of Peripheral Arterial Disease Among Thai Dialysis Patients.

Objective: To identify the prevalence and risk factors of peripheral arterial disease (PAD) in dialysis patients covering both hemodialysis and peritoneal dialysis. Material and Method: All consecutive cases of stable dialysis patients in Ramathibodi hospital from September 2013 to December 2013 were surveyed. Patients were classified as having PAD if they had ankle-brachial blood pressure index (ABI) values of ≤0.9 or >1.4. We also measured toe-brachial blood pressure index (TBI) and TBI ≤0.6 was classified as abnormal TBI. Data were analyzed to identify the prevalence and risk factors of PAD. Results: Among these 269 stable dialysis patients, the mean age was 48.8±15.1 years and 56.9% were male. The mean dialysis vintage was 52.6±41.8 months. The prevalence of PAD was 11.5% and the prevalence of abnormal TBI was 29.7%. Multivariate regression analysis found that increased body mass index (BMI), history of coronary artery disease (CAD), and increased pulse pressure were associated with PAD. Conclusion: The prevalence of PAD among long-term stable dialysis patients in Thailand was around one-tenth. The prevalence of abnormal TBI was higher than those of abnormal ABI criteria. Factors associated with PAD were increased BMI, history of CAD, and increased pulse pressure.