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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.

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.

Summary Background Hospitalized patients with hyperkalemia are heterogeneous, and cluster approaches may identify specific homogenous groups. This study aimed to cluster patients with hyperkalemia on admission using unsupervised machine learning (ML) consensus clustering approach, and to compare characteristics and outcomes among these distinct clusters. Methods Consensus cluster analysis was performed in 5133 hospitalized adult patients with admission hyperkalemia, based on available clinical and laboratory data. The standardized mean difference was used to identify each cluster’s key clinical features. The association of hyperkalemia clusters with hospital and 1-year mortality was assessed using logistic and Cox proportional hazard regression. Results Three distinct clusters of hyperkalemia patients were identified using consensus cluster analysis: 1661 (32%) in cluster 1, 2455 (48%) in cluster 2 and 1017 (20%) in cluster 3. Cluster 1 was mainly characterized by older age, higher serum chloride and acute kidney injury (AKI), but lower estimated glomerular filtration rate (eGFR), serum bicarbonate and hemoglobin. Cluster 2 was mainly characterized by higher eGFR, serum bicarbonate and hemoglobin, but lower comorbidity burden, serum potassium and AKI. Cluster 3 was mainly characterized by higher comorbidity burden, particularly diabetes and end-stage kidney disease, AKI, serum potassium, anion gap, but lower eGFR, serum sodium, chloride and bicarbonate. Hospital and 1-year mortality risk was significantly different among the three identified clusters, with highest mortality in cluster 3, followed by cluster 1 and then cluster 2. Conclusion In a heterogeneous cohort of hyperkalemia patients, three distinct clusters were identified using unsupervised ML. These three clusters had different clinical characteristics and outcomes.

Background: Lower patient survival has been observed in sickle cell disease (SCD) patients who go on to receive a kidney transplant. This study aimed to assess the post-transplant outcomes of SCD kidney transplant recipients in the contemporary era. Methods: We used the OPTN/UNOS database to identify first-time kidney transplant recipients from 2010 through 2019. We compared patient and allograft survival between recipients with SCD (n = 105) vs. all other diagnoses (non-SCD, n = 146,325) as the reported cause of end-stage kidney disease. We examined whether post-transplant outcomes improved among SCD in the recent era (2010–2019), compared to the early era (2000–2009). Results: After adjusting for differences in baseline characteristics, SCD was significantly associated with lower patient survival (HR 2.87; 95% CI 1.75–4.68) and death-censored graft survival (HR 1.98; 95% CI 1.30–3.01), compared to non-SCD recipients. The lower patient survival and death-censored graft survival in SCD recipients were consistently observed in comparison to outcomes of recipients with diabetes, glomerular disease, and hypertension as the cause of end-stage kidney disease. There was no significant difference in death censored graft survival (HR 0.99; 95% CI 0.51–1.73, p = 0.98) and patient survival (HR 0.93; 95% CI 0.50–1.74, p = 0.82) of SCD recipients in the recent versus early era. Conclusions: Patient and allograft survival in SCD kidney recipients were worse than recipients with other diagnoses. Overall SCD patient and allograft outcomes in the recent era did not improve from the early era. The findings of our study should not discourage kidney transplantation for ESKD patients with SCD due to a known survival benefit of transplantation compared with remaining on dialysis. Urgent future studies are needed to identify strategies to improve patient and allograft survival in SCD kidney recipients. In addition, it may be reasonable to assign risk adjustment for SCD patients.

AbstractBackgroundThis study aimed to assess outcomes of delivery hospitalizations, including acute kidney injury (AKI), obstetric and foetal events and resource utilization among pregnant women with kidney transplants compared with pregnant women with no known kidney disease and those with chronic kidney disease (CKD) Stages 3–5.MethodHospitalizations for delivery in the US were identified using the enhanced delivery identification method in the National Inpatient Sample dataset from the years 2009 to 2014. Diagnoses of CKD Stages 3–5, kidney transplantation, along with obstetric events, delivery methods and foetal events were identified using ICD‐9‐CM diagnosis and procedure codes. Patients with no known kidney disease group were identified by excluding any diagnoses of CKD, end stage kidney disease, and kidney transplant. Multivariable logistic regression accounting for the survey weights and matched regression was conducted to investigate the risk of maternal and foetal complications in women with kidney transplants, compared with women with no kidney transplants and no known kidney disease, and to women with CKD Stages 3–5.ResultA total of 5, 408, 215 hospitalizations resulting in deliveries were identified from 2009 to 2014, including 405 women with CKD Stages 3–5, 295 women with functioning kidney transplants, and 5, 405, 499 women with no known kidney disease. Compared with pregnant women with no known kidney disease, pregnant kidney transplant recipients were at higher odds of hypertensive disorders of pregnancy (OR = 3.11, 95% CI [2.26, 4.28]), preeclampsia/eclampsia/HELLP syndrome (OR = 3.42, 95% CI [2.54, 4.60]), preterm delivery (OR = 2.46, 95% CI [1.75, 3.45]), foetal growth restriction (OR = 1.74, 95% CI [1.01, 3.00]) and AKI (OR = 10.46, 95% CI [5.33, 20.56]). There were no significant differences in rates of gestational diabetes or caesarean section. Pregnant women with kidney transplants had 1.30‐times longer lengths of stay and 1.28‐times higher costs of hospitalization. However, pregnant women with CKD Stages 3–5 were at higher odds of AKI (OR = 5.29, 95% CI [2.41, 11.59]), preeclampsia/eclampsia/HELLP syndrome (OR = 1.72, 95% CI [1.07, 2.76]) and foetal deaths (OR = 3.20, 95% CI [1.06, 10.24]), and had 1.28‐times longer hospital stays and 1.37‐times higher costs of hospitalization compared with pregnant women with kidney transplant.ConclusionPregnant women with kidney transplant were more likely to experience adverse events during delivery and had longer lengths of stay and higher total charges when compared with women with no known kidney disease. However, pregnant women with moderate to severe CKD were more likely to experience serious complications than kidney transplant recipients.

BACKGROUND: Idiopathic recurrent pericarditis (IRP) is a debilitating condition that can be recalcitrant to conventional therapy. Some patients develop steroid dependency with the attendant risks of systemic side effects and increased future recurrence. Anakinra is a recombinant human interleukin-1 receptor antagonist that reduces systemic inflammatory responses. The aim of this study was to evaluate the therapeutic role of anakinra in IRP. Methods: We retrospectively studied consenting patients with treatment refractory IRP who received anakinra, between January 2009 and November 2013. The primary end points were symptom resolution and steroid discontinuation. Results: Nine patients were followed for a median of 16.8 (IQR 1.3-24) months. Study subjects were predominantly female (7 [78%]) with a median age of 53 (IQR 38 - 58) years. All 9 patients had failed maximum tolerated doses of NSAIDs, colchicine and prednisone (median dose 20 mg [IQR 15 - 22.5]). Primary symptom was chest pain in 9 (100%), with concurrent dyspnea in 5 (56%) patients. Symptom duration prior to anakinra initiation was 24 (IQR 13 - 71) months. Indication for anakinra was steroid sparing agent in 7 (78%) and symptom control in 2 (22%) patients. The dosage was 100 mg once daily via subcutaneous injection. Baseline LVEF was 62% (IQR 59% - 67%). Echocardiographic findings include pericardial effusion in 3 (33%), pericardial thickening in 6 (67%), and constrictive physiology in 4 (44%). Pericardial enhancement on cardiac MRI was seen in 7 (78%) patients. All 9 patients (100%) had symptom improvement with complete resolution in 8 (89%) and partial resolution in 1 (11%) patient. At last follow up, all 9 (100%) patients had discontinued NSAIDs and colchicine, 5 (56%) patients discontinued and 4 (44%) had reduced prednisone dosage. All patients remained on anakinra at the end of follow up. The only reported side effect was transient injection site reaction in 4 (44%) patients. In 2 (22%) patients, attempted anakinra weaning was unsuccessful due to symptomatic flare after 6 weeks. Conclusion: Anakinra is an effective alternative agent for the management of steroid dependent IRP. It provides remarkable symptomatic amelioration, limits steroid dependency, and is associated with minimal side effects.

Very-low-carbohydrate diets or ketogenic diets are frequently used for weight loss in adults and as a therapy for epilepsy in children. The incidence and characteristics of kidney stones in patients on ketogenic diets are not well studied. Methods: A systematic literature search was performed, using MEDLINE, EMBASE, and Cochrane Database of Systematic Reviews from the databases’ inception through April 2020. Observational studies or clinical trials that provide data on the incidence and/or types of kidney stones in patients on ketogenic diets were included. We applied a random-effects model to estimate the incidence of kidney stones. Results: A total of 36 studies with 2795 patients on ketogenic diets were enrolled. The estimated pooled incidence of kidney stones was 5.9% (95% CI, 4.6–7.6%, I2 = 47%) in patients on ketogenic diets at a mean follow-up time of 3.7 +/− 2.9 years. Subgroup analyses demonstrated the estimated pooled incidence of kidney stones of 5.8% (95% CI, 4.4–7.5%, I2 = 49%) in children and 7.9% (95% CI, 2.8–20.1%, I2 = 29%) in adults, respectively. Within reported studies, 48.7% (95% CI, 33.2–64.6%) of kidney stones were uric stones, 36.5% (95% CI, 10.6–73.6%) were calcium-based (CaOx/CaP) stones, and 27.8% (95% CI, 12.1–51.9%) were mixed uric acid and calcium-based stones, respectively. Conclusions: The estimated incidence of kidney stones in patients on ketogenic diets is 5.9%. Its incidence is approximately 5.8% in children and 7.9% in adults. Uric acid stones are the most prevalent kidney stones in patients on ketogenic diets followed by calcium-based stones. These findings may impact the prevention and clinical management of kidney stones in patients on ketogenic diets.