Citations

AbstractIntroductionWe aimed to assess the association between serum potassium and mortality in patients receiving continuous renal replacement therapy (CRRT).MethodsWe studied 1279 acute kidney injury patients receiving CRRT in a tertiary referral hospital in the United States. We used logistic regression to assess the association of serum potassium before CRRT and mean serum potassium during CRRT with 90‐day mortality after CRRT initiation, using serum potassium 4.0–4.4 mmol/L as reference group.ResultsBefore CRRT, there was a U‐shaped association between serum potassium and 90‐day mortality. There was a significant increase in mortality when serum potassium before CRRT was ≤3.4 and ≥4.5 mmol/L. During CRRT, progressively increased mortality was noted when mean serum potassium was ≥4.5 mmol/L. The odds ratio of 90‐day mortality was significantly higher when mean serum potassium was ≥4.5 mmol/L.ConclusionHypokalemia and hyperkalemia before CRRT and hyperkalemia during CRRT predicts 90‐day mortality.

ABSTRACT Background Critically ill patients with severe acute kidney injury (AKI) requiring kidney replacement therapy (KRT) have a grim prognosis. Recently, multiple studies focused on the impact of KRT initiation time [i.e., accelerated versus watchful waiting KRT initiation (WWS-KRT)] on patient outcomes. We aim to review the results of all related clinical trials. Methods In this systematic review, we searched all relevant randomized clinical trials from January 2000 to April 2021. We assessed the impacts of accelerated versus WWS-KRT on KRT dependence, KRT-free days, mortality and adverse events, including hypotension, infection, arrhythmia and bleeding. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. Results A total of 4932 critically ill patients with AKI from 10 randomized clinical trials were included in this analysis. The overall 28-day mortality rate was 38.5%. The 28-day KRT-dependence rate was 13.0%. The overall incident of KRT in the accelerated group was 97.4% and 62.8% in the WWS-KRT group. KRT in the accelerated group started 36.7 h earlier than the WWS-KRT group. The two groups had similar risks of 28-day [pooled log odds ratio (OR) 1.001, P = 0.982] and 90-day (OR 0.999, P = 0.991) mortality rates. The accelerated group had a significantly higher risk of 90-day KRT dependence (OR 1.589, P = 0.007), hypotension (OR 1.687, P < 0.001) and infection (OR 1.38, P = 0.04) compared with the WWS-KRT group. Conclusions This meta-analysis revealed that accelerated KRT leads to a higher probability of 90-day KRT dependence and dialysis-related complications without any impact on mortality rate when compared with WWS-KRT. Therefore, we suggest the WWS-KRT strategy for critically ill patients.

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.

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.

Abstract Introduction: Kidneys play a primary role in electrolyte homeostasis. The association between serum sodium level and mortality or the need for kidney replacement therapy during acute kidney injury has not been adequately explored. Methods: In this prospective cohort study, we enrolled patients admitted to the Civil Hospital of Guadalajara from August 2017 to March 2020. We divided patients into five groups based on the serum sodium level trajectories up to ten days following hospitalization, 1) stable normonatremia (serum sodium 135 and 145 mEq/L), 2) fluctuating serum sodium levels (increased/decreased in and out of normonatremia), 3) uncorrected hyponatremia, 4) corrected hyponatremia, and 5) uncorrected hypernatremia. We assessed the association of serum sodium trajectories with mortality and the need for kidney replacement therapy (secondary objective). Results: A total of 288 patients were included. The mean age was 55±18 years, and 175 (60.7%) were male. Acute kidney injury stage 3 was present in 145 (51%). Kidney replacement therapy started in 72 (25%) patients, and 45 (15.6%) died. After adjusting for confounders, 10-day hospital mortality was significantly higher in group 5 (HR, 3.12; 95% CI, 1.05 to 9.24, p = 0.03), and kidney replacement therapy initiation was higher in group 3 (HR, 2.44; 95% CI, 1.04 to 5.70, p = 0.03) compared with group 1. Conclusion: In our prospective cohort, most patients with acute kidney injury had alterations in serum sodium. Uncorrected hypernatremia was associated with death, and uncorrected hyponatremia was correlated with the need for kidney replacement therapy.

AbstractHigh‐dose methotrexate (HDMTX) pharmacokinetics (PKs), including the best estimated glomerular filtration rate (eGFR) equation that reflects methotrexate (MTX) clearance, requires investigation. This prospective, observational, single‐center study evaluated adult patients with lymphoma treated with HDMTX. Samples were collected at predefined time points up to 96 h postinfusion. MTX and 7‐hydroxy‐MTX PKs were estimated by standard noncompartmental analysis. Linear regression determined which serum creatinine‐ or cystatin C‐based eGFR equation best predicted MTX clearance. The 80 included patients had a median (interquartile range [IQR]) age of 68.6 years (IQR 59.2–75.6), 54 (67.5%) were men, and 74 (92.5%) were White. The median (IQR) dose of MTX was 7.6 (IQR 4.8–11.3) grams. Median clearance was similar across three dosing levels at 4.5–5.6 L/h and was consistent with linear PKs. Liver function, weight, age, sex, concomitant chemotherapy, and number of previous MTX doses did not impact clearance. MTX area under the curve (AUC) values varied over a fourfold range and appeared to increase in proportion to the dose. The eGFRcys (ml/min) equation most closely correlated with MTX clearance in both the entire cohort and after excluding outlier MTX clearance values (r = 0.31 and 0.51, respectively). HDMTX as a 4‐h infusion displays high interpatient pharmacokinetic variability. Population PK modeling to optimize MTX AUC attainment requires further evaluation. The cystatin C‐based eGFR equation most closely estimated MTX clearance and should be investigated for dosing and monitoring in adults requiring MTX as part of lymphoma management.