Kashani, Kianoush

Background: Acute kidney injury (AKI) survivors are at heightened risk for poor short- and long-term health outcomes. Even among those who recover after an AKI episode, the risk for chronic kidney disease is 4- to 6-fold higher than in patients without AKI, underscoring the importance of identifying methods to improve AKI survivorship. Objective: The purpose of this report was to describe the development and feasibility of a novel multidisciplinary approach to caring for AKI survivors at care transitions (ACT). Design: Observational process improvement initiative. Setting: Single academic medical center in the United States. Patients: The studied population was adults with stage 3 AKI not discharging on dialysis who were established with a primary care provider (PCP) at our institution. Methods: An electronic health record tool was developed prior to implementation to identify AKI survivors. The ACT program encompassed engaging patients in the hospital, delivering education by nephrology-trained nurses before discharge, completing recommended laboratory testing after discharge, and conducting structured kidney-focused follow-up with a pharmacist and a PCP within 7 to 14 days after discharge. Patients could be referred for nephrology evaluation at the discretion of the PCP. Results: Preliminary data demonstrated that most AKI survivors of interest could be identified, educated, and followed up with this model. This strategy appeared feasible, scalable, and maximized the unique expertise of each member of the multidisciplinary team. Limitations: Small sample size, future assessment of process, clinical, and patient-reported outcomes needed. Conclusions: The multidisciplinary ACT workflow supported by clinical decision support was feasible and addressed gaps in existing care transition models. Team-based care delivery in primary care appears to be a mechanism to extend the capacity for kidney health monitoring for AKI survivors.

AbstractBackgroundProposed phenotypes have recently been identified in cardiogenic shock (CS) populations using unsupervised machine learning clustering methods. We sought to validate these phenotypes in a mixed cardiac intensive care unit (CICU) population of patients with CS.MethodsWe included Mayo Clinic CICU patients admitted from 2007 to 2018 with CS. Agnostic K means clustering was used to assign patients to three clusters based on admission values of estimated glomerular filtration rate, bicarbonate, alanine aminotransferase, lactate, platelets, and white blood cell count. In‐hospital mortality and 1‐year mortality were analyzed using logistic regression and Cox proportional‐hazards models, respectively.ResultsWe included 1498 CS patients with a mean age of 67.8 ± 13.9 years, and 37.1% were females. The acute coronary syndrome was present in 57.3%, and cardiac arrest was present in 34.0%. Patients were assigned to clusters as follows: Cluster 1 (noncongested), 603 (40.2%); Cluster 2 (cardiorenal), 452 (30.2%); and Cluster 3 (hemometabolic), 443 (29.6%). Clinical, laboratory, and echocardiographic characteristics differed across clusters, with the greatest illness severity in Cluster 3. Cluster assignment was associated with in‐hospital mortality across subgroups. In‐hospital mortality was higher in Cluster 3 (adjusted odds ratio [OR]: 2.6 vs. Cluster 1 and adjusted OR: 2.0 vs. Cluster 2, both p < 0.001). Adjusted 1‐year mortality was incrementally higher in Cluster 3 versus Cluster 2 versus Cluster 1 (all p < 0.01).ConclusionsWe observed similar phenotypes in CICU patients with CS as previously reported, identifying a gradient in both in‐hospital and 1‐year mortality by cluster. Identifying these clinical phenotypes can improve mortality risk stratification for CS patients beyond standard measures.

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 &lt; 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.