Genovez, Jose L. Zabala

Abstract Background Serum chloride derangement is common in critically ill patients requiring continuous renal replacement therapy (CRRT). We aimed to assess the association between serum chloride levels before and during CRRT with mortality. Methods This is a retrospective cohort study of critically ill patients receiving CRRT for acute kidney injury from December 2006 through November 2015 in a tertiary referral hospital in the United States. We used logistic regression to assess serum chloride before and mean serum chloride during CRRT as predictors for 90 days mortality after CRRT initiation. The normal reference range for serum chloride was 99–108 mmol/L. Results Of 1282 eligible patients, 25%, 50%, and 25% had hypochloremia, normochloremia, and hyperchloremia, respectively. The adjusted odds ratio for 90 days mortality in patients with hypochloremia before CRRT was 1.82 (95% CI 1.29–2.55). During CRRT, 4%, 70%, 26% of patients had mean serum chloride in the hypochloremia, normochloremia, and hyperchloremia range, respectively. The adjusted odds ratio for 90 days mortality in patients with mean serum chloride during CRRT in the hypochloremia range was 2.96 (95% CI 1.43–6.12). Hyperchloremia before and during CRRT was not associated with mortality. The greater serum chloride range during CRRT was associated with increased mortality (OR 1.29; 95% CI 1.13–1.47 per 5 mmol/L increase). Conclusion Hypochloremia before and during CRRT is associated with higher mortality.

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.

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.