Clustering Based on Laboratory Data in Patients With Heart Failure Admitted to the Intensive Care Unit

Abstract

Background

Heart failure (HF) is a common condition that imposes a significant burden on healthcare systems. We aimed to identify subgroups of patients with heart failure admitted to the ICU using routinely measured laboratory biomarkers.

Methods

A large dataset (N = 1176) of patients with heart failure admitted to the ICU at the Beth Israel Deaconess Medical Center in Boston, USA, between June 1, 2001, and October 31, 2012, was analyzed. We clustered patients to identify laboratory phenotypes. Cluster profiling was then performed to characterize each cluster, using a binary logistic model.

Results

Two distinct clusters of patients were identified (N = 679 and 497). There was a significant difference in the mortality rate between Clusters 1 and 2 (50 [7.4%] vs. 109 [21.9%], respectively, p < 0.001). Patients in the Cluster 2 were significantly older (mean [SD] age = 72.35 [14.40] and 76.37 [11.61] years, p < 0.001) with a higher percentage of chronic kidney disease (167 [24.6%] vs. 262 [52.7%], respectively, p < 0.001). The logistic model was significant (Log-likelihood ratio p < 0.001, pseudo R² = 0.746) with an area under the curve of 0.905. The odds ratio for leucocyte count, mean corpuscular volume (MCV), red blood cell (RBC) distribution width, hematocrit (HcT), lactic acid, blood urea nitrogen (BUN), serum potassium, magnesium, and sodium were significant (all p < 0.05).

Conclusion

Laboratory data revealed two phenotypes of ICU-admitted patients with heart failure. The two phenotypes are of prognostic importance in terms of mortality rate. They can be differentiated using blood cell count, kidney function status, and serum electrolyte concentrations.