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Open Access Research article

Predicting hospital cost in CKD patients through blood chemistry values

Russell W Bessette1* and Randy L Carter2

Author Affiliations

1 University of Louisville, Abell Administration Center, 323 East Chestnut St. Louisville, Kentucky 40202, USA

2 State University of New York at Buffalo, School of Public Health, Department of Biostatistics and Population Observatory, Farber Hall, Main St, Buffalo, New York 14214, USA

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BMC Nephrology 2011, 12:65  doi:10.1186/1471-2369-12-65

Published: 1 December 2011



Controversy exists in predicting costly hospitalization in patients with chronic kidney disease and co-morbid conditions. We therefore tested associations between serum chemistry values and the occurrence of in-patient hospital costs over a thirteen month study period. Secondarily, we derived a linear combination of variables to estimate probability of such occurrences in any patient.


We calculated parsimonious values for select variables associated with in-patient hospitalization and compared sensitivity and specificity of these models to ordinal staging of renal disease.

Data from 1104 de-identified patients which included 18 blood chemistry observations along with complete claims data for all medical expenses.

We employed multivariable logistic regression for serum chemistry values significantly associated with in-patient hospital costs exceeding $3,000 in any single month and contrasted those results to other models by ROC area curves.


The linear combination of weighted Z scores for parathyroid hormone, phosphorus, and albumin correlated with in-patient hospital care at p < 0.005. ROC curves derived from weighted variables of age, eGFR, hemoglobin, albumin, creatinine, and alanine aminotransferase demonstrated significance over models based on non-weighted Z scores for those same variables or CKD stage alone. In contrast, the linear combination of weighted PTH, PO4 and albumin demonstrated better prediction, but not significance over non-weighted Z scores for PTH alone.


Further study is justified to explore indices that predict costly hospitalization. Such metrics could assist Accountable Care Organizations in evaluating risk adjusted compensation for providers.