Renal function and healthcare costs in patients with polycystic kidney disease.
OBJECTIVE
Characterizing relationships of kidney function to healthcare costs in polycystic kidney disease has applications for economic evaluations of standard and emerging therapies.


METHODS
The administrative records (2003 to 2006) of a private health insurer were examined to identify polycystic kidney disease patients (n = 1913) from ICD9 diagnosis codes on billing claims. The first available diagnostic claim was assumed as an index date, and baseline estimated GFR (eGFR) was computed using closest serum creatinine value. The associations of eGFR with annualized charges were modeled by nonlinear and linear regression.


RESULTS
Medical, pharmacy, and total healthcare costs varied significantly by baseline kidney function, such that mean total annualized charges (unadjusted) were approximately 5-fold higher in patients with eGFR < 15 ml/min compared with those with eGFR >or= 90 ml/min. After adjustment for age and gender, total charges did not differ significantly among patients with eGFR > 30 ml/min, and but rose precipitously with eGFR < 30 ml/min. Each ml/min decline <30 ml/min predicted approximately $5435 higher adjusted annual charges. Results were similar after adjustment for baseline diabetes and cardiovascular disease as identified in claims, while significantly higher adjusted charges were detected with eGFR = 31 to 60 ml/min versus >or=90 ml/min in a subgroup free of diabetes and cardiovascular disease.


CONCLUSIONS
Healthcare charges are associated with advanced renal dysfunction in polycystic kidney disease patients. Strategies that prevent loss of renal function below 30 ml/min have the potential to generate substantial reductions in medical charges.
Lentine
Krista L
KL
Center for Outcomes Research, Saint Louis University School of Medicine, St. Louis, Missouri 63104, USA. lentinek@slu.edu
Xiao
Huiling
H
Machnicki
Gerardo
G
Gheorghian
Adrian
A
Schnitzler
Mark A
MA
D000328:Adult; D000553:Ambulatory Care / Q000191:economics; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D003404:Creatinine / Q000097:blood; D016527:Drug Costs; D005260:Female; D005919:Glomerular Filtration Rate; D017048:Health Care Costs; D017721:Hospital Costs; D006801:Humans; D007668:Kidney / Q000503:physiopathology*; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D018803:Models, Economic; D017711:Nonlinear Dynamics; D007690:Polycystic Kidney Diseases / Q000097:blood / Q000191:economics* / Q000503:physiopathology* / Q000628:therapy; D017149:Private Sector / Q000191:economics*; D012189:Retrospective Studies; D012720:Severity of Illness Index; D014481:United States
D015415:Biomarkers; D003404:Creatinine