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Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital
Jamie L Fleet1, Stephanie N Dixon, Salimah Z Shariff
1Division of Nephrology, Department of Medicine, Western University, London, Canada.
Insights
Administrative healthcare databases can identify chronic kidney disease (CKD) patients without lab results. Algorithms using combined codes showed lower sensitivity in women and older adults, potentially underestimating CKD prevalence.
Area of Science:
- Nephrology
- Public Health
- Health Informatics
Background:
- Administrative healthcare databases offer a method for identifying chronic kidney disease (CKD) patients when laboratory results are unavailable.
- This study validates algorithms using combined hospital encounter and physician claims codes for CKD detection in Ontario, Canada.
Purpose of the Study:
- To assess the validity of algorithms for detecting chronic kidney disease (CKD) in administrative healthcare databases.
- To determine the sensitivity, specificity, and predictive values of these algorithms.
Main Methods:
- A cohort of 123,499 patients aged 65+ from 2007-2010 with baseline serum creatinine was analyzed.
- An algorithm combining physician claims and hospital encounter codes was developed to identify CKD.
- The algorithm's performance was evaluated against an estimated glomerular filtration rate (eGFR) threshold of <45 mL/min per 1.73 m².
Main Results:
- The algorithm identified 7.7% of patients as algorithm-positive for CKD.
- Sensitivity was 32.7%, notably lower in women (25.7%) and individuals over 80 (28.4%).
- Specificities exceeded 94%, with positive and negative predictive values of 65.4% and 88.8%, respectively. Algorithm-positive patients had higher creatinine and lower eGFR.
Conclusions:
- CKD patients identified by the algorithm exhibited significantly higher serum creatinine and lower eGFR values.
- The algorithm's limited sensitivity suggests it may underestimate the true prevalence of CKD in the population.
- These findings highlight the utility and limitations of administrative data for CKD surveillance.
Background:
Large, population-based administrative healthcare databases can be used to identify patients with chronic kidney disease (CKD) when serum creatinine laboratory results are unavailable. We examined the validity of algorithms that used combined hospital encounter and physician claims database codes for the detection of CKD in Ontario, Canada.
Methods:
We accrued 123,499 patients over the age of 65 from 2007 to 2010. All patients had a baseline serum creatinine value to estimate glomerular filtration rate (eGFR). We developed an algorithm of physician claims and hospital encounter codes to search administrative databases for the presence of CKD. We determined the sensitivity, specificity, positive and negative predictive values of this algorithm to detect our primary threshold of CKD, an eGFR <45 mL/min per 1.73 m² (15.4% of patients). We also assessed serum creatinine and eGFR values in patients with and without CKD codes (algorithm positive and negative, respectively).
Results:
Our algorithm required evidence of at least one of eleven CKD codes and 7.7% of patients were algorithm positive. The sensitivity was 32.7% [95% confidence interval: (95% CI): 32.0 to 33.3%]. Sensitivity was lower in women compared to men (25.7 vs. 43.7%; p <0.001) and in the oldest age category (over 80 vs. 66 to 80; 28.4 vs. 37.6 %; p < 0.001). All specificities were over 94%. The positive and negative predictive values were 65.4% (95% CI: 64.4 to 66.3%) and 88.8% (95% CI: 88.6 to 89.0%), respectively. In algorithm positive patients, the median [interquartile range (IQR)] baseline serum creatinine value was 135 μmol/L (106 to 179 μmol/L) compared to 82 μmol/L (69 to 98 μmol/L) for algorithm negative patients. Corresponding eGFR values were 38 mL/min per 1.73 m² (26 to 51 mL/min per 1.73 m²) vs. 69 mL/min per 1.73 m² (56 to 82 mL/min per 1.73 m²), respectively.
Conclusions:
Patients with CKD as identified by our database algorithm had distinctly higher baseline serum creatinine values and lower eGFR values than those without such codes. However, because of limited sensitivity, the prevalence of CKD was underestimated.
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