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Agreement Between Administrative Database and Medical Chart Review for the Prediction of Chronic Kidney Disease G
Louise Roy1, Michael Zappitelli2, Brian White-Guay3
1Faculty of Medicine, University of Montreal, University of Montreal Hospital Center, QC, Canada.
Insights
A new algorithm using administrative data accurately identifies severe chronic kidney disease (CKD G4-5ND) in older adults. This method, combining diagnosis codes, drug use, and nephrologist visits, offers a valid approach for real-world research.
Area of Science:
- Nephrology
- Public Health
- Health Informatics
Background:
- Chronic kidney disease (CKD) is a significant health concern and cardiovascular risk factor.
- Accurate identification of CKD using administrative data is crucial for drug benefit and risk assessment in real-world research.
- Existing algorithms for CKD detection have limitations, necessitating the development of improved methods.
Purpose of the Study:
- To validate a predictive algorithm for identifying CKD GFR category 4-5 (CKD G4-5ND) using administrative databases.
- To compare the algorithm's performance against estimated glomerular filtration rate (eGFR) as a reference standard.
Main Methods:
- Retrospective cohort study utilizing administrative databases and chart reviews.
- Development of algorithms based on physician claims, hospital discharge data, specific drug use, and medical interventions.
- Assessment of algorithm validity using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Main Results:
- Algorithm #3, incorporating diagnosis codes, drug use, and nephrologist visits, demonstrated high accuracy in identifying CKD G4-5ND.
- Sensitivity ranged from 82.5% to 89.0%, specificity from 97.1% to 98.9%, with PPV and NPV between 91.1% and 97.7%.
- Subgroup analyses confirmed the algorithm's accuracy in older adults, including those with diabetes and hypertension.
Conclusions:
- The developed algorithm using administrative data is a valid tool for defining severe CKD (CKD G4-5ND) in older adults.
- The findings support the use of this algorithm for real-world research on drug benefits and risks.
- Limitations include a predominantly older cohort, potentially affecting generalizability to all adult populations.
Background:
Chronic kidney disease (CKD) is a major health issue and cardiovascular risk factor. Validity assessment of administrative data for the detection of CKD in research for drug benefit and risk using real-world data is important. Existing algorithms have limitations and we need to develop new algorithms using administrative data, giving the importance of drug benefit/risk ratio in real world.
Objective:
The aim of this study was to validate a predictive algorithm for CKD GFR category 4-5 (eGFR < 30 mL/min/1.73 m2 but not receiving dialysis or CKD G4-5ND) using the administrative databases of the province of Quebec relative to estimated glomerular filtration rate (eGFR) as a reference standard.
Design:
This is a retrospective cohort study using chart collection and administrative databases.
Setting:
The study was conducted in a community outpatient medical clinic and pre-dialysis outpatient clinic in downtown Montreal and rural area.
Patients:
Patient medical files with at least 2 serum creatinine measures (up to 1 year apart) between September 1, 2013, and June 30, 2015, were reviewed consecutively (going back in time from the day we started the study). We excluded patients with end-stage renal disease on dialysis. The study was started in September 2013.
Measurement:
Glomerular filtration rate was estimated using the CKD Epidemiological Collaboration (CKD-EPI) from each patient's file. Several algorithms were developed using 3 administrative databases with different combinations of physician claims (diagnostics and number of visits) and hospital discharge data in the 5 years prior to the cohort entry, as well as specific drug use and medical intervention in preparation for dialysis in the 2 years prior to the cohort entry.
Methods:
Chart data were used to assess eGFR. The validity of various algorithms for detection of CKD groups was assessed with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Results:
A total of 434 medical files were reviewed; mean age of patients was 74.2 ± 10.6 years, and 83% were older than 65 years. Sensitivity of algorithm #3 (diagnosis within 2-5 years and/or specific drug use within 2 years and nephrologist visit ≥4 within 2-5 years) in identification of CKD G4-5ND ranged from 82.5% to 89.0%, specificity from 97.1% to 98.9% with PPV and NPV ranging from 94.5% to 97.7% and 91.1% to 94.2%, respectively. The subsequent subgroup analysis (diabetes, hypertension, and <65 and ≥65 years) and also the comparisons of predicted prevalence in a cohort of older adults relative to published data emphasized the accuracy of our algorithm for patients with severe CKD (CKD G4-5ND).
Limitations:
Our cohort comprised mostly older adults, and results may not be generalizable to all adults. Participants with CKD without 2 serum creatinine measurements up to 1 year apart were excluded.
Conclusions:
The case definition of severe CKD G4-5ND derived from an algorithm using diagnosis code, drug use, and nephrologist visits from administrative databases is a valid algorithm compared with medical chart reviews in older adults.
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