Agreement Between Administrative Database and Medical Chart Review for the Prediction of Chronic Kidney Disease G category

Abstract

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.

Comparative Effectiveness and Safety of Direct Oral Anticoagulants vs Warfarin Among Obese Patients With Atrial Fibrillation

“…Comorbidities were determined using inpatients’ and outpatients’ ICD-9 and ICD-10 diagnoses occurring in the 3 years preceding the index date. 14 , 18 , 20 Patients’ demographic characteristics and comorbidities were used to evaluate the CHADS 2 ( C ongestive Heart Failure, H ypertension, A ge ≥ 75, D iabetes, and Prior S troke/Transient Ischemic Attack) score ( Supplemental Tables S2 and S3 ), the CHA 2 DS 2 -VASc ( C ongestive Heart Failure, H ypertension, A ge [≥ 75 Years], D iabetes Mellitus, S troke, V ascular Disease, A ge [65-74] Years, S ex C ategory [Female]) score ( Supplemental Tables S2 and S4 ) and the modified HAS-BLED ( H ypertension, A bnormal Renal/Liver Function, S troke, B leeding History or Predisposition, L abile INR, E lderly [> 65 Years], D rugs/Alcohol Concomitantly) score ( Supplemental Tables S2 and S5 ). The Charlson Comorbidity Index was also calculated for each patient.…”

“…Comorbidities were determined using inpatients and outpatients ICD-9 and ICD-10 diagnoses occurring in the 3 years preceding the index date. (15,19,21) Patients' demographic characteristics and comorbidities were used to evaluate the CHADS2 score (Tables S2, S3), the CHAD2DSs-VASc score (Tables S2, S4) and the modified HAS-BLED score (Tables S2, S5). The Charlson comorbidity index was also calculated for each patient.…”

Predicting major bleeding among hospitalized patients using oral anticoagulants for atrial fibrillation after discharge

“…Associated morbidities were assessed up to 3 years prior to cohort entry. They included stroke/transient ischemic attack, hypertension, dyslipidemia, cardiomyopathy, coronary artery disease, acute myocardial infarction, peripheral vascular disease (PVD), chronic heart failure, anemia, chronic kidney disease (CKD), severe kidney disease (creatinine clearance < 30 ml /min), acute renal failure, liver disease, diabetes mellitus, asthma and chronic obstructive pulmonary disease (COPD), history of MB, and prior Helicobacter Pylori infection [ 40 , 50 , 51 ]. The CHA 2 DS 2 -VASc score (stroke risk), a modified HAS-BLED (bleeding risk) excluding labile INR, and the Charlson-Deyo comorbidity index, were assessed up to 3-years prior to cohort entry ( S2 and S3 Tables for coding algorithms).…”

Comparative Effectiveness and Safety of Low-Dose Oral Anticoagulants in Patients With Atrial Fibrillation

“…Social and economic deprivation was assessed using the Pampalon index (Pampalon et al, 2009). We determined the presence of comorbidities from specific ICD-9 or ICD-10 codes recorded during the hospital stay and those recorded for inpatient and outpatient diagnoses during the 3 years prior to the index date (Blais et al, 2012;Roy et al, 2020). Using the data on patient characteristics and associated comorbidities, we then assessed the CHA 2 DS 2 -VASc score (Supplementary Tables S2, S3), the modified HAS-BLED score (Supplementary Tables S2, S4) (Lip et al, 2010;Friberg et al, 2012;Pisters et al, 2010).…”