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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.

SUBMITTER: Roy L

PROVIDER: S-EPMC7549183 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Roy Louise L Zappitelli Michael M White-Guay Brian B Lafrance Jean-Philippe JP Dorais Marc M Perreault Sylvie S

Canadian journal of kidney health and disease 20201010

<h4>Background</h4>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.<h4>Objective</h4>The aim of this study was to validate a predictive algorithm for CKD GF ...[more]

PMID: 33101698

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Project description:BackgroundIn people with severe chronic kidney disease (CKD), there is an inverse relationship between age and kidney failure. If this relationship is the same at any age (linear), one effect (hazard ratio) will be sufficient for accurate risk prediction; if it is nonlinear, the effect will vary with age.ObjectiveTo investigate the relationship between age and kidney failure in adults with category G4 chronic kidney disease (G4 CKD).MethodsWe performed a population-based study using linked administrative databases in Alberta, Canada, to study adults with G4 CKD (estimated glomerular filtration rate [eGFR] = 15-30 mL/min/1.73 m2) and without previously documented eGFR <15 mL/min/1.73 m2 or renal replacement. We used cause-specific Cox regression to model the relationship between age and the hazard of kidney failure (the earlier of eGFR <10 mL/min/1.73 m2 or receipt of renal replacement) and death, incorporating spline terms to capture any nonlinear effect of age. We included sex, diabetes mellitus, cardiovascular disease, albuminuria, and eGFR in all models.ResultsOf the 27 823 participants (97 731 patient-years at risk; mean age = 76 years, ±13), 19% developed kidney failure and 51% died. The decline in the hazard of kidney failure associated with a given increase in age was not constant but became progressively larger as people aged; that is, the hazard ratio became progressively smaller (closer to 0). Assuming an eGFR of 25 mL/min/1.73 m2, for every 10-year increase in age, the hazard ratio declined from 0.76 (95% confidence interval = 0.73-0.79) at age 50 years to 0.43 (95% confidence interval = 34-56) at age 80 years in people without cardiovascular disease, and from 0.75 (95% confidence interval = 0.70-0.79) at age 50 years to 0.36 (95% confidence interval = 0.29-0.45) at age 80 years in people with cardiovascular disease.ConclusionsThe relationship between kidney failure and age varies with age. An age-dependent effect, rather than a constant effect, needs to be specified to accurately predict risk. These findings have implications for risk prediction and advanced care planning.

Project description:BackgroundPopulation health studies often use existing databases that are not necessarily constituted for research purposes. The question arises as to whether different data sources such as in administrative health data (AHD) and self-report questionnaires are equivalent and lead to similar information.ObjectivesThe main objective of this study was to assess the level of agreement between self-reported medical conditions and medical diagnosis captured in AHD. A secondary objective was to identify predictors of agreement among medical conditions between the two data sources. Therefore, the purposes of the study were to explore the extent to which these two methods of commonly used public health data collection provide concordant records and identify the main predictors of statistical variations.MethodsData were extracted from CARTaGENE, a population-based cohort in Québec, Canada, which was linked to the provincial health insurance records of the same individuals, namely the MED-ÉCHO database from the Régie de l'assurance maladie du Québec (RAMQ) and the fee-for-service billing records provided by the physician, for the time period 1998-2012. Agreement statistics (kappa coefficient) along with sensitivity, specificity and predictive positive value were calculated for 19 chronic conditions and 12 types of cancers. Logistic regressions were used to identify predictors of concordance between self-report and AHD from significant covariates (sex, age groups, education, region, income, heavy utilization of health care system and Charlson comorbidity index).ResultsAgreement between self-reported data and AHD across diseases ranged from kappa of 0.09 for chronic renal failure to 0.86 for type 2 diabetes. Sensitivity of self-reported data was higher than 50% for 14 out of the 31 medical conditions studied, especially for myocardial infarction (88.62%), breast cancer (86.28%), and diabetes (85.06%). Specificity was generally high with a minimum value of 89.70%. Lower concordance between data sources was observed for higher frequency of health care utilization and higher comorbidity scores.ConclusionOverall, there was moderate agreement between the two data sources but important variations were found depending on the type of disease. This suggests that CARTaGENE's participants were generally able to correctly identify the kind of diseases they suffer from, with some exceptions. These results may help researchers choose adequate data sources according to specific study objectives. These results also suggest that Québec's AHD seem to underestimate the prevalence of some chronic conditions, which might result in inaccurate estimates of morbidity with consequences for public health surveillance.

Project description:BACKGROUND:High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups. METHODS:The index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups' persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history. RESULTS:A total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment. CONCLUSION:HUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.

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

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

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