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Suitability of administrative claims databases for bariatric surgery research - is the glass half-full or half-empty?

ABSTRACT: BACKGROUND:Claims databases are generally considered inadequate for obesity research due to suboptimal capture of body mass index (BMI) measurements. This might not be true for bariatric surgery because of reimbursement requirements and changes in coding systems. We assessed the availability and validity of claims-based weight-related diagnosis codes among bariatric surgery patients. METHODS:We identified three nested retrospective cohorts of adult bariatric surgery patients who underwent adjusted gastric banding, Roux-en-Y gastric bypass, or sleeve gastrectomy between January 1, 2011 and June 30, 2018 using different components of OptumLabs® Data Warehouse, which contains linked de-identified claims and electronic health records (EHRs). We measured the availability of claims-based weight-related diagnosis codes in the 6-month preoperative and 1-year postoperative periods in the main cohort identified in the claims data. We created two claims-based algorithms to classify the presence of severe obesity (a commonly used cohort selection criterion) and categorize BMI (a commonly used baseline confounder or postoperative outcome). We evaluated their performance by estimating sensitivity, specificity, positive predictive value, negative predictive value, and weighted kappa in two sub-cohorts using EHR-based BMI measurements as the reference. RESULTS:Among the 29,357 eligible patients identified using claims only, 28,828 (98.2%) had preoperative weight-related diagnosis codes, either granular indicating BMI ranges or nonspecific denoting obesity status. Among the 27,407 patients with granular preoperative codes, 12,346 (45.0%) had granular codes and 9355 (34.1%) had nonspecific codes in the 1-year postoperative period. Among the 3045 patients with both preoperative claims-based diagnosis codes and EHR-based BMI measurements, the severe obesity classification algorithm had a sensitivity 100%, specificity 71%, positive predictive value 100%, and negative predictive value 78%. The BMI categorization algorithm had good validity categorizing the last available preoperative or postoperative BMI measurements (weighted kappa [95% confidence interval]: preoperative 0.78, [0.76, 0.79]; postoperative 0.84, [0.80, 0.87]). CONCLUSIONS:Claims-based weight-related diagnosis codes had excellent validity before and after bariatric surgical operation but suboptimal availability after operation. Claims databases can be used for bariatric surgery studies of non-weight-related effectiveness and safety outcomes that are well-captured.

SUBMITTER: Li X

PROVIDER: S-EPMC7487952 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Suitability of administrative claims databases for bariatric surgery research - is the glass half-full or half-empty?

Li Xiaojuan X Lewis Kristina H KH Callaway Katherine K Wharam J Frank JF Toh Sengwee S

BMC medical research methodology 20200907 1

<h4>Background</h4>Claims databases are generally considered inadequate for obesity research due to suboptimal capture of body mass index (BMI) measurements. This might not be true for bariatric surgery because of reimbursement requirements and changes in coding systems. We assessed the availability and validity of claims-based weight-related diagnosis codes among bariatric surgery patients.<h4>Methods</h4>We identified three nested retrospective cohorts of adult bariatric surgery patients who u ...[more]

PMID: 32894060

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Project description:BackgroundThe supplementation of electronic health records data with administrative claims data may be used to capture outcome events more comprehensively in longitudinal observational studies. This study investigated the utility of administrative claims data to identify outcomes across health systems using a comparative effectiveness study of different types of bariatric surgery as a model.MethodsThis observational cohort study identified patients who had bariatric surgery between 2007 and 2015 within the HealthCore Anthem Research Network (HCARN) database in the National Patient-Centered Clinical Research Network (PCORnet) common data model. Patients whose procedures were performed in a member facility affiliated with PCORnet Clinical Research Networks (CRNs) were selected. The outcomes included a 30-day composite adverse event (including venous thromboembolism, percutaneous/operative intervention, failure to discharge and death), and all-cause hospitalization, abdominal operation or intervention, and in-hospital death up to 5 years after the procedure. Outcomes were classified as occurring within or outside PCORnet CRN health systems using facility identifiers.ResultsWe identified 4899 patients who had bariatric surgery in one of the PCORnet CRN health systems. For 30-day composite adverse event, the inclusion of HCARN multi-site claims data marginally increased the incidence rate based only on HCARN single-site claims data for PCORnet CRNs from 3.9 to 4.2%. During the 5-year follow-up period, 56.8% of all-cause hospitalizations, 31.2% abdominal operations or interventions, and 32.3% of in-hospital deaths occurred outside PCORnet CRNs. Incidence rates (events per 100 patient-years) were significantly lower when based on claims from a single PCORnet CRN only compared to using claims from all health systems in the HCARN: all-cause hospitalization, 11.0 (95% Confidence Internal [CI]: 10.4, 11.6) to 25.3 (95% CI: 24.4, 26.3); abdominal operations or interventions, 4.2 (95% CI: 3.9, 4.6) to 6.1 (95% CI: 5.7, 6.6); in-hospital death, 0.2 (95% CI: 0.11, 0.27) to 0.3 (95% CI: 0.19, 0.38).ConclusionsShort-term inclusion of multi-site claims data only marginally increased the incidence rate computed from single-site claims data alone. Longer-term follow up captured a notable number of events outside of PCORnet CRNs. The findings suggest that supplementing claims data improves the outcome ascertainment in longitudinal observational comparative effectiveness studies.

Project description:BackgroundConfounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database.MethodsData from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics® Extended DataMart (Optum) and additionally validated in three external IBM MarketScan® databases. The model was further validated in the Optum database across a range of patient cohorts.ResultsIn the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79).ConclusionsA prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers.

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Suitability of administrative claims databases for bariatric surgery research - is the glass half-full or half-empty?

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Suitability of administrative claims databases for bariatric surgery research - is the glass half-full or half-empty?

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