Dataset Information

Validation of obesity-related diagnosis codes in claims data.

ABSTRACT:

Aim

To determine whether body mass index (BMI) can be accurately identified in epidemiological studies using claims databases.

Materials and methods

Using the Mass General Brigham Research Patient Data Repository-Medicare-linked database, we identified a cohort of patients with a BMI measurement for the periods January 1 to June 31, 2014 or January 1 to June 31, 2016, to capture both the International Classification of Disease (ICD)-9 and ICD-10 eras. Patients were divided into two groups, with or without an obesity-related ICD code in the 6 months before or after the BMI measurement date. We created two binary measures, first for composite overweight, obesity, or severe obesity (BMI ≥25 kg/m² ), and second for obesity or severe obesity (BMI ≥30 kg/m² ). We calculated accuracy measures (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) for each obesity category for the overall cohort, and stratified by type 2 diabetes and ICD-code era.

Results

The cohort included 73 644 patients with a BMI measurement in 2014 or 2016, of whom 16 280 had an obesity-related ICD code. The specificity of obesity-related ICD codes (ICD-9 and ICD-10) was 99.7% for underweight/normal weight, 97.4% for overweight, 99.7% for obese and 98.9% for severely obese. For binary categories capturing BMI ≥25 kg/m² and BMI ≥30 kg/m² , specificity was 97.0% and 98.2%, and PPV was 86.9% and 97.3%. Sensitivity was low overall (<40%). Codes for patients with type 2 diabetes and codes in the ICD-10 era had higher sensitivity, PPV and NPV.

Conclusion

Obesity-related ICD codes can accurately identify patients with obesity in epidemiological studies using claims databases.

SUBMITTER: Suissa K

PROVIDER: S-EPMC8578343 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Validation of obesity-related diagnosis codes in claims data.

Suissa Karine K Schneeweiss Sebastian S Lin Kueiyu Joshua KJ Brill Gregory G Kim Seoyoung C SC Patorno Elisabetta E

Diabetes, obesity & metabolism 20210818 12

<h4>Aim</h4>To determine whether body mass index (BMI) can be accurately identified in epidemiological studies using claims databases.<h4>Materials and methods</h4>Using the Mass General Brigham Research Patient Data Repository-Medicare-linked database, we identified a cohort of patients with a BMI measurement for the periods January 1 to June 31, 2014 or January 1 to June 31, 2016, to capture both the International Classification of Disease (ICD)-9 and ICD-10 eras. Patients were divided into tw ...[more]

PMID: 34338404

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Project description:Purpose: Previous studies identified the study cohort or outcome of carbon monoxide poisoning (COP) by using the relevant disease diagnosis codes in secondary databases, but the validity of diagnosis codes of COP is unclear in such secondary databases. This study aimed to evaluate the accuracy of case definitions for COP using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in Taiwan’s health insurance claims data. Methods: We selected a 10% simple random sample from an original cohort of patients newly undergoing carboxyhemoglobin (COHb) testing under any clinical diagnosis at four Chang Gung Memorial Hospitals in Taiwan during 2011–2020. Two clinical doctors independently ascertained the COP diagnosis by reviewing the medical records as the reference standard. We estimated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of various case definitions (e.g., ICD-9-CM code, ICD-10-CM code and the uses of hyperbaric oxygen therapy) in the claims data. Results: We randomly selected 557 cases from the original cohort of 5,571 cases newly receiving COHb test in the study hospitals. We found 90, 35, and 9 cases were true-positive, false-positive, and false-negative episodes of COP, respectively, among 278 cases with an ICD-9-CM code of 986. A further 111, 34, and 6 cases were true-positive, false-positive, and false-negative episodes of COP, respectively, among 279 cases with an ICD-10-CM code of T58. Using ICD-9-CM codes, the sensitivity, specificity, PPV and NPV for COP were 90.9, 80.4, 72, and 94.1%, respectively. Using ICD-10-CM codes they were 94.9, 79, 76.6, and 95.5%, respectively. PPV typically increased when COP was the primary diagnosis and could reach 100% if patients with ICD-CM code 986 or T58 also received hyperbaric oxygen therapy during hospitalization. Conclusion: The COP-related ICD-CM codes alone did not accurately identify COP patients, but accuracy improved after including oxygen therapy data with the ICD-CM codes in Taiwan’s claims data.

Project description:IntroductionTransfusion-related acute lung injury (TRALI), an adverse event occurring during or within 6 hours of transfusion, is a leading cause of transfusion-associated fatalities reported to the US Food and Drug Administration. There is limited information on the validity of diagnosis codes for TRALI recorded in inpatient electronic medical records (EMRs).Study designs and methodsWe conducted a validation study to establish the positive predictive value (PPV) of TRALI International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes recorded within a large hospital system between 2013 and 2015. A physician with critical care expertise confirmed the TRALI diagnosis. As TRALI is likely underdiagnosed, we used the specific code (518.7), and codes for respiratory failure (518.82) in combination with transfusion reaction (999.80, 999.89, E934.7).ResultsAmong almost four million inpatient stays, we identified 208 potential TRALI cases with ICD-9-CM codes and reviewed 195 medical records; 68 (35%) met clinical definitions for TRALI (26 [38%] definitive, 15 [22%] possible, 27 [40%] delayed). Overall, the PPV for all inpatient TRALI diagnoses was 35% (95% confidence interval (CI), 28-42). The PPV for the TRALI-specific code was 44% (95% CI, 35-54).ConclusionWe observed low PPVs (<50%) for TRALI ICD-9-CM diagnosis codes as validated by medical charts, which may relate to inconsistent code use, incomplete medical records, or other factors. Future studies using TRALI diagnosis codes in EMR databases may consider confirming diagnoses with medical records, assessing TRALI ICD, Tenth Revision, Clinical Modification codes, or exploring alternative ways for of accurately identifying TRALI in EMR databases.Key pointsIn 169 hospitals, we identified 208 potential TRALI cases, reviewed 195 charts, and confirmed 68 (35%) cases met TRALI clinical definitions. As many potential TRALI cases identified with diagnosis codes did not meet clinical definitions, medical record confirmation may be prudent.

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Dataset Information

Validation of obesity-related diagnosis codes in claims data.

Aim

Materials and methods

Results

Conclusion

Publications

Validation of obesity-related diagnosis codes in claims data.

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