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Bias Implications of Outcome Misclassification in Observational Studies Evaluating Association Between Treatments and All-Cause or Cardiovascular Mortality Using Administrative Claims.

ABSTRACT: Background The bias implications of outcome misclassification arising from imperfect capture of mortality in claims-based studies are not well understood. Methods and Results We identified 2 cohorts of patients: (1) type 2 diabetes mellitus (n=8.6 million), and (2) heart failure (n=3.1 million), from Medicare claims (2012-2016). Within the 2 cohorts, mortality was identified from claims using the following approaches: (1) all-place all-cause mortality, (2) in-hospital all-cause mortality, (3) all-place cardiovascular mortality (based on diagnosis codes for a major cardiovascular event within 30 days of death date), or (4) in-hospital cardiovascular mortality, and compared against National Death Index identified mortality. Empirically identified sensitivity and specificity based on observed values in the 2 cohorts were used to conduct Monte Carlo simulations for treatment effect estimation under differential and nondifferential misclassification scenarios. From National Death Index, 1 544 805 deaths (549 996 [35.6%] cardiovascular deaths) in the type 2 diabetes mellitus cohort and 1 175 202 deaths (523 430 [44.5%] cardiovascular deaths) in the heart failure cohort were included. Sensitivity was 99.997% and 99.207% for the all-place all-cause mortality approach, whereas it was 27.71% and 33.71% for the in-hospital all-cause mortality approach in the type 2 diabetes mellitus and heart failure cohorts, respectively, with perfect positive predicted values. For all-place cardiovascular mortality, sensitivity was 52.01% in the type 2 diabetes mellitus cohort and 53.83% in the heart failure cohort with positive predicted values of 49.98% and 54.45%, respectively. Simulations suggested a possibility for substantial bias in treatment effects. Conclusions Approaches to identify mortality from claims had variable performance compared with the National Death Index. Investigators should anticipate the potential for bias from outcome misclassification when using administrative claims to capture mortality.

SUBMITTER: Desai RJ

PROVIDER: S-EPMC7660765 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Bias Implications of Outcome Misclassification in Observational Studies Evaluating Association Between Treatments and All-Cause or Cardiovascular Mortality Using Administrative Claims.

Desai Rishi J RJ Levin Raisa R Lin Kueiyu Joshua KJ Patorno Elisabetta E

Journal of the American Heart Association 20200826 17

Background The bias implications of outcome misclassification arising from imperfect capture of mortality in claims-based studies are not well understood. Methods and Results We identified 2 cohorts of patients: (1) type 2 diabetes mellitus (n=8.6 million), and (2) heart failure (n=3.1 million), from Medicare claims (2012-2016). Within the 2 cohorts, mortality was identified from claims using the following approaches: (1) all-place all-cause mortality, (2) in-hospital all-cause mortality, (3) al ...[more]

PMID: 32844711

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Project description:BackgroundTo effectively use administrative claims for healthcare research, clinical events must be inferred from coding data according to validated algorithms. In October 2015, the United States transitioned from the International Classification of Diseases Ninth Revision (ICD-9) to the Tenth Revision (ICD-10). We describe our method to derive new ICD-10 codes for outcomes after vascular procedures from our prior, validated ICD-9 codes.MethodsWe began with validated ICD-9 coding lists known to represent outcomes after lower extremity revascularization, thoracic aortic endograft placement, abdominal aortic aneurysm reintervention, and carotid revascularization. We used the publicly available general equivalence mapping tools to derive corresponding ICD-10 codes for each of the ICD-9 codes in our current lists. The resulting lists were then manually reviewed by multiple authors to ensure clinical relevance for appropriate event detection. Clinically nonrelevant and duplicated codes were removed.ResultsA total of 475 ICD-9 codes were translated to ICD-10 with a 98-fold increase (n = 46,630) in the total number of codes. Overall, we found that 77% of codes (n = 35,833) were either duplicated or not clinically relevant upon manual review. For example, for thoracic aortic endograft placement, 97 ICD-9 codes mapped to 14,661 ICD-10 codes in total. A total of 890 codes were removed as duplicates and 9035 codes were removed during manual clinical review. The resultant, reviewed list contained 4736 ICD-10 codes representing a 49-fold increase from the initial ICD-9 list. Findings were similar across the other procedures studied.ConclusionsICD-10 has expanded the number of codes necessary to describe outcomes after vascular procedures. More than 75% of the codes obtained using the general equivalence mapping database were either duplicated or not clinically relevant. Manual review of codes by researchers with clinical knowledge of the procedures is imperative.

Project description:BackgroundThe diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing. However, its use has been limited in the context of an outcome validation study. We considered that wider recognition of the utility of DLR would enhance the practices surrounding database studies. This is particularly timely and important since the use of healthcare-related databases for pharmacoepidemiology research has greatly expanded in recent years. In this paper, we aimed to advance the use of DLR, focusing on the planning of a new database study.MethodsTheoretical frameworks were developed for an outcome validation study and a comparative cohort database study; these two were combined to form the overall relationship. Graphical presentations based on these relationships were used to examine the implications of validation study results on the planning of a database study. Additionally, novel uses of graphical presentations were explored using some examples.ResultsPositive DLR was identified as a pivotal parameter that connects the expected positive-predictive value (PPV) with the disease prevalence in the planned database study, where the positive DLR is equal to sensitivity/(1-specificity). Moreover, positive DLR emerged as a pivotal parameter that links the expected risk ratio with the disease risk of the control group in the planned database study. In one example, graphical presentations based on these relationships provided a transparent and informative summary of multiple validation study results. In another example, the potential use of a graphical presentation was demonstrated in selecting a range of positive DLR values that best represented the relevant validation studies.ConclusionsInclusion of the DLR in the results section of a validation study would benefit potential users of the study results. Furthermore, investigators planning a database study can utilize the DLR to their benefit. Wider recognition of the full utility of the DLR in the context of a validation study would contribute meaningfully to the promotion of good practice in planning, conducting, analyzing, and interpreting database studies.

Project description:BackgroundMajor adverse cardiovascular events (MACE) are increasingly used as composite outcomes in randomized controlled trials (RCTs) and observational studies. However, it is unclear how observational studies most commonly define MACE in the literature when using administrative data.MethodsWe identified peer-reviewed articles published in MEDLINE and EMBASE between January 1, 2010 to October 9, 2020. Studies utilizing administrative data to assess the MACE composite outcome using International Classification of Diseases 9th or 10th Revision diagnosis codes were included. Reviews, abstracts, and studies not providing outcome code definitions were excluded. Data extracted included data source, timeframe, MACE components, code definitions, code positions, and outcome validation.ResultsA total of 920 articles were screened, 412 were retained for full-text review, and 58 were included. Only 8.6% (n = 5/58) matched the traditional three-point MACE RCT definition of acute myocardial infarction (AMI), stroke, or cardiovascular death. None matched four-point (+unstable angina) or five-point MACE (+unstable angina and heart failure). The most common MACE components were: AMI and stroke, 15.5% (n = 9/58); AMI, stroke, and all-cause death, 13.8% (n = 8/58); and AMI, stroke and cardiovascular death 8.6% (n = 5/58). Further, 67% (n = 39/58) did not validate outcomes or cite validation studies. Additionally, 70.7% (n = 41/58) did not report code positions of endpoints, 20.7% (n = 12/58) used the primary position, and 8.6% (n = 5/58) used any position.ConclusionsComponents of MACE endpoints and diagnostic codes used varied widely across observational studies. Variability in the MACE definitions used and information reported across observational studies prohibit the comparison, replication, and aggregation of findings. Studies should transparently report the administrative codes used and code positions, as well as utilize validated outcome definitions when possible.

Dataset Information

Bias Implications of Outcome Misclassification in Observational Studies Evaluating Association Between Treatments and All-Cause or Cardiovascular Mortality Using Administrative Claims.

Publications

Bias Implications of Outcome Misclassification in Observational Studies Evaluating Association Between Treatments and All-Cause or Cardiovascular Mortality Using Administrative Claims.

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