Dataset Information

ClinicalCodes: an online clinical codes repository to improve the validity and reproducibility of research using electronic medical records.

ABSTRACT: Lists of clinical codes are the foundation for research undertaken using electronic medical records (EMRs). If clinical code lists are not available, reviewers are unable to determine the validity of research, full study replication is impossible, researchers are unable to make effective comparisons between studies, and the construction of new code lists is subject to much duplication of effort. Despite this, the publication of clinical codes is rarely if ever a requirement for obtaining grants, validating protocols, or publishing research. In a representative sample of 450 EMR primary research articles indexed on PubMed, we found that only 19 (5.1%) were accompanied by a full set of published clinical codes and 32 (8.6%) stated that code lists were available on request. To help address these problems, we have built an online repository where researchers using EMRs can upload and download lists of clinical codes. The repository will enable clinical researchers to better validate EMR studies, build on previous code lists and compare disease definitions across studies. It will also assist health informaticians in replicating database studies, tracking changes in disease definitions or clinical coding practice through time and sharing clinical code information across platforms and data sources as research objects.

SUBMITTER: Springate DA

PROVIDER: S-EPMC4062485 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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ClinicalCodes: an online clinical codes repository to improve the validity and reproducibility of research using electronic medical records.

Springate David A DA Kontopantelis Evangelos E Ashcroft Darren M DM Olier Ivan I Parisi Rosa R Chamapiwa Edmore E Reeves David D

PloS one 20140618 6

Lists of clinical codes are the foundation for research undertaken using electronic medical records (EMRs). If clinical code lists are not available, reviewers are unable to determine the validity of research, full study replication is impossible, researchers are unable to make effective comparisons between studies, and the construction of new code lists is subject to much duplication of effort. Despite this, the publication of clinical codes is rarely if ever a requirement for obtaining grants, ...[more]

PMID: 24941260

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Project description:ImportancePatients can develop multiple skin cancers, and their medical data can be spread over multiple health care systems. This fragmented care, combined with the lack of skin cancer registries, has limited our ability both to provide accurate estimates of incidence and to study the pathogenesis of multiple skin cancers.ObjectiveTo assess whether standard diagnostic and procedural codes present in the electronic health records at a single health care system are a valid proxy for estimating the number of overall skin cancers.Design, setting, and participantsRetrospective cohort study of patients seen at a single-center tertiary care hospital (ie, Vanderbilt University Medical Center) between July 1, 2008, and June 30, 2018. All patients with at least 1 electronic health record-based diagnostic or procedural code for any skin cancer and at least 1 pathology report of a skin cancer.ExposureThe number of International Classification of Disease (ICD) or Current Procedural Terminology (CPT) codes relating to skin cancer.Main outcomes and measuresPearson correlation coefficient and R2 were calculated for the total number of ICD or CPT codes for skin cancer and histologically verified skin cancers.ResultsIn this cohort study of 35 901 patients, the mean (SD) age was 70.4 (14.4) years, 20 404 (56.8%) were men, and 31 623 (88.1%) were White individuals. Of these patients, 6307 had at least 1 ICD or CPT code or pathology report for a skin cancer, of whom 5688 patients had both a CPT code related to skin malignancy and a histologically verified skin cancer. There was a strong linear correlation between the number of CPT codes and pathology records (r = 0.87). There was a poor correlation between the number of ICD codes and pathology records (r = 0.22).Conclusions and relevanceThis cohort study found that the use of ICD codes was a poor proxy measure for the number of skin cancers per patient. In contrast, CPT codes accounted for more than 75% of the variability in the number of skin cancers (R2 = 0.76) and were a better proxy measure for the total number of skin cancers per patient.

Project description:Importance:Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and health care expenditures. Accurate prediction of incident AF would enhance AF management and potentially improve patient outcomes. Objective:To validate the AF risk prediction model originally developed by the Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation (CHARGE-AF) investigators using a large repository of electronic medical records (EMRs). Design, Setting, and Participants:In this prediction model study, deidentified EMRs of 33?494 individuals 40 years or older who were white or African American and had no history of AF were reviewed and analyzed. The participants were followed up in the internal medicine outpatient clinics at Vanderbilt University Medical Center for incident AF from December 31, 2005, until December 31, 2010. Adjusting for differences in baseline hazard, the CHARGE-AF Cox proportional hazards model regression coefficients were applied to the EMR cohort. A simple version of the model with no echocardiographic variables was also evaluated. Data were analyzed from October 31, 2013, to January 31, 2014. Main Outcomes and Measures:Incident AF. Predictors in the model included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, type 2 diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval. Results:Among the 33?494 participants, the median age was 57 (interquartile range, 49-67) years; 57% of patients were women, 43% were men, 85.7% were white, and 14.3% were African American. During the mean (SD) follow-up of 4.8 (0.9) years, 2455 individuals (7.3%) developed AF. Both models had poor calibration in the EMR cohort, with underprediction of AF among low-risk individuals and overprediction of AF among high-risk individuals (10th and 90th percentiles for predicted probability of incident AF, 0.005 and 0.179, respectively). The full CHARGE-AF model had a C index of 0.708 (95% CI, 0.699-0.718) in our cohort. The simple model had similar discrimination (C index, 0.709; 95% CI, 0.699-0.718; P?=?.70 for difference between models). Conclusions and Relevance:Despite reasonable discrimination, the CHARGE-AF models showed poor calibration in this EMR cohort. This study highlights the difficulties of applying a risk model derived from prospective cohort studies to an EMR cohort and suggests that these AF risk prediction models be used with caution in the EMR setting. Future risk models may need to be developed and validated within EMR cohorts.

Dataset Information

ClinicalCodes: an online clinical codes repository to improve the validity and reproducibility of research using electronic medical records.

Publications

ClinicalCodes: an online clinical codes repository to improve the validity and reproducibility of research using electronic medical records.

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