Dataset Information

Recommendations for optimal ICD codes to study neurologic conditions: a systematic review.

ABSTRACT:

Objective

Administrative health data are frequently used for large population-based studies. However, the validity of these data for identifying neurologic conditions is uncertain.

Methods

This article systematically reviews the literature to assess the validity of administrative data for identifying patients with neurologic conditions. Two reviewers independently assessed for eligibility all abstracts and full-text articles identified through a systematic search of Medline and Embase. Study data were abstracted on a standardized abstraction form to identify ICD code-based case definitions and corresponding sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs).

Results

Thirty full-text articles met the eligibility criteria. These included 8 studies for Alzheimer disease/dementia (sensitivity: 8-86.5, specificity: 56.3-100, PPV: 60-97.9, NPV: 68.0-98.9), 2 for brain tumor (sensitivity: 54.0-100, specificity: 97.0-99.0, PPV: 91.0-98.0), 4 for epilepsy (sensitivity: 98.8, specificity: 69.6, PPV: 62.0-100, NPV: 89.5-99.1), 4 for motor neuron disease (sensitivity: 78.9-93.0, specificity: 99.0-99.9, PPV: 38.0-90.0, NPV: 99), 2 for multiple sclerosis (sensitivity: 85-92.4, specificity: 55.9-92.6, PPV: 74.5-92.7, NPV: 70.8-91.9), 4 for Parkinson disease/parkinsonism (sensitivity: 18.7-100, specificity: 0-99.9, PPV: 38.6-81.0, NPV: 46.0), 3 for spinal cord injury (sensitivity: 0.9-90.6, specificity: 31.9-100, PPV: 27.3-100), and 3 for traumatic brain injury (sensitivity: 45.9-78.0 specificity: 97.8, PPV: 23.7-98.0, NPV: 99.2). No studies met eligibility criteria for cerebral palsy, dystonia, Huntington disease, hydrocephalus, muscular dystrophy, spina bifida, or Tourette syndrome.

Conclusions

To ensure the accurate interpretation of population-based studies with use of administrative health data, the accuracy of case definitions for neurologic conditions needs to be taken into consideration.

SUBMITTER: St Germaine-Smith C

PROVIDER: S-EPMC3430709 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Publications

Recommendations for optimal ICD codes to study neurologic conditions: a systematic review.

St Germaine-Smith Christine C Metcalfe Amy A Pringsheim Tamara T Roberts Jodie Irene JI Beck Cynthia A CA Hemmelgarn Brenda R BR McChesney Jane J Quan Hude H Jette Nathalie N

Neurology 20120822 10

<h4>Objective</h4>Administrative health data are frequently used for large population-based studies. However, the validity of these data for identifying neurologic conditions is uncertain.<h4>Methods</h4>This article systematically reviews the literature to assess the validity of administrative data for identifying patients with neurologic conditions. Two reviewers independently assessed for eligibility all abstracts and full-text articles identified through a systematic search of Medline and Em ...[more]

PMID: 22914826

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Project description:BackgroundThe phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR).ObjectiveThe goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes.MethodsWe mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS.ResultsWe mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]).ConclusionsThis study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.

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Dataset Information

Recommendations for optimal ICD codes to study neurologic conditions: a systematic review.

Objective

Methods

Results

Conclusions

Publications

Recommendations for optimal ICD codes to study neurologic conditions: a systematic review.

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