Dataset Information

Defining maternal obesity in studies of birth outcomes: Comparing ICD-9 codes at delivery and measures on the birth certificate.

ABSTRACT:

Background

Using ICD-9 codes underestimates the prevalence of obesity in adults; however, the validity of these codes in studies of pregnancy-related outcomes is not known.

Objectives

To compare classification of maternal obesity based on ICD-9 codes in hospital discharge records versus data from birth certificates in the same women, examine predictors of agreement, and assess how associations between obesity and two birth outcomes differ by source of weight data.

Methods

This population-based study included 2 329 145 California births between 2007 and 2012. We compared data on obesity from childbirth hospital discharge records (ICD-9 codes for obesity) and birth certificates (pre-pregnancy body mass index (BMI) calculated from weight and height) and identified predictors of agreement between the two sources. Logistic regression models assessed whether the two definitions of obesity resulted in different estimates of the associations of obesity with caesarean birth and large-for-gestational age.

Results

Overall, 464 754 women (20.0%) had obesity based on their pre-pregnancy BMI while only 100 002 (4.3%) had an obesity-related ICD-9 code. The sensitivity of ICD-9-based obesity was low at 16.2%; however, obesity codes were highly specific at 98.7%, with a negative predictive value of 82.5% and a positive predictive value of 75.2%. Among women with obesity identified by the birth certificate, those with pre-pregnancy and pregnancy-related complications (eg diabetes and hypertension) were more likely to have an obesity-related diagnosis in their delivery hospital discharge record. Using ICD-9 codes overestimated the association of obesity with caesarean birth and newborn large-for-gestational age.

Conclusions

ICD-9 codes in childbirth discharge records captured only one in five women with pre-pregnancy obesity. Sensitivity varied by maternal characteristics and conditions. This misclassification resulted in bias when examining the association of obesity and pregnancy-related outcomes.

SUBMITTER: Wall-Wieler E

PROVIDER: S-EPMC7483732 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:The 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). OBJECTIVE:The 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. METHODS:We 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. RESULTS:We 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]). CONCLUSIONS:This 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.

Project description:Background On October 1, 2015, the Center for Medicare and Medicaid Services transitioned from the International Classification of Diseases, Ninth Revision ( ICD-9) to the ICD, Tenth Revision ( ICD-10) compendium of codes for diagnosis and billing in health care, but translation between the two is often inexact. Here we describe a validated crosswalk to translate ICD-9 codes into ICD-10 codes, with a focus on complications after carotid revascularization and endovascular aortic aneurysm repair. Methods and Results We devised an 8-step process to derive and validate ICD-10 codes from existing ICD-9 codes. We used publicly available sources, including the General Equivalence Mapping database, to translate ICD-9 codes used in prior work to ICD-10 codes. We defined ICD-10 codes as validated if they were concordant with the initial ICD-9 codes after manual comparison by two physicians. Our primary validation measure was the percent of valid ICD-10 codes out of the total ICD-10 codes obtained during translation. We began with 126 ICD-9 diagnosis codes used for complication identification after carotid revascularization procedures, and 97 ICD-9 codes for complications after endovascular aortic aneurysm procedures. Translation generated 143 ICD-10 codes for carotid revascularization, a 14% increase from the initial 126 codes. Manual comparison demonstrated 98% concordance, with 99% agreement between the reviewers. Similarly, we identified 108 ICD-10 codes for endovascular aortic aneurysm repair, an 11% increase from the initial 97 ICD-9 codes. We again noted excellent concordance and agreement (98% and 100%, respectively). Manual review identified 4 ICD-10 codes incorrectly translated from ICD-9 codes for carotid revascularization, and 3 codes incorrectly translated for endovascular aortic aneurysm repair. Conclusions Algorithms to crosswalk lists of ICD-9 codes to ICD-10 codes can leverage electronic resources to minimize the burden of code translation. However, manual review for code validation may be necessary, with collaboration across institutions for researchers to share their efforts.