Dataset Information

Evaluation of ICD-9-CM codes for craniofacial microsomia.

ABSTRACT: Craniofacial microsomia (CFM) is a congenital condition characterized by microtia and mandibular underdevelopment. Healthcare databases and birth defects surveillance programs could be used to improve knowledge of CFM. However, no specific International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code exists for this condition, which makes standardized data collection challenging. Our aim was to evaluate the validity of existing ICD-9-CM codes to identify individuals with CFM.Study sample eligibility criteria were developed by an expert panel and matched to 11 ICD-9-CM codes. We queried hospital discharge data from two craniofacial centers and identified a total of 12,254 individuals who had ?1 potentially CFM-related code(s). We reviewed all (n = 799) medical records identified at the University of North Carolina (UNC) and 500 randomly selected records at Seattle Children's Hospital (SCH). Individuals were classified as a CFM case or non-case.Thirty-two individuals (6%) at SCH and 93 (12%) at UNC met the CFM eligibility criteria. At both centers, 59% of cases and 95% of non-cases had only one code assigned. At both centers, the most frequent codes were 744.23 (microtia), 754.0 and 756.0 (nonspecific codes), and the code 744.23 had a positive predictive value (PPV) >80% and sensitivity >70%. The code 754.0 had a sensitivity of 3% (PPV <1%) at SCH and 36% (PPV = 5%) at UNC, whereas 756.0 had a sensitivity of 38% (PPV = 5%) at SCH and 18% (PPV = 26%) at UNC.These findings suggest the need for a specific CFM code to facilitate CFM surveillance and research.

SUBMITTER: Luquetti DV

PROVIDER: S-EPMC3522759 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Evaluation of ICD-9-CM codes for craniofacial microsomia.

Luquetti Daniela V DV Saltzman Babette S BS Vivaldi Daniela D Pimenta Luiz A LA Hing Anne V AV Cassell Cynthia H CH Starr Jacqueline R JR Heike Carrie L CL

Birth defects research. Part A, Clinical and molecular teratology 20120818 12

<h4>Background</h4>Craniofacial microsomia (CFM) is a congenital condition characterized by microtia and mandibular underdevelopment. Healthcare databases and birth defects surveillance programs could be used to improve knowledge of CFM. However, no specific International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code exists for this condition, which makes standardized data collection challenging. Our aim was to evaluate the validity of existing ICD-9-CM codes to ...[more]

PMID: 22903955

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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:The International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) Injury Severity Score (ICISS) is a risk adjustment model when injuries are recorded using ICD-9-CM coding. The trauma mortality prediction model (TMPM-ICD9) provides better calibration and discrimination compared with ICISS and injury severity score (ISS). Though TMPM-ICD9 is statistically rigorous, it is not precise enough mathematically and has the tendency to overestimate injury severity. The purpose of this study is to develop a new ICD-10-CM injury model which estimates injury severities for every injury in the ICD-10-CM lexicon by a combination of rigorous statistical probit models and mathematical properties and improves the prediction accuracy. Methods:We developed an injury mortality prediction (IMP-ICDX) using data of 794,098 patients admitted to 738 hospitals in the National Trauma Data Bank from 2015 to 2016. Empiric measures of severity for each of the trauma ICD-10-CM codes were estimated using a weighted median death probability (WMDP) measurement and then used as the basis for IMP-ICDX. ISS (version 2005) and the single worst injury (SWI) model were re-estimated. The performance of each of these models was compared by using the area under the receiver operating characteristic (AUC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion statistic. Results:IMP-ICDX exhibits significantly better discrimination (AUCIMP-ICDX, 0.893, and 95% confidence interval (CI), 0.887 to 0.898; AUCISS, 0.853, and 95% CI, 0.846 to 0.860; and AUCSWI, 0.886, and 95% CI, 0.881 to 0.892) and calibration (HLIMP-ICDX, 68, and 95% CI, 36 to 98; HLISS, 252, and 95% CI, 191 to 310; and HLSWI, 92, and 95% CI, 53 to 128) compared with ISS and SWI. All models were improved after the extension of age, gender, and injury mechanism, but the augmented IMP-ICDX still dominated ISS and SWI by every performance. Conclusions:The IMP-ICDX has a better discrimination and calibration compared to ISS. Therefore, we believe that IMP-ICDX could be a new viable trauma research assessment method.

Project description:Various assessment methods based on the International Classification of Diseases, Tenth Edition, Clinical Modification (ICD-10-CM), such as ICD-10-CM Injury Severity Score (ICISS), trauma mortality prediction model (TMPM-ICD10), and injury mortality prediction (IMP-ICDX), are purely anatomic trauma assessment, which need to be further improved. Traumatic injury mortality prediction (TRIMP-ICDX) is a comprehensive assessment method based on anatomic injuries and incorporating available information to determine whether it is superior to Trauma and Injury Severity Score (TRISS) and IMP-ICDX in predicting trauma outcomes. This retrospective cohort study was based on data from 704,287 trauma patients admitted to 710 trauma centers in the National Trauma Data Bank of the United States in 2016. The TRIMP-ICDX was established using anatomical injury, physiological reserves, and physiological response indicators. Its performance was compared with the IMP-ICDX and TRISS by examining the area under the receiver operating characteristic curve (AUC), calibration (Hosmer-Lemeshow goodness-of-fit test, HL), and the Akaike information criterion (AIC). The TRIMP-ICDX showed significantly better discrimination (AUCTRIMP-ICDX 0.968; 95% confidence interval (CI), 0.966–0.970, AUCTRISS 0.922; 95% CI, 0.918–0.925, and AUCIMP-ICDX 0.894; 95% CI, 0.890–0.899), better calibration (HLTRIMP-ICDX 5.6; 95% CI, 3.0–8.0, HLTRISS 72.7; 95% CI, 38.4–104.5, and HLIMP-ICDX 53.1; 95% CI, 26.6–77.8), and a lower AIC (AICTRIMP-ICDX 24,774, AICTRISS 30,753, and AICIMP-ICDX 32,780) compared with TRISS and IMP-ICDX. Similar results were found in statistical comparisons among different body regions. As a comprehensive evaluation method based on the ICD-10-CM lexicon TRIMP-ICDX is significantly better than IMP-ICDX and TRISS with respect to both discriminative power and calibration. The TRIMP-ICDX should become a research method for the comprehensive evaluation of trauma severity.

Dataset Information

Evaluation of ICD-9-CM codes for craniofacial microsomia.

Publications

Evaluation of ICD-9-CM codes for craniofacial microsomia.

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