Dataset Information

Validation of ICD-9-CM Diagnosis Codes for Surgical Site Infection and Noninfectious Wound Complications After Mastectomy.

ABSTRACT: BACKGROUND Few studies have validated ICD-9-CM diagnosis codes for surgical site infection (SSI), and none have validated coding for noninfectious wound complications after mastectomy. OBJECTIVES To determine the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes in health insurer claims data to identify SSI and noninfectious wound complications, including hematoma, seroma, fat and tissue necrosis, and dehiscence, after mastectomy. METHODS We reviewed medical records for 275 randomly selected women who were coded in the claims data for mastectomy with or without immediate breast reconstruction and had an ICD-9-CM diagnosis code for a wound complication within 180 days after surgery. We calculated the positive predictive value (PPV) to evaluate the accuracy of diagnosis codes in identifying specific wound complications and the PPV to determine the accuracy of coding for the breast surgical procedure. RESULTS The PPV for SSI was 57.5%, or 68.9% if cellulitis-alone was considered an SSI, while the PPV for cellulitis was 82.2%. The PPVs of individual noninfectious wound complications ranged from 47.8% for fat necrosis to 94.9% for seroma and 96.6% for hematoma. The PPVs for mastectomy, implant, and autologous flap reconstruction were uniformly high (97.5%-99.2%). CONCLUSIONS Our results suggest that claims data can be used to compare rates of infectious and noninfectious wound complications after mastectomy across facilities, even though PPVs vary by specific type of postoperative complication. The accuracy of coding was highest for cellulitis, hematoma, and seroma, and a composite group of noninfectious complications (fat necrosis, tissue necrosis, or dehiscence). Infect Control Hosp Epidemiol 2017;38:334-339.

SUBMITTER: Olsen MA

PROVIDER: S-EPMC5490368 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Validation of ICD-9-CM Diagnosis Codes for Surgical Site Infection and Noninfectious Wound Complications After Mastectomy.

Olsen Margaret A MA Ball Kelly E KE Nickel Katelin B KB Wallace Anna E AE Fraser Victoria J VJ

Infection control and hospital epidemiology 20161215 3

BACKGROUND Few studies have validated ICD-9-CM diagnosis codes for surgical site infection (SSI), and none have validated coding for noninfectious wound complications after mastectomy. OBJECTIVES To determine the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes in health insurer claims data to identify SSI and noninfectious wound complications, including hematoma, seroma, fat and tissue necrosis, and dehiscence, after mastecto ...[more]

PMID: 27974057

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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.

Dataset Information

Validation of ICD-9-CM Diagnosis Codes for Surgical Site Infection and Noninfectious Wound Complications After Mastectomy.

Publications

Validation of ICD-9-CM Diagnosis Codes for Surgical Site Infection and Noninfectious Wound Complications After Mastectomy.

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