Dataset Information

Validation of ICD-9-CM codes to identify gastrointestinal perforation events in administrative claims data among hospitalized rheumatoid arthritis patients.

ABSTRACT: To validate, using physician review of abstracted medical chart data as a gold standard, a claims-based algorithm developed to identify gastrointestinal (GI) perforation cases among rheumatoid arthritis (RA) patients.Patients with established RA, aged 18?years or older with hospital admissions between January 2004 and September 2009, were selected from a large US-hospital-based database. An algorithm with International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes for GI perforation and combinations of GI-related diagnosis codes and Current Procedural Terminology (CPT-4) procedure codes for relevant GI surgeries was used to identify potential GI perforation cases. Two senior experienced specialist physicians independently reviewed abstracted chart data and classified cases as confirmed or unconfirmed GI perforations. Positive predictive values (PPVs) to identify confirmed GI perforation were calculated and stratified by upper versus lower GI tract.Overall, 86 of 92 GI perforation cases were confirmed, yielding an overall PPV of 94% (95%confidence interval [CI]?=?86%-98%). PPV was 100% (95%CI?=?100%-100%) for upper GI perforation (esophagus, stomach) and 91% (95%CI?=?90%-97%) for lower GI perforation (small intestine, PPV?=?100%; large intestine, PPV?=?94%; unspecified lower GI, PPV?=?89%).This algorithm, consisting of a combination of ICD-9-CM diagnosis and CPT-4 codes, could be used in future safety studies to evaluate GI perforation risk factors in RA patients.

SUBMITTER: Curtis JR

PROVIDER: S-EPMC3227025 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Validation of ICD-9-CM codes to identify gastrointestinal perforation events in administrative claims data among hospitalized rheumatoid arthritis patients.

Curtis Jeffrey R JR Chen Shih-Yin SY Werther Winifred W John Ani A Johnson David A DA

Pharmacoepidemiology and drug safety 20110827 11

<h4>Purpose</h4>To validate, using physician review of abstracted medical chart data as a gold standard, a claims-based algorithm developed to identify gastrointestinal (GI) perforation cases among rheumatoid arthritis (RA) patients.<h4>Methods</h4>Patients with established RA, aged 18 years or older with hospital admissions between January 2004 and September 2009, were selected from a large US-hospital-based database. An algorithm with International Classification of Diseases, Ninth Revision, C ...[more]

PMID: 22020901

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Project description:BACKGROUND:Validation of administrative databases for cerebrovascular diseases is crucial for epidemiological, outcome, and health services research. The aim of this study was to validate ICD-9 codes for hemorrhagic or ischemic stroke in administrative databases, to use them for a comprehensive assessment of the burden of disease in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. METHODS:We considered the hospital discharge abstract database of the Umbria Region (890,000 residents). Source population was represented by patients aged >18 discharged from hospital with a diagnosis of hemorrhagic or ischemic stroke between 2012 and 2014 using ICD-9-CM codes in primary position. We randomly selected and reviewed medical charts of cases and non-cases from hospitals. For case ascertainment we considered symptoms and instrumental tests reported in the medical charts. Diagnostic accuracy measures were computed using 2x2 tables. RESULTS:We reviewed 767 medical charts for cases and 78 charts for non-cases. Diagnostic accuracy measures were: subarachnoid hemorrhage: sensitivity (SE) 100% (95% CI: 97%-100%), specificity (SP) 96% (90-99), positive predictive value (PPV) 98% (93-100), negative predictive value (NPV) 100% (95-100); intracerebral hemorrhage: SE 100% (97-100), SP 98% (91-100), PPV 98% (94-100), NPV 100% (95-100); other and unspecified intracranial hemorrhage: SE 100% (97-100), SP 96% (90-99), PPV 98% (93-100), NPV 100% (95-100); ischemic stroke due to occlusion and stenosis of precerebral arteries: SE 99% (94-100), SP 66 (57-75), PPV 70% (61-77), NPV 99% (93-100); occlusion of cerebral arteries: SE 100% (97-100), SP 87% (78-93), PPV 91% (84-95), NPV 100% (95-100); acute, but ill-defined, cerebrovascular disease: SE 100% (97-100), SP 78% (69-86), PPV % 83 (75-89), NPV 100% (95-100). CONCLUSIONS:Case ascertainment for both ischemic and hemorrhagic stroke showed good or high levels of accuracy within the regional healthcare databases in Umbria. This database can confidently be employed for epidemiological, outcome, and health services research related to any type of stroke.

Project description:BackgroundAdministrative healthcare databases are useful and inexpensive tools that can provide a comprehensive assessment of the burden of diseases in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. However, a crucial issue is the reliability of information gathered. The aim of this study was to validate ICD-9 codes for several major cardiovascular conditions, i.e., acute myocardial infarction (AMI), atrial fibrillation/flutter (AF), and heart failure (HF), in order to use them for epidemiological, outcome, and health services research.MethodsData from the centralised administrative database of the Umbria Region (890,000 residents, located in Central Italy) were considered. Patients with a first hospital discharge for AMI, AF/flutter, and HF, between 2012 and 2014, were identified using ICD-9-CM codes in primary position. A sample of cases and non-cases was randomly selected, and the corresponding medical charts reviewed by specifically trained investigators. For each disease, case ascertainment was based on all clinical, laboratory, and instrumental examinations available in medical charts. Sensitivity, specificity, and predictive values with 95% confidence intervals (CIs), were calculated.ResultsWe reviewed 458 medical charts, 128 for AMI, 127 for AF/flutter, 127 for HF, and 76 of non-cases for each condition. Diagnostic accuracy measures of the original discharge diagnosis were as follows. AMI: sensitivity 98% (95% CI, 94-100%), specificity 91% (95% CI, 83-97%), positive predictive value (PPV) 95% (95% CI, 89-98%), negative predictive value (NPV) 97% (95% CI, 91-100%). AF/flutter: sensitivity 95% (95% CI, 90-98%), specificity 95% (95% CI, 87-99%), PPV 97% (95% CI, 92-99%), NPV 92% (95% CI, 84-97%). HF: sensitivity 96% (95% CI, 91-99%), specificity 90% (95% CI, 81-96%), PPV 94% (95% CI, 88-97%), NPV 93% (95% CI, 85-98%).ConclusionThe case ascertainment for AMI, AF and flutter, and HF, showed a high level of accuracy (≥ 90%). The healthcare administrative database of the Umbria Region can be confidently used for epidemiological, outcome, and health services research.

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.

Dataset Information

Validation of ICD-9-CM codes to identify gastrointestinal perforation events in administrative claims data among hospitalized rheumatoid arthritis patients.

Publications

Validation of ICD-9-CM codes to identify gastrointestinal perforation events in administrative claims data among hospitalized rheumatoid arthritis patients.

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