Dataset Information

Data on coding association rules from an inpatient administrative health data coded by International classification of disease - 10th revision (ICD-10) codes.

ABSTRACT: Data presented in this article relates to the research article entitled "Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data" (Peng et al. [1]) in preparation). We provided a set of ICD-10 coding association rules in the age group of 55 to 65. The rules were extracted from an inpatient administrative health data at five acute care hospitals in Alberta, Canada, using association rule mining. Thresholds of support and confidence for the association rules mining process were set at 0.19% and 50% respectively. The data set contains 426 rules, in which 86 rules are not nested. Data are provided in the supplementary material. The presented coding association rules provide a reference for future researches on the use of association rule mining for data quality assessment.

SUBMITTER: Peng M

PROVIDER: S-EPMC5995749 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Data on coding association rules from an inpatient administrative health data coded by International classification of disease - 10th revision (ICD-10) codes.

Peng Mingkai M Sundararajan Vijaya V Williamson Tyler T Minty Evan P EP Smith Tony C TC Doktorchik Chelsea T A CTA Quan Hude H

Data in brief 20180216

Data presented in this article relates to the research article entitled "Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data" (Peng et al. [1]) in preparation). We provided a set of ICD-10 coding association rules in the age group of 55 to 65. The rules were extracted from an inpatient administrative health data at five acute care hospitals in Alberta, Canada, using association rule mining. Thresholds of support and co ...[more]

PMID: 29896537

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Project description:Administrative databases are increasingly used in research studies to capture clinical outcomes such as sepsis. This systematic review and meta-analysis examines the accuracy of International Classification of Diseases, 10th revision (ICD-10), codes for identifying sepsis in adult and pediatric patients.Data sourcesWe searched MEDLINE, EMBASE, Web of Science, CENTRAL, Epistemonikos, and McMaster Superfilters from inception to September 7, 2021.Study selectionWe included studies that validated the accuracy of sepsis ICD-10 codes against any reference standard.Data extractionThree authors, working in duplicate, independently extracted data. We conducted meta-analysis using a random effects model to pool sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated individual study risk of bias using the Quality Assessment of Diagnostic Accuracy Studies tool and assessed certainty in pooled diagnostic effect measures using the Grading of Recommendations Assessment, Development, and Evaluation framework.Data synthesisThirteen eligible studies were included in the qualitative synthesis and the meta-analysis. Eleven studies used manual chart review as the reference standard, and four studies used registry databases. Only one study evaluated pediatric patients exclusively. Compared with the reference standard of detailed chart review and/or registry databases, the pooled sensitivity for sepsis ICD-10 codes was 35% (95% CI, 22-48, low certainty), whereas the pooled specificity was 98% (95% CI: 98-99, low certainty). The PPV for ICD-10 codes ranged from 9.8% to 100% (median, 72.0%; interquartile range [IQR], 50.0-84.7%). NPV ranged from 54.7% to 99.1% (median, 95.9%; interquartile range, 85.5-98.3%).ConclusionsSepsis is undercoded in administrative databases. Future research is needed to explore if greater consistency in ICD-10 code definitions and enhanced quality measures for ICD-10 coders can improve the coding accuracy of sepsis in large databases.

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.

Dataset Information

Data on coding association rules from an inpatient administrative health data coded by International classification of disease - 10th revision (ICD-10) codes.

Publications

Data on coding association rules from an inpatient administrative health data coded by International classification of disease - 10th revision (ICD-10) codes.

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