Literature DB >> 10785573

Coronary heart disease surveillance: field application of an epidemiologic algorithm.

A R Assaf1, K L Lapane, J L McKenney, S McKinlay, R A Carleton.   

Abstract

This report describes the performance of a surveillance system and computerized algorithm for the assignment of definite or probable hospitalized cardiac events for large epidemiologic studies. The algorithm, developed by the Coordinating Committee for Community Demonstration Studies (CCCDS), evolved from the Gillum criteria, and included selected ICD-9-CM codes including codes 410 through 414 for discharge record screening, plus creatine kinase. For the small percentage of cases in which enzyme analysis was inconclusive (8%), presence of pain and/or Minnesota-coded electrocardiograms were included to define the outcome. All data items were easily obtained from medical records by trained lay record abstractors and required no interpretation. From January 1980 through December 1991, 21,183 medical records were screened for ICD-9-CM codes 410 through 414. Of all 410 to 411 ICD-9-CM codes (n = 9026), 36.9% (n = 3220) were classified as definite cardiac events and 10.6% (n = 1057) as probable events. Of all 412 through 414 codes (n = 9070), only 1.8% (n = 227) were classified as definite cardiac events and 5.4% (n = 716) as probable events. The epidemiologic diagnostic algorithm presented in this article used computerized data to assign diagnoses in a standard, objective manner, and was a lower cost alternative to classification of cardiac events on the basis of clinical review and/or more complex record abstraction approaches.

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Year:  2000        PMID: 10785573     DOI: 10.1016/s0895-4356(99)00183-3

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  2 in total

1.  Accuracy of ICD-9-coded chief complaints and diagnoses for the detection of acute respiratory illness.

Authors:  J U Espino; M M Wagner
Journal:  Proc AMIA Symp       Date:  2001

2.  A rule-based electronic phenotyping algorithm for detecting clinically relevant cardiovascular disease cases.

Authors:  Santiago Esteban; Manuel Rodríguez Tablado; Ricardo Ignacio Ricci; Sergio Terrasa; Karin Kopitowski
Journal:  BMC Res Notes       Date:  2017-07-14
  2 in total

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