Why the study?
Do computer-assigned Minnesota Codes (MC-MEANS and NOVACODE) agree with the visual standard method and have similar prognostic associations with coronary heart disease events?
Do computer-assigned Minnesota Codes (MC-MEANS and NOVACODE) agree with the visual standard method and have similar prognostic associations with coronary heart disease events?
Computer algorithms assign more and severer Minnesota Codes than the visual method but maintain similar prognostic importance for CHD events, highlighting potential issues when pooling data from different reading methods.
Computer and visual Minnesota coding yield comparable CHD risk associations in cohorts; leaves open bias from method heterogeneity in pooled analyses.
The Minnesota Code is the most widely used electrocardiogram (ECG) classification system for epidemiologic studies and has been incorporated into several Computer algorithms. The authors compared the Modular ECG Analysis System (MC-MEANS) and NOVACODE computer ECG findings with the Visual coding standard for agreement and prognostic associations with coronary heart disease (CHD) events occurring during follow-up from 1987 to 1995 in 2,116 individuals participating in the Atherosclerosis Risk in Communities (ARIC) Study. The exact agreement between Visual and computer findings was greater than 90% for all Minnesota Code categories except Q-code, which was 77% for MC-MEANS and 81% for NOVACODE. Approximately 60% of all Q-codes were assigned by computer methods only. Among the 2,116 participants, there were 246 (11.6%) new coronary events. Unadjusted relative risks for codes assigned by the three methods were similar. When computer methods disagreed on code severity, the CHD occurrence rates for MC-MEANS-detected severer code versus NOVACODE-detected severer code were 21% and 7%, respectively. This study provides clear evidence that computers assign more and severer Minnesota Codes with similar prognostic importance as does the Visual method; it also alerts researchers to potential problems in pooling Minnesota Code data read by different methods.
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Kors et al. (2000) studied this question.
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