Why the study?
Does the Minnesota Heart Failure Criteria (MHFC) improve prognostic discrimination for 2-year mortality compared to previous criteria in patients discharged after MI or HF?
Does the Minnesota Heart Failure Criteria (MHFC) improve prognostic discrimination for 2-year mortality compared to previous criteria in patients discharged after MI or HF?
The Minnesota Heart Failure Criteria provides precise prognostic discrimination for 2-year mortality using fewer variables than the full Framingham Criteria, facilitating epidemiologic case-finding.
MHFC may enhance post-MI/HF mortality stratification; hypothesis-generating and requires prospective validation before practice change.
The authors present the Minnesota Heart Failure Criteria (MHFC), derived using latent class analysis from widely available items in the Framingham Criteria. The authors used 1995 and 2000 data on hospitalized Minnesota Heart Survey subjects discharged after myocardial infarction or heart failure (N = 7,379). Selected Framingham Criteria variables (dyspnea, pulmonary rales, cardiomegaly, interstitial or pulmonary edema on chest radiograph, S(3) heart sound, tachycardia) plus left ventricular ejection fraction were used. The discriminatory power of the MHFC was evaluated using age- and sex-adjusted 2-year mortality. A five-class latent class analysis model was collapsed into cases and noncases. Mortality estimates discriminated noncases (18%) from cases (43%) (p < 0.001). The MHFC performed better than previous truncated criteria (Framingham Criteria: 26% noncases, 43% cases; Duke Criteria: 29%, 40%; Killip Score: 31%, 44%; Boston Score: 28%, 45%). In a subset of patients admitted for heart failure (n = 5,128), the MHFC identified all but 2% (116/4,746) of cases found with a nearly full version of the Framingham Criteria. In terms of prognostic value, the MHFC are as precise as or more precise than several previous sets of truncated criteria. They closely approximate a nearly full version of the Framingham Criteria but require many fewer variables and can facilitate epidemiologic case-finding for heart failure.
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Kim et al. (2006) studied this question.
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