Literature DB >> 2801520

Limitations of a conventional logistic regression model based on left ventricular ejection fraction in predicting coronary events after myocardial infarction.

J W Work1, J G Ferguson, G A Diamond.   

Abstract

The clinical utility of conventional logistic regression models based on left ventricular ejection fraction (LVEF) for the prediction of cardiac events (death or recurrent infarction) was assessed in 646 postinfarction patients undergoing radionuclide ventriculography at rest and during exercise. The discriminant power of 2 different models (LVEF at rest alone vs LVEF at rest plus LVEF at peak exercise) was quantified in terms of the area under receiver-operating characteristic curves based on knowledge of patient outcome in the year after testing and the logistic probability of that outcome. Although LVEF at rest provided a significant amount of prognostic information (receiver-operating characteristic curve area = 62 +/- 4%, p less than 0.001), several limitations were observed: (1) powerful predictors of risk were uncommon (32% of patients with an LVEF at rest less than 0.20 had a cardiac event, but only 3% of the population had such extreme values); (2) the accuracy of predictions for high risk patients was less than for low risk patients (28 vs 98%, p less than 0.001); (3) addition of exercise LVEF to the model did not improve the accuracy of prediction (receiver-operating characteristic curve area = 68 +/- 4%, p = 0.11); and (4) predictions for individual patients were very imprecise (the 95% confidence interval of percent risk for an LVEF at rest of 0.20 [11 to 36%] overlapped that for an LVEF at rest of 0.60 [0 to 14%]).(ABSTRACT TRUNCATED AT 250 WORDS)

Entities:  

Mesh:

Year:  1989        PMID: 2801520     DOI: 10.1016/0002-9149(89)90751-0

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  2 in total

1.  Acute chest pain in African Americans: factors in the delay in seeking emergency care.

Authors:  K Ell; L J Haywood; E Sobel; M deGuzman; D Blumfield; J P Ning
Journal:  Am J Public Health       Date:  1994-06       Impact factor: 9.308

2.  Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture.

Authors:  Nitchanant Kitcharanant; Pojchong Chotiyarnwong; Thiraphat Tanphiriyakun; Ekasame Vanitcharoenkul; Chantas Mahaisavariya; Wichian Boonyaprapa; Aasis Unnanuntana
Journal:  BMC Geriatr       Date:  2022-05-24       Impact factor: 4.070

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.