RESUMO
The clinician's decisions are subject to numerous distorting influences. Computer decision aids can help avoid these distortions by placing the clinician's limited personal experience into broader perspective through comparison with a larger repository of clinically relevant information; by making explicit the assumptions implied by his or her decisions; and by alerting the clinician whenever the decisions made do not appear consistent with these assumptions, with the available information or with the conventional rules of logic. Practical standards of performance with respect to the development, validation and clinical application of these decision aids are still in evolution, however, and a variety of ethical and legal issues have yet to be addressed. Despite the promise of computer decision aids, it remains to be seen whether their diffusion into medical practice will improve the quality and cost of health care.
Assuntos
Cardiologia/tendências , Tomada de Decisões Assistida por Computador , Algoritmos , Doença das Coronárias/diagnóstico , Tomada de Decisões Assistida por Computador/economia , Tomada de Decisões Assistida por Computador/legislação & jurisprudência , Tomada de Decisões Assistida por Computador/normas , Humanos , Imperícia , Modelos Teóricos , Aceitação pelo Paciente de Cuidados de Saúde , Estatística como Assunto , Terminologia como AssuntoRESUMO
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)