Is quality of life measurement likely to be a proxy for health needs assessment in patients with coronary artery disease?

BACKGROUND: The identification of patients' health needs is pivotal in optimising the quality of health care, increasing patient satisfaction and directing resource allocation. Health needs are complex and not so easily evaluated as health-related quality of life (HRQL), which is becoming increasingly accepted as a means of providing a more global, patient-orientated assessment of the outcome of health care interventions than the simple medical model. The potential of HRQL as a surrogate measure of healthcare needs has not been evaluated. OBJECTIVES AND METHOD: A generic (Short Form-12; SF-12) and a disease-specific questionnaire (Seattle Angina Questionnaire; SAQ) were tested for their potential to predict health needs in patients with acute coronary disease. A wide range of healthcare needs were determined using a questionnaire specifically developed for this purpose. RESULTS: With the exception of information needs, healthcare needs were highly correlated with health-related quality of life. Patients with limited enjoyment of personal interests, weak financial situation, greater dependency on others to access health services, and dissatisfaction with accommodation reported poorer HRQL (SF-12: p < 0.001; SAQ: p < 0.01). Difficulties with mobility, aids to daily living and activities requiring assistance from someone else were strongly associated with both generic and disease-specific questionnaires (SF-12: r = 0.46-0.55, p < 0.01; SAQ: r = 0.53-0.65, p < 0.001). Variables relating to quality of care and health services were more highly correlated with SAQ components (r = 0.33-0.59) than with SF-12 (r = 0.07-0.33). Overall, the disease-specific Seattle Angina Questionnaire was superior to the generic Short Form-12 in detecting healthcare needs in patients with coronary disease. Receiver-operator curves supported the sensitivity of HRQL tools in detecting health needs. CONCLUSION: Healthcare needs are complex and developing suitable questionnaires to measure these is difficult and time-consuming. Without a satisfactory means of measuring these needs, the extent to which disease impacts on health will continue to be underestimated. Further investigation on larger populations is warranted but HRQL tools appear to be a reasonable proxy for healthcare needs, as they identify the majority of needs in patients with coronary disease, an observation not previously reported in this patient group

Empirical evidence of bias in the design of experimental stroke studies: a metaepidemiologic approach

Background and Purpose: At least part of the failure in the transition from experimental to clinical studies in stroke has been attributed to the imprecision introduced by problems in the design of experimental stroke studies. Using a metaepidemiologic approach, we addressed the effect of randomization, blinding, and use of comorbid animals on the estimate of how effectively therapeutic interventions reduce infarct size. Methods: Electronic and manual searches were performed to identify meta-analyses that described interventions in experimental stroke. For each meta-analysis thus identified, a reanalysis was conducted to estimate the impact of various quality items on the estimate of efficacy, and these estimates were combined in a meta meta-analysis to obtain a summary measure of the impact of the various design characteristics. Results: Thirteen meta-analyses that described outcomes in 15 635 animals were included. Studies that included unblinded induction of ischemia reported effect sizes 13.1% (95% CI, 26.4% to 0.2%) greater than studies that included blinding, and studies that included healthy animals instead of animals with comorbidities overstated the effect size by 11.5% (95% CI, 21.2% to 1.8%). No significant effect was found for randomization, blinded outcome assessment, or high aggregate CAMARADES quality score. Conclusions: We provide empirical evidence of bias in the design of studies, with studies that included unblinded induction of ischemia or healthy animals overestimating the effectiveness of the intervention. This bias could account for the failure in the transition from bench to bedside of stroke therapies.