Can factors related to mortality be used to predict the follow-up health-related quality of life (HRQoL) in cardiac surgery patients?

Abstract

Optimal selection of patients and choice of treatment methods in cardiac surgery calls for methods to predict outcome both in terms of mortality and health-related quality of life (HRQoL). Our target was to evaluate whether indicators predicting mortality can also be used to predict follow-up HRQoL. Preoperative and intensive care-related data of 571 elective cardiac surgery patients treated in the Helsinki University Central Hospital were used to predict, in a stepwise (forward) binary logistic regression, the probability of being dead at six months after operation. Furthermore, Tobit regression models were employed to predict the follow-up HRQoL of patients using also treatment complications and patients' experiences of pain and restlessness during treatment as explanatory variables. The EuroSCORE, renal, respiratory and neurological complications as well as urgent sternotomy were all statistically significant predictors of mortality. By contrast, follow-up HRQoL was predicted by the baseline HRQoL, diabetes and male gender as well as experience of pain and restlessness during the ICU stay. Mortality and HRQoL after cardiac surgery appear to be explained by different factors. Pain and restlessness during ICU treatment affect follow-up HRQoL in a negative manner and as potentially modifiable factors, need attention during treatment.