Prediction of outcomes in patients with heart failure

Abstract

The main aim of this thesis is to explore risk factors associated to an increased risk of adverse outcomes for heart failure (HF) patients and improve the early re-admission or mortality prediction in HF. Data from two studies (OPERA-HF study in the UK and SAPHIRE study in US) has been used to explore a wide range of variables as potential risk factors. We found that depression is a significant and independent predictor of all-cause mortality among HF patients. Depression was also significantly associated with recurrent events: unplanned readmission or mortality. Other psychosocial or non-clinical variables independently associated with increasing risk of recurrent events in the year following discharge after a HF hospital admission were: presence of frailty, moderate-to-severe anxiety, living alone and the presence of cognitive impairment. We then used data from the OPERA-HF study to develop a 30-day composite outcome model and to explore the added predictive value of non-clinical predictors to early outcomes: 30-day unplanned readmission or mortality. The performance of the model improved by including physical frailty and social support next to clinical variables. The transportability of the model to a different geography was proved in the external validation of the model on the SAPHIRE study data. </p