Objective: To develop a comprehensive and easily applicable prognostic model predicting mortality risk in patients with moderate to severe heart failure. Design: Prospective follow up study. Setting: Seven general hospitals in the Netherlands. Patients: 152 outpatients with heart failure or patients admitted to hospital because of heart failure, who were included in a randomised trial to assess the impact of a pharmacist led intervention to improve drug compliance. Duration of follow up was at least 18 months. Main outcome measures: Multivariable logistic regression modelling was used to evaluate information from history, physical examination (for example, blood pressure), drug use, and quality of life questionnaires that independently contributed to the prediction of death. The area under receiver operating characteristic curves (AUC) was used to estimate the predictive ability of the prognostic models. Results: During the 18 months of follow up, 51 patients (34%) died. Independent predictors of mortality were diabetes mellitus, a history of renal dysfunction (or higher creatinine), New York Heart Association (NYHA) functional class III or IV, lower weight or body mass index, lower blood pressure, ankle oedema, and higher scores on a disease specific quality of life questionnaire. The use of β blockers was predictive of a better prognosis. These factors were used to derive various prediction formulas. A model based on medical history, weight, presence of oedema, and lower blood pressure had an AUC of 0.77. Addition of use of β blockers to this model improved the AUC to 0.80. Addition of NYHA class increased the AUC to 0.84. Data on quality of life did not improve the AUC further (AUC 0.85). Conclusions: A prognostic model produced on the basis of easily obtainable information from medical history and physical examination can adequately stratify heart failure patients according to their short term risk of death.

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Antidepressants have different receptor binding profiles, which are related to therapeutic action and adverse drug reactions. We constructed a model to classify antidepressants on the basis of their binding properties of most common transporter-and receptor sites. Receptor binding was quantified by calculating receptor occupancy for the 5-HT (serotonin) reuptake transporter, norepinephrinic reuptake transporter, 5-HT 2C -receptor, M 3 -receptor, H 1 -receptor and 1 -receptor. To identify groups of antidepressants that show similar patterns of receptor occupancy for different receptors, hierarchical cluster analysis (HCA) and principle component analysis (PCA) were used. In addition, to visualize (a)symmetry between binding profiles of antidepressants, radar plots were constructed. On the basis of both analyses, four clusters of antidepressants which exert similar pharmacological properties were identified. Potentially, this model could be a helpful tool in medical practice and may be used as a prediction model for adverse effects of drugs entering the market.