Decreased heart rate variability has been associated with an adverse prognosis in patients after myocardial infarction. Studies carried out in the population at large show contradictory results. The authors examined the association between heart rate variability on a standard 10-second electrocardiogram and cardiac and all-cause mortality in the Rotterdam Study, a population-based cohort study of men and women aged > or =55 years, using data collected between 1990 and 1996 (mean follow-up = 4 years). Heart rate variability, taken as the standard deviation of normal R-R intervals (SDNN), was computed by means of the Modular ECG Analysis System. After exclusion of subjects with arrhythmia and those with fewer than six normal R-R intervals, the study population consisted of 2,088 men and 3,184 women. Cox's proportional hazards model was used to examine the age- and sex-adjusted risk for cardiac, noncardiac, and total mortality in relation to quartiles of SDNN, using the third quartile of SDNN as the reference category. Subjects in the lowest quartile of SDNN relative to those in the third quartile had an 80 percent age- and sex-adjusted increased risk for cardiac mortality (hazard ratio = 1.8; 95% confidence interval: 1.0, 3.2). Interestingly, for subjects in the highest quartile of SDNN, an even more pronounced risk for cardiac mortality was present (hazard ratio = 2.3; 95% confidence interval: 1.3, 4.0). Additional adjustment for possible confounders did not materially change the risk estimates. The authors conclude that heart rate variability measured on the standard 10-second electrocardiogram can be used to identify older men and women with an increased risk for cardiac mortality. In the elderly, increased heart rate variability is an even stronger indicator of cardiac mortality than decreased heart rate variability. Further studies are needed to confirm these findings and to elucidate their physiologic meaning.

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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