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Background and aim Epidemiological studies have reported increased risk of cardiovascular disease, cancer and all-cause mortality with greater resting heart rate, however, the evidence is not consistent. Differences by gender, adjustment for confounding factors, as well as the potential impact of subclinical disease are not clear. A previous meta-analysis missed a large number of studies, and data for atrial fibrillation have not been summarized before. We therefore aimed to clarify these associations in a systematic review and meta-analysis of prospective studies. Methods and results PubMed and Embase were searched up to 29 March 2017. Summary RRs and 95% confidence intervals (CIs) were calculated using random effects models. Eighty seven studies were included. The summary RR per 10 beats per minute increase in resting heart rate was 1.07 (95% CI: 1.05–1.10, I2 = 61.9%, n = 31) for coronary heart disease, 1.09 (95% CI: 1.00–1.18, I2 = 62.3%, n = 5) for sudden cardiac death, 1.18 (95% CI: 1.10–1.27, I2 = 74.5%, n = 8) for heart failure, 0.97 (95% CI: 0.92–1.02, I2 = 91.4%, n = 9) for atrial fibrillation, 1.06 (95% CI: 1.02–1.10, I2 = 59.5%, n = 16) for total stroke, 1.15 (95% CI: 1.11–1.18, I2 = 84.3%, n = 35) for cardiovascular disease, 1.14 (95% CI: 1.06–1.23, I2 = 90.2%, n = 12) for total cancer, and 1.17 (95% CI: 1.14–1.19, I2 = 94.0%, n = 48) for all-cause mortality. There was a positive dose–response relationship for all outcomes except for atrial fibrillation for which there was a J-shaped association. Conclusion This meta-analysis found an increased risk of coronary heart disease, sudden cardiac death, heart failure, atrial fibrillation, stroke, cardiovascular disease, total cancer and all-cause mortality with greater resting heart rate.

Purpose: Inflammatory breast cancer (IBC) is a rare, aggressive and special subtype of primary breast cancer. We aimed to establish competing-risks nomograms to predict the IBC-specific death (BCSD) and other-cause-specific death (OCSD) of IBC patients. Methods: We extracted data on primary IBC patients from the SEER (Surveillance, Epidemiology, and End Results) database by applying specific inclusion and exclusion criteria. Cumulative incidence function (CIF) was used to calculate the cumulative incidence rates and Gray’s test was used to evaluate the difference between groups. Fine-Gray proportional subdistribution hazard method was applied to identify the independent predictors. We then established nomograms to predict the 1-, 3-, and 5-year cumulative incidence rates of BCSD and OCSD based on the results. The calibration curves and concordance index (C-index) were adopted to validate the nomograms. Results: We enrolled 1699 eligible IBC patients eventually. In general, the 1-, 3-, and 5-year cumulative incidence rates of BCSD were 15.3%, 41.0%, and 50.7%, respectively, while those of OCSD were 3.0%, 5.1%, and 7.4%. The following 9 variables were independent predictive factors for BCSD: race, lymph node ratio (LNR), AJCC M stage, histological grade, ER (estrogen receptor) status, PR (progesterone receptor) status, HER-2 (human epidermal growth factor-like receptor 2) status, surgery status, and radiotherapy status. Meanwhile, age, ER, PR and chemotherapy status could predict OCSD independently. These factors were integrated for the construction of the competing-risks nomograms. The results of calibration curves and C-indexes indicated the nomograms had good performance. Conclusions: Based on the SEER database, we established the first competing-risks nomograms to predict BCSD and OCSD of IBC patients. The good performance indicated that they could be incorporated in clinical practice to provide references for clinicians to make individualized treatment strategies.