Search

Aim: To evaluate the relationship between sex, age and outcome in dilated cardiomyopathy (DCM)., Methods and Results: We used proportional hazard modelling to examine the association between sex, age and all-cause mortality in consecutive patients with DCM. Overall, 881 patients (290 women, median age 52 years) were followed for a median of 4.9 years. Women were more likely to present with heart failure (64.0% vs. 54.5%; P = 0.007) and had more severe symptoms (P < 0.0001) compared to men. Women had smaller left ventricular end-diastolic volume (125 mL/m 2 vs. 135 mL/m 2 ; P < 0.001), higher left ventricular ejection fraction (40.2% vs. 37.9%; P = 0.019) and were less likely to have mid-wall late gadolinium enhancement (23.0% vs. 38.9%; P < 0.0001). During follow-up, 149 (16.9%) patients died, including 41 (4.7%) who died suddenly. After adjustment, all-cause mortality [hazard ratio (HR) 0.61, 95% confidence interval (CI) 0.41-0.92; P = 0.018] was lower in women, with similar trends for cardiovascular (HR 0.60, 95% CI 0.35-1.05; P = 0.07), non-sudden (HR 0.63, 95% CI 0.39-1.02; P = 0.06) and sudden death (HR 0.70, 95% CI 0.30-1.63; P = 0.41). All-cause mortality (per 10 years: HR 1.36, 95% CI 1.20-1.55; P < 0.0001) and non-sudden death (per 10 years: HR 1.51, 95% CI 1.26-1.82; P < 0.00001) increased with age. Cumulative incidence curves confirmed favourable outcomes, particularly in women and those <60 years. Increased all-cause mortality in patients >60 years of age was driven by non-sudden death., Conclusion: Women with DCM have better survival compared to men, which may partly be due to less severe left ventricular dysfunction and a smaller scar burden. There is increased mortality driven by non-sudden death in patients >60 years of age that is less marked in women. Outcomes with contemporary treatment were favourable, with a low incidence of sudden death., (© 2018 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

In the presence of time-dependent confounding, there are several methods available to estimate treatment effects. With correctly specified models and appropriate structural assumptions, any of these methods could provide consistent effect estimates, but with real-world data, all models will be misspecified and it is difficult to know if assumptions are violated. In this paper, we investigate five methods: inverse probability weighting of marginal structural models, history-adjusted marginal structural models, sequential conditional mean models, g-computation formula, and g-estimation of structural nested models. This work is motivated by an investigation of the effects of treatments in cystic fibrosis using the UK Cystic Fibrosis Registry data focussing on two outcomes: lung function (continuous outcome) and annual number of days receiving intravenous antibiotics (count outcome). We identified five features of this data that may affect the performance of the methods: misspecification of the causal null, long-term treatment effects, effect modification by time-varying covariates, misspecification of the direction of causal pathways, and censoring. In simulation studies, under ideal settings, all five methods provide consistent estimates of the treatment effect with little difference between methods. However, all methods performed poorly under some settings, highlighting the importance of using appropriate methods based on the data available. Furthermore, with the count outcome, the issue of non-collapsibility makes comparison between methods delivering marginal and conditional effects difficult. In many situations, we would recommend using more than one of the available methods for analysis, as if the effect estimates are very different, this would indicate potential issues with the analyses., (© 2018 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)