Results
This report is based on 39 372 patients from 30 studies: six were randomized controlled trials (24 041 patients) and 24 were registries (15 331 patients). Supplementary material online Table S1 describes each of the 30 studies. Overall, 15 851 (40.2%) patients died during a median follow-up of 2.5 years. The six largest studies (DIAMOND, DIG, CHARM, and ECHOS trials and IN-CHF and HOLA registries) contributed 75.8% of patients and also 75.8% of deaths.
There were 31 baseline variables available for inclusion in prognostic models. Table 1 provides their descriptive statistics for patients still alive and patients who died during follow-up.
Using Poisson regression models for patient survival with forward stepwise variable selection, adjusting for study (random effect) and follow-up time (higher mortality rate in early follow-up), we identified 13 independent predictor variables (Table 2). All were highly significant P < 0.002, and most were overwhelmingly significant, i.e. P < 0.0001.
Table 3 lists the extent of missing data for these 13 variables. A multiple imputation algorithm (see Methods) was used to overcome this problem. Consequently, all results are based on average estimates across 25 imputed data sets.
For continuous variables, potential non-linearity in the prediction of survival was explored, as were potential statistical interactions between predictors. Hence the associations of EF, body mass index, and serum creatinine with mortality risk were, respectively, confined to EF <40%, body mass index <30 kg/m, and serum creatinine <350 µmol/L. The mortality association of increased age was more marked with higher EF, whereas the inverse association of systolic blood pressure with mortality became more marked with lower EF.
Figure 1 displays the independent impact of each predictor on mortality risk. The impact of age (which varies with EF) is particularly strong, and hence is shown on a different scale to the other plots.
(Enlarge Image)
Figure 1.
Mortality rate ratios (and 95% CIs) for each variable in the predictive model. All charts are on the same scale except that for the interaction between ejection fraction and age, where the impact on mortality is more marked.
From the risk coefficients given in Table 2, an integer score has been created (Figure 2). For each patient, the integer amounts contributed by the risk factor's values are added up to obtain a total integer score for that patient. The bell-shaped distribution of this integer risk score for all 39 372 patients is shown in Figure 3. The median is 23 points and the range is 0–52 points, with 95% of patients in the range of 8–36 points. The curve in Figure 3 relates a patient's score to their probability of dying within 3 years. For instance, scores of 10, 20, 30, and 40 have 3-year probabilities 0.101, 0.256, 0.525, and 0.842, respectively. Table 4 details the link between any integer score and the probabilities of dying within 1 year and 3 years.
(Enlarge Image)
Figure 2.
A chart to calculate the integer risk score for each patient.
(Enlarge Image)
Figure 3.
Distribution of the integer risk score for all 39 372 patients, and its association with the risk of dying (and 95% CI) within 3 years.
Figure 4 shows mortality over 3 years for patients classified into six risk groups. Groups 1–4 comprise patients with scores 0–16, 17–20, 21–24, and 25–28, respectively, approximately the first four quintiles of risk. To give more detail at higher risk, groups 5 and 6 comprise patients with scores 29–32 and 33 or more, approximately the top two deciles of risk. The marked continuous separation of the six Kaplan–Meier curves is striking: the 3-year % dead in the bottom quintile and top decile is 10 and 70%, respectively.
(Enlarge Image)
Figure 4.
Cumulative mortality risk over 3 years for patients classified into six risk groups. Risk groups 1–4 represent the first four quintiles of risk (integer scores 0–16, 17–20, 21–24, and 25–28, respectively). Risk groups 5 and 6 represent the top two deciles of risk (integer scores 29–32 and 33 or more, respectively). 95% CIs are plotted at 1, 2, and 3 years follow-up.
Regarding model goodness-of-fit, Figure 5 compares observed and model-predicted 3-year mortality risk across the six risk groups. In the bottom two groups, the observed mortality is slightly lower than that predicted by the model, but overall the marked gradient in risk is well captured by the integer score.
(Enlarge Image)
Figure 5.
Observed vs. model-predicted 3-year mortality in six risk groups.
Table 5 and Table 6 show two separate models for patients with reduced and preserved left-ventricular function (EF <40 and ≥40%, respectively). For most predictors, the strength of mortality association is similar in both subgroups. However, the impact of age is more marked and the impact of lower SBP is less marked in patients with preserved left-ventricular function, consistent with the interactions in the overall model.
In this meta-analysis of 30 cohort studies, we explored between-study heterogeneity in mortality prediction. From fitting separate models for each study, we observe a good consistency across studies re the relative importance of the predictors (data not shown). We have also repeated the model in Table 2, now fitting study as a fixed effect (rather than a random effect). This reveals substantial between-study differences in mortality risk not explained by predictors in our model. However, a comparison of the seven randomized trials with the 23 patient registries reveals no significant difference in their mortality rates.