Discussion
Like many other disorders in medicine, the prognosis of a particular patient with HF has a stochastic - rather than deterministic - nature. As a direct result, a risk model could never have a perfect discriminatory ability for mortality, regardless of the complexity of the model. Using too many variables for a risk model not only makes it less useful for clinical practice, but also increases the risk of ‘overfitting’ - which threatens the accuracy of a model when applied to populations other than the original derivation sample [20]. Preferably, a model should follow the “law of parsimony” and contain least number of variables that has the most value, rather than including every variable that only provides a marginal increase in accuracy. Present study showed that a simple risk score only consisting of three variables have a good predictive accuracy for one-year mortality and performs rather comparably to more complex risk scores such as GWTG-HF model.
Risk models have important drawbacks that limit their usefulness. A HF risk model could give inaccurate results when applied to populations beyond their initial derivation, they are rarely accurate to predict prognosis for individual patients with HF and they can become obsolete with time [21,22]. However, they are still convenient as risk models enable a more objective assessment of the average life expectancy and they could be useful for selecting optimal management strategy for a given HF patient [21,22]. Even risk models with external validation are underutilized in daily clinical practice, perhaps not only because of the limitations but because of the inconvenience of finding and entering multiple data to calculate the final score [23]. MAGGIC risk score, which has a good evidence base for validity and a formidable c-score of 0.74 for mortality when applied to other HF cohort, needs 13 different variables to be entered [24]. GWTG-HF score had an acceptable predictive ability for one-year mortality (c-score varied between 0.64 - 0.67 for HFrEF and HFpEF, respectively), though it needed a mere 7 variables that made GWTG-HF score somewhat easier to calculate and more compatible with the law of parsimony [25]. Present findings indicate that ACEF-MDRD score could predict one-year mortality with an accuracy comparable to the GWTG score, and similar to the GWTG-HF score it could be applied to HF populations regardless of the presenting phenotype. ACEF-MDRD score had the additional advantage of using three simple and universally available parameters that makes it convenient to calculate, thus making it somewhat better suited to move beyond the “research realm” to the real world, as compared to other risk models.
The components of the ACEF score are not only used as standalone predictors of prognosis in HF, but also one or more of these variables are commonly found in nearly all HF risk scores [3,4,16,26]. Combining these variables allows an overall estimation of life expectancy, comorbidities, end-organ function and left ventricular performance. Despite the availability of multiple studies demonstrating the predictive ability of ACEF score in a multitude of different cardiovascular conditions, including patients with recent myocardial infarction or those undergoing cardiovascular surgery or percutaneous interventions, data on the prognostic usefulness of ACEF score in patients with HF is extremely limited [8-12]. Chen and associates have studied ACEF and ACEF-MDRD in 862 patients with ischemic cardiomyopathy and found that both scores had a good discriminative ability (c-statistics were 0.73 for ACEF and 0.72 for ACEF-MDRD, respectively), though it was not clear whether these patients had accompanying HF or not as this study was only presented as an abstract [27]. Present findings suggest that ACEF-MDRD score is an independent predictor of mortality in all HF patients, regardless of the underlying etiology, presentation, or phenotype, thus making it a potentially useful tool for a wide variety of patients.
To note, ACEF-MDRD score was not developed from the present sample but rather applied to it, and as such present analysis itself should be considered as a validation study. While there were many studies that have reported a more impressive predictive accuracy for their models than the figures provided in this study, they either lack external validation or their predictive accuracy is substantially lower when tested in samples other than their derivation cohorts [28]. Given that provided c-statistics rarely exceed 0.8 for nearly all models, using an index with a rather modest predictive accuracy could be justified given the sheer simplicity of the calculation (which could be done even with a pen and paper) making it practical for daily use and the lack of “overfitting” - making it suitable for use in different HF populations [22].
Available treatments for HF are numerous in the contemporary era and algorithms provided to guide management strategies are not evidence based. While the main expectation from a risk model is estimation of overall mortality, it is nonetheless more useful when it could guide treatment decisions. Several studies have already shown that risk models could indeed be utilized for this aim. For example, Seattle Heart Failure Model (SHFM) has been shown to predict mortality after left ventricular assist device implantation [29]. Whether ACEF-MDRD score could be utilized in a similar manner would be an interesting prospect to research in future studies.
Present findings indicate that ACEF-MDRD score had a rather modest discriminative ability for mortality. Adding new variables to the equation would be one way to improve the accuracy, since our findings indicate that ACEF score itself does not explain all the variability in mortality. However, this approach would violate the founding principle of ACEF score, which was using a limited number of predictors rather than every variable with statistical significance on multivariate analysis. Another way would be finding similar yet more powerful predictors of mortality to redesign ACEF-MDRD score. Although individual components of ACEF score are standalone predictors of mortality, it is not clear whether they are the best predictors, as ACEF score was not developed to predict mortality after HF. As such, better predictors could be used to replace core components of the ACEF score, but the law of parsimony should still be applied to keep the predictors at a minimum.