In this study, we developed and internally validated a risk prediction model for incident HF in an elderly cohort using commonly available clinical variables. We demonstrated that this model provides better discrimination for incident HF than the FHFRS. Moreover, we created a simple to use scoring system to classify the population at-risk into four risk categories for HF development over 5 years.
The Health ABC HF risk prediction model and score has several strengths. First, this is a clinically relevant and applicable model that has potentially important utility in the general elderly population for prediction of incident HF. This is of significant epidemiologic importance. According to the White House Conference on Aging 2005,4
approximately 12% of Americans were older than 65 years in the year 2000; this proportion will rise to 20% by the year 2050. Heart failure incidence and prevalence is highest among the elderly and the aging of the population is expected to significantly worsen the current HF epidemic.14–16
This model provides a framework for risk assessment and systematic evaluation of preventive strategies to curb the HF epidemic. Second, although in a younger adult population from an earlier era the population attributable risk for hypertension was found to be nearly 40% in men and 60% in women, the population attributable risk of even major HF risk factors like hypertension and diabetes in the elderly recently were found to be only 12.7% and 8.3% respectively.6, 34
Since most subjects have multiple risk factors in various combinations, a multi-factorial risk prediction scheme is likely to be more robust in predicting risk. Third, our model predicts risk reliably using only common, clinically available parameters and a simple scoring system ensuring ease of widespread use. Fourth, eight of nine variables in our model except age are potentially modifiable. Therefore, risk assessment based on our model can lead to interventions that can potentially modify HF risk and may facilitate close follow-up and aggressive clinical management. It is possible that identification of high-risk individuals can be used for recruitment into HF prevention trials. Finally, very importantly this is the first prediction scheme that has shown reliable risk prediction of incident HF in blacks. The FHFRS was drawn on almost exclusively white population and until now there was no incident HF prediction model that assessed the risk in blacks. With the growing understanding on race based differences in risks and outcomes for various diseases and the particular relevance of certain risk factors in blacks e.g. hypertension, the reliability of any prediction model needs to be validated in the various race based cohorts. In our study, the Health ABC HF Score predicted risk equally well in both white and black subjects.
Unlike CHD, we currently lack prediction models on how to detect at-risk HF subjects in the general population. Previous literature has identified individual risk factors associated with HF, but comprehensive and validated risk prediction models have not been developed.13
The only exception is the FHFRS, which was developed in a subgroup of community-based cohort at higher risk for HF with known CHD or VHD or hypertension.10
Such patients accounted for half the population in our study. Moreover, with the obesity, metabolic syndrome, and diabetes epidemic, the population risk profile for incident HF may be changing.5
We assessed the utility of the FHFRS in predicting incident HF in a general population of elderly subjects and found it to be suboptimal in assessing the risk of incident HF, in both the overall Health ABC cohort and also in the subgroup of patients from which it was derived.
Our nine-variable model had good discrimination and calibration, with acceptable performance in both gender- and race-based groups. Importantly, internal validation in 1000 random bootstrap samples demonstrated stable performance. Although hypertension and diabetes were significantly associated with HF in univariate analyses, after inclusion of blood pressure and serum glucose levels in the analyses, past history of hypertension and diabetes were not independently associated with incident HF. This finding suggests that the relation between blood pressure, glucose, and HF is continuous and graded, and that blood pressure and glucose levels may increase HF risk even in the normal range.35,36
A recent analysis also showed an independent relationship between glucose levels and HF hospitalization risk.37
Thus, optimal glucose and blood pressure levels to ameliorate risk for HF need further study. This becomes a central issue in light of recent studies that indicate both increasing prevalence and inadequate control of hypertension and diabetes.38, 39
Our study has several limitations. Diagnosis of HF was based on HF hospitalization. As some participants may have developed HF without hospitalization, our rates of HF are likely underestimated. Possible misclassification of HF events might have occurred, as diagnostic criteria for HF are difficult to define. Of note, although the prognostic validity of CHS criteria for diagnosis of HF has been demonstrated, these criteria are less specific than the Framingham HF criteria and may explain some of the variability in the performance of the different models.40
Echocardiography was not performed at baseline in the Health ABC study. Thus, patients with sub-clinical prevalent structural heart disease may have been included in the analysis. The outcomes of both patients with either systolic dysfunction or HF with preserved ejection fraction are uniformly poor. The discriminatory ability of the current model to predict the two types of HF needs to be assessed further.41, 42
The Health ABC study did not collect data uniformly on VHD; however it is unlikely that a very large proportion of participants had significant subclinical VHD that would impact these results. Finally, the model was developed in a relatively healthy cohort of a certain age. Thus, the validity of the Health ABC model in other age groups, or general population within this age group where the burden of comorbidity may be higher, needs to be studied.
In conclusion, we have developed and internally validated a HF risk prediction model based on nine routine clinical variables, most of which are potentially modifiable. The identification of persons at high risk for HF using the Health ABC HF Score and targeting strategies for primary prevention of HF to improve outcomes needs further study.