Predicting Survival in Heart Failure
Predicting Survival in Heart Failure
This study identifies 13 independent predictors of mortality in HF. Although all have been previously identified, the model and risk score reported here are the most comprehensive and generalizable available in the literature. They are based on 39 372 patients from 30 studies with a median follow-up of 2.5 years, the largest available database of HF patients. Also, we include patients with both reduced and preserved EF, the latter being absent from most previous models of HF prognosis.
Given the wide variety of different studies included, with a global representation, the findings are inherently generalizable to a broad spectrum of current and future patients. Conversion of the risk model into a user-friendly integer score accessible by the website www.heartfailurerisk.org facilitates its use on a routine individual patient basis by busy clinicians and nurses.
All 13 predictors in the risk score should be routinely available, though provision will be made in the website for one or two variables to be unknown for an individual. Note, the 'top five' predictors age, EF, serum creatinine, New York Heart Association (NYHA) class, and diabetes are important to know. The inverse association of EF with mortality is well established, and as previously reported, in above 40% there appears no further trend in prognosis. We included serum creatinine rather than creatinine clearance or eGFR. The latter involve formulae that include age, which would artificially diminish the huge influence of age on prognosis.
We confirm the association of body mass index with mortality, but with a cut-off of 30 kg/m, above which there appears no further trend. While others report heart rate as a significant predictor of mortality, we find that once the strong influence of beta blocker use is included, heart rate was not a strong independent predictor. A modest association of ACE-inhibitor and/or angiotensin-receptor blockers (ARB) use with lower mortality was highly significant, though many of our cohorts were established before ARBs were routinely available.
Cardiovascular disease history (e.g. myocardial infarction, angina, stroke, atrial fibrillation, LBBB) was considered in our model development. What mattered most was the time since first diagnosis of HF, best captured by whether this exceeds 18 months. Besides the powerful influence of diabetes, the other disease indicator of a poorer prognosis was prevalence of COPD. Previous myocardial infarction, atrial fibrillation, and LBBB were not sufficiently strong independent predictors of risk to be included in our model.
For patients with reduced and preserved EF, we developed separate risk models (Table 5 and Table 6). Nearly all predictors display a similar influence on mortality in both subgroups. Two exceptions are age (better prognosis of preserved EF compared with reduced EF HF is more pronounced at younger ages) and systolic blood pressure, which have a stronger inverse association with mortality in patients with reduced EF. These two interactions are incorporated into the integer risk score, as displayed in Figure 1.
Our meta-analysis of 30 cohort studies enables exploration of between-study differences in mortality risk. Separately, for each of the 10 largest studies, we calculated Poisson regression models for the same 13 predictors. Informal inspection of models across studies shows a consistent pattern to be expected, given there are no surprises among the selected predictors.
An additional model, with study included as a fixed effect (rather than a random effect), reveals some between-study variation in mortality risk not captured by the predictor variables. This may be due to geographic variations or unidentified patient-selection criteria varying across registries and clinical trials, though overall patients in registries and trials appear at similar risk. Also, calendar year may be relevant since improved treatment of HF may enhance prognosis in more recent times. We will explore these issues in a subsequent publication.
The integer risk score gives a very powerful discrimination of patients' mortality risk over 3 years, and also has excellent goodness-of-fit to the data across all 30 studies combined (Figures 3 and 4). Specifically, the score facilitates the identification of low-risk patients, e.g. score <17 has an expected 90% 3-year survival, and very high-risk patients, e.g. score ≥33 has an expected 30% 3-year survival.
We recognize some limitations. In combining evidence across multiple studies, we inevitably encountered substantial missing data (Table 3), with a few variables (e.g. body mass index, HF duration) missing in some entire cohorts. To overcome this problem, we used sophisticated computer-intensive multiple imputation methods. In addition, we have checked the robustness of our overall findings for each predictor by separate analyses within each cohort where full data for that predictor were available.
Conventional good practice seeks to validate a new risk score on external data. That is important when a risk score arises from a single cohort in one particular setting, especially when that cohort has limited size. Here, the circumstances are different. We have a global meta-analysis of 30 cohorts with the largest numbers of patients and deaths ever investigated in HF. We found an internal consistency across studies in risk predictors, but inevitably found between-cohort differences in mortality risk not attributable to known risk factors, probably due to geographic variations and differing patient-selection criteria. Thus, no single external cohort can provide a sensible, generalizable validation of our risk model. We feel that internal validation found across studies is sufficient.
There exist several other risk scores for predicting survival in HF. Best known is the Seattle Heart Failure Model. It was developed from a small database, 1125 patients in the PRAISE clinical trial, confined to patients with severe HF: NYHA class III B or IV and EF ≤30%. Such patients account for <20% of patients in our meta-analysis. Thus the robustness, applicability, and generalizability of the Seattle model are somewhat limited. Some variables in the Seattle model, e.g. serum sodium and haemoglobin, were not found to be independent predictors for inclusion in our model. Also, the Seattle model does not include diabetes, body mass index, and serum creatinine, well established risk factors in HF. A recently developed predictive model for survival is from the 3C-HF Study, but its relatively small size and only 1 year follow-up is limiting.
Any new risk score's success depends on the patient variables available for inclusion. Current knowledge of biomarkers in HF is inevitably ahead of what data are available across multiple cohort studies. For instance, natriuretic peptide level markedly influences prognosis in HF, but could not be included in our model. In principle, its inclusion would enhance further the excellent prognostic discrimination we achieved with routinely collected long-established predictors. The risk score is most applicable for patients at a stable point in their disease, the short-term impact of acute HF events being a separate matter.
In conclusion, the risk score developed here on a huge database of 30 cohort studies provides a uniquely robust and generalizable tool to quantify individual patients' prognosis in HF. The simplified integer score, accessible by the website www.heartfailurerisk.org makes findings routinely usable by busy clinicians. Such immediate awareness of a patient's risk profile is of value in determining the most appropriate management and treatment of their HF.
Discussion
This study identifies 13 independent predictors of mortality in HF. Although all have been previously identified, the model and risk score reported here are the most comprehensive and generalizable available in the literature. They are based on 39 372 patients from 30 studies with a median follow-up of 2.5 years, the largest available database of HF patients. Also, we include patients with both reduced and preserved EF, the latter being absent from most previous models of HF prognosis.
Given the wide variety of different studies included, with a global representation, the findings are inherently generalizable to a broad spectrum of current and future patients. Conversion of the risk model into a user-friendly integer score accessible by the website www.heartfailurerisk.org facilitates its use on a routine individual patient basis by busy clinicians and nurses.
All 13 predictors in the risk score should be routinely available, though provision will be made in the website for one or two variables to be unknown for an individual. Note, the 'top five' predictors age, EF, serum creatinine, New York Heart Association (NYHA) class, and diabetes are important to know. The inverse association of EF with mortality is well established, and as previously reported, in above 40% there appears no further trend in prognosis. We included serum creatinine rather than creatinine clearance or eGFR. The latter involve formulae that include age, which would artificially diminish the huge influence of age on prognosis.
We confirm the association of body mass index with mortality, but with a cut-off of 30 kg/m, above which there appears no further trend. While others report heart rate as a significant predictor of mortality, we find that once the strong influence of beta blocker use is included, heart rate was not a strong independent predictor. A modest association of ACE-inhibitor and/or angiotensin-receptor blockers (ARB) use with lower mortality was highly significant, though many of our cohorts were established before ARBs were routinely available.
Cardiovascular disease history (e.g. myocardial infarction, angina, stroke, atrial fibrillation, LBBB) was considered in our model development. What mattered most was the time since first diagnosis of HF, best captured by whether this exceeds 18 months. Besides the powerful influence of diabetes, the other disease indicator of a poorer prognosis was prevalence of COPD. Previous myocardial infarction, atrial fibrillation, and LBBB were not sufficiently strong independent predictors of risk to be included in our model.
For patients with reduced and preserved EF, we developed separate risk models (Table 5 and Table 6). Nearly all predictors display a similar influence on mortality in both subgroups. Two exceptions are age (better prognosis of preserved EF compared with reduced EF HF is more pronounced at younger ages) and systolic blood pressure, which have a stronger inverse association with mortality in patients with reduced EF. These two interactions are incorporated into the integer risk score, as displayed in Figure 1.
Our meta-analysis of 30 cohort studies enables exploration of between-study differences in mortality risk. Separately, for each of the 10 largest studies, we calculated Poisson regression models for the same 13 predictors. Informal inspection of models across studies shows a consistent pattern to be expected, given there are no surprises among the selected predictors.
An additional model, with study included as a fixed effect (rather than a random effect), reveals some between-study variation in mortality risk not captured by the predictor variables. This may be due to geographic variations or unidentified patient-selection criteria varying across registries and clinical trials, though overall patients in registries and trials appear at similar risk. Also, calendar year may be relevant since improved treatment of HF may enhance prognosis in more recent times. We will explore these issues in a subsequent publication.
The integer risk score gives a very powerful discrimination of patients' mortality risk over 3 years, and also has excellent goodness-of-fit to the data across all 30 studies combined (Figures 3 and 4). Specifically, the score facilitates the identification of low-risk patients, e.g. score <17 has an expected 90% 3-year survival, and very high-risk patients, e.g. score ≥33 has an expected 30% 3-year survival.
We recognize some limitations. In combining evidence across multiple studies, we inevitably encountered substantial missing data (Table 3), with a few variables (e.g. body mass index, HF duration) missing in some entire cohorts. To overcome this problem, we used sophisticated computer-intensive multiple imputation methods. In addition, we have checked the robustness of our overall findings for each predictor by separate analyses within each cohort where full data for that predictor were available.
Conventional good practice seeks to validate a new risk score on external data. That is important when a risk score arises from a single cohort in one particular setting, especially when that cohort has limited size. Here, the circumstances are different. We have a global meta-analysis of 30 cohorts with the largest numbers of patients and deaths ever investigated in HF. We found an internal consistency across studies in risk predictors, but inevitably found between-cohort differences in mortality risk not attributable to known risk factors, probably due to geographic variations and differing patient-selection criteria. Thus, no single external cohort can provide a sensible, generalizable validation of our risk model. We feel that internal validation found across studies is sufficient.
There exist several other risk scores for predicting survival in HF. Best known is the Seattle Heart Failure Model. It was developed from a small database, 1125 patients in the PRAISE clinical trial, confined to patients with severe HF: NYHA class III B or IV and EF ≤30%. Such patients account for <20% of patients in our meta-analysis. Thus the robustness, applicability, and generalizability of the Seattle model are somewhat limited. Some variables in the Seattle model, e.g. serum sodium and haemoglobin, were not found to be independent predictors for inclusion in our model. Also, the Seattle model does not include diabetes, body mass index, and serum creatinine, well established risk factors in HF. A recently developed predictive model for survival is from the 3C-HF Study, but its relatively small size and only 1 year follow-up is limiting.
Any new risk score's success depends on the patient variables available for inclusion. Current knowledge of biomarkers in HF is inevitably ahead of what data are available across multiple cohort studies. For instance, natriuretic peptide level markedly influences prognosis in HF, but could not be included in our model. In principle, its inclusion would enhance further the excellent prognostic discrimination we achieved with routinely collected long-established predictors. The risk score is most applicable for patients at a stable point in their disease, the short-term impact of acute HF events being a separate matter.
In conclusion, the risk score developed here on a huge database of 30 cohort studies provides a uniquely robust and generalizable tool to quantify individual patients' prognosis in HF. The simplified integer score, accessible by the website www.heartfailurerisk.org makes findings routinely usable by busy clinicians. Such immediate awareness of a patient's risk profile is of value in determining the most appropriate management and treatment of their HF.
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