Using BNP to develop a risk score for heart failure in primary care
David Adlam1,*,
Paul Silcocks2 and
Nigel Sparrow3
1Department of Cardiovascular Medicine, Queen's Medical Centre, Nottingham, UK
2Trent Institute for Health Services Research, University of Nottingham Medical School, Queen's Medical Centre, Nottingham, UK
3Newthorpe Medical Practice, Nottingham, UK
Received 25 February 2004; revised 4 December 2004; accepted 27 January 2005; online publish-ahead-of-print 15 March 2005.
* Corresponding author. Tel: +44 1865 760 177. E-mail address: davidadlam{at}dcotors.org.uk
See page 1052 for the editorial comment on this article (doi:10.1093/eurheartj/ehi244)
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Abstract
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Aims Chronic heart failure is a common condition with high mortality. Accurate diagnosis in primary care is difficult. Elevated B-type natriuretic peptide (BNP) is associated with left ventricular systolic dysfunction and increased mortality. Prognostic scoring systems using BNP may help to stratify risk in primary care patients. The aim of this research was to establish the independent variables which predict mortality in a primary care population-prescribed loop diuretics and to generate and validate a scoring system for heart failure in general practice.
Methods and results Five hundred and thirty-two patients were followed up for a mean of 6.4 years after attending a research clinic for clinical assessment, electrocardiogram (ECG), echocardiography, and BNP. Multivariate analysis was used to establish independent prognostic variables and to generate a prognostic scoring system. The score generated was [0.50xBNP+5xage+50x(CVA+sex+diabetes+ECG)]. The cut-off scores for risk groups were; 25th percentile, 411; 50th percentile, 475; 75th percentile, 524; Harrell's c=0.75.
Conclusion Developing prognostic scoring systems provides a means of risk stratifying patients without relying on a single cut-off diagnostic value for BNP. Further validation of such scoring systems may improve future management of community heart failure patients.
Key Words: Primary care Heart failure Brain natriuretic peptide Echocardiography Prognosis
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Introduction
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Chronic heart failure is a common condition, especially in the elderly. In those 75 years of age, the prevalence may exceed 80 cases per 1000.1 It is associated with a high mortality and considerable morbidity, in particular, from breathlessness and reduced exercise tolerance. The identification of patients with chronic heart failure is important as they may benefit from treatments known to improve both symptoms and mortality in heart failure. These include angiotensin-converting enzyme (ACE)-inhibitors,2,3 beta-blockers,4,5 and spironolactone.6
Most patients with chronic heart failure present in primary care. Accurate diagnosis in a primary care setting is difficult.7,8 Clinical features associated with left ventricular failure (LVF) are inconsistent, and the sensitivity and specificity of typical symptoms and clinical signs are low.911 Echocardiography is the investigation of choice to establish a diagnosis of impaired left ventricular systolic function,12 but this requires a high degree of technical expertise, and availability to primary care physicians may be limited. Even where open access echocardiography services are available, the referral for this investigation is much lower than the population prevalence of the disease.9
B-type natriuretic peptide (BNP) is closely correlated with left ventricular systolic dysfunction in most1315 but not all studies.16,17 It is also an independent predictor of mortality.18,19 The development of bedside test kits for BNP has the potential to allow rapid accurate assessment of BNP in a primary care setting.20,21 The practical application of BNP measurement to primary care practice has been limited by difficulties in establishing a cut-off threshold for diagnosis.22,23 This has led some to suggest that there is no role for BNP measurement in current clinical practice.22 The development of scoring systems in primary care, utilizing BNP and other readily available clinical information, may allow patients with a worse prognosis to be identified without relying purely on a single cut-off threshold value for BNP.
In this study, we followed up a population of patients prescribed loop diuretics in primary care, who attended a research clinic for assessment. The aim of this study was to use the independent variables predictive of mortality to develop and validate a scoring system in primary care, using BNP and other readily available clinical information.
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Methods
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Seven general practices in Nottingham, UK, with a total list size of 60 728 were surveyed between January 1995 and December 1998. The study area included both urban and rural practices, with list sizes ranging from 4000 to 12 000. This practice population was under the care of 27 full-time equivalent general practitioners. In total, 1366 patients were found to be taking loop diuretics. These patients were invited by letter to attend a research clinic at the local hospital where they underwent full clinical assessment, electrocardiography, BNP measurement, and echocardiography. Each of these elements was carried out blinded to the results of other parts of the assessment. Electrocardiograms (ECGs) were analysed by a primary care physician (N.S.) and classified as normal or abnormal. To best mimic conditions in primary care practice, the primary care physician had no prior specialized ECG training and no specific instruction on what constituted abnormality. This simple approach to ECG analysis has previously been demonstrated to be both accurate and reproducible in primary care.32 Echocardiography was performed by an experienced technician. Left ventricular ejection fractions were measured using a phased array sector scanner (Vingmed CFM 700, Oslo, Norway). The images were analysed using a computer-assisted, video overlay, echocardiographic analysis system (Thoraxcenter, Erasmus University, Rotterdam, The Netherlands). An apical four-chamber view was used for imaging, and a modified Simpson's single plane disc method was used for analysis. The BNP assay used has been described elsewhere.16 In brief, plasma samples were acidified and extracted using pre-activated Sep Pak C18 cartridge (Waters Corporation, Milford, MA, USA). The eluates were dried under vacuum using a centrifugal evaporator and stored. The precipitates were resuspended in assay buffer and assayed by radioimmunoassay (Peninsula Laboratories, St Helens, UK).
Of 737 patients who attended the research clinic, ejection fraction and BNP measurements were obtained in 570 patients. Ethical approval for this study was obtained from the Queen's Medical Centre Ethics Committee, Nottingham. The investigation conforms to the principles outlined in the Declaration of Helsinki (BMJ 1964;ii:177).
The vital status of patients was subsequently established a mean of 6.4 years (4.07.1) and median 6.3 years after assessment. Five hundred and thirty-two patients were traced using local authority records. Thirty-eight patients were lost to follow up. The derivation of the study population is shown in Figure 1.
Statistics
Variables significantly associated with a worse prognosis were identified for descriptive purposes by an initial univariate analysis. Because some of these variables might be redundant for predictive purposes, a Cox regression analysis was then performed to establish which factors retained prognostic importance when considered jointly. Cigarette consumption data were found to be incomplete in the primary care records of the patients studied, and this variable was omitted from further consideration.
A predictive model was developed using the whole data set as follows. Having excluded incomplete observations, a 100% simple random sample with replacement was drawn and a stepwise Cox regression was performed, the list of variables selected being stored. This process was repeated 100 times, and those variables which were selected in
60% of the repetitions (i.e. for which the lower 95% confidence interval for the probability of selection was >50%) were then fitted simultaneously to generate a prognostic score. This admittedly ad hoc procedure ensured that only variables consistently selected would be used for the final model, which would therefore not suffer from over-fitting and is similar to that described by Austin and Tu.24 The number of events per parameter was 11.38.
The performance of the prognostic score was then evaluated on the data set by means of Harrell's c-statistic.25 The c-statistic is a rank-based measure, a value of 1 indicating perfect concordance of the score and outcome, whereas a value of 0.5 indicates only chance agreement. The evaluation incorporated a correction for optimism by means of a 0.632 bootstrap. A simplified version of the score was also created, and its performance summarized by the c-statistic, with standard error estimated by bootstrapping. KaplanMeier survival curves were then generated dividing patient scores by quartiles to assess the discriminating power of the derived score. Finally, the variable selection process and evaluation of the prognostic score was repeated after a crude imputation of high and low for missing values.
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Results
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The characteristics of patients in this study are shown in Table 1. Using the KaplanMeier method, all-cause mortality in this population was calculated as 37.14% at 5 years (190/528 evaluable). The death certification data over the whole follow-up period show LVF as the primary cause of death in 21% (44/209) of patients. A further 18 patient certificates (9%) listed LVF as a contributing factor in the cause of death. Cardiovascular disease was given as the cause of death in 56% (117/209) of patients, cancer in 10% (21/209), and lung disease in 18% (38/209). Other causes of death account for 15% (32/209) of deaths. No information on cause of death was available in one patient.
For comparability of univariate and multiple variable analyses, further results apply to the data set without missing values (except when otherwise specified). Mortality was higher in patients with lower ejection fractions (Table 2). For those patients with ejection fraction measurements in the lowest quartile (median 25%, IQR 2228%), 56% were alive at 5 years. This compares with the patients in the highest quartile of ejection fractions (median 54%, IQR 5159%), where 69% survived 5 years. BNP level at the time of assessment was also significantly correlated with mortality (Table 3). Of patients with BNP measurements in the highest quartile (median 135 pg/mL, IQR 103165), 34% survived 5 years, whereas in those with BNP in the lowest quartile (median 28 pg/mL, IQR 2232), 81% survived 5 years.
Table 4 shows the univariate analysis. The variables significantly associated with mortality are in bold. The effect of BNP only appears small because it is expressed per increment of pg/mL, but it is statistically highly significant. When repeated stepwise, Cox regressions were performed to identify the independent variables predictive of mortality; the variables consistently selected were age at assessment, BNP, sex, past history of stroke, past history of diabetes, and the ECG assessment being normal or abnormal. Table 5 shows pairwise r2 values: those corresponding to variables consistently selected are in bold and it is clear that these variables were largely uncorrelated with each other, whether this was in the full data set or the one without missing values.
The variables consistently identified were combined in a prognostic score, on the basis of coefficients in a Cox regression. The optimal score was found to be: [0.0069xBNP (pg/mL)]+0.068xage at assessment]+[0.549xpast history of stroke (0 if no past history, 1 if past history)]+[0.486xsex (1 if male, 0 if female)]+[0.587x past history of diabetes (0 if no past history, 1 if past history)]+[0.637xECG abnormality (0 absent, 1 present)].
The proportional hazards assumption was tested for this model, using the Stata function phtest (Table 6). Overall, there is no evidence contrary to the assumption, although there is evidence that the assumption is violated for diabetes. However, in this context, the prime interest is in the performance of the overall score so the result for diabetes was discounted. The overall performance as measured by Harrell's c was 0.756 and correction for optimism by a 0.632 bootstrap only reduced this to a value of 0.748. This compares with a Harrell's c for BNP in the univariate analysis of 0.695. Thus, the combined score gives a useful improvement over absolute BNP alone. To generate a more user-friendly prognostic score, a simplified version of this score was also tested. The simple score was 0.50xBNP+5xage+50x(CVA+sex+diabetes+ECG) for which Harrell's c was 0.752 [and for which the test for non-proportional hazards gave a
2 of 1.49 (1 df) P=0.2216]. There was therefore no practical difference in the discrimination provided by the optimal and simplified scoring systems as estimated by Harrell's c, although this is a rank-based measure, it may miss small differences in discrimination.
The KaplanMeier survival estimates for the groupings of the simplified score are shown in Figure 2. The scores are grouped by quartiles, with the lowest score correlating with the best survival. A crude sensitivity analysis was also performed by imputing missing values first with the highest and next with the lowest values observed for each variable. This resulted in the same variables being identified.

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Figure 2 KaplanMeier survival estimates by groupings of simplified score: 0.50xBNP+5.00xage+50x(CVA+diabetes+ECG+sex), Harrell's c=0.752. GP score 1, 2, 3 and 4 represent patients from the lowest to highest quartile GP scores respectively.
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Figure 3 shows the relative survival in each of the quartile population groups identified by the simple scoring system (compared with that expected among the general population matched for age and sex). This shows that even those patients in this study population with the best scores have a worse prognosis than age- and sex-matched patients in the region in which this study was performed (relative survival at 5 years=63.28%, 95% CI 58.5467.64), and in effect, general mortality can be ignored for prognostic purposes in these patients.

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Figure 3 Relative survival by prognostic group. The relative survival is the proportion surviving divided by the proportion expected to survive in a group of persons of that age and sex composition (based on Trent/E Midlands rates, 19712001). GP score 1, 2, 3 and 4 represent patients from the lowest to highest quartile scores respectively.
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Discussion
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The results of this study confirm that BNP is an independent predictor of mortality in the primary care population studied. This is consistent with the findings of other community-based studies.18,19,26 The relationship between BNP and prognosis may be directly related to the association between elevated BNP and left ventricular systolic dysfunction.1315 In our study population, however, the correlation between BNP and left ventricular systolic dysfunction at the time of assessment was not as close as that found in other studies.16 It is possible that other factors associated with elevation in plasma BNP are also associated with a worse prognosis.
The precise role of BNP in the diagnosis and management of heart failure in primary care remains to be established. One of the major hurdles to practical use of this test is the difficulty in establishing a clear diagnostic cut-off value.22,23 This is a particular problem in a primary care population as BNP is known to be higher in elderly patients and in females.27,28 This study shows that using BNP as part of a scoring system, with other independent predictors of mortality such as age and sex, may be useful in stratifying patient risk. This has the advantage of using absolute BNP values in the score rather than a specific cut-off value. With the development of rapid bedside BNP measurement kits,20,21 such a score could potentially be calculated in a primary care setting.
The method used to generate the scoring system is ad hoc, but robust in the sense that (i) it is objective, (ii) the well-recognized problems of stepwise variable selection (overfitting and unstable choice of variables) have been avoided by resampling and choosing only variables selected most of the time, and (iii) the estimated performance of the score has been corrected for optimism.
Interpretation of the scoring system in this study is limited by the population studied. We studied a population of patients prescribed loop diuretics in primary care who were willing and able to attend a research clinic for assessment (i.e. prevalent cases). This population was selected to include most patients in primary care with significant left ventricular systolic dysfunction, but will also include some patients with normal left ventricular function-prescribed loop diuretics for other reasons.9,29 The scoring system may not apply equally to other populations and would need to be more widely validated. Even those patients with the lowest scores had a higher mortality relative to age- and sex-matched controls in the region. However, a BNP scoring system would probably not be used as a population screening tool30 but rather to risk stratify patients identified for assessment by their primary care physician. The clinical judgement, which leads general practitioners to prescribe loop diuretics, may by itself identify a population who would benefit from further investigation.9 Figure 1 demonstrates how the study population was derived from the primary care population as a whole. We have previously shown that our study population is, in general, younger, more likely to be male, and more likely to have a history of angina than the community population as a whole.9 This is the result of the inevitable degree of selection bias inherent to any voluntary attendance study and underlines the importance of broader future validation of this prognostic score in different patient populations.
Another key question is how to use a scoring system such as that proposed in this study to modify patient management. Clearly, it would be useful if patients with the lowest scores could be managed in primary care, whereas those with the highest scores could be referred for more urgent out-patient hospital management. This scoring system is devised from prevalent cases in a primary care population-prescribed loop diuretics. Diuretic therapy has been shown to reduce levels of circulating natriuretic peptides in patients treated for class IV heart failure.31 Untreated, newly presenting, or incident cases may behave differently to our study patients. Any such scoring system would therefore have to be prospectively assessed before an algorithm of alternative approaches to patient management could be proposed. The separation of the KaplanMeier survival estimates with our simple score suggests that it may well be possible to develop a scoring system to risk stratify patients and prioritize patients for hospital management. However, it remains to be determined if those patients with the best prognostic scores can be safely managed without echocardiography. A prospective study of this type may also be able to establish whether more intense medical therapy or surveillance in patients with higher scores improves their subsequent prognosis. There is, at present, little evidence to support the use of BNP to monitor response to treatment in patients with chronic heart failure, although this is the subject of ongoing research.22
Acknowledged limitations in this study include the fact that interpretation of ECG and echocardiography was performed by a single observer. Therefore, there is no measure of inter-observer variability. We have previously reported that the simple classification of the ECG into normal vs. abnormal by a primary care physician is reproducible and shows little variation from that of a cardiologist.32 Thirty-eight patients in this study were lost to follow-up. Most had moved from the study area. We do not feel that this unduly influenced the results presented. Smoking data was not included in our scoring system owing to a large proportion of missing data from patient primary care records. Inclusion of smoking as a variable may further refine and strengthen the score. It is possible that inclusion of other parameters not included in our analysis, such as presenting symptoms or New York Heart Association class, could further improve the prognostic accuracy of this scoring system.
The results of this study conform to those of a previous study by Nielson et al.33 that followed up 126 patients for a mean of 4.3 years from a primary care population with a history of symptoms of heart disease. The authors found that combining elevated N-terminal atrial natriuretic peptide (using a cut-off value of 0.8 nmol/L) with an abnormal ECG improved the ability to predict a poor prognosis.33 This study also found that the predictive value of these factors varies with age. Our study population is more than four times that of Nielson and Hilden and had a longer follow-up (mean 6.7 years). As a result, there was almost 10 times the number of deaths during follow-up in our analysis. We also identify the prognostic predictive value of natriuretic peptides and an abnormal ECG in a similar population and show that age and sex affect this predictive power. We continue to use this data to derive a risk score which uses absolute values of BNP rather than a cut-off and includes age and sex and important aspects of past medical history.
There is growing data on the correlation between BNP and left ventricular systolic dysfunction and mortality. If BNP is to become a clinically useful test in primary care, it must become quick and easy to perform and interpret. The development of rapid bedside test kits should allow rapid assessment of BNP. Interpretation of the prognostic implication of the results may be facilitated by the development of simple scoring systems in primary care, using BNP and other information readily obtained.
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Acknowledgements
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The study was funded by a grant from the Trent Regional Health Authority and the Nottinghamshire Multidisciplinary Audit Advisory Group. The authors wish to thank Dr A. Cowley and Dr Hetmanski for their work on the initial study and Professor J.R. Hampton for his advice during the preparation of this manuscript.
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