Skip Navigation

European Journal of Heart Failure 2003 5(4):489-497; doi:10.1016/S1388-9842(03)00053-9
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Kearney, M. T.
Right arrow Articles by Fox, K. A.A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kearney, M. T.
Right arrow Articles by Fox, K. A.A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2003 European Society of Cardiology

A prognostic index to predict long-term mortality in patients with mild to moderate chronic heart failure stabilised on angiotensin converting enzyme inhibitors

Mark T. Kearneya,*,1, James Nolanb,1, Amanda J. Leec, Paul W. Brooksbyd, Robin Prescottc, Ajay M. Shaha, Azfar G. Zamane, Dwain L. Eckbergf, H.Stephen Lindsayg, Philip D. Batind, Richard Andrewsh and Keith A.A. Foxi

a Department of Cardiology, GKT School of Medicine King's College, Bessemer Road, Denmark Hill, London SE5 9PJ, UK
b The North Staffordshire Cardiac Centre Princes Road, UK
c Medical Statistics Unit University of Edinburgh UK
d Pontefract and Wakefield Hospitals UK
e Freeman Hospital Newcastle UK
f Medical College of Virginia, Virginia Commonwealth University Virginia, USA
g Bradford Royal Infirmary, Dunkworth Lane Bradford, Yorkshire BD9 6RJ, UK
h Lincoln County Hospital Greetwell Road, Lincoln, Licolnshire LN2 5QY, UK
i Department of Cardiology University of Edinburgh, UK

* Corresponding author. Tel.: +44-207-346-4025; fax: +44-207-346-4771 E-mail address: mark.kearney{at}kcl.ac.uk


   Abstract
Top
 Notes
 Abstract
1. Introduction
 2. Methods
 3. Measured variables
 4. Results
 5. Discussion
 6. Conclusion
 References
 
Background: Mortality in patients with mild to moderate chronic heart failure remains high. At present there is no easy way of identifying patients within this population at increased risk of death in the medium to long term.

Aims: To develop a prognostic index to identify outpatients with mild to moderate chronic heart failure at increased risk of death.

Methods and results: Five hundred and fifty-three outpatients mean (S.D.) age 63(±10) years with symptoms of chronic heart failure (mean New York Heart Association functional class, 2.3(±0.5)), were recruited between December 1993 and April 1995. By April 2000, 201 patients had died. Using data from non-invasive measurements of cardiac size, electrical and autonomic function, renal function and plasma biochemistry we identified eight independent predictors of mortality (all P<0.01). To develop a prognostic index, predictors were dichotomised by group median and awarded 0 or 1 point accordingly. Serum sodium≤140 mmol/l (1 point), creatinine≥111 µmol/l (1 point), cardiothoracic ratio≥0.52 (1 point), SDNN≤112 ms (1 point), maximum corrected QT interval≥487 ms (1 point), QRS dispersion≥42.7 ms (1 point), the presence of non-sustained ventricular tachycardia (1 point) and voltage criteria for left ventricular hypertrophy on 12-lead ECG (1 point). We calculated risk scores for patients by adding the points of each independent risk factor. In the low-risk group (0–3 points) mortality at 5 years was 20% and in the high-risk group (4–8 points) 53%. The area under the receiver–operator characteristic curve using dichotomised variables was 0.74 and for continuous model 0.78.

Conclusions: Our prognostic index which uses eight non-invasive measurements and a straightforward additive points system, has good discrimination and stratifies outpatients with chronic heart failure into high and low risk. This index may be useful in clinical care and risk stratification.

Key Words: Heart failure • Prognosis • Mortality

Received January 22, 2003; Revised February 14, 2003; Accepted April 2, 2003


   1. Introduction
Top
 Notes
 Abstract
 1. Introduction
2. Methods
 3. Measured variables
 4. Results
 5. Discussion
 6. Conclusion
 References
 
Chronic heart failure is a major health problem [1]. It is well established that patients with symptomatic chronic heart failure classed as mild to moderate, have a mortality rate of approximately 10% per annum [2,3], a rate similar to that of some soft tissue tumours [4]. Despite this, there is no simple method of stratifying patients within this group at low or high risk of early mortality. The few prognostic indices currently employed in clinical practice are designed to assess patients with severe heart failure who may need cardiac transplantation [5–7]. These patients, however, account for only a small proportion of the total heart failure population.

A clinical prediction rule for mild to moderately symptomatic chronic heart failure would provide objective prognostic estimates to enhance the clinician's intuition and judgement when considering therapeutic strategies or discussions with patients and relatives. To perform accurately, such a tool should use data that offer independent prognostic information [8]. Chronic heart failure is a complex disease characterised by disturbances of autonomic [9], electrical [10,11], mechanical [12] and neurohumoral function [12]. The most efficient prognostic index would, therefore, integrate measurements of these factors taken in the outpatient department.

We used the prognostic information from a multivariate analysis in a very well characterised group of patients with mild to moderate heart failure, followed for a minimum of 5 years or until death. The data included in the present study can all be easily collected in the outpatient clinic. The present report describes, for the first time, an index employing non-invasive measurements that can stratify ambulant patients with mild to moderate heart failure at high or low risk of death.


   2. Methods
Top
 Notes
 Abstract
 1. Introduction
 2. Methods
3. Measured variables
 4. Results
 5. Discussion
 6. Conclusion
 References
 
Detailed descriptions of the methods and organisation of the UK-HEART study have been published previously [9,10]. In brief, UK-HEART was a multi-centre study carried out in 8 UK Hospitals. This investigation conforms with the principles outlined in the Declaration of Helsinki. Ambulant outpatients of either sex, 18–85 years old, with chronic heart failure were recruited. Patients were eligible for the study if they had stable clinical signs and symptoms of chronic heart failure present for at least 3 months classified as New York Heart Association functional class I–III. This had to be in association with objective evidence of cardiac dysfunction at rest (pulmonary venous congestion, pulmonary oedema or a cardiothoracic ratio >0.55 on at least one chest radiograph, or a documented radionuclide or echocardiographic ejection fraction of <0.45). Patients were excluded if they had a condition associated with impaired autonomic function (including diabetes mellitus). Patients with documented constrictive or hypertrophic cardiomyopathy, sustained non-sinus dysrhythmias, atrioventricular conduction defects, or a comorbid non-cardiac disease likely to limit survival were also excluded. Studies were carried out in accordance with the standards of the local ethical committees and with the Helsinki Declaration. All patients gave written informed consent.


   3. Measured variables
Top
 Notes
 Abstract
 1. Introduction
 2. Methods
 3. Measured variables
4. Results
 5. Discussion
 6. Conclusion
 References
 
3.1. Ambulatory electrocardiographic assessment
Twenty-four hour ambulatory electrocardiogram recordings (Tracker, Reynolds Medical) were obtained in all patients during normal, unrestricted out-of-hospital activity. Recordings were analysed with a Reynolds Medical Pathfinder system, by independent technical staff blinded to patient characteristics and outcome data. Non-sustained ventricular tachycardia was defined as three or more consecutive ventricular ectopic beats at a rate >120 beats/min. After initial arrhythmia analysis and editing, normal-to-normal RR intervals were identified and heart rate variability in the time-domain was measured, according to published guidelines [13]. We measured the standard deviation of all normal-to-normal RR intervals (SDNN), an index of total heart rate variability and a surrogate for total cardiac neurohumoral input. Frequency-domain analyses (which are thought to give an insight into different aspects of the autonomic nervous system) were made for the first 5 min of each hour and averaged. Fast Fourier transform power spectra (with linear interpolation over missing data, resampling at 0.5 Hz, and Hamming windowing) were integrated over all frequencies (total power); 0.0033–0.04 Hz (very low frequency); 0.04–0.15 Hz (low frequency); and 0.15–0.40 Hz (high frequency) [14].

3.2. 12-Lead electrocardiograms
Standard 12-lead ECGs were recorded at 25 mm/s and analysed by a senior cardiologist (W.P.B.) blinded to patient characteristics. The QTc interval and QRS duration were measured manually and corrected for heart rate using the Bazzett formula as previously described [10]. The JT interval was calculated by subtracting QRS duration from QT (means) interval in individual leads. The QT, QRS and JT dispersions were defined as the difference between the maximum and minimum QT/QRS/JT values occurring in any of the 12 ECG leads. Left ventricular hypertrophy was assessed using the Sokolow-Lyon voltage [15] (sum of the amplitude of the S wave on lead V1. and the R wave on V5 or V6>=3.5 mV).

3.3. Renal function and electrolyte homeostasis
All patients had assessment of renal function by plasma urea and creatinine. Plasma sodium and potassium were also measured.

3.4. Assessment of cardiac size and function
Transthoracic echocardiography was used to measure ejection fraction, left ventricular end diastolic and systolic dimensions according to American Society of Echocardiography guidelines. Postero–antero chest radiographs reported by Senior Radiologists were used to assess cardiothoracic ratio.

3.5. Model development and statistical methods
The outcome of interest was defined as death from all causes. Descriptive statistics are given as the mean (S.E.) for continuous variables (geometric mean for non-normally distributed data) and as percentages for categorical variables. Stepwise Cox proportional hazards regression was used to identify significant independent predictors of all cause mortality. Missing values for any variable were estimated by multiple linear regression from their relationship with other variables. The variables considered for entry into the model were: age, sex, the presence of non-sustained ventricular tachycardia and left ventricular hypertrophy, left ventricle end diastolic and systolic diameters, ejection fraction, sodium, potassium, urea, creatinine and the logarithm of the cardiothoracic ratio, the natural logarithms of the following variables: QT dispersion; QT dispersion corrected for rate, QT dispersion corrected for rate across leads V1–V6; the standard deviation of QT intervals; maximum QT interval; maximum corrected QT; QRS dispersion; JT dispersion, JT dispersion corrected for heart rate. Natural logarithms of heart rate, SDNN, very low frequency, low frequency, high frequency and total power.

Predictors of mortality in Cox multiple regression analysis were used to develop a prognostic index. The prognostic index was derived for each patient based on the Cox proportional hazards model. This model has advantages over other techniques such as logistic regression: It takes the following into consideration: variable duration of follow-up, censoring of subjects, proportionality of event occurrence and time to event [16]. Assumptions of the model were tested. The model was developed aiming for the highest {chi}2 statistic allied to ease of use (eight predictors were identified providing ~25 events per explanatory independent variable). To exclude significant relationships between the variables selected for the models, Spearman's correlation coefficients were calculated.

In order to make the index simple to use, patients were given a score of zero or one for each variable in the Cox model (depending on whether the patient has a value equivalent to or greater than the group median for each variable). The sum of the scores was taken. A maximum score of eight was possible with a lower score corresponding to a better prognosis. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for each possible binary cut-off of the index into low and high risk based on mortality at 5 years. These values were used to construct receiver-operating-characteristic (ROC) curves. ROC curves (which quantitate the diagnostic accuracy of an index) plot the positive fraction, or sensitivity against the false positive fraction (1-specificity). The threshold is varied with increasingly stringent criteria for positivity. The ROC curve thus indicates the probability of a true positive result as a function of the probability of a false positive result for all possible threshold values [17,18]. The area under the curve (AUC) was calculated. An area of 0.5 indicates that the test results are no better than those obtained by chance, whereas an area of 1.0 indicates a perfectly sensitive and specific test. To assess internal validity of our model we used the ‘bootstrapping technique’. This technique gives an indication of how much the performance of the model may deteriorate when applied to a new group of similar patients [19,20]. This involved taking a random sample of our patients (with replacement) and calculating the sensitivity of this sample. This was undertaken a total of 20 000 times and the distribution of the sensitivity (based on the chosen cut-off) was obtained. Kaplan–Meier curves were estimated and plotted for high and low risk stratum.

Statistical analyses were completed using SPSS (version 10; SPSS Inc. Headquarters, 233 S. Wacker Drive, 11th Floor, Chicago, IL 60606, USA) and SAS (versions 6.12 and 8.2; SAS Institute Inc., Cary, NC, USA), S+ software (version 4.5).


   4. Results
Top
 Notes
 Abstract
 1. Introduction
 2. Methods
 3. Measured variables
 4. Results
5. Discussion
 6. Conclusion
 References
 
4.1. Characteristics of participants
4.1.1. UK-HEART population
Five hundred and fifty-three Caucasian patients were recruited in cardiology outpatient clinics. Patients’ mean age (and range) was 62.7 (9.7) years (18–85); 76% were male and NYHA functional class was I: 2%, class II: 59% and class III: 39%. The average NYHA functional class was 2.4 (0.5). Seventy-nine percent of patients had ischemic heart disease as the etiology of their heart failure, 38% had non-sustained ventricular tachycardia and 10% left ventricular hypertrophy. Most patients (81%) were treated with angiotensin converting enzyme inhibitors (mean enalapril dose: 12 (7.2) mg/day) and loop diuretics (97%: mean furosemide dose: 75 (68) mg/day). Nineteen percent of patients were taking digoxin, (mean dose 198.0 (7.1) mcg/day), 14% amiodarone (all 200 mg/day) and 7.9% atenolol (mean dose 43.7 (1.6) mg/day). Information on deaths was recorded up to and including April 2000, allowing 5-year survival status to be determined for all patients. Overall annual mortality was 7.3% (Fig. 1).


Figure 1
View larger version (9K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 1 (a) Kaplan–Meier survival curve showing proportion of patients alive over 5-year period for population of ambulant patients with chronic heart failure. (b) Receiver operator characteristic curve for non-invasive prognostic index (AUC/C statistic=0.74) dotted line given for reference. (c) Kaplan–Meier curves for population dichotomised by score 0–3 and 4–8 (P<0.001).

 
4.1.2. Heart failure severity
The mean (S.D.) (range) left ventricular ejection fraction was 42% (17) (6–88), left ventricular end-diastolic diameter 6.2 (1.0) cm (3.2–9.4), left ventricular end-systolic diameter 5.0 (1.2) cm (1.7–9.2) and cardiothoracic ratio 0.53 (0.07) (0.34–0.88).

4.2. Cox multivariable independent predictors of mortality
Independent predictors of mortality (Table 1) were: the presence of left ventricular hypertrophy, the presence of non-sustained ventricular tachycardia, maximum corrected QT interval, cardiothoracic ratio, serum sodium, serum creatinine, SDNN and QRS dispersion. Diabetes mellitus was an exclusion criteria for the present study. However, the number of patients were found to be diabetic on random blood sugar testing including these patients and non-diabetic patients resulted in a range of blood sugar from 3.2–29.0 mmol/l incorporating blood sugar into the statistical model did not change the predictors of mortality.


View this table:
[in this window]
[in a new window]

  		Table 1 Independent predictors of all cause mortality, hazards ratios, parameter estimates and descriptive data

 
4.3. Survivors vs. non-survivors
Characteristics of survivors compared to non-survivors are detailed in Table 2. In survivors (n=352), there were 77% with ischaemic heart disease, 17% dilated cardiomyopathy and 6% hypertensive heart failure. In non-survivors, there were 82% with ischaemic heart disease, 16% dilated cardiomyopathy and 2% hypertension. There were significantly more patients with a clinical diagnosis of ischaemic heart disease in the non-survivors (P=0.04).


View this table:
[in this window]
[in a new window]

  		Table 2 Comparison of descriptive statistics for survivors and non-survivors

 
4.4. Development of index
The prognostic index was derived for each patient based on the Cox proportional hazards model by giving a score of zero or one for each of the eight variables and summing these scores to produce an index ranging from zero to eight. (i) A score of one was given for the variable if the patients value for that particular variable was less than or equal to the group median for sodium and SDNN (zero points awarded when variable above group median). (ii) Greater than or equal to the group median for cardiothoracic ratio, plasma creatinine, QRS dispersion, maximum QT interval corrected for heart rate (zero points awarded when variable below median). (iii) For the binary variables left ventricular hypertrophy and non-sustained ventricular tachycardia one point was awarded when present and zero when absent (Fig. 2). No pair of variables had a correlation coefficient >0.2.


Figure 2
View larger version (16K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 2 Chart illustrating absolute and percentage number of deaths in groups divided by points awarded using prognostic index, {chi}2 for trend P<0.001.

 
4.5. Model discrimination and internal validation
The present model had good positive and negative predictive values (Table 3). The AUC (C-statistic) was 0.74 (95% CI 0.70–0.78) (Fig. 1b). For simplicity we dichotomised values by median values, when using the absolute values (rather than zero or one for each variable) and parameter estimates the C statistic was 0.78 (95% CI 0.74–0.0.82). To assess the internal validity of the present index and to provide an estimate of how much it might deteriorate in a different cohort of patients we used the bootstrap technique. Using cut off values of low risk 0–3 points and 4–8 points high risk, our index had a sensitivity of 72 (95% CI 65–78) to identify patients likely to die (Fig. 1c). When bootstrapping was used with 20 000 samples we found that <2.5% of observations fell either side of these 95% confidence intervals, illustrating that the index has good internal validity and potential to be successfully applied in a different cohort without significant deterioration in its performance.


View this table:
[in this window]
[in a new window]

  		Table 3 Sensitivity and specificity of index over the possible range of values

 

   5. Discussion
Top
 Notes
 Abstract
 1. Introduction
 2. Methods
 3. Measured variables
 4. Results
 5. Discussion
6. Conclusion
 References
 
Previous studies have shown that the combination of clinical judgement and prognostic indices leads to more accurate assessment of prognosis [21,22]. Furthermore, it has been demonstrated that physicians often lack confidence in their prognostic estimates [22].

We have developed, to our knowledge, the first prognostic index using prospectively assembled data in a highly characterised group of patients with mild to moderate chronic heart failure. This scoring system which can be used in the outpatient setting can stratify ambulant patients with heart failure into high- and low-risk groups for mortality over a 5-year period. The strength of the index is that it is easy to perform, the variables used in multivariate analysis are each independent predictors of mortality and the index performs well in analysis of its discriminatory ability.

Wasson et al. [8] proposed methodological standards for creating and validating clinical prediction rules that should enhance future performance when met by the original investigators and heeded by subsequent users. These standards were largely met in our study. We clearly defined the event (death) to be predicted and the patient population demographics/study site. The prediction rule is highly relevant to chronic heart failure and we have evaluated the ability of our rule to discriminate between high- and low-risk patients by constructing ROC curves and internally validated it using the ‘bootstrap’ method.

5.1. Study population
We specifically selected the present study population to reflect everyday clinical practice, as such, a low ejection fraction was only one of our entry criteria. This allowed our patients to reflect the usual heart failure population in whom it has been demonstrated that up to 40% have normal ejection fraction on echocardiography [23].

We also included patients in the present study with both ischaemic and non-ischaemic etiologies for CHF. We did not include etiology as a variable in analysis for a number of reasons. It has been demonstrated that differentiation between ischaemic and non-ischaemic CHF is difficult and that prognosis in these patients is dependent on the extent of coronary artery disease rather than its presence per se [24]. Not all of our patients underwent cardiac catheterisation and studies have shown that at least 40% of patients with non-ischaemic CHF complain of typical anginal chest pain making a definite etiological diagnosis out of a specialist center difficult. It has also been shown that these patients may have regional wall motion abnormalities making echocardiographic diagnosis of ischaemic CHF also difficult in some circumstances [25].

5.2. Predictors of mortality
The eight predictors of mortality used in the present index all have data supporting their role in the pathophysiology of chronic heart failure.

5.3. Autonomic dysfunction in chronic heart failure
Heart rate variability is a measure of the cyclical variations of beat-to-beat (RR) intervals that reflects cardiac autonomic function. SDNN, a measure of global heart rate variability, is also thought to be a measure of neurohumoral activation including the renin–angiotensin–aldosterone system and the sympathetic nervous system (both of which are well established as being pivotal in the pathophysiology of chronic heart failure) [12]. The mechanism by which hyponatremia predicts mortality may also involve neurohumoral activity, including particularly, that of the renin–angiotensin–aldosterone system. Although, not demonstrated in the current cohort of patients, serum sodium correlates closely with plasma renin activity in patients with severe heart failure, before treatment with angiotensin converting enzyme inhibitors [26].

5.4. Electrical abnormalities
While left ventricular hypertrophy is well established as an independent risk factor for cardiovascular mortality in healthy subjects [27] and patients with hypertension [28], to the best of our knowledge, we are the first group to demonstrate this in patients with chronic heart failure. Data from the DIAMOND study [29] has demonstrated that patients with chronic heart failure and a prolonged QT interval (an indication of the overall repolarization status of the myocardium) have increased mortality when treated with the antiarrhythmic agent dofetilide. In an animal model of ischaemic heart failure it has been shown that QT interval prolongation renders animals an increased risk of death [30].

The work of Aaronson [5] and others [31] has demonstrated that interventricular conduction abnormalites are associated with an increased risk of death. Our data build on this work showing that the difference between the maximal QRS and minimum QRS complexes (a potential marker of abnormal ventricular depolarisation—irrespective of the presence of bundle branch block) is an independent predictor of mortality.

5.5. Cardiothoracic ratio
In the present analysis and previous studies [32] cardiothoracic ratio has been shown to offer prognostic information independent of left ventricular dimensions and ejection fraction. In our analysis there was a very weak correlation between ejection fraction and cardiothoracic ratio. The effect of cardiothoracic ratio on mortality probably reflects the role of the right ventricle and lungs in the pathophysiology of chronic heart failure.

5.6. Age and ejection fraction
It is intriguing that in the present analysis, age and left ventricular ejection fraction were no longer predictive of mortality once the other eight variables were taken into account. A lack of independent predictive value of age on mortality in patients with more severe heart failure has been reported previously [5]. The data of Aaronson [5] and our own illustrate the importance of biological age and disease burden rather than numerical age. It is likely that ejection fraction is not an independent predictor of mortality in the present analysis due to its close relationship with a number of different physiological factors, such as the neurohumoral environment (estimated by SDNN and serum sodium). The effect of right ventricular function (estimated by cardiothoracic ratio) and the electrical properties of the failing myocardium.

5.7. Study limitations
As with all prognostic indices the validity and generalizability of our index needs to be established in other locations and groups of patients of different age. UK-HEART commenced in 1993, well before publication of the landmark trials of beta-adrenoceptor blockers [2,33] and spironolactone in chronic heart failure [34]. However, a significant number of patients in our cohort had ejection fractions >40% [35] and would hence, based on current evidence, not qualify for beta-blocker therapy—our index is valuable for these patients. Our index also allows us to give patients suitable for beta-blockers a baseline risk, but requires validation in a cohort of patients stabilised on these agents. The present study excluded diabetics, however, when random blood glucose was incorporated into our statistical analysis the predictors of all cause mortality did not change, suggesting that our results may apply to the diabetic population. As one of the primary aims of UK-HEART was to assess the value of heart rate variability in risk stratification, we also excluded patients with atrial fibrillation, our index, therefore, cannot be used in these patients. Recently, novel markers of risk such as BNP [36] have emerged as potentially useful, the effect of adding these markers to the current index warrants attention.

5.8. Clinical application of index
This index could be used on a PDA or computer where the continuous (more complex) model gives a C-statistic of 0.78, when clinicians do not have access to this equipment, a simple nomogram could be used. The measurement of SDNN requires a 24-h tape (as does identification of patients with non-sustained ventricular tachycardia). The measurement of SDNN is now readily available on most analysis packages, as is QTc max on most ECG machines and measurement of QRS dispersion can be carried out in the outpatient setting. Our index requires carrying out an additional 24-h tape, which, one may not usually do in clinical practice, but from this we can gain highly relevant prognostic data. For clinicians dealing with patients with chronic heart failure, all of these non-invasive investigations could be arranged at a single visit (or before the index consultation) and a prognostic score is given to each patient. In addition to use for individual patients it could also be used to identify populations at high risk for trials of therapeutic agents, thereby reducing the number of patients required.


   6. Conclusion
Top
 Notes
 Abstract
 1. Introduction
 2. Methods
 3. Measured variables
 4. Results
 5. Discussion
 6. Conclusion
References
 
The novel prognostic index described in the current report may provide useful information for both patients and providers of healthcare, allowing identification of high and low risk ambulant patients with chronic heart failure. Furthermore, it may allow the targeting of new pharmacological or device therapies for this patient group, possibilities that warrant further investigation.


   Acknowledgements
 
Financial support for this study was by the Chest Heart and Stroke Association (Scotland) and the Northern and Yorkshire Research and Development Directorate. M.T.K, K.A.A.F and A.M.S are supported by The British Heart Foundation.


   Notes
Top
 Notes
Abstract
 1. Introduction
 2. Methods
 3. Measured variables
 4. Results
 5. Discussion
 6. Conclusion
 References
 
1 Contributed equally to this work. Back


   References
Top
 Notes
 Abstract
 1. Introduction
 2. Methods
 3. Measured variables
 4. Results
 5. Discussion
 6. Conclusion
 References
 

  1. Ghali J.K., Cooper R., Ford E. Trends in hospitalization for heart failure in the United States 1973–1986: evidence for increasing population prevalence. Arch Intern Med (1990) 150:769–773.[Abstract/Free Full Text]
  2. MERIT-HF Study Group. Effect of metoprolol CR/XL in chronic heart failure: Metoprolol CR/XL randomised intervention trial in congestive heart failure (MERIT-HF). Lancet (1999) 353:2001–2007.[CrossRef][Web of Science][Medline]
  3. The Captopril-Digoxin Multicentre Research group. Comparative effects of therapy with captopril and digoxin in mild to moderate heart failure. JAMA (1988) 259:539–544.[Abstract/Free Full Text]
  4. Stewart S., MacIntyre K., Hole D.J., Capewell S., McMurray J.J. More ‘malignant’ than cancer? Five-year survival following a first admission for heart failure. Eur J Heart Fail (2001) 3:315–322.[Abstract/Free Full Text]
  5. Aaronson K.D., Schwartz S.J., Chen T.M., et al. Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. Circulation (1997) 95:2660–2667.[Abstract/Free Full Text]
  6. Gronda E.G., Barbieri P., Frigerio M., et al. Prognostic indices in heart transplant candidates after the first hospitalization triggered by the need for intravenous pharmacologic circulatory support. J Heart Lung Transpl (1999) 18:654–663.[CrossRef][Web of Science][Medline]
  7. Campana C., Gavazzi A., Berzuini C., et al. Predictors of prognosis in patients awaiting heart transplantation. J Heart Lung Transpl (1993) 12:756–765.[Web of Science][Medline]
  8. Wasson J., Sox J., Neff R., Goldman L. Clinical prediction rules: applications and methodological standards. New Engl J Med (1985) 313:793–799.[Abstract]
  9. Nolan J., Batin P.D., Andrews R., et al. Prospective study of heart rate variability and mortality in chronic heart failure. Results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-Heart). Circulation (1998) 98:1510–1516.[Abstract/Free Full Text]
  10. Brooksby P., Batin P.D., Nolan J., et al. The relationship between QT intervals and mortality in ambulant patients with chronic heart failure. The United Kingdom heart failure evaluation and assessment of risk trial (UK-HEART). Eur Heart J (1999) 20:1335–1341.[Abstract/Free Full Text]
  11. Sweeney M.O. Sudden death in heart failure associated with reduced left ventricular function: substrates, mechanisms, and evidence-based management, Part I. J Pacing Clin Electr (2001) 24:871–887.[CrossRef]
  12. Mann D.L. Mechanisms and models in heart failure a combinatorial approach. Circulation (1999) 100:999–1008.[Free Full Text]
  13. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 1996;93:1043–1065.
  14. Saul J.P., Arai Y., Berger R.D., et al. Assessment of autonomic regulation in chronic congestive heart failure by spectral heart rate analysis. Am J Cardiol (1988) 61:1292–1299.[CrossRef][Web of Science][Medline]
  15. Sokolow M., Lyon T.P. The ventricular complex in left ventricular hypertrophy as obtained by unipolar precordial and limb leads. Am Heart J (1949) 37:161–186.[CrossRef][Web of Science][Medline]
  16. Cox D.R. Regression models and life tables. J Roy Stat Soc (1972) 34:187–220.
  17. Hanley J., McNeil B. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology (1982) 143:29–36.[Abstract/Free Full Text]
  18. Harrell F.E. Jr., Lee K.L., Mark D.B. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med (1996) 15:361–387.[CrossRef][Web of Science][Medline]
  19. Justice A.C., Covinsky K.E., Berlin J.A. Assessing the generalizability of prognostic information. Ann Intern Med (1999) 130:515–524.[Abstract/Free Full Text]
  20. Knaus W.A., Harrell F.E., Lynn J., et al. The SUPPORT prognostic model: objective estimates of survival for seriously ill hospitalised older adults. Ann Intern Med (1995) 122:191–203.[Abstract/Free Full Text]
  21. Teno J.M., Harrell F.E., Knaus W.A., et al. Prediction of survival for older hospitalized patients: the HELP survival model. J Am Geriatr Soc (2000) 48(Suppl):16–24.
  22. Christakis N.A., Iwashyna T.J. Attitude and self-reported practice regarding prognostication in a national sample of internists. Arch Intern Med (1998) 158:2389–2395.[Abstract/Free Full Text]
  23. Vasan R.S., Larson M.G., Benjamin E.J., et al. Congestive heart failure in subjects with normal versus reduced left ventricular ejection fraction: prevalence and mortality in a population-based cohort. J Am Coll Cardiol (1999) 33:1948–1955.[Abstract/Free Full Text]
  24. Felker G.M., Shaw L.K., O'Connor C.M. A standardized definition of ischemic cardiomyopathy for use in clinical research. J Am Coll Cardiol (2002) 39:210–218.[Abstract/Free Full Text]
  25. Diaz R.A., Nihoyannopoulos P., Athanassopoulos G., Oakley C.M. Usefulness of echocardiography to differentiate dilated cardiomyopathy from coronary-induced congestive heart failure. Am J Cardiol (1991) 17:373–383.
  26. Lee W.H., Packer M. Prognostic importance of serum sodium concentration and its modification by converting-enzyme inhibition in patients with severe chronic heart failure. Circulation (1986) 73:257–267.[Abstract/Free Full Text]
  27. Kannel W.B. Left ventricular hypertrophy as a risk factor: the Framingham experience. J Hypertens Suppl (1991) 9:S3–S8.[Medline]
  28. Sundström J, Lind L, Ärnlöv J, Zethelius B, et al. Echocardiographic and Electrocardiographic Diagnoses of Left Ventricular Hypertrophy Predict Mortality Independently of Each Other in a Population of Elderly Men. Circulation 2001;103:2346–2351.
  29. Brendorp B, Elming H, Jun L, et al. QTc interval as a guide to select those patients with congestive heart failure and reduced left ventricular systolic function who will benefit from antiarrhythmic treatment with dofetilide. Circulation 2001;103:1422–1427.
  30. Adamson P.B., Vanioli E. Early autonomic and repolarization abnormalities contribute to lethal arrhythmias in chronic ischemic heart failure: characteristics of a novel heart failure model in dogs with postmyocardial infarction left ventricular dysfunction. J Am Coll Cardiol (2001) 37:1741–1748.[Abstract/Free Full Text]
  31. Shamin W., Francis D.P., Yousufuddin M., et al. Intraventricular conduction delay: a prognostic marker in chronic heart failure. Int J Cardiol (1999) 70:171–178.[CrossRef][Web of Science][Medline]
  32. Eichorn E.J. Prognosis determination in heart failure. Am J Med (2001) 110(7A):14S–35S.[CrossRef][Medline]
  33. CIBIS-II Investigators and Committees. The cardiac insufficiency bisoprolol study II (CIBIS-II): a randomised trial. Lancet (1999) 353:9–13.[CrossRef][Web of Science][Medline]
  34. Pitt B., Zannad F., Remme W.J., et al. The effect of spironolactone on morbidity and mortality in patients with severe heart failure. New Engl J Med (1999) 341:709–717.[Abstract/Free Full Text]
  35. Kearney M.T., Fox K.A., Lee A.J., et al. Predicting death due to progressive heart failure in patients with mild-to-moderate chronic heart failure. J Am Coll Cardiol (2002) 40:1801–1808.[Abstract/Free Full Text]
  36. Berger B., Huelsman M., Strecker K., et al. B-type natriuretic peptide predicts sudden death in patients with chronic heart failure. Circulation (2002) 105:2392–2397.[Abstract/Free Full Text]

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Eur Heart JHome page
R. Vazquez, A. Bayes-Genis, I. Cygankiewicz, D. Pascual-Figal, L. Grigorian-Shamagian, R. Pavon, J. R. Gonzalez-Juanatey, J. M. Cubero, L. Pastor, J. Ordonez-Llanos, et al.
The MUSIC Risk score: a simple method for predicting mortality in ambulatory patients with chronic heart failure
Eur. Heart J., May 1, 2009; 30(9): 1088 - 1096.
[Abstract] [Full Text] [PDF]

Home page
 Eur J Heart FailHome page
R. K.G. Moore, D. G. Groves, P. E. Barlow, K. A.A. Fox, A. Shah, J. Nolan, and M. T. Kearney
Heart rate turbulence and death due to cardiac decompensation in patients with chronic heart failure
Eur J Heart Fail, October 1, 2006; 8(6): 585 - 590.
[Abstract] [Full Text] [PDF]

Home page
 Eur Heart JHome page
S. J. Pocock, D. Wang, M. A. Pfeffer, S. Yusuf, J. J.V. McMurray, K. B. Swedberg, J. Ostergren, E. L. Michelson, K. S. Pieper, C. B. Granger, et al.
Predictors of mortality and morbidity in patients with chronic heart failure
Eur. Heart J., January 1, 2006; 27(1): 65 - 75.
[Abstract] [Full Text] [PDF]

Home page
 J. Am. Soc. Nephrol.Home page
R. N. Foley, A. M. Murray, S. Li, C. A. Herzog, A. M. McBean, P. W. Eggers, and A. J. Collins
Chronic Kidney Disease and the Risk for Cardiovascular Disease, Renal Replacement, and Death in the United States Medicare Population, 1998 to 1999
J. Am. Soc. Nephrol., February 1, 2005; 16(2): 489 - 495.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Kearney, M. T.
Right arrow Articles by Fox, K. A.A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kearney, M. T.
Right arrow Articles by Fox, K. A.A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?