Abstract

INTRODUCTION
Symptomatic heart failure continues to confer a poor prognosis, with 1-year mortality averaging 50% [1]. Risk or prognostic stratification is one of the pivotal activities in medical practice. Heart failure patients are distinguishing in ethiology, clinic of this syndrome and co-diseases [2, 3]. That’s way an individual approach is needed to evaluate patients survival probability. Virtually all patient management decisions are driven by the clinician's assessment of the patient's prognosis. Using a standard data set of history, physical examination, laboratory test data, the physician formulates a working diagnosis and risk assessment and selects an initial management strategy.

OBJECTIVES
The purpose of the study was to develop the prognostic model to predict survival in elderly heart failure patients.

MATERIAL AND METHODS
We used prospectively collected information from 104 hospitalized heart failure patients 75 years and older, mean age 80±0.8, 80 men, with NYHA class HF II-IV, with systolic and combined systolic and diastolic dysfunction. During hospitalization commonly obtained clinical variables (150 in total): age, gender, NYHA class, medications, laboratory values such as sodium, hemoglobin, and cholesterol and therapy were evaluated. Also, after 12 days of standard drug in-patient therapy (optimal doses of ACE-inhibitors or ARA, ß-blockers, diuretics and spironolactone) BNP was measured to each participant with the use of chemiluminescent immunoassay («AXSIM», Abbot, USA). All patients were followed up by gerontologists for a median period of 2.5 years. Outcomes for prognosis were limited to mortality. Cox proportional hazards model was used to assess the association of variables with mortality.

RESULTS
Participants were followed for a 2.5 years after hospital discharge. Table 1 gives the baseline patient characteristics.
Table 1

Within the 2.5 years follow-up period 40% of 104 CHF patients died. The strongest predictors were plasma BNP level and age. Age-BNP survival mathematical model is defined in the following way: S(t)=e^{(0,143*В+0,001*К)}*Н(t), where S(t) is a survival function evaluated at time t, B is the age in years, K – plasma BNP level, H(t) is the basic risk function. For easy clinician's using of Age-BNP Survival Model we proposed a few graphics, illustrating relationships between age, plasma BNP level and survival probability (Fig. 1-4). Knowing patient's age and his plasma BNP level we can define his 2.5-year prognosis (survival probability) from day of BNP blood analysis. For example, patient F. 78 years old with plasma BNP level 1000 pg/ml had from 75% to 79% survival probability for 20 month (Fig. 5).

Fig. 1. Age-BNP Survival Model in heart failure patients 75-77,5 years old, showing the cumulative survival according to predischarge BNP cut-off value's - 25, 450, 875, 1300, 1720, 2150, 2564 pg/ml (from top to bottom).
Fig. 2. Age-BNP Survival Model in heart failure patients 77.5-82.5 years old, showing the cumulative survival according to predischarge BNP cut-off value's - 25, 450, 875, 1300, 1720, 2150, 2564 pg/ml (from top to bottom).
Fig. 3. Age-BNP Survival Model in heart failure patients 82.5-87.5 years old, showing the cumulative survival according to predischarge BNP cut-off value's - 25, 450, 875, 1300, 1720, 2150, 2564 pg/ml (from top to bottom).
Fig. 4. Age-BNP Survival Model in patients 87.5-92.5 years old, showing the cumulative survival according to predischarge BNP cut-off value's - 25, 450, 875, 1300, 1720, 2150, 2564 pg/ml (from top to bottom).

Fig. 5. Example of assessment of survival probability using Age-BNP Survival Model

DISCUSSION
Patients 75 years and older are the majority of heart failure population [4, 5, 6]. Patients of this age have two features: many co-diseases and low intensity of ambulatory care. It is known, that today in real practice out-patient with CHF are surveyed and treated less intensively, than it is offered existing international standards [7]: echocardiography to such patients it is spent much less often, than it is necessary; the basic medications such as beta blockers and ACE-inhibitors in 30 % - 50 % of cases were not prescribed; therapists and gerontologists prefer to begin therapy CHF with diuretics [3, 8]. Possibly, at an estimation of the forecast at patients with CHF it is necessary to consider, including, intensity and level of the out-patient help. An accurate prognosis estimating can help clinician's to make optimal treatment decision.
Age-BNP Survival Model identified patients having low or high survival probability. The identification of patients with low survival probability may enable clinicians to better advise patients about prognosis, adjust management accordingly, and permit consideration of palliative care in those anticipated to have particularly poor short-term survival. Conversely, patients with a more favorable prognosis may be suitable candidates for more aggressive interventions, such as an implantable defibrillator, although individual patient factors and preferences would still require careful consideration. Although the 2 prognostic predictors identified in our model are not currently amenable to modification, they still provide clinically important information for stratifying risk in elderly patients with HF at the time of admission to help guide diagnostic and therapeutic decision making, including appropriate selection of invasive and costly interventions. Prognostic indicators can also identify those areas of HF management requiring further investigation.
Our model for predicting HF mortality is unique in several respects. Unlike other prognostic models, Age-BNP Survival Model predicts individualized survival probability every month during 2.5 years follow-up and based just on measurement of brain natriuretic peptide concentration and allows prediction of survival in heart failure patients 75 years old and elderly – the majority of heart failure population.
Study limitations. The study was conducted at a single Veteran hospital, and the sample size was relatively small. For these reasons, results of this study may not be generalizable to all HF populations.

CONCLUSIONS
1. Prognosis of elderly patients hospitalized with HF is highly variable. A simple risk score, based on 2 variables readily obtainable at the time of hospital admission, can effectively stratify patients into individualized survival probability categories. This information can be used as an aid to guide diagnostic and therapeutic decision making.

2. The Age-BNP Survival Model provides accurate estimate of 2.5 years survival probability among elderly patients with heart failure and easy used in clinical practice.

REFERENCES

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