***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background — Heart failure has an annual mortality rate ranging from 5 % to 75 % . The purpose of the study was to develop and vali date a multivariate risk model to predict 1- , 2- , and 3-year survival in heart failure patients with the use of easily obtainable characteristics relating to clinical status , therapy ( pharmacological as well as devices ) , and laboratory parameters . Methods and Results — The Seattle Heart Failure Model was derived in a cohort of 1125 heart failure patients with the use of a multivariate Cox model . For medications and devices not available in the derivation data base , hazard ratios were estimated from published literature . The model was prospect ively vali date d in 5 additional cohorts totaling 9942 heart failure patients and 17 307 person-years of follow-up . The accuracy of the model was excellent , with predicted versus actual 1-year survival rates of 73.4 % versus 74.3 % in the derivation cohort and 90.5 % versus 88.5 % , 86.5 % versus 86.5 % , 83.8 % versus 83.3 % , 90.9 % versus 91.0 % , and 89.6 % versus 86.7 % in the 5 validation cohorts . For the lowest score , the 2-year survival was 92.8 % compared with 88.7 % , 77.8 % , 58.1 % , 29.5 % , and 10.8 % for scores of 0 , 1 , 2 , 3 , and 4 , respectively . The overall receiver operating characteristic area under the curve was 0.729 ( 95 % CI , 0.714 to 0.744 ) . The model also allowed estimation of the benefit of adding medications or devices to an individual patient 's therapeutic regimen . Conclusions — The Seattle Heart Failure Model provides an accurate estimate of 1- , 2- , and 3-year survival with the use of easily obtained clinical , pharmacological , device , and laboratory characteristics BACKGROUND Risk stratification of patients with end-stage congestive heart failure is a critical component of the transplant c and i date selection process . Accurate identification of individuals most likely to survive without a transplant would facilitate more efficient use of scarce donor organs . METHODS AND RESULTS Multivariable proportional hazards survival models were developed with the use of data on 80 clinical characteristics from 268 ambulatory patients with advanced heart failure ( derivation sample ) .  Invasive and noninvasive models ( with and without catheterization-derived data ) were constructed . A prognostic score was determined for each patient from each model . Stratum-specific likelihood ratios were used to develop three prognostic-score risk groups . The models were prospect ively vali date d on 199 similar patients ( validation sample ) by calculation of the area under the receiver operating characteristic curve for 1-year event-free survival , the censored c-index for event-free survival , and comparison of event-free survival curves for prognostic-score risk strata . Outcome events were defined as urgent transplant or death without transplant . The noninvasive model performed well in both sample s , and increased performance was not attained by the addition of catheterization-derived variables . Prognostic-score risk groups derived from the noninvasive model in the derivation sample effectively stratified the risk of an outcome event in both sample s ( 1-year event-free survival for derivation and validation sample s , respectively : low risk , 93 % and 88 % ; medium risk , 72 % and 60 % ; high risk , 43 % and 35 % ) . CONCLUSIONS Selection of c and i date s for cardiac transplantation may be improved by use of this noninvasive risk-stratification model BACKGROUND Current guidelines do not recommend implantable cardioverter-defibrillator ( ICD ) implantation  in patients with a life expectancy of < 1 year . Better methods are needed for identifying patients at high risk for early mortality despite ICD therapy . OBJECTIVE To develop and vali date a risk prediction score to identify patients at high risk for death within 1 year despite ICD therapy . DESIGN Detailed clinical data were collected on a large observational cohort of ICD patients from 3 tertiary care centers . One-third of the patients were r and omly selected to form the prediction group ( PG ) from which a risk score was developed using logistic regression . This score was then applied to the remaining two-thirds of the cohort ( validation group [ VG ] ) to assess the risk score 's predictive accuracy . RESULTS The total cohort included 2717 ICD patients ( mean age = 64.6 ± 14.5 , male = 77.2 % , primary prevention = 74.7 % ) . A simple risk score incorporating peripheral arterial disease , age ≥ 70 years , creatinine ≥ 2.0 mg/dL , and ejection fraction ≤20 % ( PACE ) accurately predicted 1-year mortality in the VG . Patients with a risk score of ≥3 had a > 4-fold excess 1-year mortality compared with patients with a risk score of <3 ( 16.5 % vs 3.5 % ; P < .0001 ) . LIMITATION Risk reduction provided by ICD therapy in this cohort is not known given the lack of a control group . CONCLUSIONS A simple risk score accurately predicts 1-year mortality in ICD patients , as patients with a PACE risk score of ≥3 are at high risk despite ICD therapy CONTEXT Patients with chronic heart failure have impaired long-term survival , but their own expectations regarding prognosis have not been well studied . OBJECTIVES To quantify expectations for survival in patients with heart failure , to compare patient expectations to model predictions , and to identify factors associated with discrepancies between patient-predicted and model-predicted prognosis . DESIGN , SETTING , AND PARTICIPANTS Prospect i ve face-to-face survey of patients from the single-center Duke Heart Failure Disease Management Program between July and December 2004 , with follow-up through February 2008 . Patient-predicted life expectancy  was obtained using a visual analog scale . Model-predicted life expectancy was calculated using the Seattle Heart Failure Model . Actuarial-predicted life expectancy , based on age and sex alone , was calculated using life tables . Observed survival was determined from review of medical records and search of the Social Security Death Index . MAIN OUTCOME MEASURE Life expectancy ratio ( LER ) , defined as the ratio of patient-predicted to model-predicted life expectancy . RESULTS The cohort consisted of 122 patients ( mean age , 62 years ; 47 % African American , 42 % New York Heart Association [ NYHA ] class III or IV ) . On average , patients overestimated their life expectancy relative to model-predicted life expectancy ( median patient-predicted life expectancy , 13.0 years ; model-predicted expectancy , 10.0 years ) . Median LER was 1.4 ( interquartile range , 0.8 - 2.5 ) . Younger age , increased NYHA class , lower ejection fraction , and less depression were the most significant predictors of higher LER . During a median follow-up of 3.1 years , 29 % of the original cohort died . There was no association between higher LER and improved survival ( adjusted hazard ratio for overestimated compared with concordant LER , 1.05 ; 95 % confidence interval , 0.46 - 2.42 ) . CONCLUSIONS Ambulatory patients with heart failure tended to substantially overestimate their life expectancy compared with model-based predictions for survival . Because differences in perceived survival could affect decision making regarding advanced therapies and end-of-life planning , the causes of these discordant predictions warrant further study AIMS The performance of a US-American scoring system ( Heart Failure Survival Score , HFSS ) was prospect ively evaluated in a sample of ambulatory patients with congestive heart failure ( CHF ) . Additionally , it was investigated whether the HFSS might be simplified by assessment of the  distance ambulated during a 6-min walk test ( 6'WT ) instead of determination of peak oxygen uptake ( peak VO(2 ) ) . METHODS AND RESULTS In 208 middle-aged CHF patients ( age 54+/-10 years , 82 % male , NYHA class 2.3+/-0.7 ; follow-up 28+/-14 months ) the seven variables of the HFSS : CHF aetiology ; heart rate ; mean arterial pressure ; serum sodium concentration ; intraventricular conduction time ; left ventricular ejection fraction ( LVEF ) ; and peak VO(2 ) , were determined . Additionally , a 6'WT was performed . The HFSS allowed discrimination between patients at low , medium and high risk , with mortality rates of 16 , 39 and 50 % , respectively . However , the prognostic power of the HFSS was not superior to a two-variable model consisting only of LVEF and peak VO(2 ) . The areas under the receiver operating curves ( AUC ) for prediction of 1-year survival were even higher for the two-variable model ( 0.84 vs. 0.74 , P<0.05 ) . Replacing peak VO(2 ) with 6'WT result ed in a similar AUC ( 0.83 ) . CONCLUSION The HFSS continued to predict survival when applied to this patient sample . However , the HFSS was inferior to a two-variable model containing only LVEF and either peak VO(2 ) or 6'WT . As the 6'WT requires no sophisticated equipment , a simplified two-variable model containing only LVEF and 6'WT may be more widely applicable , and is therefore recommended The analytical effect of the number of events per variable ( EPV ) in a proportional hazards regression analysis was evaluated using Monte Carlo simulation techniques for data from a r and omized trial containing 673 patients and 252 deaths , in which seven predictor variables had an original significance level of p < 0.10 . The 252 deaths and 7 variables correspond to 36 events per variable analyzed in the full data set . Five hundred simulated analyses were conducted for these seven variables at EPVs of 2 , 5 , 10 , 15 , 20 , and 25 . For each simulation , a r and om exponential survival time was generated for each of the 673 patients , and the simulated results were compared with their original counterparts . As EPV decreased , the regression coefficients became more biased relative to the true value ; the 90 % confidence limits about the simulated values did not have a coverage of 90 % for the original value ; large sample properties did not hold for variance estimates from the proportional hazards model , and the Z statistics used to test the significance of the regression coefficients lost validity under the null hypothesis . Although a single boundary level for avoiding problems is not easy to choose , the value of EPV = 10 seems most prudent . Below this value for EPV , the results of proportional hazards regression analyses should be interpreted with caution because the statistical model may not be valid Management of heart failure ( HF ) remains complex with low 5-year survival . The Seattle Heart Failure Model ( SHFM ) is a recently described risk score derived predominantly from clinical trial population s that may enable the prediction of survival in patients with HF . This study sought to vali date the SHFM in an independent , non clinical trial-based HF population . Patients ( n = 4,077 ) from the hospital-based Intermountain Heart Collaborative Study registry with a diagnosis of HF were evaluated using prospect ively collected data ( mean + /- SD follow-up 4.4 + /- 3.1 years ) . The SHFM was used to calculate a risk score for each patient . Receiver-operating characteristic area under the curve provided SHFM predictive ability for a composite end point of survival free from death , transplantation , or left ventricular assist device implantation . Addition of creatinine , serum urea nitrogen , diabetes status , and B-type natriuretic peptide ( BNP ) to the SHFM was also evaluated . Patient age averaged 67 + /- 13 years and 61 % were men . Area under the curves were 0.70 ( 95 % confidence interval 0.66 to 0.70 ) , 0.67 ( 95 % confidence interval 0.66 to 0.69 ) , 0.67 ( 95 % confidence interval 0.065 to 0.68 ) , and 0.66 ( 95 % confidence interval 0.63 to 0.67 ) for 1- , 2- , 3- , and 5-year survivals , respectively . Area under the curves were slightly attenuated in patients > 75 years of age ( n = 1,339 ) , implantable cardioverter-defibrillator recipients ( n = 693 ) , and patients with an ejection fraction > 40 % ( n = 1,634 ) . BNP added significantly to the model ( area under the curve + 0.06 ) . BNP was found to add additional predictive ability at 1 year ( area under the curve change + 0.05 ) and nominally at 5 years ( area under the curve change + 0.02 ) . In conclusion , the SHFM  predicts survival in patients with HF in a hospital-based population , with areas under the curve similar to those from data on which models were initially fit AIMS To determine the prevalence , incidence rate , lifetime risk and prognosis of heart failure . METHODS AND RESULTS The Rotterdam Study is a prospect i ve population -based cohort study in 7983 participants aged > or = 55 .  Heart failure was defined according to criteria of the European Society of Cardiology . Prevalence was higher in men and increased with age from 0.9 % in subjects aged 55 - 64 to 17.4 % in those aged > or = 85 . Incidence rate of heart failure was 14.4/1000 person-years 
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Among the remaining 15 models , 6 were vali date d by bootstrapping ( c-statistic , 0.74 - 0.85 ) ; the rest were not vali date d. CONCLUSIONS Externally vali date d heart failure models showed inconsistent performance .
The Heart Failure Survival Score and Seattle Heart Failure Model demonstrated modest discrimination and question able calibration .