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10.1016/j.healun.2016.01.016 http://scihub22266oqcxt.onion/10.1016/j.healun.2016.01.016 C4917454!4917454!27021278     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27021278&cmd=llinks): Failed to open stream: HTTP request failed! 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From Statistical Significance to Clinical Relevance: A Simple Algorithm to Integrate BNP and the Seattle Heart Failure Model for Risk Stratification in Heart Failure |pdf=|usr=}}{{27021278}} gab.com Text From Statistical Significance to Clinical Relevance: A Simple Algorithm to Integrate BNP and the Seattle Heart Failure Model for Risk Stratification in Heart Failure JO: J Heart Lung Transplant http://www.kidney.de/pget.php?&tw=1&pmid=27021278 #FOAvip Background: Heart failure (HF) guidelines recommend brain natriuretic peptide (BNP) and multivariable risk-scores such as the Seattle HF Model (SHFM) to predict risk in HF with reduced ejection fraction (HFrEF). A practical way to integrate information from these two prognostic tools is lacking. We sought to establish a SHFM+BNP risk-stratification algorithm. Methods: The retrospective derivation cohort included consecutive patients with HFrEF at Mayo. One-year outcome (death, transplantation or ventricular assist device) was assessed. The SHFM+BNP algorithm was derived by stratifying patients within SHFM-predicted risk categories (?2.5%, 2.6??10%, >10%) according to BNP above or below 700 pg/mL and comparing SHFM-predicted and observed event rates within each SHFM+BNP category. The algorithm was validated in a prospective, multicenter HFrEF registry (Penn HF Study). Results: Derivation (n=441; one-year event rate 17%) and validation (n=1513; one-year event rate 12%) cohorts differed with the former being older and more likely ischemic with worse symptoms, lower EF, worse renal function, higher BNP and SHFM scores. In both cohorts, across the three SHFM-predicted risk strata, a BNP>700 pg/ml consistently identified patients with approximately three-fold the risk that the SHFM would have otherwise estimated regardless stage of HF, intensity and duration of HF-therapy, and comorbidities. Conversely, the SHFM was appropriately calibrated in patients with a BNP<700 pg/ml. Conclusion: The simple SHFM+BNP algorithm displays stable performance across diverse HFrEF cohorts and may enhance risk stratification to enable appropriate decisions regarding HF therapeutic or palliative strategies. Twit Text FOAVip From Statistical Significance to Clinical Relevance: A Simple Algorithm to Integrate BNP and the Seattle Heart Failure Model for Risk Stratification in Heart Failure ????J Heart Lung Transplant http://www.kidney.de/pget.php?&tw=1&pmid=27021278 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27021278 #FOAvip English Wikipedia <ref>{{Cite pmid|27021278}}</ref> From Statistical Significance to Clinical Relevance: A Simple Algorithm to Integrate BNP and the Seattle Heart Failure Model for Risk Stratification in Heart Failure #MMPMID27021278 AbouEzzeddine OF; French B; Mirzoyev SA; Jaffe AS; Levy WC; Fang JC; Sweitzer NK; Cappola TP; Redfield MM J Heart Lung Transplant 2016[Jun]; 35 (6): 714-21 PMID27021278show ga Background: Heart failure (HF) guidelines recommend brain natriuretic peptide (BNP) and multivariable risk-scores such as the Seattle HF Model (SHFM) to predict risk in HF with reduced ejection fraction (HFrEF). A practical way to integrate information from these two prognostic tools is lacking. We sought to establish a SHFM+BNP risk-stratification algorithm. Methods: The retrospective derivation cohort included consecutive patients with HFrEF at Mayo. One-year outcome (death, transplantation or ventricular assist device) was assessed. The SHFM+BNP algorithm was derived by stratifying patients within SHFM-predicted risk categories (?2.5%, 2.6??10%, >10%) according to BNP above or below 700 pg/mL and comparing SHFM-predicted and observed event rates within each SHFM+BNP category. The algorithm was validated in a prospective, multicenter HFrEF registry (Penn HF Study). Results: Derivation (n=441; one-year event rate 17%) and validation (n=1513; one-year event rate 12%) cohorts differed with the former being older and more likely ischemic with worse symptoms, lower EF, worse renal function, higher BNP and SHFM scores. In both cohorts, across the three SHFM-predicted risk strata, a BNP>700 pg/ml consistently identified patients with approximately three-fold the risk that the SHFM would have otherwise estimated regardless stage of HF, intensity and duration of HF-therapy, and comorbidities. Conversely, the SHFM was appropriately calibrated in patients with a BNP<700 pg/ml. Conclusion: The simple SHFM+BNP algorithm displays stable performance across diverse HFrEF cohorts and may enhance risk stratification to enable appropriate decisions regarding HF therapeutic or palliative strategies. äFrom Statistical Significance to Clinical Relevance: A Simple Algorith(epmc).pdf DeepDyve Pubget Overpricing