Incorporation of Natriuretic Peptides with Clinical Risk‐scores to Predict Heart Failure Among Individuals with Dysglycemia

Author(s):  
Matthew W. Segar ◽  
Muhammad Shahzeb Khan ◽  
Kershaw V. Patel ◽  
Muthiah Vaduganathan ◽  
Vaishnavi Kannan ◽  
...  
2007 ◽  
Vol 52 (3) ◽  
pp. 8-13 ◽  
Author(s):  
H. Sinclair ◽  
M Paterson ◽  
S. Walker ◽  
G Beckett ◽  
K.A.A. Fox

Background Accurate risk stratification soon after admission for patients with acute coronary syndromes (ACS) is vital in guiding management. Clinical risk scores and B-type natriuretic peptide (BNP) can predict mortality and re-infarction in ACS, but it is unknown whether BNP provides prognostic information over and above that of the clinical risk scores. Methods 142 unselected patients with ACS were prospectively studied. BNP was measured and patients were stratified according to BNP and Global Registry of Acute Coronary Events (GRACE) score. In-hospital and 30-day events were characterised. Results 20.4% of ACS subjects had ST-elevation myocardial infarction (MI), 14.1%, non-ST elevation MI and 65.5% unstable angina. Elevated BNP predicted inhospital and 30-day heart failure (p<0.01), and the risk of in-hospital recurrent ACS (p<0.05). Increasing GRACE score predicted in-hospital recurrent ACS (p<0.05), heart failure (p<0.001), arrhythmias (p<0.05) and angioplasty (p<0.05). GRACE score also predicted 30-day heart failure (p<0.05). In contrast, the predictive accuracy of troponin elevation was less robust. Conclusion BNP and the GRACE score predict complementary outcomes from ACS, but both predicted heart failure. BNP is a powerful indicator of heart failure in patients with ACS and provides prognostic information above and beyond conventional biomarkers and risk scores.


2011 ◽  
Vol 161 (5) ◽  
pp. 923-930.e2 ◽  
Author(s):  
Sachin Gupta ◽  
Anand Rohatgi ◽  
Colby R. Ayers ◽  
Parag C. Patel ◽  
Susan A. Matulevicius ◽  
...  

EP Europace ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1463-1469
Author(s):  
Bastiaan Geelhoed ◽  
Christin S Börschel ◽  
Teemu Niiranen ◽  
Tarja Palosaari ◽  
Aki S Havulinna ◽  
...  

Abstract Aims Natriuretic peptides are extensively studied biomarkers for atrial fibrillation (AF) and heart failure (HF). Their role in the pathogenesis of both diseases is not entirely understood and previous studies several single-nucleotide polymorphisms (SNPs) at the NPPA-NPPB locus associated with natriuretic peptides have been identified. We investigated the causal relationship between natriuretic peptides and AF as well as HF using a Mendelian randomization approach. Methods and results N-terminal pro B-type natriuretic peptide (NT-proBNP) (N = 6669), B-type natriuretic peptide (BNP) (N = 6674), and mid-regional pro atrial natriuretic peptide (MR-proANP) (N = 6813) were measured in the FINRISK 1997 cohort. N = 30 common SNPs related to NT-proBNP, BNP, and MR-proANP were selected from studies. We performed six Mendelian randomizations for all three natriuretic peptide biomarkers and for both outcomes, AF and HF, separately. Polygenic risk scores (PRSs) based on multiple SNPs were used as genetic instrumental variable in Mendelian randomizations. Polygenic risk scores were significantly associated with the three natriuretic peptides. Polygenic risk scores were not significantly associated with incident AF nor HF. Most cardiovascular risk factors showed significant confounding percentages, but no association with PRS. A causal relation except for small causal betas is unlikely. Conclusion In our Mendelian randomization approach, we confirmed an association between common genetic variation at the NPPA-NPPB locus and natriuretic peptides. A strong causal relationship between natriuretic peptides and incidence of AF as well as HF at the community-level was ruled out. Therapeutic approaches targeting natriuretic peptides will therefore very likely work through indirect mechanisms.


2018 ◽  
Vol 71 (11) ◽  
pp. A830
Author(s):  
Chiara Arzilli ◽  
Alberto Aimo ◽  
Giuseppe Vergaro ◽  
Alessandra Gabutti ◽  
Andrea Ripoli ◽  
...  

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
K.A.I Neoh ◽  
L Sevdynidis ◽  
J Hatherley ◽  
J Tay ◽  
H Douglas ◽  
...  

Abstract Introduction Acute decompensated heart failure (ADHF) is associated with frailty and co-morbidities which influence prognosis. The Rockwood Frailty Score (RFS) and age-adjusted Charlson co-morbidity index (CCI) have been used to predict outcomes in hospitalised ADHF patients. Purpose To describe the relationship of CCI, RFS and clinical risk score -Get With The Guidelines Score (GWTG) with mortality in ADHF treated as outpatients (OP) versus hospitalised inpatients (IP). Methods This retrospective analysis compared 2 cohorts of consecutive ADHF patients - hospitalised in-patients (IP) versus outpatients (OP) who were treated with bolus intravenous diuretics in a specialist heart failure nurse delivered OP HF unit (Ambulatory HF Unit -AHFU) with input from various specialties (renal, palliative, ascitic, pleural teams) from Nov 16 to Dec 17. Mean follow-up duration was similar for both groups (IP=19.5±4.1 months; OP=19.3±3.9 months, p=0.6). Mortality was compared at 1, 3 and 12 months based on RFS (no frailty &lt;5, mild to moderate frailty 5/6, severe frailty - 7 to 9). Results were expressed as mean±SD and analysed using One-Way ANOVA and Chi-squared with Fisher's exact test test. Results 410 consecutive patients (482 admissions) were hospitalised (inpatients -IP) and 231 OP (289 OP visits) were treated in the AHFU. IP group had significantly higher mean CCI (IP=6.55±2; OP=6.10±1.9; p=0.006) and mean RFS (IP 5.2±1.2; OP 4.9±1.1; p=0.002). Mean Clinical Risk Score GWTG was similar (IP=38.9±7.2; OP=38.4±6.6; p=0.44). Mean survival was significantly lower in IP (IP=378±270 days; OP=437±228; P=0.003). As shown in the table higher RFS predicts increased mortality risk (1 month, 3 month and 12 month). Conclusions Rockwood Frailty Score predicts mortality in ADHF and assessment of RFS can play an important role in risk stratifying and decision-making in addition to clinical risk-scores, with regards to suitability for outpatient treatment of ADHF. Funding Acknowledgement Type of funding source: None


2017 ◽  
Vol 44 (4) ◽  
pp. 239-244
Author(s):  
Álvaro Aceña ◽  
Maria Luisa Martín-Mariscal ◽  
Nieves Tarín ◽  
Carmen Cristóbal ◽  
Ana Huelmos ◽  
...  

No clinical risk score is universally accepted for coronary artery disease. In 603 patients (mean age, 61.2 ± 12.3 yr) with stable coronary artery disease, we investigated the predictive power of clinical risk scores derived from the Framingham, the Long-term Intervention with Pravastatin in Ischemic Disease (LIPID), and the Vienna and Ludwigshafen Coronary Artery Disease (VILCAD) studies. Secondary outcomes were the recurrence of an acute thrombotic event (coronary events, strokes, or transient ischemic attacks), or heart failure or death. The primary outcome was the combination of secondary outcomes. During follow-up (duration, 2.08 ± 0.97 yr), 42 patients had an acute thrombotic event; 22, heart failure or death; and 60, the primary outcome. The Framingham score predicted acute thrombotic events: hazard ratio (HR)=1.05; 95% confidence interval (CI), 1.01–1.08; P=0.03; net reclassification index (NRI, calculated to evaluate improvement in prediction gained by adding different risk scores to models constructed with variables excluded from the calculation of that score)=9.7% (95% CI, 9.6–9.8). The LIPID (HR=1.13; 95% CI, 1.04–1.22; P=0.005) and VILCAD scores (HR=1.99; 95% CI, 1.48–2.67; P &lt;0.001) predicted heart failure or death with NRIs of 5.8% (95% CI, 5.7–5.9) and 18.6% (95% CI, 18.3–18.9), respectively. The primary outcome was predicted by the LIPID (HR=1.1; 95% CI, 1.03–1.17; P=0.005) and VILCAD scores (HR=1.39; 95% CI, 1.13–1.70; P=0.003). The NRIs (95% CIs) were 3.4% (3.3–3.5) and 19.4% (19.3–19.6), respectively. We conclude that the accuracy of these risk scores varies in accordance with the outcome studied.


2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C Boerschel ◽  
B Geelhoed ◽  
T Niiranen ◽  
A.S Havulinna ◽  
C.J.K Fouodo ◽  
...  

Abstract Background Natriuretic peptides are extensively studied biomarkers for atrial fibrillation (AF) and heart failure (HF). Their role in the pathogenesis of both diseases is not entirely understood and in previous studies several single nucleotide polymorphisms (SNPs) at the NPPA-NPPB locus associated with natriuretic peptides have been identified. Purpose We investigated whether a causal relationship exists between natriuretic peptides and AF as well as HF using a Mendelian randomization approach. Methods N-terminal pro B-type natriuretic peptide (NT-proBNP) (N=6669), B-type natriuretic peptide (BNP) (N=6674) and mid-regional pro atrial natriuretic peptide (MR-proANP) (N=6813) were measured in the FINRISK 1997 cohort. Thirty common SNPs related to NT-proBNP, BNP and MR-proANP were selected from prior studies. We performed six Mendelian randomizations for all three natriuretic peptide biomarkers and for both outcomes, AF and HF separately. Polygenic risk scores (PRS) based on multiple SNPs were used as the genetic instrumental variable in Mendelian randomizations. Results PRS were significantly associated with the three natriuretic peptides. PRS were not significantly associated with incident AF nor HF. Most cardiovascular risk factors showed significant confounding percentages, but no association with PRS. A causal relation, other than a weak one, is unlikely. Conclusion In our Mendelian randomization approach, based on common genetic variation at the NPPA-NPPB locus, associations of the common polymorphisms with natriuretic peptides and the protein biomarkers themselves with incident disease could be confirmed. A strong causal relationship between natriuretic peptides and incidence of AF as well as HF was ruled out. Therapeutic approaches targeting natriuretic peptides will therefore very likely work through indirect mechanisms. Comparison of hazard ratios Funding Acknowledgement Type of funding source: Public grant(s) – EU funding. Main funding source(s): European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, German Ministry of Research and Education


2020 ◽  
Vol 75 (11) ◽  
pp. 1094
Author(s):  
Jesus Alvarez-Garcia ◽  
Mercedes Rivas-Lasarte ◽  
Juan Fernandez Martinez ◽  
Alba Maestro Benedicto ◽  
Laura Lopez Lopez ◽  
...  

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
R Herman ◽  
M Vanderheyden ◽  
B Vavrik ◽  
M Beles ◽  
T Palus ◽  
...  

Abstract Background Heart failure (HF) is a heterogenous syndrome with complex pathophysiology. Biomarkers and clinical risk scores often fail to provide optimal patient-level precision in the prognostic stratification. As utilizing single observational timepoint, they do not capture the entire care pathway with variations in individual patient management. Electronic patient records provide an opportunity to develop new artificial intelligence (AI) strategies for comprehensive prognostic re-stratification reflecting diagnostic and therapeutic management. Purpose We sought to use deep artificial intelligence (AI) and develop an unbiased predictive algorithm for all-cause mortality in a cohort of patients hospitalized with a de novo or worsened HF. Methods In a cohort of 2449 HF patients hospitalized between 2011–2017, we utilized 151 451 patient exams from 422 parameters. They included clinical phenotyping, medication, ECG, laboratory, echocardiography, catheterization data or percutaneous and surgical interventions gathered on a routine clinical basis reflecting standard of care as captured in individual electronic records. The AI model development consisted of 101 iterations of repeated random subsampling splits into balanced training and validation sets. Results AI models yielded performance ranging from 0.83 to 0.89 AUC on the outcome-balanced validation set in predicting all-cause mortality at 30-, 90-, 180-, 360- and 720-day time-limits (Figure 1). The primary endpoint, 1-year mortality prediction model, recorded an 0.85 AUC accuracy. We observed stable model performance across all HF phenotypes: HFpEF 0.83 AUC, HFmrEF 0.85 AUC and HFrEF 0.86 AUC, respectively). Conclusion Our findings present a novel, patient-level, AI-based risk prediction of all-cause mortality in heart failure with a robust accuracy across its phenotypes. This suggests the potential of AI based predictive models in a point-of-care approach to guide clinical risk stratification. FUNDunding Acknowledgement Type of funding sources: Foundation. Main funding source(s): VZW Cardiovascular Research Center Aalst


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