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  • 1
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    Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study
    BMJ ; 2022
    In:  BMJ
    Details
    In: BMJ, BMJ
    Abstract: To evaluate the diagnostic performance of N-terminal pro-B-type natriuretic peptide (NT-proBNP) thresholds for acute heart failure and to develop and validate a decision support tool that combines NT-proBNP concentrations with clinical characteristics. Design Individual patient level data meta-analysis and modelling study. Setting Fourteen studies from 13 countries, including randomised controlled trials and prospective observational studies. Participants Individual patient level data for 10 369 patients with suspected acute heart failure were pooled for the meta-analysis to evaluate NT-proBNP thresholds. A decision support tool (Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF)) that combines NT-proBNP with clinical variables to report the probability of acute heart failure for an individual patient was developed and validated. Main outcome measure Adjudicated diagnosis of acute heart failure. Results Overall, 43.9% (4549/10 369) of patients had an adjudicated diagnosis of acute heart failure (73.3% (2286/3119) and 29.0% (1802/6208) in those with and without previous heart failure, respectively). The negative predictive value of the guideline recommended rule-out threshold of 300 pg/mL was 94.6% (95% confidence interval 91.9% to 96.4%); despite use of age specific rule-in thresholds, the positive predictive value varied at 61.0% (55.3% to 66.4%), 73.5% (62.3% to 82.3%), and 80.2% (70.9% to 87.1%), in patients aged 〈 50 years, 50-75 years, and 〉 75 years, respectively. Performance varied in most subgroups, particularly patients with obesity, renal impairment, or previous heart failure. CoDE-HF was well calibrated, with excellent discrimination in patients with and without previous heart failure (area under the receiver operator curve 0.846 (0.830 to 0.862) and 0.925 (0.919 to 0.932) and Brier scores of 0.130 and 0.099, respectively). In patients without previous heart failure, the diagnostic performance was consistent across all subgroups, with 40.3% (2502/6208) identified at low probability (negative predictive value of 98.6%, 97.8% to 99.1%) and 28.0% (1737/6208) at high probability (positive predictive value of 75.0%, 65.7% to 82.5%) of having acute heart failure. Conclusions In an international, collaborative evaluation of the diagnostic performance of NT-proBNP, guideline recommended thresholds to diagnose acute heart failure varied substantially in important patient subgroups. The CoDE-HF decision support tool incorporating NT-proBNP as a continuous measure and other clinical variables provides a more consistent, accurate, and individualised approach. Study registration PROSPERO CRD42019159407.
    Type of Medium: Online Resource
    ISSN: 1756-1833
    URL: Article
    DOI: 10.1136/bmj-2021-068424
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 1479799-9
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  • 2
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    Rationale and design of the preserved versus reduced ejection fraction biomarker registry and precision medicine database for ambulatory patients with heart failure (PREFER-HF) study
    BMJ ; 2021
    In:  Open Heart Vol. 8, No. 2 ( 2021-10), p. e001704-
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    In: Open Heart, BMJ, Vol. 8, No. 2 ( 2021-10), p. e001704-
    Abstract: Patients with heart failure (HF) are classically categorised by left ventricular ejection fraction (LVEF). Efforts to predict outcomes and response to specific therapy among LVEF-based groups may be suboptimal, in part due to the underlying heterogeneity within clinical HF phenotypes. A multidimensional characterisation of ambulatory patients with and without HF across LVEF groups is needed to better understand and manage patients with HF in a more precise manner. Methods and analysis To date, the first cohort of 1313 out of total planned 3000 patients with and without HF has been enroled in this single-centre, longitudinal observational cohort study. Baseline and 1-year follow-up blood samples and clinical characteristics, the presence and duration of comorbidities, serial laboratory, echocardiographic data and images and therapy information will be obtained. HF diagnosis, aetiology of disease, symptom onset and clinical outcomes at 1 and 5 years will be adjudicated by a team of clinicians. Clinical outcomes of interest include all-cause mortality, cardiovascular mortality, all-cause hospitalisation, cardiovascular hospitalisation, HF hospitalisation, right-sided HF and acute kidney injury. Results from the Preserved versus Reduced Ejection Fraction Biomarker Registry and Precision Medicine Database for Ambulatory Patients with Heart Failure (PREFER-HF) trial will examine longitudinal clinical characteristics, proteomic, metabolomic, genomic and imaging data to better understand HF phenotypes, with the ultimate goal of improving precision medicine and clinical outcomes for patients with HF. Ethics and dissemination Information gathered in this research will be published in peer-reviewed journals. Written informed consent for PREFER-HF was obtained from all participants. All study procedures were approved by the Mass General Brigham Institutional Review Board in Boston, Massachusetts and performed in accordance with the Declaration of Helsinki (Protocol Number: 2016P000339). Trial registration number PREFER-HF ClinicalTrials.gov identifier: NCT03480633 .
    Type of Medium: Online Resource
    ISSN: 2053-3624
    URL: Article
    DOI: 10.1136/openhrt-2021-001704
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2747269-3
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  • 3
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    Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus
    BMJ ; 2019
    In:  Open Heart Vol. 6, No. 1 ( 2019-05), p. e000955-
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    In: Open Heart, BMJ, Vol. 6, No. 1 ( 2019-05), p. e000955-
    Abstract: Patients with diabetes mellitus (DM) are at substantial risk of developing peripheral artery disease (PAD). We recently developed a clinical/proteomic panel to predict obstructive PAD. In this study, we compare the accuracy of this panel for the diagnosis of PAD in patients with and without DM. Methods and results The HART PAD panel consists of one clinical variable (history of hypertension) and concentrations of six biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1 and eotaxin-1). In a prospective cohort of 354 patients undergoing peripheral and/or coronary angiography, performance of this diagnostic panel to detect ≥50% stenosis in at least one peripheral vessel was assessed in patients with (n=94) and without DM (n=260). The model had an area under the receiver operating characteristic curve (AUC) of 0.85 for obstructive PAD. At optimal cut-off, the model had 84% sensitivity, 75% specificity, positive predictive value (PPV) of 84% and negative predictive value (NPV) of 75% for detection of PAD among patients with DM, similar as in those without DM. In those with DM, partitioning the model into five levels resulted in a PPV of 95% and NPV of 100% in the highest and lowest levels, respectively. Abnormal scores were associated with a shorter time to revascularisation during 4.3 years of follow-up. Conclusion A clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM. Trial registration number NCT00842868 .
    Type of Medium: Online Resource
    ISSN: 2053-3624
    URL: Article
    DOI: 10.1136/openhrt-2018-000955
    Language: English
    Publisher: BMJ
    Publication Date: 2019
    detail.hit.zdb_id: 2747269-3
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  • 4
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    Multiple biomarker panel to screen for severe aortic stenosis: results from the CASABLANCA study
    BMJ ; 2018
    In:  Open Heart Vol. 5, No. 2 ( 2018-11), p. e000916-
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    In: Open Heart, BMJ, Vol. 5, No. 2 ( 2018-11), p. e000916-
    Abstract: Severe aortic valve stenosis (AS) develops via insidious processes and can be challenging to correctly diagnose. We sought to develop a circulating biomarker panel to identify patients with severe AS. Methods We enrolled study participants undergoing coronary or peripheral angiography for a variety of cardiovascular diseases at a single academic medical centre. A panel of 109 proteins were measured in blood obtained at the time of the procedure. Statistical learning methods were used to identify biomarkers and clinical parameters that associate with severe AS. A diagnostic model incorporating clinical and biomarker results was developed and evaluated using Monte Carlo cross-validation. Results Of 1244 subjects (age 66.4±11.5  years, 28.7% female), 80 (6.4%) had severe AS (defined as aortic valve area (AVA) 〈 1.0  cm 2 ). A final model included age, N-terminal pro-B-type natriuretic peptide, von Willebrand factor and fetuin-A. The model had good discrimination for severe AS (OR=5.9, 95% CI 3.5 to 10.1, p 〈 0.001) with an area under the curve of 0.76 insample and 0.74 with cross-validation. A diagnostic score was generated. Higher prevalence of severe AS was noted in those with higher scores, such that 1.6% of those with a score of 1 had severe AS compared with 15.3% with a score of 5 (p 〈 0.001), and score values were inversely correlated with AVA (r=−0.35; p 〈 0.001). At optimal model cut-off, we found 76% sensitivity, 65% specificity, 13% positive predictive value and 98% negative predictive value. Conclusions We describe a novel, multiple biomarker approach for diagnostic evaluation of severe AS. Trial registration number NCT00842868 .
    Type of Medium: Online Resource
    ISSN: 2053-3624
    URL: Article
    URL: Article
    DOI: 10.1136/openhrt-2018-000916
    DOI: 10.1136/openhrt-2018-000916.supp1
    Language: English
    Publisher: BMJ
    Publication Date: 2018
    detail.hit.zdb_id: 2747269-3
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