A clinical and proteomics approach to predict the presence of obstructive peripheral arterial disease: From the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study

McCarthy, Cian P.; Ibrahim, Nasrien E.; Kimmenade, Roland R.J. van; Gaggin, Hanna K.; Simon, Mandy L.; Gandhi, Parul U.; Kelly, Noreen; Motiwala, Shweta R.; Mukai, Renata; Magaret, Craig A.; Barnes, Grady; Rhyne, Rhonda F.; Garasic, Joseph M.; Januzzi, James L.

doi:10.1002/clc.22939

Cited by 17 publications

(12 citation statements)

References 25 publications

(40 reference statements)

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“…As such, ML models might contribute to identify asymptomatic PAD patients with great functional limitations, that otherwise would be lost to PAD diagnosis by regular tests [ 142 ]. Other authors described that the combination of proteomic data and clinical information rendered algorithms able to predict angiographically significant PAD [ 143 ]. Regarding CV risk stratification, Ross EG et al reported that state of the art ML algorithms outperformed stepwise logistic regression models for the identification of PAD and the prognostication of mortality risk in this population [ 144 ].…”

Section: Machine Learning and Padmentioning

confidence: 99%

The Role of Circulating Biomarkers in Peripheral Arterial Disease

Saenz-Pipaon

Martínez-Aguilar

Orbe

et al. 2021

IJMS

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Peripheral arterial disease (PAD) of the lower extremities is a chronic illness predominantly of atherosclerotic aetiology, associated to traditional cardiovascular (CV) risk factors. It is one of the most prevalent CV conditions worldwide in subjects >65 years, estimated to increase greatly with the aging of the population, becoming a severe socioeconomic problem in the future. The narrowing and thrombotic occlusion of the lower limb arteries impairs the walking function as the disease progresses, increasing the risk of CV events (myocardial infarction and stroke), amputation and death. Despite its poor prognosis, PAD patients are scarcely identified until the disease is advanced, highlighting the need for reliable biomarkers for PAD patient stratification, that might also contribute to define more personalized medical treatments. In this review, we will discuss the usefulness of inflammatory molecules, matrix metalloproteinases (MMPs), and cardiac damage markers, as well as novel components of the liquid biopsy, extracellular vesicles (EVs), and non-coding RNAs for lower limb PAD identification, stratification, and outcome assessment. We will also explore the potential of machine learning methods to build prediction models to refine PAD assessment. In this line, the usefulness of multimarker approaches to evaluate this complex multifactorial disease will be also discussed.

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Section: Machine Learning and Padmentioning

confidence: 99%

The Role of Circulating Biomarkers in Peripheral Arterial Disease

Saenz-Pipaon

Martínez-Aguilar

Orbe

et al. 2021

IJMS

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“…Though prevalent in patients with DM, PAD is a challenge to identify and manage; tools typically used for diagnosis of PAD are often less accurate in those with DM. Accordingly, we wished to verify the performance of a biomarker-leveraged scoring system derived using machine learning recently found to predict angiographically significant PAD 8. We demonstrate excellent performance of the model in patients with DM.…”

Section: Discussionmentioning

confidence: 94%

“…The derivation of the HART PAD panel has been previously described 8. Briefly, using the same 354 patients included in this study, we used machine learning, a subset of artificial intelligence, to identify predictors of significant PAD.…”

Section: Methodsmentioning

confidence: 99%

“…Imaging modalities are also used to diagnose PAD but imaging is expensive, has variable availability and requires intravenous contrast and/or ionising radiation. For these reasons, we recently developed a clinical/proteomic panel (HART PAD) using machine learning, capable of diagnosing obstructive PAD with high accuracy 8. In this study, we compare the accuracy of this panel for the diagnosis of obstructive PAD in patients with and without DM a population at high risk for PAD that is particularly challenging to evaluate and manage.…”

Section: Introductionmentioning

confidence: 99%

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Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus

et al. 2019

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BackgroundPatients 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 resultsThe 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.ConclusionA clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM.Trial registration numberNCT00842868.

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“…Machine learning (ML) encompasses a wide variety of methods whereby artificial intelligence learns to perform tasks when exposed to large amounts of data. The application of a ML algorithm may expedite management and identification of ACS for more accurate diagnosis, appropriate signposting to the correct clinical pathway and early initiation of appropriate ACS pharmacotherapy [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Improving the Detection of Acute Coronary Syndrome Using Machine Learning of Blood Biomarkers

Rjoob¹,

McGilligan²,

Bond³

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Background: Acute coronary syndrome (ACS) is one of the main causes of death worldwide. The 12-lead electrocardiogram (ECG) is used to help diagnose ACS, along with clinical risk factors (smoking, diabetes mellitus, hypertension, hscTn and positive family history of ACS. These methods however are associated with many limitations resulting in variable sensitivity/specificity. The aim of this study was to use a machine learning approach to develop an optimum panel of blood protein biomarkers capable of independently diagnosing ACS. Methods: A hybrid feature selection and ML prediction algorithms including two classifiers: 1) decision tree (DT) and 2) logistic regression were applied to protein biomarkers (327 proteins) collected from patients with ACS n=91 or non-ACS n=97. Results: Using this approach, 20 proteins out of 327 proteins were able to accurately distinguish between ACS and non-ACS (logistic regression ROC-AUC=0.8 and accuracy=82.5% and DT ROC-AUC=0.6 and accuracy=64.9%. Conclusion: Logistic regression obtained a higher performance compared to DT and showed promising results uncovering a panel of 20 protein biomarkers which included those associated with progressive atherosclerotic plaques, myocardial injury and inflammation. This approach was able to accurately discriminate between patients with ACS and non-ACS.

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A clinical and proteomics approach to predict the presence of obstructive peripheral arterial disease: From the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study

Abstract: A clinical/biomarker score demonstrates high accuracy for predicting the presence of PAD.

Cited by 17 publications

References 25 publications

The Role of Circulating Biomarkers in Peripheral Arterial Disease

The Role of Circulating Biomarkers in Peripheral Arterial Disease

Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus

Improving the Detection of Acute Coronary Syndrome Using Machine Learning of Blood Biomarkers

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