Evidence map›Paper›PMID 33175247›Full record

ArticleJournal of medical systems2020

Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort.

Ankush D Jamthikar, Deep Gupta, Amer M Johri, Laura E Mantella, Luca Saba, Raghu Kolluri, Aditya M Sharma, Vijay Viswanathan, Andrew Nicolaides, Jasjit S Suri

Abstract read
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Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  8. Machine Learning and the Conundrum of Stroke Risk Prediction.Arrhythmia & electrophysiology review · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ankush D JamthikarDepartment of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, MH, Nagpur, India.
Deep GuptaDepartment of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, MH, Nagpur, India.
Amer M JohriDepartment of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada.
Laura E MantellaDepartment of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada.
Luca SabaDepartment of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy.
Raghu KolluriOhio Health Heart and Vascular, Columbus, OH, USA.
Aditya M SharmaDivision of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA.
Vijay ViswanathanMV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India.
Andrew NicolaidesVascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia, Cyprus.
Jasjit S SuriStroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA. jasjit.suri@atheropoint.com.ORCID http://orcid.org/0000-0002-4600-8513

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study developed an office-based cardiovascular risk calculator using a machine learning (ML) algorithm that utilized a focused carotid ultrasound. The design of this study was divided into three steps. The first step involved collecting 18 office-based biomarkers consisting of six clinical risk factors (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, and smoking) and 12 carotid ultrasound image-based phenotypes. The second step consisted of the design of an ML-based cardiovascular risk calculator-called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0

Indexed as

Cardiovascular DiseasesCross-Sectional StudiesFemaleHeart Disease Risk FactorsHumansInfant, NewbornMachine LearningMaleRetrospective StudiesRisk AssessmentRisk Factorsand artificial intelligence, “AtheroEdge composite risk score 2.0”Cardiovascular diseaseMachine learningNon-laboratory biomarkersOffice-basedRisk assessmentStroke

Identifiers

PMID33175247

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.