Evidence map›Paper›PMID 42894238›Full record

ReviewCardiovascular ultrasound2026

Time-aligned segmental strain as a diagnostic marker for obstructive coronary artery disease in patients with chronic chest pain.

Assami Rösner, Hatice Akay Caglayan, Mehran Jazbani, Semra Øztemel Sari, Pablo Marti Castellote, Didrik Kjønås, Sandro Queirós

Abstract readReview
In one paragraph

Review in Cardiovascular ultrasound, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Assami RösnerDepartment of Cardiology, Division of Cardiothoracic and Respiratory Medicine, University Hospital of North Norway, Tromsø, 9019, Norway. assami.rosner@unn.no.
Hatice Akay CaglayanDepartment of Cardiology, Division of Cardiothoracic and Respiratory Medicine, University Hospital of North Norway, Tromsø, 9019, Norway.
Mehran JazbaniDepartment of Cardiology, Stavanger University Hospital, Stavanger, Norway.
Semra Øztemel SariDepartment of Cardiology, Division of Cardiothoracic and Respiratory Medicine, University Hospital of North Norway, Tromsø, 9019, Norway.
Pablo Marti CastelloteDepartment of Information and Communication Technologies (DTIC), Universitat Pompeu Fabra (UPF), Barcelona, Spain.
Didrik KjønåsDepartment of Cardiology, Division of Cardiothoracic and Respiratory Medicine, University Hospital of North Norway, Tromsø, 9019, Norway.
Sandro QueirósLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMyocardial strain curves provide valuable insights into coronary artery disease (CAD) beyond conventional peak strain measurements. This study aimed to evaluate the diagnostic utility of time-aligned segmental strain and strain rate (S/SR) curves to identify obstructive CAD in consecutive chest pain patients.

methodsA total of 510 chest-pain patients referred to coronary computed tomography angiography (CCTA) were included. Segmental strain curves were time-aligned using aortic valve closure (AVC) and cardiac cycle length as reference points. Average S/SR curves were generated for 18 myocardial segments, establishing reference curves from a group of 314 chronic chest-pain patients without CAD (No-CAD). Deviations were defined based on thresholds of peak S/SR or average S/SR curve deviation over systole and the first half of diastole.

resultsSignificant differences were observed between groups, particularly between the revascularized MI, CABG, or 3-vessel CAD groups compared to the No-CAD group (p<0.05). ROC curve analysis demonstrated excellent diagnostic performance for CABG patients (AUC up to 0.933), while the detection rate for PCI was limited (AUC < 0.7). Global longitudinal strain rate in early diastole (GLSR E) and segmental peak SR E showed the highest diagnostic accuracy. These findings confirm that time-aligned strain analysis improves the detection of significant CAD but remains limited for identifying patients with mild disease.

conclusionTime-aligned segmental strain analysis improved detection of high-grade disease requiring CABG but could not distinguish single-vessel from non-obstructive CAD. Pathological strain patterns became significant only in advanced CAD, indicating that segmental resting strain analysis has clinical utility primarily for identifying advanced CAD.

Indexed as

Chest PainComputed Tomography AngiographyCoronary Artery DiseaseCoronary VesselsAgedChronic DiseaseCoronary AngiographyFemaleGlobal Longitudinal StrainHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesChronic coronary artery diseaseSegmental strainSpeckle tracking analysisTime-aligned strainTime-aligned Strain rate

Identifiers

PMID42894238
PMCPMC13678925

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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.