000162320 001__ 162320
000162320 005__ 20251017144612.0
000162320 0247_ $$2doi$$a10.1016/j.jocmr.2025.101919
000162320 0248_ $$2sideral$$a144863
000162320 037__ $$aART-2025-144863
000162320 041__ $$aeng
000162320 100__ $$aBurns, Richard
000162320 245__ $$aRelationships between heart shape, function, and disease in 38,858 UK biobank participants
000162320 260__ $$c2025
000162320 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162320 5203_ $$aBackground. Cardiac functional metrics such as ejection fraction, strain, and valve excursion are important diagnostic and prognostic measures of cardiac disease. However, they ignore a large amount of systolic shape change information available from modern cardiovascular magnetic resonance (CMR) examinations.
We aimed to automatically quantify multidimensional shape and motion scores from CMR, investigate covariates, and test their discrimination of disease in the UK Biobank compared against standard functional metrics.
Methods. An automated analysis pipeline was used to obtain quality-controlled three-dimensional left and right ventricular shape models in 38,858 UK Biobank participants, 5149 of whom had one or more diagnoses of cardiovascular or cardiometabolic disease. Principal component analysis was used to obtain a statistical shape atlas and quantify each participant’s left and right ventricular shape at both end-diastole and end-systole simultaneously. Systolic strain was obtained from arc length changes computed from the shape model, and mitral/tricuspid annular plane systolic excursion (MAPSE/TAPSE) was computed from the displacement of the valves. Discrimination for prevalent disease was quantified using linear discriminant analysis area under the receiver operating characteristic curve.
Results. The first 25 principal component scores captured >90% of the total shape variance. Significantly stronger discrimination for atrial fibrillation, heart failure, diabetes, ischemic disease, and conduction disorders (p<0.001 for each) was obtained using shape scores compared with volumes, ejection fractions, strains, MAPSE, and TAPSE.
Conclusion. Automatically derived shape and motion z-scores capture more discriminative information on disease effects than standard metrics, including volumes, ejection fraction, strain and valve excursions.
000162320 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031413-I
000162320 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000162320 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162320 700__ $$aDal Toso, Laura
000162320 700__ $$aMauger, Charlène A.
000162320 700__ $$aSojoudi, Alireza
000162320 700__ $$aSuinesiaputra, Avan
000162320 700__ $$aPetersen, Steffen E.
000162320 700__ $$0(orcid)0000-0003-4130-5866$$aRamírez, Julia$$uUniversidad de Zaragoza
000162320 700__ $$aMunroe, Patricia B.
000162320 700__ $$aYoung, Alistair A.
000162320 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000162320 773__ $$g27, 2 (2025), 101919 [10 pp.]$$pJ. Cardiov. Magn. Reson.$$tJOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE$$x1097-6647
000162320 8564_ $$s2844337$$uhttps://zaguan.unizar.es/record/162320/files/texto_completo.pdf$$yVersión publicada
000162320 8564_ $$s2207171$$uhttps://zaguan.unizar.es/record/162320/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162320 909CO $$ooai:zaguan.unizar.es:162320$$particulos$$pdriver
000162320 951__ $$a2025-10-17-14:18:06
000162320 980__ $$aARTICLE