168741 20260217214838.0 doi 10.1016/j.medcli.2025.107283 sideral 147976 ART-2026-147976 eng Pérez Abad, Laura Universidad de Zaragoza Unlocking the potential of nailfold videocapillaroscopy in diagnosing and staging wild-type transthyretin amyloidosis: A preliminary approach 2026 Background. Wild-type transthyretin amyloidosis (ATTRwt) is a serious condition. At early stages, symptoms resemble those of heart failure with preserved ejection fraction (HFpEF). Our aim was to perform software-supported nailfold videocapillaroscopy (NVC) analysis to identify hallmarks useful for diagnosis and build machine learning (ML)-based models to assess severity. Methods. Thirty-two ATTRwt patients underwent NVC. Nineteen initiated TTR-stabilizing therapy and had a new NVC 12 months afterwards. Forty-one capillary-related variables were analyzed. Thirty NVCs were randomly chosen to train models to discriminate between poorer or less poor prognosis according to N-terminal pro-B-type natriuretic peptide (NT-proBNP) or Cheng score (cut-offs: 2000 pg/mL and 4 points, respectively). The remaining 21 NVCs were used for validation purposes. A control population of 99 patients with heart failure with preserved ejection fraction (HFpEF) but without signs of amyloidosis was included. Results. A profound disorganization in the nailfold capillary architecture was generally observed. The models achieved accuracies of 0.81 and 0.90, respectively, in predicting disease severity. An additional model designed to distinguish a profile suggestive of amyloidosis (vs. HFpEF controls) achieved an accuracy of 0.73. Conclusions. NVC-based ML models may contribute to early diagnosis and staging of ATTRwt. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/IISAragón/GIIS-009 info:eu-repo/grantAgreement/ES/IISAragón/GIIS-084 info:eu-repo/semantics/embargoedAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Aibar Arregui, Miguel Ángel (orcid)0000-0002-1774-6488 Aramburu Llorente, Jimena Ramón y Cajal Calvo, Juan Universidad de Zaragoza Andrés Gracia, Alejandro Universidad de Zaragoza (orcid)0000-0003-3501-0121 Revilla Martí, Pablo Universidad de Zaragoza Atienza Ayala, Saida (orcid)0000-0003-0715-2843 Lahuerta Pueyo, Carmen Campos Sáenz de Santamaría, Amelia Universidad de Zaragoza Ramos Ibañez, Eduardo Gracia Tello, Borja del Carmelo Universidad de Zaragoza (orcid)0000-0003-3248-2908 1011 770 Universidad de Zaragoza Dpto. Microb.Ped.Radio.Sal.Pú. Área Radiol. y Medicina Física 1007 610 Universidad de Zaragoza Dpto. Medicina, Psiqu. y Derm. Area Medicina 166, 2 (2026), 107283 [8 pp.] Med. clín. Medicina clinica 0025-7753 294116 http://zaguan.unizar.es/record/168741/files/texto_completo.pdf Postprint info:eu-repo/date/embargoEnd/2027-02-14 1096121 http://zaguan.unizar.es/record/168741/files/texto_completo.jpg?subformat=icon icon Postprint info:eu-repo/date/embargoEnd/2027-02-14 oai:zaguan.unizar.es:168741 articulos driver 2026-02-17-20:14:41 ARTICLE