doi:10.1016/j.medcli.2025.107283engPérez Abad, LauraAibar Arregui, Miguel ÁngelAramburu Llorente, JimenaRamón y Cajal Calvo, JuanAndrés Gracia, AlejandroRevilla Martí, PabloAtienza Ayala, SaidaLahuerta Pueyo, CarmenCampos Sáenz de Santamaría, AmeliaRamos Ibañez, EduardoGracia Tello, Borja del CarmeloUnlocking the potential of nailfold videocapillaroscopy in diagnosing and staging wild-type transthyretin amyloidosis: A preliminary approachART-2026-147976Background. 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.2026http://zaguan.unizar.es/record/16874110.1016/j.medcli.2025.107283http://zaguan.unizar.es/record/168741oai:zaguan.unizar.es:168741info:eu-repo/grantAgreement/ES/IISAragón/GIIS-009info:eu-repo/grantAgreement/ES/IISAragón/GIIS-084Medicina clinica 166, 2 (2026), 107283 [8 pp.]by-nc-ndhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esinfo:eu-repo/semantics/embargoedAccess