Unlocking the potential of nailfold videocapillaroscopy in diagnosing and staging wild-type transthyretin amyloidosis: A preliminary approach

Pérez Abad, Laura (Universidad de Zaragoza) ; Aibar Arregui, Miguel Ángel ; Aramburu Llorente, Jimena ; Ramón y Cajal Calvo, Juan (Universidad de Zaragoza) ; Andrés Gracia, Alejandro (Universidad de Zaragoza) ; Revilla Martí, Pablo (Universidad de Zaragoza) ; Atienza Ayala, Saida ; Lahuerta Pueyo, Carmen ; Campos Sáenz de Santamaría, Amelia (Universidad de Zaragoza) ; Ramos Ibañez, Eduardo ; Gracia Tello, Borja del Carmelo (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.medcli.2025.107283

Año: 2026

Publicado en: Medicina clinica 166, 2 (2026), 107283 [8 pp.]
ISSN: 0025-7753

Financiación: info:eu-repo/grantAgreement/ES/IISAragón/GIIS-009
Financiación: info:eu-repo/grantAgreement/ES/IISAragón/GIIS-084

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Radiol. y Medicina Física (Dpto. Microb.Ped.Radio.Sal.Pú.)
Área (Departamento): Area Medicina (Dpto. Medicina, Psiqu. y Derm.)