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.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material.
Fecha de embargo : 2027-02-14
Exportado de SIDERAL (2026-02-17-20:14:41)
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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.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material. Fecha de embargo : 2027-02-14
Exportado de SIDERAL (2026-02-17-20:14:41)
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Record created 2026-02-17, last modified 2026-02-17