Biomarker and clinical data–based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study
MMT biomarkers and clinical data may be associated in a causal manner with ultrafiltration failure (local effect) and cardiovascular events (systemic effect) in PD. The machine learning-based software MAUXI provides applicability of ET-LDA models with ≤38 variables to predict PD endurance and type of PD technique failure related to peritoneal membrane deterioration.
Authors
- Pilar Sandoval (ORCID: https://orcid.org/0000-0002-5893-7037)
- Gloria del Peso-Gilsanz
- Manuel López‐Cabrera (ORCID: https://orcid.org/0000-0002-0976-9719)
- Pedro Majano (ORCID: https://orcid.org/0000-0002-5495-1413)
- Marta Ossorio-González
- Patricia Albar-Vizcaíno
- M. Auxiliadora Bajo (ORCID: https://orcid.org/0000-0001-7364-557X)
- Guadalupe Tirma Gónzalez-Mateo (ORCID: https://orcid.org/0000-0002-8144-904X)
- Eva María Arriero‐País (ORCID: https://orcid.org/0000-0001-7719-7022)
- Roberto Arrojo-García
Institutions
- Consejo Superior de Investigaciones Científicas (ES)
- Hospital Universitario La Paz (ES)
- Hospital Universitario de Guadalajara (ES)
- Hospital La Paz Institute for Health Research (ES)
- Unidad de Investigación en cuidados y servicios de salud (Investén-isciii) (ES)
- Hospital Universitario de La Princesa (ES)
- Centro de Biología Molecular Severo Ochoa (ES)
- Universidad Autónoma de Madrid (ES)
Publication Details
- Journal
- BMJ Health & Care Informatics
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1136/bmjhci-2024-101138
- Citations
- 2
- Primary Topic
- Dialysis and Renal Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- Universidad Autónoma de Madrid
- Ministerio de Ciencia e Innovación
- Instituto de Salud Carlos III
- H2020 Marie Skłodowska-Curie Actions