RT Journal Article
T1 Machine learning methods to evaluate fluctuation in oxygenation and peripheral tissue temperature, in sacral and trochanteric area under pressure in healthy subjects and institutionalized patients. A prospective non-randomized trial
A1 Lupiáñez-Pérez, Inmaculada
A1 Muñoz Lupiáñez, Marina
A1 Gómez-González, Alberto José
A1 Morilla-Herrera, Juan Carlos
A1 Pimentel-Sánchez, Ernesto
A1 Durán-Muñoz, Francisco Javier
A1 García-Luque, Rafael
A1 Marfil-Gómez, Raquel María
A1 Pérez-Ardanaz, Bibiana
A1 Morales-Asencio, José Miguel
K1 Ecografía Doppler
K1 Temperatura corporal
AB peripheral oxygenation and local temperature of sacral and trochanteric areas exposed to pressure, comparing a healthy population with one at risk of impaired skin integrity.Methods: The study protocol is registered in ClinicalTrials.gov (NCT02736838). Non-randomised experimental study in two phases. Volunteers were recruited at the University and nursing homes from Nov 2017 to June 2020. The parameters were measured in the sacral and trochanteric areas under pressure using a laser Doppler monitor and near-infrared spectroscopy over 2 h. Body positions varied by area: supine for the sacral region and lateral supine for the trochanteric region.Results: Comparative analysis between both groups of the sacral area showed significant fluctuations in perfusion (p = .035) and local temperature (p = .013). In the trochanteric area, the comparative analysis showed sig nificant fluctuations in perfusion (p = .044), local temperature (p > .001), and oxygenation (p = .049). Nonlinear patterns were identified in the variables examined in both anatomical areas. This prompted the adoption of an algorithm based on the Random Forest Regressor technique to predict the parameters associated with the development of pressure injuries.Conclusions: Individuals at risk of developing pressure injuries show a more limited compensatory physiological response compared to healthy individuals in terms of blood flow, oxygenation, and tissue temperature in pressure areas. Notably, at the 90-min, a turning point is observed, marking a critical threshold from which these pa rameters begin to decline.
PB Elsevier
YR 2026
FD 2026
LK https://hdl.handle.net/10630/45934
UL https://hdl.handle.net/10630/45934
LA eng
NO Lupiáñez-Pérez, Inmaculada, Muñoz Lupiáñez, Marina, Gómez-González, Alberto José, Morilla-Herrra, Juan Carlos, Pimentel-Sánchez, Ernesto, Durán-Muñoz, Francisco Javier, García-Luque, Rafael, Marfil-Gómez, Raquel María, Pérez-Ardanaz, Bibliana, Morales-Asencio, José Miguel (2026). Machine learning methods to evaluate fluctuation in oxygenation and peripheral tissue temperature, in sacral and trochanteric area under pressure in healthy subjects and institutionalized patients. A prospective non-randomized trial. Journal of Tissue Viability. Elsvier. Vol. 25, nº 2, May, DOI: 10.1016/j.jtv.2026.100992
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 18 mar 2026