Caggiari Silvia, Aylward-Wotton Nicci, Kent Bridie, Worsley Peter R
Skin Sensing Research Group, School of Health Sciences, University of Southampton, Southampton, SO17 1BJ, UK.
Skin Sensing Research Group, School of Health Sciences, University of Southampton, Southampton, SO17 1BJ, UK; Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin PL31 2QN, UK.
J Tissue Viability. 2024 Nov;33(4):693-700. doi: 10.1016/j.jtv.2024.07.005. Epub 2024 Jul 14.
Individuals in the community with reduced mobility are at risk of exposure to prolonged lying and sitting postures, which may cause pressure ulcers. The present study combines continuous pressure monitoring technology and intelligent algorithms to evaluate posture, mobility, and pressure profiles in a cohort of community dwelling patients, who had acquired pressure ulcers.
This study represents a secondary analysis of the data from the Quality Improvement project 'Pressure Reduction through COntinuous Monitoring In the community SEtting (PROMISE)'. 22 patients with pressure ulcers were purposely selected from 105 recruited community residents. Data were collected using a commercial continuous pressure monitoring system over a period of 1-4 days, and analysed with an intelligent algorithm using machine learning to determine posture and mobility events. Duration and magnitude of pressure signatures of each static posture and exposure thresholds were identified based on a sigmoid relationship between pressure and time.
Patients revealed a wide range of ages (30-95 years), BMI (17.5-47 kg/m) and a series of co-morbidities, which may have influenced the susceptibility to skin damage. Posture, mobility, and pressure data revealed a high degree of inter-subject variability. Largest duration of static postures ranged between 1.7 and 19.8 h, with 17/22 patients spending at least 60 % of their monitoring period in static postures which lasted >2 h. Data revealed that many patients spent prolonged periods with potentially harmful interface pressure conditions, including pressure gradients >60 mmHg/cm.
This study combined posture, mobility, and pressure data from a commercial pressure monitoring technology through an intelligent algorithm. The community residents who had acquired a pressure ulcer at the time of monitoring exhibited trends which exposed their skin and subdermal tissues to prolonged high pressures during static postures. These indicators need further validation through prospective clinical trials.
社区中行动不便的个体面临长时间躺卧和坐姿的风险，这可能会导致压疮。本研究结合连续压力监测技术和智能算法，对一组患有压疮的社区居住患者的姿势、活动能力和压力分布进行评估。
本研究是对“社区环境中通过连续监测降低压力（PROMISE）”质量改进项目数据的二次分析。从105名招募的社区居民中特意挑选出22名患有压疮的患者。使用商业连续压力监测系统在1 - 4天内收集数据，并通过机器学习智能算法进行分析，以确定姿势和活动事件。基于压力与时间之间的S形关系，确定每个静态姿势的压力特征持续时间和大小以及暴露阈值。
患者年龄范围广泛（30 - 95岁），体重指数（BMI）为17.5 - 47kg/m²，还有一系列合并症，这些可能影响了皮肤损伤的易感性。姿势、活动能力和压力数据显示个体间差异很大。静态姿势的最长持续时间在1.7至19.8小时之间，22名患者中有17名在监测期间至少60%的时间处于持续超过2小时的静态姿势。数据显示，许多患者在可能有害的界面压力条件下停留时间较长，包括压力梯度>60mmHg/cm。
本研究通过智能算法将商业压力监测技术的姿势、活动能力和压力数据结合起来。在监测时已患有压疮的社区居民呈现出一些趋势，即他们的皮肤和皮下组织在静态姿势期间会暴露于长时间的高压之下。这些指标需要通过前瞻性临床试验进一步验证。
