School of Health and Society, University of Salford, Salford, UK.
Swift Medical Inc, Toronto, Ontario, Canada.
Int Wound J. 2024 Jul;21(7):e70000. doi: 10.1111/iwj.70000.
This study aimed to improve the predictive accuracy of the Braden assessment for pressure injury risk in skilled nursing facilities (SNFs) by incorporating real-world data and training a survival model. A comprehensive analysis of 126 384 SNF stays and 62 253 in-house pressure injuries was conducted using a large calibrated wound database. This study employed a time-varying Cox Proportional Hazards model, focusing on variations in Braden scores, demographic data and the history of pressure injuries. Feature selection was executed through a forward-backward process to identify significant predictive factors. The study found that sensory and moisture Braden subscores were minimally contributive and were consequently discarded. The most significant predictors of increased pressure injury risk were identified as a recent (within 21 days) decrease in Braden score, low subscores in nutrition, friction and activity, and a history of pressure injuries. The model demonstrated a 10.4% increase in predictive accuracy compared with traditional Braden scores, indicating a significant improvement. The study suggests that disaggregating Braden scores and incorporating detailed wound histories and demographic data can substantially enhance the accuracy of pressure injury risk assessments in SNFs. This approach aligns with the evolving trend towards more personalized and detailed patient care. These findings propose a new direction in pressure injury risk assessment, potentially leading to more effective and individualized care strategies in SNFs. The study highlights the value of large-scale data in wound care, suggesting its potential to enhance quantitative approaches for pressure injury risk assessment and supporting more accurate, data-driven clinical decision-making.
本研究旨在通过整合真实世界的数据并训练生存模型，提高Braden 评估在熟练护理机构（SNF）中预测压力性损伤风险的准确性。使用大型校准伤口数据库，对 126384 次 SNF 入住和 62253 例院内压力性损伤进行了全面分析。本研究采用了时变 Cox 比例风险模型，重点关注 Braden 评分、人口统计学数据和压力性损伤史的变化。通过前向-后向过程进行特征选择，以确定显著的预测因素。研究发现，感觉和湿度 Braden 子评分的贡献最小，因此被丢弃。增加压力性损伤风险的最显著预测因素是 Braden 评分最近（21 天内）下降、营养、摩擦和活动的低子评分以及压力性损伤史。与传统的 Braden 评分相比，该模型的预测准确性提高了 10.4%，表明有显著改善。研究表明，分解 Braden 评分并纳入详细的伤口史和人口统计学数据可以大大提高 SNF 中压力性损伤风险评估的准确性。这种方法符合向更个性化和详细的患者护理发展的趋势。这些发现为压力性损伤风险评估提出了一个新的方向，可能会导致 SNF 中更有效和个体化的护理策略。该研究强调了大规模数据在伤口护理中的价值，表明其有可能增强压力性损伤风险评估的定量方法，并支持更准确、数据驱动的临床决策。
