PDF(Pubmed)
Abstract：
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.
摘要：
这项研究旨在通过结合现实世界的数据和训练生存模型来提高Braden评估对熟练护理机构（SNF）中压力损伤风险的预测准确性。使用大型校准伤口数据库对126384SNF停留和62253内部压力伤害进行了综合分析。这项研究采用了时变的Cox比例危险模型，关注布雷登分数的变化，人口统计数据和压力伤害史。通过前后过程执行特征选择以识别重要的预测因素。研究发现，感觉和湿度Braden子分数的贡献很小，因此被丢弃。压力伤害风险增加的最重要预测因素被确定为Braden评分最近（21天内）下降，营养方面的低分，摩擦和活动，和压力伤的历史。与传统的Braden评分相比，该模型的预测准确性提高了10.4%，表明有了显著的改善。研究表明，对Braden评分进行分类并纳入详细的伤口历史和人口统计数据可以大大提高SNF中压力性损伤风险评估的准确性。这种方法与更个性化和详细的患者护理的发展趋势相一致。这些发现为压力损伤风险评估提供了新的方向，可能导致SNF中更有效和个性化的护理策略。这项研究强调了大规模数据在伤口护理中的价值，表明它有可能增强压力损伤风险评估的定量方法，并支持更准确的方法，数据驱动的临床决策。
