Hatanaka N, Yamamoto Y, Ichihara K, Mastuo S, Nakamura Y, Watanabe M, Iwatani Y
Department of Clinical Pathology, Tenri Hospital, Nara, Japan.
J Clin Pathol. 2008 Apr;61(4):514-8. doi: 10.1136/jcp.2007.050195.
Various scales have been devised to predict development of pressure ulcers on the basis of clinical and laboratory data, such as the Braden Scale (Braden score), which is used to monitor activity and skin conditions of bedridden patients. However, none of these scales facilitates clinically reliable prediction.
To develop a clinical laboratory data-based predictive equation for the development of pressure ulcers.
Subjects were 149 hospitalised patients with respiratory disorders who were monitored for the development of pressure ulcers over a 3-month period. The proportional hazards model (Cox regression) was used to analyse the results of 12 basic laboratory tests on the day of hospitalisation in comparison with Braden score.
Pressure ulcers developed in 38 patients within the study period. A Cox regression model consisting solely of Braden scale items showed that none of these items contributed to significantly predicting pressure ulcers. Rather, a combination of haemoglobin (Hb), C-reactive protein (CRP), albumin (Alb), age, and gender produced the best model for prediction. Using the set of explanatory variables, we created a new indicator based on a multiple logistic regression equation. The new indicator showed high sensitivity (0.73) and specificity (0.70), and its diagnostic power was higher than that of Alb, Hb, CRP, or the Braden score alone.
The new indicator may become a more useful clinical tool for predicting presser ulcers than Braden score. The new indicator warrants verification studies to facilitate its clinical implementation in the future.
已经设计了各种量表,用于根据临床和实验室数据预测压疮的发生,例如用于监测卧床患者活动和皮肤状况的Braden量表(Braden评分)。然而,这些量表均无法实现临床可靠的预测。
开发一种基于临床实验室数据的压疮发生预测方程。
研究对象为149例患有呼吸系统疾病的住院患者,对其进行为期3个月的压疮发生监测。使用比例风险模型(Cox回归)分析住院当天12项基本实验室检查结果,并与Braden评分进行比较。
在研究期间,38例患者发生了压疮。仅由Braden量表项目组成的Cox回归模型显示,这些项目均对压疮的显著预测没有贡献。相反,血红蛋白(Hb)、C反应蛋白(CRP)、白蛋白(Alb)、年龄和性别的组合产生了最佳预测模型。利用这组解释变量,我们基于多元逻辑回归方程创建了一个新指标。新指标显示出高敏感性(0.73)和特异性(0.70),其诊断能力高于单独的Alb、Hb、CRP或Braden评分。
与Braden评分相比,新指标可能成为预测压疮更有用的临床工具。新指标有待进行验证研究,以促进其未来在临床上的应用。