Department of Epidemiology and Health Statistics, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Service Management, The Affiliated Hospital of Qingdao University, Qingdao, China.
Int Wound J. 2020 Aug;17(4):974-986. doi: 10.1111/iwj.13362. Epub 2020 Apr 7.
This 1:5 case-control study aimed to identify the risk factors of hospital-acquired pressure injuries (HAPIs) and to develop a mathematical model of nomogram for the risk prediction of HAPIs. Data for 370 patients with HAPIs and 1971 patients without HAPIs were extracted from the adverse events and the electronic medical systems. They were randomly divided into two sets: training (n = 1951) and validation (n = 390). Significant risk factors were identified by univariate and multivariate analyses in the training set, followed by a nomogram constructed. Age, independent movement, sensory perception and response, moisture, perfusion, use of medical devices, compulsive position, hypoalbuminaemia, an existing pressure injury or scarring from a previous pressure injury, and surgery sufferings were considered significant risk factors and were included to construct a nomogram. In both of the training and validation sets, the areas of 0.90 under the receiver operating characteristic curves showed excellent discrimination of the nomogram; calibration plots demonstrated a good consistency between the observed probability and the nomogram's prediction; decision curve analyses exhibited preferable net benefit along with the threshold probability in the nomogram. The excellent performance of the nomogram makes it a convenient and reliable tool for the risk prediction of HAPIs.
本 1:5 病例对照研究旨在确定医院获得性压疮(HAPI)的风险因素,并建立 HAPI 风险预测的列线图数学模型。从不良事件和电子病历系统中提取了 370 例 HAPI 患者和 1971 例无 HAPI 患者的数据。将其随机分为两组:训练集(n = 1951)和验证集(n = 390)。在训练集中,通过单因素和多因素分析确定了显著的风险因素,然后构建了一个列线图。年龄、独立活动、感觉知觉和反应、湿度、灌注、使用医疗器械、强制性体位、低白蛋白血症、现有的压疮或先前压疮的疤痕以及手术痛苦被认为是显著的风险因素,并被纳入构建列线图。在训练集和验证集中,ROC 曲线下的面积为 0.90,表明该列线图具有出色的区分度;校准图显示观察概率与列线图预测之间具有良好的一致性;决策曲线分析显示,该列线图在阈值概率下具有较好的净获益。该列线图性能优异,是一种方便可靠的 HAPI 风险预测工具。