目的:本研究的主要目的是综合住院患者压力性损伤的流行预测模型,目的是确定与住院患者压力损伤相关的常见预测因素。这项努力有可能为临床护士提供有价值的参考,为高风险患者提供有针对性的护理。
背景:压力伤害(PI)是全世界经常发生的健康问题。关于已报告和发表的PI风险预测模型的研究越来越多。然而,模型的预测性能尚不清楚。
方法:系统评价和荟萃分析:Cochrane图书馆,PubMed,Embase,CINAHL,WebofScience和中国数据库,包括CNKI(中国国家知识基础设施),万方数据库,维普数据库和CBM(中国生物医学)。
方法:本系统评价是根据PRISMA的建议进行的。Cochrane图书馆的数据库,PubMed,Embase,CINAHL,WebofScience,和CNKI,Weipu数据库,搜索了2023年9月之前发表的所有研究的万方数据库和煤层气。我们纳入了队列研究,案例控制设计,报告风险模型的发展,并已在住院患者中进行外部和内部验证。两名研究人员根据纳入和排除标准选择了检索到的研究,并根据CHARMS清单严格评估研究质量。PRISMA指南用于报告系统评价和荟萃分析。
结果:纳入了62项研究,其中包含99个压力伤害风险预测模型。据报道,32个预测模型的AUC(ROC曲线下面积)范围为.70至.99,而38个模型的验证AUC范围为.70至.98。性别(OR=1.41,CI:.99~1.31),年龄(WMD=8.81,CI:8.11~9.57),糖尿病(OR=1.64,CI:1.36~1.99),机械通气(OR=2.71,CI:2.05~3.57),住院时间(WMD=7.65,CI:7.24~8.05)是压力性损伤最常见的预测因素。
结论:住院患者PIs风险预测模型研究具有较高的研究质量,风险预测模型也具有良好的预测性能。然而,一些纳入的研究缺乏内部或外部建模验证,影响了稳定性和可扩展性。老年人,ICU的男性患者,白蛋白,血细胞比容,低血红蛋白水平,糖尿病,机械通气和住院时间是住院患者压力性损伤的高危因素。在未来,建议临床护士,在实践中,选择性能较好的预测模型,根据实际情况识别高危患者,并针对高危因素提供护理,以预防疾病的发生。
结论:风险预测模型是一种有效的工具,用于识别有发生PIs风险的患者。借助风险预测工具,护士可以识别高危患者和常见的预测因素,预测发展PI的可能性,然后提供具体的预防措施,以改善这些患者的预后。
■CRD42023445258。
OBJECTIVE: The main aim of this study is to synthesize the prevalent predictive models for pressure injuries in hospitalized patients, with the goal of identifying common predictive factors linked to pressure injuries in hospitalized patients. This endeavour holds the potential to provide clinical nurses with a valuable reference for providing targeted care to high-risk patients.
BACKGROUND: Pressure injuries (PIs) are a frequently occurring health problem throughout the world. There are mounting studies about risk prediction model of PIs reported and published. However, the prediction performance of the models is still unclear.
METHODS: Systematic review and meta-analysis: The Cochrane Library, PubMed, Embase, CINAHL, Web of Science and Chinese databases including CNKI (China National Knowledge Infrastructure), Wanfang Database, Weipu Database and CBM (China Biology Medicine).
METHODS: This systematic review was conducted following PRISMA recommendations. The databases of Cochrane Library, PubMed, Embase, CINAHL, Web of Science, and CNKI, Weipu Database, Wanfang Database and CBM were searched for all studies published before September 2023. We included studies with cohort, case-control designs, reporting the development of risk model and have been validated externally and internally among the hospitalized patients. Two researchers selected the retrieved studies according to the inclusion and exclusion criteria, and critically evaluated the quality of studies based on the CHARMS checklist. The PRISMA guideline was used to report the systematic review and meta-analysis.
RESULTS: Sixty-two studies were included, which contained 99 pressure injuries risk prediction models. The AUC (area under ROC curve) of modelling in 32 prediction models were reported ranged from .70 to .99, while the AUC of verification in 38 models were reported ranged from .70 to .98. Gender (OR = 1.41, CI: .99 ~ 1.31), age (WMD = 8.81, CI: 8.11 ~ 9.57), diabetes mellitus (OR = 1.64, CI: 1.36 ~ 1.99), mechanical ventilation (OR = 2.71, CI: 2.05 ~ 3.57), length of hospital stay (WMD = 7.65, CI: 7.24 ~ 8.05) were the most common predictors of pressure injuries.
CONCLUSIONS: Studies of PIs risk prediction model in hospitalized patients had high research quality, and the risk prediction models also had good predictive performance. However, some of the included studies lacked of internal or external validation in modelling, which affected the stability and extendibility. The aged, male patient in ICU, albumin, haematocrit, low haemoglobin level, diabetes, mechanical ventilation and length of stay in hospital were high-risk factors for pressure injuries in hospitalized patients. In the future, it is recommended that clinical nurses, in practice, select predictive models with better performance to identify high-risk patients based on the actual situation and provide care targeting the high-risk factors to prevent the occurrence of diseases.
CONCLUSIONS: The risk prediction model is an effective tool for identifying patients at the risk of developing PIs. With the help of risk prediction tool, nurses can identify the high-risk patients and common predictive factors, predict the probability of developing PIs, then provide specific preventive measures to improve the outcomes of these patients.
UNASSIGNED: CRD42023445258.