Ma Yuxia, He Xiang, Yang Tingting, Yang Yifang, Yang Ziyan, Gao Tian, Yan Fanghong, Yan Boling, Wang Juan, Han Lin
Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China.
The First Hospital of Lanzhou University, Lanzhou, China.
J Clin Nurs. 2025 Jun;34(6):2117-2137. doi: 10.1111/jocn.17367. Epub 2024 Jul 29.
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.
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.
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).
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.
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.
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.
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.
REGISTRATION NUMBER (PROSPERO): CRD42023445258.
本研究的主要目的是综合目前流行的住院患者压力性损伤预测模型,旨在识别与住院患者压力性损伤相关的常见预测因素。这一努力有可能为临床护士为高危患者提供针对性护理提供有价值的参考。
压力性损伤(PIs)是全球范围内常见的健康问题。关于压力性损伤风险预测模型的研究报告和发表数量不断增加。然而,这些模型的预测性能仍不明确。
系统评价和荟萃分析:检索Cochrane图书馆、PubMed、Embase、CINAHL、Web of Science以及包括中国知网(CNKI)、万方数据库、维普数据库和中国生物医学文献数据库(CBM)在内的中文数据库。
本系统评价按照PRISMA指南进行。检索Cochrane图书馆、PubMed、Embase、CINAHL、Web of Science、CNKI、维普数据库、万方数据库和CBM中2023年9月之前发表的所有研究。纳入队列研究、病例对照研究,报告风险模型的建立,并在住院患者中进行了内部和外部验证。两名研究人员根据纳入和排除标准筛选检索到的研究,并根据CHARM清单严格评估研究质量。采用PRISMA指南报告系统评价和荟萃分析结果。
纳入62项研究,包含99个压力性损伤风险预测模型。32个预测模型建模的AUC(ROC曲线下面积)报告范围为0.70至0.99,38个模型验证的AUC报告范围为0.70至0.98。性别(OR = 1.41,CI:0.99至1.31)、年龄(WMD = 8.81,CI:8.11至9.57)、糖尿病(OR = 1.64,CI:1.36至1.99)、机械通气(OR = 2.71,CI:2.05至3.57)、住院时间(WMD = 7.65,CI:7.24至8.05)是压力性损伤最常见的预测因素。
住院患者压力性损伤风险预测模型的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,部分纳入研究在建模过程中缺乏内部或外部验证,影响了模型的稳定性和可推广性。年龄较大、ICU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。未来建议临床护士在实践中根据实际情况选择性能较好的预测模型识别高危患者,并针对高危因素提供护理措施,预防疾病发生。
风险预测模型是识别有发生压力性损伤风险患者的有效工具。借助风险预测工具,护士可以识别高危患者和常见预测因素,预测发生压力性损伤的概率,进而提供具体的预防措施以改善这些患者的结局。
注册号(PROSPERO):CRD42023445258