压力性损伤发生风险预测工具：对报告模型开发与验证方法的系统评价的伞状综述
Risk prediction tools for pressure injury occurrence: an umbrella review of systematic reviews reporting model development and validation methods.
作者信息
Hillier Bethany, Scandrett Katie, Coombe April, Hernandez-Boussard Tina, Steyerberg Ewout, Takwoingi Yemisi, Velickovic Vladica, Dinnes Jacqueline
机构信息
Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.
出版信息
Diagn Progn Res. 2025 Jan 14;9(1):2. doi: 10.1186/s41512-024-00182-4.
BACKGROUND
Pressure injuries (PIs) place a substantial burden on healthcare systems worldwide. Risk stratification of those who are at risk of developing PIs allows preventive interventions to be focused on patients who are at the highest risk. The considerable number of risk assessment scales and prediction models available underscores the need for a thorough evaluation of their development, validation, and clinical utility. Our objectives were to identify and describe available risk prediction tools for PI occurrence, their content and the development and validation methods used.
METHODS
The umbrella review was conducted according to Cochrane guidance. MEDLINE, Embase, CINAHL, EPISTEMONIKOS, Google Scholar, and reference lists were searched to identify relevant systematic reviews. The risk of bias was assessed using adapted AMSTAR-2 criteria. Results were described narratively. All included reviews contributed to building a comprehensive list of risk prediction tools.
RESULTS
We identified 32 eligible systematic reviews only seven of which described the development and validation of risk prediction tools for PI. Nineteen reviews assessed the prognostic accuracy of the tools and 11 assessed clinical effectiveness. Of the seven reviews reporting model development and validation, six included only machine learning models. Two reviews included external validations of models, although only one review reported any details on external validation methods or results. This was also the only review to report measures of both discrimination and calibration. Five reviews presented measures of discrimination, such as the area under the curve (AUC), sensitivities, specificities, F1 scores, and G-means. For the four reviews that assessed the risk of bias assessment using the PROBAST tool, all models but one were found to be at high or unclear risk of bias.
CONCLUSIONS
Available tools do not meet current standards for the development or reporting of risk prediction models. The majority of tools have not been externally validated. Standardised and rigorous approaches to risk prediction model development and validation are needed.
TRIAL REGISTRATION
The protocol was registered on the Open Science Framework ( https://osf.io/tepyk ).
背景
压力性损伤（PI）给全球医疗系统带来了沉重负担。对有发生PI风险的人群进行风险分层，有助于将预防性干预措施集中于风险最高的患者。现有的大量风险评估量表和预测模型凸显了对其开发、验证及临床效用进行全面评估的必要性。我们的目标是识别并描述现有的PI发生风险预测工具、其内容以及所采用的开发和验证方法。
方法
按照Cochrane指南进行伞状综述。检索MEDLINE、Embase、CINAHL、EPISTEMONIKOS、谷歌学术以及参考文献列表，以识别相关的系统综述。使用改编后的AMSTAR - 2标准评估偏倚风险。以叙述方式描述结果。所有纳入的综述都有助于构建风险预测工具的综合列表。
结果
我们识别出32篇符合条件的系统综述，其中仅有7篇描述了PI风险预测工具的开发和验证。19篇综述评估了工具的预测准确性，11篇评估了临床有效性。在7篇报告模型开发和验证的综述中，6篇仅纳入了机器学习模型。2篇综述纳入了模型的外部验证，不过只有1篇综述报告了外部验证方法或结果的任何细节。这也是唯一一篇报告区分度和校准度指标的综述。5篇综述给出了区分度指标，如曲线下面积（AUC）、灵敏度、特异度、F1分数和G均值。对于4篇使用PROBAST工具评估偏倚风险的综述，除1个模型外，所有模型均被发现存在高偏倚风险或偏倚风险不明确。
结论
现有工具不符合风险预测模型开发或报告的当前标准。大多数工具尚未经过外部验证。需要采用标准化且严格的方法来进行风险预测模型的开发和验证。
试验注册
该方案已在开放科学框架（https://osf.io/tepyk）上注册。
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