兰州大学机构库 >护理学院
手术患者压力性损伤风险预测模型的系统评价及效果研究
Alternative TitleSystematic review and effectiveness study of pressure injury risk prediction model for surgical patients
张娇燕
Subtype硕士
Thesis Advisor田金徽
2023-05-24
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name护理硕士
Degree Discipline护理
Keyword术中获得性压力性损伤 Intraoperative Acquired Pressure Injury 风险预测模型 Risk Prediction Model 系统评价 Systematic Review 风险评估量表 Risk Assessment Scale Scott Triggers评分表 Scott Triggers Braden量表 Braden Scale
Abstract

目的:本研究旨在对手术患者术中获得性压力性损伤(Intraoperatively Acquired Pressure Injury,IAPI)风险预测模型进行系统评价,为IAPI风险预测模型的构建及优化提供参考。将IAPI风险预测模型应用于心血管手术患者IAPI风险评估,探究其在临床实践中应用的准确性及有效性,同时对比分析风险评估量表与风险预测模型这两类评估工具在同一人群中的应用效果,为心血管手术患者IAPI风险评估工具的选取提供依据。

方法:①IAPI风险预测模型的系统评价:系统检索主要的中英文数据库并追溯纳入文献的参考文献,获取手术患者IAPI风险预测模型相关研究,对模型性能及模型呈现等进行汇总分析。采用PROBAST对纳入的模型进行偏倚风险及适用性评价。②心血管手术患者IAPI风险预测模型的初步应用:根据系统评价结果,便利选取性能良好、操作简便、可及性强的心血管手术患者IAPI风险预测模型(Lu CX预测模型),于2022年1月-2023年3月对兰州市的两家三级甲等医院的心血管手术患者进行评估。同时将其与专用于手术患者IAPI风险评估的Scott Triggers评分表(ST评分表)以及目前研究机构常规在使用的Braden量表进行效果对比。采用Excel和SPSS软件建立数据库,通过MedCalc软件绘制受试者工作特征(Receiver Operating Characteristic, ROC)曲线,计算ROC曲线下面积(Area Under the ROC Curve, AUC)、敏感度、特异度等指标,分析两类评估工具的实际预测效果。

结果:①IAPI风险预测模型的系统评价:共纳入21个IAPI风险预测模型,包括20个模型开发研究和1个模型验证研究。近5年发表的模型有18个(85.71%),目标人群明确为心血管手术患者的预测模型有7个。大部分模型(16个)基于Logistic回归算法建立。14个模型报告的AUC值介于0.727~0.897之间,5个模型报告的一致性指数介于0.725~0.815之间,2个模型报告的C统计量分别为0.78和0.984。11个模型报告了模型的分类能力指标,敏感度介于8.11%~98%之间,特异度介于19%~100%之间,阳性预测值介于6.0%~100%之间,阴性预测值介于76.71%~99.1%之间。8个模型以校准度图的形式报告校准度,4个模型报告了Hosmer-Lemeshow拟合优度检验。12个模型进行了内部验证。模型纳入的预测因子数量介于2~13个之间,出现频次排名前3位的预测因子分别为手术时间、年龄和体质指数(Body Mass Index, BMI)。纳入模型最终呈现的模型类别主要为列线图模型和回归模型。根据PROBAST评价标准,21个模型的适用性均较好,但存在一定的偏倚风险,高风险主要集中于“研究对象”(9个模型)和“分析”(21个模型)两个领域。②心血管手术患者IAPI风险预测模型(Lu CX预测模型)的初步应用:共221例患者参与研究,其中29例患者发生了IAPI,发生率13.12%,IAPI程度为Ⅰ期和Ⅱ期,IAPI部位主要为骶尾部,IAPI面积介于0.25cm2~50cm2之间。对三种评估工具(Lu CX预测模型、ST评分表、Braden量表)的预测效果进行分析,敏感度分别为68.97%、96.55%、93.10%,特异度分别为70.83%、72.40%、45.31%。Lu CX预测模型识别IAPI患者的能力低于另外两种评估工具。ST评分表具有较好的发现IAPI患者和非IAPI患者的能力。Braden量表识别IAPI患者的能力较好,但识别非IAPI患者的能力较弱。Lu CX预测模型、ST评分表2种评估工具的得分均与预测结果呈负相关(P<0.05)。以-0.29分为分界值时,Lu CX预测模型的AUC为0.712(0.591,0.833),对IAPI诊断有一定的准确性。以2分为分界值时,ST评分表的AUC为0.785(0.725,0.837),对IAPI诊断具有中等预测价值。以12分为分界值时,Braden量表的AUC为0.598(0.530,0.663),小于0.6,对心血管手术患者IAPI的预测价值较低。

结论:目前构建的IAPI风险预测模型具有良好的区分度,整体性能表现可观,大部分模型采用内部验证对模型的性能指标进行考察,未来研究者应积极开展模型的外部验证及更新完善,明确不同模型的可重复性及可推广性。IAPI风险预测模型(Lu CX预测模型)用于心血管手术患者IAPI风险评估时操作较为简便,具有中等区分能力,未来应继续探索心血管手术患者IAPI发生的独立危险因素,对风险预测模型进一步优化和完善后再推广到临床实践中。

Other Abstract

Objectives: This study aims to systematically evaluate the risk prediction model of Intraoperatively Acquired Pressure Injury (IAPI) in surgical patients, and provide a reference for the construction and optimization of the IAPI risk prediction model. The IAPI risk prediction model was applied to the IAPI risk assessment of cardiovascular surgery patients to explore its accuracy and effectiveness in clinical practice. At the same time, the application effect of the two types of assessment tools, the risk assessment scale and the risk prediction model, were compared and analyzed in the same surgical population, so as to provide a basis for the selection of IAPI risk assessment tools for patients undergoing cardiovascular surgery.

Methods: ①Systematic review of the IAPI risk prediction model: Major English and Chinese databases were systematically searched and references in the included literature were traced to relevant research on IAPI risk prediction models for surgical patients. The performance and model presentation were summarized and analyzed, and the risk of bias and applicability of the included models were evaluated using the PROBAST. ②Preliminary application of the IAPI risk prediction model for cardiovascular surgery patients: According to the results of the systematic review, a well-performing, easy-to-operate, and accessible IAPI risk prediction model for cardiovascular surgery patients (Lu CX prediction model) was facilitated and evaluated for cardiovascular surgery patients in two tertiary care hospitals in Lanzhou from January 2022 to March 2023. At the same time, the prediction model was compared with the IAPI risk assessment scale (the Scott Triggers score scale and the Braden scale). Excel and SPSS software were used to establish a database, and MedCalc software was used to draw the Receiver Operating Characteristic (ROC) curve. The area under the ROC curve (AUC), Sensitivity, specificity were calculated to analyses the actual prediction effect of the two types of evaluation tools.

Results: ①Systematic review of IAPI risk prediction models: A total of 21 IAPI risk prediction models were included, including 20 model development studies and 1 model validation study. Eighteen models (85.71%) were published in the past five years. Seven models had a target population explicitly aimed at patients undergoing cardiovascular surgery. Most of the models (16 models) were established based on the Logistic regression algorithm. Fourteen models reported AUC values ranging from 0.727 to 0.897, five models reported C-index ranging from 0.725 to 0.815, and two models reported C-statistics of 0.78 and 0.984, respectively. Eleven models reported classification ability indicators for the models with sensitivities ranging from 8.11% to 98%, specificities ranging from 19% to 100%, positive predictive values ranging from 6.0% to 100%, and negative predictive values ranged from 76.71% to 99.1%. Calibration was reported as a calibration plot for 8 models and Hosmer-Lemeshow goodness-of-fit test for 4 models. Twelve models were validated internally. The number of predictors included in the model ranged from 2 to 13, and the top three predictors in terms of frequency of occurrence were operation time, age and body mass index. The final model categories of the included models are mainly nomogram models and regression models. According to the PROBAST evaluation criteria, the applicability of the 21 models was good, but there was a certain risk of bias, with the high risk mainly in the two fields of "study population" (9 models) and "analysis" (21 models). ②Preliminary application of the IAPI risk prediction model (Lu CX prediction model) in patients undergoing cardiovascular surgery: A total of 221 patients participated in the study, of whom 29 patients developed IAPI, with an incidence rate of 13.12%. The degree of IAPI is stage Ⅰ and stage Ⅱ, and the site of IAPI is mainly in the sacrococcygeal region, and the area of IAPI ranged from 0.25cm2 to 50cm2. The predictive effects of three assessment tools (Lu CX predictive model, ST score scale, Braden scale) were analyzed. The sensitivities of the three assessment tools were 68.97%, 96.55%, 93.10%, and the specificities were 70.83%, 72.40%, 45.31%, respectively. The ability of the Lu CX predictive model to identify IAPI patients from non-IAPI patients was lower than that of the other two assessment tools. The ST score scale had better ability to detect IAPI patients and non-IAPI patients. The Braden scale had better ability to screen IAPI patients but weaker ability to identify non-IAPI patients. The scores of the Lu CX prediction model and the ST score scale were negatively correlated with the prediction outcome (P<0.05). The AUC of the Lu CX prediction model was 0.712 (0.591, 0.833) at a cut-off value of -0.29, which had a certain accuracy for the diagnosis of IAPI. The AUC of the ST score scale was 0.785 (0.725, 0.837) at a cut-off value of 2, which had a moderate predictive value for the diagnosis of IAPI. The AUC of Braden scale was 0.598 (0.530, 0.663) at a cut-off value of 12, which was less than 0.6 and had a low predictive value for IAPI in patients undergoing cardiovascular surgery.

Conclusion: The IAPI risk prediction models constructed so far have good discrimination and impressive overall performance. Most of the models adopt internal validation to examine the performance indexes of the models, and future researchers should actively carry out external validation and update the models to clarify the reproducibility and generalizability of different models. The IAPI risk prediction model (Lu CX prediction model) is relatively simple to operate and has moderate distinguishing ability when used for IAPI risk assessment in patients undergoing cardiovascular surgery. In the future, independent risk factors for the occurrence of IAPI in cardiovascular surgery patients should continue to be explored, and the risk prediction model should be further optimized and improved before being extended to clinical practice.

MOST Discipline Catalogue医学 - 护理* - 护理
URL查看原文
Language中文
Other Code262010_220200921730
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/536536
Collection护理学院
Affiliation
兰州大学护理学院
Recommended Citation
GB/T 7714
张娇燕. 手术患者压力性损伤风险预测模型的系统评价及效果研究[D]. 兰州. 兰州大学,2023.
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