Constructing a Predictive Model for Psychological Distress of Young- and Middle-Aged Gynaecological Cancer Patients

IF 2.1 4区医学 Q3 HEALTH CARE SCIENCES & SERVICES

Journal of evaluation in clinical practice Pub Date : 2024-11-20 DOI:10.1111/jep.14244

Yitong Qu, Yinan Zhang, Xueying Zhou, Linan Wang, Xinran Zhu, Shimei Jin, Shumei Zhuang

{"title":"Constructing a Predictive Model for Psychological Distress of Young- and Middle-Aged Gynaecological Cancer Patients","authors":"Yitong Qu, Yinan Zhang, Xueying Zhou, Linan Wang, Xinran Zhu, Shimei Jin, Shumei Zhuang","doi":"10.1111/jep.14244","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Cancer patients experience substantial psychological distress which causes the reduction of the quality of life. However, the risk of psychological distress has not been well predicted especially in young- and middle-aged gynaecological cancer patients. This study aimed to develop a prediction model for psychological distress in young- and middle-aged gynaecological cancer patients using the artificial neural network (ANN).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A cross-sectional study of young- and middle-aged gynaecological cancer patients (<i>n</i> = 368) was conducted between March and December 2022. We used the univariate analysis to determine the factors affecting psychological distress. ANN was used for psychological distress prediction in young- and middle-aged gynaecological cancer patients. Also, a traditional logistic regression (LR) model was constructed for comparison. The area under the receiver's operating characteristic curve (AUC) was used to evaluate the model's predictive performance.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>ANN and LR showed that self-efficacy, economic income and sleep duration were the top risk variables for psychological distress in young- and middle-aged gynaecological cancer patients. The AUC of the ANN was 0.977, the sensitivity was 94.83% and the specificity was 86.44%, whereas logistic regression's were 0.920, 85.57% and 82.76%, respectively.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Compared with the LR model, the ANN model shows obvious superiority across all assessment index outcomes, and it may be used as a decision-support tool for early identification of young- and middle-aged gynaecological cancer patients suffering from psychological distress.</p>\n </section>\n </div>","PeriodicalId":15997,"journal":{"name":"Journal of evaluation in clinical practice","volume":"31 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of evaluation in clinical practice","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jep.14244","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Cancer patients experience substantial psychological distress which causes the reduction of the quality of life. However, the risk of psychological distress has not been well predicted especially in young- and middle-aged gynaecological cancer patients. This study aimed to develop a prediction model for psychological distress in young- and middle-aged gynaecological cancer patients using the artificial neural network (ANN).

Methods

A cross-sectional study of young- and middle-aged gynaecological cancer patients (n = 368) was conducted between March and December 2022. We used the univariate analysis to determine the factors affecting psychological distress. ANN was used for psychological distress prediction in young- and middle-aged gynaecological cancer patients. Also, a traditional logistic regression (LR) model was constructed for comparison. The area under the receiver's operating characteristic curve (AUC) was used to evaluate the model's predictive performance.

Results

ANN and LR showed that self-efficacy, economic income and sleep duration were the top risk variables for psychological distress in young- and middle-aged gynaecological cancer patients. The AUC of the ANN was 0.977, the sensitivity was 94.83% and the specificity was 86.44%, whereas logistic regression's were 0.920, 85.57% and 82.76%, respectively.

Conclusion

Compared with the LR model, the ANN model shows obvious superiority across all assessment index outcomes, and it may be used as a decision-support tool for early identification of young- and middle-aged gynaecological cancer patients suffering from psychological distress.

查看原文本刊更多论文

构建中青年妇科癌症患者心理压力预测模型

背景癌症患者会经历巨大的心理压力，导致生活质量下降。然而，人们还不能很好地预测心理困扰的风险，尤其是中青年妇科癌症患者。本研究旨在利用人工神经网络（ANN）建立中青年妇科癌症患者心理困扰的预测模型。方法在 2022 年 3 月至 12 月期间对中青年妇科癌症患者（n = 368）进行了横断面研究。我们采用单变量分析来确定影响心理困扰的因素。ANN 被用于预测中青年妇科癌症患者的心理困扰。此外，我们还构建了一个传统的逻辑回归（LR）模型进行比较。接受者工作特征曲线下面积（AUC）用于评估模型的预测性能。结果 ANN 和 LR 显示，自我效能感、经济收入和睡眠时间是中青年妇科癌症患者心理困扰的首要风险变量。ANN 的 AUC 为 0.977，灵敏度为 94.83%，特异度为 86.44%，而逻辑回归的 AUC 分别为 0.920、85.57% 和 82.76%。结论与 LR 模型相比，ANN 模型在所有评估指标结果中均表现出明显的优越性，可作为早期识别中青年妇科癌症患者心理困扰的决策支持工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of evaluation in clinical practice 医学-卫生保健

CiteScore

4.80

自引率

4.20%

发文量

143

审稿时长

3-8 weeks

期刊介绍： The Journal of Evaluation in Clinical Practice aims to promote the evaluation and development of clinical practice across medicine, nursing and the allied health professions. All aspects of health services research and public health policy analysis and debate are of interest to the Journal whether studied from a population-based or individual patient-centred perspective. Of particular interest to the Journal are submissions on all aspects of clinical effectiveness and efficiency including evidence-based medicine, clinical practice guidelines, clinical decision making, clinical services organisation, implementation and delivery, health economic evaluation, health process and outcome measurement and new or improved methods (conceptual and statistical) for systematic inquiry into clinical practice. Papers may take a classical quantitative or qualitative approach to investigation (or may utilise both techniques) or may take the form of learned essays, structured/systematic reviews and critiques.