Anxiety and depression in patients with non-site-specific cancer symptoms: data from a rapid diagnostic clinic

Author:

Monroy-Iglesias Maria J.,Russell Beth,Martin Sabine,Fox Louis,Moss Charlotte,Bruno Flaminia,Millwaters Juliet,Steward Lindsay,Murtagh Colette,Cargaleiro Carlos,Bater Darren,Lavelle Grace,Simpson Anna,Onih Jemima,Haire Anna,Reeder Clare,Jones Geraint,Smith Sue,Santaolalla Aida,Van Hemelrijck Mieke,Dolly Saoirse

Abstract

BackgroundRapid diagnostic clinics (RDCs) provide a streamlined holistic pathway for patients presenting with non-site specific (NSS) symptoms concerning of malignancy. The current study aimed to: 1) assess the prevalence of anxiety and depression, and 2) identify a combination of patient characteristics and symptoms associated with severe anxiety and depression at Guy’s and St Thomas’ Foundation Trust (GSTT) RDC in Southeast London. Additionally, we compared standard statistical methods with machine learning algorithms for predicting severe anxiety and depression.MethodsPatients seen at GSTT RDC between June 2019 and January 2023 completed the General Anxiety Disorder Questionnaire (GAD-7) and Patient Health Questionnaire (PHQ-8) questionnaires, at baseline. We used logistic regression (LR) and 2 machine learning (ML) algorithms (random forest (RF), support vector machine (SVM)) to predict risk of severe anxiety and severe depression. The models were constructed using a set of sociodemographic and clinical variables.ResultsA total of 1734 patients completed GAD-7 and PHQ-8 questionnaires. Of these, the mean age was 59 years (Standard Deviation: 15.5), and 61.5% (n:1067) were female. Prevalence of severe anxiety (GAD-7 score ≥15) was 13.8% and severe depression (PHQ-8 score≥20) was 9.3%. LR showed that a combination of previous mental health condition (PMH, Adjusted Odds Rario (AOR) 3.28; 95% confidence interval (CI) 2.36–4.56), symptom duration >6 months (AOR 2.20; 95%CI 1.28–3.77), weight loss (AOR 1.88; 95% CI 1.36–2.61), progressive pain (AOR 1.71; 95%CI 1.26–2.32), and fatigue (AOR 1.36; 95%CI 1.01–1.84), was positively associated with severe anxiety. Likewise, a combination PMH condition (AOR 3.95; 95%CI 2.17–5.75), fatigue (AOR 2.11; 95%CI 1.47–3.01), symptom duration >6 months (AOR 1.98; 95%CI 1.06–3.68), weight loss (AOR 1.66; 95%CI 1.13–2.44), and progressive pain (AOR 1.50; 95%CI 1.04–2.16), was positively associated with severe depression. LR and SVM had highest accuracy levels for severe anxiety (LR: 86%, SVM: 85%) and severe depression (SVM: 89%, LR: 86%).ConclusionHigh prevalence of severe anxiety and severe depression was found. PMH, fatigue, weight loss, progressive pain, and symptoms >6 months emerged as combined risk factors for both these psychological comorbidities. RDCs offer an opportunity to alleviate distress in patients with concerning symptoms by expediting diagnostic evaluations.

Publisher

Frontiers Media SA

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3