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
Translational Oncology and Urology Research, King's College London, Faculty of Life Sciences and Medicine, London, United Kingdom.
Medical Oncology, Guy's and St Thomas' National Health System (NHS) Foundation Trust, London, United Kingdom.
Front Oncol. 2024 Jun 3;14:1358888. doi: 10.3389/fonc.2024.1358888. eCollection 2024.
Rapid 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.
Patients 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.
A 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%).
High 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.
快速诊断诊所(RDC)为出现与恶性肿瘤相关的非特定部位(NSS)症状的患者提供了一条简化的整体就医途径。本研究旨在:1)评估焦虑和抑郁的患病率,以及2)确定伦敦东南部盖伊和圣托马斯国民保健服务信托基金(GSTT)的RDC中与严重焦虑和抑郁相关的患者特征和症状组合。此外,我们比较了用于预测严重焦虑和抑郁的标准统计方法与机器学习算法。
2019年6月至2023年1月期间在GSTT的RDC就诊的患者在基线时完成了广泛性焦虑障碍问卷(GAD - 7)和患者健康问卷(PHQ - 8)。我们使用逻辑回归(LR)和两种机器学习(ML)算法(随机森林(RF)、支持向量机(SVM))来预测严重焦虑和严重抑郁的风险。模型使用一组社会人口统计学和临床变量构建。
共有1734名患者完成了GAD - 7和PHQ - 8问卷。其中,平均年龄为59岁(标准差:15.5),61.5%(n = 1067)为女性。严重焦虑(GAD - 7评分≥15)的患病率为13.8%,严重抑郁(PHQ - 8评分≥20)的患病率为9.3%。LR显示,既往心理健康状况(PMH,调整后比值比(AOR)3.28;95%置信区间(CI)2.36 - 4.56)、症状持续时间>6个月(AOR 2.20;95%CI 1.28 - 3.77)、体重减轻(AOR 1.88;95%CI 1.36 - 2.61)、进行性疼痛(AOR 1.71;95%CI 1.26 - 2.32)和疲劳(AOR 1.36;95%CI 1.01 - 1.84)的组合与严重焦虑呈正相关。同样,PMH状况(AOR 3.95;95%CI 2.17 - 5.75)、疲劳(AOR 2.11;95%CI 1.47 - 3.01)、症状持续时间>6个月(AOR 1.98;95%CI 1.06 - 3.68)、体重减轻(AOR 1.66;95%CI 1.13 - 2.44)和进行性疼痛(AOR 1.50;95%CI 1.04 - 2.16)的组合与严重抑郁呈正相关。LR和SVM对严重焦虑(LR:86%,SVM:85%)和严重抑郁(SVM:89%,LR:86%)具有最高的准确率。
发现严重焦虑和严重抑郁的患病率较高。PMH、疲劳、体重减轻、进行性疼痛和症状>6个月是这两种心理合并症的综合危险因素。RDC通过加快诊断评估为有相关症状的患者缓解痛苦提供了一个机会。