TY - JOUR
T1 - Anxiety and depression in patients with non-site-specific cancer symptoms
T2 - data from a rapid diagnostic clinic
AU - Monroy-Iglesias, Maria J
AU - Russell, Beth
AU - Martin, Sabine
AU - Fox, Louis
AU - Moss, Charlotte
AU - Bruno, Flaminia
AU - Millwaters, Juliet
AU - Steward, Lindsay
AU - Murtagh, Colette
AU - Cargaleiro, Carlos
AU - Bater, Darren
AU - Lavelle, Grace
AU - Simpson, Anna
AU - Onih, Jemima
AU - Haire, Anna
AU - Reeder, Clare
AU - Jones, Geraint
AU - Smith, Sue
AU - Santaolalla, Aida
AU - Van Hemelrijck, Mieke
AU - Dolly, Saoirse
N1 - Publisher Copyright:
Copyright © 2024 Monroy-Iglesias, Russell, Martin, Fox, Moss, Bruno, Millwaters, Steward, Murtagh, Cargaleiro, Bater, Lavelle, Simpson, Onih, Haire, Reeder, Jones, Smith, Santaolalla, Van Hemelrijck and Dolly.
PY - 2024/6/3
Y1 - 2024/6/3
N2 - BACKGROUND: 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.METHODS: 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.RESULTS: 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%).CONCLUSION: 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.
AB - BACKGROUND: 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.METHODS: 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.RESULTS: 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%).CONCLUSION: 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.
UR - http://www.scopus.com/inward/record.url?scp=85196119053&partnerID=8YFLogxK
U2 - 10.3389/fonc.2024.1358888
DO - 10.3389/fonc.2024.1358888
M3 - Article
C2 - 38887232
SN - 2234-943X
VL - 14
SP - 1358888
JO - Frontiers in oncology
JF - Frontiers in oncology
M1 - 1358888
ER -
