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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.
RESUMO
OBJECTIVE: To analyse the effectiveness of rapid diagnostic clinics (RDCs) as an alternative pathway for patients with concerning symptoms and a faecal immunochemical test (FIT) result <10. Our primary endpoint was rate of colorectal cancer (CRC) detection. Second endpoints were rates of other cancers and gastrointestinal (GI) serious benign conditions. Finally, we analysed the specific pathway followed by FIT <10 patients with cancer at Guy's and St Thomas NHS Foundation Trust (GSTT) RDC. DESIGN: A retrospective and prospective cohort study. SETTING: GSTT RDC, one of England's largest single-centre RDCs. Sociodemographic and clinical characteristics of FIT <10 patients were analysed descriptively. PARTICIPANTS: Patients with an FIT result <10, seen at GSTT RDC between 1 January 2020 and 5 May 2023. RESULTS: A total of 1299 patients with an FIT<10 were seen at GSTT RDC between January 2020 and May 2023. Of these, 66% (n=861) reported weight loss, 62% (n=805) pain, 37% (n=481) fatigue, 34% (n=444) were anaemic and 23% (n=301) had nausea and vomiting. Among these patients, 7% (n=88) received a cancer diagnosis, 36% (n=462) were identified as having a serious benign condition. Within the patients with cancer, 9% (n=8) were diagnosed with CRC. Among patients with serious benign conditions, 7% (n=31) were referred to colorectal, hepatopancreatobiliary, or upper GI specialists. CONCLUSION: This study demonstrates the effectiveness of RDCs as an alternate pathway for FIT <10 patients with ongoing clinical concerns. These results contribute to enhancing patient care and optimising resource allocation within the healthcare system.