Search

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 1.362.61), 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. [ABSTRACT FROM AUTHOR]

While previous reviews found a positive association between pre‐existing cancer diagnosis and COVID‐19‐related death, most early studies did not distinguish long‐term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher‐quality evidence on risk of COVID‐19‐related death for people with recent/active cancer (compared to people without) in the pre‐COVID‐19‐vaccination period. We searched the WHO COVID‐19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk‐of‐bias assessment was based on the Newcastle‐Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse‐variance random‐effects models. Random‐effects meta‐regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID‐19‐related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36‐1.61, I2 = 0; people with COVID‐19: aOR = 1.58, 95% CI: 1.41‐1.77, I2 = 0.58; inpatients with COVID‐19: aOR = 1.66, 95% CI: 1.34‐2.06, I2 = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4‐4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68‐2.68, I2 = 0.43), and for metastatic cancers. Meta‐regression suggested risk of COVID‐19‐related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37‐1.75) at 1 year and aOR = 0.98 (95% CI: 0.80‐1.20) at 5 years post‐cancer diagnosis/treatment. In conclusion, before COVID‐19‐vaccination, risk of COVID‐19‐related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment. [ABSTRACT FROM AUTHOR]

Background: Using an updated dataset with more patients and extended follow-up, we further established cancer patient characteristics associated with COVID-19 death. Methods: Data on all cancer patients with a positive reverse transcription-polymerase chain reaction swab for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) at Guy's Cancer Centre and King's College Hospital between 29 February and 31 July 2020 was used. Cox proportional hazards regression was performed to identify which factors were associated with COVID-19 mortality. Results: Three hundred and six SARS-CoV-2-positive cancer patients were included. Seventy-one had mild/moderate and 29% had severe COVID-19. Seventy-two patients died of COVID-19 (24%), of whom 35 died <7 days. Male sex [hazard ratio (HR): 1.97 (95% confidence interval (CI): 1.15–3.38)], Asian ethnicity [3.42 (1. 59–7.35)], haematological cancer [2.03 (1.16–3.56)] and a cancer diagnosis for >2–5 years [2.81 (1.41–5.59)] or ≥5 years were associated with an increased mortality. Age >60 years and raised C-reactive protein (CRP) were also associated with COVID-19 death. Haematological cancer, a longer-established cancer diagnosis, dyspnoea at diagnosis and raised CRP were indicative of early COVID-19-related death in cancer patients (<7 days from diagnosis). Conclusions: Findings further substantiate evidence for increased risk of COVID-19 mortality for male and Asian cancer patients, and those with haematological malignancies or a cancer diagnosis >2 years. These factors should be accounted for when making clinical decisions for cancer patients. [ABSTRACT FROM AUTHOR]

We investigated the association between metabolic syndrome (MetS) and its components and risk of prostate cancer (PCa) in a cohort of men enrolled in the UK Biobank. Our study cohort included 220 622 PCa‐free men with baseline measurements of triglycerides (TGs), HDL‐cholesterol (HDL), glycated hemoglobin (HbA1c), blood pressure (BP), and waist circumference (WC). Multivariable Cox proportional hazards regression was used to analyze associations with PCa for: individual metabolic components (TG, HDL, HbA1c, BP, WC), combinations of two and three components, and MetS overall (three or more components). We conducted mediation analyses to examine potential hormonal and inflammatory pathways (total testosterone [TT], C‐reactive protein [CRP], insulin‐like growth factor 1 [IGF‐1]) through which MetS components may influence PCa risk. A total of 5409 men in the study developed PCa during a median follow‐up of 6.9 years. We found no significant association between MetS and PCa risk (hazard ratio [HR] = 0.99, 95% confidence interval [CI] = 0.92‐1.06). No associations were found with PCa risk and individual measurements of TG, HDL, BP, or WC. However, an inverse association was observed with elevated HbA1c (≥42 mmol/mol) (HR = 0.89, 95% CI = 0.79‐0.98). Consistent inverse associations were observed between HbA1c and risk of PCa. Mediation analysis revealed TT, CRP, and IGF‐1 as potential mediating factors for this association contributing 10.2%, 7.1%, and 7.9% to the total effect, respectively. Overall MetS had no association with PCa risk. However, a consistent inverse association with PCa risk was found for HbA1c. This association may be explained in part through hormonal and inflammatory pathways. What's new? Risk of prostate cancer is variously linked to obesity, type 2 diabetes mellitus, and other components of metabolic syndrome (MetS). Evidence for a clear association between prostate cancer and MetS and its components, however, remains elusive. In this large, prospective study, the authors found that MetS is not significantly associated with risk of prostate cancer. Glycated hemoglobin (HbA1c), however, was found to be inversely associated with risk. Analyses examining potential hormonal and inflammatory pathways identified total testosterone, C‐reactive protein, and insulin‐like growth factor 1 as potential mediating factors for the association between HbA1c and prostate cancer. [ABSTRACT FROM AUTHOR]