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The value of axillary, facial, occipital, subclavian and common carotid arteries ultrasound in the diagnosis of giant cell arteritis.
OBJECTIVE: To assess the diagnostic value for GCA in adding the axillary arteries (AX) to the temporal artery (TA) ultrasound, particularly in patients with a cranial phenotype of the disease; and to investigate the utility of facial (FA), occipital (OC), subclavian (SC), and common carotid (CC) ultrasound in patients with suspected GCA. METHODS: Patients with new-onset GCA and a positive ultrasound of the TA, AX, FA, OC, SC or CC, followed at the rheumatology departments of two academic centres, were retrospectively included. RESULTS: 230 patients were assessed. TA halo sign was identified in 206/230 (89.6%) cases, FA in 40/82 (48.8%), OC in 17/69 (24.6%), AX in 56/230 (24.3%), SC in 31/57 (54.4%), and CC in 14/68 (20.6%). Negative TA ultrasound was found in 24/230 (10.4%) patients: 22 had AX involvement, 1 exclusive OC involvement and 1 exclusive SC involvement. Adding AX evaluation to the TA ultrasound increased the diagnostic yield for GCA in 9.6%, whereas adding OC or SCs to the TA and AX ultrasound increased it in 1.4% and 1.8%, respectively. No value was found in adding the FA or CCs. Notably, 13 patients with cranial symptoms and 4 with exclusively cranial symptoms showed negative TA ultrasound but positive AX ultrasound. CONCLUSION: Adding the evaluation of AXs to the TA ultrasound increased the number of patients diagnosed with GCA, even in cases of predominantly cranial symptoms. In the subset of patients where these arteries were assessed, no substantial benefit was found in adding the FA, OC, SC or CC arteries to the TA and AX ultrasonographic assessment.
Collateral effects of the COVID-19 pandemic on endocrine treatments for breast and prostate cancer in the UK: a cohort study.
BACKGROUND: The COVID-19 pandemic affected cancer screening, diagnosis and treatments. Many surgeries were substituted with bridging therapies during the initial lockdown, yet consideration of treatment side effects and their management was not a priority. OBJECTIVES: To examine how the changing social restrictions imposed by the pandemic affected incidence and trends of endocrine treatment prescriptions in newly diagnosed (incident) breast and prostate cancer patients and, secondarily, endocrine treatment-related outcomes (including bisphosphonate prescriptions, osteopenia and osteoporosis), in UK clinical practice from March 2020 to June 2022. DESIGN: Population-based cohort study using UK primary care Clinical Practice Research Datalink GOLD database. METHODS: There were 13,701 newly diagnosed breast cancer patients and 12,221 prostate cancer patients with ⩾1-year data availability since diagnosis between January 2017 and June 2022. Incidence rates (IR) and incidence rate ratios (IRR) were calculated across multiple time periods before and after lockdown to examine the impact of changing social restrictions on endocrine treatments and treatment-related outcomes, including osteopenia, osteoporosis and bisphosphonate prescriptions. RESULTS: In breast cancer patients, aromatase inhibitor (AI) prescriptions increased during lockdown versus pre-pandemic [IRR: 1.22 (95% confidence interval (CI): 1.11-1.34)], followed by a decrease post-first lockdown [IRR: 0.79 (95% CI: 0.69-0.89)]. In prostate cancer patients, first-generation antiandrogen prescriptions increased versus pre-pandemic [IRR: 1.23 (95% CI: 1.08-1.4)]. For breast cancer patients on AIs, diagnoses of osteopenia, osteoporosis and bisphosphonate prescriptions were reduced across all lockdown periods versus pre-pandemic (IRR range: 0.31-0.62). CONCLUSION: During the first 2 years of the pandemic, newly diagnosed breast and prostate cancer patients were prescribed more endocrine treatments compared to pre-pandemic due to restrictions on hospital procedures replacing surgeries with bridging therapies. But breast cancer patients had fewer diagnoses of osteopenia and osteoporosis and bisphosphonate prescriptions. These patients should be followed up in the coming years for signs of bone thinning. Evidence of poorer management of treatment-related side effects will help assess resource allocation for patients at high risk for bone-related complications.
SARS-CoV-2 infection is associated with an increase in new diagnoses of schizophrenia spectrum and psychotic disorder: A study using the US national COVID cohort collaborative (N3C).
Amid the ongoing global repercussions of SARS-CoV-2, it is crucial to comprehend its potential long-term psychiatric effects. Several recent studies have suggested a link between COVID-19 and subsequent mental health disorders. Our investigation joins this exploration, concentrating on Schizophrenia Spectrum and Psychotic Disorders (SSPD). Different from other studies, we took acute respiratory distress syndrome (ARDS) and COVID-19 lab-negative cohorts as control groups to accurately gauge the impact of COVID-19 on SSPD. Data from 19,344,698 patients, sourced from the N3C Data Enclave platform, were methodically filtered to create propensity matched cohorts: ARDS (n = 222,337), COVID-19 positive (n = 219,264), and COVID-19 negative (n = 213,183). We systematically analyzed the hazard rate of new-onset SSPD across three distinct time intervals: 0-21 days, 22-90 days, and beyond 90 days post-infection. COVID-19 positive patients consistently exhibited a heightened hazard ratio (HR) across all intervals [0-21 days (HR: 4.6; CI: 3.7-5.7), 22-90 days (HR: 2.9; CI: 2.3 -3.8), beyond 90 days (HR: 1.7; CI: 1.5-1.)]. These are notably higher than both ARDS and COVID-19 lab-negative patients. Validations using various tests, including the Cochran Mantel Haenszel Test, Wald Test, and Log-rank Test confirmed these associations. Intriguingly, our data indicated that younger individuals face a heightened risk of SSPD after contracting COVID-19, a trend not observed in the ARDS and COVID-19 negative groups. These results, aligned with the known neurotropism of SARS-CoV-2 and earlier studies, accentuate the need for vigilant psychiatric assessment and support in the era of Long-COVID, especially among younger populations.
Statistical analysis plan for the TRANSLATE (TRANSrectal biopsy versus Local Anaesthetic Transperineal biopsy Evaluation of potentially clinically significant prostate cancer) multicentre randomised controlled trial
Background: The TRANSLATE (TRANSrectal biopsy versus Local Anaesthetic Transperineal biopsy Evaluation) trial assesses the clinical and cost-effectiveness of two biopsy procedures in terms of detection of clinically significant prostate cancer (PCa). This article describes the statistical analysis plan (SAP) for the TRANSLATE randomised controlled trial (RCT). Methods/design: TRANSLATE is a parallel, superiority, multicentre RCT. Biopsy-naïve men aged ≥ 18 years requiring a prostate biopsy for suspicion of possible PCa are randomised (computer-generated 1:1 allocation ratio) to one of two biopsy procedures: transrectal (TRUS) or local anaesthetic transperineal (LATP) biopsy. The primary outcome is the difference in detection rates of clinically significant PCa (defined as Gleason Grade Group ≥ 2, i.e. any Gleason pattern ≥ 4 disease) between the two biopsy procedures. Secondary outcome measures are th eProBE questionnaire (Perception Part and General Symptoms) and International Index of Erectile Function (IIEF, Domain A) scores, International Prostate Symptom Score (IPSS) values, EQ-5D-5L scores, resource use, infection rates, complications, and serious adverse events. We describe in detail the sample size calculation, statistical models used for the analysis, handling of missing data, and planned sensitivity and subgroup analyses. This SAP was pre-specified, written and submitted without prior knowledge of the trial results. Discussion: Publication of the TRANSLATE trial SAP aims to increase the transparency of the data analysis and reduce the risk of outcome reporting bias. Any deviations from the current SAP will be described and justified in the final study report and results publication. Trial registration: International Standard Randomised Controlled Trial Number ISRCTN98159689, registered on 28 January 2021 and registered on the ClinicalTrials.gov (NCT05179694) trials registry.
Table 3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Breakdown of case and control classification in the test data (<i>N</i> = 25,369) using 10-year risks from the Gail model with and without PRS<sub>BC</sub>, with a fixed proportion (2.88%) of women considered at “moderate risk”.</p>
Table 3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Breakdown of case and control classification in the test data (<i>N</i> = 25,369) using 10-year risks from the Gail model with and without PRS<sub>BC</sub>, with a fixed proportion (2.88%) of women considered at “moderate risk”.</p>
Figure 3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Decision curve analysis for Tyrer–Cuzick and Gail 10-year risk scores with and without PRS<sub>BC</sub> in test data (<i>N</i> = 25,359). Plots show net benefit calculated in full available follow-up time. The gray line indicates net benefit if all women were screened, and the black line indicates net benefit if no women were screened.</p>
Table 2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Breakdown of case and control classification in the test data (<i>N</i> = 25,369) using 10-year risks from the Tyrer-Cuzick model with and without PRS<sub>BC</sub>, with a fixed proportion (6.47%) of women considered at “moderate risk”.</p>
Data from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<div>AbstractBackground:<p>Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorization needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI).</p>Methods:<p>We analyzed data from 126,490 post-menopausal women of “White British” ancestry, aged 40 to 69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer–Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected.</p>Results:<p>The Harrell's C statistic of the 10-year risk from the Tyrer–Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models [0.080 (95% confidence interval (CI), 0.053–0.104) and 0.051 (95% CI, 0.030–0.073), respectively], with negligible impact on controls.</p>Conclusions:<p>The addition of a PRS for breast cancer to the well-established Tyrer–Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification.</p>Impact:<p>These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.</p></div>
Supplementary Figure S1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Figure S1: Analysis population exclusion flowchart. This illustrates the selection of eligible study participants from the UK Biobank.</p>
Supplementary Table S8 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S8: Model performance using Tyrer-Cuzick v8 approach to Genomics plc PRS inclusion, in test data (N=25,369).</p>
Supplementary Table S4 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S4: Hazard ratios (HR) associated with variables in the Tyrer-Cuzick model</p>
Supplementary Table S6 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S6: Reclassification tables for Tyrer-Cuzick model using fixed 5% 10-year risk threshold in test data (N=25,369).</p>
Supplementary Table S4 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S4: Hazard ratios (HR) associated with variables in the Tyrer-Cuzick model</p>
Supplementary Table S1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S1: Derivation of variables for the Tyrer-Cuzick model using UK Biobank data.</p>
Table 1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Model performance with and without PRS<sub>BC</sub> in test data (<i>N</i> = 25,369).</p>
Figure 2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Time-dependent ROC plots at 10 years of follow up for both the Tyrer–Cuzick (left) and Gail (right) models, with and without PRS<sub>BC</sub>, as well as PRS<sub>BC</sub> alone in test data (<i>N</i> = 25,369).</p>
Figure 1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Calibration plots of 10-year observed versus predicted risk by risk decile, for the Tyrer–Cuzick and Gail models, plotting the observed proportion of women with breast cancer by 10 years of follow-up against the predicted 10-year risk from the models. Top row, predicted risks in training dataset from Tyrer–Cuzick and Gail models. Middle row, predicted risks in test dataset from Tyrer–Cuzick and Gail models, calibrated using the training dataset. Bottom row, predicted risks in test dataset from Cox model containing Tyrer–Cuzick or Gail model and PRS<sub>BC</sub>, developed in training dataset.</p>
Supplementary Figure S5 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Figure S5: Density plot of 10-year predicted risk with and without PRSBC, split by breast cancer cases and controls in test data (N=25,369).</p>