Validating a Pragmatic Measure of Evidence-Based Practice (EBP) Delivery: Therapist Reports of EBP Strategy Delivery and Associations with Child Outcome Trajectories.

Pragmatic measures of evidence-based practice (EBP) implementation can support and evaluate implementation efforts. We examined the predictive validity of therapist reports of EBP strategy delivery for children's mental health outcomes. Data were obtained from 1,380 sessions with 248 children delivered by 76 therapists in two county systems. Children (Mage=11.8 years, SD = 3.7) presented with internalizing (52%), externalizing (27%), trauma (16%), and other (5%) concerns. Therapists reported their delivery of EBP strategies on a revised version of the EBP Concordant Care Assessment (ECCA; Brookman-Frazee, et al., Administration and Policy in Mental Health and Mental Health Services Research, 48, 155-170, 2021) that included 25 content (e.g., parenting, cognitive behavioral) and 12 technique strategies (e.g., modeling, practice/role-play). On average, 5.6 ECCA session reports (SD = 2.3) were obtained for each client, and caregivers reported symptoms on the Brief Problem Checklist (Chorpita, et al., Journal of Consulting and Clinical Psychology, 78(4), 526-536, 2010) at baseline, weekly over two months, and again at four months. Multilevel models examined whether the mean extensiveness of each EBP strategy predicted trajectories of child outcomes. More individual technique (6 of 12) than content strategies (1 of 25) were associated with outcome trajectories. For techniques, more extensive use of Performance Feedback and Live Coaching and less extensive use of Addressing Barriers were associated with greater declines in total symptoms, and more extensive use of Establishing/Reviewing Goals, Tracking/Reviewing Progress, and Assigning/Reviewing Homework was associated with declines in externalizing symptoms. For content, more extensive use of Cognitive Restructuring was associated with declines in total symptoms. In addition, higher average extensiveness ratings of the top content strategy across sessions was associated with greater declines in total and externalizing symptoms. Therapist-reported delivery of some EBP strategies showed evidence of predictive validity and may hold utility in indexing quality of care.

Incidence of menstrual symptoms suggestive of possible endometriosis in adolescents: and variance in these by ethnicity and socio-economic status.

BACKGROUND: Dysmenorrhoea affects up to 94% of adolescents who menstruate; approximately one third miss school and activities. Dysmenorrhoea can occur without identified pelvic pathology (primary dysmenorrhoea) or in association with other conditions (secondary dysmenorrhoea). In adolescence, the commonest cause of secondary dysmenorrhoea is endometriosis. The incidence of symptoms in adolescence suggesting possible endometriosis has not been previously documented in GP records. AIM: To document incidence of adolescent endometriosis and symptoms associated with endometriosis in English GP records. METHOD: Data from the QResearch primary care database were used for adolescent females aged 10- 19 years between 1 January 2011 and 30 June 2021, reported using descriptive statistics. RESULTS: The population cohort included 2 843 347 female adolescents; 98 887 participants had coded dysmenorrhoea (3.48%) and 1994 (0.07%) had documented endometriosis. The cumulative incidence for the cohort who turned 10 years old in 2011 was 7.2% for dysmenorrhoea and 0.12% for endometriosis. The period prevalence of coded symptoms during adolescence potentially associated with dysmenorrhoea and endometriosis includes: heavy menstrual bleeding (3.73%), irregular menstrual bleeding (2.21%), pelvic pain (0.63%), dyspareunia (0.40%), premenstrual syndrome (PMS)/premenstrual dysphoric disorder (PMDD) (0.22%), cystitis (8.45%), and irritable bowel syndrome (IBS) (1.00%). Disparities in coding were observed for these variables by ethnicity and socioeconomic status. Incidence of prescribed hormonal medication, with and without coded dysmenorrhoea, varied by ethnicity. This was less apparent for non-steroidal anti-inflammatory medications. CONCLUSION: Prevalence of coded dysmenorrhoea in GP records is significantly lower than community surveys suggest; however, adolescent menstrual symptoms are commonly encountered in primary care, and deserve specific guidance and resources. There are demographic patterns, likely structural, that warrant further exploration.

Uptake, effectiveness and safety of COVID-19 vaccines in individuals at clinical risk due to immunosuppressive drug therapy or transplantation procedures: a population-based cohort study in England.

BACKGROUND: Immunocompromised individuals are at increased risk of severe COVID-19 outcomes, underscoring the importance of COVID-19 vaccination in this population. The lack of comprehensive real-world data on vaccine uptake, effectiveness and safety in these individuals presents a critical knowledge gap, highlighting the urgency to better understand and address the unique challenges faced by immunocompromised individuals in the context of COVID-19 vaccination. METHODS: We analysed data from 12,274,946 people in the UK aged > 12 years from 01/12/2020 to 11/04/2022. Of these, 583,541 (4.8%) were immunocompromised due to immunosuppressive drugs, organ transplants, dialysis or chemotherapy. We undertook a cohort analysis to determine COVID-19 vaccine uptake, nested case-control analyses adjusted for comorbidities and sociodemographic characteristics to determine effectiveness of vaccination against COVID-19 hospitalisation, ICU admission and death, and a self-controlled case series assessing vaccine safety for pre-specified adverse events of interest. RESULTS: Overall, 93.7% of immunocompromised individuals received at least one COVID-19 vaccine dose, with 80.4% having received three or more doses. Uptake reduced with increasing deprivation (hazard ratio [HR] 0.78 [95%CI 0.77-0.79] in the most deprived quintile compared to the least deprived quintile for the first dose). Estimated vaccine effectiveness against COVID-19 hospitalisation 2-6 weeks after the second and third doses compared to unvaccinated was 78% (95%CI 72-83) and 91% (95%CI 88-93) in the immunocompromised population, versus 85% (95%CI 83-86) and 86% (95%CI 85-89), respectively, for the general population. Results showed COVID-19 vaccines were protective against intensive care unit (ICU) admission and death in both populations, with effectiveness of over 92% against COVID-19-related death and up to 95% in reducing ICU admissions for both populations following the third dose. COVID-19 vaccines were generally safe for immunocompromised individuals, though specific doses of ChAdOx1, mRNA-1273 and BNT162b2 raised risks of specific cardiovascular/neurological conditions. CONCLUSIONS: COVID-19 vaccine uptake is high in immunocompromised individuals on immunosuppressive drug therapy or who have undergone transplantation procedures, with documented disparities by deprivation. Findings suggest that COVID-19 vaccines are protective against severe COVID-19 outcomes in this vulnerable population, and show a similar safety profile in immunocompromised individuals and the general population, despite some increased risk of adverse events. These results underscore the importance of ongoing vaccination prioritisation for this clinically at-risk population to maximise protection against severe COVID-19 outcomes.

Hormone replacement therapy and cancer mortality in women with 17 site-specific cancers: a cohort study using linked medical records.

BACKGROUND: There is limited evidence on the safety of Hormone Replacement Therapy (HRT) in women with cancer. Therefore, we systematically examined HRT use and cancer-specific mortality in women with 17 site-specific cancers. METHODS: Women newly diagnosed with 17 site-specific cancers from 1998 to 2019, were identified from general practitioner (GP) records, hospital diagnoses or cancer registries in Scotland, Wales and England. Breast cancer patients were excluded because HRT is contraindicated in breast cancer patients. The primary outcome was time to cancer-specific mortality. Time-dependent Cox regression models were used to calculate adjusted hazard ratios (HR) and 95% confidence intervals (95% CIs) for cancer-specific mortality by systemic HRT use. RESULTS: The combined cancer cohorts contained 182,589 women across 17 cancer sites. Overall 7% of patients used systemic HRT after their cancer diagnosis. There was no evidence that HRT users, compared with non-users, had higher cancer-specific mortality at any cancer site. In particular, no increase was observed in common cancers including lung (adjusted HR = 0.98 95% CI 0.90, 1.07), colorectal (adjusted HR = 0.79 95% CI 0.70, 0.90), and melanoma (adjusted HR = 0.77 95% CI 0.58, 1.02). CONCLUSIONS: We observed no evidence of increased cancer-specific mortality in women with a range of cancers (excluding breast) receiving HRT.

Safety outcomes following COVID-19 vaccination and infection in 5.1 million children in England.

The risk-benefit profile of COVID-19 vaccination in children remains uncertain. A self-controlled case-series study was conducted using linked data of 5.1 million children in England to compare risks of hospitalisation from vaccine safety outcomes after COVID-19 vaccination and infection. In 5-11-year-olds, we found no increased risks of adverse events 1-42 days following vaccination with BNT162b2, mRNA-1273 or ChAdOX1. In 12-17-year-olds, we estimated 3 (95%CI 0-5) and 5 (95%CI 3-6) additional cases of myocarditis per million following a first and second dose with BNT162b2, respectively. An additional 12 (95%CI 0-23) hospitalisations with epilepsy and 4 (95%CI 0-6) with demyelinating disease (in females only, mainly optic neuritis) were estimated per million following a second dose with BNT162b2. SARS-CoV-2 infection was associated with increased risks of hospitalisation from seven outcomes including multisystem inflammatory syndrome and myocarditis, but these risks were largely absent in those vaccinated prior to infection. We report a favourable safety profile of COVID-19 vaccination in under-18s.

Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model.

BACKGROUND: The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways. METHODS: Population-based cohort study of adults aged 30-85 years at type 2 diabetes diagnosis (2010-2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal-external cross-validation. RESULTS: Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell's C-index 0.802 (95% CI: 0.797-0.817)), the highest clinical utility, and was well calibrated. The model's highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity. DISCUSSION: A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging.

Risk of bleeding amongst warfarin and direct oral anticoagulant users prescribed immediate antibiotics for respiratory tract infection: Cohort study.

PURPOSE: Incidence of bleeding amongst warfarin and direct oral anticoagulant (DOAC) users is greater following a respiratory tract infection (RTI). It is unclear whether immediate antibiotics modify this association. We estimated the risk of bleeding amongst warfarin and DOAC users with RTI by antibiotic treatment. METHODS: This retrospective cohort study used data from the Clinical Practice Research Datalink (CPRD) GOLD for adults in England prescribed warfarin or a DOAC, who sought primary care for an RTI between 1st January 2011 and 31st December 2019. Outcomes were major bleeding (hospital admission for intracranial or gastrointestinal bleeding), and non-major bleeding (hospital admission or General Practice consult for epistaxis, haemoptysis, or haematuria). Cox models derived hazard ratios (HRs) and 95% confidence intervals (CIs) for each outcome, adjusting for confounders using inverse probability of treatment weighting. RESULTS: Of 14 817 warfarin and DOAC users consulting for an RTI, 8768 (59%) were prescribed immediate antibiotics and 6049 (41%) were not. Approximately 49% were female, and median age was 76 years. Antibiotics were associated with reduced risk of major bleeding (adjusted HR 0.38, 95% CI 0.25 to 0.58). This was consistent across several sensitivity analyses. Antibiotics were also associated with a reduced risk of non-major bleeding (adjusted HR 0.78, 95% CI 0.61 to 0.99). CONCLUSIONS: Immediate antibiotics were associated with reduced risk of bleeding amongst warfarin and DOAC users with an RTI. Further work is needed to understand mechanisms and confirm whether a lower threshold for antibiotic use for RTI in this population may be beneficial.

Development and validation of a new algorithm for improved cardiovascular risk prediction.

QRISK algorithms use data from millions of people to help clinicians identify individuals at high risk of cardiovascular disease (CVD). Here, we derive and externally validate a new algorithm, which we have named QR4, that incorporates novel risk factors to estimate 10-year CVD risk separately for men and women. Health data from 9.98 million and 6.79 million adults from the United Kingdom were used for derivation and validation of the algorithm, respectively. Cause-specific Cox models were used to develop models to predict CVD risk, and the performance of QR4 was compared with version 3 of QRISK, Systematic Coronary Risk Evaluation 2 (SCORE2) and atherosclerotic cardiovascular disease (ASCVD) risk scores. We identified seven novel risk factors in models for both men and women (brain cancer, lung cancer, Down syndrome, blood cancer, chronic obstructive pulmonary disease, oral cancer and learning disability) and two additional novel risk factors in women (pre-eclampsia and postnatal depression). On external validation, QR4 had a higher C statistic than QRISK3 in both women (0.835 (95% confidence interval (CI), 0.833-0.837) and 0.831 (95% CI, 0.829-0.832) for QR4 and QRISK3, respectively) and men (0.814 (95% CI, 0.812-0.816) and 0.812 (95% CI, 0.810-0.814) for QR4 and QRISK3, respectively). QR4 was also more accurate than the ASCVD and SCORE2 risk scores in both men and women. The QR4 risk score identifies new risk groups and provides superior CVD risk prediction in the United Kingdom compared with other international scoring systems for CVD risk.

Effectiveness and safety of COVID-19 vaccination in people with blood cancer.

BACKGROUND: People with blood cancer have increased risk of severe COVID-19 outcomes and poor response to vaccination. We assessed the safety and effectiveness of COVID-19 vaccines in this vulnerable group compared to the general population. METHODS: Individuals aged ≥12 years as of 1st December 2020 in the QResearch primary care database were included. We assessed adjusted COVID-19 vaccine effectiveness (aVE) against COVID-19-related hospitalisation and death in people with blood cancer using a nested matched case-control study. Using the self-controlled case series methodology, we compared the risk of 56 pre-specified adverse events within 1-28 days of a first, second or third COVID-19 vaccine dose in people with and without blood cancer. FINDINGS: The cohort comprised 12,274,948 individuals, of whom 81,793 had blood cancer. COVID-19 vaccines were protective against COVID-19-related hospitalisation and death in people with blood cancer, although they were less effective, particularly against COVID-19-related hospitalisation, compared to the general population. In the blood cancer population, aVE against COVID-19-related hospitalisation was 64% (95% confidence interval [CI] 48%-75%) 14-41 days after a third dose, compared to 80% (95% CI 78%-81%) in the general population. Against COVID-19-related mortality, aVE was >80% in people with blood cancer 14-41 days after a second or third dose. We found no significant difference in risk of adverse events 1-28 days after any vaccine dose between people with and without blood cancer. INTERPRETATION: Our study provides robust evidence which supports the use of COVID-19 vaccinations for people with blood cancer.

Dynamic updating of clinical survival prediction models in a changing environment.

BACKGROUND: Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to remain useful. In this study, we investigate dynamic model updating of clinical survival prediction models. In contrast to discrete or one-time updating, dynamic updating refers to a repeated process for updating a prediction model with new data. We aim to extend previous research which focused largely on binary outcome prediction models by concentrating on time-to-event outcomes. We were motivated by the rapidly changing environment seen during the COVID-19 pandemic where mortality rates changed over time and new treatments and vaccines were introduced. METHODS: We illustrate three methods for dynamic model updating: Bayesian dynamic updating, recalibration, and full refitting. We use a simulation study to compare performance in a range of scenarios including changing mortality rates, predictors with low prevalence and the introduction of a new treatment. Next, the updating strategies were applied to a model for predicting 70-day COVID-19-related mortality using patient data from QResearch, an electronic health records database from general practices in the UK. RESULTS: In simulated scenarios with mortality rates changing over time, all updating methods resulted in better calibration than not updating. Moreover, dynamic updating outperformed ad hoc updating. In the simulation scenario with a new predictor and a small updating dataset, Bayesian updating improved the C-index over not updating and refitting. In the motivating example with a rare outcome, no single updating method offered the best performance. CONCLUSIONS: We found that a dynamic updating process outperformed one-time discrete updating in the simulations. Bayesian updating offered good performance overall, even in scenarios with new predictors and few events. Intercept recalibration was effective in scenarios with smaller sample size and changing baseline hazard. Refitting performance depended on sample size and produced abrupt changes in hazard ratio estimates between periods.

External validation of the QCovid 2 and 3 risk prediction algorithms for risk of COVID-19 hospitalisation and mortality in adults: a national cohort study in Scotland.

OBJECTIVE: The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. In this study, we assess their performance in Scotland. METHODS: We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 national data platform consisting of individual-level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR virology testing, hospitalisation and mortality data. We assessed the discrimination and calibration of the QCovid 2 and 3 algorithms in predicting COVID-19 hospitalisations and deaths between 8 December 2020 and 15 June 2021. RESULTS: Our validation dataset comprised 465 058 individuals, aged 19-100. We found the following performance metrics (95% CIs) for QCovid 2 and 3: Harrell's C 0.84 (0.82 to 0.86) for hospitalisation, and 0.92 (0.90 to 0.94) for death, observed-expected ratio of 0.24 for hospitalisation and 0.26 for death (ie, both the number of hospitalisations and the number of deaths were overestimated), and a Brier score of 0.0009 (0.00084 to 0.00096) for hospitalisation and 0.00036 (0.00032 to 0.0004) for death. CONCLUSIONS: We found good discrimination of the QCovid 2 and 3 algorithms in Scotland, although performance was worse in higher age groups. Both the number of hospitalisations and the number of deaths were overestimated.

Seasonal trends in antidepressant prescribing, depression, anxiety and self-harm in adolescents and young adults: an open cohort study using English primary care data.

BACKGROUND: There is an increasing demand for mental health services for young people, which may vary across the year. OBJECTIVE: To determine whether there are seasonal patterns in primary care antidepressant prescribing and mental health issues in adolescents and young adults. METHODS: This cohort study used anonymised electronic health records from general practices in England contributing to QResearch. It included 5 081 263 males and females aged 14-18 (adolescents), 19-23 and 24-28 years between 2006 and 2019. The incidence rates per 1000 person-years and the incidence rate ratios (IRRs) were calculated for the first records of a selective serotonin reuptake inhibitor (SSRI) prescription, depression, anxiety and self-harm. The IRRs were adjusted for year, region, deprivation, ethnic group and number of working days. FINDINGS: There was an increase in SSRI prescribing, depression and anxiety incidence in male and female adolescents in the autumn months (September-November) that was not seen in older age groups. The IRRs for SSRI prescribing for adolescents peaked in November (females: 1.75, 95% CI 1.67 to 1.83, p<0.001; males: 1.72, 95% CI 1.61 to 1.84, p<0.001, vs in January) and for depression (females: 1.29, 95% CI 1.25 to 1.33, p<0.001; males: 1.29, 95% CI 1.23 to 1.35, p<0.001). Anxiety peaked in November for females aged 14-18 years (1.17, 95% CI 1.13 to 1.22, p<0.001) and in September for males (1.19, 95% CI 1.12 to 1.27, p<0.001). CONCLUSIONS: There were higher rates of antidepressant prescribing and consultations for depression and anxiety at the start of the school year among adolescents. CLINICAL IMPLICATIONS: Support around mental health issues from general practitioners and others should be focused during autumn.

Development and validation of a novel risk prediction algorithm to estimate 10-year risk of oesophageal cancer in primary care: prospective cohort study and evaluation of performance against two other risk prediction models.

Background: Methods to identify patients at increased risk of oesophageal cancer are needed to better identify those for targeted screening. We aimed to derive and validate novel risk prediction algorithms (CanPredict) to estimate the 10-year risk of oesophageal cancer and evaluate performance against two other risk prediction models. Methods: Prospective open cohort study using routinely collected data from 1804 QResearch® general practices. We used 1354 practices (12.9 M patients) to develop the algorithm. We validated the algorithm in 450 separate practices from QResearch (4.12 M patients) and 355 Clinical Practice Research Datalink (CPRD) practices (2.53 M patients). The primary outcome was an incident diagnosis of oesophageal cancer found in GP, mortality, hospital, or cancer registry data. Patients were aged 25-84 years and free of oesophageal cancer at baseline. Cox proportional hazards models were used with prediction selection to derive risk equations. Risk factors included age, ethnicity, Townsend deprivation score, body mass index (BMI), smoking, alcohol, family history, relevant co-morbidities and medications. Measures of calibration, discrimination, sensitivity, and specificity were calculated in the validation cohorts. Finding: There were 16,384 incident cases of oesophageal cancer in the derivation cohort (0.13% of 12.9 M). The predictors in the final algorithms were: age, BMI, Townsend deprivation score, smoking, alcohol, ethnicity, Barrett's oesophagus, hiatus hernia, H. pylori infection, use of proton pump inhibitors, anaemia, lung and blood cancer (with breast cancer in women). In the QResearch validation cohort in women the explained variation (R2) was 57.1%; Royston's D statistic 2.36 (95% CI 2.26-2.46); C statistic 0.859 (95% CI 0.849-0.868) and calibration was good. Results were similar in men. For the 20% at highest predicted risk, the sensitivity was 76%, specificity was 80.1% and the observed risk at 10 years was 0.76%. The results from the CPRD validation were similar. Interpretation: We have developed and validated a novel prediction algorithm to quantify the absolute risk of oesophageal cancer. The CanPredict algorithms could be used to identify high risk patients for targeted screening. Funding: Innovate UK and CRUK (grant 105857).

Predicting 10-year breast cancer mortality risk in the general female population in England: a model development and validation study.

BACKGROUND: Identifying female individuals at highest risk of developing life-threatening breast cancers could inform novel stratified early detection and prevention strategies to reduce breast cancer mortality, rather than only considering cancer incidence. We aimed to develop a prognostic model that accurately predicts the 10-year risk of breast cancer mortality in female individuals without breast cancer at baseline. METHODS: In this model development and validation study, we used an open cohort study from the QResearch primary care database, which was linked to secondary care and national cancer and mortality registers in England, UK. The data extracted were from female individuals aged 20-90 years without previous breast cancer or ductal carcinoma in situ who entered the cohort between Jan 1, 2000, and Dec 31, 2020. The primary outcome was breast cancer-related death, which was assessed in the full dataset. Cox proportional hazards, competing risks regression, XGBoost, and neural network modelling approaches were used to predict the risk of breast cancer death within 10 years using routinely collected health-care data. Death due to causes other than breast cancer was the competing risk. Internal-external validation was used to evaluate prognostic model performance (using Harrell's C, calibration slope, and calibration in the large), performance heterogeneity, and transportability. Internal-external validation involved dataset partitioning by time period and geographical region. Decision curve analysis was used to assess clinical utility. FINDINGS: We identified data for 11 626 969 female individuals, with 70 095 574 person-years of follow-up. There were 142 712 (1·2%) diagnoses of breast cancer, 24 043 (0·2%) breast cancer-related deaths, and 696 106 (6·0%) deaths from other causes. Meta-analysis pooled estimates of Harrell's C were highest for the competing risks model (0·932, 95% CI 0·917-0·946). The competing risks model was well calibrated overall (slope 1·011, 95% CI 0·978-1·044), and across different ethnic groups. Decision curve analysis suggested favourable clinical utility across all age groups. The XGBoost and neural network models had variable performance across age and ethnic groups. INTERPRETATION: A model that predicts the combined risk of developing and then dying from breast cancer at the population level could inform stratified screening or chemoprevention strategies. Further evaluation of the competing risks model should comprise effect and health economic assessment of model-informed strategies. FUNDING: Cancer Research UK.

Risk prediction of covid-19 related death or hospital admission in adults testing positive for SARS-CoV-2 infection during the omicron wave in England (QCOVID4): cohort study.

OBJECTIVES: To derive and validate risk prediction algorithms (QCOVID4) to estimate the risk of covid-19 related death and hospital admission in people with a positive SARS-CoV-2 test result during the period when the omicron variant of the virus was predominant in England, and to evaluate performance compared with a high risk cohort from NHS Digital. DESIGN: Cohort study. SETTING: QResearch database linked to English national data on covid-19 vaccinations, SARS-CoV-2 test results, hospital admissions, and cancer and mortality data, 11 December 2021 to 31 March 2022, with follow-up to 30 June 2022. PARTICIPANTS: 1.3 million adults in the derivation cohort and 0.15 million adults in the validation cohort, aged 18-100 years, with a positive test result for SARS-CoV-2 infection. MAIN OUTCOME MEASURES: Primary outcome was covid-19 related death and secondary outcome was hospital admission for covid-19. Risk equations with predictor variables were derived from models fitted in the derivation cohort. Performance was evaluated in a separate validation cohort. RESULTS: Of 1 297 922 people with a positive test result for SARS-CoV-2 infection in the derivation cohort, 18 756 (1.5%) had a covid-19 related hospital admission and 3878 (0.3%) had a covid-19 related death during follow-up. The final QCOVID4 models included age, deprivation score and a range of health and sociodemographic factors, number of covid-19 vaccinations, and previous SARS-CoV-2 infection. The risk of death related to covid-19 was lower among those who had received a covid-19 vaccine, with evidence of a dose-response relation (42% risk reduction associated with one vaccine dose and 92% reduction with four or more doses in men). Previous SARS-CoV-2 infection was associated with a reduction in the risk of covid-19 related death (49% reduction in men). The QCOVID4 algorithm for covid-19 explained 76.0% (95% confidence interval 73.9% to 78.2%) of the variation in time to covid-19 related death in men with a D statistic of 3.65 (3.43 to 3.86) and Harrell's C statistic of 0.970 (0.962 to 0.979). Results were similar for women. QCOVID4 was well calibrated. QCOVID4 was substantially more efficient than the NHS Digital algorithm for correctly identifying patients at high risk of covid-19 related death. Of the 461 covid-19 related deaths in the validation cohort, 333 (72.2%) were in the QCOVID4 high risk group and 95 (20.6%) in the NHS Digital high risk group. CONCLUSION: The QCOVID4 risk algorithm, modelled from data during the period when the omicron variant of the SARS-CoV-2 virus was predominant in England, now includes vaccination dose and previous SARS-CoV-2 infection, and predicted covid-19 related death among people with a positive test result. QCOVID4 more accurately identified individuals at the highest levels of absolute risk for targeted interventions than the approach adopted by NHS Digital. QCOVID4 performed well and could be used for targeting treatments for covid-19 disease.

An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK.

INTRODUCTION: At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine. OBJECTIVES: To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK. METHODS: We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine. RESULTS: The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828). CONCLUSION: This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks.

Disparities in care and outcomes for primary liver cancer in England during 2008-2018: a cohort study of 8.52 million primary care population using the QResearch database.

Background: Liver cancer has one of the fastest rising incidence and mortality rates among all cancers in the UK, but it receives little attention. This study aims to understand the disparities in epidemiology and clinical pathways of primary liver cancer and identify the gaps for early detection and diagnosis of liver cancer in England. Methods: This study used a dynamic English primary care cohort of 8.52 million individuals aged ≥25 years in the QResearch database during 2008-2018, followed up to June 2021. The crude and age-standardised incidence rates, and the observed survival duration were calculated by sex and three liver cancer subtypes, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (CCA), and other specified/unspecified primary liver cancer. Regression models were used to investigate factors associated with an incident diagnosis of liver cancer, emergency presentation, late stage at diagnosis, receiving treatments, and survival duration after diagnosis by subtype. Findings: 7331 patients were diagnosed with primary liver cancer during follow-up. The age-standardised incidence rates increased over the study period, particularly for HCC in men (increased by 60%). Age, sex, socioeconomic deprivation, ethnicity, and geographical regions were all significantly associated with liver cancer incidence in the English primary care population. People aged ≥80 years were more likely to be diagnosed through emergency presentation and in late stages, less likely to receive treatments and had poorer survival than those aged <60 years. Men had a higher risk of being diagnosed with liver cancer than women, with a hazard ratio (HR) of 3.9 (95% confidence interval 3.6-4.2) for HCC, 1.2 (1.1-1.3) for CCA, and 1.7 (1.5-2.0) for other specified/unspecified liver cancer. Compared with white British, Asians and Black Africans were more likely to be diagnosed with HCC. Patients with higher socioeconomic deprivation were more likely to be diagnosed through the emergency route. Survival rates were poor overall. Patients diagnosed with HCC had better survival rates (14.5% at 10-year survival, 13.1%-16.0%) compared to CCA (4.4%, 3.4%-5.6%) and other specified/unspecified liver cancer (12.5%, 10.1%-15.2%). For 62.7% of patients with missing/unknown stage in liver cancer, their survival outcomes were between those diagnosed in Stages III and IV. Interpretation: This study provides an overview of the current epidemiology and the disparities in clinical pathways of primary liver cancer in England between 2008 and 2018. A complex public health approach is needed to tackle the rapid increase in incidence and the poor survival of liver cancer. Further studies are urgently needed to address the gaps in early detection and diagnosis of liver cancer in England. Funding: The Early Detection of Hepatocellular Liver Cancer (DeLIVER) project is funded by Cancer Research UK (Early Detection Programme Award, grant reference: C30358/A29725).

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study.

OBJECTIVE: To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches. DESIGN: Population based cohort study. SETTING: QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers. PARTICIPANTS: 141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020. MAIN OUTCOME MEASURES: Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility. RESULTS: During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model's random effects meta-analysis pooled estimate for Harrell's C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell's C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell's C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches. CONCLUSION: In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.