Retrospective cohort study of admission timing and mortality following COVID-19 infection in England.

OBJECTIVES: We investigated whether the timing of hospital admission is associated with the risk of mortality for patients with COVID-19 in England, and the factors associated with a longer interval between symptom onset and hospital admission. DESIGN: Retrospective observational cohort study of data collected by the COVID-19 Hospitalisation in England Surveillance System (CHESS). Data were analysed using multivariate regression analysis. SETTING: Acute hospital trusts in England that submit data to CHESS routinely. PARTICIPANTS: Of 14 150 patients included in CHESS until 13 May 2020, 401 lacked a confirmed diagnosis of COVID-19 and 7666 lacked a recorded date of symptom onset. This left 6083 individuals, of whom 15 were excluded because the time between symptom onset and hospital admission exceeded 3 months. The study cohort therefore comprised 6068 unique individuals. MAIN OUTCOME MEASURES: All-cause mortality during the study period. RESULTS: Timing of hospital admission was an independent predictor of mortality following adjustment for age, sex, comorbidities, ethnicity and obesity. Each additional day between symptom onset and hospital admission was associated with a 1% increase in mortality risk (HR 1.01; p<0.005). Healthcare workers were most likely to have an increased interval between symptom onset and hospital admission, as were people from Black, Asian and minority ethnic (BAME) backgrounds, and patients with obesity. CONCLUSION: The timing of hospital admission is associated with mortality in patients with COVID-19. Healthcare workers and individuals from a BAME background are at greater risk of later admission, which may contribute to reports of poorer outcomes in these groups. Strategies to identify and admit patients with high-risk and those showing signs of deterioration in a timely way may reduce the consequent mortality from COVID-19, and should be explored

Evidence against the proposition that “UK cancer survival statistics are misleading”: simulation study with National Cancer Registry data

Objectives To simulate each of two hypothesised errors in the National Cancer Registry (recording of the date of recurrence of cancer, instead of the date of diagnosis, for registrations initiated from a death certificate; long term survivors who are never notified to the registry), to estimate their possible effect on relative survival, and to establish whether lower survival in the UK might be due to one or both of these errors

PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.

INTRODUCTION: The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. METHODS: Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. RESULTS: Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75). CONCLUSIONS: We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Diagnosing cancer in primary care: results from the National Cancer Diagnosis Audit.

BACKGROUND: Continual improvements in diagnostic processes are needed to minimise the proportion of patients with cancer who experience diagnostic delays. Clinical audit is a means of achieving this. AIM: To characterise key aspects of the diagnostic process for cancer and to generate baseline measures for future re-audit. DESIGN AND SETTING: Clinical audit of cancer diagnosis in general practices in England. METHOD: Information on patient and tumour characteristics held in the English National Cancer Registry was supplemented by information from GPs in participating practices. Data items included diagnostic timepoints, patient characteristics, and clinical management. RESULTS: Data were collected on 17 042 patients with a new diagnosis of cancer during 2014 from 439 practices. Participating practices were similar to non-participating ones, particularly regarding population age, urban/rural location, and practice-based patient experience measures. The median diagnostic interval for all patients was 40 days (interquartile range [IQR] 15-86 days). Most patients were referred promptly (median primary care interval 5 days [IQR 0-27 days]). Where GPs deemed diagnostic delays to have occurred (22% of cases), patient, clinician, or system factors were responsible in 26%, 28%, and 34% of instances, respectively. Safety netting was recorded for 44% of patients. At least one primary care-led investigation was carried out for 45% of patients. Most patients (76%) had at least one existing comorbid condition; 21% had three or more. CONCLUSION: The findings identify avenues for quality improvement activity and provide a baseline for future audit of the impact of 2015 National Institute for Health and Care Excellence guidance on management and referral of suspected cancer

Cohort profile: prescriptions dispensed in the community linked to the national cancer registry in England.

PURPOSE: The linked prescriptions cancer registry data resource was set up to extend our understanding of the pathway for patients with cancer past secondary care into the community, to ultimately improve patient outcomes. PARTICIPANTS: The linked prescriptions cancer registry data resource is currently available for April to July 2015, for all patients diagnosed with cancer in England with a dispensed prescription in that time frame.The dispensed prescriptions data are collected by National Health Service (NHS) Prescription Services, and the cancer registry data are processed by Public Health England. All data are routine healthcare data, used for secondary purposes, linked using a pseudonymised version of the patient's NHS number and date of birth.Detailed demographic and clinical information on the type of cancer diagnosed and treatment is collected by the cancer registry. The dispensed prescriptions data contain basic demographic information, geography measures of the dispensed prescription, drug information (quantity, strength and presentation), cost of the drug and the date that the dispensed prescription was submitted to NHS Business Services Authority. FINDINGS TO DATE: Findings include a study of end of life prescribing in the community among patients with cancer, an investigation of repeat prescriptions to derive measures of prior morbidity status in patients with cancer and studies of prescription activity surrounding the date of cancer diagnosis. FUTURE PLANS: This English linked resource could be used for cancer epidemiological studies of diagnostic pathways, health outcomes and inequalities; to establish primary care comorbidity indices and for guideline concordance studies of treatment, particularly hormonal therapy, as a major treatment modality for breast and prostate cancer which has been largely delivered in the community setting for a number of years

30-day mortality after systemic anticancer treatment for breast and lung cancer in England: a population-based, observational study

Background: 30-day mortality might be a useful indicator of avoidable harm to patients from systemic anticancer treatments, but data for this indicator are limited. The Systemic Anti-Cancer Therapy (SACT) dataset collated by Public Health England allows the assessment of factors affecting 30-day mortality in a national patient population. The aim of this first study based on the SACT dataset was to establish national 30-day mortality benchmarks for breast and lung cancer patients receiving SACT in England, and to start to identify where patient care could be improved. Methods: In this population-based study, we included all women with breast cancer and all men and women with lung cancer residing in England, who were 24 years or older and who started a cycle of SACT in 2014 irrespective of the number of previous treatment cycles or programmes, and irrespective of their position within the disease trajectory. We calculated 30-day mortality after the most recent cycle of SACT for those patients. We did logistic regression analyses, adjusting for relevant factors, to examine whether patient, tumour, or treatment-related factors were associated with the risk of 30-day mortality. For each cancer type and intent, we calculated 30-day mortality rates and patient volume at the hospital trust level, and contrasted these in a funnel plot. Findings: Between Jan 1, and Dec, 31, 2014, we included 23 228 patients with breast cancer and 9634 patients with non-small cell lung cancer (NSCLC) in our regression and trust-level analyses. 30-day mortality increased with age for both patients with breast cancer and patients with NSCLC treated with curative intent, and decreased with age for patients receiving palliative SACT (breast curative: odds ratio [OR] 1·085, 99% CI 1·040–1·132; p<0·0001; NSCLC curative: 1·045, 1·013–1·079; p=0·00033; breast palliative: 0·987, 0·977–0·996; p=0·00034; NSCLC palliative: 0·987, 0·976–0·998; p=0·0015). 30-day mortality was also significantly higher for patients receiving their first reported curative or palliative SACT versus those who received SACT previously (breast palliative: OR 2·326 99% CI 1·634–3·312; p<0·0001; NSCLC curative: 3·371, 1·554–7·316; p<0·0001; NSCLC palliative: 2·667, 2·109–3·373; p<0·0001), and for patients with worse general wellbeing (performance status 2–4) versus those who were generally well (breast curative: 6·057, 1·333–27·513; p=0·0021; breast palliative: 6·241, 4·180–9·319; p<0·0001; NSCLC palliative: 3·384, 2·276–5·032; p<0·0001). We identified trusts with mortality rates in excess of the 95% control limits; this included seven for curative breast cancer, four for palliative breast cancer, five for curative NSCLC, and seven for palliative NSCLC. Interpretation: Our findings show that several factors affect the risk of early mortality of breast and lung cancer patients in England and that some groups are at a substantially increased risk of 30-day mortality. The identification of hospitals with significantly higher 30-day mortality rates should promote review of clinical decision making in these hospitals. Furthermore, our results highlight the importance of collecting routine data beyond clinical trials to better understand the factors placing patients at higher risk of 30-day mortality, and ultimately improve clinical decision making. Our insights into the factors affecting risk of 30-day mortality will help treating clinicians and their patients predict the balance of harms and benefits associated with SACT. Funding: Public Health England

PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Introduction The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Methods Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. Results Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75). Conclusions We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort

Investigation of the international comparability of population-based routine hospital data set derived comorbidity scores for patients with lung cancer

Introduction: The International Cancer Benchmarking Partnership (ICBP) identified significant international differences in lung cancer survival. Differing levels of comorbid disease across ICBP countries has been suggested as a potential explanation of this variation but, to date, no studies have quantified its impact. This study investigated whether comparable, robust comorbidity scores can be derived from the different routine population-based cancer data sets available in the ICBP jurisdictions and, if so, use them to quantify international variation in comorbidity and determine its influence on outcome. Methods: Linked population-based lung cancer registry and hospital discharge data sets were acquired from nine ICBP jurisdictions in Australia, Canada, Norway and the UK providing a study population of 233 981 individuals. For each person in this cohort Charlson, Elixhauser and inpatient bed day Comorbidity Scores were derived relating to the 4–36 months prior to their lung cancer diagnosis. The scores were then compared to assess their validity and feasibility of use in international survival comparisons. Results: It was feasible to generate the three comorbidity scores for each jurisdiction, which were found to have good content, face and concurrent validity. Predictive validity was limited and there was evidence that the reliability was questionable. Conclusion: The results presented here indicate that interjurisdictional comparability of recorded comorbidity was limited due to probable differences in coding and hospital admission practices in each area. Before the contribution of comorbidity on international differences in cancer survival can be investigated an internationally harmonised comorbidity index is required

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

BACKGROUND PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. METHODS Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. RESULTS In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. CONCLUSIONS The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer