Summary 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.
IMPORTANCE: Cutaneous squamous cell carcinoma (cSCC) is the most common skin cancer with metastatic potential, but epidemiologic data are poor. Changes to the National Cancer Registration and ...Analysis Service (NCRAS) in England have allowed more accurate data analysis of primary and metastatic cSCC since 2013. OBJECTIVE: To assess the national incidence of cSCC and metastatic cSCC (mcSCC) in England from 2013 through 2015. DESIGN, SETTING, AND PARTICIPANTS: This national population-based study identified a cohort of patients with cSCC and mcSCC in England from January 1, 2013, through December 31, 2015. Patients were identified using diagnostic codes derived from pathology reports in the NCRAS. Data were analyzed from March 1, 2017, through March 1, 2018. MAIN OUTCOMES AND MEASURES: Incidence rates across sex and risk factors for cSCC were derived from the NCRAS data. Risk of occurrence of mcSCC among the population with cSCC was assessed with Cox proportional hazards regression analysis to determine indicators of mcSCC. RESULTS: Among the 76 977 patients with first primary cSCC in 2013 through 2015 (62.7% male; median age, 80 years interquartile range, 72-86 years), the age-standardized rates for the first registered cSCC in England from 2013 through 2015 were 77.3 per 100 000 person-years (PY) (95% CI, 76.6-78.0) in male patients and 34.1 per 100 000 PY (95% CI, 33.7-34.5) in female patients. Increased primary cSCC tumor count was observed in older, white male patients in lower deprivation quintiles. After a maximum follow-up of 36 months, cumulative incidence of mcSCC developed in 1.1% of women and 2.4% of men with a primary cSCC. Significant increases in the risk of metastasis with adjusted hazard rates of approximately 2.00 were observed in patients who were aged 80 to 89 years (hazard ratio HR, 1.23; 95% CI, 1.07-1.43), 90 years or older (HR, 1.35; 95% CI, 1.09-1.66), male (HR, 1.79; 95% CI, 1.52-2.10), immunosuppressed (HR, 1.99; 95% CI, 1.64-2.42), and in higher deprivation quintiles (HR for highest quintile, 1.64; 95% CI, 1.35-2.00). Primary cSCC located on the ear (HR, 1.70; 95% CI, 1.42-2.03) and lip (HR, 1.85; 95% CI, 1.29-2.63) were at highest risk of metastasis. CONCLUSIONS AND RELEVANCE: This study presents the first national study of the incidence of mcSCC. With limited health care resources and an aging population, accurate epidemiologic data are essential for informing future health care planning, identifying high-risk patients, and evaluating skin cancer prevention policies.
ObjectivesWe 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.DesignRetrospective observational cohort study of data collected by the COVID-19 Hospitalisation in England Surveillance System (CHESS). Data were analysed using multivariate regression analysis.SettingAcute hospital trusts in England that submit data to CHESS routinely.ParticipantsOf 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 measuresAll-cause mortality during the study period.ResultsTiming 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.ConclusionThe 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.
The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research ...activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project's outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases-Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)-as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
There are concerns that the COVID-19 pandemic has had a negative effect on cancer care but there is little direct evidence to quantify any effect. This study aims to investigate the impact of the ...COVID-19 pandemic on the detection and management of colorectal cancer in England.
Data were extracted from four population-based datasets spanning NHS England (the National Cancer Cancer Waiting Time Monitoring, Monthly Diagnostic, Secondary Uses Service Admitted Patient Care and the National Radiotherapy datasets) for all referrals, colonoscopies, surgical procedures, and courses of rectal radiotherapy from Jan 1, 2019, to Oct 31, 2020, related to colorectal cancer in England. Differences in patterns of care were investigated between 2019 and 2020. Percentage reductions in monthly numbers and proportions were calculated.
As compared to the monthly average in 2019, in April, 2020, there was a 63% (95% CI 53–71) reduction (from 36 274 to 13 440) in the monthly number of 2-week referrals for suspected cancer and a 92% (95% CI 89–95) reduction in the number of colonoscopies (from 46 441 to 3484). Numbers had just recovered by October, 2020. This resulted in a 22% (95% CI 8–34) relative reduction in the number of cases referred for treatment (from a monthly average of 2781 in 2019 to 2158 referrals in April, 2020). By October, 2020, the monthly rate had returned to 2019 levels but did not exceed it, suggesting that, from April to October, 2020, over 3500 fewer people had been diagnosed and treated for colorectal cancer in England than would have been expected. There was also a 31% (95% CI 19–42) relative reduction in the numbers receiving surgery in April, 2020, and a lower proportion of laparoscopic and a greater proportion of stoma-forming procedures, relative to the monthly average in 2019. By October, 2020, laparoscopic surgery and stoma rates were similar to 2019 levels. For rectal cancer, there was a 44% (95% CI 17–76) relative increase in the use of neoadjuvant radiotherapy in April, 2020, relative to the monthly average in 2019, due to greater use of short-course regimens. Although in June, 2020, there was a drop in the use of short-course regimens, rates remained above 2019 levels until October, 2020.
The COVID-19 pandemic has led to a sustained reduction in the number of people referred, diagnosed, and treated for colorectal cancer. By October, 2020, achievement of care pathway targets had returned to 2019 levels, albeit with smaller volumes of patients and with modifications to usual practice. As pressure grows in the NHS due to the second wave of COVID-19, urgent action is needed to address the growing burden of undetected and untreated colorectal cancer in England.
Cancer Research UK, the Medical Research Council, Public Health England, Health Data Research UK, NHS Digital, and the National Institute for Health Research Oxford Biomedical Research Centre.
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.
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.
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.
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.
Accurate prediction of the individualized survival benefit of adjuvant therapy is key to making informed therapeutic decisions for patients with early invasive breast cancer. Machine learning ...technologies can enable accurate prognostication of patient outcomes under different treatment options by modelling complex interactions between risk factors in a data-driven fashion. Here, we use an automated and interpretable machine learning algorithm to develop a breast cancer prognostication and treatment benefit prediction model—Adjutorium—using data from large-scale cohorts of nearly one million women captured in the national cancer registries of the United Kingdom and the United States. We trained and internally validated the Adjutorium model on 395,862 patients from the UK National Cancer Registration and Analysis Service (NCRAS), and then externally validated the model among 571,635 patients from the US Surveillance, Epidemiology, and End Results (SEER) programme. Adjutorium exhibited significantly improved accuracy compared to the major prognostic tool in current clinical use (PREDICT v2.1) in both internal and external validation. Importantly, our model substantially improved accuracy in specific subgroups known to be under-served by existing models. Adjutorium is currently implemented as a web-based decision support tool (https://vanderschaar-lab.com/adjutorium/) to aid decisions on adjuvant therapy in women with early breast cancer, and can be publicly accessed by patients and clinicians worldwide.Methods are available to support clinical decisions regarding adjuvant therapies in breast cancer, but they have limitations in accuracy, generalizability and interpretability. Alaa et al. present an automated machine learning model of breast cancer that predicts patient survival and adjuvant treatment benefit to guide personalized therapeutic decisions.
Background The National Cancer Registration and Analysis Service (NCRAS), in Public Health England (PHE), collects and quality assures clinical information on all cases of cancer that occur in people ...living in England. The data collected supports the delivery of effective NHS cancer services.
Aim NCRAS aims to curate a clinically rich data resource, which this abstract describes.
Approach The data collected by NCRAS comes from several sources including Multi-Disciplinary Team (MDT) meetings and pathology reports. Data is reviewed by Cancer Registration Officers with the assistance of automated tools for data linkage and de-duplication of identical data sources. The data collected includes: patient identifiers; patient characteristics (e.g. sex, ethnicity); cancer specific fields (e.g. diagnosis date, cancer site, morphology, laterality, stage at diagnosis); cancer treatment fields; death information (e.g. date and cause of death); health care system information (e.g. hospital of diagnosis); and geography at diagnosis (based on patient’s postcode of residence).
Results Data is submitted by 162 healthcare providers, covering over 1,700 MDTs. Population-based official statistics are available for cancers diagnosed since 1971. The data is routinely used to inform and allow evidence-based decisions about NHS service provision and patient care and to longitudinally track cancer incidence rates and survival statistics. In recent years, completeness of specific items such as stage has greatly improved. However, non-death outcomes, such as quality of life assessments and recurrence, are currently difficult to identify.
Conclusion Detailed clinical information on cancer across the patient pathway enables the measurement of the diagnosis, treatment and survival outcome of all patients diagnosed with cancer. This population-based national data resource is critical to support service provision, clinical audit, commissioning, planning of services, public health and epidemiological research; all of which contribute to improved outcomes for people diagnosed with cancer.
Background The Systemic Anti-Cancer Therapy (SACT) dataset is a unique source of national, real-world data reporting chemotherapy activity delivered by NHS treatment providers in England. SACT data ...are collected with the intent of improving disease-free or progression-free survival.
Main Aim The SACT dataset aims to provide a detailed resource on systemic treatments delivered in secondary and tertiary settings in England for patients with cancer.
Approach The SACT dataset is collected and curated by the National Cancer Registration and Analysis Service (NCRAS) at Public Health England. The dataset was launched in a phased implementation from April 2012. From April 2014 submission of SACT data became mandatory for all NHS treatment providers. Data are collected on the patient, tumour, regimen, treatment cycle, drug details, and treatment outcomes. The SACT dataset can be linked to other data held by NCRAS, for example the cancer registry1, Hospital Episodes Statistics2, and the national Radiotherapy Dataset to provide a complete picture of the cancer patient pathway.
Results The SACT dataset is being used: for the appraisal of drugs funded through the Cancer Drugs Fund; to review the quality of cancer care as part of the National Cancer Audits; and for observational research purposes, including investigating 30-day mortality following chemotherapy3, and sociodemographic variation in the use of chemotherapy in patients with stage IV lung, oesophageal, stomach and pancreatic cancer4. The data are also released to external researchers via the Office for Data Release.
Conclusion The SACT dataset is a unique resource in terms of the breadth and depth of information on chemotherapy prescribed to cancer patients. The key strengths are: the national coverage; real-world data; detailed clinical information beyond cancer registration data; and the ability to link to other cancer datasets.
References
Henson KE, Elliss-Brookes L, Coupland VH, Payne E, Vernon S, Rous B et al. Data Resource Profile: National Cancer Registration Dataset in England. Int J Epidemiol 2019; doi 10.1093/ije/dyz076.
Herbert A, Wijlaars L, Zylbersztejn A, Cromwell D, Hardelid P. Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). Int J Epidemiol 2017; 46(4): 1093-1093i; e-pub ahead of print 2017/03/25; doi 10.1093/ije/dyx015.
Wallington M, Saxon EB, Bomb M, Smittenaar R, Wickenden M, McPhail S et al. 30-day mortality after systemic anticancer treatment for breast and lung cancer in England: a population-based, observational study. The Lancet Oncology 2016; 17(9): 1203-1216; doi 10.1016/s1470-2045(16)30383-7.
Henson KE, Fry A, Lyratzopoulos G, Peake M, Roberts KJ, McPhail S. Sociodemographic variation in the use of chemotherapy and radiotherapy in patients with stage IV lung, oesophageal, stomach and pancreatic cancer: evidence from population-based data in England during 2013-2014. Br J Cancer 2018; doi 10.1038/s41416-018-0028-7.
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