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Erdem S, Warschkow R, Studer P, Tsai C, Nussbaum D, Schmied BM, Blazer D, Worni M. The Impact of Age in the Treatment of Non-comorbid Patients with Rectal Cancer: Survival Outcomes from the National Cancer Database. World J Surg 2023; 47:2023-2038. [PMID: 37097321 DOI: 10.1007/s00268-023-07008-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND Multimodal therapy has improved survival outcomes for rectal cancer (RC) significantly with an exemption for older patients. We sought to assess whether older non-comorbid patients receive substandard oncological treatment for localized RC referring to the National Comprehensive Cancer Network (NCCN) guidelines and whether it affects survival outcomes. METHODS This is a retrospective study using patient data from the National Cancer Data Base (NCDB) for histologically confirmed RC from 2002 to 2014. Non-comorbid patients between ≥50 and ≤85 years and defined treatment for localized RC were included and assigned to a younger (<75 years) and an older group (≥75 years). Treatment approaches and their impact on relative survival (RS) were analyzed using loess regression models and compared between both groups. Furthermore, mediation analysis was performed to measure the independent relative effect on age and other variables on RS. Data were assessed using the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) checklist. RESULTS Of 59,769 included patients, 48,389 (81.0%) were assigned to the younger group (<75 years). Oncologic resection was performed in 79.6% of the younger patients compared to 67.2% of the older patients (p < 0.001). Chemotherapy (74.3% vs. 56.1%) and radiotherapy (72.0% vs. 58.1%) were provided less often in older patients, respectively (p < 0.001). Increasing age was associated with enhanced 30- and 90-day mortality with 0.6% and 1.1% in the younger and 2.0% and 4.1% in the elderly group (p < 0.001) and worse RS rates [multivariable adjusted HR: 1.93 (95% CI 1.87-2.00), p < 0.001]. Adherence to standard oncological therapy resulted in a significant increase in 5-year RS (multivariable adjusted HR: 0.80 (95% CI 0.74-0.86), p < 0.001). Mediation analysis revealed that RS was mainly affected by age itself (84%) rather than the choice of therapy. CONCLUSIONS The likelihood to receive substandard oncological therapy increases in the older population and negatively affects RS. Since age itself has a major impact on RS, better patient selection should be performed to identify those that are potentially eligible for standard oncological care regardless of their age.
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Affiliation(s)
- Suna Erdem
- University of California San Diego, La Jolla, CA, USA
- Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Basel, Switzerland
| | - Rene Warschkow
- Department of Surgery, Kantonsspital St. Gallen, 9007, St. Gallen, Switzerland
| | - Peter Studer
- Department of Surgery, Hirslanden Clinic Beau Site, Bern, Switzerland
| | | | | | - Bruno M Schmied
- Department of Surgery, Kantonsspital St. Gallen, 9007, St. Gallen, Switzerland
| | - Dan Blazer
- Department of Surgery, Duke University, Durham, USA
| | - Mathias Worni
- Department of Surgery, Hirslanden Clinic Beau Site, Bern, Switzerland.
- Department of Surgery, Duke University, Durham, USA.
- Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Basel, Switzerland.
- Swiss Institute for Translational and Entrepreneurial Medicine, Stiftung Lindenhof, Campus SLB, Bern, Switzerland.
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Teece L, Sweeting MJ, Hall M, Coles B, Oliver-Williams C, Welch CA, de Belder MA, Deanfield J, Weston C, Rutherford MJ, Paley L, Kadam UT, Lambert PC, Peake MD, Gale CP, Adlam D. Impact of a Prior Cancer Diagnosis on Quality of Care and Survival Following Acute Myocardial Infarction: Retrospective Population-Based Cohort Study in England. Circ Cardiovasc Qual Outcomes 2023; 16:e009236. [PMID: 37339190 PMCID: PMC10281182 DOI: 10.1161/circoutcomes.122.009236] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 02/06/2023] [Indexed: 06/22/2023]
Abstract
BACKGROUND An increasing proportion of patients with cancer experience acute myocardial infarction (AMI). We investigated differences in quality of AMI care and survival between patients with and without previous cancer diagnoses. METHODS A retrospective cohort study using Virtual Cardio-Oncology Research Initiative data. Patients aged 40+ years hospitalized in England with AMI between January 2010 and March 2018 were assessed, ascertaining previous cancers diagnosed within 15 years. Multivariable regression was used to assess effects of cancer diagnosis, time, stage, and site on international quality indicators and mortality. RESULTS Of 512 388 patients with AMI (mean age, 69.3 years; 33.5% women), 42 187 (8.2%) had previous cancers. Patients with cancer had significantly lower use of ACE (angiotensin-converting enzyme) inhibitors/angiotensin receptor blockers (mean percentage point decrease [mppd], 2.6% [95% CI, 1.8-3.4]) and lower overall composite care (mppd, 1.2% [95% CI, 0.9-1.6]). Poorer quality indicator attainment was observed in patients with cancer diagnosed in the last year (mppd, 1.4% [95% CI, 1.8-1.0]), with later stage disease (mppd, 2.5% [95% CI, 3.3-1.4]), and with lung cancer (mppd, 2.2% [95% CI, 3.0-1.3]). Twelve-month all-cause survival was 90.5% in noncancer controls and 86.3% in adjusted counterfactual controls. Differences in post-AMI survival were driven by cancer-related deaths. Modeling improving quality indicator attainment to noncancer patient levels showed modest 12-month survival benefits (lung cancer, 0.6%; other cancers, 0.3%). CONCLUSIONS Measures of quality of AMI care are poorer in patients with cancer, with lower use of secondary prevention medications. Findings are primarily driven by differences in age and comorbidities between cancer and noncancer populations and attenuated after adjustment. The largest impact was observed in recent cancer diagnoses (<1 year) and lung cancer. Further investigation will determine whether differences reflect appropriate management according to cancer prognosis or whether opportunities to improve AMI outcomes in patients with cancer exist.
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Affiliation(s)
- Lucy Teece
- Department of Health Sciences (L.T., M.J.S., B.C., C.O.-W., C.A.W., M.J.R., U.T.K., P.C.L.), University of Leicester, United Kingdom
- National Cancer Registration and Analysis Service, NHS Digital, London, United Kingdom (L.T., M.J.S., B.C., C.O.-W., C.A.W., L.P., M.D.P.)
| | - Michael J. Sweeting
- Department of Health Sciences (L.T., M.J.S., B.C., C.O.-W., C.A.W., M.J.R., U.T.K., P.C.L.), University of Leicester, United Kingdom
- National Cancer Registration and Analysis Service, NHS Digital, London, United Kingdom (L.T., M.J.S., B.C., C.O.-W., C.A.W., L.P., M.D.P.)
| | - Marlous Hall
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, United Kingdom (M.H., C.P.G.)
| | - Briana Coles
- Department of Health Sciences (L.T., M.J.S., B.C., C.O.-W., C.A.W., M.J.R., U.T.K., P.C.L.), University of Leicester, United Kingdom
- National Cancer Registration and Analysis Service, NHS Digital, London, United Kingdom (L.T., M.J.S., B.C., C.O.-W., C.A.W., L.P., M.D.P.)
| | - Clare Oliver-Williams
- Department of Health Sciences (L.T., M.J.S., B.C., C.O.-W., C.A.W., M.J.R., U.T.K., P.C.L.), University of Leicester, United Kingdom
- National Cancer Registration and Analysis Service, NHS Digital, London, United Kingdom (L.T., M.J.S., B.C., C.O.-W., C.A.W., L.P., M.D.P.)
| | - Cathy A. Welch
- Department of Health Sciences (L.T., M.J.S., B.C., C.O.-W., C.A.W., M.J.R., U.T.K., P.C.L.), University of Leicester, United Kingdom
- National Cancer Registration and Analysis Service, NHS Digital, London, United Kingdom (L.T., M.J.S., B.C., C.O.-W., C.A.W., L.P., M.D.P.)
| | - Mark A. de Belder
- National Institute for Cardiovascular Outcomes Research, Barts Health NHS Trust, London, United Kingdom (M.A.d.B., J.D., C.W.)
| | - John Deanfield
- National Institute for Cardiovascular Outcomes Research, Barts Health NHS Trust, London, United Kingdom (M.A.d.B., J.D., C.W.)
- Institute of Cardiovascular Science, University College London, United Kingdom (J.D.)
| | - Clive Weston
- National Institute for Cardiovascular Outcomes Research, Barts Health NHS Trust, London, United Kingdom (M.A.d.B., J.D., C.W.)
- Department of Cardiology, Glangwili General Hospital, Carmarthen, United Kingdom (C.W.)
| | - Mark J. Rutherford
- Department of Health Sciences (L.T., M.J.S., B.C., C.O.-W., C.A.W., M.J.R., U.T.K., P.C.L.), University of Leicester, United Kingdom
| | - Lizz Paley
- National Cancer Registration and Analysis Service, NHS Digital, London, United Kingdom (L.T., M.J.S., B.C., C.O.-W., C.A.W., L.P., M.D.P.)
| | - Umesh T. Kadam
- Department of Health Sciences (L.T., M.J.S., B.C., C.O.-W., C.A.W., M.J.R., U.T.K., P.C.L.), University of Leicester, United Kingdom
- Leicester Diabetes Centre, United Kingdom (U.T.K.)
| | - Paul C. Lambert
- Department of Health Sciences (L.T., M.J.S., B.C., C.O.-W., C.A.W., M.J.R., U.T.K., P.C.L.), University of Leicester, United Kingdom
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (P.C.L.)
| | - Michael D. Peake
- Department of Respiratory Medicine (M.D.P.), University of Leicester, United Kingdom
- National Cancer Registration and Analysis Service, NHS Digital, London, United Kingdom (L.T., M.J.S., B.C., C.O.-W., C.A.W., L.P., M.D.P.)
| | - Chris P. Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, United Kingdom (M.H., C.P.G.)
| | - David Adlam
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre (D.A.), University of Leicester, United Kingdom
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Syriopoulou E, Mozumder SI, Rutherford MJ, Lambert PC. Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv. BMC Med Res Methodol 2022; 22:226. [PMID: 35963987 PMCID: PMC9375409 DOI: 10.1186/s12874-022-01666-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 06/24/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND When interested in a time-to-event outcome, competing events that prevent the occurrence of the event of interest may be present. In the presence of competing events, various estimands have been suggested for defining the causal effect of treatment on the event of interest. Depending on the estimand, the competing events are either accommodated or eliminated, resulting in causal effects with different interpretations. The former approach captures the total effect of treatment on the event of interest while the latter approach captures the direct effect of treatment on the event of interest that is not mediated by the competing event. Separable effects have also been defined for settings where the treatment can be partitioned into two components that affect the event of interest and the competing event through different causal pathways. METHODS We outline various causal effects that may be of interest in the presence of competing events, including total, direct and separable effects, and describe how to obtain estimates using regression standardisation with the Stata command standsurv. Regression standardisation is applied by obtaining the average of individual estimates across all individuals in a study population after fitting a survival model. RESULTS With standsurv several contrasts of interest can be calculated including differences, ratios and other user-defined functions. Confidence intervals can also be obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer to allow the reader to replicate the analysis and further explore the different effects of interest. CONCLUSIONS Several causal effects can be defined in the presence of competing events and, under assumptions, estimates of those can be obtained using regression standardisation with the Stata command standsurv. The choice of which causal effect to define should be given careful consideration based on the research question and the audience to which the findings will be communicated.
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Affiliation(s)
- Elisavet Syriopoulou
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Sarwar I Mozumder
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
| | - Mark J Rutherford
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
| | - Paul C Lambert
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
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Smith A, Lambert PC, Rutherford MJ. Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility. BMC Med Res Methodol 2022; 22:176. [PMID: 35739465 PMCID: PMC9229142 DOI: 10.1186/s12874-022-01654-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on. METHODS We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to generate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented. RESULTS We successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research. CONCLUSIONS We evaluate the effectiveness of the methods for constructing synthetic data, as well as providing evidence that there is minimal risk that a given patient from the original data could be identified from their individual unique patient information. Synthetic datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research.
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Affiliation(s)
- Aiden Smith
- Department of Health Sciences, Centre for Medicine, University of Leicester, University Road, Leicester, LE1 7RH, UK.
| | - Paul C Lambert
- Department of Health Sciences, Centre for Medicine, University of Leicester, University Road, Leicester, LE1 7RH, UK
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mark J Rutherford
- Department of Health Sciences, Centre for Medicine, University of Leicester, University Road, Leicester, LE1 7RH, UK
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