OBJECTIVES:Evidence about the comparative effects of new treatments is typically collected in randomized controlled trials (RCTs). In some instances, RCTs are not possible, or their value is limited by an inability to capture treatment effects over the longer term or in all relevant population subgroups. In these cases, nonrandomized studies (NRS) using real-world data (RWD) are increasingly used to complement trial evidence on treatment effects for health technology assessment (HTA). However, there have been concerns over a lack of acceptability of this evidence by HTA agencies. This article aims to identify the barriers to the acceptance of NRS and steps that may facilitate increases in the acceptability of NRS in the future. METHODS:Opinions of the authorship team based on their experience in real-world evidence research in academic, HTA, and industry settings, supported by a critical assessment of existing studies. RESULTS:Barriers were identified that are applicable to key stakeholder groups, including HTA agencies (eg, the lack of comprehensive methodological guidelines for using RWD), evidence generators (eg, avoidable deviations from best practices), and external stakeholders (eg, data controllers providing timely access to high-quality RWD). Future steps that may facilitate future acceptability of NRS include improvements in the quality, integration, and accessibility of RWD, wider use of demonstration projects to highlight the value and applicability of nonrandomized designs, living, and more detailed HTA guidelines, and improvements in HTA infrastructure relating to RWD. CONCLUSION:NRS can represent a crucial source of evidence on treatment effects for use in HTA when RCT evidence is limited.

Across its clinical development program, ocrelizumab demonstrated efficacy in improving clinical outcomes in multiple sclerosis, including annualized relapse rates and confirmed disability progression. However, as with any new treatment, it was unclear how this efficacy would translate into real-world clinical practice. The objective of this study was to systematically collate the published real-world clinical effectiveness data for ocrelizumab in relapsing remitting multiple sclerosis and primary progressive multiple sclerosis. A search strategy was developed in MEDLINE and Embase to identify articles reporting real-world evidence in people with relapsing remitting multiple sclerosis or primary progressive multiple sclerosis receiving treatment with ocrelizumab. The search focused on English language articles only but was not limited by the country in which the study was conducted or the time frame of the study. Additional manual searches of relevant websites were also performed. Fifty-two studies were identified reporting relevant evidence. Real-world effectiveness data for ocrelizumab were consistently favorable, with reductions in relapse rate and disease progression rates similar to those reported in the OPERA I/OPERA II and ORATORIO clinical trials, including in studies with more diverse patient populations not well represented in the pivotal trials. Although direct comparisons are confounded by lack of randomization of treatments, outcomes reported suggest that ocrelizumab has a similar or greater efficacy than other therapy options. Initial real-world effectiveness data for ocrelizumab appear favorable and consistent with results reported in clinical trials, providing clinicians with an efficacious option to treat patients with multiple sclerosis.

Objective Estimate the prevalence of diagnosed Alzheimer’s disease (AD) and early Alzheimer’s disease (eAD) overall and stratified by age, sex and deprivation and combinations thereof in England on 1 January 2020.Design Cross-sectional.Setting Primary care electronic health record data, the Clinical Practice Research database linked with secondary care data, Hospital Episode Statistics (HES) and patient-level deprivation data, Index of Multiple Deprivation (IMD).Outcome measures The prevalence per 100 000 of the population and corresponding 95% CIs for both diagnosed AD and eAD overall and stratified by covariates. Sensitivity analyses were conducted to assess the sensitivity of the population definition and look-back period.Results There were 448 797 patients identified in the Clinical Practice Research Datalink that satisfied the study inclusion criteria and were eligible for HES and IMD linkage. For the main analysis of AD and eAD, 379 763 patients are eligible for inclusion in the denominator. This resulted in an estimated prevalence of diagnosed AD of 378.39 (95% CI, 359.36 to 398.44) per 100 000 and eAD of 292.81 (95% CI, 276.12 to 310.52) per 100 000. Prevalence estimates across main and sensitivity analyses for the entire AD study population were found to vary widely with estimates ranging from 137.48 (95% CI, 127.05 to 148.76) to 796.55 (95% CI, 768.77 to 825.33). There was significant variation in prevalence of diagnosed eAD when assessing the sensitivity with the look-back periods, as low as 120.54 (95% CI, 110.80 to 131.14) per 100 000, and as high as 519.01 (95% CI, 496.64 to 542.37) per 100 000.Conclusions The study found relatively consistent patterns of prevalence across both AD and eAD populations. Generally, the prevalence of diagnosed AD increased with age and increased with deprivation for each age category. Women had a higher prevalence than men. More granular levels of stratification reduced patient numbers and increased the uncertainty of point prevalence estimates. Despite this, the study found a relationship between deprivation and prevalence of AD.

Internal validity is often the primary concern for health technology assessment agencies when assessing comparative effectiveness evidence. However, the increasing use of real-world data from countries other than a health technology assessment agency’s target population in effectiveness research has increased concerns over the external validity, or “transportability”, of this evidence, and has led to a preference for local data. Methods have been developed to enable a lack of transportability to be addressed, for example by accounting for cross-country differences in disease characteristics, but their consideration in health technology assessments is limited. This may be because of limited knowledge of the methods and/or uncertainties in how best to utilise them within existing health technology assessment frameworks. This article aims to provide an introduction to transportability, including a summary of its assumptions and the methods available for identifying and adjusting for a lack of transportability, before discussing important considerations relating to their use in health technology assessment settings, including guidance on the identification of effect modifiers, guidance on the choice of target population, estimand, study sample and methods, and how evaluations of transportability can be integrated into health technology assessment submission and decision processes.

IntroductionGiven the emergence of combination of programmed cell death protein-1 and CTLA4 pathway blockade as effective treatment options in malignant pleural mesothelioma (MPM), there is interest in the extent to which programmed death-ligand 1 (PD-L1) expression may be prognostic of clinical outcomes and predictive of response to anti–programmed death (ligand) 1 (PD-[L]1) therapies.MethodsMEDLINE and EMBASE electronic databases were searched until November 4, 2020. English-language randomized trials and observational studies that reported clinical outcomes and PD-L1 expression in adult patients (>18 or >20 y) with MPM were included. Forest plots were used to descriptively summarize clinical outcome data across studies.ResultsA total of 29 publications were identified providing data on the research question. Among the studies in which anti–PD-(L)1 therapies were not specified to have been used, 63% (10 of 16) found patients with tumors expressing PD-L1 (typically >1%) to have poorer survival than those with tumors expressing lower levels of PD-L1. Among the studies in which anti–PD-(L)1 therapies were used, 83% (five of six) did not reveal an association between survival and PD-L1 tumor expression. The single study directly comparing outcomes between those treated and untreated with anti–PD-(L)1 therapies across different PD-L1 cutoffs did not identify any differences between the groups.ConclusionsThe quality and consistency of the existing evidence base are currently insufficient to draw conclusions regarding a prognostic or predictive role of PD-L1 in MPM. Furthermore, high-quality studies on this topic are required to support the use of PD-L1 as a biomarker in MPM.

Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates, which has led to much criticism of NRS by HTA agencies. Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding and adjust effect estimates from NRS. Key considerations for application in HTA proposed in this article reflect the need to balance methodological complexity with ease of application and interpretation, and the need to ensure the methods fit within the existing frameworks used to assess nonrandomized evidence by HTA bodies.

Journal of Comparative Effectiveness ResearchVol. 11, No. 5 CommentaryOpen AccessHealth technology assessments and real-world evidence: tell us what you want, what you really, really wantFrank Griesinger, Oliver Cox, Cormac Sammon, Sreeram V Ramagopalan & Sanjay PopatFrank GriesingerDepartment of Medical Oncology, Pius-Hospital Oldenburg, Oldenburg, 26121, GermanySearch for more papers by this author, Oliver CoxGlobal Access, F Hoffmann-La Roche, Basel, 4070, SwitzerlandSearch for more papers by this author, Cormac SammonPHMR, London, NW1 8XY, UKSearch for more papers by this author, Sreeram V Ramagopalan*Author for correspondence: E-mail Address: sreeram.ramagopalan@roche.comGlobal Access, F Hoffmann-La Roche, Basel, 4070, SwitzerlandSearch for more papers by this author & Sanjay PopatLung Unit, Royal Marsden National Health Service Foundation Trust, Chelsea, London, SW3 6JJ, UKSearch for more papers by this authorPublished Online:25 Feb 2022https://doi.org/10.2217/cer-2021-0296AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail Keywords: HTAhealth technology assessmentreal-world evidencereimbursementsingle-arm trialsHealth Technology Assessment (HTA) involves the synthesis of a broad body of clinical, humanistic and economic evidence in order to determine the relative benefit of health technologies. Guidelines set out by HTA bodies seek to provide general guidance on the methodological standards for evidence to be submitted for their consideration, and some insight into the relative value that will be assigned to different types of evidence during decision-making. The extent to which specific pieces of evidence influence HTA decisions appears to be subjectively determined by the relationship between the body of evidence available for a specific technology and the values, preferences and constraints of a given HTA body [1]. As such, a single piece of evidence might influence decision making for a single product very differently across different HTA bodies and the same type of evidence may impact the assessment of different technologies by the same HTA body differentially. Given these nuances, alongside organizational experience, manufacturers must rely not only on formal guidelines to support their decision making around evidence generation activities, but also on critical review of the outputs of HTAs carried out for other products in a similar therapeutic area and/or with a comparable evidence base. It is therefore important that clarity regarding the relative value of different pieces of evidence to the decision-making process is provided in all of these outputs.Where a relatively new type of evidence comes into use within HTA, the lack of transparency around its role in decision making processes overall and relative to other types of evidence is perhaps even more of an issue. This is currently the case for the use of real-world evidence (RWE) to estimate treatment effects as, while the potential of RWE to provide treatment effects in HTA have been purported, there remains limited detail in policies regarding the role of such RWE in HTA decision-making. Manufacturers have therefore increasingly looked to published HTA outputs to gain some insight into this. However, as has been recently highlighted [2], the contribution of RWE on treatment effects to HTA decisions in many assessments is unclear. In some cases, this obscurity can be stark, with assessment reports deficient in any commentary on the submitted RWE, as was the case for a real-world comparator arm submitted as part of the assessment of alectinib in second-line anaplastic lymphoma kinase (ALK) + non-small-cell lung cancer by the Australian Pharmaceutical Benefits Advisory Committee (PBAC) [3]. More commonly, the limitations associated with RWE have been broadly noted in the commentary of the assessments, but a detailed critique of the RWE, and its contribution to the decision has been absent. This was the case for example in the assessments of ocrelizumab in relapsing-remitting multiple sclerosis and tocilizumab in giant cell arteritis by the PBAC, the National Institute for Health and Care Excellence (NICE) and the Canadian Agency for Drugs and Technologies in Health (CADTH) [4–9]. The opaqueness in these examples may be related to the fact that in each of them RWE was used to inform secondary treatment effects of relatively minor importance to the decision problem. However, cases such as these nonetheless leave a lack of clarity regarding the value of generating supportive RWE of this nature for HTA submissions.In cases where RWE has informed a treatment effect that is pivotal to the decision problem, greater transparency has been forthcoming in HTA reports. For example, in assessments of alectinib, blinatumomab and polatuzumab vedotin, CADTH provided relatively thorough critiques of the treatment effects estimated utilizing RWE but it is difficult to determine the role the evidence played in their decisions. In contrast, in their assessment of alectinib in second line ALK+ non-small-cell lung cancer the German Institute for Quality and Efficiency in HealthCare (IQWiG) provided detailed critiques of the analyses utilizing real world comparator arms and left little ambiguity that these data could not be used to support decision making [10]. Similarly, the Haute Autorité de Santé and Norwegian Medicines agency provided similar feedback on a comparator arm including real-world patients included in submissions for tisagenlecleucel in relapsed/refractory diffuse large B cell lymphoma [11,12]. While the clarity provided in these latter cases is notable and welcome, the critiques typically point to a number of issues inherent to RWE on treatment effects, such as the potential for unmeasured confounding, and do not offer any suggestions as to a preferred way they could have been overcome using RWE. This, combined with the lack of guidelines from HTA bodies on methods for the generation of such evidence, results in inconsistency and again makes understanding the benefit of generating RWE for use in HTA submissions unclear.Interestingly, even within the HTA re-assessment setting, where the potential of RWE on treatment effects to add value is potentially highest, and collection of real-world data are often mandated, its role remains obscure. For example, while the use of observational data from the systemic anticancer therapy dataset was reported to be a key element of the NICE cancer drugs fund its use in revaluating therapies used under the cancer drugs fund was not fully transparent and appears to be very limited at best [13].We appreciate that in some of the cases we have highlighted the manufacturer(s) involved in a HTA process may have obtained further transparency on the role of RWE within the process and potential alternative options, both verbally within meetings or within unpublished documents and correspondence. However, if this is the case, we believe there would be substantial gain in publishing any such insights within the main HTA outputs in order to allow a wider set of stakeholders to benefit from them.We also appreciate that the lack of detailed feedback in some of these cases is likely to reflect the absence of straightforward solutions to the challenges encountered in using RWE in this setting. As such, we would emphasize that the responsibility to define best practices does not lie with HTA bodies alone and that a collaborative effort including regulators, payers/HTA bodies, manufacturers, academic methodologists and healthcare professionals is needed to develop and validate approaches to overcome these challenges. For example, with an appropriate framework for their use, methods such as quantitative bias analysis may provide a tool to support the assessment of submissions using RWE in the context of their potential biases [14]. While guideline and process documents updates will serve as the primary guide for best practice in using RWE in HTA, these tend to be relatively static documents and can lack the granularity to address the nuances of specific technologies. We therefore believe that, even with the development of guidelines, HTA assessment outputs represent an important opportunity to provide detailed insights into specific use cases and the latest opinions held by a HTA body regarding best practices.Importantly, the potential implications of this observation extend beyond resource allocation issues to ethical considerations. As others have recently noted [15], with the role of RWE in decision-making being relatively opaque, the benefit of generating RWE is unclear, a situation which calls into question the ethics of utilizing patient data to generate such evidence. Ethical concerns can pose a tangible barrier to recruitment of patients into individual real-world studies and, perhaps more importantly for RWE, can contribute negatively to the debate regarding the set-up and use of comprehensive linked databases of patient healthcare data, as was seen with the failure of the Care.Data initiative in England [16]. As such, it is vital that any role for RWE in supporting patient access to medicines is clarified in order to enhance patient consent for the sharing and use of their RWD and ultimately drive the development of better quality RWE for HTA.In conclusion, while it can be challenging to communicate the role of a specific piece of evidence plays in a complex decision-making process, we believe there is a need for greater clarity regarding the role of RWE in HTA. This clarity will primarily be provided in updated methodological processes and guidelines. However, any such guidelines are unlikely to remain up to date and to address all the nuances encountered in specific HTA submissions. Outputs describing the HTA assessment of specific technologies represent an important and complementary forum which HTA bodies should utilize to provide further clarity regarding the role of RWE in HTA and evolving expectations regarding its use.Financial & competing interests disclosureO Cox and SV Ramagopalan are employees of F. Hoffmann-La Roche. S Popat is a consultant to Amgen, AstraZeneca, Bayer, Beigene, Blueprint, Boehringer Ingelheim, Daiichi Sankyo, GSK, Guardant Health, Incyte, Janssen, Eli Lilly, Merck KGaA, Novartis, Roche, Takeda, Pfizer, Seattle Genetics, Turning Point Therapeutics, Xcovery outside of the submitted work. F Griesinger has consulted or provided expert opinion for Amgen, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Celgene, GSK, Lilly, MSD, Novartis, Pfizer, Roche, Siemens, and Takeda outside of the submitted work. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.Open accessThis work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.References1. Angelis A, Lange A, Kanavos P. Using health technology assessment to assess the value of new medicines: results of a systematic review and expert consultation across eight European countries. Eur. J. Health Econ. 19(1), 123–152 (2018).Crossref, Medline, Google Scholar2. Patel D, Grimson F, Mihaylova E et al. Use of external comparators for health technology assessment submissions based on single-arm trials. Value Health 24(8), 1118–1125 (2021).Crossref, Medline, Google Scholar3. PBAC. Public Summary Document – alectinib – July 2017. www.pbs.gov.au/industry/listing/elements/pbac-meetings/psd/2017-07/files/alectinib-psd-july-2017.pdfGoogle Scholar4. CADTH. common drug review – ocrelizumab (2017). https://cadth.ca/ocrelizumabGoogle Scholar5. CADTH. common drug review – tocilizumab (2018). www.cadth.ca/index.php/tocilizumab-3 Google Scholar6. NICE. Technology appraisal guidance [TA518] – tocilizumab for treating giant cell arteritis (2018). www.nice.org.uk/guidance/ta518 Google Scholar7. NICE. Technology appraisal guidance [TA585] – ccrelizumab for treating primary progressive multiple sclerosis (2019). www.nice.org.uk/guidance/ta585.Google Scholar8. PBAC. Public Summary Document – ocrelizumab (2020). www.pbs.gov.au/industry/listing/elements/pbac-meetings/psd/2020-07/files/ocrelizumab-psd-july-2020.pdf Google Scholar9. PBAC. public summary document – tocilizumab (2019). www.pbs.gov.au/industry/listing/elements/pbac-meetings/psd/2019-03/files/tocilizumab-GCA-psd-march-2019.pdf Google Scholar10. IQWIG. Extract of dossier assessment A17-19 – Alectinib (non-small cell lung cancer) (2017). www.iqwig.de/download/a17-19_alectinib_extract-of-dossier-assessment_v1-0.pdf?rev=185028 Google Scholar11. Haute Autorité de Santé (HAS). Commission De La Transparence - Tisagenlecleucel (2018). www.has-sante.fr/upload/docs/application/pdf/2018-12/kymriah_ldgcb_pic_ins_avis3_ct17238.pdf Google Scholar12. Norwegian Medicines Agency. Single Technology assessment - Tisagenlecleucel (Kymriah) for the treatment of second or later relapsed/refractory diffuse large B cell lymphoma (DLBCL) (2019). https://legemiddelverket.no/Documents/Offentlig%20finansiering%20og%20pris/Metodevurderinger/K/Kymriah_DLBCL_2019.pdf Google Scholar13. Macdonald H, Goldacre B. Does the reformed cancer drug fund generate evidence on effectiveness? A cross-sectional analysis on publicly accessible documentation. medRxiv doi: doi:10.1101/2020.03.06.19014944 (2020) (Epub ahead of print).Google Scholar14. Sammon CJ, Leahy TP, Gsteiger S, Ramagopalan S. Real-world evidence and nonrandomized data in health technology assessment: using existing methods to address unmeasured confounding? J. Comp. Eff. Res. 9(14), 969–972 (2020).Link, Google Scholar15. Brown JP, Douglas IJ, Hanif S, Thwaites RMA, Bate A. Measuring the effectiveness of real-world evidence to ensure appropriate impact. Value Health 24(9), 1241–1244 (2021).Crossref, Medline, Google Scholar16. Carter P, Laurie GT, Dixon-Woods M. The social licence for research: why care.data ran into trouble. J. Med. Ethics 41(5), 404 (2015).Crossref, Medline, Google ScholarFiguresReferencesRelatedDetails Vol. 11, No. 5 Follow us on social media for the latest updates Metrics Downloaded 642 times History Received 3 December 2021 Accepted 10 February 2022 Published online 25 February 2022 Published in print April 2022 Information© 2022 The AuthorsKeywordsHTAhealth technology assessmentreal-world evidencereimbursementsingle-arm trialsFinancial & competing interests disclosureO Cox and SV Ramagopalan are employees of F. Hoffmann-La Roche. S Popat is a consultant to Amgen, AstraZeneca, Bayer, Beigene, Blueprint, Boehringer Ingelheim, Daiichi Sankyo, GSK, Guardant Health, Incyte, Janssen, Eli Lilly, Merck KGaA, Novartis, Roche, Takeda, Pfizer, Seattle Genetics, Turning Point Therapeutics, Xcovery outside of the submitted work. F Griesinger has consulted or provided expert opinion for Amgen, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Celgene, GSK, Lilly, MSD, Novartis, Pfizer, Roche, Siemens, and Takeda outside of the submitted work. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.Open accessThis work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.PDF download

Due to uncertainty regarding the potential impact of unmeasured confounding, health technology assessment (HTA) agencies often disregard evidence from nonrandomised studies when considering new technologies. Quantitative bias analysis (QBA) methods provide a means to quantify this uncertainty but have not been widely used in the HTA setting, particularly in the context of cost-effectiveness modelling (CEM). This study demonstrated the application of an aggregate and patient-level QBA approach to quantify and adjust for unmeasured confounding in a simulated nonrandomised comparison of survival outcomes. Application of the QBA output within a CEM through deterministic and probabilistic sensitivity analyses and under different scenarios of knowledge of an unmeasured confounder demonstrates the potential value of QBA in HTA.

Objective: To derive a score for finishing in the top three positions of the television show, Love Island, UK. Design: A retrospective study was undertaken using data from all previous contestants. Results: A score predicting show success termed DO-BITS (different coupling approaches [islanders pursuing one or many people on the show], Original islander [being on the show from the start], [being] Brunette, [having] intimate relationships on screen, Tradesman [occupation before being on the show] and Short name [having a four-letter first name]) was developed. The accuracy of this score in this derivation cohort yielded a C-statistic of 0.85. Conclusion: This simple, novel score provides a practical tool to assess the likelihood of success on Love Island.

This cohort study compares the findings of combined pertuzumab, trastuzumab, and docetaxel therapy in women with metastatic breast cancer with those in the CLEOPATRA trial.

Background: Guidelines indicate that oral anticoagulant (OAC) treatment decisions in atrial fibrillation should be based on a balanced consideration of thromboembolic and bleeding risk. Materials & methods: A retrospective cohort of nonvalvular atrial fibrillation patients were identified. Univariate logistic regression and conditional inference trees were used to quantify the importance of the CHA2DS2-VASc and modified HAS-BLED scores and their individual components on OAC treatment decisions. Results: The individual components of these risk scores provided more distinguishability between treated and untreated patients than the risk scores themselves, with bleeding risk factors strongly associated with nontreatment. Conclusion: While individual components of risk scores drive OAC treatment decisions according to guidelines, the relationship between bleeding risk factors and nontreatment warrants further consideration.

BACKGROUND:Significant improvements in mortality among patients with non-small cell lung cancer (NSCLC) in the USA over the past two decades have been reported based on Surveillance, Epidemiology, and End Results (SEER) data. The timing of these improvements led to suggestions that they result from the introduction of new treatments; however, few studies have directly investigated this. The aim of this study was to investigate the extent to which population level improvements in survival of advanced and/or metastatic NSCLC (admNSCLC) patients were associated with changes in treatment patterns.METHODS:We utilized a de-identified database to select three cohorts of patients with admNSCLC: (1) patients with non-oncogene (EGFR/ALK/ROS1/BRAF) positive tumors, (2) patients with ALK-positive (ALK+) tumors, and (3) patients with EGFR-positive (EGFR+) tumors. All patients were diagnosed with admNSCLC between 2012 and 2019. Multivariable Cox models adjusting for baseline characteristics and receipt of targeted and immunotherapy were utilized to explore the relationship between these variables and changes in the hazard of death by calendar year in each cohort.RESULTS:We included 28,154 admNSCLC patients with non-oncogene positive tumors, 598 with ALK+ tumors, and 2464 with EGFR+ tumors eligible for analysis. After adjustment for differences in baseline characteristics, the hazard of death in patients who had non-oncogene positive tumors diagnosed in 2015, 2016, 2017, 2018 ,and 2019 was observed to be 12%, 11%, 17%, 20%, and 21% lower respectively than that for those diagnosed in 2012. Upon additionally adjusting for receipt of first line or second line immunotherapy, the decrease in the hazard of death by calendar year was no longer observed, suggesting improvements in survival observed over time may be explained by the introduction of these treatments. Similarly, decreases in the hazard of death were only observed in patients with ALK+ tumors diagnosed between 2017 and 2019 relative to 2012 but were no longer observed following adjustment for the use of 1st and later generation ALK inhibitors. Among patients with EGFR+ tumors, the hazard of death did not improve significantly over time.CONCLUSION:Our findings expand on the SEER data and provide additional evidence suggesting improvements in survival of patients with advanced and metastatic NSCLC over the past decade could be explained by the change in treatment patterns over this period.

Head-to-head comparisons of the efficacy of treatments for gastroenteropancreatic neuroendocrine tumours (GEP-NETs) have not yet been reported. This study used a series of matching-adjusted indirect comparisons to indirectly compare the effectiveness of [177Lu]Lu-DOTA-TATE to everolimus, sunitinib and best supportive care (BSC) for extending progression-free survival and overall survival in patients with advanced, unresectable gastrointestinal (GI)-NETs and P-NETs. The results of the main analysis suggest that after accounting for differences in key prognostic variables, the hazard of progression was 62% (hazard ratio [HR], 0.38; confidence interval [CI]95 0.25–0.58) and 65% (HR 0.35 CI95 0.21–0.59) lower in patients with GI-NETs treated with [177Lu]Lu-DOTA-TATE than in those treated with everolimus and BSC, respectively. Similarly, the hazard of progression was 64% (HR 0.36 CI95 0.18–0.70), 54% (HR 0.46 CI95 0.30–0.71) and 79–87% (HR 0.21 CI95 0.13–0.32; HR 0.13 CI95 0.08–0.22) lower in patients with P-NET treated with [177Lu]Lu-DOTA-TATE than in those treated with sunitinib, everolimus and BSC, respectively. The hazard of death was 58% (HR 0.42 CI95 0.25–0.72), 47% (HR 0.53 CI95 0.33–0.87) and 44–64% (HR 0.56 CI95 0.36–0.90; HR 0.34 CI95 0.20–0.57) lower in P-patients with NET treated with [177Lu]Lu-DOTA-TATE than in those treated with sunitinib, everolimus and BSC, respectively. While our results must be interpreted with caution given the non-randomised nature of the comparisons and the potential for residual confounding, the magnitude of the effect sizes we observe and their consistency across comparators suggest that [177Lu]Lu-DOTA-TATE may be a more effective treatment option than everolimus, sunitinib and BSC in advanced, unresectable GEP-NETs.

9090 Background: Significant improvements in mortality among NSCLC cancer patients in the US over the past two decades have recently been reported based on SEER data. The timing of these improvements led to suggestions that they are primarily a result of the introduction of new and innovative treatments, however few studies have directly investigated this. Methods: We utilised the US Flatiron Health database to identify a cohort of non-biomarker (EGFR/ALK/ROS1/BRAF) positive metastatic NSCLC (mNSCLC) patients and a separate cohort of ALK positive (ALK+) patients diagnosed between 2012 and 2019. Multivariable Cox models adjusting for baseline characteristics and receipt of targeted and immunotherapy were utilised to explore the relationship between these variables and changes in the hazard of death by calendar year in each cohort. Results: We identified cohorts of 30,076 (54.7% Males) non-biomarker positive and 652 (45.4% males) ALK+ mNSCLC cancer patients in the database eligible for the analysis. Survival in both cohorts improved over time. After adjustment for differences in baseline characteristics the hazard of death in non-biomarker positive patients diagnosed in 2015, 2016, 2017, 2018 and 2019 was observed to be 14%, 13%, 16% 19% and 21% lower respectively than that in those diagnosed in 2012. Upon additionally adjusting for receipt of first line or second line immunotherapy the decrease in the hazard of death by calendar year was no longer observed, suggesting improvements in survival observed over time may be explained by the introduction of these innovative treatments. Similarly, decreases in the hazard of death were only observed in ALK+ patients diagnosed in 2018 and 2019 relative to 2012 and were no longer observed following adjustment for the use of ALK inhibitors. Conclusions: Our findings expand on the SEER data and provide direct evidence linking improvements in survival of NSCLC patients over the past decade with the introduction of innovative therapies.[Table: see text]

Aim: There are different methods to identify chronic kidney disease (CKD) in Clinical Practice Research Datalink (CPRD)-Hospital Episode Statistics (HES). Methods: Using CPRD-HES, nonvalvular atrial fibrillation patients were classified according to CKD category. Results: Using glomerular filtration rate/estimated glomerular filtration rate tests only to identify patients with CKD resulted in 3.5% stage 2, 2.7% stage 3, 0.3% stage 4 and 0.03% stage 5. Using data from diagnostic codes to identify patients with CKD resulted in 1.4% stage 3, 0.4% stage 4 and 0.3% stage 5. Using test records and codes resulted in 3.5% stage 2, 4.0% stage 3, 0.6% stage 4 and 0.4% stage 5. Conclusion: To identify CKD status in CPRD-HES, a combination of test records and codes should be used. Using diagnostic codes only significantly underestimates CKD prevalence.

Dementia is a common comorbidity in patients with atrial fibrillation (AF) and treatment guidelines recommend oral anticoagulant (OAC) therapy for AF patients with dementia unless concordance cannot be ensured by the caregiver. Despite this, the literature reports a low prescribing of OAC treatment in these patients. This study investigated possible factors associated with non-prescribing of OAC treatment in dementia patients newly diagnosed with non-valvular atrial fibrillation (NVAF) at age= 65 years between 2013 and 2017 using the Clinical Practice Research Datalink and Hospital Episodes Statistics databases. Of 1090 dementia patients newly diagnosed with NVAF, 693 (63.6%) patients did not have a prescription for an OAC in the year following their diagnosis. The likelihood of experiencing a thromboembolic event was high, with 97% of the population having a CHA(2)DS(2)-VASc score> 2; however, little difference in the presence of stroke risk factors was observed between the prescribed and non-prescribed groups. The presence of bleeding risk factors was high; only 28 (2.6%) of patients did not have a previous fall or a HAS-BLED bleeding risk factor. A history of falls [OR= 0.76, 95% confidence intervals (CIs) (0.58, 0.98)], previous major bleed [OR= 0.56, 95% CI (0.43, 0.73)] and care home residence [OR= 0.47, 95% CI (0.30, 0.74)] were associated with not having an OAC prescription. The results suggest that dementia patients with NVAF and certain risk bleeding risk factors are less likely to be prescribed an OAC. Further work is needed to establish possible relationships between bleeding risk factors and other potential drivers of OAC prescribing.

IntroductionAnti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) is a rare, serious and often life-threatening disease. The use of available treatments options (immunosuppressants and glucocorticoids (GCs)) improves the prognosis of AAV greatly; however, GC use is associated with significant toxicity related morbidities and the management of AAV is costly. However, information of the costs associated with AAV in the United Kingdom is limited. This study aimed to quantify the burden of AAV using a large England and Wales source of real-world data, the Clinical Practice Research Datalink (CPRD) Hospital Episode Statistics (HES) linked database, to identify healthcare resource utilization and generate estimates of costs.MethodsIncident patients (n = 220) were included if ≥ eighteen years, with diagnosis read codes G754.00/G75A.00; ICD codes M31.3/M31.7 from January 1997 to December 2017. Costs were taken from Unit Costs of Social and Health Care, National Health Service reference costs and electronic drug tariff. Distinction was made between type of consultations, outpatient visits and inpatient admission based on Healthcare Resource Grouping. Costs were summarised as mean per member per year (PMPY) in 2016 prices and presented before and after diagnosis.ResultsIn the year preceding AAV diagnosis, mean costs PMPY were GBP12,012 [USD15,400], (GBP5,339 [USD6,845] inpatient, GBP766 [USD982] outpatient, GBP314 [USD403] GP, GBP5,594 [USD7,172] GP prescribing). In the year of AAV diagnosis (Y0) costs PMPY were GBP28,252 [USD36,220], GBP15,436 [USD19,790] inpatient, GBP1,863 [USD2,388] outpatient, GBP2,407 [USD3,086] GBP8,545 [USD10,956] GP prescribing). Costs in the years post-diagnosis remained higher than pre-diagnosis with a low of GBP22,839 [USD29,281] in Y4. The prescribing costs (GC, methotrexate and azathioprine) were the largest contributor in Y0-Y4 (GBP15,047 [USD19,291] Y1; GBP12,325 [USD15,801] Y4).ConclusionsDiagnosis of AAV is associated with increased healthcare costs, including higher inpatients costs in the year of diagnosis and subsequently higher prescribing costs in the community. Given the incidence (17.2 cases per million) and considering only costs in the year of diagnosis, an additional GBP15.6 million [USD24.6 million] of healthcare resource utilization occurs every year from new diagnoses of AAV. However, this will likely be underestimated due to the lack of secondary care prescribing data in CPRD-HES and prescribing of immunosuppressant treatments in this setting.