Clinical Trial Eligibility Criteria Research Articles

Eligibility for Anti-Amyloid Treatment in a Population-Based Study of Cognitive Aging.

Treatment options for Alzheimer disease (AD) are limited and have focused mainly on symptomatic therapy and improving quality of life. Recently, lecanemab, an anti-β-amyloid monoclonal antibody (mAb), received accelerated approval by the US Food and Drug Administration for treatment in the early stages of biomarker-confirmed symptomatic AD. An additional anti-β-amyloid mAb, aducanumab, was approved in 2021, and more will potentially become available in the near future. Research on the applicability and generalizability of the anti-β-amyloid mAb eligibility criteria on adults with biomarkers available in the general population has been lacking. The study's primary aim was to apply the clinical trial eligibility criteria for lecanemab treatment to participants with early AD of the population-based Mayo Clinic Study of Aging (MCSA) and assess the generalizability of anti-amyloid treatment. The secondary aim of this study was to apply the clinical trial eligibility criteria for aducanumab treatment in MCSA participants. This cross-sectional study aimed to apply the clinical trial eligibility criteria for lecanemab and aducanumab treatment to participants with early AD of the population-based MCSA and assess the generalizability of anti-amyloid treatment. Two hundred thirty-seven MCSA participants (mean age [SD] 80.9 [6.3] years, 54.9% male, and 97.5% White) with mild cognitive impairment (MCI) or mild dementia and increased brain amyloid burden by PiB PET comprised the study sample. Lecanemab trial's inclusion criteria reduced the study sample to 112 (47.3% of 237) participants. The trial's exclusion criteria further narrowed the number of potentially eligible participants to 19 (overall 8% of 237). Modifying the eligibility criteria to include all participants with MCI (instead of applying additional cognitive criteria) resulted in 17.4% of participants with MCI being eligible for lecanemab treatment. One hundred four participants (43.9% of 237) fulfilled the aducanumab clinical trial's inclusion criteria. The aducanumab trial's exclusion criteria further reduced the number of available participants, narrowing those eligible to 12 (5.1% of 237). Common exclusions were related to other chronic conditions and neuroimaging findings. Findings estimate the limited eligibility in typical older adults with cognitive impairment for anti-β-amyloid mAbs.

LeafAI: query generator for clinical cohort discovery rivaling a human programmer.

Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex clinical trial eligibility criteria. The task of query creation from eligibility criteria requires solving several text-processing problems, including named entity recognition and relation extraction, sequence-to-sequence transformation, normalization, and reasoning. We incorporated hybrid deep learning and rule-based modules for these, as well as a knowledge base of the Unified Medical Language System (UMLS) and linked ontologies. To enable data-model agnostic query creation, we introduce a novel method for tagging database schema elements using UMLS concepts. To evaluate our system, called LeafAI, we compared the capability of LeafAI to a human database programmer to identify patients who had been enrolled in 8 clinical trials conducted at our institution. We measured performance by the number of actual enrolled patients matched by generated queries. LeafAI matched a mean 43% of enrolled patients with 27 225 eligible across 8 clinical trials, compared to 27% matched and 14 587 eligible in queries by a human database programmer. The human programmer spent 26 total hours crafting queries compared to several minutes by LeafAI. Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base. We demonstrate that LeafAI can rival an experienced human programmer in finding patients eligible for clinical trials.

Eligibility criteria in clinical trials in breast cancer: a cohort study

BackgroundBreast cancer (BC) is the most common cancer type in women. The purpose of this study was to assess the eligibility criteria in recent clinical trials in BC, especially those that can limit the enrollment of older patients as well as those with comorbidities and poor performance status.MethodsData on clinical trials in BC were extracted from ClinicalTrials.gov. Co-primary outcomes were proportions of trials with different types of the eligibility criteria. Associations between trial characteristics and the presence of certain types of these criteria (binary variable) were determined with univariate and multivariate logistic regression.ResultsOur analysis included 522 trials of systemic anticancer treatments started between 2020 and 2022. Upper age limits, strict exclusion criteria pertaining to comorbidities, and those referring to inadequate performance status of the patient were used in 204 (39%), 404 (77%), and 360 (69%) trials, respectively. Overall, 493 trials (94%) had at least one of these criteria. The odds of the presence of each type of the exclusion criteria were significantly associated with investigational site location and trial phase. We also showed that the odds of the upper age limits and the exclusion criteria involving the performance status were significantly higher in the cohort of recent trials compared with cohort of 309 trials started between 2010 and 2012 (39% vs 19% and 69% vs 46%, respectively; p < 0.001 for univariate and multivariate analysis in both comparisons). The proportion of trials with strict exclusion criteria was comparable between the two cohorts (p > 0.05). Only three of recent trials (1%) enrolled solely patients aged 65 or 70 and older.ConclusionsMany recent clinical trials in BC exclude large groups of patients, especially older adults, individuals with different comorbidities, and those with poor performance status. Careful modification of some of the eligibility criteria in these trials should be considered to allow investigators to assess the benefits and harms of investigational treatments in participants with characteristics typically encountered in clinical practice.

Expanding eligibility criteria for renal and liver function in NCI-sponsored immunotherapy clinical trials.

e13610 Background: Stringent eligibility criteria can slow accrual, impede evaluation of potentially promising anticancer therapies, and limit applicability of trial results to the larger population. Immuno-Oncology (IO) clinical trials utilizing anti-PD1/PDL1 monoclonal antibodies (mAb) are ideal candidates to broaden organ function (fcn) eligibility, as their kinetics are independent of renal or hepatic fcn. Herein we present an analysis of the current landscape of renal and hepatic eligibility criteria in NCI CTEP-sponsored IO clinical trials to determine if these meet the recommended guidelines for broadened criteria. Methods: We conducted a retrospective analysis of renal and hepatic fcn eligibility criteria of NCI-sponsored IO trials in the CTEP networks initiated by the data cut-off date of 4/24/22. At least one treatment arm on all included trials had an anti-PD1/PDL1 mAb either alone or in combination with other agents. ASCO and Friends of Cancer Research (ASCO/Friends) recommendations and the NCI broadened eligibility criteria were used to assess the trials. ASCO/Friends broadens inclusion criteria for renal fcn to a creatinine clearance (CrCl) of &gt; 30 mL/min but gives no specific hepatic fcn cutoff. NCI recommends broadened criteria to glomerular filtration rate (GFR) of ≥ 60 mL/min/1.73m2 for the Experimental Therapeutics Clinical Trials Network and all phase 1 trials or GFR ≥ 50 mL/min/1.73m2 for National Clinical Trials Network (NCTN) trials, unless safety data exists for lower renal fcn values, and no lower than 30 mL/min/1.73m2. NCI suggests hepatic fcn criteria with total bilirubin ≤ institutional upper limit of normal (ULN) and AST/ALT ≤ 3 × ULN. CrCl and GFR were deemed interchangeable in this analysis. Results: A total of 182 IO trials were identified. Of these, 45 (25%) required CrCl &gt; 30 mL/min. Three trials (2%) allowed for CrCl &lt; 30 mL/min: one trial each requiring CrCl &gt; 25 mL/min, 20 mL/min, and 15 mL/min. One trial had no renal criteria. Ninety-five trials (52%) were compliant with the suggested NCI template guidance but not ASCO/Friends: 20 trials with CrCl &gt; 60 mL/min, 48 trials with &gt; 50 mL/min, 10 trials with &gt; 45 mL/min, and 17 trials with &gt; 40 mL/min as the renal eligibility criteria. Thirteen trials (7%) did not abide by either guidance: 6 NCTN trials with CrCl &gt; 60 mL/min and 4 trials requiring CrCl &gt; 70 mL/min. Serum creatinine alone was utilized for renal fcn in 22 trials. For hepatic criteria, 82 trials (45%) complied with NCI recommendations. The other 100 trials (55%) did not utilize the suggested NCI template cutoff for AST/ALT of 3 x ULN, but 81 of these trials allowed for AST/ALT at 2.5 x ULN with 27 of the 81 trials allowing for 5 x ULN for AST/ALT if liver metastases were present. Conclusions: Our results demonstrate that clinical trials have updated toward broadening hepatic eligibility criteria but require further guidance to facilitate a less cautious approach with renal fcn.

Embracing Myeloma Chimeric Antigen Receptor-T: From Scientific Design to Clinical Impact.

Despite recent advancement of treatment strategies in multiple myeloma (MM), patients with relapsed/refractory MM disease, particularly after triple-class refractoriness, continue to have poor prognosis. Chimeric antigen receptor (CAR-T) cells were developed and applied to improve outcomes in this setting, and two products, idecabtagene vicleucel and ciltacabtagene autoleucel, both targeting B-cell maturation antigen, have been approved by the Food and Drug Administration in the United States and European Medicines Agency in Europe. Both have shown unprecedented clinical outcomes with high response rate and prolonged progression-free survival and overall survival in this patient population with grim prognosis. Currently, further investigations are ongoing for CAR-T targeting different tumor antigens such as G protein-coupled receptor, class C, group 5, member D or with different combinations of intracellular signaling domains, as well as fourth-generation CAR-T with antigen-unrestricted inducible cytokines. Although CAR-T therapies hold hopes and enthusiasm from the myeloma community, several hurdles remain before these treatments become available for all patients in need. These barriers include CAR-T-cell manufacturing availability, access to administering centers, financial cost, caregivers' availability, and socioeconomic and racial disparities. Expanding clinical trial eligibility criteria and real-world data collection and analysis is crucial to understand the efficacy and safety of CAR-T in the patient cohort who tends to be excluded from current trials.

0342 Are Clinical Trials for Insomnia Recruiting Real World Patients?

Abstract Introduction Studies that have resulted in recent approval of hypnotics for chronic insomnia disorder (CID) have extremely restrictive inclusion criteria. We sought to determine how many patients seen in a sleep medicine clinic would be eligible to participate in these trials in order to assess the generalizability of these findings to clinical populations. Methods Six-hundred and twenty-eight patients were screened to identify 100 patients who were ≥18 years old, had a diagnosis of CID, and were prescribed a hypnotic medication at a sleep medicine clinic in a quaternary level healthcare center. Information regarding age, sex, body mass index, comorbid diagnoses, sleep schedule, medications, alcohol/tobacco/illicit drug use, caffeine intake, as well as Epworth Sleepiness Scale, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Insomnia Severity Index scores were abstracted. These data were compared against the inclusion and exclusion criteria for recent regulatory Phase III trials of orexin receptor antagonists (suvorexant, lemborexant, daridorexant). Results Of the 100 patients with CID (mean age=56.2 years; 57% female), only 3 patients met eligibility criteria for inclusion. A majority of patients (80%) would have been excluded due to a prior/concurrent trial of cognitive behavioral therapy for insomnia. If this criterion was set aside, only 11 patients would have been eligible to participate. The remaining top reasons for potential exclusion from the regulatory trials were: presence of medical comorbidities (36%), daytime napping (33%), and sleep apnea (30%). Conclusion A very small number of patients with CID seen in a clinical setting met eligibility criteria for clinical trials of hypnotic medications resulting in regulatory approval. This substantially limits the generalizability of findings from these trials and restricts our ability to determine the efficacy of these drugs in clinical populations. Future trials should enroll patients with less restrictive criteria to help determine the effectiveness of these medications in real world settings. Support (if any)

The art of clinical trial design in pulmonary fibrosis

The design of clinical trials is an art on its own. There are numerous, partially conflicting aspects a clinical trial has to satisfy. First and most importantly, the trial, especially in phase III trials, should ideally demonstrate a statistically significant effect of the drug under investigation on a clinically relevant primary endpoint. To accomplish this task, several aspects need to be considered. A crucial aspect at this stage is the expected effect size of the drug and the known variation of the primary endpoint measure, which together govern the power calculation and the sample size needed. As any treatment effect will need time to develop, time is also an important component: if the study is too short important effects or relevant but rare adverse events may be missed; if the study is too long patient retention may be difficult and dropouts may lead to missing data. A realistic estimate of the expected treatment effect on the primary endpoint and a sound power calculation are the mainstay of a successful study. Eligibility criteria for clinical trials need critical consideration <https://bit.ly/3mWl8Lz>

Abstract 5538: Prevalence of factors serving as common cancer clinical trial eligibility criteria by race and ethnicity

Abstract Background: Racial/ethnic minority patients remain underrepresented in clinical trials potentially due to restrictive trial eligibility criteria that disproportionally affect minority cancer patients. Objective: To examine the prevalence of existing medical conditions and abnormal lab values that commonly serve as clinical trial eligibility criteria among cancer patients by race and ethnicity. Methods: A cross-sectional analysis was conducted among patients new to Moffitt Cancer Center in 2011-2021 with multiple myeloma (n=3,967), breast (n=14,348), lung (n=10,492), and prostate (n=7,823) cancers. Demographics, existing medical conditions, and lab values were obtained from the Electronic Health Record, whereas history of cancer and metastatic disease at diagnosis were obtained from the Cancer Registry. Prevalence of medical conditions and abnormal lab values were reported among all patients and compared by race groups (White, Black, Hispanic, and other races) using age-adjusted logistic regression. For factors with prevalence higher than 5%, stratified analysis was conducted with respect to cancer type and adjusted for multiple comparisons. Results: Compared to White patients, Black (B) and Hispanic (H) patients were found to have higher prevalence of diabetes (OR [odds ratio] = 2.26 [B]/1.40 [H]), organ transplantation (OR=1.58 [B]/1.77 [H]), hepatitis (OR=1.74 [B]/1.48 [H]), HIV (OR=4.25 [B]/1.92 [H]), and abnormal creatinine value (OR=1.77 [B]/1.23 [H]). In addition, Black patients were more likely to have hypertension (OR=1.41) while patients of other races were more likely to be diabetic (OR=1.36). Similar patterns were observed across cancer types. Conclusion: Restrictive cancer clinical trial eligibility criteria may post a structural barrier that disproportionately impact racial/ethnic minority patients. Investigators should consider leveraging real-world data to define and design appropriate trial eligibility criteria. Prevalence of factors commonly included as clinical trial eligibility by race/ethnicity. Factors All White Black Hispanic Other Prevalence (%) % % OR (95% CI) % OR (95% CI) % OR (95% CI) Diabetes 6.30 5.90 10.00 2.26 (1.97-2.58) 6.50 1.40 (1.20-1.63) 6.50 1.36 (1.07-1.70) Chronic obstructive pulmonary disease 10.30 11.40 6.20 0.61 (0.52-0.72) 6.10 0.61 (0.52-0.71) 5.90 0.57 (0.44-0.72) Hypertension 18.00 18.50 19.80 1.41 (1.27-1.55) 13.40 0.87 (0.78-0.97) 13.50 0.84 (0.71-1.00) Heart condition 3.00 3.10 2.60 1.10 (0.86-1.39) 2.00 0.80 (0.61-1.03) 1.80 0.70 (0.44-1.05) Organ transplant 0.90 0.90 1.40 1.58 (1.11-2.20) 1.50 1.77 (1.28-2.41) 0.40 0.46 (0.16-1.00) Autoimmune disease 2.50 2.60 2.10 0.84 (0.63-1.09) 1.90 0.78 (0.59-1.01) 2.20 0.90 (0.60-1.30) Hepatitis 0.70 0.70 1.20 1.74 (1.17-2.49) 1.00 1.48 (0.99-2.15) 0.40 0.60 (0.21-1.30) HIV infection 0.30 0.20 0.90 4.25 (2.62-6.74) 0.40 1.92 (1.01-3.40) 0.20 0.71 (0.12-2.30) History of any cancer 15.90 17.20 10.30 0.69 (0.60-0.78) 10.30 0.69 (0.61-0.77) 10.90 0.71 (0.59-0.85) Metastasis at diagnosis 21.20 21.40 22.60 1.13 (1.02-1.24) 19.80 0.96 (0.87-1.05) 18.60 0.88 (0.75-1.02) Abnormal neutrophil count 19.70 20.10 20.20 1.00 (0.86-1.16) 16.60 0.79 (0.67-0.93) 18.20 0.89 (0.69-1.13) Abnormal creatinine 14.00 13.50 19.30 1.77 (1.57-1.99) 14.10 1.23 (1.08-1.39) 13.40 1.13 (0.93-1.38) Abnormal glomerular filtration rate 14.00 13.50 22.40 2.32 (1.99-2.70) 11.20 1.07 (0.88-1.30) 10.10 0.91 (0.66-1.22) Abnormal bilirubin 2.40 2.50 2.40 1.02 (0.69-1.45) 2.40 1.03 (0.70-1.46) 2.40 0.99 (0.52-1.69) Abnormal aspartate aminotransferase 9.40 9.40 9.20 0.88 (0.72-1.07) 9.70 0.92 (0.76-1.12) 8.50 0.82 (0.59-1.12) Citation Format: Yayi Zhao, Rossybelle P. Amorrortu, Rachel Howard, Kedar S. Kirtane, Susan T. Vadaparampil, Dana E. Rollison. Prevalence of factors serving as common cancer clinical trial eligibility criteria by race and ethnicity. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5538.

Abstract 3167: The need for structured pathology data in matching human subjects to cancer clinical trial criteria: A methodological approach

Abstract Introduction: Identification of human subjects who qualify for a cancer trial can be challenging, particularly given narrow time frames for enrollment and highly specific criteria for eligibility. We sought to identify patients on a timely basis by matching trial criteria to data present in both the Pathology and clinical information systems. In a separate abstract submitted to the AACR meeting, we described the enrollment outcomes of our effort for two clinical trials. In this companion abstract, we describe the query methods that we employed. Methods: Method #1 involved querying structured data fields in the EMR, using dashboards or SQL queries of relational databases. Method #2 utilized natural language processing (NLP) queries of narrative text in pathology reports. We assessed the useability of each query approach, on a near-real-time basis, against eligibility criteria for open clinical trials at our institution. Results: We examined the first 25 open clinical trials for treatment of solid tumors listed publicly on our institutional website. For each trial, eligibility and query criteria were compiled by two reviewers (DC,YZ); discrepancies between reviewers were resolved by a third reviewer (NY). We found that of the 33 trials examined: 26 trials (79%) could be successfully analyzed by NLP in the pathology report. 2 trials (6%) required SQL query of discrete data fields in clinical databases. 2 trials (6%) required both methods to be used together. 3 trials (9%) required data elements outside the scope of either method. The data fields provided by NLP were: tumor morphology; tumor stage; specimen margin; and tumor receptor status. The data fields provided by SQL were: ICD 10 codes, CPT codes, Chemotherapy, and quantitative lab results. The data fields not accessible by either approach were: tumor resectability and risk calculation. Conclusion: There is no single method that is best for all trials, and availability of a broad set of tools is needed to craft a fitting query for each trial. However, 26 of 33 trials (79%) required NLP to parse eligibility criteria from narrative pathology reports. Cancer clinical trials enrollment may be substantially enhanced by NLP extraction of structured data from pathology reports as part of a near-real-time workflow for identifying eligible Human Subjects. Citation Format: Nalan Yurtsever, Yonah Ziemba, Daniel A. King, Dylan J. Cooper, Sergio Garza, Cheryl B. Schleicher, Sharon S. Fox, James M. Crawford, Joseph Herman. The need for structured pathology data in matching human subjects to cancer clinical trial criteria: A methodological approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3167.

Abstract 6655: A real world analysis: Impact of clinical trial eligibility criteria on clinical outcomes among patients with advanced non-small cell lung cancer (aNSCLC) receiving second line (2L) immune checkpoint inhibitor (ICI) therapy

Abstract Background. Clinical trials (CT) include patients (pts) satisfying eligibility criteria including normal organ function and low comorbidity. Real-world outcomes for pts with aNSCLC receiving ICIs in 2L were compared based on whether pts met CT eligibility criteria. Methods. 2L- associated outcomes were compared between pts with aNSCLC diagnosed between 04/01/2015 - 12/31/2020 in US community health systems that fulfilled or failed to fulfill CT eligibility criteria. CT-eligible (CTE) pts had at 2L: performance status of 0-1; normal renal, hepatic, and bone marrow function; no evidence of Hepatitis B or C, HIV, rheumatic diseases, or interstitial lung disease. Pts were CT-ineligible (CTI) if they failed to meet one or more of these criteria. Follow-up spanned 2L start to the first of: outcome of interest, death, or study end on 06/30/2021. Outcomes were evaluated in pts receiving ICI monotherapy (monotherapy) or ICI + chemotherapy (combination). Results. Of the 505 pts in this analysis, 81% and 19% received monotherapy and combination, respectively; 19% of pts were CTE (76% monotherapy, 24% combination, Table). Overall, in ICI subgroups among monotherapy pts, pembrolizumab-treated pts had longer OS and TTNT; time to treatment discontinuation (TTD) was less variable across subgroups. CTE pts in the monotherapy group had longer OS, shorter TTNT, and similar TTD compared to CTI. Among combination pts, CTE had similar OS and TTD, but shorter TTNT compared to CTI. Conclusions. CTE pts appear to have longer survival than CTI pts with aNSCLC receiving ICI monotherapy, but not among pts receiving combination. TTNT was longer for CTI pts in both groups. In addition to CTE pts, some CTI pts appear to benefit from 2L ICI therapy. Time-to-event outcomes (months), median (95% CI) in the study cohort Monotherapy (N = 412) Monotherapy (N = 412) Monotherapy (N = 412) Monotherapy (N = 412) Combination (N = 93) Combination (N = 93) Combination (N = 93) Combination (N = 93) N (%) OS TTNT TTD N (%) OS TTNT TTD Atezolizumab 37 (9%) 7 (3-16) 9 (7-NA) 2 (1-3) 9 (10%) 6 (5-NA) NA (5-NA) 2 (1-NA) Nivolumab 234 (57%) 14 (9-18) 20 (15-NA) 2 (2-3) 9 (10%) 36 (6-NA) 23 (6-NA) 5 (3-NA) Pembrolizumab 138 (34%) 20 (16-28) NA (31-NA) 2 (1-3) 75 (80%) 12 (8-NA) 20 (15-NA) 1 (1-3) Overall - 15 (13-18) 23 (18-NA) 2 (2-2) - 11 (7-NA) 20 (10-NA) 2 (1-3) Trial-Eligible (all regimens) 72 (18%) 20 (12-NA) 19 (10-NA) 2 (2-3) 23 (25%) 11 (5-NA) 10 (7-NA) 3 (1-6) Trial-Ineligible(all regimens) 292 (70%) 13 (8-16) 26 (18-NA) 2 (2-2) 64 (69%) 11 (7-NA) 20 (9-NA) 2 (1-3) Unknown Trial Eligibility 47 (12%) - - - 6 (6%) - - - Citation Format: Hina Mohammed, Miso Kim, Mahder Teka, Monika Izano, Frank Wolf, Thomas Brown. A real world analysis: Impact of clinical trial eligibility criteria on clinical outcomes among patients with advanced non-small cell lung cancer (aNSCLC) receiving second line (2L) immune checkpoint inhibitor (ICI) therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6655.

Contextual Vulnerability Should Guide Fair Subject Selection in Xenotransplantation Clinical Trials

Contextual Vulnerability Should Guide Fair Subject Selection in Xenotransplantation Clinical Trials

Outcomes of Chemoimmunotherapy Among Patients With Extensive-Stage Small Cell Lung Cancer According to Potential Clinical Trial Eligibility

Chemoimmunotherapy is the standard first-line therapy for patients with extensive-stage small cell lung cancer (ES-SCLC). However, whether findings from pivotal trials can be extrapolated to the clinical practice setting remains unclear. To compare treatment outcome gaps following first-line chemoimmunotherapy for patients with ES-SCLC between those who met and did not meet the eligibility criteria used in previous clinical trials. A prospective cohort study was conducted from September 1, 2019, to September 30, 2020, at 32 hospitals in Japan, with at least 12 months of follow-up. Participants included consecutive patients with ES-SCLC who received carboplatin and etoposide with atezolizumab as first-line therapy. Patients who met eligibility criteria for pivotal phase 3 clinical trials were considered trial-eligible. The primary outcome was 6-month progression-free survival. The secondary outcomes were differences in progression-free survival, overall survival, and safety according to whether key clinical trial eligibility criteria were met. A total of 207 patients were analyzed (median age, 72 years; range, 46-87 years; 170 [82%] were male). Sixty-four patients (31%) were older adults (age ≥75 years), and most (184 [89%]) had an Eastern Cooperative Oncology Group performance status of 0 or 1. There were 132 (64%) trial-eligible patients. The 6-month progression-free survival rate for all patients was 38.8% (95% CI, 32.4%-45.7%). The median progression-free survival was 5.1 months in trial-eligible patients and 4.7 months in trial-ineligible patients (hazard ratio, 0.72; 95% CI, 0.53-0.97; P = .03). The proportion of patients who achieved disease control was 93% (118 of 127) in trial-eligible patients and 77% (55 of 71) in trial-ineligible patients (P = .002). The median overall survival was 15.8 months in trial-eligible patients and 13.1 months in trial-ineligible patients (hazard ratio, 0.73; 95% CI, 0.51-1.07; P = .10). The rate of severe adverse events was numerically higher among trial-ineligible patients than among trial-eligible patients (39% vs 27%; P = .07). In this cohort study, the overall treatment outcome was comparable to that reported in pivotal clinical trials. However, treatment outcomes after chemoimmunotherapy might differ between trial-eligible and trial-ineligible patients. These findings suggest that trial-eligibility criteria may be useful in clinical practice, and further studies using data from clinical practice settings are required to inform regulatory approval and clinical decision-making.

Call to action: overcoming enrollment disparities in cancer clinical trials with modernized eligibility criteria.

Traditional clinical trial eligibility criteria restrict study populations, perpetuating enrollment disparities. We aimed to assess implementation of modernized eligibility criteria guidelines among pancreatic cancer (PC) clinical trials. Interventional PC trials in the United States since January 1, 2014, were identified via clinicaltrials.gov with December 31, 2017, as the transition for pre- and postguidance eras. Trials were assessed for guideline compliance and compared using Fisher exact test. In total, 198 trials were identified: 86 (43.4%) were pre- and 112 (56.6%) postguidance era. Improvements were seen in allowing patients with history of HIV (8.6% vs 43.8%; P < .0001), prior cancer (57.0% vs 72.3%; P = .034), or concurrent and/or stable cancer (2.1% vs 31.1%; P < .0001) to participate. Most (>95%) trials were compliant with laboratory reference ranges, QT interval corrected for heart rate (QTc) cutoffs, and rationalizing excluding prior therapies both pre- and postguidance eras. However, overall compliance with modernized criteria remains poor. We advocate for stakeholders to update protocols and scrutinize traditionally restrictive eligibility criteria.

Evaluation of eligibility criteria in advanced prostate cancer clinical trials based on ASCO-FCR recommendations.

34 Background: The American Society of Clinical Oncology (ASCO) and the Friends of Cancer Research (FCR) published a joint statement addressing clinical eligibility for characteristics lacking adequate representation in recent studies (Kim et al., Clin Cancer Res 2021). Herein, we aimed to identify the frequency of other potentially excessive exclusion criteria in the context of metastatic prostate cancer. Methods: The eligibility criteria of advanced prostate cancer studies with start dates between June 30, 2012 and June 30, 2022 were identified through Clinicaltrials.gov. MeSH (Medical Subject Headings) terms in our query were “(metastatic OR advanced OR stage IV OR unresectable) AND (prostate cancer OR prostate adenocarcinoma)”. Our study included trials conducted worldwide and examined patients aged 18 and over in phase I-III trials. To narrow the scope of study, trials including more than one cancer type (basket trials) as well as those focusing on radiation therapy were not included in the study. Descriptive statistics were used to determine the frequency of eligibility criteria while the Fisher’s exact test or chi-square test were performed to demonstrate association between type of treatment and exclusion criteria. Results: 265 of 699 (37.9%) clinical trials within the specified search had sufficiently available data for evaluation. Of these, 136 (51.3%), 51 (19.2%), 26 (9.8%), 20 (7.5%), 19 (7.2%), and 7 (2.6%) of trials investigated treatment with combination therapy, hormone therapy, targeted therapy, immunotherapy, radioligand therapy, and chemotherapy, respectively. The most common ASCO-FCR-cited exclusion criteria in the studies were brain metastases (161/265, 60.8 %), HIV positivity (123/265, 46.4%), HBV/HCV positivity (122/265, 46.0%), and concurrent malignancies (41/265, 16.6%). Statistically significant relationships were identified between type of treatment along with status of brain metastases, HIV, and HBV/HCV (p=0.011, &lt;0.001, and 0.001, respectively. Conclusions: This study represents the first effort to analyze this specific subset of exclusion criteria in relation to type of treatment in advanced prostate cancer. Attention to the ASCO-FCR consensus statement may maximize patient representation in future studies and increase generalizability of data. [Table: see text]

Abstract WP126: Predictors Of Intracranial Hemorrhage Volume Expansion In Patients Receiving Factor Xa Inhibitors In The Annexa-4 Trial

Introduction: Andexanet, a specific reversal agent for FXa inhibitors, showed an effective hemostasis in 82% of patients with major bleeding in the ANNEXA-4 trial. However, little is known about the predictors associated with hematoma expansion in patients with FXa inhibitor-associated intracranial hemorrhage (ICrH) receiving andexanet. Methods: ANNEXA-4 was a prospective, single-arm, open-label study of andexanet in patients with acute major bleeding within 18h after taking a FXa inhibitor. Hematoma volumes at baseline and 12h after andexanet treatment were measured utilizing a computerized-assisted volumetric method. Univariate and multivariate logistic regression analyses of baseline clinical and non-clinical parameters were performed to identify factors predictive of different volumes of hematoma expansion. Results: A total of 305 patients with both baseline and follow-up imaging were included (mean age 80 years; 60.2% with intracerebral hemorrhage; 55.4% taking apixaban; 32.5% taking rivaroxaban). Overall median baseline hematoma volume was 10.4 mls (IQR 2.7-28.1) and time from symptom onset to baseline CT was 3.3h (1.5-6.8). Mean baseline anti-FXa activity levels were 148 ng/mL (SD 107.5). In follow up scans, 48 cases (15.7%) showed evidence of ICrH expansion ≥6 mls. Utilizing a four category ICrH Expansion Scale as the dependent variable, ordinal logistic regression identified Glasgow Coma Scale (GCS) score (OR 0.79; 95% CI 0.64-0.97), baseline ICrH volume (OR 1.02; 95% CI 1.01-1.04) and time from symptom onset to CT (OR 0.73 (0.53-0.99) as significant predictors of ICrH expansion. Baseline anti-FXA activity (OR 1.54; 95% CI 0.95-2.51; p value 0.08) and multiple compartment ICrH (OR 2.32; 95% CI 0.98-5.51; p value 0.06) showed trend as predictors of ICrH expansion. ICrH expansion dichotomized by ≥6 mls or &lt;6 mls as dependent variable, identified similar predictors of ICrH expansion. Conclusions: Shorter time from onset of symptoms to CT, lower GCS score and larger hematoma volumes at presentation increased the probability of ICrH expansion in FXa-associated ICrH treated with andexanet. These findings can inform the eligibility criteria of clinical trials targeting hemostatic efficacy in patients with FXa-associated ICrH.

Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design

Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients’ clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual’s EHRs can determine the subphenotypes—homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same subgroup not only share similar clinical characteristics but also at similar risk levels of encountering severe adverse events (SAEs). We evaluated our algorithm on two previously conducted clinical trials with EHRs from the OneFlorida+ Clinical Research Consortium. Our model can clearly identify the patient subgroups who are more likely to suffer or not suffer from SAEs as subphenotypes in a transparent and interpretable way. Our approach identified a set of clinical topics and derived novel patient representations based on them. Each clinical topic represents a certain clinical event composition pattern learned from the patient EHRs. Tested on both trials, patient subgroup (#SAE=0) and patient subgroup (#SAE>0) can be well-separated by k-means clustering using the inferred topics. The inferred topics characterized as likely to align with the patient subgroup (#SAE>0) revealed meaningful combinations of clinical features and can provide data-driven recommendations for refining the exclusion criteria of clinical trials. The proposed supervised topic modeling approach can infer the clinical topics from the subphenotypes with or without SAEs. The potential rules for describing the patient subgroups with SAEs can be further derived to inform the design of clinical trial eligibility criteria.

A review of research on eligibility criteria for clinical trials.

The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries.

Characterization of Patients With Metastatic Renal Cell Carcinoma Experiencing Complete Response to First-line Therapies: Results From the International Metastatic Renal Cell Carcinoma Database Consortium.

Clinical trials have demonstrated higher complete response rates in the immuno-oncology-based combination arms than in the tyrosine kinase inhibitor arms in patients with metastatic renal cell carcinoma. We aimed to characterize real-world patients who experienced complete response to the contemporary first-line therapies. Using the International Metastatic Renal Cell Carcinoma Database Consortium, response-evaluable patients who received frontline immuno-oncology-based combination therapy or tyrosine kinase inhibitor monotherapy were analyzed. Baseline characteristics of patients and post-landmark overall survival were compared based on best overall response, as per RECIST 1.1. A total of 52 (4.6%) of 1,126 and 223 (3.0%) of 7,557 patients experienced complete response to immuno-oncology-based and tyrosine kinase inhibitor therapies, respectively (P = .005). An adjusted odds ratio for complete response achieved by immuno-oncology-based combination therapy (vs tyrosine kinase inhibitor monotherapy) was 1.56 (95% CI 1.11-2.17; P = .009). Among patients who experienced complete response, the immuno-oncology-based cohort had a higher proportion of non-clear cell histology (15.9% and 4.7%; P = .016), sarcomatoid dedifferentiation (29.8% and 13.5%; P = .014), and multiple sites of metastases (80.4% and 50.0%; P < .001) than the tyrosine kinase inhibitor cohort. Complete response was independently associated with post-landmark overall survival benefit in both the immuno-oncology-based and tyrosine kinase inhibitor cohorts, giving respective adjusted hazard ratios of 0.17 (95% CI 0.04-0.72; P = .016) and 0.28 (95% CI 0.21-0.38; P < .001). The complete response rate was not as high in the real-world population as in the clinical trial population. Among those who experienced complete response, several adverse clinicopathological features were more frequently observed in the immuno-oncology-based cohort than in the tyrosine kinase inhibitor cohort. Complete response was an indicator of favorable overall survival.

Implementation of a clinical trial recruitment support system based on fast healthcare interoperability resources (FHIR) in a cardiology department

Abstract Background Clinical Trial Recruitment Support Systems can booster patient inclusion of clinical trials by automatically analyzing eligibility criteria based on electronic health records. However, missing interoperability has hindered introduction of those systems on a broader scale. Purpose Our aim was to develop a recruitment support system based on FHIR R4 and evaluate its usage and features in a cardiology department. Methods/Implementation Clinical conditions, anamnesis, examinations, allergies, medication, laboratory data and echocardiography results were imported as FHIR resources. Trial study nurses and physicians were enabled to add new and edit trial information and input inclusion and exclusion criteria using a web-browser user interface in the hospital intranet. All information were recorded on the server side as the FHIR resources “ResearchStudy” and “Group”. Eligibility criteria linked by the logical operation “OR” were represented by using multiple FHIR Group resources for enrollment. On the client side, eligibility criteria were transformed to a tree-like structure (see Figure 1). Upon user demand, all hospitalized and ambulatory patients in the cardiology department were instantly screened for trial eligibility using the FHIR eligibility criteria on the existing patients' FHIR resources. Furthermore, study personal was able to manually edit trial status (i.e. ineligible, on-study, ...) of patients, which was implemented using the FHIR resource “ResearchSubject”. Results This implementation of a CTRSS based on FHIR R4 was evaluated in clinical practice: Beginning from 1st April 2021 the application was used as an additional patient screening tool for the four trials CLOSURE-AF, FAIR-HF2, SPRIRIT-HF and TORCH-PLUS of the German Centre for Cardiovascular Research. As the COVID-19 pandemic is prohibiting any proper comparison of patient inclusion rates, efficacy of the recruitment support system was tested by comparing the numbers of patients identified by the recruitment support system and enrolled in a trial to the actual number of enrolled patients irrespective of the screening method from 1st April 2021 to 23rd November 2021. The system was able to identify 52 of 55 patients included in those four clinical trials. Conclusion Use of FHIR for defining eligibility criteria of clinical trials may facilitate interoperability and allow automatic screening for eligible patients at multiple sites of different healthcare providers in the future. Upcoming changes in FHIR should allow easier description of “OR”-linked eligibility criteria. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Deutsche Forschungsgemeinschaft

Classification of Stiff-Knee Gait Kinematic Severity after Stroke Using Retrospective k-Means Clustering Algorithm.

Nowadays, a classification system for unilateral stiff-knee gait (SKG) kinematic severity in hemiparetic adult patients after stroke does not exist. However, such classification would be useful to the clinicians. We proposed the use of the k-means method in order to define unilateral SKG severity clusters in hemiparetic adults after stroke. A retrospective k-means cluster analysis was applied to five selected knee kinematic parameters collected during gait in 96 hemiparetic adults and 19 healthy adults from our clinical gait analysis database. A total of five discrete knee kinematic clusters were determined. Three clusters of SKG were identified, based on which a three-level severity classification was defined: unbend-knee gait, braked-knee gait, and frozen-limb gait. Preliminary construct validity of the classification was obtained. All selected knee kinematic parameters defining the five clusters and the majority of usual kinematic parameters of the lower limbs showed statistically significant differences between the different clusters. We recommend diagnosing SKG for values strictly below 40° of knee flexion during the swing phase. Clinicians and researchers are now able to specify the level of kinematic severity of SKG in order to optimize treatment choices and future clinical trial eligibility criteria.