bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trial eligibility criteria (EC) and diversity among patients with advanced non small cell lung cancer (advNSCLC).
Authors: Kaur Maneet , Frahm Filip , Ascha Mustafa , Guadamuz Jenny , Gottesman Adam , Leybovich Barry , Sondhi Arjun , Zhao Yihua , Meropol Neal J. , Royce Trevor Joseph 
Published: June 2023

1566 Background: FDA guidance encourages using real world data in developing diversity plans for clinical trials. Diversity plans may incorporate more inclusive (or expanded) EC, which may increase enrollment of minoritized populations. Whether broadened EC affect the diversity of eligible participants is unknown. Methods: Using the nationwide Flatiron Health electronic health record-derived deidentified database, we included patients with advNSCLC from 1/1/17-11/30/22 with a line of therapy (LOT) (N=61596). EC were defined by FDA, NCI/CTEP, and ASCO/FoCR standards, and included ECOG, ALT, AST, ALP, bilirubin, eGFR, CrCl, HGB, ANC & PLT -90 to +7 days from first LOT. Claims-linkage identified brain mets (BM), congestive heart failure (CHF), hepatitis C (HCV) & B (HBV), HIV and myocardial infarction (MI) within 6 months of first LOT. Logistic regression estimated odds ratios (OR) for exclusion by criterion associated with gender, age, race/ethnicity and area-level socioeconomic status (SES). Adjusting for clinical characteristics, Cox regression estimated hazard ratios (HR) for real world overall survival (rwOS). Results: Female patients (vs male patients) had greater odds of exclusion by CrCl (OR 2.9), eGFR (OR 1.27) & BM (OR 1.30). Compared to non-Latinx (NL) White patients, NL Black patients had greater odds of exclusion by ECOG (OR 1.17), CrCl (OR 1.33), eGFR (OR 1.61), HGB (OR 2.2), ANC (OR 1.71), HIV (OR 5.31), CHF (OR 1.31), HBV (OR 2.29) and HCV (OR 2.73); NL Asian patients by ALT (OR 1.64), ALP (OR 1.71), CrCl (OR 1.63), BM (OR 1.45) and HBV (OR 26.2); and Latinx patients by ALP (OR 1.56), HIV (OR 2.23) and HCV (OR 1.47). Compared to younger patients (18-49), older patients (75-84) had greater odds of exclusion by ECOG (OR 2.12), bilirubin (OR 1.53), CrCl (OR 41.9), eGFR (OR 9.54), CHF (OR 4.9) and MI (OR 4.7). Patients with the lowest (vs highest) quintile of SES had greater odds of exclusion by ECOG (OR 1.29) and HGB (OR 1.52). Among patients with complete data on all non-claims criteria (N=22117), the impact of expanding EC on diversity is shown, except SES which was similar. Compared to standard EC, the group with expanded EC had lower rwOS (adj HR 1.32; 95% CI 1.28-1.37). Conclusions: Expanding EC has the potential to increase the proportion and absolute number of Black, older, and female patients in advNSCLC trials. The resulting more diverse eligible population may have different clinical outcomes compared to a cohort defined by more restrictive EC. Future work will explore generalizability of findings to other clinical contexts.[Table: see text]
Toxicity reporting consistency and subjective minimizing language use in colorectal cancer (CRC) and pancreatic cancer (PaC) clinical trials: A systematic review of phase III randomized controlled trials (RCTs) presented at ASCO between 2012-2022.
Authors: Yu James , Mohammed Turab J , Chin-Yee Benjamin , Skorupski Clarissa , Gyawali Bishal , Lyman Gary H. , Adkins Lauren , Hicks Lisa K. , Kuderer Nicole M. 
Published: June 2023

1567 Background: Objective and complete toxicity reporting in clinical trials is critical for patient-centered shared decision-making. Conference abstracts inform initial impressions of practice-changing treatments. Methods: We performed a systematic review of all abstracts of CRC and PaC phase 3 RCTs presented at ASCO annual meetings between 2012 – 2022; long-term follow-up, supportive care, and solely non-pharmacological studies were excluded. Objective minimization of adverse event (AE) reporting was defined as absent and/or incomplete reporting of cumulative grade 3-5 CTCAE (common terminology criteria for AE). We also assessed the use of subjective minimizing language (Chin-Yee et al ASH 2022), defined as use of “acceptable,” “tolerable”, “manageable”, “favorable” (primary minimization terms), or “feasible”, “safe”, “patients did well”, “limited” (secondary minimization terms), terms that falsely imply patients deemed the therapy as such. Presence/absence of PRO or QOL data was also assessed. Results: 63 RCTs met entry criteria (42 CRC, 21 PaC), detailed in Table. Most trials studied chemotherapy +/- other drugs (52; 83%). 17% of all abstracts did not provide any information on AE. Quantitative data on AEs were reported by 38 (60%) of abstracts. However, serious AE reporting was frequently absent (Table), with some trials reporting only specific toxicities (e.g. cytopenias) instead of cumulative CTCAE. Only 7 (11%) of abstracts noted the occurrence or absence of fatal AE. Any subjective-minimizing language was used in 15 (24%) abstracts. Notably, none of the abstracts using subjective-minimizing language provided information on fatal AE rates, nor reported on the patient perspective via QOL or PRO. Average grade ≥ 3 AE in the experimental arm were similar in abstracts with vs without minimizing language (44% vs 45%). Conclusions: Our systematic review of ph 3 RCTs in GI oncology presented at ASCO annual meetings reveals that subjective minimizing language is often used to describe serious toxicities, and without formally assessing the patient voice. Serious AE reporting is frequently absent or incomplete.[Table: see text]
Technology-enabled clinical trials: Intentional capture of source data (IDC) in the electronic-health record (EHR) and direct transfer to trial database (electronic data capture [EDC]) in a phase II multicenter trial.
Authors: Royce Trevor Joseph , Falk Sam , Xiao Forrest , Boyd Mariel , Bramwell Lindsay , Shelley Addison , Sutton Lauren , Morgan Ruth M. , Mader Patricia , Taiclet Teresa , Alve Abigail 
Published: June 2023

1568 Background: Traditional data collection for clinical trials can be cumbersome and inefficient. FDA guidance now promotes capturing clinical trial source data in electronic form and transmitting it to the EDC, recognizing that this should reduce double data entry and improve data quality and timeliness. Electronic data originators can include the EHR, which can be used during routine clinical care and for gathering data during a clinical investigation. We evaluated a pilot of structured EHR data transfer to EDC in a clinical trial setting. Methods: IDC capability in the EHR was developed and applied to a subset of patients for an open label prospective Phase II study. EHR variables, that include free text, standard dictionaries (eg CTCAEv5, CDISC, First Databank), and derived responses, were mapped to the electronic case report form (eCRF). Bulk transmission of IDC clinical data (adverse events, AEs; concomitant medications, CMs; past medical history, PMH) from the EHR to EDC was executed at a designated time point during study conduct. We assessed the quantity (records) and quality (queries) of data captured and transferred, and timeliness of entry. Data to be used for the production analysis of the trial was collected via eCRF in an EDC system in parallel per usual standards. Results: Data for 102 patients across 8 sites of care by 99 different users was captured in the EHR via IDC from 3/1/2021 to 9/9/2022 and transferred from EHR to EDC in bulk on 9/9/22. 3,958 data records were transferred (852 AEs; 1,581 CMs; 1,525 PMHs). Of the AEs, 99% had an associated grade. Of the CMs and PMHs, none were copy-forwarded (all were entered de novo). Regarding queries in the EDC for transcription error validation among matching records, EHR data produced 205 opened edit check queries from 12 edit checks across all domains (149 AEs, 0 CMs, and 56 PMHs). Regarding timeliness, 95% of matched records were entered in IDC prior to entry in the production EDC. Conclusions: For an open label Phase II study, clinical trial source data was captured electronically through IDC using the EHR as the data originator, and directly bulk-transmitted to EDC. This may enable the elimination of transcription of source data entry into eCRF and result in more timely access to the data in the EDC. Additional investigation into how IDC with EHR-to-EDC compares in quality to traditional EDC is warranted. Planned follow up research includes interviewing site users to evaluate their experiences with IDC data entry.
Utilizing Drug Combo to improve the design of phase 1 trials for anticancer drugs.
Authors: Wang Lei , Zhang Shijun , Wei Lai , Owen Dwight Hall , Li Lang 
Published: June 2023

1571 Background: Accurate prior knowledge of a single drug or drug combination on toxicity, pharmacokinetics (PK), maximum tolerated dose (MTD), and dose-limiting toxicity (DLT) can help oncologists and statisticians design an efficient phase 1 clinical trial. However, most of the relevant knowledge is shattered in various databases. More importantly, MTD and DLT are missing in any databases. This study constructed a comprehensive knowledge base with expert-curated MTD and DLT knowledge from published anticancer drug combination phase 1 clinical trial results. We also demonstrated the utility of the knowledgebase to design a more efficient phase 1 trial. Methods: Toxicity, pharmacology, and PK knowledge were integrated from publicly available data sources, including DrugBank, SIDER, and drug labels. An interdisciplinary team manually curated the MTD and DLT knowledge. Relevant papers were discovered by PubMed keyword search (drug name + MTD or RP2D). Details regarding patient eligibility, treatment information (dose, formula, and schedule), trial design method, dose levels, DLT definition, MTD or Recommended phase 2 dose, and DLT incident were curated in the structured data format and stored in a database. Rxnorm and MedDRA were used for normalizing drug names and DLT. Results: Currently, 2358 papers on cancer drugs phase 1 trial have been curated. Additionally, pharmacological and PK knowledge of 197 cancer drugs, ADE information from 1094 drug labels, and more than 500000 pieces of DDI evidence were integrated into the knowledge base. This knowledge base (drugcombo.info) could help researchers design an efficient phase 1 trial. For example, based on knowledge from Drug Combo, the combination of nivolumab and axitinib did not have potential PK DDI and severe overlapping toxicity. The reviewing physician considered a phase 1b study to start at a curated 5 mg maximum dose of axitinib with dose reduction to 3 and 2 mg and maintenance of a 3-mg/kg dose of nivolumab based on guidelines. With such additional preliminary knowledge, the reviewing statistician felt the adaptive statistical methods could be applied to redesign this phase I combination trial and minimize patient exposure to sub-therapeutic dose levels. Conclusions: To our knowledge, Drug Combo is the first knowledge that integrated curated MTD and DLT knowledge. It would promote applying curated knowledge to better design phase I trials to treat cancer.
Survival-inferred fragility index of FDA-approved cancer drugs using surrogate endpoints.
Authors: Ofer Jonathan , Gilboa Shai , Pras Yarden , Markel Gal , Bomze David , Meirson Tomer 
Published: June 2023

1573 Background: The survival-inferred fragility index (SIFI) quantifies the robustness of positive trials by estimating the number of changes in a survival analysis that would render the results insignificant. Here, we use a variation of SIFI (SIFI-median) in which the median patient is reassigned between study groups until significance is lost. Our goal was to study the robustness of oncology randomized controlled trials (RCTs) leading to drug approval by the FDA based on surrogate time-to-event endpoints. Methods: In this cross-sectional study, we identified phase II-III RCTs between 2010 and 2020. We reconstructed individual patient data from published Kaplan-Meier plots of surrogate time-to-event endpoints and calculated the SIFI-median, i.e., the minimum number of median survivor reassignments from the intervention group to the control group that would cause the loss of statistical significance. Results: A total of 101 studies comprising 60697 patients met the inclusion criteria. The most common surrogate endpoint was PFS 86 (85.1%), followed by DFS 7 (6.9%). The SIFI-median was 23 (interquartile range [IQR], 12 to 37) and the SIFI-median as a proportion of sample size was 5.3% (IQR, 2.7% to 8.1%). Forty-five studies (44.5%) had SIFI-median less than 5% of the total sample size which was often less than the number of censored patients during early follow-up. The SIFI-median was less than 1% of the total sample size in 10 studies (9.9%). Conclusions: Low fragility indices indicates that the statistical conclusions of clinical studies could be reversed with a small number of variations. The lack of statistical robustness in multiple FDA-approved drugs suggests higher uncertainty in their clinical benefit.
Use of gender-neutral language in clinical trial protocols in select cancers.
Authors: Gupta Garima , Myint Zin , Yan Donglin , Patel Reema Anil 
Published: June 2023

1574 Background: Around 21% of Generation Z Americans self-identify as lesbian, gay, bisexual, transgender (LGBT), nearly double the proportion of millennials. Despite Affordable Care Act Section 1557 in 2020, transgender individuals continue to have poor access to quality cancer care, including clinical trials. The National Institutes of Health (NIH) recognizes that using gender-neutral language in healthcare is one of many ways to improve sexual and gender minorities (SGM) related health disparities. This study examines use of gender-neutral language in clinical trial protocols in select cancers. Methods: Data was collected from clinicaltrials.gov, a commonly used resource for patients and providers. Phase III clinical trials with all recruitment statuses in the US from 2017 to 2022 were included. Cancer types were limited to those with highest risk of using gender-biased language: breast, ovarian, endometrial, fallopian tube, cervical, prostate, testicular, and penile. For each trial, frequency of use of following terms was recorded by searching under study details section: male, female, she, her, he, his, him, man/men, woman/women, breastfeeding/nursing/lactating/pregnant mother or female or woman. Mean frequency was compared between 2017-19 and 2020-22 using Wilcoxon signed-rank test. Funder type, eligible sex, gender-based eligibility and gender eligibility description were also collected. Data were compared between 2017-19 (pre-1557) and 2020-22 (post-1557) using Fisher exact test. Results: 243 studies were included in the analysis; 130 were pre- and 113 were post-1557. 103/130 (80%) studies between 2017-19 and 78/113 (70%) between 2020-22 included at least one gender-biased term or “male/female” outside of eligible sex description ( p = 0.05), depicting a trend towards improvement; stronger improvement was noted in non-NIH funded studies versus NIH-funded studies. Mean frequency of gender-biased terms was higher in 2017-19 compared to 2020-22 (2.25 vs 1.88, p = 0.02). 27/130 studies (21%) between 2017-19 and 21/113 (19%) of studies between 2020-22 required gender-based eligibility ( p = 0.40). Amongst these, 100% of studies between 2017-19 and 95% of studies between 2020-22 either provided no description or used this clause incorrectly, commenting on sex assigned at birth and not gender identity. This indicates lack of awareness regarding difference between sex assigned at birth and gender identity. While it is evident to investigators during registration on clinicaltrials.gov that eligible sex refers to biological sex, it is not to anyone reviewing the trial for enrollment, resulting in implicit bias. Conclusions: Our study highlights the dire need to address SGM-related health disparities and brings attention towards paucity of gender-neutral language in clinical trial protocols. This step is critical in providing more inclusive care and capturing the rising population of LGBT individuals.
Quantifying the growth of clinical actionability in precision oncology using OncoKB.
Authors: Suehnholz Sarah P. , Kundra Ritika , Nandakumar Subhiksha , Nissan Moriah H , Zhang Hongxin , Lu Calvin , Dhaneshwar Amanda , Fernandez Nicole , Carrero Stephanie , Arcila Maria E. , Ladanyi Marc , Berger Michael F. , Syed Aijazuddin , Brannon Angela Rose , Levine Ross L. , Dogan Ahmet , Drilon Alexander E. , Solit David B. , Schultz Nikolaus , Chakravarty Debyani 
Published: June 2023

1577 Background: OncoKB ( www.oncokb.org ), Memorial Sloan Kettering's (MSK) precision oncology knowledge base, contains curated information about the oncogenic effect and therapeutic implications of somatic alterations in cancer and is the first cancer variant database to be partially recognized by the US-FDA. Individual mutations and structural alterations in OncoKB are assigned a level of evidence based on whether the alteration is a predictive biomarker of response or resistance to a genomically matched standard care or investigational drug in a specific cancer subtype. As of February 2023, OncoKB includes annotations for >6200 alterations in >700 cancer-associated genes, including 50 level 1 or 2 standard care genes (specified in the FDA drug label or professional guidelines) as well as 9 level 3A and 12 level 4 genes (predictive of drug response based on well-powered clinical studies or compelling biological evidence, respectively). OncoKB data is used internally to annotate >15,000 MSK patient sequencing reports annually, and its content is integrated into the cBioPortal for Cancer Genomics. Methods: To quantitate the expansion of the clinical actionability landscape in precision oncology, we annotated the clinical sequencing results of 47,271 solid tumor samples from the AACR Project GENIE cohort (GENIE 11.0 - public) using two versions of OncoKB, the first from March 2017 and the second from November 2022. Results: From 2017 to 2022, we observed a 22.7% increase in the fraction of samples with a standard care biomarker (levels 1 and 2), and a 21.4% decrease in the fraction of samples with a driver alteration that was not clinically actionable. The tumor agnostic approvals of the anti-PD1 antibody pembrolizumab in microsatellite instability high (MSI-H) and tumor mutational burden high (TMB-H) solid tumors resulted in a 11.1%increase of samples with a level 1 biomarker. In sum, of the 38,722 samples across 66 cancer types with limited to no actionability in 2017 (levels 3B, 4 or no level), 21.7% became clinically actionable (levels 1, 2 or 3A) by November 2022. This is an upper bound estimate as it includes samples that may have been eligible for an FDA-approved precision oncology therapy based on cancer subtype alone. Conclusions: Our analysis highlights the significant expansion of precision oncology-based therapeutic options for patients with cancer over the past 5 years, as well as the ongoing unmet need for novel precision oncology approaches designed to address oncogenic but not currently actionable drug targets. [Table: see text]
Clinical pathways innovation: A lesson on the value of clinician based feedback and collaboration with Flatiron Health.
Authors: Davella Christopher , Bange Erin Mary , Singh Aditi Puri , Dougherty David W. , Gabriel Peter Edward , Maniago Rebecca Lawrence , Miao Jennifer , Harvey Colin , Ricottone Marcello , Shulman Lawrence N. 
Published: June 2023

1578 Background: Clinical pathways have emerged as a strategy to provide value-based care by reducing treatment variation across oncology practice. Despite evidence for cost reduction and improved patient outcomes, adoption of clinical pathways has met resistance in the oncology community given concerns regarding the impact on workflow, and cost. Flatiron Assist, an EMR based pathways tool, is a promising solution to minimizing workflow friction by proving a novel, streamlined user interface. Methods: The Abramson Cancer Center at the University of Pennsylvania and Flatiron Health conducted a pilot study from August 2021-December 2022 to test the use of Flatiron Assist within its thoracic oncology practices. The goal of the pilot was to increase utilization as well as usability of the pathways tool over time through clinician and developer iterative collaboration. Penn’s clinical pathways were incorporated into the tool and integrated into the EMR. An education program trained clinicians on the tool’s functionality. Based on clinician feedback, the Penn team identified two areas of improvement for the tool: speed and organization of the tool keys and prompts. Changes made over time included: 1) increasing the speed of the tool 2) restructuring the organization of keys and prompts, and 3) creation of a “hot button” for three common clinical scenarios in order to increase efficiency. Speed of the application (session length in seconds), utilization of the hot button (number of sessions/overall sessions) and overall physician adoption (treatment ordering with Flatiron Assist/total thoracic orders) were calculated during the pilot. Results: Provider utilization of the pathways tool increased over time with a baseline adoption rate of 29% in August 2021 and a peak of 74% in April 2022. Median session length decreased over time from 106.4 seconds (August 2021) to 39.9 seconds (December 2022). Peak performance for session length was 38.2 seconds (November 2022). Usage of the hot button introduced in August 2022 has increased over time from 27.6% of sessions to 41.8 % in November 2022. Thoracic providers ordered, on average, 1.7 new treatment plans per week, resulting in less than 1.5 extra minutes of work per week with optimal use of the pathways tool. Conclusions: Collaboration between Penn and Flatiron Health effectively reduced specific areas of friction associated with the use of Flatiron Assist. Changes made to the speed, button organization and functionality of the tool contributed to increased clinician utilization over time. Future development of EMR based pathway tools should incorporate clinician engagement and feedback.