scholarly journals A graph-based method for reconstructing entities from coordination ellipsis in medical text

2020 ◽  
Vol 27 (9) ◽  
pp. 1364-1373 ◽  
Author(s):  
Chi Yuan ◽  
Yongli Wang ◽  
Ning Shang ◽  
Ziran Li ◽  
Ruxin Zhao ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Global Optimization ◽  
Concept Mapping ◽  
Biomedical Literature ◽  
Eligibility Criteria ◽  
Medical Text ◽  
Representation Model ◽  
Biomedical Publications ◽  
Linguistic Phenomenon

Abstract Objective Coordination ellipsis is a linguistic phenomenon abound in medical text and is challenging for concept normalization because of difficulty in recognizing elliptical expressions referencing 2 or more entities accurately. To resolve this bottleneck, we aim to contribute a generalizable method to reconstruct concepts from medical coordinated elliptical expressions in a variety of biomedical corpora. Materials and Methods We proposed a graph-based representation model and built a pipeline to reconstruct concepts from coordinated elliptical expressions in medical text (RECEEM). There are 4 modules: (1) identify all possible candidate conjunct pairs from original coordinated elliptical expressions, (2) calculate coefficients for candidate conjuncts using the embedding model, (3) select the most appropriate decompositions by global optimization, and (4) rebuild concepts based on a pathfinding algorithm. We evaluated the pipeline’s performance on 2658 coordinated elliptical expressions from 3 different medical corpora (ie, biomedical literature, clinical narratives, and eligibility criteria from clinical trials). Precision, recall, and F1 score were calculated. Results The F1 scores for biomedical publications, clinical narratives, and research eligibility criteria were 0.862, 0.721, and 0.870, respectively. RECEEM outperformed 2 previously released methods. By incorporating RECEEM into 2 existing NLP tools, the F1 scores increased from 0.248 to 0.460 and from 0.287 to 0.630 on concept mapping of 1125 coordination ellipses. Conclusions RECEEM improves concept normalization for medical coordinated elliptical expressions in a variety of biomedical corpora. It outperformed existing methods and significantly enhanced the performance of 2 notable NLP systems for mapping coordination ellipses in the evaluation. The algorithm is open sourced online (https://github.com/chiyuan1126/RECEEM).

scholarly journals The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

10.2196/21169 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e21169
Author(s):  
Lyndsey Elaine Gates ◽  
Ahmed Abdeen Hamed
Keyword(s):  
Clinical Trials ◽  
Language Processing ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Drug Candidates ◽  
Fast Development ◽  
Drug Knowledge ◽  
Biomedical Publications ◽  
Multiple Drugs

Background Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments. Objective The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. Methods To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called “diversity.” A diversity score for a given drug was calculated by measuring how “diverse” a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease. Results From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride. Conclusions The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

scholarly journals The Anatomy of SARS-CoV-2 BioMedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation (Preprint)

2020 ◽  
Author(s):  
Lyndsey Elaine Gates ◽  
Ahmed Abdeen Hamed
Keyword(s):  
Clinical Trials ◽  
Language Processing ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Drug Candidates ◽  
Fast Development ◽  
Drug Knowledge ◽  
Biomedical Publications ◽  
Multiple Drugs

BACKGROUND Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug to save our fellow humans, we explored the landscape of the SARS-CoV-2 biomedical publications to satisfy our objectives. OBJECTIVE The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. METHODS To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called “diversity.” A diversity score for a given drug was calculated by measuring how “diverse” a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease. RESULTS From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. CONCLUSIONS The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

scholarly journals Real-World Data for Planning Eligibility Criteria and Enhancing Recruitment: Recommendations from the Clinical Trials Transformation Initiative

2021 ◽  
Author(s):  
Scott R. Evans ◽  
Dianne Paraoan ◽  
Jane Perlmutter ◽  
Sudha R. Raman ◽  
John J. Sheehan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real World ◽  
Real World Data ◽  
Eligibility Criteria ◽  
World Data ◽  
Study Planning ◽  
Efficiency And Effectiveness ◽  
Multi Stakeholder ◽  
Functional Teams ◽  
Data Driven Decisions

AbstractThe growing availability of real-world data (RWD) creates opportunities for new evidence generation and improved efficiency across the research enterprise. To varying degrees, sponsors now regularly use RWD to make data-driven decisions about trial feasibility, based on assessment of eligibility criteria for planned clinical trials. Increasingly, RWD are being used to support targeted, timely, and personalized outreach to potential trial participants that may improve the efficiency and effectiveness of the recruitment process. This paper highlights recommendations and resources, including specific case studies, developed by the Clinical Trials Transformation Initiative (CTTI) for applying RWD to planning eligibility criteria and recruiting for clinical trials. Developed through a multi-stakeholder, consensus- and evidence-driven process, these actionable tools support researchers in (1) determining whether RWD are fit for purpose with respect to study planning and recruitment, (2) engaging cross-functional teams in the use of RWD for study planning and recruitment, and (3) understanding patient and site needs to develop successful and patient-centric approaches to RWD-supported recruitment. Future considerations for the use of RWD are explored, including ensuring full patient understanding of data use and developing global datasets.

scholarly journals On the design of early-phase Alzheimer’s disease clinical trials with cerebrospinal fluid tau outcomes

2021 ◽  
pp. 174077452110344
Author(s):  
Michelle M Nuño ◽  
Joshua D Grill ◽  
Daniel L Gillen ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cerebrospinal Fluid ◽  
Phosphorylated Tau ◽  
Inclusion Criteria ◽  
Eligibility Criteria ◽  
Total Tau ◽  
Prodromal Alzheimer’S Disease ◽  
Study Power

Background/Aims: The focus of Alzheimer’s disease studies has shifted to earlier disease stages, including mild cognitive impairment. Biomarker inclusion criteria are often incorporated into mild cognitive impairment clinical trials to identify individuals with “prodromal Alzheimer’s disease” to ensure appropriate drug targets and enrich for participants likely to develop Alzheimer’s disease dementia. The use of these eligibility criteria may affect study power. Methods: We investigated outcome variability and study power in the setting of proof-of-concept prodromal Alzheimer’s disease trials that incorporate cerebrospinal fluid levels of total tau (t-tau) and phosphorylated (p-tau) as primary outcomes and how differing biomarker inclusion criteria affect power. We used data from the Alzheimer’s Disease Neuroimaging Initiative to model trial scenarios and to estimate the variance and within-subject correlation of total and phosphorylated tau. These estimates were then used to investigate the differences in study power for trials considering these two surrogate outcomes. Results: Patient characteristics were similar for all eligibility criteria. The lowest outcome variance and highest within-subject correlation were obtained when phosphorylated tau was used as an eligibility criterion, compared to amyloid beta or total tau, regardless of whether total tau or phosphorylated tau were used as primary outcomes. Power increased when eligibility criteria were broadened to allow for enrollment of subjects with either low amyloid beta or high phosphorylated tau. Conclusion: Specific biomarker inclusion criteria may impact statistical power in trials using total tau or phosphorylated tau as the primary outcome. In concert with other important considerations such as treatment target and population of clinical interest, these results may have implications to the integrity and efficiency of prodromal Alzheimer’s disease trial designs.

scholarly journals Efficacy and Safety of Intravenous rtPA in Ischemic Strokes Due to Small-Vessel Occlusion: Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Bartosz Karaszewski ◽  
Adam Wyszomirski ◽  
Bartosz Jabłoński ◽  
David J. Werring ◽  
Dominika Tomaka
Keyword(s):  
Clinical Trials ◽  
Ischemic Stroke ◽  
Randomized Clinical Trials ◽  
Small Vessel Disease ◽  
Small Vessel ◽  
Efficacy And Safety ◽  
Eligibility Criteria ◽  
Vessel Occlusion ◽  
Excellent Outcome ◽  
Symptomatic Intracerebral Hemorrhage

AbstractIntravenous recombinant tissue plasminogen activator (iv-rtPA) has been routinely used to treat ischemic stroke for 25 years, following large clinical trials. However, there are few prospective studies on the efficacy and safety of this therapy in strokes attributed to cerebral small vessel disease (SVD). We evaluated functional outcome (modified Rankin scale, mRS) and symptomatic intracerebral hemorrhage (sICH) using all available data on the effects of iv-rtPA in SVD-related ischemic stroke (defined either using neuroimaging, clinical features, or both). Using fixed-effect and random-effects models, we calculated the pooled effect estimates with regard to excellent and favorable outcomes (mRS=0–1 and 0–2 respectively, at 3 months), and the rate of sICH. Twenty-three studies fulfilled the eligibility criteria, 11 of which were comparative, and there were only 3 randomized clinical trials. In adjusted analyses, there was an increased odds of excellent outcome (adjusted OR=1.53, 95% CI: 1.29–1.82, I2: 0%) or favorable outcome (adjusted OR=1.68, 95% CI: 1.31–2.15,I2: 0%) in patients who received iv-rtPA compared with placebo. Across the six studies which reported it, the incidence of sICH was higher in the treatment group (M-H RR = 8.83, 95% CI: 2.76–28.27). The pooled rate of sICH in patients with SVD administered iv-rtPA was only 0.72% (95% CI: 0.12%–1.64%). We conclude that when ischemic stroke attributed to SVD is considered separately, available data on the effects of iv-rtPA therapy are insufficient for the highest level of recommendation, but it seems to be safe. Although further therapeutic trials in SVD-related ischemic stroke appear to be justified, our findings should not prevent its continued use for this group of patients in clinical practice.

scholarly journals Effectiveness and Safety of Immune Checkpoint Inhibitors for Patients with Advanced Non Small-Cell Lung Cancer in Real-World: Review and Meta-Analysis

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1388
Author(s):  
Manlio Mencoboni ◽  
Marcello Ceppi ◽  
Marco Bruzzone ◽  
Paola Taveggia ◽  
Alessia Cavo ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real World ◽  
Group Performance ◽  
Checkpoint Inhibitors ◽  
Meta Analysis ◽  
Performance Status ◽  
Eastern Cooperative Oncology Group ◽  
Small Cell ◽  
Small Cell Lung ◽  
Eligibility Criteria

Immunotherapy based on anti PD-1/PD-L1 inhibitors is the new standard of advanced non-small cell lung cancers. Pembrolizumab, nivolumab and atezolizumab are used in clinical practice. The strict eligibility criteria of clinical trials do not allow researchers to fully represent treatment effects in the patients that will ultimately use these drugs. We performed a systematic review and a meta-analysis to evaluate the effectiveness and safety of these drugs, and more generally of ICIs, as second-line therapy in NSCLC patients in real world practice. MEDLINE, PubMed, Scopus and Web of Science were searched to include original studies published between January 2015 and April 2020. A total of 32 studies was included in the meta-analysis. The overall radiological response rate (ORR), disease control rate (DCR), median progression-free survival (PFS) and overall survival (OS) were 21%, 52%, 3.35 months and 9.98 months, respectively. The results did not change when analysis was adjusted for Eastern Cooperative Oncology Group performance status (ECOG PS) and age. A unitary increase in the percent of patients with liver and CNS metastases reduced the occurrence of DCR by 7% (p < 0.001) and the median PFS by 2% (p = 0.010), respectively. The meta-analysis showed that the efficacy and safety of immunotherapy in everyday practice is comparable to that in clinical trials.

Eligibility criteria and clinical trials: An FDA perspective

2021 ◽  
pp. 106515
Author(s):  
Mili Duggal ◽  
Leonard Sacks ◽  
Kaveeta Vasisht
Keyword(s):  
Clinical Trials ◽  
Eligibility Criteria

scholarly journals Outcomes of patients with solid tumour malignancies treated with first-line immuno-oncology agents who do not meet eligibility criteria for clinical trials

2021 ◽  
Vol 151 ◽  
pp. 115-125
Author(s):  
Chun L. Gan ◽  
Igor Stukalin ◽  
Daniel E. Meyers ◽  
Shaan Dudani ◽  
Heidi A.I. Grosjean ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Solid Tumour ◽  
First Line ◽  
Eligibility Criteria

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
The United States ◽  
Design Stage ◽  
Common Data Model ◽  
Free Text ◽  
Eligibility Criteria ◽  
Health Records ◽  
Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

Improving hematology/oncology clinical research recruitment via universal prescreening: The VA Connecticut (VACT) Cancer Center experience.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 79-79
Author(s):  
Jenny Jing Xiang ◽  
Alicia Roy ◽  
Christine Summers ◽  
Monica Delvy ◽  
Jessica Lee O'Donovan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Clinical Research ◽  
Cancer Patients ◽  
Cancer Center ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Research Recruitment ◽  
Lung Cancer Patients ◽  
Study Enrollment

79 Background: Patient-trial matching is a critical step in clinical research recruitment that requires extensive review of clinical data and trial requirements. Prescreening, defined as identifying potentially eligible patients using select eligibility criteria, may streamline the process and increase study enrollment. We describe the real-world experience of implementing a standardized, universal clinical research prescreening protocol within a VA cancer center and its impact on research enrollment. Methods: An IRB approved prescreening protocol was implemented at the VACT Cancer Center in March 2017. All patients with a suspected or confirmed diagnosis of cancer are identified through tumor boards, oncology consults, and clinic lists. Research coordinators perform chart review and manually enter patient demographics, cancer type and stage, and treatment history into a REDCap (Research Electronic Data Capture) database. All clinical trials and their eligibility criteria are also entered into REDCap and updated regularly. REDCap generates real time lists of potential research studies for each patient based on his/her recorded data. The primary oncologist is alerted to a patient’s potential eligibility prior to upcoming clinic visits and thus can plan to discuss clinical research enrollment as appropriate. Results: From March 2017 to December 2020, a total of 2548 unique patients were prescreened into REDCAP. The mean age was 71.5 years, 97.5% were male, and 15.5% were African American. 32.57 % patients had genitourinary cancer, 17.15% had lung cancer, and 46.15% were undergoing malignancy workup. 1412 patients were potentially eligible after prescreening and 556 patients were ultimately enrolled in studies. The number of patients enrolled on therapeutic clinical trials increased after the implementation of the prescreening protocol (35 in 2017, 64 in 2018, 78 in 2019, and 55 in 2020 despite the COVID19 pandemic). Biorepository study enrollment increased from 8 in 2019 to 15 in 2020. The prescreening protocol also enabled 200 patients to be enrolled onto a lung nodule liquid biopsy study from 2017 to 2019. Our prescreening process captured 98.57% of lung cancer patients entered into the cancer registry during the same time period. Conclusions: Universal prescreening streamlined research recruitment operations and was associated with yearly increases in clinical research enrollment at a VA cancer center. Our protocol identified most new lung cancer patients, suggesting that, at least for this malignancy, potential study patients were not missed. The protocol was integral in our program becoming the top accruing VA site for NCI’s National Clinical Trial Network (NCTN) studies since 2019.

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