scholarly journals Clinical Trial Information As a Measure of Quality Cancer Care

2010 ◽  
Vol 6 (3) ◽  
pp. 170-171 ◽  
Author(s):  
Wei Chua ◽  
Stephen J. Clarke
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Cancer Care ◽  
Treatment Options ◽  
Small Cell ◽  
Small Cell Lung ◽  
Quality Cancer Care ◽  
Improved Outcomes ◽  
New Treatment ◽  
Clinical Trial Information

Participation in clinical trials enables patients to access new treatment options. Evidence shows improved outcomes in participants compared with nonparticipants in non–small-cell, lung, breast, colorectal, and testicular cancers.

scholarly journals Are clinical trial eligibility criteria an accurate reflection of a real-world population of advanced non-small-cell lung cancer patients?

2018 ◽  
Vol 25 (4) ◽  
Author(s):  
K. Al-Baimani ◽  
H. Jonker ◽  
T. Zhang ◽  
G.D. Goss ◽  
S.A. Laurie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Advanced Nsclc ◽  
Small Cell ◽  
Small Cell Lung ◽  
Eligibility Criteria ◽  
Ecog Ps

Background Advanced non-small-cell lung cancer (nsclc) represents a major health issue globally. Systemic treatment decisions are informed by clinical trials, which, over years, have improved the survival of patients with advanced nsclc. The applicability of clinical trial results to the broad lung cancer population is unclear because strict eligibility criteria in trials generally select for optimal patients.Methods We performed a retrospective chart review of all consecutive patients with advanced nsclc seen in outpatient consultation at our academic institution between September 2009 and September 2012, collecting data about patient demographics and cancer characteristics, treatment, and survival from hospital and pharmacy records. Two sets of arbitrary trial eligibility criteria were applied to the cohort. Scenario A stipulated Eastern Cooperative Oncology Group performance status (ecog ps) 0–1, no brain metastasis, creatinine less than 120 μmol/L, and no second malignancy. Less-strict scenario B stipulated ecog ps 0–2 and creatinine less than 120 μmol/L. We then used the two scenarios to analyze treatment and survival of patients by trial eligibility status.Results The 528 included patients had a median age of 67 years, with 55% being men and 58% having adenocarcinoma. Of those 528 patients, 291 received at least 1 line of palliative systemic therapy. Using the scenario A eligibility criteria, 73% were trial-ineligible. However, 46% of “ineligible” patients actually received therapy and experienced survival similar to that of the “eligible” treated patients (10.2 months vs. 11.6 months, p = 0.10). Using the scenario B criteria, only 35% were ineligible, but again, the survival of treated patients was similar in the ineligible and eligible groups (10.1 months vs. 10.9 months, p = 0.57).Conclusions Current trial eligibility criteria are often strict and limit the enrolment of patients in clinical trials. Our results suggest that, depending on the chosen drug, its toxicities and tolerability, eligibility criteria could be carefully reviewed and relaxed.

Phenotyping clinical trial treatment regimens in cancer: An integrative ontology, artificial intelligence and knowledge engineering approach.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14054-e14054
Author(s):  
Yun Mai ◽  
Kyeryoung Lee ◽  
Zongzhi Liu ◽  
Zhiqiang Li ◽  
Scott Jones ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Real World ◽  
Small Cell ◽  
Small Cell Lung ◽  
Treatment Regimens

e14054 Background: Matching clinical attributes (i.e. indications, lab tests, treatment regimens) of clinical trial eligibility criteria with real world patient data is extremely challenging. Attribute phenotyping is one of the key components of Trial2Patient, a customized system developed by Sema4 to find patients for clinical trials. Transforming treatment regimens to a standard ontology and encoding drugs with standard nomenclatures will facilitate the semantic retrieval of treatments mentioned in clinical trial criteria. This will also enable the interoperation between different data sources that is often required for fast-learning and scalable healthcare information system. Methods: Free text containing treatment regimen/medication terms were extracted and preprocessed from three sources: 1) clinical trials listed in a commercial database citeline.com, 2) clinical trials listed in clinicaltrials.gov, and 3) National Comprehensive Cancer Network (NCCN) Guidelines. The regimen terms such as neoadjuvant therapy for non-small cell lung cancer, checkpoint inhibitor, EGFR inhibitor, androgen deprivation therapy (ADT), among many others, were profiled by AI methods (i.e. pattern reorganization and rule-based) and knowledge engineering via Sema4’s in-house knowledge base (CAV), Pharmaprojects in citline.com and NCCN Guidelines. The drugs related to each regimen were identified and mapped to RxCUI via RxNorm ontology. Results: We identified 76 regimen terms for non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and prostate cancer (e.g. PD-L1 ≥1% nonsquamous NSCLC, bone antiresorptive therapy for M1 castration resistant prostate cancer), and 14,476 drug-category pair (e.g. pembrolizumab is a PD-1 inhibitor, pembrolizumab is used as the third line and beyond systemic therapy for M1 CRPC). All drugs identified were mapped to RxCUI for real world patient matching. Conclusions: This approach systematically extracted and normalized regimens and medications mentioned in clinical trials in NSCLC, SCLC and prostate cancer to standard codes. These standardized data can be used in mapping treatment histories of a patient to the eligibility criteria for clinical studies or for identifying studies relevant to a patient. The outcome of profiling cancer treatment regimens through standard ontology RxNorm may be particularly valuable in cancer studies based on real-world evidence.

Phenotyping of clinical trial eligibility text from cancer studies into computable criteria in electronic health records.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6592-6592
Author(s):  
Yun Mai ◽  
Kyeryoung Lee ◽  
Zongzhi Liu ◽  
Meng Ma ◽  
Christopher Gilman ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Knowledge Base ◽  
Small Cell ◽  
Rater Agreement ◽  
Small Cell Lung ◽  
Eligibility Criteria ◽  
Health Records ◽  
Cancer Studies

6592 Background: Clinical trial phenotyping is the process of extracting clinical features and patient characteristics from eligibility criteria. Phenotyping is a crucial step that precedes automated cohort identification from patient electronic health records (EHRs) against trial criteria. We establish a clinical trial phenotyping pipeline to transform clinical trial eligibility criteria into computable criteria and enable high throughput cohort selection in EHRs. Methods: Formalized clinical trial criteria attributes were acquired from a natural-language processing (NLP)-assisted approach. We implemented a clinical trial phenotyping pipeline that included three components: First, a rule-based knowledge engineering component was introduced to annotate the trial attributes into a computable and customizable granularity from EHRs. The second component involved normalizing annotated attributes using standard terminologies and pre-defined reference tables. Third, a knowledge base of computable criteria attributes was built to match patients to clinical trials. We evaluated the pipeline performance by independent manual review. The inter-rater agreement of the annotation was measured on a random sample of the knowledge base. The accuracy of the pipeline was evaluated on a subset of randomly selected matched patients for a subset of randomly selected attributes. Results: Our pipeline phenotyped 2954 clinical trials from five cancer types including Non-Small Cell Lung Cancer, Small Cell Lung Cancer, Prostate Cancer, Breast Cancer, and Multiple Myeloma. We built a knowledge base of 256 computable attributes that included comorbidities, comorbidity-related treatment, previous lines of therapy, laboratory tests, and performance such as ECOG and Karnofsky score. Among 256 attributes, 132 attributes were encoded using standard terminologies and 124 attributes were normalized to customized concepts. The inter-rater agreement of the annotation measured by Cohen’s Kappa coefficient was 0.83. We applied the knowledge base to our EHRs and efficiently identified 33258 potential subjects for cancer clinical trials. Our evaluation on the patient matching indicated the F1 score was 0.94. Conclusions: We established a clinical trial phenotyping pipeline and built a knowledge base of computable criteria attributes that enabled efficient screening of EHRs for patients meeting clinical trial eligibility criteria, providing an automated way to efficiently and accurately identify clinical trial cohorts. The application of this knowledge base to patient matching from EHR data across different institutes demonstrates its generalization capability. Taken together, this knowledge base will be particularly valuable in computer-assisted clinical trial subject selection and clinical trial protocol design in cancer studies based on real-world evidence.

scholarly journals Use of New Treatment Modalities for Non-small Cell Lung Cancer Care in the Medicare Population

CHEST Journal ◽  
2013 ◽  
Vol 143 (2) ◽  
pp. 429-435 ◽  
Author(s):  
Michael T. Vest ◽  
Jeph Herrin ◽  
Pamela R. Soulos ◽  
Roy H. Decker ◽  
Lynn Tanoue ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Cancer Care ◽  
Small Cell ◽  
Treatment Modalities ◽  
Small Cell Lung ◽  
Medicare Population ◽  
New Treatment

Feasibility of integrating the Outcomes4Me smartphone navigation application into the care of breast cancer patients (FIONA).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1570-1570
Author(s):  
Steven J. Isakoff ◽  
Maya Said ◽  
Agnes H. Kwak ◽  
Eva Glieberman ◽  
Amanda Stroiney ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Pilot Study ◽  
Patient Engagement ◽  
Medical Center ◽  
Treatment Options ◽  
Academic Medical Center ◽  
Patient Reported ◽  
Clinical Trial Information

1570 Background: Patients diagnosed with breast cancer (BC) face complex decisions about their care and many studies have shown that improved patient engagement results in increased satisfaction and better outcomes. Patient engagement includes education, treatment option selection, symptom tracking and reporting, and clinical trial opportunities. We conducted a pilot study to determine the feasibility of introducing the Outcomes4Me patient engagement app into the standard of care experience of BC patients. Methods: This was a pilot study (NCT04262518) conducted at an academic medical center. Eligible patients had any subtype of stage 1-4 BC and were on any type of chemo-, hormonal-, targeted-, or radiation-therapy for BC during the study period. Participants downloaded the app on their smartphone and their app usage was evaluated. Surveys were administered at baseline and end of study. Clinicians caring for patients using the app were surveyed at the end of the study. The primary endpoint was feasibility, defined as at least 40% of patients engaging with the app at least 3 times over the 12-week study period. Additional endpoints included usability, satisfaction, correlation of patient reported data with the EHR, clinical trial matching, and patient experience. Results: Between June 2020 and December 2020, 107 patients enrolled; results are reported for 90 patients with complete data as of 1/24/21. Baseline demographics: median age 53 (range: 27-77); 90% White, 4% Black, 3% Asian; 66% had hormone positive/HER2-, 20% HER2+, and 13% triple negative BC; 31% had stage 4 disease. At study entry, 93% had never used an app to help with their disease or treatment options. Over the 12 week study period, 58% of patients engaged with the app at least 3 times, meeting the primary feasibility endpoint. Patients engaged with the app on average 5.5 days (range: 0-40) with 20% engaging on more than 10 days during the study. The mean System Usability Score was 71 (median = 76) and was similar across age groups. The 5 app features deemed most (‘somewhat’ or ‘very’) helpful were: background about their BC (76%), information about treatment options (74%), newsfeed about their BC (70%), symptom tracking (65%), and clinical trial information (65%). 53% said that the app helped them keep track of symptoms and 33% said they are more likely to explore or enroll in a clinical trial after using the app. Conclusions: Integration of the Outcomes4Me app into the care management of BC patients is feasible with acceptable usability. Our results suggest that use of a patient smartphone app may be helpful for many aspects of patient education and engagement for patients with BC. The results also suggest that this type of intervention can help patients better track their symptoms and make them aware of clinical trials, potentially facilitating the management of side effects and accelerating clinical trials recruitment. Clinical trial information: NCT04262518.

scholarly journals Potential Influence on Clinical Trials of Long-Term Survivors of Stage IV Non-small cell Lung Cancer

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Jennifer S Davis ◽  
Erin Prophet ◽  
Ho-Lan Peng ◽  
Hwa Young Lee ◽  
Rebecca S S Tidwell ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Small Cell Lung Cancer ◽  
Stage Iv ◽  
Small Cell ◽  
Small Cell Lung ◽  
Term Survival ◽  
Long Term Survivors

Abstract Background New, effective treatments have resulted in long-term survival for small subgroups of metastatic non-small cell lung cancer (NSCLC) patients. However, knowledge of long-term survivor frequency and characteristics prior to modern therapies is lacking. Methods Surveillance Epidemiology and End Results (SEER) patients with stage IV NSCLC diagnosed from 1991 to 2007 and followed through 2012 were dichotomized by survival time into the 10% who lived 21 months or longer (long-term survivors) vs the remaining 90% and compared with participants in a representative clinical trial of molecular profiling and targeted therapies (CUSTOM). Results Among the 44 387 SEER patients, the 10% identified as long-term survivors were distinguishable from the remaining 90% by younger age, female sex, Asian race, adenocarcinoma histology, tumor grade, tumor site, and surgery. From 1991–1994 to 2003–2007, median survival increased by 6 months from 30 to 36 months among long-term survivors but by only 1 month from 3 to 4 months among the remaining 90%. Among the 165 participants in the CUSTOM trial, 54% met our SEER criterion of long-term survival by living for 21 months or longer. Conclusions Among SEER patients with stage IV NSCLC, long-term survivors had a median survival approximately 10 times that of the remaining 90%. Long-term survivors accounted for more than one-half of the participants in a representative clinical trial. Caution is required when extrapolating the outcomes of participants in clinical trials to patients in routine clinical practice.

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