Javascript must be enabled to continue!
Impact of patient travel time on disparities in precision oncology clinical trials.
View through CrossRef
3113 Background: Precision oncology revolutionizes cancer care, allowing personalized treatments to improve outcomes. Precision oncology often entails participation in genotype-matched clinical trials, resulting in referrals from institutions providing comprehensive genomic profiling (CGP) testing to those conducting these trials. It is still being determined whether there are regional disparities in precision oncology. Methods: We conducted a retrospective review of 1127 patients (pts) referred to National Cancer Center Hospital (NCCH) for participation in genotype-matched clinical trials following CGP testing performed between June 2020 and June 2022. Travel distance and time were calculated utilizing Google Maps from the patient's residence to the NCCH. A total of 23 covariates were preselected and dichotomized per previous research or expert consensus as follows: age, sex, performance status, body mass index, tumor type, number of lines of prior therapies, number of metastatic sites, liver metastases, brain metastases, pleural or peritoneal effusions, biopsiability, neutrophil, hemoglobin, platelet, albumin, creatinine, total bilirubin, LDH, AST, CRP, place of residence (urban vs. rural), referring hospitals, and household income. All independent variables associated with participation in genotype-matched trials (P < 0.20) were included in a multivariable model, and variable importance was calculated using a machine learning (ML) model (gradient-boosted decision tree). Results: A total of 127 (11%) of 1127 pts were enrolled in the genotype-matched trials. Of 127 pts, 82 (65%) and 45 (35%) participated in phase 1 trials and phase 2/3 trials, respectively. The overall median travel distance and time were 38 km (interquartile range [IQR] 21–107) and 55 minutes (IQR 35–110), respectively. In multivariable regression, travel distance (≥100km vs. <100km) was not associated with the proportion of genotype-matched trial participation (9% vs. 12%; odds ratio [OR], 0.68; 95% confidence interval [CI], 0.42–1.07; P = 0.11); however, in pts with travel time ≥120 minutes, the proportion of genotype-matched trial participation was significantly lower than those with travel time < 120 minutes (7% vs. 13%; OR, 0.54; 95% CI, 0.32–0.89; P = 0.019). The proportion of genotype-matched trial participation decreased as travel time increased from <40 minutes to 40–120 minutes to ≥120 minutes (13% vs. 12% vs. 7%, respectively; OR, 0.67; 95% CI, 0.50–0.88; P = 0.006). Travel time was also identified as an important factor in the ML model, whereas low-income and residence in rural areas were of minor importance. Conclusions: Patients with travel time ≥120 minutes were less likely to participate in genotype-matched clinical trials than those with travel time <120 minutes. Regional disparities may be creating inequities in precision oncology, which warrant immediate action, including decentralization of clinical trials.
American Society of Clinical Oncology (ASCO)
Title: Impact of patient travel time on disparities in precision oncology clinical trials.
Description:
3113 Background: Precision oncology revolutionizes cancer care, allowing personalized treatments to improve outcomes.
Precision oncology often entails participation in genotype-matched clinical trials, resulting in referrals from institutions providing comprehensive genomic profiling (CGP) testing to those conducting these trials.
It is still being determined whether there are regional disparities in precision oncology.
Methods: We conducted a retrospective review of 1127 patients (pts) referred to National Cancer Center Hospital (NCCH) for participation in genotype-matched clinical trials following CGP testing performed between June 2020 and June 2022.
Travel distance and time were calculated utilizing Google Maps from the patient's residence to the NCCH.
A total of 23 covariates were preselected and dichotomized per previous research or expert consensus as follows: age, sex, performance status, body mass index, tumor type, number of lines of prior therapies, number of metastatic sites, liver metastases, brain metastases, pleural or peritoneal effusions, biopsiability, neutrophil, hemoglobin, platelet, albumin, creatinine, total bilirubin, LDH, AST, CRP, place of residence (urban vs.
rural), referring hospitals, and household income.
All independent variables associated with participation in genotype-matched trials (P < 0.
20) were included in a multivariable model, and variable importance was calculated using a machine learning (ML) model (gradient-boosted decision tree).
Results: A total of 127 (11%) of 1127 pts were enrolled in the genotype-matched trials.
Of 127 pts, 82 (65%) and 45 (35%) participated in phase 1 trials and phase 2/3 trials, respectively.
The overall median travel distance and time were 38 km (interquartile range [IQR] 21–107) and 55 minutes (IQR 35–110), respectively.
In multivariable regression, travel distance (≥100km vs.
<100km) was not associated with the proportion of genotype-matched trial participation (9% vs.
12%; odds ratio [OR], 0.
68; 95% confidence interval [CI], 0.
42–1.
07; P = 0.
11); however, in pts with travel time ≥120 minutes, the proportion of genotype-matched trial participation was significantly lower than those with travel time < 120 minutes (7% vs.
13%; OR, 0.
54; 95% CI, 0.
32–0.
89; P = 0.
019).
The proportion of genotype-matched trial participation decreased as travel time increased from <40 minutes to 40–120 minutes to ≥120 minutes (13% vs.
12% vs.
7%, respectively; OR, 0.
67; 95% CI, 0.
50–0.
88; P = 0.
006).
Travel time was also identified as an important factor in the ML model, whereas low-income and residence in rural areas were of minor importance.
Conclusions: Patients with travel time ≥120 minutes were less likely to participate in genotype-matched clinical trials than those with travel time <120 minutes.
Regional disparities may be creating inequities in precision oncology, which warrant immediate action, including decentralization of clinical trials.
Related Results
Autonomy on Trial
Autonomy on Trial
Photo by CHUTTERSNAP on Unsplash
Abstract
This paper critically examines how US bioethics and health law conceptualize patient autonomy, contrasting the rights-based, individualist...
Accuracy of medical oncology prognosis for patients with metastatic cancer evaluated for enrollment onto an ongoing randomized clinical trial.
Accuracy of medical oncology prognosis for patients with metastatic cancer evaluated for enrollment onto an ongoing randomized clinical trial.
12063 Background: For patients with metastatic cancer, a key aspect of interdisciplinary care has involved the overall prognosis provided by Medical Oncology, which often dictates...
Patient-centered perspectives: Examining quality-of-life integration in phase III lung cancer trials (2019-2023).
Patient-centered perspectives: Examining quality-of-life integration in phase III lung cancer trials (2019-2023).
e23181 Background: In the dynamic landscape of lung cancer treatment, marked by precision medicine advancements, addressing the persistent global health challenge of lung cancer r...
Current therapeutic strategies for erectile function recovery after radical prostatectomy – literature review and meta-analysis
Current therapeutic strategies for erectile function recovery after radical prostatectomy – literature review and meta-analysis
Radical prostatectomy is the most commonly performed treatment option for localised prostate cancer. In the last decades the surgical technique has been improved and modified in or...
Perceptions of Telemedicine and Rural Healthcare Access in a Developing Country: A Case Study of Bayelsa State, Nigeria
Perceptions of Telemedicine and Rural Healthcare Access in a Developing Country: A Case Study of Bayelsa State, Nigeria
Abstract
Introduction
Telemedicine is the remote delivery of healthcare services using information and communication technologies and has gained global recognition as a solution to...
Rotavirus vaccine clinical trials: a cross-sectional analysis of clinical trials registries
Rotavirus vaccine clinical trials: a cross-sectional analysis of clinical trials registries
Abstract
Background
Rotavirus is a primary infectious virus causing childhood diarrhoea and is associated with significant mortality in children. Th...
Disparities in disaster healthcare: A review of past disasters
Disparities in disaster healthcare: A review of past disasters
Objective: To review the literature on the effects seen after disaster on those with poor social determinants of health (SDOH) and individual social needs.Design: The Disaster Prep...
Risks Management in Travel Business: Peculiarities, Types Criteria of Estimation
Risks Management in Travel Business: Peculiarities, Types Criteria of Estimation
The article considers important issues on the security of travel business as a component of the state’s social and economic system and methods of its impact on risks. The major pur...

