scholarly journals TACL'ing supportive care needs in pediatric early phase clinical trials for acute leukemia: A report from the therapeutic advances in childhood leukemia & lymphoma (TACL) consortium supportive care committee

2017 ◽  
Vol 34 (6-7) ◽  
pp. 409-417
Author(s):  
E Orgel ◽  
J J Auletta
2020 ◽  
Vol 38 (29_suppl) ◽  
pp. 26-26
Author(s):  
Debra Lundquist ◽  
Dejan Juric ◽  
Rachel Jimenez ◽  
Virginia Capasso ◽  
Casandra McIntyre ◽  
...  

26 Background: Early phase CTs investigate novel therapeutic approaches for patients with cancer, but little is known about the use of supportive care services among participants in early phase CTs. Methods: We conducted a retrospective chart review of consecutive patients enrolled in Phase 1 CTs from 2017-2019, capturing sociodemographics, clinical data, and use of supportive care services from the electronic health record. We calculated the Royal Marsden Hospital (RMH) prognostic score using data at the time of CT trial enrollment based on patients’ lactate dehydrogenase, albumin, and number of sites of metastasis. The RMH score ranges from 0-3, with scores of 2+ indicating a poor prognosis. We explored differences in patient characteristics, supportive care use, and clinical outcomes based on the RMH prognosis score. Results: Among 426 patients treated on Phase 1 CTs during the study period, the median age was 63.0 years (range 20.5-85.2 years), and most were female (56.1%), white race (85.1%), and had metastatic cancer (97.7%). The most common cancer types were gastrointestinal (22.1%), lung (20.0%), and breast (10.6%) cancer. Under half (31.6%) had an RMH score indicating a poor prognosis. Patients with a poor prognosis score had a worse performance status (ECOG ≥1: 80.2% v 58.3%, p < .001) and more prior treatment (3+ prior lines: 49.5% v 35.0%, p = .001) compared to those without a poor prognosis score. Those with a poor prognosis score were more likely to receive palliative care before or during CT participation (40.5% v 27.1%, p = .011). We observed no significant differences in the rates of nutrition (69.1% v 64.0%), social work (62.2% v 63.8%), or physical therapy (64.5% v 61.7%) consults between those with and without a poor prognosis score. We found that those with an RMH score indicating a poor prognosis had a shorter time on trial (median: 49 vs 87 days, p < .001) and worse survival (median: 139 v 379 days, p < .001). Conclusions: Early phase CT participants represent an advanced cancer population with unique supportive care needs, and we identified a group with a particularly poor prognosis for whom earlier intervention with supportive care services may be needed. Our findings highlight the need to prospectively examine these characteristics along with patient-reported outcomes to better understand the distinct supportive care needs of this population and guide the development of targeted interventions.


2017 ◽  
Vol 28 ◽  
pp. v558
Author(s):  
N. Cook ◽  
L. Carter ◽  
S. Aruketty ◽  
C. O'Brien ◽  
F. Thistlethwaite ◽  
...  

2005 ◽  
Vol 2 (6) ◽  
pp. 467-478 ◽  
Author(s):  
Peter F Thall ◽  
Leiko H Wooten ◽  
Nizar M Tannir

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shinjo Yada

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.


Vaccine ◽  
2019 ◽  
Vol 37 (47) ◽  
pp. 6951-6961 ◽  
Author(s):  
Sofiya Fedosyuk ◽  
Thomas Merritt ◽  
Marco Polo Peralta-Alvarez ◽  
Susan J Morris ◽  
Ada Lam ◽  
...  

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