Delivering intensive therapies to older adults with hematologic malignancies: strategies to personalize care

Olin, Rebecca L.

doi:10.1182/blood.2019001300

Cited by 8 publications

(3 citation statements)

References 69 publications

(62 reference statements)

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“…This score includes age, ADL, IADL, and comorbidities according to Charlson scale 10 . These same factors are also particularly predictive of overall survival in elderly patients treated with induction chemotherapy for AML 43 . Hamaker et al show that physical ability impairment is associated with the highest risk of mortality in hematology 13 .…”

Section: Discussionmentioning

confidence: 99%

Impact of a comprehensive geriatric assessment on decision‐making in older patients with hematological malignancies

Garric

Sourdet

Steinmeyer

et al. 2021

European J of Haematology

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Objective Hematological treatment decisions in older adults with hematological malignancies are complex. Our objective is to study the impact of a comprehensive geriatric assessment on hematological treatment decision in older patients and the factors associated with change in treatment plan. Methods We conducted a cross‐sectional analysis of patients aged 65 years and above with hematological malignancies, hospitalized between 2008 and 2019 at the University Cancer Institute of Toulouse. They were assessed by a geriatrician/nurse team using a comprehensive geriatric assessment (CGA). A penalized logistic regression model with elastic net regularization was used to identify factors associated with change in hematological treatment plan. Results A total of 424 patients were included. Main hematological malignancies were lymphoma (36.1 %), acute myeloid leukemia (26.9 %) and myelodysplastic syndrome (19.8%). Change in hematological treatment plan was suggested after CGA for 92 patients (21.7%). Factors associated with change in treatment plan were functional impairment according to ADL and IADL scale, mobility impairment, the presence of comorbidity defined by the Charlson score >1 and increasing age. Conclusion A CGA has a significant impact on hematological treatment decision in older patients. Functional and mobility impairment, comorbidities and age are predictive factors of change in treatment plan.

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Section: Discussionmentioning

confidence: 99%

Impact of a comprehensive geriatric assessment on decision‐making in older patients with hematological malignancies

Garric

Sourdet

Steinmeyer

et al. 2021

European J of Haematology

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“…2 In addition to chronological age, disease status, comorbidities, novel risk factors identified through comprehensive geriatric assessment, including impairments in instrumental activities of daily living (IADL), nutrition status, cognition, and physical function have emerged as predictors of survival in older adults with hematologic cancers. [3][4][5][6][7][8][9][10] Furthermore, frailty, a state of age-associated decreased physiologic reserve, increases the risk of poor health outcomes after HSCT. The incidence of frailty in patients undergoing HSCT has been reported to be 8%-25%.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Recovery Stem-Cell Transplantation: Multidisciplinary Efforts to Improve Outcomes in Older Adults Undergoing Hematopoietic Stem-Cell Transplant

Ngo‐Huang

Ombres

Saliba

et al. 2023

JCO Oncology Practice

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PURPOSE: Older adults have unique risk factors for poor outcomes after hematopoietic stem-cell transplant (HSCT). We sought to determine the impact of our multidisciplinary supportive care program, Enhanced Recovery after stem-cell transplant (ER-SCT), on survival outcomes in patients age 65 years and older who underwent HSCT. PATIENTS AND METHODS: In this retrospective study, clinicodemographic data, nonrelapse mortality (NRM), overall survival (OS), and relapse were compared between 64 patients age 65 years and older who underwent allogeneic stem-cell transplant during ER-SCT program's first year, October 2017 through September 2018, and 140 historical controls age 65 years and older who underwent allogeneic HSCT, January 2015 through September 2017. RESULTS: In the ER-SCT cohort, 41% (26 of 64) of patients were women, and the median (range) age was 68 (65-74) years; in the control cohort, 38% (53 of 140) of patients were women, and the median (range) age was 67 (65-79) years. Hematopoietic cell transplant comorbidity index and donor type/cell source were similar between cohorts. The ER-SCT cohort had a lower 1-year NRM rate (13% v 26%, P = .03) and higher 1-year OS rate (74% v 53%, P = .007). Relapse rate did not differ significantly between cohorts. In multivariate analyses, ER-SCT was associated with improved 1-year NRM (hazard ratio, 0.4; 95% CI, 0.2 to 0.9; P = .02) and improved 1-year OS (hazard ratio, 0.5; 95% CI, 0.3 to 0.9; P = .03). CONCLUSION: A multidisciplinary supportive care program may improve NRM and OS in older patients undergoing allogeneic HSCT. Randomized studies are warranted to confirm this benefit and explore which program components most contribute to the improved outcomes.

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“…The idea that individualised therapies are more cost-effective than generic ones seems credible because the same treatment is obviously not suitable for every patient since not all cases are similar. Several publications have discussed the direction that medicine is taking in this respect [ 21 , 22 , 23 , 24 ] and its popularity has grown in recent years. Although all these authors agree that personalised treatment will increase the effectiveness of existing drugs, to the best of our knowledge, there has been no attempt to put it into practice in the case of cancer treatment, making this goal a priority in cancer research.…”

Section: Introductionmentioning

confidence: 99%

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Banegas-Luna

Peña‐García

Iftene

et al. 2021

IJMS

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Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.

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Delivering intensive therapies to older adults with hematologic malignancies: strategies to personalize care

Abstract: These 2 reviews respectively examine the value and utility of geriatric assessment (GA) tools and discuss the role of GA in the clinical management of patients with hematologic malignancies.

Cited by 8 publications

References 69 publications

Impact of a comprehensive geriatric assessment on decision‐making in older patients with hematological malignancies

Impact of a comprehensive geriatric assessment on decision‐making in older patients with hematological malignancies

Enhanced Recovery Stem-Cell Transplantation: Multidisciplinary Efforts to Improve Outcomes in Older Adults Undergoing Hematopoietic Stem-Cell Transplant

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

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