Li CJ, Gong SM, Shi YJ, Guo YN, Song NN, Jiang LM, Wang YY, Zhang CJ, Wang YB, Li ZP, Wang P, Ruan YH, Shi Z, Li HY, Zhang QJ, Fu WP. Application of comprehensive geriatric assessment in oncology nursing: A literature review on optimizing treatment decisions and patient outcomes. World J Clin Oncol 2025; 16(4): 104785 [DOI: 10.5306/wjco.v16.i4.104785]
Corresponding Author of This Article
Wei-Ping Fu, Assistant Professor, Chief Physician, Second Department of Orthopedics, The Fifth Affiliated Hospital of Zhengzhou University, No. 3 Kangfuqian Street, Erqi District, Zhengzhou 450052, Henan Province, China. fwp_2024@139.com
Research Domain of This Article
Nursing
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Cheng-Jin Li, Yu-Juan Shi, Ya-Nan Guo, Na-Na Song, Li-Min Jiang, Yan-Yan Wang, Chang-Jiang Zhang, Yao-Bin Wang, Zhi-Peng Li, Peng Wang, Yu-Hua Ruan, Zhen Shi, Hao-Yu Li, Wei-Ping Fu, Second Department of Orthopedics, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
Shu-Mei Gong, Director of Medical Association Construction and Management Office, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
Yan-Yan Wang, Henan Key Laboratory for Helicobacter pylori and Digestive Tract Microecology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
Co-corresponding authors: Qiu-Jun Zhang and Wei-Ping Fu.
Author contributions: Li CJ and Gong SM contributed equally to this manuscript as co-first authors. Li CJ was responsible for conceptualization, methodology, and writing of the original draft; Gong SM handled software; Shi YJ and Guo YN conducted formal analysis; Song NN and Jiang LM contributed to methodology; Wang YY and Wang YB supervised the work; Li ZP and Wang P performed validation; Ruan YH, Shi Z, and Li HY managed data curation; Zhang CJ contributed to project administration; Zhang QJ and Fu WP oversaw visualization, formal analysis, and manuscript review and editing. All authors participated in drafting the manuscript and have read, contributed to, and approved the final version. Zhang QJ and Fu WP are co-corresponding authors who made significant contributions to distinct and critical aspects of the study, including collaborative writing, final review, and obtaining funding. Fu WP led formal analysis, data visualization, and methodological refinement, while Zhang QJ provided supervision, project direction, and critical revisions, ensuring the manuscript’s accuracy and clarity. Both were instrumental in securing key grants (e.g., Henan Province Key Research and Development Program and Henan Province Medical Science and Technology Key Project), and their leadership and collaboration were indispensable for shaping the study and bringing it to completion.
Supported by Henan Province Key Research and Development Program, No. 231111311000; Henan Provincial Science and Technology Research Project, No. 232102310411; Henan Province Medical Science and Technology Key Project, No. LHGJ20220566 and No. LHGJ20240365; Henan Province Medical Education Research Project, No. WJLX2023079; and Zhengzhou Medical and Health Technology Innovation Guidance Program, No. 2024YLZDJH022.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Wei-Ping Fu, Assistant Professor, Chief Physician, Second Department of Orthopedics, The Fifth Affiliated Hospital of Zhengzhou University, No. 3 Kangfuqian Street, Erqi District, Zhengzhou 450052, Henan Province, China. fwp_2024@139.com
Received: January 1, 2025 Revised: January 23, 2025 Accepted: March 10, 2025 Published online: April 24, 2025 Processing time: 84 Days and 17.5 Hours
Abstract
With the global population aging, the care of elderly cancer patients has become increasingly complex and significant. Comprehensive geriatric assessment (CGA), a multidimensional evaluation tool, has been widely implemented in oncology nursing to enhance the precision of treatment decisions and improve patient outcomes. This review examines the application of CGA in oncology nursing, drawing on literature published between 2010 and 2024 in major databases using keywords such as “Comprehensive Geriatric Assessment” and “Oncology Nursing”. It highlights how CGA contributes to optimizing treatment selection, monitoring the treatment process, and improving patients’ quality of life and long-term outcomes. CGA provides a comprehensive evaluation of elderly cancer patients, including physical, psychological, and social aspects, enabling the identification of high-risk patients and reducing treatment-related side effects and complications. It also offers a critical foundation for developing personalized care plans. The article discusses various practical examples of CGA implementation across different countries and regions, including multidisciplinary collaborative models in France, the United States, and Australia, demonstrating CGA’s flexible application in diverse healthcare settings. Although significant progress has been made in applying CGA in oncology nursing, numerous challenges remain in its implementation, such as resource limitations and insufficient personnel training. Future research will focus on integrating CGA with emerging technologies, such as artificial intelligence and precision medicine, to further improve the quality of care and treatment outcomes for elderly cancer patients. By summarizing the current status and challenges of CGA in oncology nursing, this review provides guidance for future research and clinical practice, emphasizing the importance of advancing CGA application to meet the growing demands of elderly oncology care.
Core Tip: Comprehensive geriatric assessment (CGA) is crucial in optimizing oncology nursing for elderly cancer patients. CGA enables a thorough evaluation of physical, psychological, social, and functional status, allowing for more personalized treatment plans and improving patient outcomes. The integration of CGA into oncology care helps in identifying high-risk patients, reducing treatment-related side effects, and enhancing quality of life. Despite its proven benefits, challenges in implementation remain, such as resource limitations and the need for standardized procedures. Future advancements may include the integration of artificial intelligence and precision medicine to further refine CGA’s effectiveness in geriatric oncology care.
Citation: Li CJ, Gong SM, Shi YJ, Guo YN, Song NN, Jiang LM, Wang YY, Zhang CJ, Wang YB, Li ZP, Wang P, Ruan YH, Shi Z, Li HY, Zhang QJ, Fu WP. Application of comprehensive geriatric assessment in oncology nursing: A literature review on optimizing treatment decisions and patient outcomes. World J Clin Oncol 2025; 16(4): 104785
Global population aging has become one of the primary public health challenges faced by countries worldwide. Statistics show that the proportion of individuals aged 65 and above is increasing annually, with projections indicating that by 2030, there will be over 2 billion elderly people globally[1]. Concurrently, the incidence of cancer in the elderly population is also rising significantly, with patients aged 65 and older accounting for more than 60% of all cancer cases[1,2]. The increasing proportion of elderly cancer patients has led to a growing demand for oncology care, imposing a substantial burden on healthcare systems. Elderly cancer patients not only face the challenges associated with cancer treatment but often have multiple comorbid chronic diseases, further complicating the care process[2].
The complexity of caring for elderly cancer patients is primarily reflected in the decline of physiological functions and the management of multiple comorbidities. These patients often have concurrent chronic conditions, such as cardiovascular diseases, diabetes, and cognitive impairment[1,3]. Additionally, the physical condition of elderly patients differs significantly from that of younger patients, making them more susceptible to severe side effects and complications when undergoing treatments such as chemotherapy and radiotherapy. These complications include postoperative cognitive dysfunction, increased risk of infections, and malnutrition[2,4]. These factors underscore the importance of individualized assessment and care for elderly cancer patients to optimize treatment outcomes and improve quality of life.
Comprehensive geriatric assessment (CGA) is a multidimensional tool designed to evaluate the physical, psychological, social, and functional status of elderly patients[5]. In oncology care, CGA is widely used to help healthcare teams develop personalized treatment plans. Instead of assessing “chronological age”, CGA evaluates the patient’s “functional age”, providing insights into physical functioning, cognitive status, emotional health, nutritional status, and social support. This enables the identification of high-risk patients and the optimization of treatment decisions[6,7]. Multiple studies have confirmed that CGA can reduce treatment-related side effects, extend survival, and improve the quality of life, establishing it as the “gold standard” in elderly oncology care[2].
This review aims to systematically explore the application of CGA in elderly oncology care, particularly its role in optimizing treatment decisions, monitoring the treatment process, and enhancing patient quality of life and prognosis. We will review the practical experiences of CGA in different countries and regions, analyze the challenges in clinical implementation, and explore how emerging technologies such as artificial intelligence (AI) can be integrated to further improve care quality. Ultimately, this review will provide reference points for future clinical practice and research[1,2].
LITERATURE RETRIEVAL STRATEGY
In this study, we conducted a comprehensive literature search using major databases such as PubMed, Web of Science, and Embase for articles published up to 2024. We employed relevant keywords and MeSH terms including “Comprehensive Geriatric Assessment”, “Oncology Nursing”, “Elderly Cancer Patients”, and “Multidimensional Assessment” in various combinations. Studies were included if they focused on the application of CGA in oncology nursing, discussed its impact on treatment decisions and patient outcomes, or explored the integration of emerging technologies. Non-English articles, case reports, and those lacking substantial CGA data were excluded. Two reviewers independently screened the titles and abstracts of the retrieved articles, and full-text versions of potentially relevant studies were then assessed for inclusion. Any discrepancies were resolved by consensus. By targeting publications from 2010 to 2024, this review encompasses the most current and pertinent research in the field.
BASIC CONCEPTS OF CGA
The multi-dimensional structure of CGA: Assessment of physical, psychological, social, and functional status
The CGA is a multi-dimensional assessment tool designed to thoroughly analyze the health status of elderly patients[8] (Table 1). Its structure encompasses various domains including physical, psychological, social, and functional statuses - to provide a basis for formulating personalized treatment plans through comprehensive evaluation[2,9]. In assessing physical status, CGA evaluates factors such as physical strength, nutritional status, chronic disease management, and medication usage[10,11]. Understanding a patient’s physical functional status is particularly crucial in oncology care, as it aids in predicting treatment tolerance and potential side effects[1,12]. The psychological assessment focuses on evaluating cognitive function, emotional state, and potential psychological issues like depression and anxiety. Cognitive decline and emotional disorders are relatively common among elderly cancer patients; neglecting these issues may adversely affect treatment decisions and patient compliance[13,14]. Assessing social support involves evaluating the strength of a patient’s social network, including family support and community resources. This is a significant factor influencing quality of life and the recovery process. CGA helps determine if additional social services are needed during treatment by assessing social support[2]. Functional status is assessed by evaluating a patient’s abilities in activities of daily living and instrumental activities of daily living[15]. This helps determine whether patients can live independently and cope with the physical and psychological challenges of treatment[16,17].
Cognitive function, emotional state (depression, anxiety), psychological problem screening
Social support
Family support, community resources, economic status, ability for self-care
Functional status
ADL, IADL, physical function testing
Frailty index and CGA: Key evaluation tools in geriatric oncology care
CGA is a multidimensional evaluation tool[8]. Based on the diverse health metrics provided by CGA, the frailty index (FI) employs a cumulative deficit model to quantify the degree of frailty in patients[18]. A higher FI score indicates more deficits in functional, nutritional, cognitive, and other areas. Current research demonstrates that FI is a significant predictor of treatment tolerance, hospitalization rates, complication occurrence, and mortality in older patients, especially those with cancer. Compared to traditional assessment methods, FI exhibits superior sensitivity and specificity[19,20].
In clinical practice for older cancer patients, the close integration of FI and CGA provides a comprehensive and quantifiable reference for individualized treatment decisions. Through CGA, clinicians can identify potential issues in areas such as nutritional status, cognitive function, and social support. FI is then used to score these deficits cumulatively, enabling rapid identification of high-risk patients. This approach allows clinicians to adjust treatment intensity or intervention plans accordingly[21]. For instance, in patients with high FI scores and lower treatment tolerance, healthcare providers can minimize unnecessary high-intensity interventions during chemotherapy or surgery and enhance perioperative management and rehabilitation to reduce complication rates[2,22]. Moreover, studies have reported that patients receiving targeted interventions based on FI assessments experience significant improvements in quality of life during treatment, as early identification of issues such as malnutrition, emotional disturbances, and functional decline can be addressed promptly[23-25].
In alignment with this, the International Society of Geriatric Oncology and various clinical studies recommend incorporating frailty assessments into routine care for older cancer patients[22]. This practice provides a more scientific basis for developing and understanding treatment plans, making clinical decisions more precise. To improve clinical efficiency, researchers have also developed simplified versions of the FI, such as the FI-CGA-10, which maintains assessment validity while reducing complexity, making it easier to implement in multidisciplinary teams (MDTs)[20,22].
Looking forward, as AI and big data analytics continue to evolve, real-time monitoring and dynamic assessment techniques are expected to further optimize the FI, allowing for more precise capture of overall health changes in older patients. These advancements will enhance its value in managing cancer and other chronic diseases. As interdependent tools, the FI and CGA have demonstrated significant potential in geriatric medicine and oncology care. Their roles in risk assessment, treatment planning, quality of life improvement, and individualized health management are irreplaceable. Through continuous refinement and integration with emerging technologies, this comprehensive assessment system is poised to provide more precise and holistic care for older patients in the future.
The unique role of CGA in oncology care
Compared to traditional unidimensional assessments, CGA offers more targeted and precise insights in oncology care. By evaluating a patient’s “functional age” rather than their chronological age, it enables clinicians to formulate more individualized treatment plans[26] (Figure 1). Its uniqueness lies in assessing not only tumor-related conditions but also considering comorbidities, cognitive function, and functional status - factors that are especially important for elderly patients[27]. CGA helps identify high-risk patients, anticipates potential treatment complications, and allows for ongoing monitoring during therapy, thereby reducing unnecessary treatment-related risks[28]. Studies have shown that utilizing CGA significantly improves the prognosis of elderly cancer patients. It not only reduces treatment-related side effects but also helps extend survival periods[1,29,30]. Additionally, conducting CGA at the onset of treatment provides better insight into a patient’s quality of life goals, allowing for a balanced approach between cancer control and quality of life in treatment planning[2,30].
Figure 1 The four elements of functional age assessment in comprehensive geriatric assessment.
This Figure employs a circular structure to visually represent the four core aspects of functional age assessment in comprehensive geriatric assessment: Cognitive ability, assessed using tools like Mini-Mental State Examination and Montreal Cognitive Assessment to identify early dementia or cognitive impairments; physical function, evaluated through the level of independence in activities of daily living using the Barthel Index or modified Barthel Index; mental health, assessed using the Geriatric Depression Scale to evaluate emotional states such as depression and anxiety; and social support, which focuses on social interaction skills, family, and social support systems to understand social adaptation and overall quality of life. CGA: Comprehensive geriatric assessment; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; GDS: Geriatric Depression Scale; ADL: Activities of daily living; BI: Barthel Index; MBI: Modified Barthel Index.
Comparison and advantages of CGA over traditional assessment tools
There are multiple limitations to traditional assessment tools in evaluating older patients. Many such tools such as the Karnofsky performance status and the Eastern Cooperative Oncology Group scoring systems - primarily focus on physical functional status and fail to adequately incorporate cognitive, psychological, and social factors, leading to an incomplete understanding of older patients’ overall health[23,31]. In addition, these tools often rely on chronological age or simplified functional scores, which may not provide sufficient information for personalized treatment planning and can result in some older patients missing out on appropriate therapy or support[2,23]. Moreover, although traditional tools can assess functional status, they are generally less effective than the CGA in predicting treatment-related complications and overall survival[32,33]. For example, both Karnofsky performance status and Eastern Cooperative Oncology Group scores often overlook comorbidities and mental health - critical factors for predicting prognosis in older cancer patients (Table 2).
Table 2 Overview of common geriatric assessment tools: Key features, advantages, and limitations.
Tool name
Key features
Advantages
Limitations
KPS
Assesses the patient’s ability to perform daily activities
Simple, widely used
Focuses only on physical function; lacks a multidimensional perspective
ECOG
Evaluates the physical status of cancer patients
Convenient for clinical use
Does not account for comorbidities or mental health
MMSE
A screening tool for cognitive function
Capable of identifying cognitive impairment
Focuses solely on cognition; does not address other health domains
GDS
A screening tool for depression
Specifically addresses mental health
Does not consider physical or social factors
CGA offers multiple advantages over traditional assessment tools. Traditional oncology assessments mainly focus on basic information like tumor severity and patient age, lacking comprehensive consideration of physiological, psychological, and social factors unique to elderly patients[34,35]. In contrast, CGA provides a more accurate prediction of patient responses to treatment and potential risks by analyzing multidimensional data[8,36]. In clinical practice, CGA offers the following advantages. Firstly, CGA can precisely identify elderly patients who, despite advanced age, have good physical function and can tolerate more aggressive treatment regimens; simultaneously, it can screen younger patients with poor functional status, indicating the need for more conservative treatment strategies[2,37]. Secondly, the comprehensive evaluation results provided by CGA enable the formulation of individualized treatment plans for elderly cancer patients, ensuring that clinical decisions consider both tumor characteristics and the patient’s functional needs and quality of life[38,39]. Finally, by assessing patients’ overall health status, CGA aids clinicians in preventing potential complications during treatment, reducing unnecessary hospitalizations and medical interventions, and optimizing the utilization of medical resources[40] (Table 3).
Table 3 Comparison table of comprehensive geriatric assessment and traditional assessment.
Comparison dimensions
CGA
Traditional assessment
Assessment scope
Multidimensional assessment, including physical, psychological, social, and functional status
Primarily focuses on tumor size, staging, and patient age
Precision
Accurately assess patients’ functional status and identify high-risk patients
Pays less attention to individual patient differences
Personalized treatment
Develop personalized treatment plans based on assessment results
Typically employs standardized treatment protocols
Prognosis prediction
Predict patients’ treatment tolerance and long-term prognosis
Mainly relies on tumour markers, offering a limited prognosis prediction
Overall, CGA not only demonstrates significant practical value in clinical care but also provides a scientific basis for improving the quality of life and treatment outcomes for elderly cancer patients. Consequently, CGA has become an indispensable tool in geriatric oncology care worldwide and has been widely adopted in clinical practice across many countries[41].
APPLICATION OF CGA IN TREATMENT DECISION-MAKING
How CGA improves the selection of treatment plans
The CGA provides crucial support for treatment decisions in oncology care for elderly patients. By evaluating patients’ multidimensional health status including physical, cognitive, psychological, and functional aspects - CGA helps clinicians more accurately determine patients’ treatment tolerability and risks, thereby optimizing treatment plans[32]. CGA excels in identifying elderly patients who are unsuitable for intensive treatments or who require individualized treatment adjustments. Studies have shown that CGA can effectively predict risks associated with surgery, chemotherapy, and radiotherapy, allowing for early adjustments in treatment intensity or the selection of alternative options[32].
Formulation of personalized treatment plans: Application of CGA in decision-making processes for surgery, chemotherapy, and radiotherapy
By comprehensively analyzing patients’ functional and health status, CGA can tailor personalized treatment plans for elderly cancer patients. In surgical treatment, CGA can identify patients with good physical reserves who may be more suitable for invasive surgical procedures[42]. For chemotherapy, CGA helps identify high-risk patients and adjusts chemotherapy dosages or treatment frequencies for them, reducing toxicity and side effects[43,44]. In radiotherapy decision-making, CGA similarly assists in adjusting the intensity and duration of radiotherapy based on patients’ physical and psychological conditions, ensuring that patients can tolerate treatment and reducing unnecessary complications[45].
The role of CGA in predicting treatment risks and prognosis
The multidimensional evaluation of CGA gives it significant advantages in predicting treatment-related risks and patient prognosis. CGA can quantify patients’ physical fitness, cognitive function, and nutritional status to identify which patients have a higher risk of complications when undergoing surgery or chemotherapy[32]. By identifying these high-risk individuals in advance, clinicians can choose to reduce dosages, extend treatment courses, or opt for other gentler treatment options, thereby enhancing treatment safety and efficacy[42]. Additionally, CGA can predict patients’ long-term prognosis, helping determine whether an individual is suitable for aggressive treatment or should focus on palliative care[32].
CGA’s contribution to reducing treatment-related side effects and complications
By integrating multiple aspects such as physical health, cognitive ability, and nutritional status, CGA can significantly reduce side effects and complications experienced by elderly cancer patients during treatment[38,46]. CGA not only helps reduce toxic reactions in chemotherapy and radiotherapy but also lowers the incidence of postoperative complications through personalized treatment adjustments[47,48]. Studies have shown that early intervention and treatment plan adjustments using CGA can effectively improve patients’ quality of life, reducing unnecessary hospitalizations and medical interventions during treatment[32,42]. In summary, CGA provides a scientific basis for treatment decisions in elderly cancer patients, not only enhancing the precision of treatment but also effectively reducing treatment-related risks and side effects. This tool has been widely applied globally and is gradually becoming a standard assessment method in geriatric oncology care (Figure 2).
Figure 2 Application flow chart of comprehensive geriatric assessment in tumor nursing.
This flowchart illustrates the step-by-step application of comprehensive geriatric assessment in oncology nursing. It begins with a multidimensional evaluation of the patient, covering physical, mental, social, and functional domains. The results are integrated for risk stratification, categorizing patients as high-risk or low-risk. Based on this, personalized treatment plans are formulated, including adjustments to surgery, chemotherapy, or radiotherapy. Continuous health monitoring ensures dynamic treatment modifications, and the process concludes with an evaluation of therapeutic outcomes and long-term prognosis, optimizing care for elderly cancer patients. CGA: Comprehensive geriatric assessment.
THE IMPACT OF CGA ON PATIENT QUALITY OF LIFE AND LONG-TERM PROGNOSIS
The importance of CGA in enhancing the quality of life of elderly cancer patients
The CGA as a multidimensional tool, has been proven to significantly improve the quality of life in elderly cancer patients. These patients often face complex health challenges, including multiple comorbidities, functional decline, and cognitive impairments, all of which may worsen during treatment. CGA helps identify these potential health risks through comprehensive evaluations of physiological, psychological, functional, and social support aspects, providing a basis for personalized care interventions[1,49]. This process not only aids in alleviating treatment-related toxic reactions from chemotherapy and radiotherapy but also optimizes patients’ self-management abilities, reducing the decline in quality of life[49]. Multiple studies have shown that after CGA interventions, patients experience significant improvements in independence and satisfaction in daily activities, and enhancements in quality of life also help alleviate cancer-related psychological stress[31,50,51].
Monitoring the treatment process through CGA and its impact on long-term prognosis
CGA provides a scientific framework for continuous monitoring of patients’ health status during treatment. Through regular assessments and adjustments of treatment strategies, CGA can ensure that patients maintain optimal health throughout the treatment process. For elderly cancer patients, timely identification of changes such as functional decline, malnutrition, or psychological issues is critical for determining treatment success[49]. Moreover, the monitoring role of CGA helps reduce hospitalization rates and the incidence of complications, especially among high-risk patients. This dynamic monitoring not only improves patients’ short-term quality of life but also enhances long-term disease-free survival and overall survival rates[1].
The impact of multidimensional assessments of physical health, functional status, and psychological well-being on patient outcomes
A core advantage of CGA lies in its multidimensional assessment of physical health, functional status, and psychological well-being. These assessment results can accurately predict patients’ treatment tolerability and long-term prognosis[36,52]. For example, changes in physical functions such as decreased muscle strength or gait instability - are often closely associated with poor prognosis in elderly cancer patients. Mental health issues like depression or anxiety may affect patients’ treatment adherence and overall quality of life[53,54]. By identifying these problems early and providing interventions, CGA not only improves treatment outcomes but also significantly reduces mortality rates due to treatment-related complications[55,56]. In summary, through multidimensional assessments and personalized treatment interventions, CGA significantly enhances the quality of life for elderly cancer patients and improves their long-term prognosis. It not only optimizes treatment decisions but also provides essential support for the long-term health management of elderly patients.
GLOBAL APPLICATION OF CGA IN ONCOLOGY NURSING: CASE STUDIES
France: Multidisciplinary collaboration model and the practice of CGA in geriatric oncology care
In France, the CGA is widely applied in geriatric oncology nursing and is integrated through a MDT collaboration model[57]. This approach ensures the involvement of experts from various fields including geriatric medicine, oncology, nutrition, and psychology - to jointly develop treatment plans and optimize patient care pathways[32]. The extensive use of CGA in France has shown significant effectiveness, particularly in reducing treatment-related toxic reactions and complications[18]. For instance, a study found that 16.7% of patients assessed with CGA had their treatment plans adjusted, avoiding unnecessary invasive procedures, especially for those with limited physical function or cognitive impairments[32,56], these advancements have substantially enhanced patient satisfaction with clinical nursing care and improved overall nursing safety. Additionally, France’s MDT model emphasizes personalized treatment, ensuring a balance between tumor control and the patient’s quality of life during therapy.
United States: Clinical practice experience and challenges of CGA
In the United States, CGA has been incorporated into standardized procedures for geriatric oncology care, especially in assessing chemotherapy toxicity risks and surgical suitability. However, the United States still faces challenges in the widespread implementation of CGA, particularly regarding resource allocation and the scale of implementation[58]. The execution of CGA requires multidisciplinary collaboration and long-term follow-up, which poses challenges for some resource-limited medical institutions. Moreover, the underrepresentation of elderly patients in oncology clinical trials limits the broad application of CGA in cancer care[56]. Despite these challenges, multiple studies have demonstrated that the application of CGA in the United States effectively reduces chemotherapy-related toxic reactions and improves patient prognosis through tailored treatment plans[58]. Particularly among high-risk patients, CGA helps physicians find the optimal balance between treatment intensity and the patient’s functional status[59].
Australia: Integration and innovation of CGA in oncology care
Australia has shown significant advantages in the integration and innovative application of CGA. By establishing a nationwide geriatric oncology care network, Australia has fully incorporated CGA into the formulation process of oncology treatment plans[60]. MDTs in Australia use CGA data to develop personalized care pathways for elderly cancer patients, encompassing surgery, chemotherapy, and palliative care[56]. Furthermore, Australia’s innovative practices include combining CGA with digital technology, optimizing its implementation through telemedicine and electronic health record systems. This integration ensures effective application even in community settings and remote areas[58]. These initiatives have not only improved patients’ quality of life but also reduced hospitalization rates and treatment-related complications.
Experiences from other countries and regions
Other countries and regions are also actively promoting the application of CGA in oncology nursing[61]. For example, the United Kingdom and Germany have established specialized geriatric oncology care teams to ensure the comprehensive application of CGA in cancer treatment[56,58,62]. In these nations, CGA is used not only for inpatient assessments but is also widely promoted in outpatient care and community nursing. Meanwhile, some low-resource countries face limitations in infrastructure and human resources when implementing CGA[63]. Nonetheless, by utilizing simplified versions of CGA, these countries can still significantly improve the prognosis and quality of life for elderly cancer patients[56].
In summary, the global application of CGA in oncology nursing exhibits diverse developmental paths. Whether in resource-rich developed countries or resource-limited regions, CGA effectively improves treatment outcomes and quality of life for elderly cancer patients through its comprehensive assessment capabilities. Experiences from various countries indicate that, despite certain challenges during implementation, CGA can provide significant support for geriatric oncology care through multidisciplinary collaboration and innovative technologies (Table 4).
Table 4 Comparative studies on comprehensive geriatric assessment vs standard care in oncology nursing.
Study
Country/region
Key findings
Cancer types studied
Outcomes measured
French OGS tool
France
Developed and validated a short decision-making algorithm-OGS with high sensitivity to identify frail patients requiring CGA, optimizing treatment decisions
Various cancers
Identification of frail patients, treatment decision-making[18]
CGA in older cancer patients
United States
Meta-analysis of 28 trials (4959 subjects) showed CGA effectively identifies patients benefiting from geriatric evaluation, reducing morbidity and improving physical and cognitive function
Various cancers
Morbidity reduction, physical and cognitive function improvement[23,59]
CGA in cancer care
Australia
Integrating CGA into oncology care improved treatment completion rates, reduced grade 3+ chemotherapy toxicity, and enhanced quality of life scores
Various cancers
Treatment completion rates, chemotherapy toxicity, quality of life scores[27]
Geriatrician-delivered CGA impact
United Kingdom
Demonstrated that geriatrician-led CGA is associated with better outcomes for older patients undergoing chemotherapy compared to standard care
CHALLENGES AND LIMITATIONS IN THE IMPLEMENTATION OF CGA
The impact of resource constraints on CGA implementation
The comprehensive implementation of the CGA is often affected by resource limitations, particularly in regions and medical institutions with scarce resources. CGA involves multidimensional assessments including physical, psychological, social, and functional statuses - and requires the collaboration of MDTs. This necessitates additional investments in manpower, time, and equipment[56]. In environments where resources are limited, medical institutions often lack sufficient professionals and equipment to conduct thorough CGA evaluations, which restricts its widespread adoption, especially in community or remote areas[64]. Moreover, insufficient financial resources impact the sustained application of CGA and the quality of subsequent care, limiting its effective implementation among high-risk elderly patients[65].
Insufficient personnel training and obstacles in multidisciplinary collaboration
The effective implementation of CGA relies on the collaboration of MDTs, including geriatricians, oncologists, nutritionists, and psychologists[66]. However, many medical institutions lack personnel who are specially trained to execute these complex assessment processes[64]. Additionally, due to differing focal points among disciplines, MDTs often face challenges in communication and coordination during collaboration. Especially when handling complex cases, inconsistencies in assessment and treatment opinions may arise[67]. Effective collaboration and communication among team members are crucial for ensuring the smooth implementation of CGA, but this is often difficult to achieve in practice, particularly when team resources are insufficient[64].
Difficulties in standardizing CGA assessment procedures in clinical practice
Despite the widely recognized utility of CGA in geriatric oncology care, standardizing its assessment procedures in clinical practice still faces numerous challenges. First, significant differences in healthcare systems and resource allocations across countries and regions lead to a lack of consistency in CGA assessment procedures among different institutions[56,68]. Second, different CGA tools and assessment standards have not been fully unified globally, making cross-institutional and cross-border comparisons and promotion of results challenging[64]. Additionally, since CGA involves comprehensive evaluations across multiple health dimensions, standardizing the interpretation of assessment results and subsequent decision-making also presents difficulties[67]. Establishing unified assessment standards and procedures is crucial for further promoting the application of CGA in global oncology care. In summary, although CGA has significant advantages in improving the quality of care for elderly cancer patients, its comprehensive implementation still faces multiple challenges, including resource constraints, personnel training, and the standardization of procedures. Addressing these issues will contribute to the widespread promotion of CGA and further optimize the prognosis and quality of life for elderly cancer patients.
FUTURE DEVELOPMENT DIRECTIONS: INTEGRATION OF CGA WITH EMERGING TECHNOLOGIES
Potential applications of AI and precision medicine in CGA
With the rapid development of AI and precision medicine, the application prospects of the CGA have greatly expanded. AI technology, by analyzing large amounts of patient data, can provide personalized diagnostic and treatment recommendations, thereby enhancing the quality of geriatric oncology care. Leveraging AI-driven precision medicine, patients’ genomic, pathological, and lifestyle data can be integrated to help identify their response patterns to specific treatments[69] and optimize treatment plans. For example, machine learning algorithms have been successfully applied to predict cancer treatment outcomes, especially in identifying patients with poor prognosis and timely adjusting treatment strategies[70-72]. This not only improves the assessment accuracy of CGA but also accelerates the data analysis process, making clinical decisions faster and more precise.
The combination of AI and big data technology further promotes the application of CGA in oncology care. AI helps clinicians better understand the efficacy and toxicity of different treatment plans by analyzing large-scale real-world data[73,74]. AI has been used to assess the effectiveness of immunotherapy in elderly patients, optimizing personalized treatment decisions by distinguishing between robust and frail patients, reducing toxic reactions, and improving treatment outcomes. Moreover, the integration of AI with telemedicine allows elderly patients to be monitored in real-time through wearable devices and electronic health record systems, enhancing the efficiency of CGA assessments and ensuring that patients in remote areas also receive high-quality care[74,75]. By analyzing multidimensional health data through machine learning, AI can predict side effects of chemotherapy and immunotherapy, identify high-risk patients in advance, and optimize interventions during treatment, thereby enhancing the effectiveness of geriatric oncology care[73,74].
How to use big data and machine learning to enhance the efficiency and precision of CGA assessments
The application of big data and machine learning technologies in CGA can significantly improve its assessment efficiency and accuracy. By collecting and analyzing multidimensional patient data, machine learning models can identify complex relationships between different health variables, providing more targeted assessments for elderly cancer patients[76,77]. For example, algorithms based on big data can analyze multiple factors such as patients’ physical strength, nutritional status, and cognitive function - to predict in advance which patients are more likely to develop treatment-related complications[78]. Additionally, AI models can continuously learn and optimize, enhancing the application of CGA in oncology care and providing more effective tools for personalized care.
Integration of CGA with telemedicine and digital health technologies
Telemedicine and digital health technologies offer new avenues for the widespread application of CGA. Through wearable devices, remote monitoring, and electronic health record systems, clinicians can track patients’ health status in real-time and conduct CGA assessments remotely[78]. This is particularly important for elderly patients living in remote areas or with mobility difficulties. Telemedicine can also shorten communication time between patients and MDTs, improving the efficiency of care[79]. The further development of digital health technologies will help CGA to be more widely applied globally, thereby enhancing the quality of life and care outcomes for elderly cancer patients.
Exploring innovative models combining CGA with oncology care
With technological advancements, innovative models combining CGA with oncology care are gradually emerging. AI-driven CGA assessments are not limited to evaluating the current status of patients but can also identify potential health risks by analyzing vast amounts of data[78]. In the future, predictive models based on big data will enable more personalized treatment plans, reducing adverse reactions in elderly patients during cancer treatment. Furthermore, the standardization and digitization of CGA will accelerate, especially in the context of global aging, where the innovative application of CGA is expected to play an even more significant role in geriatric oncology care[8,44]. By integrating emerging technologies such as AI, big data, and telemedicine, the application prospects of CGA are very promising. This integration not only enhances the precision and efficiency of assessments but also provides more comprehensive care plans for elderly cancer patients (Table 5).
Table 5 Prospects of combining comprehensive geriatric assessment with emerging technologies table.
Emerging technologies
Potential applications
Advantages
Future outlook
AI
Analyze patient data to provide personalized diagnostic and treatment recommendations
Enhance assessment precision
Real-time decision support systems
Big data
Integrate patient data to identify relationships between health variables
Improve assessment efficiency
Creation of patient stratification models
Telemedicine
Real-time monitoring of patient health status
Expand the scope of assessment
Provision of continuous health monitoring
LIMITATIONS
This review comprehensively explores the application of CGA in oncology nursing but has several limitations. It is based on existing literature and lacks novel primary data, making the conclusions reliant on the quality and scope of cited studies. The variability in CGA protocols across regions and institutions limits the generalizability of findings, while the underrepresentation of low-resource settings restricts its applicability in areas with scarce healthcare resources. Although the potential integration of emerging technologies like AI, big data, and telemedicine is discussed, practical challenges such as data privacy, technical barriers, and healthcare professionals’ readiness are not deeply explored. The review focuses broadly on oncology nursing without delving into specific cancer types or patient subgroups, and it lacks quantitative meta-analysis of CGA’s effectiveness in improving treatment outcomes. Furthermore, the potential influence of publication bias in the included studies may affect the robustness of the conclusions. Addressing these limitations in future research will enhance the understanding and practical application of CGA in diverse clinical settings.
CONCLUSION
The critical role of CGA in geriatric oncology care
The CGA has been established as an indispensable tool in geriatric oncology care, effectively assessing patients’ multidimensional health status across physiological, psychological, and functional domains. Through CGA’s comprehensive analysis, clinicians can develop more personalized treatment plans, enhancing patients’ treatment tolerability and reducing adverse reactions[1,80]. Numerous studies have shown that CGA has significant advantages in predicting treatment risks for elderly patients, improving quality of life, and enhancing long-term prognosis[81,82].
Future research directions: Promoting the integration of CGA with emerging technologies to further improve care quality
Future development should focus on integrating CGA with emerging technologies such as AI, big data, and precision medicine. The application of AI and machine learning technologies can provide clinicians with real-time decision support by analyzing large volumes of patient data, helping to identify high-risk patients and optimize treatment plans[69,83]. Additionally, the use of big data contributes to improving the efficiency and precision of CGA, enhancing its applicability in different healthcare settings[78]. Future research should also aim to develop more automated and standardized CGA tools to further shorten assessment time and enhance care outcomes.
Continuing to promote CGA application to meet the growing demand for geriatric oncology care
With the acceleration of global aging, the number of elderly cancer patients is continuously increasing. To meet this demand, it is particularly urgent to continue promoting the application of CGA worldwide. Clinical practices in various countries have demonstrated that CGA can significantly improve the prognosis of elderly patients and reduce treatment-related complications[1,80]. In resource-limited areas, the combination of digital technology and telemedicine can further promote the use of CGA, benefiting more patients[78].
Recommendations for clinical practice and the necessity of policy support
To ensure the widespread application of CGA, policy support and standardization in clinical practice are crucial. Firstly, countries should develop unified CGA implementation guidelines to ensure its standardized application across different medical institutions[84]. Secondly, healthcare systems need to increase investment in MDTs to ensure proper training and technical support for professionals. Furthermore, governments and medical institutions should promote further research and technological development of CGA, ensuring its seamless integration with AI and big data technologies, thereby providing higher-quality care for elderly cancer patients[1]. In conclusion, CGA plays a critical role in geriatric oncology care. By integrating with new technologies in the future, CGA is expected to further enhance care quality and meet the growing global demand for elderly patient care.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Oncology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B, Grade C, Grade C
Novelty: Grade B, Grade B, Grade C, Grade C
Creativity or Innovation: Grade B, Grade B, Grade C, Grade C
Scientific Significance: Grade B, Grade B, Grade C, Grade C
P-Reviewer: Batta A; Oviedo RJ; Wang SB S-Editor: Wang JJ L-Editor: A P-Editor: Zhao YQ
Alexander K, Korc-grodzicki B.
Comprehensive Geriatric Assessment (CGA) for Cancer Patients. In: Extermann M. Geriatric Oncology. Cham: Springer, 2020: 419–432.
[PubMed] [DOI][Cited in This Article: ]
Diaz FC, Velazquez AI, Tsang M. Geriatric Assessments in Older Adults With Cancer.JAMA Oncol. 2023;9:1592.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Reference Citation Analysis (0)]
Wildiers H, Heeren P, Puts M, Topinkova E, Janssen-Heijnen ML, Extermann M, Falandry C, Artz A, Brain E, Colloca G, Flamaing J, Karnakis T, Kenis C, Audisio RA, Mohile S, Repetto L, Van Leeuwen B, Milisen K, Hurria A. International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer.J Clin Oncol. 2014;32:2595-2603.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1234][Cited by in RCA: 1243][Article Influence: 113.0][Reference Citation Analysis (0)]
Li D, Sun CL, Kim H, Soto-Perez-de-Celis E, Chung V, Koczywas M, Fakih M, Chao J, Cabrera Chien L, Charles K, Hughes SFDS, Katheria V, Trent M, Roberts E, Jayani R, Moreno J, Kelly C, Sedrak MS, Dale W. Geriatric Assessment-Driven Intervention (GAIN) on Chemotherapy-Related Toxic Effects in Older Adults With Cancer: A Randomized Clinical Trial.JAMA Oncol. 2021;7:e214158.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 65][Cited by in RCA: 259][Article Influence: 64.8][Reference Citation Analysis (0)]
Soo WK, King MT, Pope A, Parente P, Dārziņš P, Davis ID. Integrated Geriatric Assessment and Treatment Effectiveness (INTEGERATE) in older people with cancer starting systemic anticancer treatment in Australia: a multicentre, open-label, randomised controlled trial.Lancet Healthy Longev. 2022;3:e617-e627.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 3][Cited by in RCA: 79][Article Influence: 26.3][Reference Citation Analysis (0)]
Hosoi T, Yamana H, Tamiya H, Matsui H, Fushimi K, Akishita M, Yasunaga H, Ogawa S. Association between comprehensive geriatric assessment and short-term outcomes among older adult patients with stroke: A nationwide retrospective cohort study using propensity score and instrumental variable methods.EClinicalMedicine. 2020;23:100411.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 4][Cited by in RCA: 4][Article Influence: 0.8][Reference Citation Analysis (0)]
Li D, Sun CL, Allen R, Crook CJ, Levi A, Ballena R, Klepin HD, Elias R, Mohile SG, Tew WP, Owusu C, Muss HB, Lichtman SM, Gross CP, Chapman AE, Gajra A, Cohen HJ, Katheria V, Hurria A, Dale W. Risk Factors for Hospitalizations Among Older Adults with Gastrointestinal Cancers.Oncologist. 2022;27:e37-e44.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Cited by in RCA: 1][Article Influence: 0.3][Reference Citation Analysis (0)]
Stover AM, Mayer DK, Muss H, Wheeler SB, Lyons JC, Reeve BB. Quality of life changes during the pre- to postdiagnosis period and treatment-related recovery time in older women with breast cancer.Cancer. 2014;120:1881-1889.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 33][Cited by in RCA: 33][Article Influence: 3.0][Reference Citation Analysis (0)]
Pembroke L, Sherman KA, Dhillon HM, Francis H, Gillatt D, Gurney H. Online Group Cognitive Rehabilitation Program for Prostate Cancer Survivors: Development Using Codesign and the Theoretical Domains Framework.Semin Oncol Nurs. 2024;40:151695.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Nehlsen A, Agarwal P, Mazumdar M, Dutta P, Goldstein NE, Dharmarajan KV. Impact of radiotherapy on daily function among older adults living with advanced cancer (RT impact on function in advanced cancer).J Geriatr Oncol. 2022;13:46-52.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Strafella C, Caputo V, Termine A, Fabrizio C, Calvino G, Megalizzi D, Ruffo P, Toppi E, Banaj N, Bassi A, Bossù P, Caltagirone C, Spalletta G, Giardina E, Cascella R. Identification of Genetic Networks Reveals Complex Associations and Risk Trajectory Linking Mild Cognitive Impairment to Alzheimer's Disease.Front Aging Neurosci. 2022;14:821789.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Cited by in RCA: 1][Article Influence: 0.3][Reference Citation Analysis (0)]
Nishijima TF, Shimokawa M, Esaki T, Morita M, Toh Y, Muss HB. A 10-Item Frailty Index Based on a Comprehensive Geriatric Assessment (FI-CGA-10) in Older Adults with Cancer: Development and Construct Validation.Oncologist. 2021;26:e1751-e1760.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 3][Cited by in RCA: 16][Article Influence: 4.0][Reference Citation Analysis (0)]
Thibaud V, Billy C, Prud'homm J, Garin J, Hue B, Cattenoz C, Somme D, Corvol A. Inside the Black Box: A Narrative Review on Comprehensive Geriatric Assessment-Driven Interventions in Older Adults with Cancer.Cancers (Basel). 2022;14:1642.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Cited by in RCA: 1][Article Influence: 0.3][Reference Citation Analysis (0)]
Fusco D, Ferrini A, Pasqualetti G, Giannotti C, Cesari M, Laudisio A, Ballestrero A, Scabini S, Odetti PR, Colloca GF, Monzani F, Nencioni A, Antonelli Incalzi R, Monacelli F; Oncogeriatrics Group of the Italian Society of Gerontology, Geriatrics. Comprehensive geriatric assessment in older adults with cancer: Recommendations by the Italian Society of Geriatrics and Gerontology (SIGG).Eur J Clin Invest. 2021;51:e13347.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 6][Cited by in RCA: 6][Article Influence: 1.5][Reference Citation Analysis (0)]
Disalvo D, Moth E, Soo WK, Garcia MV, Blinman P, Steer C, Amgarth-Duff I, Power J, Phillips J, Agar M. The effect of comprehensive geriatric assessment on care received, treatment completion, toxicity, cancer-related and geriatric assessment outcomes, and quality of life for older adults receiving systemic anti-cancer treatment: A systematic review.J Geriatr Oncol. 2023;14:101585.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 3][Cited by in RCA: 5][Article Influence: 2.5][Reference Citation Analysis (0)]
Tometich DB, Small BJ, Carroll JE, Zhai W, Luta G, Zhou X, Kobayashi LC, Ahles T, Saykin AJ, Clapp JD, Jim HSL, Jacobsen PB, Hurria A, Graham D, McDonald BC, Denduluri N, Extermann M, Isaacs C, Dilawari A, Root J, Rini C, Mandelblatt JS; Thinking and Living with Cancer (TLC) Study. Pretreatment Psychoneurological Symptoms and Their Association With Longitudinal Cognitive Function and Quality of Life in Older Breast Cancer Survivors.J Pain Symptom Manage. 2019;57:596-606.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 24][Cited by in RCA: 30][Article Influence: 5.0][Reference Citation Analysis (0)]
Mohile SG, Epstein RM, Hurria A, Heckler CE, Canin B, Culakova E, Duberstein P, Gilmore N, Xu H, Plumb S, Wells M, Lowenstein LM, Flannery MA, Janelsins M, Magnuson A, Loh KP, Kleckner AS, Mustian KM, Hopkins JO, Liu JJ, Geer J, Gorawara-Bhat R, Morrow GR, Dale W. Communication With Older Patients With Cancer Using Geriatric Assessment: A Cluster-Randomized Clinical Trial From the National Cancer Institute Community Oncology Research Program.JAMA Oncol. 2020;6:196-204.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 131][Cited by in RCA: 187][Article Influence: 37.4][Reference Citation Analysis (0)]
Leo S, Arnoldi E, Repetto L, Coccorullo Z, Cinieri S, Fedele P, Cazzaniga M, Lorusso V, Latorre A, Campanella G, Ciccarese M, Accettura C, Pisconti S, Rinaldi A, Brunetti C, Raffaele M, Coltelli L, Spazzapan S, Fratino L, Petrucelli L, Biganzoli L. Eribulin Mesylate as Third or Subsequent Line Chemotherapy for Elderly Patients with Locally Recurrent or Metastatic Breast Cancer: A Multicentric Observational Study of GIOGer (Italian Group of Geriatric Oncology)-ERIBE.Oncologist. 2019;24:e232-e240.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 10][Cited by in RCA: 11][Article Influence: 1.6][Reference Citation Analysis (0)]
Caillet P, Canoui-Poitrine F, Vouriot J, Berle M, Reinald N, Krypciak S, Bastuji-Garin S, Culine S, Paillaud E. Comprehensive geriatric assessment in the decision-making process in elderly patients with cancer: ELCAPA study.J Clin Oncol. 2011;29:3636-3642.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 301][Cited by in RCA: 315][Article Influence: 22.5][Reference Citation Analysis (0)]
Decoster L, Van Puyvelde K, Mohile S, Wedding U, Basso U, Colloca G, Rostoft S, Overcash J, Wildiers H, Steer C, Kimmick G, Kanesvaran R, Luciani A, Terret C, Hurria A, Kenis C, Audisio R, Extermann M. Screening tools for multidimensional health problems warranting a geriatric assessment in older cancer patients: an update on SIOG recommendations†.Ann Oncol. 2015;26:288-300.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 469][Cited by in RCA: 513][Article Influence: 46.6][Reference Citation Analysis (0)]
Kenis C, Bron D, Libert Y, Decoster L, Van Puyvelde K, Scalliet P, Cornette P, Pepersack T, Luce S, Langenaeken C, Rasschaert M, Allepaerts S, Van Rijswijk R, Milisen K, Flamaing J, Lobelle JP, Wildiers H. Relevance of a systematic geriatric screening and assessment in older patients with cancer: results of a prospective multicentric study.Ann Oncol. 2013;24:1306-1312.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 232][Cited by in RCA: 235][Article Influence: 19.6][Reference Citation Analysis (0)]
Meixner E, Lang K, König L, Sandrini E, Lischalk JW, Debus J, Hörner-Rieber J. Postoperative Radiotherapy for Endometrial Cancer in Elderly (≥80 Years) Patients: Oncologic Outcomes, Toxicity, and Validation of Prognostic Scores.Cancers (Basel). 2021;13:6264.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Cited by in RCA: 1][Article Influence: 0.3][Reference Citation Analysis (0)]
Extermann M, Brain E, Canin B, Cherian MN, Cheung KL, de Glas N, Devi B, Hamaker M, Kanesvaran R, Karnakis T, Kenis C, Musolino N, O'Donovan A, Soto-Perez-de-Celis E, Steer C, Wildiers H; International Society of Geriatric Oncology. Priorities for the global advancement of care for older adults with cancer: an update of the International Society of Geriatric Oncology Priorities Initiative.Lancet Oncol. 2021;22:e29-e36.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 30][Cited by in RCA: 64][Article Influence: 16.0][Reference Citation Analysis (0)]
Kwon M, Kim SA, Roh JL, Lee SW, Kim SB, Choi SH, Nam SY, Kim SY. An Introduction to a Head and Neck Cancer-Specific Frailty Index and Its Clinical Implications in Elderly Patients: A Prospective Observational Study Focusing on Respiratory and Swallowing Functions.Oncologist. 2016;21:1091-1098.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 38][Cited by in RCA: 28][Article Influence: 3.1][Reference Citation Analysis (0)]
Hamid M, Hannan M, Myo Oo N, Lynch P, Walsh DJ, Matthews T, Madden S, O'Connor M, Calvert P, Horgan AM. Chemotherapy Toxicity in Older Adults Optimized by Geriatric Assessment and Intervention: A Non-Comparative Analysis.Curr Oncol. 2022;29:6167-6176.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 7][Cited by in RCA: 8][Article Influence: 2.7][Reference Citation Analysis (0)]
Malik Z, Di Lorenzo G, Pichler A, De Giorgi U, Hitier S, Ecstein-Fraisse E, Ozatilgan A, Carles J. Effect of Baseline Characteristics on Cabazitaxel Treatment Duration in Patients with Metastatic Castration-Resistant Prostate Cancer: A Post Hoc Analysis of the Compassionate Use/Expanded Access Programs and CAPRISTANA Registry.Cancers (Basel). 2020;12:995.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Cited by in RCA: 2][Article Influence: 0.4][Reference Citation Analysis (0)]
Dwane L, Behan FM, Gonçalves E, Lightfoot H, Yang W, van der Meer D, Shepherd R, Pignatelli M, Iorio F, Garnett MJ. Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets.Nucleic Acids Res. 2021;49:D1365-D1372.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 52][Cited by in RCA: 42][Article Influence: 10.5][Reference Citation Analysis (0)]
Vallet H, Bianco C, Thomas C.
Comprehensive Geriatric Assessment (CGA). In: Flaatten H, Guidet B, Vallet H. The Very Old Critically Ill Patients. Lessons from the ICU. Cham: Springer, 2022: 219–228.
[PubMed] [DOI][Cited in This Article: ]
Chun YJ, Kim KJ, Lee JY, Choi JW, Kim TH, Kim HR, Cho BC, Hong MH, Kim CO. The prognostic value of comprehensive geriatric assessment on the management of older patients with small cell lung cancer.Korean J Intern Med. 2023;38:254-263.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Gołębiowska J, Szymala-Pędzik M, Żórawska J, Sobieszczańska M, Agrawal S. Identification of Factors Affecting the Increased Percentage of CGA Recommendations among Patients on Geriatric Ward.Int J Environ Res Public Health. 2023;20:2065.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Trevino KM, Nelson CJ, Saracino RM, Korc-Grodzicki B, Sarraf S, Shahrokni A. Is screening for psychosocial risk factors associated with mental health care in older adults with cancer undergoing surgery?Cancer. 2020;126:602-610.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 6][Cited by in RCA: 7][Article Influence: 1.2][Reference Citation Analysis (0)]
Jittla P, Graham DM, Zhou C, Halliwell J, O'Reilly S, Aruketty S, Azizi A, Germetaki T, Lowe J, Little M, Punnett G, McMahon P, Benson L, Carter L, Krebs MG, Thistlethwaite FC, Darlington E, Yorke J, Cook N. EPIC: an evaluation of the psychological impact of early-phase clinical trials in cancer patients.ESMO Open. 2022;7:100550.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Reid-Agboola C, Klukowska A, Malcolm FL, Harrison C, Parks RM, Cheung KL. Comprehensive Geriatric Assessment for Older Women with Early-Stage (Non-Metastatic) Breast Cancer-An Updated Systematic Review of the Literature.Curr Oncol. 2023;30:8294-8309.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Sum G, Nicholas SO, Nai ZL, Ding YY, Tan WS. Health outcomes and implementation barriers and facilitators of comprehensive geriatric assessment in community settings: a systematic integrative review [PROSPERO registration no.: CRD42021229953].BMC Geriatr. 2022;22:379.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 26][Cited by in RCA: 27][Article Influence: 9.0][Reference Citation Analysis (0)]
Cooper H, Ganjam S, Badawi A, Mcintosh A, Marshall E. 2050 Scoping an Oncogeriatric Pathway in Acute Care.Age Ageing. 2024;53:afad246.032.
[PubMed] [DOI][Cited in This Article: ]
Naughton C, Galvin R, McCullagh R, Horgan F. Comprehensive geriatric assessment-where are we now, where do we need to be in the context of global ageing?Age Ageing. 2023;52:afad210.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Daly RM, Iuliano S, Fyfe JJ, Scott D, Kirk B, Thompson MQ, Dent E, Fetterplace K, Wright ORL, Lynch GS, Zanker J, Yu S, Kurrle S, Visvanathan R, Maier AB. Screening, Diagnosis and Management of Sarcopenia and Frailty in Hospitalized Older Adults: Recommendations from the Australian and New Zealand Society for Sarcopenia and Frailty Research (ANZSSFR) Expert Working Group.J Nutr Health Aging. 2022;26:637-651.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 23][Cited by in RCA: 22][Article Influence: 7.3][Reference Citation Analysis (0)]
Giger AW, Ditzel HM, Ditzel HJ, Ewertz M, Jørgensen TL, Pfeiffer P, Lund CM, Ryg J. Effects of comprehensive geriatric assessment-guided interventions on physical performance and quality of life in older patients with advanced cancer: A randomized controlled trial (PROGNOSIS-RCT).J Geriatr Oncol. 2024;15:101658.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 3][Cited by in RCA: 3][Article Influence: 3.0][Reference Citation Analysis (0)]
Kalsi T, Babic-Illman G, Ross PJ, Maisey NR, Hughes S, Fields P, Martin FC, Wang Y, Harari D. The impact of comprehensive geriatric assessment interventions on tolerance to chemotherapy in older people.Br J Cancer. 2015;112:1435-1444.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 174][Cited by in RCA: 193][Article Influence: 19.3][Reference Citation Analysis (0)]
Orcel V, Banh L, Bastuji-Garin S, Renard V, Boutin E, Gouja A, Caillet P, Paillaud E, Audureau E, Ferrat E. Effectiveness of comprehensive geriatric assessment adapted to primary care when provided by a nurse or a general practitioner: the CEpiA cluster-randomised trial.BMC Med. 2024;22:414.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Chen YS, Zhou ZN, Glynn SM, Frey MK, Balogun OD, Kanis M, Holcomb K, Gorelick C, Thomas C, Christos PJ, Chapman-Davis E. Financial toxicity, mental health, and gynecologic cancer treatment: The effect of the COVID-19 pandemic among low-income women in New York City.Cancer. 2021;127:2399-2408.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 30][Cited by in RCA: 12][Article Influence: 3.0][Reference Citation Analysis (0)]
Gast KC, Benedict JA, Grogan M, Janse S, Saphire M, Kumar P, Bertino EM, Agne JL, Presley CJ. Impact of an Embedded Palliative Care Clinic on Healthcare Utilization for Patients With a New Thoracic Malignancy.Front Oncol. 2022;12:835881.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Murri R, Masciocchi C, Lenkowicz J, Fantoni M, Damiani A, Marchetti A, Sergi PDA, Arcuri G, Cesario A, Patarnello S, Antonelli M, Bellantone R, Bernabei R, Boccia S, Calabresi P, Cambieri A, Cauda R, Colosimo C, Crea F, De Maria R, De Stefano V, Franceschi F, Gasbarrini A, Landolfi R, Parolini O, Richeldi L, Sanguinetti M, Urbani A, Zega M, Scambia G, Valentini V; Gemelli against Covid Group. A real-time integrated framework to support clinical decision making for covid-19 patients.Comput Methods Programs Biomed. 2022;217:106655.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 3][Cited by in RCA: 2][Article Influence: 0.7][Reference Citation Analysis (0)]
Tack L, Schofield P, Boterberg T, Chandler R, Parris CN, Debruyne PR. Practical Implementation of the Comprehensive Geriatric Assessment to Optimise Care for Older Adults with Cancer.Geriatrics (Basel). 2023;8:18.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Abadir P, Chellappa R. Artificial Intelligence in Geriatrics: Riding the Inevitable Tide of Promise, Challenges, and Considerations.J Gerontol A Biol Sci Med Sci. 2024;79:glad279.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 2][Reference Citation Analysis (0)]
Long SW, Olsen RJ, Christensen PA, Bernard DW, Davis JJ, Shukla M, Nguyen M, Saavedra MO, Yerramilli P, Pruitt L, Subedi S, Kuo HC, Hendrickson H, Eskandari G, Nguyen HAT, Long JH, Kumaraswami M, Goike J, Boutz D, Gollihar J, McLellan JS, Chou CW, Javanmardi K, Finkelstein IJ, Musser JM. Molecular Architecture of Early Dissemination and Massive Second Wave of the SARS-CoV-2 Virus in a Major Metropolitan Area.mBio. 2020;11:e02707-20.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 85][Cited by in RCA: 76][Article Influence: 15.2][Reference Citation Analysis (0)]
Wang X, Xiong W. Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning.Sci Technol Adv Mater. 2020;21:626-634.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 12][Reference Citation Analysis (0)]
Chang A.
The Role of Artificial Intelligence in Digital Health. In: Meyers A. Digital Health Entrepreneurship. Health Informatics. Cham: Springer, 2023: 75–85.
[PubMed] [DOI][Cited in This Article: ]
O'Shaughnessy Í, Robinson K, O'Connor M, Conneely M, Ryan D, Steed F, Carey L, Leahy A, Shanahan E, Quinn C, Galvin R. Effectiveness of acute geriatric unit care on functional decline, clinical and process outcomes among hospitalised older adults with acute medical complaints: a systematic review and meta-analysis.Age Ageing. 2022;51.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Cited by in RCA: 14][Article Influence: 4.7][Reference Citation Analysis (0)]
Yang L, Su Y, Dong S, Wu T, Zhang Y, Qiu H, Gu W, Qiu H, Xu Y, Wang J, Chen J, Fang Y. Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world practice in China.Front Pharmacol. 2022;13:954973.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Reference Citation Analysis (0)]
