Abstract
Background
As the global population ages rapidly, the number of older adults experiencing critical illness is increasing significantly. This growing demographic presents substantial challenges for critical care systems worldwide. However, older patients are frequently underrepresented in clinical research and often receive suboptimal care due to assumptions about low productivity, the presence of multiple comorbidities, polypharmacy, and altered physiological responses to illness and treatment. These factors contribute to a paucity of targeted studies and evidence-based guidelines for managing critical illness in geriatric populations. In contrast to younger patients, older adults often place greater emphasis on long-term outcomes and quality of life rather than survival alone. As a result, the clinical management of critical illness in this population involves distinct and complex considerations.
Methods
We propose the adoption of a practical and scalable ABCDEF framework: A, Artificial Intelligence; B, Value-Based Medicine; C, Comprehensive Care Planning; D, Doctor–Patient Shared Decision-Making; E, Enhanced Recovery for Surgical and Critically Ill Patients; F, Frailty Assessment and Early Identification of High-Risk Older Adults. This framework is our core contribution, designed specifically to integrate key geriatric principles into the high-intensity setting of critical care in a structured and actionable manner.
Conclusions
The ABCDEF framework offers a structured, holistic approach to geriatric critical care that improves patient-centered outcomes, optimizes resource utilization, and addresses existing gaps in research and clinical practice, ultimately enabling more compassionate, effective, and sustainable care delivery. We advocate for this framework as a proactive strategy to transform the standard of care for a growing and vulnerable patient population, moving beyond mere survival towards meaningful recovery.
Keywords: Critical illness, geriatrics, patient-centered care
KEY MESSAGES
The growing older adult population represents a significant challenge for the field of critical care.
There remains a notable lack of systematic research and evidence-based guidance specifically tailored to the management of critical illness in geriatrics.
We propose the adoption of a practical and scalable ABCDEF framework to address these challenges.
Introduction
The global demographic landscape is undergoing rapid changes, with China being home to the largest geriatric population cohort. In 2023, the proportion of the population aged 60 and above is 21.1%, with expectations soaring above 30% by 2035. The number of older person aged 80 years and above will reach 250 million, with expectations soaring to 17% by 2035 [1]. This demographic shift correlates with a notable surge in geriatric patients admissions to intensive care units (ICUs), accentuating the escalating demand for intensive care services [2,3].
Aging leads to a decline in organ functional reserve and reduced tolerance to physiological stressors. Concurrently, compensatory mechanisms progressively deteriorate with advancing age, contributing to chronic inflammation and metabolic dysregulation [4]. The cardiopulmonary system is particularly vulnerable to these age-related changes. In the heart, aging is characterized by cardiomyocyte loss, ventricular wall thickening, reduced cardiac output, and associated immunometabolic disturbances accompanied by increased inflammatory activity [5]. Pulmonary aging involves decreased airway macrophage density, diminished lung elastic recoil, and impaired alveolar gas exchange efficiency, which elevate the risk of respiratory infections and compromise systemic oxygenation [6,7]. Immunosenescence further disrupts host defense mechanisms, thereby increasing susceptibility to infections, malignancies, and autoimmune disorders [4]. The growing population of older adults and the clinical complexities of aging present significant challenges in critical care [8].
Managing of critical illness in geriatrics is inherently complex, involving not only medical care but also ethical, social, and organizational challenges. A key aspect is the need for humanized care that maintains patient dignity and autonomy, especially in high-pressure settings like emergency and prehospital care. For example, ambulance personnel must balance life-saving interventions with respecting the values and experiences of geriatric patients [9].
Training inadequacies further complicate care. A study on caring for children with serious illness reveal significant gaps in training for palliative and end-of-life care, issues that are equally relevant for geriatrics [10]. Effective management of critical illness in the geriatrics requires training that addresses multiple comorbidities, pain management, cognitive impairments, and end-of-life decisions.
Post-discharge rehabilitation is another critical area, where geriatric patients often struggle due to complex health conditions and limited self-management abilities. Remote Health Monitoring (RHM) technology, utilizing wearable devices to collect physiological data, shows promise in enhancing rehabilitation for geriatric patients recovering from critical illnesses [11].
However, most clinical trials in critical care focus on younger populations due to societal priorities that view younger populations as having higher potential for recovery and productivity [12]. In contrast, geriatrics presents with multiple comorbidities, polypharmacy, and altered physiological responses to both illness and treatment [13].
These complicate clinical decision-making, as healthcare providers must meticulously balance treatment risks and benefits to mitigate adverse outcomes such as medication interactions, delirium, and prolonged hospital stays. Effectively addressing critical illnesses in the geriatric patients remains a perplexing quandary [14]. This gap highlights the urgent need for a comprehensive management framework with critical illness in geriatrics.
Discussion
For geriatric patients, the ultimate objective of intensive care treatment is to achieve long-term survival with the high quality of life, especially for critical illnesses in patients over 80, who face limited life expectancy due to their age. For many geriatric patients, the foremost concern isn’t death itself but rather becoming dependent.
Therefore, When an critically ill older patient is presented for admission to the ICU, key clinical data is needed to rapidly assess the patient’s short and long-term prognosis and determine the appropriateness of ICU admission. These data [15], in addition to demographics, reason for admission, severity of illness, also include health status prior to hospital admission, usually before the acute deterioration (14 days prior to admission frequently used), frailty status, cognitive decline, activities of daily living and comorbidities exercise tolerance, etc. The incidence of comorbidities (such as hypertension, diabetes, chronic obstructive pulmonary disease, etc.) also need to be considered [16].
The physiology of aging and available evidence are applied, and patients and their families are included in the scope of diagnosis and treatment [17]. In addition, risk factors for diseases that may affect the quality of life of critically ill geriatrics need to be considered [18]. Nonetheless, practical challenges are associated with provision of cares for geriatric patients as highlighted below.
Advanced age often accompanies a multitude of comorbidities, predisposing patients to rapid disease fluctuations. Instances such as recurrent acute illness, rapid disease progression, multi-organ dysfunction, and the development of persistent inflammation, immunosuppression, and catabolism syndrome (PICS) are frequent occurrences [19].
In addition, factors such as drug–drug interactions, malnutrition, cognitive impairment, decreased metabolic rates, sarcopenia, immunosenescence, as well as declining cardiac and respiratory reserves, significantly influence therapeutic decisions and care management in the geriatric patients.
Finally, acute geriatric patients often present with nonspecific complaints, lacking distinctive clinical manifestations, thereby heightening the risk of misdiagnosis or underdiagnosis. Additionally, prevalent cognitive impairments among the older patients affect the reliability of self-reported quality of life, further complicating care provision [20].
Therefore, managing geriatric patients in acute and emergency situations necessitates specific strategies focusing on ‘risk assessment’ and ‘step down’. Within the ICU settings, emphasizing ‘adjustment and balance’, practicing ‘moderation and compensation’, preventing decompensation, and averting Iatrogenic injury are imperative [21]. Early targeted assessments and effective interventions addressing common critical organ-related issues in the geriatric patients require meticulous attention.
With the emergence of new medications and innovative technologies, a forward-looking care framework are urgently needed to be tailored specifically for critical illness in geriatrics. We propose the adoption of a straightforward and implementable ABCDEF framework (Table 1).
Table 1.
The ABCDEF framework for managing critical illness in geriatrics.
| Component | Key elements |
|---|---|
| A: Artificial Intelligence (AI) |
|
| B: Value-based medicine |
|
| C: Comprehensive Care Plan |
|
| D: Doctor–Patient Shared Decision-Making (SDM) |
|
| E: Enhanced Recovery for Surgical or Critically illness |
|
| F: Frailty Assessment and Early Identification |
|
Artificial intelligence (AI)
AI technology is used to integrate multi-dimensional comprehensive information including geriatric syndrome, biological age, acute disease severity, combined with doctor–patient joint decision-making, to provide intelligent and accurate diagnosis and treatment decisions for critical illness in geriatrics. AI will help to realize the interconnection and interoperability of patient information among multi-level medical institutions. Superior medical institutions provide remote assistance to geriatric patients with acute or critically ill conditions in primary medical institutions to ensure the safety of the transitional care and provide optimal monitoring and timely treatment.
In addition, AI can support clinical decision-making by guiding therapy strategies, managing multiple chronic conditions, ensuring the safe use of polypharmacy, and predicting adverse clinical events [22–24]. Specifically, AI can play a crucial role in developing individualized care plans (C), identifying high-risk patients (C), improving doctor–patient communication (D), optimizing comorbidity management (E), and enabling more accurate frailty assessments (F).
However, several issues warrant careful consideration when applying AI to critical illness in geriatrics. First, AI models must be externally validated specifically in cohorts of older adults with critical illness and undergo targeted fine-tuning or transfer learning to mitigate performance degradation in the very old and frail [23]. Second, clinician-facing human–AI interfaces should be systematically designed to optimize the cognitive workflow between clinicians and AI systems, thereby reducing risks associated with both over-reliance and under-reliance on algorithmic support [24]. Finally, robust safeguards for patient privacy are imperative; data storage and access must strictly comply with applicable legal and regulatory requirements [23,24].
Value-based medicine
Value-based medicine refers to a medical management model in which healthcare institutions monitor the medical efficacy of a specific patient group, analyzes the resources and costs required to achieve expected effects, obtains medical effects per unit currency, and takes the optimal value as the goal to continuously improve the treatment strategy [25]. The essence of value medicine is patient-centered, aiming to obtain the best treatment effect with the least cost [26].
Value-based medicine focuses on the factors that affect health, shifting from disease-centered to health-centered, from focusing on the treatment of existing diseases to the treatment of non-disease, from relying only on the health system to the overall society [21]. Value-based medicine pays more attention to the integrity, coordination and realizability of people’s health, pays more attention to integrated medical and health services in service provision, pays more attention to multi-level health protection in medical insurance services, and pays more attention to the full cycle effect evaluation in medical services.
ICU treatments incur significant costs. During the treatment of critical illness in geriatrics, it needs to consider the quality of medical care and the cost performance of medical services. In the future, it might be prudent to formulate guidelines and consensus rooted in the paradigm of value-based medicine, complementing traditional evidence-based approaches. While the term ‘Value-based Medicine’ may seem broad, its core principle—maximizing health outcomes per unit of cost—is paramount in resource-intensive critical care for older patients.
Comprehensive care plan
Managing critical illness in geriatrics involves a comprehensive continuum of care that spans home-based settings, community-supported services, nursing homes, emergency departments, triage, ICUs, post-ICU care, rehabilitation facilities, and family involvement, among other components. During ICU admission, patients require serial assessments of functional status, disability-free survival, health-related quality of life, cognitive performance, and delirium, as well as prognostic indicators such as expected duration of mechanical ventilation, ICU length of stay, mortality risk, discharge destination, and readmission risk.
Clinical management should align with patient-centered goals or those of surrogate decision-makers, informed by trajectories of functional recovery and overall prognosis [27], with high-risk individuals proactively identified and supported through individualized care plans—potentially enhanced by AI-enabled decision-support tools [28,29].
Following ICU discharge, care should encompass structured follow-up, rehabilitation programs targeting muscle strength, balance, and activities of daily living, optimization of chronic disease management, convalescent care for residual effects of acute illness, simple remote monitoring, and integration of community-based support services [30,31].
For geriatric patients with complex conditions, especially those at end-of-life, care must expect beyond the ICU to include robust community healthcare services. This necessitates strengthened support from RHM, nurses with specialized geriatric training, and comprehensive community services. In China, the central government had issued a series of policies to promote the construction of an integrated health service system that unifies the management and delivery of medical and health services across the continuum, including health promotion, disease prevention, treatment and hospice care. These initiatives aim to coordinate all levels of healthcare institutions according to patient needs, ensuring consistent, lifelong care.
Doctor–patient shared decision-making (SDM)
Promoting SDM between clinicians and patients enhances patient engagement throughout the treatment process. This involves patients actively participating in medical decisions, fostering comprehensive communication between doctors and patients regarding the advantages and disadvantages of various treatment options.
Patient-specific factors such as preferences, socio-cultural background, education level, and economic status are systematically integrated, resulting in a collaborative decision-making process between clinicians and patients. Through SDM, patients are supported in selecting treatment options that align with both clinical evidence and their personal values.
However, in the ICU setting, many older patients are unconscious or cognitively impaired, necessitating SDM with legally designated surrogates or family members using advance directives, previously expressed wishes, or best-interest standards. Given that delirium and decision-making capacity can fluctuate over time, repeated assessments and timely re-engagement of geriatric patients are essential once attention and comprehension are restored [27,32].
The use of time-limited trials (TLTs) is recommended in cases of clinical uncertainty [27]. A TLT involves an agreement between clinicians and patients or their families to administer specific therapies for a defined period while monitoring the patient’s response against predetermined clinical outcomes. If improvement occurs, treatment continues with a disease-focused approach; conversely, if deterioration is observed, therapies are discontinued, often transitioning from curative to palliative care. Persistent uncertainty may warrant renegotiation of another trial [33].
Finally, within the framework of SDM, AI can streamline medical documentation, improve illness understanding among patients and surrogates, support comprehensive risk-benefit analyses, and provide basic psychological support, thereby facilitating a more informed and patient-centered decision-making process [34].
Enhanced recovery for surgical or critically illness cases
Given the high prevalence of comorbidities in older adults and the clinical objectives of managing disease progression or alleviating symptoms, it is essential to implement early rehabilitation interventions aimed at mitigating complications such as delirium, venous thromboembolism, sarcopenia, and bone loss.
Geriatric interdisciplinary teams play a critical role in supporting rapid recovery from acute and critical illnesses through Acute Care for Elders (ACE) models or in promoting functional independence and self-management via Comprehensive Geriatric Assessment and Management (GEM) [35]. Co-management by geriatricians and surgical teams can reduce hospital length of stay and perioperative complications through standardized preoperative evaluations, expedited surgical procedures, and optimized postoperative care [36].
These interventions are closely aligned with key patient-centered outcomes in geriatrics, including cognitive function, quality of life, and long-term survival. Furthermore, emerging applications of AI and machine learning show considerable promise in enhancing the prediction and management of these comorbidities [37,38].
Frailty assessment and early identification of high-risk older patients
All older adults with critical illness should undergo frailty assessment upon ICU admission [27]. The Clinical Frailty Scale (CFS) is the most widely reported tool for frailty assessment in the ICU [39]. However, the CFS has limitations compared to other instruments such as the Frailty Index (FI). While convenient to use, it lacks the comprehensiveness needed to capture conditions like depression, anxiety, or heart failure [40]. In contrast, the FI offers a more comprehensive evaluation of frailty and is better suited for repeated assessments over time.
Looking ahead, emerging tools such as the electronic Frailty Index (eFI) enable more detailed and real-time monitoring of frailty, supporting dynamic treatment adjustments in the ICU setting [41]. Moreover, AI is introducing novel capabilities into frailty assessment: AI-enabled standardization of CFS scoring can assist ICU clinicians without specialized geriatric training in rapidly and consistently obtaining reliable assessments [42].
Frailty assessment in geriatric patients requires multidisciplinary collaboration, particularly between intensivists and geriatricians. Upon ICU admission, intensivists focus on immediate life support and stabilization of the patient’s condition, while geriatricians conduct frailty assessments to identify high-risk individuals and inform personalized treatment plans aligned with patient values and overall health status.
As clinical stabilization is achieved, care transitions to a collaborative model in which intensivists and geriatricians jointly assess frailty, balance the intensity of interventions according to prognosis, manage acute medical issues, and adjust life-sustaining therapies as appropriate. After the acute phase, geriatricians assume primary responsibility for developing rehabilitation and chronic disease management strategies [27], while intensivists continue to manage residual acute complications during recovery.
Conclusions
The adoption of new technologies in geriatric health monitoring is essential to effectively address the multifaceted challenges associated with critical illness in older adults.
Implementing the ABCDEF framework – comprising AI, Value-Based Medicine, Comprehensive Care Planning, Shared Decision-Making between Clinicians and Patients, Enhanced Recovery for Surgical or Critically Ill Patients, and Frailty Assessment and Early Identification of High-Risk Older Adults – can offer significant clinical and operational benefits. By integrating this framework, healthcare providers can deliver more compassionate, competent, and individualized care, thereby bridging the gap between research and clinical practice. Ultimately, this structured approach has the potential to improve patient-centered outcomes while enhancing the efficiency and effectiveness of geriatric critical care systems.
Acknowledgments
This work was supported by National Natural Science Foundation of China (82172153).
Funding Statement
This work was supported by National Natural Science Foundation of China (82172153).
Disclosure statement
The authors report no conflict of interest.
Data availability statement
Data sharing is not applicable to this article as no data were created or analysed in this study.
References
- 1.Wu W, Long S, Cerda AA, et al. Population ageing and sustainability of healthcare financing in China. Cost Eff Resour Alloc. 2023;21(1):97. doi: 10.1186/s12962-023-00505-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Harrison DA, Creagh-Brown BC, Rowan KM.. Timing and burden of persistent critical illness in UK intensive care units: an observational cohort study. J Intensive Care Soc. 2023;24(2): 139–146. doi: 10.1177/17511437211047180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Van Heerden PV, Sviri S, Beil M, et al. The wave of very old people in the intensive care unit: a challenge in decision-making. J Crit Care. 2020;60:290–293. doi: 10.1016/j.jcrc.2020.08.030. [DOI] [PubMed] [Google Scholar]
- 4.Li X, Li C, Zhang W, et al. Inflammation and aging: signaling pathways and intervention therapies. Signal Transduct Target Ther. 2023;8(1):239. doi: 10.1038/s41392-023-01502-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ribeiro ASF, Zerolo BE, López-Espuela F, et al. Cardiac system during the aging process. Aging Dis. 2023;14(4):1105–1122. doi: 10.14336/AD.2023.0115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Schneider JL, Rowe JH, Garcia-de-Alba C, et al. The aging lung: physiology, disease, and immunity. Cell. 2021;184(8):1990–2019. doi: 10.1016/j.cell.2021.03.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wong CK, Smith CA, Sakamoto K, et al. Aging impairs alveolar macrophage phagocytosis and increases influenza-induced mortality in mice. J Immunol. 2017;199(3):1060–1068. doi: 10.4049/jimmunol.1700397. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Akinosoglou K, Schinas G, Almyroudi MP, et al. The impact of age on intensive care. Ageing Res Rev. 2023;84:101832. doi: 10.1016/j.arr.2022.101832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hjalmarsson A, Östlund G, Asp M, et al. Balancing power: ambulance personnel’s lived experience of older persons’ participation in care in the presence of municipal care personnel. Scand J Caring Sci. 2023;37(3):766–776. doi: 10.1111/scs.13162. [DOI] [PubMed] [Google Scholar]
- 10.Porter AS, Zalud K, Applegarth J, et al. Community hospice nurses’ perspectives on needs, preferences, and challenges related to caring for children with serious illness. JAMA Netw Open. 2021;4(10):e2127457. doi: 10.1001/jamanetworkopen.2021.27457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.LeBaron V. Challenges and opportunities in designing and deploying remote health monitoring technology for older adults with cancer. Innov Aging. 2022;6(6):igac057. doi: 10.1093/geroni/igac057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Wernly B, Beil M, Bruno RR, VIP2 study group ., et al. Provision of critical care for the elderly in Europe: a retrospective comparison of national healthcare frameworks in intensive care units. BMJ Open. 2021;11(6):e046909. doi: 10.1136/bmjopen-2020-046909. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.van Dam CS, Labuschagne HA, van Keulen K, et al. Polypharmacy, comorbidity and frailty: a complex interplay in older patients at the emergency department. Eur Geriatr Med. 2022;13(4):849–857. doi: 10.1007/s41999-022-00664-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Roussel M, Teissandier D, Yordanov Y, FHU IMPEC-IRU SFMU Collaborators; FHU IMPEC − IRU SFMU Collaborators, et al. Overnight stay in the emergency of Department and mortality in older patients. JAMA Intern Med. 2023;183(12):1378–1385. doi: 10.1001/jamainternmed.2023.5961. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Guidet B, Vallet H, Flaatten H, et al. The trajectory of very old critically ill patients. Intensive Care Med. 2024;50(2):181–194. doi: 10.1007/s00134-023-07298-z. [DOI] [PubMed] [Google Scholar]
- 16.Glattacker M, Giesler JM, Bablok I, et al. Availability and quality of assessment instruments on patient-centredness in older adults with multimorbidity: a systematic review. Age Ageing. 2023;52(3):afac314. doi: 10.1093/ageing/afac314. [DOI] [PubMed] [Google Scholar]
- 17.Beil M, Flaatten H, Guidet B, et al. The management of multi-morbidity in the elderly patients: ready yet for precision medicine in intensive care? Crit Care. 2021;25(1):330. doi: 10.1186/s13054-021-03750-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Mousai O, Tafoureau L, Yovell T, et al. Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU. Intensive Care Med. 2022;48(12):1726–1735. doi: 10.1007/s00134-022-06868-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Suganuma S, Idei M, Nakano H, et al. Impact of persistent inflammation, immunosuppression, and catabolism syndrome during intensive care admission on each post-intensive care syndrome component in a PICS clinic. J Clin Med. 2023;12(16):5427. doi: 10.3390/jcm12165427. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Vincent JL, Creteur J.. Appropriate care for the elderly in the ICU. J Intern Med. 2022;291(4):458–468. doi: 10.1111/joim.13371. [DOI] [PubMed] [Google Scholar]
- 21.Flaatten H, Beil M, Guidet B.. Elderly patients in the intensive care unit. Semin Respir Crit Care Med. 2021;42(1):10–19. doi: 10.1055/s-0040-1710571. [DOI] [PubMed] [Google Scholar]
- 22.Hyland SL, Faltys M, Hüser M, et al. Early prediction of circulatory failure in the intensive care unit using machine learning. Nat Med. 2020;26(3):364–373. doi: 10.1038/s41591-020-0789-4. [DOI] [PubMed] [Google Scholar]
- 23.FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. 2025;388:r340. doi: 10.1136/bmj.r340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Cecconi M, Greco M, Shickel B, et al. Implementing artificial intelligence in critical care medicine: a consensus of 22. Crit Care. 2025;29(1):290. doi: 10.1186/s13054-025-05532-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Luna A, Price A, Srivastava U, et al. Critical patient insights from the same-day feedback programme at Stanford Health Care. BMJ Open Qual. 2020;9(3):e000773. doi: 10.1136/bmjoq-2019-000773. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Peiffer V, Yock C, Yock P, et al. Value-based care: a review of key challenges and opportunities relevant to medical technology innovators. J Med Devices. 2019;13(2):020801. doi: 10.1115/1.4042794. [DOI] [Google Scholar]
- 27.Beil M, Alberto L, Bourne RS, et al. ESICM consensus-based recommendations for the management of very old patients in intensive care. Intensive Care Med. 2025;51(2):287–301. doi: 10.1007/s00134-025-07794-4. [DOI] [PubMed] [Google Scholar]
- 28.Tomašev N, Glorot X, Rae JW, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116–119. doi: 10.1038/s41586-019-1390-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Choi Y, Oh S, Huh JW, et al. Deep reinforcement learning extracts the optimal sepsis treatment policy from treatment records. Commun Med (Lond). 2024;4(1):245. doi: 10.1038/s43856-024-00665-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Nakanishi N, Liu K, Hatakeyama J, et al. Post-intensive care syndrome follow-up system after hospital discharge: a narrative review. J Intensive Care. 2024;12(1):2. doi: 10.1186/s40560-023-00716-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Lai DJ, Liu Z, Johnston E, et al. Exploring the effectiveness of eHealth interventions in treating Post Intensive Care Syndrome (PICS) outcomes: a systematic review. Crit Care. 2024;28(1):317. doi: 10.1186/s13054-024-05089-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kon AA, Davidson JE, Morrison W, et al. Shared decision-making in intensive care units. executive summary of the American College of Critical Care Medicine and American Thoracic Society Policy Statement. Am J Respir Crit Care Med. 2016;193(12):1334–1336. doi: 10.1164/rccm.201602-0269ED. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Mousai O, Tafoureau L, Yovell T, et al. The role of clinical phenotype in decisions to limit life-sustaining treatment for very old patients in the ICU. Ann Intensive Care. 2023;13(1):40. doi: 10.1186/s13613-023-01136-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Li X, Chen S, Meng M, et al. Research progress and implications of the application of large language model in shared decision-making in China’s healthcare field. Front Public Health. 2025;13:1605212. doi: 10.3389/fpubh.2025.1605212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Geen O, Rochwerg B, Wang XM.. Optimizing care for critically ill older adults. CMAJ. 2021;193(39):E1525–E1533. doi: 10.1503/cmaj.210652. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Van Heghe A, Mordant G, Dupont J, et al. Effects of orthogeriatric care models on outcomes of hip fracture patients: a systematic review and meta-analysis. Calcif Tissue Int. 2022;110(2):162–184. doi: 10.1007/s00223-021-00913-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Nguyen A, Lee P, Rodriguez EK, et al. Addressing the growing burden of musculoskeletal diseases in the ageing US population: challenges and innovations. Lancet Healthy Longev. 2025;6(5):100707. doi: 10.1016/j.lanhl.2025.100707. [DOI] [PubMed] [Google Scholar]
- 38.Du X, Novoa-Laurentiev J, Plasek JM, et al. Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes. EBioMedicine. 2024;109:105401. doi: 10.1016/j.ebiom.2024.105401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Moïsi L, Mino JC, Guidet B, et al. Frailty assessment in critically ill older adults: a narrative review. Ann Intensive Care. 2024;14(1):93. doi: 10.1186/s13613-024-01315-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Muscedere J, Bagshaw SM, Kho M, et al. Frailty, Outcomes, Recovery and Care Steps of Critically Ill Patients (FORECAST): a prospective, multi-centre, cohort study. Intensive Care Med. 2024;50(7):1064–1074. doi: 10.1007/s00134-024-07404-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Ferrante LE, Szczeklik W.. Frailty is crucial in FORECASTing outcomes in critical care. Intensive Care Med. 2024;50(7)::1119–1122. doi: 10.1007/s00134-024-07518-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Kee XLJ, Sng GGR, Lim DYZ, et al. Use of a large language model with instruction-tuning for reliable clinical frailty scoring. J Am Geriatr Soc. 2024;72(12):3849–3854. doi: 10.1111/jgs.19114. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data sharing is not applicable to this article as no data were created or analysed in this study.