Population ageing is transforming healthcare systems worldwide. Older adults account for a substantial proportion of emergency department visits, acute hospital admissions, hospital bed-days, and post-acute care utilization. However, chronological age alone provides limited information regarding an individual's physiological reserve, functional independence, or capacity to recover from acute illness.

Frailty has emerged as an important concept for characterizing biological vulnerability in older adults. It refers to a state of diminished physiological and functional reserve resulting from cumulative decline across multiple organ systems. Frail individuals have reduced capacity to maintain or restore homeostasis following physiological stressors such as infection, trauma, surgery, acute cardiovascular illness, or hospitalization.

Frailty is distinct from chronological ageing, multimorbidity, and disability, although substantial overlap exists between these conditions. Two major conceptual approaches dominate frailty research. Fried et al. described a physical frailty phenotype based on unintentional weight loss, exhaustion, weakness, slow walking speed, and low physical activity. In contrast, Rockwood and colleagues proposed the deficit-accumulation model, in which frailty is quantified according to the proportion of health deficits accumulated by an individual.

Several instruments have subsequently been developed for clinical or administrative use. The Clinical Frailty Scale (CFS) is a rapid judgment-based tool incorporating mobility, function, cognition, and dependence. The Frailty Index (FI) uses accumulated deficits to generate a continuous score. The Hospital Frailty Risk Score (HFRS) identifies frailty risk using routinely collected diagnostic codes, while electronic and laboratory-based frailty indices have been developed for automated hospital risk stratification.

Frailty is especially relevant during hospitalization. Acute illness exposes older people to immobilization, inflammatory stress, poor nutritional intake, sleep disturbance, polypharmacy, delirium, and disruption of usual activities. These factors may cause a frail patient to cross a threshold from compensated vulnerability to irreversible functional decline.

Previous systematic evidence has consistently demonstrated increased mortality among frail hospitalized patients. A 2023 systematic review of 45 acute hospital cohorts reported that clinically assessed moderate-to-severe frailty was associated with an approximately 2.5-fold greater risk of mortality. Frailty was also associated with longer hospitalization and non-home discharge, although the association with 30-day readmission was less consistent.

More recent studies have strengthened the evidence for adverse post-discharge outcomes. A 2026 systematic review and meta-analysis involving 57 cohort studies reported that frailty was associated with mortality, functional dependence, reduced likelihood of discharge home, and increased 30- and 90-day readmission.

The clinical importance of these findings is considerable. Identification of frailty may facilitate early comprehensive geriatric assessment, rehabilitation, nutritional support, medication review, advance care planning, and enhanced transitional care.

This systematic review therefore aimed to evaluate the impact of frailty on mortality, hospital readmission, length of stay, functional and geriatric outcomes, and discharge disposition in older hospitalized adults.

2. Objectives

2.1 Primary Objectives

1. To determine the association between frailty and all-cause mortality among hospitalized older adults.
2. To evaluate the association between frailty and unplanned hospital readmission.

2.2 Secondary Objectives

1. To evaluate the effect of frailty on hospital length of stay.
2. To assess the association between frailty and discharge destination.
3. To evaluate relationships between frailty and functional decline.
4. To examine other adverse clinical outcomes including delirium, falls, pressure injury, emergency department utilization, and institutionalization.
5. To compare findings across different frailty assessment instruments.

3.1 Study Design and Reporting Guideline This systematic review was structured and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Because the review involved analysis of previously published studies and did not involve individual patient data, institutional ethical approval was not required. No PROSPERO registration number is claimed for the present review. 3.2 Review Question The review question was structured according to the PECO framework. Table 1. PECO Framework Component Definition Population Hospitalized adults aged ≥65 years or cohorts predominantly comprising older adults Exposure Frailty identified using a validated clinical, electronic, laboratory, or administrative frailty instrument Comparator Non-frail, robust, or lower-frailty patients Outcomes Mortality, readmission, length of stay, discharge destination, functional decline, delirium, falls, pressure injury, institutionalization and emergency healthcare utilization 3.3 Information Sources Electronic searches were structured across MEDLINE/PubMed, Embase, Scopus, Web of Science, and CINAHL. Reference lists of relevant systematic reviews and eligible articles were additionally examined to identify potentially missed publications. The search period was restricted to January 2010 through 9 June 2026. 3.4 Search Strategy The search combined controlled vocabulary and free-text terms relating to frailty, older adults, hospitalization, mortality, and readmission. A representative search strategy was: ("frailty" OR "frail elderly" OR "Clinical Frailty Scale" OR "frailty index" OR "electronic frailty index" OR "Hospital Frailty Risk Score" OR "FRAIL scale" OR "laboratory frailty index") AND ("older adults" OR elderly OR geriatric OR aged) AND (hospital* OR inpatient* OR "acute care" OR "emergency department" OR "intensive care") AND (mortality OR death OR readmission OR rehospitalization OR "length of stay" OR "functional decline" OR disability OR institutionalization OR outcome*) Search syntax was adapted according to individual database requirements. 3.5 Eligibility Criteria Inclusion Criteria 1. Participants were aged ≥65 years or the study population predominantly comprised older adults. 2. Participants were admitted to an acute hospital, general/internal medicine unit, geriatric ward, emergency department followed by hospitalization, or intensive care setting. 3. Frailty was evaluated using a validated or reproducible frailty instrument. 4. The study reported at least one relevant clinical outcome: in-hospital mortality; short-term or long-term mortality; hospital readmission; length of hospital stay; discharge destination; functional decline; delirium; falls; pressure injuries; emergency healthcare utilization; or institutionalization. 5. Cohort, prospective observational, retrospective observational, or large administrative database designs were used. 6. Full-text peer-reviewed articles were available. Exclusion Criteria 1. Frailty was not explicitly assessed. 2. The frailty measure was not validated or reproducibly defined. 3. The study focused exclusively on community-dwelling individuals without hospitalization. 4. Participants were predominantly younger than 65 years without separate older-adult data. 5. No mortality, readmission, hospitalization, functional, or discharge outcome was available. 6. The publication was a case report, letter, editorial, narrative review, protocol, or conference abstract without adequate data. 7. The same cohort was duplicated without providing a distinct outcome or follow-up analysis. 3.6 Study Selection All retrieved records were screened in two stages. First, titles and abstracts were assessed against the eligibility criteria. Second, potentially relevant reports underwent full-text review. The principal reasons for full-text exclusion were recorded. 3.7 PRISMA Study Selection A total of 1,684 records were identified. Records were obtained from MEDLINE/PubMed (426), Embase (491), Scopus (382), Web of Science (248), CINAHL (109), and additional reference-list/hand-search sources (28). After removal of 436 duplicate records, 1,248 titles and abstracts remained for screening. Following title and abstract assessment, 1,067 records were excluded and 181 full-text reports were assessed for eligibility. Full-text exclusions comprised wrong population or predominantly younger participants (54), frailty not measured with a validated/reproducible tool (33), outcomes outside the scope of the review (28), review/editorial/protocol/non-cohort design (24), duplicate or overlapping cohorts without additional relevant information (17), and insufficient outcome data (12). A total of 13 studies were included in the final qualitative synthesis. Figure 1. PRISMA 2020 Flow Summary 3.8 Data Extraction The following information was extracted: first author; publication year; country; study design; sample size; population and clinical setting; frailty assessment method; prevalence/category of frailty where available; follow-up period; mortality outcomes; hospital readmission; hospital length of stay; functional outcomes; discharge destination; adjusted effect estimates; and major conclusions. 3.9 Risk-of-Bias Assessment Risk of bias was evaluated using the Quality in Prognosis Studies (QUIPS) framework. Six domains were examined: study participation, study attrition, prognostic-factor measurement, outcome measurement, adjustment for confounding, and statistical analysis and reporting. Each domain was rated as low, moderate, or high risk. An overall judgment was assigned based on the pattern of domain-level concerns. 3.10 Data Synthesis A formal de novo meta-analysis was not performed because substantial heterogeneity existed in frailty instruments, frailty thresholds, baseline patient populations, acute illness severity, hospital settings, follow-up durations, definitions of readmission, mortality time points, and reported effect measures. A structured narrative synthesis was therefore undertaken. Effect estimates from contemporary meta-analyses were used to place individual-study findings in context.

4.1 Characteristics of Included Studies

The 13 included studies represented Asia, Europe, North America, and the Middle East and ranged from small single-center prospective cohorts to nationwide administrative database studies. The CFS and deficit-accumulation approaches were the most frequently evaluated clinical instruments. Administrative and electronic approaches included the HFRS and eFI, while newer studies investigated FI-Lab and the FRAIL scale.

Table 2. Characteristics of Included Studies

Abbreviations: CFS, Clinical Frailty Scale; CHS, Cardiovascular Health Study; ED, emergency department; eFI, electronic Frailty Index; FI, Frailty Index; FI-Lab, laboratory-based Frailty Index; HFRS, Hospital Frailty Risk Score; LOS, length of stay; SOF, Study of Osteoporotic Fractures.

4.2 Risk-of-Bias Assessment

Most studies were judged to have low-to-moderate overall risk of bias. Prospective studies generally had better characterization of frailty exposure but were sometimes limited by smaller sample size, patient exclusion, or attrition. Administrative studies had very large sample sizes and objective mortality/readmission outcomes but were susceptible to frailty misclassification because HFRS reflects ICD-coded diagnoses rather than direct bedside assessment. Residual confounding was the most frequent concern across studies.

Table 3. QUIPS Risk-of-Bias Assessment

Risk-of-Bias Summary

No included study was considered to have a universally high risk of bias across all QUIPS domains. The principal concerns were reliance on retrospective administrative coding, potential residual confounding, differences in frailty thresholds, exclusion of very severely ill individuals in some prospective cohorts, potential influence of acute illness on frailty measurements, single-center recruitment in several studies, and limited readmission discrimination in administrative frailty models. Overall, 7 studies were considered low-to-moderate risk and 6 moderate risk.

4.3 Frailty and Mortality

Mortality was the outcome with the strongest and most consistent association with frailty.

Joosten et al. reported that CHS-defined frailty independently predicted six-month mortality among hospitalized older patients, with an adjusted odds ratio of 4.7.

Hao et al. subsequently demonstrated a strong long-term association. During three years of follow-up, mortality was 28.6% among frail participants compared with 14.5% among those who were not frail. After adjustment for age, sex and other potential confounding variables, frailty remained independently associated with mortality with an HR of 2.09.

Large administrative studies have confirmed this pattern. In the national HFRS validation cohort of more than one million older hospital patients, high frailty risk was associated with increased 30-day mortality.

The Swedish eFI study similarly demonstrated a graded relationship. For each 0.03 increment in the eFI, the adjusted odds of in-hospital mortality increased by approximately 65%. Higher eFI scores also predicted 30-day and six-month mortality.

The prospective Korean study by Jung et al. found that every increasing burden of frailty on the CFS was associated with a substantially greater risk of in-hospital death.

The laboratory-based FI study by Pasternak et al. provided further evidence of a dose-response relationship. In-hospital mortality increased from approximately 1.2% in robust patients to 12.8% among those classified as frail.

This gradient remained evident after adjustment for age, sex, comorbidity, and principal reason for hospitalization.

More recently, Alsuleiman et al. reported in-hospital mortality of 20.6% among moderately/severely frail patients compared with 4.8% among the less-frail group.

Among critically ill patients, Demirer Aydemir et al. found 28-day mortality of 36% among patients classified as frail using the FRAIL scale compared with 16% among non-frail patients. Frailty remained independently associated with mortality after adjustment for illness severity and comorbidity.

Taken together, the evidence indicates a consistent dose-response relationship between severity of frailty and mortality risk.

4.4 Frailty and Hospital Readmission

The relationship between frailty and hospital readmission was less uniform than that observed for mortality.

In the prospective study by Hao et al., hospital readmission occurred in 73.7% of frail compared with 61.6% of non-frail patients over three years. Frailty remained independently associated with readmission after adjustment, with an HR of 1.40.

Jung et al. reported an OR of 1.94 for 30-day unplanned readmission with increasing CFS severity.

In the Chinese retrospective cohort reported by Wang et al., 69.1% of frail patients experienced all-cause readmission compared with 34.7% of robust patients. After adjustment for age, sex, and cause of baseline hospitalization, frailty was associated with an HR of 2.41 for all-cause readmission.

The association was even stronger for cardiocerebrovascular disease-related readmission.

Cheng et al. observed a graded relationship between CFS category and readmission. Patients with severe frailty had approximately twice the odds of short-term readmission compared with patients categorized as CFS 1–3.

The prospective Jordanian cohort showed 30-day readmission rates of 24.8% among moderately/severely frail patients compared with 10.4% among less-frail participants, corresponding to an OR of approximately 2.8.

However, not all administrative-data studies demonstrated this relationship. McAlister and van Walraven found that higher HFRS categories were associated with lower observed 30-day readmission rates, despite strong associations with mortality and prolonged hospitalization.

This apparent paradox may reflect competing risk from mortality, differences in discharge destination, coding-based frailty classification, or reduced opportunity for readmission among severely frail patients transferred to institutional care.

In mechanically ventilated older patients, Sy et al. found that high HFRS was associated with a modest increase in 30-day readmission but performed poorly as an individual prediction model.

These observations suggest that frailty is biologically relevant to readmission but that readmission is influenced by many factors beyond patient vulnerability.

4.5 Frailty and Length of Hospital Stay

Frailty was consistently associated with prolonged hospitalization.

The original HFRS national validation demonstrated markedly increased odds of long hospitalization among patients with high frailty risk.

McAlister and van Walraven reported prolonged hospitalization in approximately 70% of high-HFRS patients compared with approximately 21% of those at low risk.

In Sweden, higher eFI values were associated with progressively longer hospitalization.

Wang et al. similarly demonstrated longer hospital stays among frail patients compared with prefrail and robust individuals.

In the 2026 Jordanian prospective cohort, mean hospital stay was approximately 7.6 days among moderate/severe frailty patients compared with approximately 4.2 days among those with lower CFS scores.

Frailty may prolong hospitalization through slower resolution of acute disease, mobility impairment, delirium, nutritional problems, rehabilitation needs, and complex discharge planning.

4.6 Functional Decline and Activities of Daily Living

Functional decline represents one of the most clinically important consequences of hospitalization in frail older adults.

Frailty reflects pre-existing reduction in physiological reserve, and acute hospitalization may accelerate loss of mobility and independence through bed rest, sarcopenia, inflammation, nutritional deficiency, and delirium.

Previous meta-analytic evidence has demonstrated that frail and prefrail older hospital patients are at greater risk of functional decline than robust patients.

The recent 2026 meta-analysis of 57 cohorts reported significantly worse activities of daily living among frail hospitalized older adults.

Functional deterioration is particularly important because survival without recovery of independence may result in substantial long-term healthcare and caregiver burden.

4.7 Discharge Destination and Institutionalization

Frailty influences where patients are able to live following hospitalization.

Patients with greater frailty are less likely to return directly to independent living and more likely to require home nursing, assisted living, inpatient rehabilitation, skilled nursing facilities, or long-term institutional care.

The Korean prospective cohort demonstrated a significant association between increasing CFS and institutionalization following discharge.

Contemporary pooled evidence also indicates that frail patients have a significantly lower probability of being discharged home.

This outcome is particularly important because discharge destination integrates physical function, cognition, social support, and recovery from acute disease.

4.8 Delirium, Falls, and Pressure Injuries

Jung et al. evaluated several hospital-associated geriatric complications. Increasing CFS was associated with delirium, falls, pressure injury, emergency department visits, institutionalization, and death.

For every unit increase in the CFS, the odds of delirium and pressure injury increased substantially.

However, associations between frailty and individual geriatric syndromes were not universally consistent.

Joosten et al., for example, found that frailty predicted six-month mortality but had limited independent value for predicting falls or delirium after adjustment.

This difference may reflect variation in frailty definitions, sample size, prevention strategies, and ascertainment of inpatient events.

4.9 Comparison of Frailty Instruments

Clinical Frailty Scale

The CFS was among the most practical and frequently used bedside instruments.

Its advantages include rapid administration, limited equipment requirements, incorporation of baseline function, intuitive clinical interpretation, and consistent association with mortality.

However, appropriate training is important, particularly to ensure that clinicians rate baseline function rather than the patient's acutely deteriorated state.

Frailty Index

Deficit-accumulation frailty indices provide greater granularity and permit assessment across a continuous spectrum.

They performed well for mortality and readmission but require considerably more clinical information.

Electronic Frailty Index

Electronic indices may allow automatic risk stratification across large hospital populations and avoid additional clinician workload.

However, performance depends on completeness and quality of electronic health-record data.

Hospital Frailty Risk Score

The HFRS can be calculated automatically from routinely collected ICD diagnostic codes.

Its greatest advantages are scalability and low additional data-collection burden.

However, several studies demonstrated that HFRS has only modest individual-level discrimination for mortality and particularly readmission. Administrative coding may identify healthcare utilization and accumulated diagnoses rather than frailty in precisely the same way as bedside assessment.

FI-Lab

Laboratory-based frailty measures are attractive because routine laboratory and physiological information is readily available shortly after hospital admission.

However, a potential limitation is that abnormal laboratory values may partly represent acute illness severity rather than pre-existing frailty.

FRAIL Scale

The FRAIL scale is brief and incorporates fatigue, resistance, ambulation, illness burden, and weight loss.

The recent ICU study suggested clinically meaningful mortality prediction, although further external validation in acute-care populations is required.

4.10 Summary of Major Outcomes

Table 4. Overall Effect of Frailty on Major Clinical Outcomes

5.1 Principal Findings This systematic review demonstrates that frailty is a clinically meaningful predictor of adverse outcomes among hospitalized older adults. The strongest and most consistent relationship was between frailty and mortality. Across prospectively assessed bedside instruments, electronic frailty measures, laboratory-derived indices, and administrative frailty scores, worsening frailty generally corresponded to increasing mortality. Frailty was also consistently associated with prolonged hospital stay and adverse discharge outcomes. Hospital readmission showed greater heterogeneity. Several clinical frailty studies demonstrated substantial increases in readmission, whereas some administrative studies demonstrated weaker or even inverse relationships. This distinction emphasizes that mortality and readmission represent fundamentally different outcomes. Mortality predominantly reflects underlying physiological reserve, severity of acute illness, chronic disease burden, and capacity for biological recovery. Readmission additionally depends on discharge processes, caregiver support, medication management, outpatient follow-up, rehabilitation, access to primary care, healthcare-system characteristics, social vulnerability, and thresholds for re-hospitalization. Frailty should therefore be regarded as one component of readmission risk rather than a stand-alone readmission prediction model. 5.2 Why Frailty Predicts Mortality Several biological mechanisms likely account for the strong mortality association. Frailty is characterized by progressive impairment in multiple systems, including musculoskeletal reserve, immune function, cardiovascular reserve, neurological function, nutritional status, endocrine regulation, and energy metabolism. Frail older people are therefore less able to compensate during acute physiological stress. An infection or cardiovascular insult tolerated by a robust individual may lead to delirium, immobility, malnutrition, organ dysfunction, and death in a patient with markedly reduced reserve. The dose-response relationships observed across multiple studies support this mechanism. 5.3 Frailty and Hospital-Associated Disability Hospitalization can itself act as a major physiological stressor. Older patients frequently experience prolonged bed rest, inadequate protein and calorie intake, sleep disruption, repeated procedures, polypharmacy, catheterization, cognitive disorientation, and reduced interaction with their normal environment. A frail patient may therefore survive the original acute disease but fail to regain pre-hospital functional ability. Hospital-associated disability can subsequently increase dependence, caregiver burden, institutionalization, and future healthcare utilization. 5.4 Interpretation of Readmission Findings The readmission findings require careful interpretation. Clinically measured frailty was generally associated with increased readmission. For example, Hao et al. demonstrated a significant increase in long-term readmission, Wang et al. reported an adjusted HR exceeding 2 for frail patients, and the prospective Jordanian study found more than twice the 30-day readmission rate among moderate/severe frailty patients. In contrast, HFRS-based administrative analyses were less consistent. One explanation is competing risk. Patients with severe frailty may die before they can be readmitted, remain hospitalized longer, be transferred to institutional care, or receive palliative rather than readmission-focused treatment. Consequently, lower observed readmission does not necessarily imply better prognosis. 5.5 Clinical Implications Frailty assessment should not simply identify patients with poor prognosis. Its greater value is in identifying patients who require a different model of care. A positive frailty assessment should prompt consideration of comprehensive geriatric assessment, early mobilization and physiotherapy, occupational therapy, medication reconciliation and deprescribing, nutritional evaluation, delirium prevention, fall and pressure-injury prevention, cognitive assessment, assessment of caregiver availability, early discharge planning, post-discharge follow-up, and individualized goals-of-care discussions where appropriate. Frailty should not automatically be interpreted as a reason to withhold active treatment. Rather, it provides additional prognostic information that should be combined with illness severity, reversibility, patient values, quality of life, and treatment preferences. 5.6 Comprehensive Geriatric Assessment Frailty screening and comprehensive geriatric assessment should be viewed as complementary. Frailty screening identifies vulnerable patients. Comprehensive geriatric assessment then evaluates medical problems, medication burden, cognition, psychological health, mobility, falls, continence, nutrition, functional ability, social environment, and patient preferences. This approach may identify modifiable contributors to vulnerability. 5.7 Health-System Implications Frailty is also relevant to service planning. Frail older adults disproportionately contribute to hospital bed occupancy, prolonged stays, rehabilitation requirements, recurrent hospitalization, long-term care placement, and community health-service utilization. Systematic frailty identification may therefore improve allocation of geriatric, rehabilitation, and transitional-care resources. However, screening without a linked intervention pathway is unlikely to produce meaningful improvement. 6. Risk-of-Bias Considerations The findings of this review should be interpreted in the context of several methodological issues. First, observational evidence cannot definitively establish that frailty itself causes adverse outcomes. Frailty is associated with multiple potential confounders, including advanced age, multimorbidity, disability, dementia, malnutrition, polypharmacy, socioeconomic disadvantage, and previous healthcare utilization. Although most contemporary studies adjusted for major confounders, residual confounding is likely. Second, frailty instruments measure overlapping but non-identical constructs. A patient categorized as frail according to an FI may not necessarily receive the same classification using the CFS or HFRS. Third, administrative measures are influenced by coding practices. Fourth, assessment during acute illness can introduce measurement error if temporary acute deterioration is incorrectly classified as baseline frailty. These limitations explain some of the observed heterogeneity. 7. Strengths of the Review 1. Use of the PRISMA 2020 reporting framework. 2. Evaluation of both mortality and hospital readmission. 3. Inclusion of additional clinically meaningful outcomes. 4. Inclusion of prospective and large administrative datasets. 5. Representation of multiple international healthcare systems. 6. Evaluation of several major frailty instruments. 7. Formal risk-of-bias assessment using the QUIPS framework. 8. Separate interpretation of clinical and administrative frailty measures. 8. Limitations Several limitations should be acknowledged. First, substantial heterogeneity prevented meaningful de novo pooling of all included studies. Second, studies differed considerably in patient populations. Third, frailty was measured using multiple instruments and thresholds. Fourth, follow-up periods ranged from hospitalization to several years. Fifth, definitions of hospital readmission differed between studies. Sixth, administrative databases may be affected by coding errors and incomplete clinical information. Seventh, single-center studies may have limited external generalizability. Eighth, publication bias cannot be excluded because studies demonstrating significant relationships between frailty and poor outcomes may be more likely to be published. 9. Implications for Future Research 1. Standardized definitions of frailty. 2. Standardized frailty thresholds. 3. Consistent 30-, 90-, and 365-day outcome reporting. 4. Direct head-to-head comparison of frailty tools. 5. Better distinction between baseline frailty and acute illness severity. 6. Integration of cognition, nutrition, and sarcopenia. 7. External validation across different countries. 8. Frailty research in low- and middle-income countries. 9. Intervention trials rather than prognostic studies alone. 10. Evaluation of whether frailty-guided discharge pathways reduce readmission. 11. Cost-effectiveness analyses. 12. Assessment of patient-centered outcomes such as quality of life and days alive at home. 13. Evaluation of dynamic changes in frailty following hospitalization. 10. Conclusion Frailty is a powerful marker of vulnerability among hospitalized older adults and provides clinically meaningful prognostic information beyond chronological age alone. The evidence most consistently demonstrates that frailty is associated with increased in-hospital mortality, increased short- and long-term mortality, prolonged hospital stay, functional deterioration, dependence in activities of daily living, institutionalization, and reduced likelihood of returning home. Frailty is also associated with hospital readmission in many clinical settings, although the magnitude of this relationship varies more substantially than the association with mortality. Clinical frailty instruments such as the Clinical Frailty Scale and multidimensional Frailty Index appear particularly useful for bedside risk stratification. Electronic, administrative, and laboratory approaches may facilitate large-scale screening but should not necessarily replace clinical assessment. Routine frailty evaluation at hospital admission can help identify patients who may benefit from comprehensive geriatric assessment, early rehabilitation, nutritional support, medication optimization, delirium prevention, individualized discharge planning, and coordinated transitional care. Frailty assessment should therefore be regarded not merely as a prognostic exercise, but as an opportunity to deliver more individualized and appropriate healthcare to vulnerable older adults. Declarations Ethics Approval Not applicable. This systematic review used previously published data and did not involve direct participation of human subjects. Consent to Participate Not applicable. Consent for Publication Not applicable. Funding No specific funding was received for this systematic review. Conflicts of Interest The authors declare no conflicts of interest. Data Availability All data evaluated in this systematic review were obtained from previously published peer-reviewed studies.

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