A state of increased “vulnerability”, resulting from aging-associated decline in reserve and function across multiple physiologic systems, may lead to “multidimensional frailty”, considered a dynamic state with a lost in one or more domains (physical, mental, nutritional, and social) which increases the risk of adverse outcomes. Moreover, the main problem is the identification and quantization of this condition. According to this definition, the most used tool for multidimensional frailty recognition is the “Rockwood deficit–accumulation frailty index” (FI), expressed as the ratio between the number of deficits found in a patient and the total number of deficits investigated. Recently, we have developed and validated an Italian modified version of FI, the Italian Frailty index (IFi) but, despite the numerous advantages and reliability, IFi shows a prolonged administration time (≈30 min.). To overcome this limitation, we have recently created and validated a rapid tool for the evaluation of multidimensional frailty called “Fr-AGILE” that has been proved to be reliable with a diagnostic power comparable to that of the IFi with a high predictive value on adverse clinical outcomes, and more importantly, with an administration time of less than 5 min. Fr-AGILE was built by selecting among the 40 items of Italian version of Frailty Index the 10 ones most predictive of mortality. The total score ranged from 0 to 10, with higher score indicate more severe frailty. The Fr-AGILE scores were divided into degrees: absent (0), light (1–3), moderate (4–7) and severe frailty (8–10). However, Fr-AGILE has been validated in non-institutionalized out-patients.

We aimed to validate Fr-AGILE as a new tool for the assessment of multidimensional frailty in various hospital care settings.

The study protocol will evaluate the degree of frailty of each patient through the Edmonton Frail Scale (EFS, a validated method) and Fr-AGILE (a method to be validated). The tools will be administered in the clinical stability phase (pre-discharge) and a series of intra-hospital events will be evaluated. Mortality, disability (a reduction in the ADL score ≥1) and new hospitalizations at 6, 13, and 24 months of follow-up will be collected via a telephone interview.

HIGH INTENSITY CARE (HC): Division of Sub-intensive Medical Therapy, AOU Federico II, Naples, Italy (n.94); Division of Emergency Medicine AOU San Giovanni di Dio e Ruggi di Aragona, Salerno, Italy (n.32); Division of Internal Medicine DEA and clinical Complexity, AORN Antonio Cardarelli, Naples, Italy (n.7); for a total of 133 patients.

LOW-MODERATE INTENSITY CARE (LC): Division of Rehabilitation and Metabolic Internal Medicine AOU Federico II, Naples, Italy (n.57); Division of Geriatrics - AORN San Giuseppe Moscati, Avellino, Italy (n.59); Division of Internal Medicine, AOU San Giovanni di Dio e Ruggi di Aragona, Salerno, Italy (n.49); Division of Geriatrics, AO Sant'Anna e San Sebastiano, Caserta, Italy (n.53); Division of Internal Medicine 3, AORN Antonio Cardarelli, Naples, Italy (n.78); for a total of 296 patients.

The total number of enrolled patients is n. 429.

The mean age of the sample is 77.8±6.7 with a percentage of male of 57.1%.

The clinical problems for the admission were: pneumonia and re-acutization of chronic obstructive pulmonary disease (25.4%), decompensated heart failure (14.5%), severe anemia (11.4%), syncope (8.5%), severe sepsis and septic shock (8.3%), acute kidney failure (7.1%), hyperkinetic arrhythmias (4.3%), glucometabolic decompensations (4.0%), gastrointestinal hemorrhage (3.7%), urinary tract infections (3.7%).

The mean score of EFS was 8.31±3.4 (8.7±3.1 in HC vs. 8.1±3.5 in LC, p=0.20) while fr-AGILE was 3.5±2.1 (3.6±2.1 in HC vs. 3.5±2.1, p=0.80). Comorbidity evaluated by CIRS-C (cumulative) was 1.5±1.4 (1.6±1.3 in HC vs.1.5±1.4, p=0.78) and CIRS-G (gravity) was 2.7±2.8 (4.2±2.5 in HC vs.1.8 ±0.3 in LC, p=0.01). Disability evaluated by ADL was 1.8±2.1 (1.9±2.1 in HC vs. 1.8±2.2 in LC, p=0.51) and IADL was 3.3±2.8 (3.4±2.7 in HC vs. 3.2±2.9 in LC, p=0.04). Cognitive impairment evaluated by MMSE was 23.3±23.1 (24.6±28.8 in HC vs 22.5±19.2 in LC, p=0.41) while depressive symptomatology evaluated by GDS was 4.7±3.5 (5.0±3.5 in HC vs. 4.6±3.4 in LC, p=0.53).

Thus, the sample studied shows a medium level of multidimensional frailty identified with both scales associated to a moderate degree of comorbidity, disability cognitive impairment and depressive symptomatology. The follow-up fr-AGILE is expected to be an effective and easily applicable tool for the diagnosis and estimation of the degree of multidimensional frailty of in-hospital elderly patients.

Background

Frailty constitutes a significant challenge in geriatric medicine, characterized by multisystem physiological decline that increases susceptibility to adverse outcomes and loss of autonomy. Health literacy is a critical determinant in the prevention and management of frailty, facilitating patient comprehension, adherence to therapeutic regimens, and engagement in health-promoting behaviours. Conversely, inadequate health literacy correlates with poorer clinical outcomes and accelerated frailty progression. Both international frameworks, such as WHO-ICOPE, and regional policies exemplified by the Tuscany Region, advocate for early risk stratification and multidisciplinary intervention. In alignment with these guidelines, a proactive protocol has been implemented to enable early detection and individualized management of frailty within primary care settings.

Methods

This observational study involves 75-year-old subjects registered with a GP affiliated with AFT Novoli-Piagge, contacted by the research team via telephone. After informed consent, participants undergo first-level frailty screening and chair rise tests at the ‘Le Piagge’ House of Community, with concurrent assessment of health literacy (HL). Those scoring ≥6.5 at the frailty test proceed to a second-level multidimensional assessment covering ICOPE domains: nutrition, sarcopenia, hearing, vision, mobility, depression, cognition, and autonomy. Results are sent to the GP for evaluation of referral to geriatric outpatient services. Participants who screen negative undergo a 12-month follow-up with repeated first-level tests. Longitudinal follow-ups include repeated multidimensional assessments at 6 (T1), 12 (T2), and 24 months (T3), with HL reassessed at 12 months. The study is currently ongoing.

Results

The initial cohort included 436 seventy-five-year-old subjects invited to participate in the study, of whom 341 were successfully reached. Over 75% of the subjects were contacted, indicating a successful preliminary engagement phase. However, a high refusal rate was observed (174 out of 341, 51%), and 46 subjects did not complete the evaluation process despite initial contact. Additionally, a considerable number of invalid phone numbers (n=44) and unreachable subjects (n=51) highlighted challenges related to the quality of contact information, emphasizing the need for enhanced contact strategies in future recruitment efforts.

Regarding the health literacy level of the 122 evaluated subjects, responses to the question about autonomy in understanding informational materials (“How often do you need someone to help you when reading instructions, brochures, or other materials provided by your doctor or pharmacist?”) revealed that only 42.5% of participants (out of 120 responses) reported never needing assistance. This finding suggests that more than half of the participants may face difficulties comprehending health-related information. In the first-level frailty screening, 19.7% of the assessed subjects (24 out of 122) scored 6.5 or higher, indicating a positive screening result. This underscores the importance of early identification and timely intervention to effectively manage frailty risk. Of the 24 subjects identified as frail, 22 completed the multidimensional assessment (9 males and 13 females), while 2 did not attend the scheduled evaluation appointment. The second-level multidimensional screening confirmed frailty in all 22 individuals, with at least one positive domain detected per subject.

Main implication of the research

The main implications of this research are that early identification and multidimensional assessment of frailty can improve patient outcomes by enabling timely interventions. Our findings emphasize the critical role of primary care professionals and highlight the need to enhance health literacy, which is essential not only to increase participation in screening programs but also to empower older adults in managing their health effectively. Improving health literacy can reduce barriers to care, facilitate better understanding of health information, and promote adherence to preventive and therapeutic measures. These results support the development of integrated care pathways and highlight areas for future research on optimizing frailty management and health literacy interventions.

Background

Frailty reflects vulnerability to adverse outcomes and represents a growing public health challenge in aging populations. This study evaluated how frailty relates to the risk of several major diseases, to guide prevention and risk stratification.

Methods

We used data from 20975 adults enrolled in the population-based Moli-sani study (age≥35 yr, 48% men; Italy 2005-10, median follow-up 15 yr; Project Age-It). Frailty was measured at baseline using a 29-item Frailty Index (FI), and multivariable Cox models were used to estimate hazard ratios (HR) for 18 health outcomes, accounting for cause-specific competing risks.

Results

Higher frailty was associated with higher risk of a wide range of adverse outcomes. The strongest associations were observed for cardiometabolic conditions: each 1-standard deviation increase in the FI was associated with a higher risk of diabetes (HR=1.82; 95% CI: 1.73–1.92; 1541 events) and coronary heart disease (HR=1.56; 1.46–1.66; 979). The FI was associated with an increased risk of Parkinson (HR=1.24; 1.05–1.47; 158) and non-Alzheimer-dementia (HR=1.31; 1.11–1.54; 150), but not with Alzheimer. Associations with cancer varied by site: FI was associated with increased risk of lung cancer (HR=1.30; 1.10–1.55; 151), decreased risk for colorectal cancer (HR=0.84; 0.74–0.96; 313), and was not associated with breast, prostate, or renal cancer. FI was associated with higher risk of hospitalization (HR=1.31; 1.28–1.34; 13465), all-cause mortality (HR=1.35; 1.30–1.40; 2631), and cause-specific mortality, with HRs ranging from 1.17 for cancer (927) to 1.50 for cardiovascular death (918).

Conclusions

Frailty predicted a broad range of adverse outcomes, with strongest associations for diabetes, cardiovascular diseases, and all-cause mortality. Its impact on neurodegenerative and cancer outcomes was more mixed. These results support including frailty valuation in prevention strategies and chronic disease surveillance, particularly in aging populations.

Background: The aging population poses an increasing challenge for healthcare systems, bringing with it a higher prevalence of age-associated conditions such as frailty and comorbidity. Both conditions are predictive of adverse outcomes such as hospitalizations, institutionalization, and mortality, but their relative contribution to clinical outcomes remains a matter of ongoing research. Comorbidity, defined as the coexistence of two or more chronic diseases, is highly prevalent among older adults and tends to remain stable in advanced age. Frailty, on the other hand, is a distinct clinical state characterized by reduced physiological reserves and increased vulnerability to stressors. Its prevalence increases progressively with age and is determined not only by coexisting diseases but also by functional, cognitive, and social deficits. However, the two concepts are not interchangeable and in spite of their bidirectional relationship the relative contribution of comorbidity—independently or in combination with frailty—to medium- to long-term mortality risk remains to be clarified. We aimed to determine whether comorbidities or frailty have a greater impact on mortality in a well-characterized cohort of community-dwelling older adults from the FRASNET study.

Methods: The study is based on the prospective FRASNET cohort, consisting of adults aged ≥65 years enrolled between 2017 and 2020, with mortality follow-up through 2023. Frailty was assessed using a modified Frailty Phenotype (FP), replacing unintentional weight loss with low BMI and a Frailty Index (FI), calculated from 49 variables collected through a comprehensive geriatric assessment, based on the deficit accumulation approach described by Teou et al. We used the number of chronic drugs as a proxy of comorbidities. The primary outcome was all-cause mortality at 6 years from enrollment. The association between frailty, comorbidity, and mortality was analyzed using age- and sex-adjusted Cox proportional hazards models.

Results: Out of the 1250 participants enrolled in the FRASNET study, 1114 were considered for this analysis. Exclusions comprised 19 institutionalized patients, 91 individuals with more than 20% missing data for the Frailty Index calculation, and 26 participants with missing body composition data. The study sample had a median age of 72 years and was composed for the 60.5% by females. Age and sex adjusted cox regression analyses showed a stronger association between frailty and mortality (HR 59.5, 95% C.I. 7.0 – 506.5, p < 0.001) than comorbidity (HR 1.17, 95% C.I. 1.1 -1.3, p = 0.002).

Conclusions: In this cohort of well-characterized community-dwelling older adults, frailty emerged as a stronger predictor of six-year all-cause mortality than comorbidity. Unlike comorbidity, which primarily reflects the presence of chronic diseases—many of which may be well-managed and thus have a limited impact on mortality—frailty captures a broader range of vulnerabilities, including functional impairments and social deficits. These multidimensional aspects can significantly influence hard outcomes such as death and are essential to consider in the prognostication of geriatric patients. Integrating comprehensive frailty assessments into routine clinical practice may therefore provide a more accurate estimation of mortality risk and help tailor individualized care strategies in older populations.

Background:
Multimorbidity, commonly defined as the coexistence of two or more chronic conditions, represents a growing challenge in clinical and public health settings. However, its predictive value for hospital outcomes and its relationship with frailty are still unclear.

Objective:
This study aimed to compare quantitative and qualitative definitions of multimorbidity and to evaluate the prognostic performance of these multimorbidity patterns alongside a frailty index (FI) retrospectively calculated using a health deficit accumulation model.

Methods:
We analyzed 4,244 patients (65+ years) from the ReportAGE Study using data collected between 2011 and 2021. A 30-item FI was computed, and multimorbidity was assessed both quantitatively (disease count) and qualitatively (latent class analysis, LCA). Cox regression models examined associations with in-hospital mortality and length of stay (LoS), adjusting for covariates including acute illnesses and patient-reported outcomes.

Results:

4,244 patients aged 84.9 (SD:6.7) years and predominantly women (56.7%) were included in the study. The prevalence of quantitative multimorbidity and frailty was relatively high (92.9% and 61.6%, respectively). The median (IQR) LoS was 9 (6-12) days with a mortality rate of 7.5%. Despite the very high prevalence of quantitative multimorbidity, simple disease count was not independently associated with in-hospital mortality or LoS. In contrast, frailty showed strong, independent predictive value for both outcomes. LCA identified five clinically meaningful multimorbidity patterns: the neurovascular cluster had the lowest mortality rate and LoS and was then used as the reference; the cardiorespiratory and renal cluster and the mixed cluster were associated with both increased mortality and LoS; the sensory and HF cluster and the geriatric syndromes cluster were instead characterized by high mortality and low LoS. Incorporating LCA patterns significantly improved model discrimination compared to disease count alone (C-index 0.70 vs. 0.65, p<0.001). Incorporating both frailty and multimorbidity latent classes into the model further improved discrimination (C-index: 0.75), compared to models including only multimorbidity classes (C-index: 0.70) or frailty alone (C-index: 0.72).

Conclusion:
Frailty outperforms disease count in predicting hospital outcomes, while qualitative multimorbidity patterns offer additional prognostic insight. Future models should incorporate both frailty and disease clustering to enhance risk stratification in older inpatients.

Extended Abstract

Background:
Frailty reflects a state of increased vulnerability in aging populations. Traditional clinical assessments, while informative, may miss early or biologically driven stages of frailty. The Novara Cohort Study (NCS) integrates multidimensional data, including biomarkers, to enhance frailty characterization and predictive power for adverse health outcomes.

Methods:
We examined baseline data from 300 NCS participants (mean age 62.1 ± 17.5 years). Frailty was assessed using a 38-item Clinical Frailty Index (FI) and a laboratory-based frailty index (FI-Lab) derived via multivariate regression from 75 blood biomarkers. Thirty-one biomarkers significantly associated with FI were identified; 28 with known reference ranges were used to calculate FI-Lab scores.

Results:
FI and FI-Lab scores were moderately correlated (r = 0.38, p < 0.001). FI-Lab correlated more strongly with chronological age (r = 0.34) than the clinical FI (r = 0.25). The combined use of FI and FI-Lab improved the discriminative performance of cognitive impairment (MoCA <26) with an AUROC of 0.649 (vs. 0.585 with FI alone), and moderate IDI and NRI gains. Associative prediction value for physical decline (SPPB <10) was limited. Key biomarkers associated with frailty included markers of inflammation (CRP, RDW), metabolic dysfunction (HbA1c, BUN), and decreased renal and nutritional status (eGFR, albumin).

Conclusions:
The integrated FI-Lab provides a biologically grounded, scalable measure of frailty that complements clinical tools and enhances early detection of cognitive decline. This multidimensional approach supports precision aging strategies and may inform targeted interventions to mitigate frailty-related risks.