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Gait smoothness, perceived when a person walks continuously and uninterruptedly, is associated with an undisrupted gait pattern, good sensorimotor control, and a lower risk of falling. The spectral arc length (SPARC) is a quantitative metric proposed for the evaluation of movement smoothness from the signal obtained by wearable sensors. In this small exploratory case–control study, older persons with and without a history of injurious falls underwent a turn-test while wearing an accelerometer: gait smoothness was estimated by calculating SPARC during the straight and turning phases. Cases seemed to exhibit lower SPARC values during the turning phase, in comparison with control.

Background: Electronic health databases are used to identify people at risk of poor outcomes. Using electronic regional health databases (e-RHD), we aimed to develop and validate a frailty index (FI), compare it with a clinically based FI, and assess its association with health outcomes in community-dwellers with SARS-CoV-2. Methods: Data retrieved from the Lombardy e-RHD were used to develop a 40-item FI (e-RHD-FI) in adults (i.e., aged ≥18 years) with a positive nasopharyngeal swab polymerase chain reaction test for SARS-CoV-2 by May 20, 2021. The considered deficits referred to the health status before SARS-CoV-2. The e-RHD-FI was validated against a clinically based FI (c-FI) obtained from a cohort of people hospitalized with COVID-19 and in-hospital mortality was evaluated. e-RHD-FI performance was evaluated to predict 30-day mortality, hospitalization, and 60-day COVID-19 WHO clinical progression scale, in Regional Health System beneficiaries with SARS-CoV-2. Results: We calculated the e-RHD-FI in 689,197 adults (51.9% females, median age 52 years). On the clinical cohort, e-RHD-FI correlated with c-FI and was significantly associated with in-hospital mortality. In a multivariable Cox model, adjusted for confounders, each 0.1-point increment of e-RHD-FI was associated with increased 30-day mortality (Hazard Ratio, HR 1.45, 99% Confidence Intervals, CI: 1.42–1.47), 30-day hospitalization (HR per 0.1-point increment = 1.47, 99%CI: 1.46–1.49), and WHO clinical progression scale (Odds Ratio = 1.84 of deteriorating by one category, 99%CI 1.80–1.87). Conclusion: The e-RHD-FI can predict 30-day mortality, 30-day hospitalization, and WHO clinical progression scale in a large population of community-dwellers with SARS-CoV-2 test positivity. Our findings support the need to assess frailty with e-RHD.

Background Little is known on how frailty influences clinical outcomes in persons with specific multimorbidity patterns. Aims To investigate the interplay between multimorbidity and frailty in the association with mortality in older individuals living in nursing homes (NH). Methods We considered 4,131 NH residents aged 60 years and over, assessed through the interRAI LTCF instrument between 2014 and 2018. Follow-up was until 2019. Considering four multimorbidity patterns identified via principal component analysis, subjects were stratified in tertiles (T) with respect to their loading values. Frailty Index (FI) considered 23 variables and a cut-off of 0.24 distinguished between high and low frailty levels. For each pattern, all possible combinations of tertiles and FI were evaluated. Their association (Hazard Ratio [HR] and 95% confidence interval) with mortality was tested in Cox regression models. Results In the heart diseases and dementia and sensory impairments patterns, the hazard of death increases progressively with patterns expression and frailty severity (being HR T3 vs. T1 = 2.36 [2.01–2.78]; HR T3 vs. T1 = 2.12 [1.83–2.47], respectively). In heart, respiratory and psychiatric diseases and diabetes, musculoskeletal and vascular diseases patterns, frailty seems to have a stronger impact on mortality than patterns’ expression. Discussion Frailty increases mortality risk in all the patterns and provides additional prognostic information in NH residents with different multimorbidity patterns. Conclusions These findings support the need to routinely assess frailty. Older people affected by specific groups of chronic diseases need a specific care approach and have high risk of negative health outcomes.

Background: There is a growing interest in generating precise predictions of survival to improve the assessment of health and life-improving interventions. We aimed to (a) test if observable characteristics may provide a survival prediction independent of chronological age; (b) identify the most relevant predictors of survival; and (c) build a metric of multidimensional age. Methods: Data from 3 095 individuals aged >= 60 from the Swedish National Study on Aging and Care in Kungsholmen. Eighty-three variables covering 5 domains (diseases, risk factors, sociodemographics, functional status, and blood tests) were tested in penalized Cox regressions to predict 18-year mortality. Results: The best prediction of mortality at different follow-ups (area under the receiver operating characteristic curves [AUROCs] 0.878-0.909) was obtained when 15 variables from all 5 domains were tested simultaneously in a penalized Cox regression. Significant prediction improvements were observed when chronological age was included as a covariate for 15- but not for 5- and 10-year survival. When comparing individual domains, we find that a combination of functional characteristics (ie, gait speed, cognition) gave the most accurate prediction, with estimates similar to chronological age for 5- (AUROC 0.836) and 10-year (AUROC 0.830) survival. Finally, we built a multidimensional measure of age by regressing the predicted mortality risk on chronological age, which displayed a stronger correlation with time to death (R = -0.760) than chronological age (R = -0.660) and predicted mortality better than widely used geriatric indices. Conclusions: Combining easily accessible characteristics can help in building highly accurate survival models and multidimensional age metrics with potentially broad geriatric and biomedical applications.

Objectives: The aim was to characterize multimorbidity patterns in a large sample of older individuals living in nursing homes (NHs) and to investigate their association with mortality, also considering the effect of functional status. Design: Observational and retrospective study. Setting and Participants: We analyzed data on 4131 NH residents in Italy, aged 60 years and older, assessed through the interRAI long-term care facility instrument. Entry date was between 2014 and 2018, and participants were followed until 2019. Methods: Multimorbidity patterns were identified through principal component analysis; for the identified components, subjects were stratified in quintiles (Q) with respect to their loading values, with the higher quantiles indicating greater expression of the component's pattern. Their association [hazard ratio (HR) and 95% CI] with mortality was tested in Cox regression models. Analyses were stratified by disability status. Results: Four patterns of multimorbidity were identified: (1) heart diseases; (2) dementia and sensory impairments; (3) heart, respiratory, and psychiatric diseases; and (4) diabetes, musculoskeletal, and vascular diseases. For the heart diseases pattern [HR Q5 vs Q1 = 1.83 (1.53–2.20)] and the dementia and sensory impairments pattern [HR Q5 vs Q1 = 1.23 (1.06–1.42)], as the specific multimorbidity expression increases, the risk of mortality increases. On stratifying by disability status, the association between the multimorbidity patterns and mortality was not always present. Conclusions and Implications: Different multimorbidity patterns are differentially associated with mortality in older residents of NHs, confirming that multimorbidity's prognosis is strictly dependent on the underlying disease combinations. This knowledge may be useful to implement personalized preventive and therapeutic care pathways for institutionalized older adults, which respond to individuals’ health needs.

In patients with COVID-19, frailty has been shown to better predict outcomes than age alone. We investigated factors associated with mechanical ventilation (MV) during hospitalization for COVID-19 among older adults in a multicentre study during the first two waves in Italy. Using data from the FRACOVID project, we included consecutive patients admitted to the participating centres during the first and second waves. We recorded sociodemographics, comorbidities, time since symptom onset, ventilatory support at admission, and chest X-ray findings. Frailty was assessed using a frailty index (FI). Results are reported as hazard ratios (HR) with 95%CI. 1,344 patients were included; 487 females (36.2%), median age 68 (56; 79) years; 52.4% had hypertension, 10.6% had chronic obstructive pulmonary disease, 15.2% were obese. Median FI was 0.088 (0.03, 0.20), and 67% had bilateral consolidations at admission. Median time since symptom onset was 7 days (4, 10). During hospitalization, 47 patients (3.6%, 95%CI 0.33-13.6%) received MV. Multivariable Cox regression analysis found that the likelihood of intubation decreased with increasing age (HR 0.945 (95%CI 0.921-0.969), p[removed]110bpm (HR 3.429 (95%CI 1.583-7.429), p=0.0018), and need for continuous positive airway pressure (CPAP) at admission (HR 2.626 (95%CI 1.330-5.186), p=0.0054) were significantly associated with a greater likelihood of intubation. Older patients are less likely to receive intubation, while those with heart rate >110 bpm and need for CPAP at admission are more likely to receive MV during hospitalization for COVID-19.

Background Most COVID-19-related deaths have occurred in older persons with comorbidities. Specific patterns of comorbidities related to COVID-19 deaths have not been investigated. Methods A random sample of 6085 individuals in Italy who died in-hospital with confirmed COVID-19 between February and December 2020 were included. Observed to expected (O/E) ratios of disease pairs were computed and logistic regression models were used to determine the association between disease pairs with O/E values ≥ 1.5. Results Six pairs of diseases exhibited O/E values ≥ 1.5 and statistically significant higher odds of co-occurrence in the crude and adjusted analyses: (1) ischemic heart disease and atrial fibrillation, (2) atrial fibrillation and heart failure, (3) atrial fibrillation and stroke, (4) heart failure and COPD, (5) stroke and dementia, and (6) type 2 diabetes and obesity. Conclusion In those deceased in-hospital due to COVID-19 in Italy, disease combinations defined by multiple cardio-respiratory, metabolic, and neuropsychiatric diseases occur more frequently than expected. This finding indicates a need to investigate the possible role of these clinical profiles in the chain of events that lead to death in individuals who have contracted SARS-CoV-2.

Introduction: We investigate dementia risk in older adults with different disease patterns and explore the role of inflammation and apolipoprotein E (APOE) genotype. Methods: A total of 2,478 dementia-free participants with two or more chronic diseases (ie, multimorbidity) part of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) were grouped according to their multimorbidity patterns and followed to detect clinical dementia. The potential modifier effect of C-reactive protein (CRP) and apolipoprotein E (APOE) genotype was tested through stratified analyses. Results: People with neuropsychiatric, cardiovascular, and sensory impairment/cancer multimorbidity had increased hazards for dementia compared to the unspecific (Hazard ration (HR) 1.66, 95% confidence interval [CI] 1.13-2.42; 1.61, 95% CI 1.17-2.29; 1.32, 95% CI 1.10-1.71, respectively). Despite the lack of statistically significant interaction, high CRP increased dementia risk within these patterns, and being APOE epsilon 4 carriers heightened dementia risk for neuropsychiatric and cardiovascular multimorbidity. Discussion: Individuals with neuropsychiatric, cardiovascular, and sensory impairment/cancer patterns are at increased risk for dementia and APOE epsilon 4, and inflammation may further increase the risk. Identifying such high-risk groups might allow tailored interventions for dementia prevention.

Background: We evaluated whether frailty and multimorbidity predict in-hospital mortality in patients with COVID-19 beyond chronological age. Method: A total of 165 patients admitted from March 8th to April 17th, 2020, with COVID-19 in an acute geriatric ward in Italy were included. Predisease frailty was assessed with the Clinical Frailty Scale (CFS). Multimorbidity was defined as the co-occurrence of >= 2 diseases in the same patient. The hazard ratio (HR) of in-hospital mortality as a function of CFS score and number of chronic diseases in the whole population and in those aged 70+ years were calculated. Results: Among the 165 patients, 112 were discharged, 11 were transferred to intensive care units, and 42 died. Patients who died were older (81.0 vs 65.2 years, p < .001), more frequently multimorbid (97.6 vs 52.8%; p < .001), and more likely frail (37.5 vs 4.1%; p < .001). Less than 2.0% of patients without multimorbidity and frailty, 28% of those with multimorbidity only, and 75% of those with both multimorbidity and frailty died. Each unitary increment in the CFS was associated with a higher risk of in-hospital death in the whole sample (HR = 1.3; 95% CI = 1.05-1.62) and in patients aged 70+ years (HR = 1.29; 95% CI = 1.04-1.62), whereas the number of chronic diseases was not significantly associated with higher risk of death. The CFS addition to age and sex increased mortality prediction by 9.4% in those aged 70+ years. Conclusions: Frailty identifies patients with COVID-19 at risk of in-hospital death independently of age. Multimorbidity contributes to prognosis because of the very low probability of death in its absence.

Objectives: The aim was to evaluate patterns of multimorbidity that increase the risk of institutionalization in older persons, also exploring the potential buffering effect of formal and informal care. Design: Prospective cohort study. Setting and Participants: The population-based Swedish National study on Aging and Care in Kungsholmen, Stockholm, Sweden. Measures: In total, 2571 community-dwelling older adults were grouped at baseline according to their underlying multimorbidity patterns, using a fuzzy c-means cluster algorithm, and followed up for 6 years to test the association between multimorbidity patterns and institutionalization. Results: Six patterns of multimorbidity were identified: psychiatric diseases; cardiovascular diseases, anemia, and dementia; metabolic and sleep disorders; sensory impairments and cancer; musculoskeletal, respiratory, and gastrointestinal diseases; and an unspecific pattern including diseases of which none were overrepresented. In total, 110 (4.3%) participants were institutionalized during the follow-up, ranging from 1.7% in the metabolic and sleep disorders pattern to 8.4% in the cardiovascular diseases, anemia, and dementia pattern. Compared with the unspecific pattern, only the cardiovascular diseases, anemia, dementia pattern was significantly associated with institutionalization [relative risk ratio ( RRR) = 2.23; 95% confidence interval (CI) 1.07-4.65)], after adjusting for demographic characteristics and disability status at baseline. In stratified analyses, those not receiving formal care in the psychiatric diseases pattern (RRR 3.34; 95% CI 1.20-9.32) and those not receiving formal or informal care in the 'cardiovascular diseases, anemia, dementia' pattern (RRR 2.99; 95% CI 1.20-7.46; RRR 2.79; 95% CI 1.16-6.71, respectively) had increased risks of institutionalization. Conclusions and Implications: Older persons suffering from specific multimorbidity patterns have a higher risk of institutionalization, especially if th

The presence of multiple chronic conditions (i.e., multimorbidity) increases the risk of hospitalisation in older adults. We aimed to examine the association between different multimorbidity patterns and unplanned hospitalisations over 5 years. To that end, 2,250 community-dwelling individuals aged 60 years and older from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) were studied. Participants were grouped into six multimorbidity patterns using a fuzzy c-means cluster analysis. The associations between patterns and outcomes were tested using Cox models and negative binomial models. After 5 years, 937 (41.6%) participants experienced at least one unplanned hospitalisation. Compared to participants in the unspecific multimorbidity pattern, those in the cardiovascular diseases, anaemia and dementia pattern, the psychiatric disorders pattern and the metabolic and sleep disorders pattern presented with a higher hazard of first unplanned hospitalisation (hazard ratio range: 1.49-2.05; p < 0.05 for all), number of unplanned hospitalisations (incidence rate ratio (IRR) range: 1.89-2.44; p < 0.05 for all), in-hospital days (IRR range: 1.91-3.61; p < 0.05 for all), and 30-day unplanned readmissions (IRR range: 2.94-3.65; p < 0.05 for all). Different multimorbidity patterns displayed a differential association with unplanned hospital care utilisation. These findings call for a careful primary care follow-up of older adults with complex multimorbidity patterns.

Introduction: Delirium is a frequent condition in hospitalized older patients and it usually has a negative prognostic value. A direct effect of SARS-COV-2 on the central nervous system (CNS) has been hypothesized. Objective: To evaluate the presence of delirium in older patients admitted for a suspected diagnosis of COVID-19 and its impact on in-hospital mortality. Setting and subjects: 91 patients, aged 70-years and older, admitted to an acute geriatric ward in Northern Italy from March 8th to April 17th, 2020. Methods: COVID-19 cases were confirmed by reverse transcriptase-polymerase chain reaction assay for SARS-Cov-2 RNA from nasal and pharyngeal swabs. Delirium was diagnosed by two geriatricians according to the Diagnostic and Statistical Manual of Mental Disorders V (DMS V) criteria. The number of chronic diseases was calculated among a pre-defined list of 60. The pre-disease Clinical Frailty Scale (CFS) was assessed at hospital admission. Results: Of the total sample, 39 patients died, 49 were discharged and 3 were transferred to ICU. Twenty-five patients (27.5%) had delirium. Seventy-two percent of patients with delirium died during hospitalization compared to 31.8% of those without delirium. In a multivariate logistic regression model adjusted for potential confounders, patients with delirium were four times more likely to die during hospital stay compared to those without delirium (OR= 3.98;95%CI = 1.05-17.28; p = 0.047). Conclusions: Delirium is common in older patients with COVID-19 and strongly associated with in-hospital mortality. Regardless of causation, either due to a direct effect of SARS-COV-2 on the CNS or to a multifactorial cause, delirium should be interpreted as an alarming prognostic indicator in older people.

Frailty is a syndrome characterized by reduced physiological reserves, increased vulnerability to stressors and adverse health outcomes. Frailty can change the prognosis and treatment approach of several chronic diseases, including heart failure (HF). The aim of this study was to conduct a systematic review and meta-analysis assessing the association of HF with frailty and pre-frailty. We employed PRISMA guidelines for reporting the results. We searched PubMed, Web of Science, and Embase from 01/01/2002 to 29/11/2019.The quality of the studies was evaluated with the Newcastle Ottawa Scale. Pooled estimates were obtained through random-effect models and Mantel-Haenszel weighting. Homogeneity (I2) and publication bias were assessed. We selected 54 studies (52 cross-sectional, one longitudinal, and one with both designs). The pooled prevalence of pre-frailty in individuals with HF was 46% (95% CI = 38–53; I2 = 93.1%) and 40% (95% CI = 31–48; I2 = 97%) for frailty. The proportion of pre-frail individuals with HF was 20% (95%CI = 15–25; I2 = 99.2%) and the proportion of frail individuals with HF was 31% (95% CI = 17–45; I2 = 98.7%). Two studies using the same frailty definition reported estimates for the association between frailty and HF (pooled OR = 3.44; 95% CI = 0.75–15.73; I2 = 95.8%). In conclusion, frailty and pre-frailty are frequent in people with HF. Persons with HF have 3.4-fold increased odds of frailty. Longitudinal studies examining bidirectional pathophysiological pathways between HF and frailty are needed to further clarify this relationship and to assess if specific treatment for HF may prevent or delay the onset of frailty and vice versa.

Aim: To assess the pattern of use of oral corticosteroids (OC) in primary care patients with severe asthma. Methods: Data derived from the Health Search Database (HSD) gathering information on 700 Italian general practitioners. A cohort of severe asthma patients was identified between 2013 and 2017 and followed-up for one year. The association between candidate predictors and the incident escalation to OC was tested through a multivariate Cox regression model. Results: Among patients with asthma (N = 55,075), 284 were diagnosed with severe asthma. Among them, the proportion of OC users decreased from 82.2% in 2013 to 75.3% in 2017. For what concerns the determinants of OC prescriptions, among 284 patients being defined at baseline (2013-2016) as those suffering from severe asthma, 216 (76.1%) were first-ever prescribed with OC at least once during one year of follow-up. The presence of gastroesophageal reflux disease (HR 1.37; 95% CI 1.02-1.85), osteoarthritis (HR 1.54; 95% CI 1.12-2.12) and moderate asthma exacerbations (HR 1.72; 95% CI 1-02-2.93) was significantly associated with the outcome. Conclusions: The optimization of asthma treatment and the management of comorbidities may be potential leverages to reduce the inappropriate use of OC in patients with severe asthma.

The frailty index (FI) is one of the most widespread tools used to predict poor, health-related outcomes in older persons. The selection of clinical and functional deficits to include in a FI is mostly based on the users' clinical experience. However, this approach may not be sufficiently accurate to predict health outcomes in particular subgroups of individuals. In this study, we implemented an optimization algorithm, the genetic algorithm, to create a highly performant (FI) based on our prediction goals, rather than on a predetermined clinical selection of deficits, using data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) and 109 potential deficits identified in the dataset. The algorithm was personalized to obtain a FI with high discrimination ability in the prediction of mortality. The resulting FI included 40 deficits and showed areas under the curve consistently higher than 0.80 (range 0.81-0.90) in the prediction of 3-year and 6-year mortality in the whole sample and in sex and age subgroups. This methodology represents a promising opportunity to optimize the exploitation of medical and administrative databases in the construction of clinically relevant frailty indices.

Background: The identification of individuals at increased risk of poor health-related outcomes is a priority. Geriatric research has proposed several indicators shown to be associated with these outcomes, but a head-to-head comparison of their predictive accuracy is still lacking. We therefore aimed to compare the accuracy of five geriatric health indicators in predicting different outcomes among older persons: frailty index (FI), frailty phenotype (FP), walking speed (WS), multimorbidity, and a summary score including clinical diagnoses, functioning, and disability (the Health Assessment Tool; HAT). Methods: Data were retrieved from the Swedish National Study on Aging and Care in Kungsholmen, an ongoing longitudinal study including 3363 people aged 60+. To inspect the accuracy of geriatric health indicators, we employed areas under the receiver operating characteristic curve (AUC) for the prediction of 3-year and 5-year mortality, 1-year and 3-year unplanned hospitalizations (1+), and contacts with healthcare providers in the 6 months before and after baseline evaluation (2+). Results: FI, WS, and HAT showed the best accuracy in the prediction of mortality [AUC(95%CI) for 3-year mortality 0.84 (0.82-0.86), 0.85 (0.83-0.87), 0.87 (0.85-0.88) and AUC(95%CI) for 5-year mortality 0.84 (0.82-0.86), 0.85 (0.83-0.86), 0.86 (0.85-0.88), respectively]. Unplanned hospitalizations were better predicted by the FI [AUC(95%CI) 1-year 0.73 (0.71-0.76); 3-year 0.72 (0.70-0.73)] and HAT [AUC(95%CI) 1-year 0.73 (0.71-0.75); 3-year 0.71 (0.69-0.73)]. The most accurate predictor of multiple contacts with healthcare providers was multimorbidity [AUC(95%CI) 0.67 (0.65-0.68)]. Predictions were generally less accurate among younger individuals (< 78 years old). Conclusion: Specific geriatric health indicators predict clinical outcomes with different accuracy. Comprehensive indicators (HAT, FI, WS) perform better in predicting mortality and hospitalization. Multimorbidity exhibits the

Objective: To assess the incidence and determinants of the triple inhaled therapy in chronic obstructive pulmonary disease (COPD) primary care patients. Methods: Data derived from the Health Search Database (HSD) gathering information on 700 Italian general practitioners. A cohort of COPD patients, prescribed for the first time with inhaled treatments, was followed-up between January 2002 and December 2014. The outcome was the first incident prescription of a triple inhaled therapy, namely the combination of inhaled corticosteroids (ICS), long-acting beta agonists (LABA), and long-acting muscarinic antagonists (LAMA). Cox regressions were used to test the association (hazard ratios, HR) between candidate determinants and the outcome. Results: Out of 17589 patients (mean age 71.1 +/- 11.3 years; 37.4% females), 3693 (21%) were prescribed with a triple inhaled therapy during follow-up. Older age (HR=1.79 to 2.61), current and former smoking habit (HR=1.72 and 1.66), higher GOLD stage (HR=1.45 to 2.79), the number of moderate and severe COPD exacerbations (HR=1.10 to 2.63), and heart failure (HR=1.17) resulted statistically significantly associated with an increased incident prescription of the triple inhaled therapy. Female sex (HR=0.80) and some co-morbidities (HR=0.21 to 0.87) resulted negatively associated with the outcome. Furthermore, patients initially treated with LAMA (HR=1.5) and LABA/ICS (HR=1.23) were more likely to escalate to the triple therapy, than those on LABA. Conversely, patients initially treated with ICS presented a negative hazard (HR=0.72). Conclusions: The knowledge of demographic and clinical determinants of the escalation to the triple inhaled therapy in real-world COPD patients may help clinicians to better personalize respiratory pharmacological treatments of their patients, and inform international societies that issue clinical guidelines.

Background Frailty and multimorbidity are both strongly associated with poor health-related outcomes, including mortality. Being multimorbidity one of the major determinants of frailty, we aimed to explore whether, and to what extent, frailty without multimorbidity plays an independent role in shortening life. Methods We used data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). Among the 3363 adults aged 60+ enrolled at baseline, those without multimorbidity (i.e.: less than two chronic diseases) (N = 1115) have been characterized according to Fried's frailty phenotype (i.e. robust, prefrail, and frail). The association between frailty and mortality was estimated using piecewise proportional hazard regression models in three five-year time periods. Results Among participants without multimorbidity, 424 (38%) were prefrail and 19 (2%) were frail. During the 15-year follow-up, 263 (24%) participants died: 19%, 29%, and 63% of those who were robust, prefrail, and frail at baseline, respectively. Within the first 5 years of follow-up, prefrail and frail participants had more than doubled mortality risk in comparison to robust ones (HR for pre-frailty 2.08, 95% CI 1.15-3.76; HR for frailty 2.69, 95% CI 1.22-5.97). Beyond 5 years, a trend of increased mortality rate was still detectable for prefrail and frail subjects in comparison to robust ones. Conclusions Physical frailty and pre-frailty are associated with short-term mortality in a cohort of older adults free from multimorbidity. Frailty could be a clinical indicator of increased risk of negative health outcomes even among subjects without multiple chronic conditions.