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The prompt identification of frailty in primary care is the first step to offer personalized care to older individuals. We aimed to detect and quantify frailty among primary care older patients, by developing and validating a primary care frailty index (PC-FI) based on routinely collected health records and providing sex-specific frailty charts. The PC-FI was developed using data from 308,280 primary care patients ≥ 60 years old part of the Health Search Database (HSD) in Italy (baseline 2013–2019) and validated in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K; baseline 2001–2004), a well-characterized population-based cohort including 3363 individuals ≥ 60 years old. Potential health deficits part of the PC-FI were identified through ICD-9, ATC, and exemption codes and selected through an optimization algorithm (i.e., genetic algorithm), using all-cause mortality as the main outcome for the PC-FI development. The PC-FI association at 1, 3 and 5 years, and discriminative ability for mortality and hospitalization were tested in Cox models. The convergent validity with frailty-related measures was verified in SNAC-K. The following cut-offs were used to define absent, mild, moderate and severe frailty: < 0.07, 0.07–0.14, 0.14–0.21, and ≥ 0.21. Mean age of HSD and SNAC-K participants was 71.0 years (55.4% females). The PC-FI included 25 health deficits and showed an independent association with mortality (hazard ratio range 2.03–2.27; p < 0.05) and hospitalization (hazard ratio range 1.25–1.64; p < 0.05) and a fair-to-good discriminative ability (c-statistics range 0.74–0.84 for mortality and 0.59–0.69 for hospitalization). In HSD 34.2%, 10.9% and 3.8% were deemed mildly, moderately, and severely frail, respectively. In the SNAC-K cohort, the associations between PC-FI and mortality and hospitalization were stronger than in the HSD and PC-FI scores were associated with physical frailty (odds ratio 4.25 for each 0.1 increase; p < 0.05; area under the curve 0.84), poor physical performance, disability, injurious falls, and dementia. Almost 15% of primary care patients ≥ 60 years old are affected by moderate or severe frailty in Italy. We propose a reliable, automated, and easily implementable frailty index that can be used to screen the primary care population for frailty. [ABSTRACT FROM AUTHOR]

Several systems, sensors, and devices are now available for the instrumental evaluation of physical function in persons with Chronic Obstructive Pulmonary Disease (COPD). We aimed to systematically review the literature about such technologies. The literature search was conducted in all major scientific databases, including articles published between January 2001 and April 2022. Studies reporting measures derived from the instrumental assessment of physical function in individuals with COPD were included and were divided into application and validation studies. The quality of validation studies was assessed with the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) risk of bias tool. From 8752 articles retrieved, 21 application and 4 validation studies were included in the systematic review. Most application studies employed accelerometers, gait analysis systems, instrumented mattresses, or force plates to evaluate walking. Surface electro-myography or near-infrared spectroscopy were employed in four studies. Validation studies were heterogeneous and presented a risk of bias ranging from inadequate to doubtful. A variety of data regarding physical function can be retrieved from technologies used in COPD studies. However, a general lack of standardization and limitations in study design and sample size hinder the implementation of the instrumental evaluation of function in clinical practice. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multimorbidity, the coexistence of multiple chronic diseases in an individual, is highly prevalent and challenging for healthcare systems. However, its risk factors remain poorly understood. To systematically review studies reporting multimorbidity risk factors. A PRISMA-compliant systematic review was conducted, searching electronic databases (MEDLINE, EMBASE, Web of Science, Scopus). Inclusion criteria were studies addressing multimorbidity transitions, trajectories, continuous disease counts, and specific patterns. Non-human studies and participants under 18 were excluded. Associations between risk factors and multimorbidity onset were reported. Of 20,806 identified studies, 68 were included, with participants aged 18–105 from 23 countries. Nine risk factor categories were identified, including demographic, socioeconomic, and behavioral factors. Older age, low education, obesity, hypertension, depression, low pysical function were generally positively associated with multimorbidity. Results for factors like smoking, alcohol consumption, and dietary patterns were inconsistent. Study quality was moderate, with 16.2% having low risk of bias. Several risk factors seem to be consistently associated with an increased risk of accumulating chronic diseases over time. However, heterogeneity in settings, exposure and outcome, and baseline health of participants hampers robust conclusions. • Multimorbidity, affects most older persons and is more prevalent in adults with low socioeconomic status • The identification of risk factors for multimorbidity may inform public health policies and prevention programs • Older age, low education, obesity, hypertension, depression, low pysical function were generally positively associated with multimorbidity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • Frailty and pre-frailty are common among individuals affected by heart failure. • Frailty may help risk stratification in patients with heart failure. • Studies investigating their longitudinal association are lacking. [ABSTRACT FROM AUTHOR]

Introduction: The development of new pharmacological treatments for chronic obstructive pulmonary disease (COPD) has improved health‐related quality of life of patients. However, suboptimal adherence may limit its potential. Objective: The aim of the present study was to assess the adherence to free triple inhaled therapy and to investigate poor adherence determinants among primary care patients. Methods: Data were derived from a primary care database in Italy. Patients aged 40+ affected by COPD and prescribed with inhaled corticosteroids, long‐acting beta agonists and long‐acting muscarinic antagonists (N = 3177) were enrolled. Low adherence was defined as a proportion of days covered (PDC) by medications prescription lower than 80%. Predictors of low adherence were tested using logistic regression models. Results and conclusions: The 85% of enrolled patients showed poor adherence to free triple inhaled therapy. Comorbidities, such as heart failure (OR 1.78, 95%CI 1.19‐2.75), depression (OR 1.41, 95%CI 1.06‐1.88) and peripheral vascular disease (OR 1.32, 95%CI 1.01‐1.74) were associated with poor adherence. Former (OR 0.52, 95%CI 0.34‐0.78) or current smokers (OR 0.61, 95%CI 0.41‐0.93) and patients with more severe airways obstruction or history of severe exacerbations (OR 0.64, 95%CI 0.52‐0.79) were less likely to exhibit poor adherence. Real‐world adherence to triple inhaled therapy with different inhalers is generally low. Higher GOLD airways obstruction stage and current or former smoking status are associated with increased adherence to treatment. Reduced perceived benefit on symptoms control is probably linked to poorer adherence to free triple therapy. [ABSTRACT FROM AUTHOR]

Abstract 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. Highlights • Forty percent of subjects without multimorbidity are frail or prefrail. • Frailty and pre-frailty increase mortality risk even in absence of multimorbidity. • Screening for frailty in individuals without multimorbidity might be beneficial. • Future research should focus on causes of frailty other than chronic diseases. [ABSTRACT FROM AUTHOR]

Multimorbidity, the co-existence of multiple chronic diseases in the same individual, is not only extremely common in older persons but is also strongly associated with several poor health outcomes [[1]]. Based on data from more than 4750 persons, Ricci-Cabello et al. [[11]] investigated the association between self-reported safety outcomes and multimorbidity. The mixed methods systematic review by González-González and colleagues [[12]] synthesized the results of 22 studies evaluating early clarification of end-of-life care preferences in persons with multimorbidity. [Extracted from the article]

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 best accuracy in the prediction of multiple contacts with providers. [ABSTRACT FROM AUTHOR]

Background This study aims to examine temporal trends in frailty state transitions, and years spent frail, in older Swedish adults. Methods We followed the Swedish National Study on Aging and Care in Kungsholmen participants from baseline (2001–2004) for 15 (median: 9.6) years. A 40-deficit frailty index (FI) was constructed to identify 3 frailty states: robust (FI ≤ 0.125), mild frailty (0.125 < FI ≤   0.25), and moderate and severe frailty (FI  > 0.25). Multistate survival analyses were implemented to obtain hazard ratios (HRs) for frailty state transitions, with birth year and sex as predictors. To examine temporal trends, frailty state-specific life expectancies at age 60 were forecasted for robust persons born in different years (1900, 1910, 1920, 1930, and 1940), also by sex. Results At baseline, the 2 941 participants' mean age was 75 years and 65% were women. Predicted life expectancy and time spent frail from age 60 followed an increasing trend by birth year. Hazards of transitioning from mild frailty to death (HR: 0.89; 95% confidence interval [CI]: 0.83–0.97) and moderate and severe frailty to death (HR: 0.98; 95% CI: 0.97–1.00) were lower for those born later. Women were less likely to transition from robust to mild frailty (HR: 0.81; 95% CI: 0.70–0.93), mild frailty to moderate and severe frailty (HR: 0.80; 95% CI: 0.68–0.93), and moderate and severe frailty to death (HR: 0.68; 95% CI: 0.59–0.78), but spent more time frail. Conclusions Our results point to an expansion of time spent frail among older Swedish adults over time. [ABSTRACT FROM AUTHOR]

Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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<0.0001), while heart rate >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. [ABSTRACT FROM AUTHOR]

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. Observational and retrospective study. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

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. Prospective cohort study. The population-based Swedish National study on Aging and Care in Kungsholmen, Stockholm, Sweden. 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. 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. Older persons suffering from specific multimorbidity patterns have a higher risk of institutionalization, especially if they lack formal or informal care. Interventions aimed at preventing the clustering of diseases could reduce the associated burden on residential long-term care. Formal and informal care provision may be effective strategies in reducing the risk of institutionalization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]