Patterns of Multimorbidity in a Population-Based Cohort of Older People: Sociodemographic, Lifestyle, Clinical, and Functional Differences

Interactions between patterns of multimorbidity and functional status among hospitalized older patients: a novel approach using cluster analysis and association rule mining

BACKGROUND

Multimorbidity (MM) is generally defined as the presence of 2 or more chronic diseases in the same patient and seems to be frequently associated with frailty and poor quality of life. However, the complex interplay between MM and functional status in hospitalized older patients has not been fully elucidated so far. Here, we implemented a 2-step approach, combining cluster analysis and association rule mining to explore how patterns of MM and disease associations change as a function of disability.

METHODS

This retrospective cohort study included 3366 hospitalized older patients discharged from acute care units of Ancona and Cosenza sites of Italian National Institute on Aging (INRCA-IRCCS) between 2011 and 2017. Cluster analysis and association rule mining (ARM) were used to explore patterns of MM and disease associations in the whole population and after stratifying by dependency in activities of daily living (ADL) at discharge. Sensitivity analyses in men and women were conducted to test for robustness of study findings.

RESULTS

Out of 3366 included patients, 78% were multimorbid. According to functional status, 22.2% of patients had no disability in ADL (functionally independent group), 22.7% had 1 ADL dependency (mildly dependent group), and 57.4% 2 or more ADL impaired (moderately-severely dependent group). Two main MM clusters were identified in the whole general population and in single ADL groups. ARM revealed interesting within-cluster disease associations, characterized by high lift and confidence. Specifically, in the functionally independent group, the most significant ones involved atrial fibrillation (AF)-anemia and chronic kidney disease (CKD) (lift = 2.32), followed by coronary artery disease (CAD)-AF and heart failure (HF) (lift = 2.29); in patients with moderate-severe ADL disability, the most significant ARM involved CAD-HF and AF (lift = 1.97), thyroid dysfunction and AF (lift = 1.75), cerebrovascular disease (CVD)-CAD and AF (lift = 1.55), and hypertension-anemia and CKD (lift = 1.43).

CONCLUSIONS

Hospitalized older patients have high rates of MM and functional impairment. Combining cluster analysis to ARM may assist physicians in discovering unexpected disease associations in patients with different ADL status. This could be relevant in the view of individuating personalized diagnostic and therapeutic approaches, according to the modern principles of precision medicine.

Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions

INTRODUCTION

Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD.

METHODS

We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi-label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors.

RESULTS

Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictive power, syndemic factors related to dementia, social behaviors, mental and physical health can improve model performance further.

DISCUSSION

Our study demonstrated that the MCC patterns among people with ADRD can be learned using a machine-learning approach with syndemic framework adjustments.

HIGHLIGHTS

Machine learning models can learn the MCC patterns for people with ADRD. The learned MCC patterns should be adjusted and individualized by syndemic factors. The model can predict which disease is developing based on existing MCC patterns. As a result, this model enables early specific MCC identification and prevention.

Learning prevalent patterns of co-morbidities in multichronic patients using population-based healthcare data

The prevalence of longstanding chronic diseases has increased worldwide, along with the average age of the population. As a result, an increasing number of people is affected by two or more chronic conditions simultaneously, and healthcare systems are facing the challenge of treating multimorbid patients effectively. Current therapeutic strategies are suited to manage each chronic condition separately, without considering the whole clinical condition of the patient. This approach may lead to suboptimal clinical outcomes and system inefficiencies (e.g. redundant diagnostic tests and inadequate drug prescriptions). We develop a novel methodology based on the joint implementation of data reduction and clustering algorithms to identify patterns of chronic diseases that are likely to co-occur in multichronic patients. We analyse data from a large adult population of multichronic patients living in Tuscany (Italy) in 2019 which was stratified by sex and age classes. Results demonstrate that (i) cardio-metabolic, endocrine, and neuro-degenerative diseases represent a stable pattern of multimorbidity, and (ii) disease prevalence and clustering vary across ages and between women and men. Identifying the most common multichronic profiles can help tailor medical protocols to patients' needs and reduce costs. Furthermore, analysing temporal patterns of disease can refine risk predictions for evolutive chronic conditions.

Additive effect of frailty with distinct multimorbidity patterns on mortality amongst middle-aged and older adults in Taiwan: A 16-year population-based study

AIM

This study aimed to explore the association between multimorbidity patterns with/without frailty and future mortality among Taiwanese middle-aged and older adults through a population-based cohort study design.

METHODS

Data were collected from the Taiwan Longitudinal Study on Aging. The data were obtained from Wave 3, with the multimorbidity patterns in the years of 1996 being analyzed through latent class analysis. Frailty was defined using the modified Fried criteria. The association between each disease group with/without frailty and mortality was examined using logistic regression, with the reference group as the Relatively healthy group without frailty. Survival analysis was performed using Cox regression, and the follow-up period of mortality was from 1 January 1996 to 31 December 2012.

RESULTS

A total of 4748 middle-aged and older adults with an average age of 66.3 years (SD: 9.07 years) were included. Four disease patterns were identified in 1996, namely the Cardiometabolic (21.0%), Arthritis-cataract (11.9%), Relatively healthy (61.6%), and Multimorbidity (5.5%) groups. After adjusting for all covariates, the Relatively healthy group with frailty showed the highest risk for mortality (odds ratio: 3.66, 95% confidence interval [95% CI]: 2.24-5.95), followed by the Cardiometabolic group with frailty (odds ratio: 3.58, 95% CI: 1.96-6.54), Multimorbidity group with frailty (odds ratio: 2.28, 95% CI: 1.17-4.44), Multimorbidity group without frailty (odds ratio: 1.44, 95% CI: 1.01-2.04), and the Cardiometabolic group without frailty (odds ratio: 1.24, 95% CI: 1.04-1.49).

CONCLUSIONS

Frailty plays an important role in mortality among middle-aged and older adults with distinct multimorbidity patterns. Middle-aged and older adults with a relatively healthy multimorbidity pattern or a cardiometabolic multimorbidity pattern with frailty encountered dismal outcomes. Geriatr Gerontol Int 2023; 23: 684-691.

Social determinants of multimorbidity patterns: A systematic review

Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.

Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records

BACKGROUND

Multimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults.

OBJECTIVE

This study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults.

METHODS

Data from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions.

RESULTS

In total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women.

CONCLUSIONS

Multimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity.

Association between multimorbidity trajectories and incident disability among mid to older age adults: China Health and Retirement Longitudinal Study

BACKGROUND

Although multimorbidity is a risk factor for disability, the relationship between the accumulative patterns of multimorbidity and disability remains poorly understood. The objective of this study was to identify the latent groups of multimorbidity trajectories among mid to older age adults and to examine their associations with incident disability.

METHODS

We included 5,548 participants aged ≥ 45 years who participated in the China Health and Retirement Longitudinal Study from 2011 to 2018 and had no multimorbidity (≥ 2 chronic conditions) at baseline. The group-based multi-trajectory modeling was used to identify distinct trajectory groups of multimorbidity based on the latent dimensions underlying 13 chronic conditions. The association between multimorbidity trajectories and incident disability was analyzed using the generalized estimating equation model adjusting for potential confounders.

RESULTS

Of the 5,548 participants included in the current analysis, 2,407 (43.39%) developed multimorbidity during the follow-up. Among participants with new-onset multimorbidity, four trajectory groups were identified according to the combination of newly diagnosed diseases: "Cardiometabolic" (N = 821, 34.11%), "Digestive-arthritic" (N = 753, 31.28%), "Cardiometabolic/Brain" (N = 618, 25.68%), and "Respiratory" (N = 215, 8.93%). Compared to participants who did not develop multimorbidity, the risk of incident disability was most significantly increased in the "Cardiometabolic/Brain" trajectory group (OR = 2.05, 95% CI: 1.55-2.70), followed by the "Cardiometabolic" (OR = 1.96, 95% CI: 1.52 -2.53) and "Digestive-arthritic" (OR = 1.70, 95% CI: 1.31-2.20) trajectory groups.

CONCLUSIONS

The growing burden of multimorbidity, especially the comorbid of cardiometabolic and brain diseases, may be associated with a significantly increased risk of disability for mid to older age adults. These findings improve our understanding of multimorbidity patterns that affect the independence of living and inform the development of strategies for the primary prevention of disability.

Identifying multimorbidity clusters among Brazilian older adults using network analysis: Findings and perspectives

In aging populations, multimorbidity (MM) is a significant challenge for health systems, however there are scarce evidence available in Low- and Middle-Income Countries, particularly in Brazil. A national cross-sectional study was conducted with 11,177 Brazilian older adults to evaluate the occurrence of MM and related clusters in Brazilians aged ≥ 60 years old. MM was assessed by a list of 16 physical and mental morbidities and it was defined considering ≥ 2 morbidities. The frequencies of MM and its associated factors were analyzed. After this initial approach, a network analysis was performed to verify the occurrence of clusters of MM and the network of interactions between coexisting morbidities. The occurrence of MM was 58.6% (95% confidence interval [CI]: 57.0–60.2). Hypertension (50.6%) was the most frequent morbidity and it was present all combinations of morbidities. Network analysis has demonstrated 4 MM clusters: 1) cardiometabolic; 2) respiratory + cancer; 3) musculoskeletal; and 4) a mixed mental illness + other diseases. Depression was the most central morbidity in the model according to nodes’ centrality measures (strength, closeness, and betweenness) followed by heart disease, and low back pain. Similarity in male and female networks was observed with a conformation of four clusters of MM and cancer as an isolated morbidity. The prevalence of MM in the older Brazilians was high, especially in female sex and persons living in the South region of Brazil. Use of network analysis could be an important tool for identifying MM clusters and address the appropriate health care, research, and medical education for older adults in Brazil.

Magnitude, pattern and correlates of multimorbidity among patients attending chronic outpatient medical care in Bahir Dar, northwest Ethiopia: The application of latent class analysis model

Objective

This study aimed to investigate the magnitude, pattern and associated factors of multimorbidity in Bahir Dar, northwest Ethiopia.

Methods

A multi-centered facility-based study was conducted among 1440 participants aged 40+ years attending chronic outpatient medical care. Two complementary methods (interview and review of medical records) were employed to collect data on socio-demographic, behavioral and disease related characteristics. The data were analyzed by STATA V.16 and R Software V.4.1.0. We fitted logistic regression and latent class analyses (LCA) models to identify the factors associated with multimorbidity and determine patterns of disease clustering, respectively. Statistical significance was considered at P-value <0.05.

Results

The magnitude of individual chronic conditions ranged from 1.4% (cancer) to 37.9% (hypertension), and multimorbidity was identified in 54.8% (95% CI = 52.2%-57.4%) of the sample. The likelihood of having multimorbidity was higher among participants aged 45–54 years (AOR: 1.6, 95%CI = 1.1, 2.2), 55–64 years (AOR: 2.6, 95%CI = 1.9, 3.6) and 65+ years (AOR: 2.6, 95%CI = 1.9, 3.6) compared to those aged 40–44 years. The odds of multimorbidity was also higher among individuals classified as overweight (AOR: 1.6, 95%CI = 1.2, 2.1) or obese (AOR: 1.9, 95%CI = 1.3, 3.0) than the normal weight category. Four patterns of multimorbidity were identified; the cardiovascular category being the largest class (50.2%) followed by the cardio-mental, (32.6%), metabolic (11.5%) and respiratory (5.7%) groups. Advanced age, being overweight and obesity predicted latent class membership, adjusting for relevant confounding factors.

Conclusions

The magnitude of multimorbidity in this study was high, and the most prevalent conditions shaped the patterns of multimorbidity. Advanced age, being overweight and obesity were the factors correlated with multimorbidity. Further research is required to better understand the burden of multimorbidity and related factors in the population, and to determine the impact of multimorbidity on individuals’ well-being and functioning.

The Effect of Dual Sensory Impairment and Multimorbidity Patterns on Functional Impairment: A Longitudinal Cohort of Middle-Aged and Older Adults in China

Objective

There is an urgent need to evaluate the contribution of several co-existing diseases on health. This study aims to explore the combined effect of dual sensory impairment (DSI) and multimorbidity patterns on functional impairment among middle-aged and older adults in China.

Methods

Data were from 10,217 adults aged 45 or older from four waves of the China Health and Retirement Longitudinal Study (CHARLS). Sensory impairments were self-reported measures. Multimorbidity patterns were identified by using k-means cluster analyses. Functional impairment was defined using activities of daily living (ADL) scale and instrumental activities of daily living (IADL) scale. Generalized estimating equation models were estimated to assess the effect of co-occurring DSI and multimorbidity on functional impairment.

Results

DSI prevalence was 50.4%, and multimorbidity prevalence was 37.7% at the baseline. The simultaneous presence of DSI and multimorbidity was associated with increased odds of ADL limitations (OR = 2.27, 95% CI: 2.11–2.43) and IADL limitations (OR = 1.89, 95% CI: 1.77–2.02). Five multimorbidity patterns were identified: the cardio-cerebrovascular pattern, the stomach-arthritis pattern, the respiratory pattern, the hepatorenal pattern, and the unspecified pattern. Compared to DSI only, DSI plus the hepatorenal pattern was most strongly associated with functional impairment (for ADL: OR = 2.70, 95% CI: 2.34–3.12; for IADL: OR = 2.04, 95% CI: 1.77–2.36).

Conclusion

Middle-aged and older adults with co-occurrence of DSI and multimorbidity are at increased risk of functional impairment, especially those with multimorbidity characterized by the hepatorenal pattern. These findings imply that integrated care for DSI and multimorbidity may be a potent pathway in improving functional status.

Specific multimorbidity patterns modify the impact of an exercise intervention in older hospitalized adults

BACKGROUND

Different multimorbidity patterns present with different prognoses, but it is unknown to what extent they may influence the effectiveness of an individualized multicomponent exercise program offered to hospitalized older adults.

METHODS

This study is a secondary analysis of a randomized controlled trial conducted in the Department of Geriatric Medicine of a tertiary hospital. In addition to the standard care, an exercise-training multicomponent program was delivered to the intervention group during the acute hospitalization period. Multimorbidity patterns were determined through fuzzy c-means cluster analysis, over 38 chronic diseases. Functional, cognitive and affective outcomes were considered.

RESULTS

Three hundred and six patients were included in the analyses (154 control; 152 intervention), with a mean age of 87.2 years, and 58.5% being female. Four patterns of multimorbidity were identified: heart valves and prostate diseases (26.8%); metabolic diseases and colitis (20.6%); psychiatric, cardiovascular and autoimmune diseases (16%); and an unspecific pattern (36.6%). The Short Physical Performance Battery (SPPB) test improved across all patterns, but the intervention was most effective for patients in the metabolic/colitis pattern (2.48-point difference between intervention/control groups, 95% CI 1.60-3.35). Regarding the Barthel Index and the Mini Mental State Examination (MMSE), the differences were significant for all multimorbidity patterns, except for the psychiatric/cardio/autoimmune pattern. Differences concerning quality of life were especially high for the psychiatric/cardio/autoimmune pattern (16.9-point difference between intervention/control groups, 95% CI 4.04, 29.7).

CONCLUSIONS

Patients in all the analyzed multimorbidity patterns improved with this tailored program, but the improvement was highest for those in the metabolic pattern. Understanding how different chronic disease combinations are associated with specific functional and cognitive responses to a multicomponent exercise intervention may allow further tailoring such interventions to older patients' clinical profile.

Multimorbidity patterns of chronic conditions and geriatric syndromes in older patients from the MoPIM multicentre cohort study

OBJECTIVES

To estimate the frequency of chronic conditions and geriatric syndromes in older patients admitted to hospital because of an exacerbation of their chronic conditions, and to identify multimorbidity clusters in these patients.

DESIGN

Multicentre, prospective cohort study.

SETTING

Internal medicine or geriatric services of five general teaching hospitals in Spain.

PARTICIPANTS

740 patients aged 65 and older, hospitalised because of an exacerbation of their chronic conditions between September 2016 and December 2018.

PRIMARY AND SECONDARY OUTCOME MEASURES

Active chronic conditions and geriatric syndromes (including risk factors) of the patient, a score about clinical management of chronic conditions during admission, and destination at discharge were collected, among other variables. Multimorbidity patterns were identified using fuzzy c-means cluster analysis, taking into account the clinical management score. Prevalence, observed/expected ratio and exclusivity of each chronic condition and geriatric syndrome were calculated for each cluster, and the final solution was approved after clinical revision and discussion among the research team.

RESULTS

740 patients were included (mean age 84.12 years, SD 7.01; 53.24% female). Almost all patients had two or more chronic conditions (98.65%; 95% CI 98.23% to 99.07%), the most frequent were hypertension (81.49%, 95% CI 78.53% to 84.12%) and heart failure (59.86%, 95% CI 56.29% to 63.34%). The most prevalent geriatric syndrome was polypharmacy (79.86%, 95% CI 76.82% to 82.60%). Four statistically and clinically significant multimorbidity clusters were identified: osteoarticular, psychogeriatric, cardiorespiratory and minor chronic disease. Patient-level variables such as sex, Barthel Index, number of chronic conditions or geriatric syndromes, chronic disease exacerbation 3 months prior to admission or destination at discharge differed between clusters.

CONCLUSIONS

In older patients admitted to hospital because of the exacerbation of chronic health problems, it is possible to define multimorbidity clusters using soft clustering techniques. These clusters are clinically relevant and could be the basis to reorganise healthcare circuits or processes to tackle the increasing number of older, multimorbid patients.

TRIAL REGISTRATION NUMBER

NCT02830425.

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

BACKGROUND

Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients' clinical phenotypes and analyse the differential clinical course.

METHODS

We performed a hierarchical cluster analysis based on Ward's Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry.

RESULTS

A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients' prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27-3.62; HR 3.42, 95%CI 2.72-4.31; HR 2.79, 95%CI 2.32-3.35), and Cluster 1 (HR 1.88, 95%CI 1.48-2.38; HR 2.50, 95%CI 1.98-3.15; HR 2.09, 95%CI 1.74-2.51) reported a higher risk for the three outcomes respectively.

CONCLUSIONS

In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes.

Multimorbidity in Patients With Acute Coronary Syndrome Is Associated With Greater Mortality, Higher Readmission Rates, and Increased Length of Stay: A Systematic Review

OBJECTIVE

The aims of this systematic review were to determine the magnitude and impact of multimorbidity (≥2 chronic conditions) on mortality, length of stay, and rates of coronary intervention in patients with acute coronary syndrome (ACS) and to compare the prevalence of cardiovascular versus noncardiovascular multimorbidities.

METHODS

MEDLINE, PubMed, MedlinePlus, EMBASE, OVID, and CINAHL databases were searched for studies published between 2009 and 2019. Eight original studies enrolling patients with ACS and assessing cardiovascular and noncardiovascular comorbid conditions met the inclusion criteria. Study quality was evaluated using the Crowe Critical Appraisal Tool.

RESULTS

The most frequently examined cardiovascular multimorbidities included hypertension, diabetes, heart failure, atrial fibrillation, stroke/transient ischemic attack, coronary heart disease, and peripheral vascular disease; the most frequently examined noncardiovascular multimorbidities included cancer, anemia, chronic obstructive pulmonary disease, renal disease, liver disease, and depression. The prevalence of multimorbidity in the population with ACS is high (25%-95%). Patients with multimorbidities receive fewer evidence-based treatments, including coronary intervention and high-dose statins. Patients with multimorbidities experience higher in-hospital mortality (5%-13.9% vs 2.6%-6.1%), greater average length of stay (5-9 vs 3-4 days), and lower rates of revascularization (9%-14% vs 39%-42%) than nonmultimorbid patients. Women, despite being the minority in all sample populations, exhibited greater levels of multimorbidity than men.

CONCLUSIONS

Multimorbid patients with ACS are at a greater risk for worse outcomes than their nonmultimorbid counterparts. Lack of consistent measurement makes interpretation of the impact of multimorbidity challenging and emphasizes the need for more research on multimorbidity's effects on postdischarge healthcare utilization.

Clusters of Physical Frailty and Cognitive Impairment and Their Associated Comorbidities in Older Primary Care Patients

(1) Objectives: We aimed to identify clusters of physical frailty and cognitive impairment in a population of older primary care patients and correlate these clusters with their associated comorbidities. (2) Methods: We used a latent class analysis (LCA) as the clustering technique to separate different stages of mild cognitive impairment (MCI) and physical frailty into clusters; the differences were assessed by using a multinomial logistic regression model. (3) Results: Four clusters (latent classes) were identified: (1) highly functional (the mean and SD of the “frailty” test 0.58 ± 0.72 and the Mini-Mental State Examination (MMSE) test 27.42 ± 1.5), (2) cognitive impairment (0.97 ± 0.78 and 21.94 ± 1.95), (3) cognitive frailty (3.48 ± 1.12 and 19.14 ± 2.30), and (4) physical frailty (3.61 ± 0.77 and 24.89 ± 1.81). (4) Discussion: The comorbidity patterns distinguishing the clusters depend on the degree of development of cardiometabolic disorders in combination with advancing age. The physical frailty phenotype is likely to exist separately from the cognitive frailty phenotype and includes common musculoskeletal diseases.

Profiles of Frailty among Older People Users of a Home-Based Primary Care Service in an Urban Area of Barcelona (Spain): An Observational Study and Cluster Analysis

Background: The multidimensional assessment of frailty allows stratifying it into degrees; however, there is still heterogeneity in the characteristics of people in each stratum. The aim of this study was to identify frailty profiles of older people users of a home-based primary care service. Methods: We carried out an observational study from January 2018 to January 2021. Participants were all people cared for a home-based primary care service. We performed a cluster analysis by applying a k-means clustering technique. Cluster labeling was determined with the 22 variables of the Frail-VIG index, age, and sex. We computed multiple indexes to assess the optimal number of clusters, and this was selected based on a clinical assessment of the best options. Results: Four hundred and twelve participants were clustered into six profiles. Three of these profiles corresponded to a moderate frailty degree, two to a severe frailty degree and one to a mild frailty degree. In addition, almost 75% of the participants were clustered into three profiles which corresponded to mild and moderate degree of frailty. Conclusions: Different profiles were found within the same degree of frailty. Knowledge of these profiles can be useful in developing strategies tailored to these differentiated care needs.

The Role of Artificial Intelligence in Managing Multimorbidity and Cancer

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

Patterns of multimorbidity and pharmacotherapy: a total population cross-sectional study

BACKGROUND

Treatment of multimorbid patients can be improved. Development of patient-centred care of high-quality requires context-bound understanding of the multimorbid population's patterns of demographics, co-morbidities and medication use.

OBJECTIVE

The aim of this study was to identify patterns of multimorbidity in the total population of Region Stockholm, Sweden, by exploring demographics, claimed prescription drugs, risk of mortality and non-random association of conditions.

METHODS

In this cross-sectional descriptive population-based cohort study, we extracted data from the Swedish VAL database (N = 2 323 667) including all consultations in primary and specialized outpatient care, all inpatient care and all prescriptions claimed during 2017. We report number of chronic conditions and claimed prescription drugs, physical and mental co-morbidity, and 1-year mortality. We stratified the analyses by sex. We examined non-random associations between diseases using cluster analysis.

RESULTS

In total, 21.6% had multimorbidity (two or more chronic conditions) and 24.1% had polypharmacy (more than five claimed prescription drugs). Number of claimed drugs, co-occurrence of mental and physical conditions, and 1-year mortality increased as multimorbidity increased. We identified seven multimorbidity clusters with clinically distinct characteristics. The smallest cluster (7% of individuals) had prominent cardiovascular disease, the highest 1-year mortality rate, high levels of multimorbidity and polypharmacy, and was much older. The largest cluster (27% of individuals) was younger and heterogenous, with primarily mental health problems.

CONCLUSIONS

Individuals with chronic conditions often show clinical complexity with both concordant and discordant conditions and polypharmacy. This study indicates that clinical guidelines addressing clustering of conditions may be one strategy for managing complexity.

[COVID-19 in old age-The geriatric perspective]

Predominantly the older population is affected by a severe course of COVID-19. The mortality of hospitalized patients with COVID-19 above the age of 80 years is up to 54% in international studies. These observations indicate the necessity to highlight the geriatric perspective on this disease. The diagnostics and treatment of COVID-19 do not differ between younger and older patients but atypical symptoms should be expected more frequently in old age. Older subjects show an increased need for rehabilitation after COVID-19. Paradoxically, increasing rehabilitation demands go along with a reduced availability of geriatric rehabilitation options, the latter being a consequence of closure or downsizing of rehabilitation departments during the pandemic. In general, measures of isolation and quarantine should be diligently balanced as the health and emotional consequences of such measures may be severe in older persons. In light of the poor prognosis of older COVID-19 patients, advanced care planning becomes even more relevant. Caregivers and physicians should be encouraged to compose advanced care directives that also reflect the specific circumstances of COVID-19. Fortunately, current data suggest that the effectiveness of the vaccination with the mRNA-vaccines approved in Germany may be equally high in older compared to younger persons.

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

Patterns of multimorbidity and risk of disability in community-dwelling older persons

The aim was to analyze the association between specific patterns of multimorbidity and risk of disability in older persons. Data were gathered from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K); 2066 60 + year-old participants living in the community and free from disability at baseline were grouped according to their multimorbidity patterns and followed-up for six years. The association between multimorbidity patterns and disability in basic (ADL) and instrumental (IADL) activities of daily living was examined through multinomial models. Throughout the follow-up, 434 (21.0%) participants developed at least one ADL and 310 (15.0%) at least one IADL. Compared to the unspecific pattern, which included diseases not exceeding their expected prevalence in the total sample, belonging to the cardiovascular/anemia/dementia, the sensory impairment/cancer and the musculoskeletal/respiratory/gastrointestinal patterns was associated with a higher risk of developing both ADL and IADL, whereas subjects in the metabolic/sleep disorders pattern showed a higher risk of developing only IADL. Multimorbidity patterns are differentially associated with incident disability, which is important for the design of future prevention strategies aimed at delaying functional impairment in old age, and for a better healthcare resource planning.

Multimorbidity burden and dementia risk in older adults: The role of inflammation and genetics

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 ε4 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 ε4, and inflammation may further increase the risk. Identifying such high‐risk groups might allow tailored interventions for dementia prevention.

Identification of the multimorbidity training needs of primary care professionals: Protocol of a survey

Current epidemiological situation has prompted the consideration of multimorbility (MM) as a prevalent condition, influenced by age, educational level and social support, related to unfavorable social and health determinants. Primary Care (PC) has a key role in its approach but further training of professionals in MM is required. The evidence on the effectiveness of training interventions in MM is still limited. Knowing the experiences, opinions and training needs of professionals is essential to enhance training interventions.

OBJECTIVES

Identify perceived training needs by PC health professionals (doctors and nurses) in MM and polypharmacy.

METHODS

Design: Cross-sectional study based on an online survey (anonymous-ad hoc questionnaire). Participants and recruitment: 384 doctors and nurses working in healthcare centers and out-of-hospital emergencies of the Spanish National Health System. Non-probabilistic convenience sampling via email addressed to Health Institutions, and social networks.

DATA

Demographic characteristics and professional profile data (close-ended and multiple-choice questions) will be collected. Open-ended questions will be used to identify training needs, difficulties and resources about MM; required skills to care patients with MM will be assessed using a 4-item ordinal scale.

ANALYSIS

Coding of data prior to analysis. Descriptive statistical analysis, participation and completion rates of the questionnaire and estimation of absolute and relative frequencies and 95% confidence intervals in close-ended questions. Content analysis with inductive methodology in open-ended questions. Ethics: Ethical approval, Online informed consent.

CONCLUSIONS

The identification of training needs of health professionals who care for patients with MM will be necessary data for developing highly effective training activities.

Multimorbidity Patterns and Unplanned Hospitalisation in a Cohort of Older Adults

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.

Cluster Analysis of the Associations among Physical Frailty, Cognitive Impairment and Mental Disorders

Background

Physical frailty, cognitive impairment, and symptoms of anxiety and depression frequently co-occur in later life, but, to date, each has been assessed separately. The present study assessed their patterns in primary care patients aged ≥60 years.

Material/Methods

This cross-sectional study evaluated 263 primary care patients aged ≥60 years in eastern Croatia in 2018. Physical frailty, cognitive impairment, anxiety and depression, were assessed using the Fried phenotypic model, the Mini-Mental State Examination (MMSE), the Geriatric Anxiety Scale (GAS), and the Geriatric Depression Scale (GDS), respectively. Patterns were identified by latent class analysis (LCA), Subjects were assorted by age, level of education, and domains of psychological and cognitive tests to determine clusters.

Results

Subjects were assorted into four clusters: one cluster of relatively healthy individuals (61.22%), and three pathological clusters, consisting of subjects with mild cognitive impairment (23.95%), cognitive frailty (7.98%), and physical frailty (6.85%). A multivariate, multinomial logistic regression model found that the main determinants of the pathological clusters were increasing age and lower mnestic functions. Lower performance on mnestic tasks was found to significantly determine inclusion in the three pathological clusters. The non-mnestic function, attention, was specifically associated with cognitive impairment, whereas psychological symptoms of anxiety and dysphoria were associated with physical frailty.

Conclusions

Clustering of physical and cognitive performances, based on combinations of their grades of severity, may be superior to modelling of their respective entities, including the continuity and non-linearity of age-related accumulation of pathologic conditions.

Untangling the complexity of multimorbidity with machine learning

The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.

Multimorbidity patterns in chronic older patients, potentially inappropriate prescribing and adverse drug reactions: protocol of the multicentre prospective cohort study MoPIM

INTRODUCTION

Multimorbidity is a major challenge for current healthcare systems and professionals. From the different approaches that have been proposed to analyse this issue, the hypothesis of the existence of association patterns of different chronic conditions is gaining visibility. In addition, multimorbidity can be associated to polypharmacy, which can lead to a higher risk of potentially inappropriate prescribing (PIP) and consequently to adverse drug reactions (ADRs). The general objective of this novel study is to identify the association between PIP, multimorbidity patterns, polypharmacy and the presence of ADRs in older patients admitted for exacerbation of chronic diseases.

METHODS AND ANALYSIS

The MoPIM (morbidity, potentially inappropriate medication) study is a multicentre prospective cohort study of an estimated sample of 800 older (≥65 years) patients admitted to five general hospitals in Spain due to an exacerbation of a chronic disease. Patients referred to home hospitalisation, admitted due to an acute process or with a fatal outcome expected at the time of admission are excluded. Sociodemographic data, chronic morbidities and geriatric syndromes, number of chronic prescribed medications, PIP at admission to hospital and on discharge, according to the newest screening tool of older screening tool of older person's potentially inappropriate prescriptions/screening tool to alert doctors to right treatment criteria, and ADRs during hospitalisation are being collected. Multimorbidity patterns will be identified using cluster analyses techniques, and the frequency of polypharmacy, PIP and ADRs will be calculated. Finally, the possible relationship between those indicators will be identified through bivariate and multivariate analyses.

ETHICS AND DISSEMINATION

The project has been approved by the clinical research ethics committees of each centre: Comité Ético de investigación Clínica del Parc Taulí, Comitè Ètic d'Investigació Clínica Osona per a la Recerca i Educació Sanitàries (FORES), Comité de Ètica de la Investigación con Medicamentos (CEIm)-Parc de Salut MAR, Comité Ético de Investigación Clínica de Euskadi, Comité de Ética de Investigación del Hospital Universitario de Canarias. The results will be actively and mainly disseminated through publication in peer-reviewed journals and communications in scientific conferences.

TRIAL REGISTRATION NUMBER

NCT02830425.

Associations between complex multimorbidity, activities of daily living and mortality among older Norwegians. A prospective cohort study: the HUNT Study, Norway

Background

With increasing age, having multiple chronic conditions is the norm. It is of importance to study how co-existence of diseases affects functioning and mortality among older persons. Complex multimorbidity may be defined as three or more conditions affecting at least three different organ systems. The aim of this study was to investigate how complex multimorbidity affects activities of daily living and mortality amongst older Norwegians.

Methods

Participants were 60–69-year-olds at baseline in the Nord-Trøndelag Health Study 1995-1997 (HUNT2) n = 9058. Multinomial logistic regression models were used to investigate the association between complex multimorbidity in HUNT2, basic and instrumental activities of daily living in HUNT3 (2006–2008) and mortality during follow-up (n = 5819/5836). Risk ratios (RR) and risk differences (RD) in percentage points (pp) with 95% confidence intervals (CI) were reported.

Results

47.8% of 60–69-year-olds met the criteria of complex multimorbidity at baseline (HUNT2). Having complex multimorbidity was strongly associated with the need for assistance in IADL in HUNT3 11 years later (RR = 1.80 (1.58–2.04) and RD = 8.7 (6.8–10.5) pp) and moderately associated with mortality during the follow-up time (RR = 1.22 (1.12–1.33) and RD = 5.1 (2.9–7.3) pp). Complex multimorbidity was to a lesser extent associated with basic activities of daily living 11 years later (RR = 1.24 (0.85–1.83) and RD = 0.4 (− 0.3–1.1) pp).

Conclusions

This is the first study to show an association between complex multimorbidity and activities of daily living. Complex multimorbidity should receive more attention in order to prevent future disability amongst older persons.