Disease clusters in older adults: rationale and need for investigation

Comorbidity of patients with noncommunicable diseases in general practice. Eurasian guidelines

Создание руководства поддержано Советом по терапевтическим наукам отделения клинической медицины Российской академии наук.

Multimorbidity clusters in adults 50 years or older with and without a history of cancer: National Health Interview Survey, 2018

BACKGROUND

Multimorbidity is increasing among adults in the United States. Yet limited research has examined multimorbidity clusters in persons aged 50 years and older with and without a history of cancer. An increased understanding of multimorbidity clusters may improve the cancer survivorship experience for survivors with multimorbidity.

METHODS

We identified 7580 adults aged 50 years and older with 2 or more diseases-including 811 adults with a history of primary breast, colorectal, cervical, prostate, or lung cancer-from the 2018 National Health Interview Survey. Exploratory factor analysis identified clusters of multimorbidity among cancer survivors and individuals without a history of cancer (controls). Frequency tables and chi-square tests were performed to determine overall differences in sociodemographic characteristics, health-related characteristics, and multimorbidity between groups.

RESULTS

Cancer survivors reported a higher prevalence of having 4 or more diseases compared to controls (57% and 38%, respectively). Our analysis identified 6 clusters for cancer survivors and 4 clusters for controls. Three clusters (pulmonary, cardiac, and liver) included the same diseases for cancer survivors and controls.

CONCLUSIONS

Diseases clustered differently across adults ≥ 50 years of age with and without a history of cancer. Findings from this study may be used to inform clinical care, increase the development and dissemination of multilevel public health interventions, escalate system improvements, and initiate innovative policy reform.

Endothelial Senescence and the Chronic Vascular Diseases: Challenges and Therapeutic Opportunities in Atherosclerosis

Atherosclerosis is probably one of the paradigms of disease linked to aging. Underlying the physiopathology of atherosclerosis are cellular senescence, oxidative stress, and inflammation. These factors are increased in the elderly and from chronic disease patients. Elevated levels of oxidative stress affect cellular function and metabolism, inducing senescence. This senescence modifies the cell phenotype into a senescent secretory phenotype. This phenotype activates immune cells, leading to chronic systemic inflammation. Moreover, due to their secretory phenotype, senescence cells present an increased release of highlighted extracellular vesicles that will change nearby/neighborhood cells and paracrine signaling. For this reason, searching for specific senescent cell biomarkers and therapies against the development/killing of senescent cells has become relevant. Recently, senomorphic and senolityc drugs have become relevant in slowing down or eliminating senescence cells. However, even though they have shown promising results in experimental studies, their clinical use is still yet to be determined.

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.

Role of Interprofessional primary care teams in preventing avoidable hospitalizations and hospital readmissions in Ontario, Canada: a retrospective cohort study

BACKGROUND

Improving health system value and efficiency are considered major policy priorities internationally. Ontario has undergone a primary care reform that included introduction of interprofessional teams. The purpose of this study was to investigate the relationship between receiving care from interprofessional versus non-interprofessional primary care teams and ambulatory care sensitive condition (ACSC) hospitalizations and hospital readmissions.

METHODS

Population-based administrative databases were linked to form data extractions of interest between the years of 2003-2005 and 2015-2017 in Ontario, Canada. The data sources were available through ICES. The study design was a retrospective longitudinal cohort. We used a "difference-in-differences" approach for evaluating changes in ACSC hospitalizations and hospital readmissions before and after the introduction of interprofessional team-based primary care while adjusting for physician group, physician and patient characteristics.

RESULTS

As of March 31st, 2017, there were a total of 778 physician groups, of which 465 were blended capitation Family Health Organization (FHOs); 177 FHOs (22.8%) were also interprofessional teams and 288 (37%) were more conventional group practices ("non-interprofessional teams"). In this period, there were a total of 13,480 primary care physicians in Ontario of whom 4848 (36%) were affiliated with FHOs-2311 (17.1%) practicing in interprofessional teams and 2537 (18.8%) practicing in non-interprofessional teams. During that same period, there were 475,611 and 618,363 multi-morbid patients in interprofessional teams and non-interprofessional teams respectively out of a total of 2,920,990 multi-morbid adult patients in Ontario. There was no difference in change over time in ACSC admissions between interprofessional and non-interprofessional teams between the pre- and post intervention periods. There were no statistically significant changes in all cause hospital readmission s between the post- and pre-intervention periods for interprofessional and non-interprofessional teams.

CONCLUSIONS

Our study findings indicate that the introduction of interprofessional team-based primary care was not associated with changes in ACSC hospitalization or hospital readmissions. The findings point for the need to couple interprofessional team-based care with other enablers of a strong primary care system to improve health services utilization efficiency.

Facing multimorbidity in the precision medicine era

The clinical picture of multimorbidity is heterogeneous and it is characterized by great complexity. Precision medicine is an innovative approach to provide personalized care focused on individual characteristics and to deliver the right treatments, at the right time, to the right person. The precision medicine approach, which represents an epochal change in the field of chronic diseases, has been poorly implemented in patients with multimorbidity. Several factors can limit this application. First, the precision medicine approach has been successfully applied in the treatment of mono-factorial diseases while multimorbidity is multifactorial. Second, there is lack of understanding of risk factors in the development and evolution of multimorbidity. Third, precision medicine is mainly focused on understanding genetic aspects of diseases and neglects other characteristics contributing to the definition of individual profiles. Finally, individual pathways may lead to the development of different multimorbidity phenotypes. A possible solution to simplify the application of precision medicine to this condition is to reduce its complexity and to find homogeneous patterns of chronic diseases that may work as targets of preventive and therapeutic strategies. This approach can lead to better understanding how these factors interact at individual level and to define interventions that might target multimorbidity.

The association between multimorbidity and hospitalization is modified by individual demographics and physician continuity of care: a retrospective cohort study

Background

Multimorbidity poses a significant clinical challenge and has been linked to greater health services use, including hospitalization; however, we have little knowledge about the influence of contextual factors on outcomes in this population. Objectives: To describe the extent to which the association between multimorbidity and hospitalization is modified by age, gender, primary care practice model, or continuity of care (COC) among adults with at least one chronic condition.

Methods

A retrospective cohort study with linked population-based administrative data.

Setting: Ontario, Canada. Cohort: All individuals 18 and older with at least one of 16 priority chronic conditions as of April 1, 2009 (baseline). Main Outcome Measures: Any hospitalization, 3 or more hospitalizations, non-medical discharge delay, and 30-day readmission within the 1 year following baseline.

Results

Of 5,958,514 individuals, 484,872 (8.1 %) experienced 646,347 hospitalizations. There was a monotonic increase in the likelihood of hospitalization and related outcomes with increasing multimorbidity which was modified by age, gender, and COC but not primary care practice model. The effect of increasing multimorbidity was greater in younger adults than older adults and in those with lower COC than with higher COC. The effect of increasing multimorbidity on hospitalization was greater in men than women but reversed for the other outcomes.

Conclusions

The effect of multimorbidity on hospitalization is influenced by age and gender, important considerations in the development of person-centred care models. Greater continuity of physician care lessened the effect of multimorbidity on hospitalization, further demonstrating the need for care continuity across providers for people with chronic conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1415-5) contains supplementary material, which is available to authorized users.

An investigation of multimorbidity measures as risk factors for pneumonia in elderly frail patients admitted to hospital

OBJECTIVES

To investigate the association of different chronic comorbidities, considered singularly and together in Cumulative Illness Rating Scale (CIRS) indexes, with pneumonia diagnosis in a group of elderly frail hospitalized patients.

DESIGN AND METHODS

With a retrospective cohort design, all clinical records of frail (Rockwood ≥ 5) nonterminal patients ≥ 65 years old acutely admitted over a 8-month span in an internal medicine ward were evaluated. Pneumonia status and its categorization (community-acquired, CAP, vs healthcare-associated, HCAP) were defined according to chest radiology findings and validated criteria. Chronic comorbidities, CIRS Comorbidity Score and CIRS Severity Index were collected for each participant through a standardized methodology. Multivariate logistic regression models were applied to assess the association of each comorbid condition or scores with pneumonia.

RESULTS

1199 patients (546 M, median age 81.9, IQR 72.8-87.9 years), of whom 239 with pneumonia (180 CAP, 59 HCAP) were evaluated. CIRS Comorbidity Score was significantly associated with pneumonia, both at an age- and sex-adjusted model and at a multivariate model (OR for each unitary increase 1.03, 95% CI 1.001-1.062, p=0.04), together with provenience from nursing home (OR 1.96, 95% CI 1.41-2.73, p<0.001). Among single comorbidities, only COPD (OR 2.7, 95% CI 1.9-3.6, p<0.001) and dementia (OR 2.3, 95% CI 1.7-3.3, p<0.001) were associated with pneumonia, while stroke, cancer, cardiovascular, chronic liver and kidney disease were not.

CONCLUSIONS

In a small cohort of elderly frail hospitalized patients, measures of multimorbidity, like CIRS, are significantly associated with the risk of pneumonia. COPD and dementia are the main conditions concurring to define this risk.

Deconstructing Complex Multimorbidity in the Very Old: Findings from the Newcastle 85+ Study

Objectives. To examine the extent and complexity of the morbidity burden in 85-year-olds; identify patterns within multimorbidity; and explore associations with medication and healthcare use. Participants. 710 men and women; mean (SD) age 85.5 (0.4) years. Methods. Data on 20 chronic conditions (diseases and geriatric conditions) ascertained from general practice records and participant assessment. Cluster analysis within the multimorbid sample identified subgroups sharing morbidity profiles. Clusters were compared on medication and healthcare use. Results. 92.7% (658/710) of participants had multimorbidity; median number of conditions: 4 (IQR 3–6). Cluster analysis (multimorbid sample) identified five subgroups sharing similar morbidity profiles; 60.0% (395/658) of participants belonged to one of two high morbidity clusters, with only 4.9% (32/658) in the healthiest cluster. Healthcare use was high, with polypharmacy (≥5 medications) in 69.8% (459/658). Between-cluster differences were found in medication count (p = 0.0001); hospital admissions (p = 0.022); and general practitioner (p = 0.034) and practice nurse consultations (p = 0.011). Morbidity load was related to medication burden and use of some, but not all, healthcare services. Conclusions. The majority of 85-year-olds had extensive and complex morbidity. Elaborating participant clusters sharing similar morbidity profiles will help inform future healthcare provision and the identification of common underlying biological mechanisms.

Acute and chronic diseases as part of multimorbidity in acutely hospitalized older patients

BACKGROUND

To describe the prevalence of multimorbidity and to study the association between acute and chronic diseases in acutely hospitalized older patients

METHODS

Prospective cohort study conducted between 2006 and 2008 in three teaching hospitals in the Netherlands. 639 patients aged 65 years and older, hospitalized for >48 h were included. Two physicians scored diseases, using ICD-9 codes. Chronic multimorbidity was defined as the presence of ≥2 chronic diseases, and acute multimorbidity as ≥1 acute diseases upon pre-existent chronic diseases. Logistic regression analyses were conducted to analyse cluster associations between a chronic index disease and the concurrent chronic or acute disease, corrected for age and sex.

RESULTS

The mean age of patients was 78 years, over 50% had ADL impairments. Prevalence of chronic multimorbidity was 69%, and acute multimorbidity was present in 88%. Hypertension (OR 1.16; 95% CI 1.08-1.24), diabetes (type I or type 2) (OR 1.12; 95% CI 1.04-1.21), heart failure (OR 1.25; 95% CI 1.14-1.38) and COPD (OR 1.19; 95% CI 1.05-1.34) were associated with acute renal failure. Hypertension (OR 1.10; 95% CI 1.04-1.17) and atrial fibrillation (OR 1.17; 95% CI 1.08-1.27) were associated with an adverse drug event. Gastro-intestinal bleeding was clustered with atrial fibrillation (OR 1.11; 95% CI 1.04-1.19) and gastric ulcer (OR 1.16; 95% CI 1.07-1.25).

CONCLUSION

Both acute and chronic multimorbidity was frequently present in hospitalized older patients. We identified specific associations between acute and chronic diseases. There is a need for strategies addressing multimorbidity during the exacerbation of chronic diseases.

[Rheumatic diseases and multimorbidity]

In the past decade, investigators' undeniable and justified interest has not been fading in comorbidities in the presence of rheumatic diseases. The terms "comorbidity" and "multimorbidity" are frequently and not always consciously used as interinterchangeable, confusing the terminology and accordingly the elaboration of strategies for further researches. The concepts "(co-occurring disease" and "multimodality" are not mutually exclusive or contradictory, but these should be considered from another point of view than "comorbidity". The problem of multimorbid disease is the rule rather than the exception for clinicians treating primarily "typical" rheumatic patients. Recent researches could outline a few key areas for further study of the concept of multimorbidity in rheumatologic practice, which will be able to turn the international research community from rheumatic disease to the patient as a whole.

Multimorbidity and rheumatic conditions-enhancing the concept of comorbidity

The concept of multimorbidity is still poorly understood and not well integrated into medical care and research. For clinicians involved in rheumatology care for an ageing patient population who have multiple diseases, multimorbidity is the rule not the exception. The interaction of different diseases and the impact they have on important clinical outcomes, such as physical function, quality of life and mortality, should all be considered by the rheumatologist. Treatment decisions must be adapted for the patient with multimorbidity to best serve the individual and society. This Perspectives article describes the concept of multimorbidity, how it differs from comorbidity, and outlines why an increased understanding of multimorbiditiy will enhance our overall clinical practice and research focus.

Epidemiological strategies for adapting clinical practice guidelines to the needs of multimorbid patients

Background

Clinical practice guidelines have been developed to improve the quality of health care. However, adherence to current monomorbidity-focused, mono-disciplinary guidelines may result in undesirable effects for persons with several comorbidities, in adverse interactions between drugs and diseases, conflicting management strategies, and polypharmacy. This is why new types of guidelines that address the problem of interacting medical interventions and conditions in multimorbid patients are needed.

Discussion

Previous research projects investigated patterns of multimorbidity and were able to identify combinations of the most prevalent chronic conditions, or clusters of comorbidities. These results represent potential methodological starting points for the development of guidelines that account for multimorbidity. The objective of these efforts is to identify frequent reasons for interactions and adverse events that may occur when the current type of guideline is rigorously applied in multimorbid patients.

Summary

The epidemiologic approaches described above may help guideline developers as a kind of check list of disease combinations that should systematically be considered during guideline development. Given the risk of worse outcomes in a huge group of vulnerable patients, researchers, guideline developers, and funding institutions should give first priority to the development of guidelines more appropriate for use in multimorbid persons.

Comparison of disease clusters in two elderly populations hospitalized in 2008 and 2010

BACKGROUND

As chronicity represents one of the major challenges in the healthcare of aging populations, the understanding of how chronic diseases distribute and co-occur in this part of the population is needed.

OBJECTIVES

The aims of this study were to evaluate and compare patterns of diseases identified with cluster analysis in two samples of hospitalized elderly.

METHODS

Data were obtained from the multicenter 'Registry Politerapie SIMI (REPOSI)' that included people aged 65 or older hospitalized in internal medicine and geriatric wards in Italy during 2008 and 2010. The study sample from the first wave included 1,411 subjects enrolled in 38 hospitals wards, whereas the second wave included 1,380 subjects in 66 wards located in different regions of Italy. To analyze patterns of multimorbidity, a cluster analysis was performed including the same diseases (19 chronic conditions with a prevalence >5%) collected at hospital discharge during the two waves of the registry.

RESULTS

Eight clusters of diseases were identified in the first wave of the REPOSI registry and six in the second wave. Several diseases were included in similar clusters in the two waves, such as malignancy and liver cirrhosis; anemia, gastric and intestinal diseases; diabetes and coronary heart disease; chronic obstructive pulmonary disease and prostate hypertrophy.

CONCLUSION

These findings strengthened the idea of an association other than by chance of diseases in the elderly population.

Patterns of comorbidity and multimorbidity in the oldest old: the Octabaix study

BACKGROUND

Multimorbidity is associated with higher mortality, increased disability, a decline in functional status and a lower quality of life. The objective of the study is to explore patterns of multimorbidity in an elderly population.

METHODS

328 community inhabitants aged 85 years were included. Socio-demographic variables and data from the global geriatric assessment were evaluated. Information on the presence of sixteen common chronic conditions was collected: hypertension, diabetes mellitus, dyslipidemia, ischemic cardiomyopathy, heart failure, stroke, chronic obstructive pulmonary disease, (COPD), atrial fibrillation, peripheral arterial disease, Parkinson's disease, cancer, dementia, anemia, chronic kidney disease (CKD), visual impairment and deafness. Hierarchical cluster analysis was performed.

RESULTS

The rate of multimorbidity (>1 disease) was 95.1%. Men had a higher percentage of COPD and malignancy. Four main clusters were identified. The highest value of the bivariate correlation matrix was that between heart failure and visual impairment. These two diseases were included in a cluster with atrial fibrillation, CKD, heart failure, stroke, high blood pressure and diabetes mellitus.

CONCLUSIONS

The large majority of oldest old subjects had multimorbidity. The results confirm the non-random co-occurrence of certain diseases in this age group.

Does multimorbidity influence the occurrence rates of chronic conditions? A claims data based comparison of expected and observed prevalence rates

Objective

Multimorbidity is a complex phenomenon with an almost endless number of possible disease combinations with unclear implications. One important aspect in analyzing the clustering of diseases is to distinguish between random coexistence and statistical dependency. We developed a model to account for random coexistence based on stochastic distribution. We analyzed if the number of diseases of the patients influences the occurrence rates of chronic conditions.

Methods

We analyzed claims data of 121,389 persons aged 65+ using a list of 46 chronic conditions. Expected prevalences were simulated by drawing without replacement from all observed diseases using observed overall prevalences as initial probability weights. To determine if a disease occurs more or less frequently than expected by chance we calculated observed-minus-expected deltas for each disease. We defined clinical relevance as |delta| ≥ 5.0%. 18 conditions were excluded because of a prevalence < 5.0%.

Results

We found that (1) two chronic conditions (e.g. hypertension) were more frequent than expected in patients with a low number of comorbidities; (2) four conditions (e.g. renal insufficiency) were more frequent in patients with many comorbidities; (3) six conditions (e.g. cancer) were less frequent with many comorbidities; and (4) 16 conditions had an average course of prevalences.

Conclusion

A growing extent of multimorbidity goes along with a rapid growth of prevalences. This is for the largest part merely a stochastic effect. If we account for this effect we find that only few diseases deviate from the expected prevalence curves. Causes for these deviations are discussed. Our approach also has methodological implications: Naive analyses of multimorbidity might easily be affected by bias, because the prevalence of all chronic conditions necessarily increases with a growing extent of multimorbidity. We should therefore always examine and discuss the stochastic interrelations between the chronic conditions we analyze.