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

Marisa Baré, Susana Herranz, Albert Roso-Llorach, Rosa Jordana, Concepción Violán, Marina Lleal, Pere Roura-Poch, Marta Arellano, Rafael Estrada, Gloria Julia Nazco, MoPIM study group, Marisa Baré, Susana Herranz, Rosa Jordana, Maria Queralt Gorgas, Sara Ortonobes, Marina Lleal, Pere Roura-Poch, Daniel Sevilla, Núria Solà, Javier González, Núria Molist, Mariona Espauella, Oscar Mascaró, Elisabet de Jaime, Olivia Ferrandez, Maria Sala, Miguel Ángel Márquez, Marta Arellano, Carlos Clemente, Olga Sabartés, Núria Carballo, Marta de Antonio, Rafael Estrada, Maria Olatz Ibarra, Candelaria Martin, Gloria Julia Nazco, Rubén Hernández, Marisa Baré, Susana Herranz, Albert Roso-Llorach, Rosa Jordana, Concepción Violán, Marina Lleal, Pere Roura-Poch, Marta Arellano, Rafael Estrada, Gloria Julia Nazco, MoPIM study group, Marisa Baré, Susana Herranz, Rosa Jordana, Maria Queralt Gorgas, Sara Ortonobes, Marina Lleal, Pere Roura-Poch, Daniel Sevilla, Núria Solà, Javier González, Núria Molist, Mariona Espauella, Oscar Mascaró, Elisabet de Jaime, Olivia Ferrandez, Maria Sala, Miguel Ángel Márquez, Marta Arellano, Carlos Clemente, Olga Sabartés, Núria Carballo, Marta de Antonio, Rafael Estrada, Maria Olatz Ibarra, Candelaria Martin, Gloria Julia Nazco, Rubén Hernández

Abstract

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.

Keywords: geriatric medicine; internal medicine; quality in health care; statistics & research methods.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Distribution of the number of chronic conditions (excluding the following risk factors: hypertension, dyslipidaemia, obesity, osteoporosis and drug-related conditions) in relation to age groups.
Figure 2
Figure 2
Observed/expected (O/E) ratio and prevalence of chronic conditions and geriatric syndromes/risk factors per multimorbidity cluster. Conditions with exclusivity >25% and O/E ratios >1 in each cluster are represented. Conditions are ordered by O/E ratio and from cluster 1 to 4. COPD, chronic obstructive pulmonary disease.

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Source: PubMed

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