{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:26:51Z","timestamp":1792790811458,"version":"4.5.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NHS Grampian Endowment","award":["18\/10"],"award-info":[{"award-number":["18\/10"]}],"id":[{"id":"https:\/\/ror.org\/0229evh14","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multimorbidity (multiple coexisting chronic health conditions) is common and increasing worldwide, and makes care challenging for both patients and healthcare systems. To ensure care is patient-centred rather than specialty-centred, it is important to know which conditions commonly occur together and identify the corresponding patient profile. To date, no studies have described multimorbidity clusters within an unselected hospital population. Our aim was to identify and characterise multimorbidity clusters, in a large, unselected hospitalised patient population. Linked inpatient hospital episode data were used to identify adults admitted to hospital in Grampian, Scotland in 2014 who had\u2009\u2265\u20092 of 30 chronic conditions diagnosed in the 5 years prior. Cluster analysis (Gower distance and Partitioning around Medoids) was used to identify groups of patients with similar conditions. Clusters of conditions were defined based on clinical review and assessment of prevalence within patient groups and labelled according to the most prevalent condition. Patient profiles for each group were described by age, sex, admission type, deprivation and urban\u2013rural area of residence. 11,389 of 41,545 hospitalised patients (27%) had\u2009\u2265\u20092 conditions. Ten clusters of conditions were identified: hypertension; asthma; alcohol misuse; chronic kidney disease and diabetes; chronic kidney disease; chronic pain; cancer; chronic heart failure; diabetes; hypothyroidism. Age ranged from 51 (alcohol misuse) to 79 (chronic heart failure). Women were a higher proportion in the chronic pain and hypothyroidism clusters. The proportion of patients from the most deprived quintile of the population ranged from 6% (hypertension) to 14% (alcohol misuse). Identifying clusters of conditions in hospital patients is a first step towards identifying opportunities to target patient-centred care towards people with unmet needs, leading to improved outcomes and increased efficiency. Here we have demonstrated the face validity of cluster analysis as an exploratory method for identifying clusters of conditions in hospitalised patients with multimorbidity.<\/jats:p>","DOI":"10.1038\/s41598-022-08690-3","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T02:03:00Z","timestamp":1648173780000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Identifying multimorbidity clusters in an unselected population of hospitalised patients"],"prefix":"10.1038","volume":"12","author":[{"given":"Lynn","family":"Robertson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rute","family":"Vieira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Butler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marjorie","family":"Johnston","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Sawhney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corri","family":"Black","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"issue":"7","key":"8690_CR1","doi-asserted-by":"publisher","first-page":"e102149","DOI":"10.1371\/journal.pone.0102149","volume":"9","author":"C Violan","year":"2014","unstructured":"Violan, C. et al. Prevalence, determinants and patterns of multimorbidity in primary care: A systematic review of observational studies. PLoS ONE 9(7), e102149 (2014).","journal-title":"PLoS ONE"},{"issue":"3","key":"8690_CR2","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1093\/ageing\/afx201","volume":"47","author":"A Kingston","year":"2018","unstructured":"Kingston, A., Robinson, L., Booth, H., Knapp, M. & Jagger, C. MODEM project Projections of multi-morbidity in the older population in England to 2035: Estimates from the Population Ageing and Care Simulation (PACSim) model. Age Ageing 47(3), 374\u2013380 (2018).","journal-title":"Age Ageing"},{"key":"8690_CR3","unstructured":"The Academy of Medical Sciences. Multimorbidity: A Priority for Global Health Research. https:\/\/acmedsci.ac.uk\/policy\/policy-projects\/multimorbidity. Accessed October, 2020."},{"key":"8690_CR4","unstructured":"Medical Research Council. 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