Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

Robertson, Lynn; Ayansina, Dolapo; Johnston, Marjorie C.; Marks, Angharad; Black, Corri

doi:10.23889/ijpds.v4i1.461

Cited by 10 publications

(13 citation statements)

References 40 publications

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“…As a first step to understanding the implications of disease clusters, we aimed to identify and characterise multimorbidity clusters in a cohort of patients hospitalised in the Grampian region of Scotland. This builds on our previous work describing the overall extent of hospital multimorbidity 10 , 11 .…”

Section: Introductionmentioning

confidence: 84%

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Identifying multimorbidity clusters in an unselected population of hospitalised patients

Robertson

Vieira

Butler

et al. 2022

Sci Rep

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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 ≥ 2 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–rural area of residence. 11,389 of 41,545 hospitalised patients (27%) had ≥ 2 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.

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Section: Introductionmentioning

confidence: 84%

“…The region consists of one large urban centre and is spread over approximately 3000 square miles of city, town, village and rural communities 14 . Full details have been previously published 10 , 11 .…”

Section: Methodsmentioning

confidence: 99%

Identifying multimorbidity clusters in an unselected population of hospitalised patients

Robertson

Vieira

Butler

et al. 2022

Sci Rep

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“…For example, a study comparing two multimorbidity measures reported that while 12 conditions were common to both measures, only 3 of these had identical ICD-10 codes. 11 All coding can be affected by variation in coding practices. In Scotland for instance, guidelines are provided regarding the coding of comorbidities, in which a list of comorbidity groups are prioritized as important conditions to record.…”

Section: Main Findingsmentioning

confidence: 99%

“…10 People with multimorbidity have more hospital admissions, stay longer in hospital, are more likely to be readmitted and have a higher risk of mortality than individuals without multimorbidity. 7,11 Patients with multimorbidity may be discharged from hospital with ongoing care needs, which can have an impact on their families and carers. 12 Identifying patients with multimorbidity at the point of admission has the potential for facilitating more informed care, taking account of the management of multiple conditions.…”

Section: Introductionmentioning

confidence: 99%

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Urban–rural and socioeconomic status: Impact on multimorbidity prevalence in hospitalized patients

et al. 2020

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Objective: The aim of this study was to describe multimorbidity prevalence in hospitalized adults, by urban–rural area of residence and socioeconomic status (SES). Methods: Linked hospital episode data were used. Adults (≥18 years) admitted to hospital as an inpatient during 2014 in Grampian, Scotland, were included. Conditions were identified from admissions during the 5 years prior to the first admission in 2014. Multimorbidity was defined as ≥2 conditions and measured using Tonelli et al. based on International Classification of Diseases-10 coding (preselected list of 30 conditions). We used proportions and 95% confidence intervals (CIs) to summarize the prevalence of multimorbidity by age group, sex, urban–rural category and deprivation. The association between multimorbidity and patient characteristics was assessed using the χ 2 test. Results: Forty one thousand five hundred and forty-five patients were included (median age 62, 52.6% female). Overall, 27.4% (95% CI 27.0, 27.8) of patients were multimorbid. Multimorbidity prevalence was 28.8% (95% CI 28.1, 29.5) in large urban versus 22.0% (95% CI 20.9, 23.3) in remote rural areas and 28.7% (95% CI 27.2, 30.3) in the most deprived versus 26.0% (95% CI 25.2, 26.9) in the least deprived areas. This effect was consistent in all age groups, but not statistically significant in the age group 18–29 years. Multimorbidity increased with age but was similar for males and females. Conclusion: Given the scarcity of research into the effect of urban–rural area and SES on multimorbidity prevalence among hospitalized patients, these findings should inform future research into new models of care, including the consideration of urban–rural area and SES.

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Positive scores on the 4AT delirium assessment tool at hospital admission are linked to mortality, length of stay and home time: two-centre study of 82,770 emergency admissions

Anand

Cheng²,

Ibitoye

et al. 2022

Age and Ageing

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Background Studies investigating outcomes of delirium using large-scale routine data are rare. We performed a two-centre study using the 4 ‘A’s Test (4AT) delirium detection tool to analyse relationships between delirium and 30-day mortality, length of stay and home time (days at home in the year following admission). Methods The 4AT was performed as part of usual care. Data from emergency admissions in patients ≥65 years in Lothian, UK (n = 43,946) and Salford, UK (n = 38,824) over a period of $\sim$3 years were analysed using logistic regression models adjusted for age and sex. Results 4AT completion rates were 77% in Lothian and 49% in Salford. 4AT scores indicating delirium (≥4/12) were present in 18% of patients in Lothian, and 25% of patients in Salford. Thirty-day mortality with 4AT ≥4 was 5.5-fold greater than the 4AT 0/12 group in Lothian (adjusted odds ratio (aOR) 5.53, 95% confidence interval [CI] 4.99–6.13) and 3.4-fold greater in Salford (aOR 3.39, 95% CI 2.98–3.87). Length of stay was more than double in patients with 4AT scores of 1–3/12 (indicating cognitive impairment) or ≥ 4/12 compared with 4AT 0/12. Median home time at 1 year was reduced by 112 days (Lothian) and 61 days (Salford) in the 4AT ≥4 group (P < 0.001). Conclusions Scores on the 4AT used at scale in practice are strongly linked with 30-day mortality, length of hospital stay and home time. The findings highlight the need for better understanding of why delirium is linked with poor outcomes and also the need to improve delirium detection and treatment.

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Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

Cited by 10 publications

References 40 publications

Identifying multimorbidity clusters in an unselected population of hospitalised patients

Identifying multimorbidity clusters in an unselected population of hospitalised patients

Urban–rural and socioeconomic status: Impact on multimorbidity prevalence in hospitalized patients

Positive scores on the 4AT delirium assessment tool at hospital admission are linked to mortality, length of stay and home time: two-centre study of 82,770 emergency admissions

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