SP - Big Data

1. Introduction

Both increased frailty and multi-morbidity are independently associated with high mortality and increased risk for nursing home placement.

There is limited data on the best ways of assessing frailty and complex comorbidities to guide patient selection for rehabilitation.

It is important we do not deprive an individual of the chance of inpatient rehabilitation, but this needs to be balanced with potential poor outcomes at one year due to frailty and comorbidities.

2. Method

Data was collated retrospectively on all discharged patients over a 90-day period from May to July 2023.

A sub-analysis was undertaken to evaluate one-year outcomes, based on clinical frailty scales on discharge, Barthel's index, their length of admission and number of subsequent hospital admissions.

3. Results

153 patients were discharged over the 90 day period with mean age of 84.

At one year 31 % had died, 12% had gone to placement and 57% remain alive at home.

Higher clinical frailty scores and lower Barthel's index at discharge were correlated with poorer outcomes with mortality & placement.

Higher length of stay, increased subsequent hospital admissions, and more advanced age were associated with unfavourable outcomes.

Among those died, 42% were transferred back to the acute hospital due to acute instability, and 15% had been discharged to placement.

Among those gone to placement, 27% were transferred back to the acute hospital due to acute instability.

Length of stay in rehab is shorter in those still alive and living at home.

4. Conclusion

The results make us consider in more details the risks and benefits of an admission for rehabilitation, as this may account for 10% of an individual’s last year of life.

We aim to relook and refine our pathways to ensure the right patients are accessing rehabilitation.

We will repeat this study in a years’ time.

Introduction

Globally, about one-third of community-dwelling older adults suffer from complex multimorbidity. Complex multimorbidity (three or more chronic diseases and affecting three or more different body systems) have worse outcomes than multimorbidity, such as more frequent hospitalizations, and premature mortality. The effect of sociodemographic factors in the progression of multimorbidity has been found, but the lifestyle and polypharmacy remain unclear. This study aims to explore impact of lifestyle and polypharmacy on the progression of multimorbidity among community-dwelling older adults.

Methods

The study used data from the health examination records of older adults residing in Southern China in 2017 and 2020 (n=3647). The outcome was occurrence of the status of the older adults changed from multimorbidity to complex multimorbidity after 3 years. Logistic regression model was used to analyze the influence of lifestyle (diet, physical activity, smoking and drinking) and polypharmacy of baseline on the progression of multimorbidity. Demographic variables were also included in the model as confounding variables.

Results

Totally 13.5% (n=491) of older adults with multimorbidity had developed into complex multimorbidity. The proportion of complex multimorbidity increased from 32.1% to 45.6%. The logistic regression analysis indicated that, compared with who exercise daily, those who don't exercise (OR=1.561, 95%CI:1.233-1.976, p<0.001) and those exercise occasionally (OR=1.670, 95%CI:1.328-2.100, p<0.001) are more possibly to have complex multimorbidity. The smokers have a higher risk than non-smokers (OR=1.636, 95%CI:1.137-2.353, p<0.01). Those widowed are more likely to developing complex multimorbidity than those married (OR=1.532, 95%CI:1.221-1.923, p<0.001). Diet, drinking and polypharmacy had no significant effect on the progression of multimorbidity.

Conclusions

Lack of exercise, smoking and loss of spouse can significantly increase risk of the progression of multimorbidity and developing into complex multimorbidity among community-dwelling older adults with multimorbidity. Future research could focus on developing and implementing exercise-based interventions to delay the progression of multimorbidity.

Background: Our recent research found significant visit-to-visit variability in nurse-assessed Clinical Frailty Scale (CFS) scores in Emergency Departments (ED), potentially limiting their reliability across patient encounters. This study investigated whether laboratory-based frailty indices could provide more stable assessments while maintaining clinical utility.

Methods: We conducted a retrospective cohort study focusing on patients with multiple ED attendances between July 2017 and December 2021 across two London hospitals. From 23,956 patients with repeated visits (total visits = 60,381), we used linear mixed effects models to compare the visit-to-visit stability of nurse-assessed CFS scores against various automated frailty index configurations. We tested base, short-period, mean-type, high-features, and low-features configurations, plus a novel drug-adjusted version incorporating medication data.

Results: Nurse-assessed CFS scores showed marked visit-to-visit variability, with only 35% of score variance attributable to underlying patient characteristics (ICC=0.35). In contrast, automated measures demonstrated significantly higher stability (ICC range 0.48-0.74), with the drug-adjusted frailty index showing the highest consistency (ICC=0.74). While nurse assessments were significantly influenced by presenting complaints and illness severity (NEWS scores β=0.12, p<0.001), automated measures remained stable across these acute factors while maintaining meaningful associations with age (β range 0.006-0.013, p<0.001) and clinical outcomes (c-statistic 0.718 for 90-day mortality).

Conclusions: The higher stability of automated measures suggests they could serve as valuable adjuncts to clinical assessment, particularly in helping establish a patient's baseline status from two weeks prior to admission - a key requirement of proper CFS scoring that can be challenging in busy ED settings. Whereas nurse assessments showed superior outcome discrimination, combining automated baseline data with clinical expertise could enhance the accuracy and efficiency of frailty assessment in emergency care. This synergistic approach could be particularly valuable in settings where comprehensive patient history may be difficult to obtain.

Background: Data are limited on whether the causes of Emergency Department (ED) attendance and clinical outcomes vary by frailty status.

Methods: Using the Nationwide ED Sample, causes of attendance were stratified by Hospital Frailty Risk Score (HFRS). Logistic regression was used to determine adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) of ED and overall mortality.

Results: A total of 155,497,048 ED attendances were included, of which 125,809,960 (80.9%) had a low HFRS (<5), 27,205,257 (17.5%) had an intermediate HFRS (5-15) and 2,481,831 (1.6%) had a high HFRS (>15). The most common cause of ED attendance in the high HFRS group was infectious diseases (43.0%), followed by cardiovascular diseases (CVD) (24.0%) and respiratory diseases (10.2%). For the low HFRS group musculoskeletal disease was the most common cause (21.2%) followed by respiratory diseases (20.6%), and gastrointestinal diseases (18.5%). On adjusted analysis, high-risk patients had overall mortality (combined ED and in-hospital) across most attendance causes, compared to their low-risk counterparts (p<0.001). High HFRS patients with infectious diseases, CVD and respiratory diseases had an increased risk of overall mortality, compared to their low-risk counterparts (aOR 23.88 95% CI 23.42-24.34 for the infectious disease cohort, aOR 2.58 95% CI 2.55-2.61 for the CVD cohort and aOR 36.90 95% CI 36.18-37.62 for respiratory disease cohort).

Conclusions: Frailty is present in a significant proportion of ED attendances, with the cause varying by frailty status. Frailty is associated with decreased ED and increased overall mortality across most attendance causes.

Background:

Frailty has both health + health economic consequences. There are however few data concerning occurrence of frailty in different ethnic groups in the United Kingdom (UK). The aim of this analysis was to determine frailty prevalence across an ethnically diverse city and to explore the influence of age/social-disadvantage/ethnicity on occurrence. We looked also at frailty related risk of severe illness in relation to COVID-19 infection.

Methods:

Using data from the Greater Manchester Health Record(GMCR), we defined frailty index based on the presence/absence of up to 36 deficits scaled 0-1. We defined frailty based on those with 9 or more deficits (out of total=36) and electronic frailty index (eFi) as the total number of deficits present, divided by 36 (range 0-1).

Results:

There were 534567 people aged 60+years on 1January2020 in Greater Manchester. There was noticeable variation in frailty prevalence across general practices. The majority were white (84%) with 4.7% self-describing as Asian/Asian British, and 1.3% Black/Black British. The prevalence of moderate to severe frailty (eFI>0.24) was 22.1%. Prevalence was higher in women than men (25.3% vs 18.5%) and increased with age. Compared to the prevalence of frailty in Whites (22.5%) prevalence was higher in Asian/Asian British ethnicity people (28.1%) and lower in those of Black/Black British descent (18.7%). Prevalence increased with increasing social disadvantage (p=0.002 for trend across disadvantage quintiles). Among those with a positive COVID-19 test those with frailty were more likely to require hospital admission within 28-days, with increased risk for Asian/Asian British descent (OR=1.47; 95% CI 1.34-1.61) and Black/Black British descent (OR 1.86; 95% CI 1.56-2.20) people vs Whites.

Conclusion:

There is marked variation in occurrence of frailty across Greater Manchester. Frailty is more common in Asian/Asian British people than Whites and less common among Black/Black British with a gradient that relates to social disadvantage.

Introduction:

Many UK care home (CH) residents live with multiple long-term conditions, leading to high levels of healthcare utilisation. Previous studies have used routine data to describe their health and social care characteristics separately. Accurately identifying when an individual is admitted to a CH from routine data is challenging. This study aims to provide a combined health and social care profile of a cohort of long-stay CH residents, at the point of admission, using linked primary, secondary and social care data.

Methods:

Individuals aged 65 and over registered to a GP practice contributing to the ‘Connected Bradford’ dataset who were admitted to a CH between January 2016 and December 2019 were included. Start and end dates for social care packages (nursing and residential) were identified from local authority social care data. Respite and reablement packages were excluded. Complete self-funders were not identified with this method. Linked secondary and primary care data were used to describe health characteristics. CH residents identified using primary care records and local authority data will be compared.

Results:

2,801 individuals were admitted to a CH during the study period of whom 1998 (71%) were long-stay residents (>6 weeks). Only 72% of participants identified using local authority data, had a primary care code indicating CH residency in their primary care records. Median length of stay was 272 days (IQR 63 to 480). Mean age at admission was 85 years (SD 8), median Index of Multiple Deprivation decile five. 59% of residents required nursing care from admission. 79% of individuals were taking 5 or more medications.

Conclusions:

Using local authority data offers a novel way to identify and characterise CH residents. Linkage of primary care records to local authority data improves identification of CH residents using routine data. Additional linkage with address history would further improve accuracy.

Introduction

Reference nutrient intake for protein amongst the general population is 0.75 grammes of protein per kilogram of body weight per day (g/kg BW/d). Expert groups recommend healthy adults over 65years have 1.0-1.2g/kg BW/d to support good health and maintain functionality (Deutz, Bauer and Barrazoni, Clinical Nutrition, 33(6):929-36). A recent paper suggested age specific recommendations of 1.2g/kg BW/d (Dorrington, Fallaize and Hobbs, Journal of Nutrition, 150(9):2245-2256).

This study aimed to quantify percentage of community dwelling older adults meeting recommendations for protein intake and explore factors associated with low consumption.

Methods

The study population comprised >65s completing the NDNS survey years 9-11 (2016-2019)*. Dietary intake was recorded in food diaries. Protein consumption was calculated as grammes per kilogram adjusted body weight per day (g/kg aBW/d). Adjustment made for body mass index (BMI) below 22kg/m² and above 27kg/m². Percentage of participants meeting protein recommendations for 0.75, 1.0 and 1.2g/kg BW/d was calculated. Chi-squared test for independence was utilised to determine association between social, health and lifestyle factors and low protein intake.

Results

Data from 385 participants were included; 43% male, 98% white. Mean protein intake was 0.98g/kg aBW/d (SD ±0.25). Prevalence of protein intake below 0.75g/kg aBW/d was 16.4% (n=63), below 1.0g/kg aBW/d was 52.2% (n=201) and below 1.2g/kg aBW/d 82.1% (n=316).

Current and ex-regular smoking was associated with protein intake <1g/kg aBW/d (p=0.01). No other analysis reached statistical significance although prevalence of low protein intake was higher in those without their own teeth (p=0.08), use of dentures (p=0.14) and BMI of 27-30kg/m² (p=0.09).

Conclusion

A large percentage of older adults are below expert recommendations for protein intake. There is a need for clarity over recommendations so that a clear public message can be given to optimise health and function in ageing. Factors influencing poor protein intake require further examination.

*University of Cambridge, MRC Epidemiology Unit, NatCen Social Research. (2023). National Diet and Nutrition Survey Years 1-11, 2008-2019. [data collection]. 19th Edition. UK Data Service. SN: 6533, DOI: http://doi.org/10.5255/UKDA-SN-6533-19

Introduction:

Multimorbidity is complex and impacts patients' quality of life, health outcomes, and health care utilisation. This project aims to identify multimorbidity patterns and their impact on long-term care admissions in community-dwelling older adults.

Methods:

Multimorbidity was ascertained using primary care data Tū Ora COMPASS Health. Adults aged 65+ (55+ for Māori and Pasifika) were included in the analysis. Aged residential care (ARC) admission was determined from interRAI. Twelve conditions ascertained were hypertension, ischaemia, congestive heart failure, stroke, diabetes, cancer, chronic obstructive pulmonary disease, depression, hypothyroid, osteoporosis, dementia, and neurological diseases. Latent class analyses were completed to identify multimorbidity patterns by ethnicity, i.e., Māori, Pasifika, and nonMāori/non-Pasifika (nMP). For the latter group, analyses were also completed by age groups (<80 years and ≥80 years. Cox-regression models were used to examine the association between multimorbidity patterns and 5-year ARC admission.

Results:

The sample comprises 45,178 older adults: nMP (88%), Māori (8%), and 1,755 Pasifika (4%). The average age for Māori and Pasifika was 65.1, respectively, and nMP was 74.1. We identified three multimorbidity patterns for Māori and Pasifika, and four for nMP (<80 and ≥80). All twelve conditions clustered differently in these samples. Eleven-per-cent Māori were in a 'complex-cluster', and they had a three times higher risk of ARC admission than 'healthier-cluster' [aHR(95%CI): 2.96 (1.81-4.36)]. We did not observe an association between condition clusters and ARC admission risk in the Pasifika sample. In the nM/nP<80y sample, those in 'complex-cluster' (4%) had a 5.5 times higher risk of ARC admission (5.48, 4.68-6.41) than in the 'healthier-cluster'; a similar association was observed in nM/nP≥80y in 'complex-cluster' (8%) when compared to 'healthier-cluster' (4.08, 3.67-4.53).

Conclusions:

Complex clusters were associated with an increased risk of five-year ARC admission. Multimorbidity patterns are helpful for a more strategic approach to managing multimorbidity better in primary care settings.

Background: Guidelines recommend that all older hospital patients are screened for cognitive comorbidity (i.e. dementia, delirium) and frailty to inform care and target multidisciplinary team resources, based mainly on evidence from studies in elective or specialty-specific settings. Unselected hospital-wide data are needed to inform guidance and service design and delivery, so we set up the Oxford Cognitive Comorbidity and Ageing Research Database (ORCHARD) using routinely-acquired electronic patient record (EPR) data.

Methods: ORCHARD includes pseudonymised EPR data on all patients >65 years with unplanned admission to one of four general hospitals in Oxfordshire, serving a population of 660,000. Data collected include cognitive screening (mandatory for >70 years) comprising dementia history, delirium diagnosis (Confusion Assessment Method—CAM), and 10-point Abbreviated Mental Test; together with nursing risk assessments, frailty, diagnoses, comorbidities (Charlson index), observations, illness acuity, laboratory tests and brain imaging. Outcomes include length of stay, delayed transfers of care, discharge destination, readmissions, death and dementia through linkage to electronic mental health records.

Results: ORCHARD (2017-2019) includes data from 99,147 consecutive, unselected hospital admissions across all specialties (n=67,585 [68%] inpatient versus n=31,562 [32%] day case; n=73,385 [81%] medical versus n=16,918 [19%] surgical/other). Admissions data were linked to 48,333 unique individuals (n=24,466 [51%] female) with a mean/SD age of 78/10, Index of Multiple Deprivation Decile of 7.6/2.1 and Braden Score of 18.7/3.5 at first admission. Frailty was prevalent, with 15,320 (32%) scoring moderate and 3,233 (7%) high on the Hospital Frailty Risk Score. Complete cognitive screening data are available for 13,102 (67%) unique individuals ≥70 years with inpatient admission.

Conclusion: ORCHARD is a large and rich data resource that will enable studies on the burden and impact of cognitive and physical frailty in-hospital, with relevance to the design and delivery of clinical services and understanding of healthcare resource use hospital-wide and by specialty.

Introduction: Moving into a care home is a significant, life-changing experience which occurs to address care needs which cannot be supported elsewhere. UK health policy recommends against moving into a care home from the acute hospital. However, this occurs in practice. Better understanding pathways into care homes could improve support for individuals and families, service planning and policymaking. Our aim was to characterise individuals who move-in to a care home from hospital and those moving-in from the community, identifying factors associated with moving-in from hospital.

Method: A retrospective observational cohort study was conducted involving adults moving into care homes in Scotland between 1/3/13-31/3/16 using the Scottish Care Home Census (SCHC), a national individual-level social care dataset. SCHC data were linked to routine data sources including hospital admissions, community prescribing and mortality. The data were split into those moving-in from hospital and those moving-in from the community. Descriptive statistics characterising the two groups were generated and multivariate regression undertaken to identify factors associated with moving-in from hospital.

Results: A total of 23,892 individuals were included in the analysis, of whom 13,564 (56.8%) moved-in from hospital. A third came directly from an acute hospital, with 57.7% from rehabilitation or community hospitals and 7.1% from inpatient psychiatry. Being male, receiving nursing care, high frailty risk, increasing numbers of hospital admissions and diagnoses of any fracture or stroke in the six months before moving-into the care home were all significant predictors of moving-in from hospital.

Conclusions: The population moving-in to care homes from hospital are clinical distinct from those moving-in from the community. National cross-sectoral data linkage of health and social care data is feasible, but the available data are dominated by health characteristics. There is an urgent need to operationalise other meaningful variables which shape care pathways to enhance understanding and evidence.