Malnutrition is a common but underrecognized clinical problem in the UK. In the ageing population, malnutrition is associated with sarcopenia, frailty, and multiple long-term conditions. The prevalence of people with malnutrition in the community, and those admitted to the hospital is around 30% in people over 65 years. Malnutrition is linked to increased treatment costs, lengths of stay, and poor recovery in secondary care (hospitals). The annual clinical and social care costs are large: malnutrition (£23bn), sarcopenia (£2.5bn), frailty (£6bn), and consequent conditions e.g. osteoporosis and fragility fractures (£4bn). Combatting malnutrition, and undernutrition has the potential for significant cost savings for the health service. Understanding, detecting, and addressing the prevalence of malnutrition is important regionally and nationally.
Malnutrition can be prevented through early detection, but it is difficult to reverse. The dearth of simple, effective screening tools and diagnostic tests for routine detection of those at risk of malnutrition prevents treatment. Our project aims to support the development of a digital application and risk-stratification alerts pathway, EAMIT (East Anglian Malnutrition Identification Tool), which will use routine clinical healthcare data including clinical biochemistry components and established malnutrition screening tools, for earlier identification of malnutrition in the community.
This feasibility study will examine data availability for a future study actually to develop the tool. Its findings are needed to develop a grant application to investigate how EAMIT could be used for earlier identification of malnutrition in the community. The terms of our funding for this current work are for a feasibility study.
We will explore data availability in older patients since January 2010 to support a later project on the development of EAMIT (East Anglian Malnutrition Identification Tool), for the earlier identification of malnutrition in primary care settings using routine clinical healthcare data.
Our objectives:
1. To find the number/percentages of malnutrition in primary care by year, gender, age at screening/malnutrition detection, region, ethnicity, IMD (patient level). As malnutrition may be under-diagnosed, we will also tabulate patients screened using the Malnutrition Universal Screening Tool (MUST).
2. To describe a range of healthcare data, including related diagnoses (such as muscle wasting and appetite loss), diagnostic biochemical tests (full blood count, albumin, haemoglobin, CRP, and HbA1c), medication use and smoking in people with and without clinically measured malnutrition.
3. To examine the completeness and frequency of recording of nutritional markers such as vitamin D, calcium, vitamin B12, folate and ferritin levels plus vitamin K, copper, zinc and selenium levels for some patients.
We request HES APC linkage to determine the number of malnutrition cases listed as primary/secondary diagnosis at hospitalisation to better inform policy changes for malnourished patients.
We request linked IMD data to help address any inequalities in patient care and/or patient outcomes.
If malnutrition is sufficiently diagnosed in primary or secondary care or if there is sufficient MUST screening, we will tabulate key healthcare data as described above (and within population sub-strata) to see which variables are largely recorded in the year before diagnosis.
If sufficient data are identified to characterise the patients, this will enable us to design a study (informed by which definition of malnutrition is used) to examine the predictors of malnutrition in the older population, for which we will submit a later full protocol.
A. Health Outcomes to be Measured
For all patients:
Malnutrition detected by read codes / deficiency of nutritional markers/prescription of nutritional supplements/ dehydration markers.
Clinical Biochemistry factors
- Full blood count,
- Vitamins levels and deficiency (vitamin D, calcium, vitamin B12, vitamin K, Vitamin B, Vitamin B6, Vitamin B12, Vitamin K)
- Mineral levels and deficiency (zinc, copper, iron, magnesium, selenium, thiamine)
- Cholesterol levels (serum total cholesterol, serum triglycerides, high-density lipoproteins (HDL), low-density lipoproteins (LDL))
- Anaemia and iron status (haemoglobin, haematocrit, mean cell volume, plasma ferritin, folate and ferritin levels)
- Diabetes (glycated haemoglobin HbA1c, glucose)
- Inflammatory status or infection (lymphocyte count, white blood cell count, serum C-reactive protein (CRP))
- Renal function (eGFR, plasma creatinine)
- Risk of bone disease (plasma 25-hydroxy vitamin D (vitamin D status))
Demographic factors
- Gender,
- Age at malnutrition detection,
- IMD Deprivation at patient level,
- Marital status,
- Smoking use(packs),
- alcohol/usage in units,
- ethnicity
- Weight/height/BMI (in the previous 6 months), loss of weight (in the previous 6 months).
All-cause mortality (Death date and death status)
Medications (in the previous 6 months)
Antihypertensives, Antidiabetics, Anticoagulants, Antiplatelets, Lipid-lowering medications (including statins), Analgesics, and sedative-hypnotics (including benzodiazepines, barbiturates, and various hypnotics), antidepressants, Anticholinergics.
Medical conditions at follow-up
Dementia, Systemic inflammatory response syndrome (SIRS), Heart failure, gastrointestinal disease, dysphagia, sarcopenia.
Ailsa Welch - Chief Investigator - University of East Anglia
Jane Skinner - Corresponding Applicant - University of East Anglia
Elena Kulinskaya - Collaborator - University of East Anglia
Helen Parretti - Collaborator - University of East Anglia
James Holmes - Collaborator - University of East Anglia
Kathryn Richardson - Collaborator - University of East Anglia
Jane Skinner - Collaborator - University of East Anglia
HES Admitted Patient Care;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation (Standard)