360 PROFILING FRAILTY IN A POPULATION OF OLDER FARMERS IN THE WEST OF IRELAND

Background

Identification of different groups at risk of frailty is a key research focus. Farmers frequently have higher prevalence of some health problems; however, they undertake high levels of physical activity which may provide protection against the development of frailty in this cohort. To date, no published research has examined prevalence of frailty levels in older farmers in Ireland. This research aimed to profile frailty levels of older farmers in the west of Ireland, and, establish if there is an association between frailty levels in this group and different farming specific demographics/socioeconomic factors such as; type of farming undertaken, amount of time spent farming outdoors and amount of land owned by farmers

Methods

This cross-sectional study recruited older farmers via livestock marts in the west of Ireland. A questionnaire regarding participants characteristics and farming demographics was provided to participants. This questionnaire also included the Program on Research for Integrating Services for the Maintenance of Autonomy (PRISMA-7) questionnaire and the short 5-question assessment of Fatigue, Resistance, Aerobic capacity, Illnesses, and Loss of weight (FRAIL) questionnaire to assess frailty. Data were analysed using STATA statistical software.

Results

In total, 58 older farmers participated in the study. Results showed found that two participants (3.5%) were ‘frail’, with 27 participants (47%) classified as ‘prefrail’. An association existed between the type of farming undertaken and frailty levels based on the FRAIL scale (p=0.009). No association was found between frailty levels and the amount of land owned by farmers (p=0.34) or the amount of time spent farming outdoors (p=0.18).

Conclusion

A minority of participants in this study were classified as frail. The type of farming undertaken by farmers may have an association with frailty levels. Results should be interpreted with caution secondary to the small sample size and the small geographical scope of recruitment.

Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals

Background

One reason for the introduction of digital technologies into health care has been to try to improve safety and patient outcomes by providing real-time access to patient data and enhancing communication among health care professionals. However, the adoption of such technologies into clinical pathways has been less examined, and the impacts on users and the broader health system are poorly understood. We sought to address this by studying the impacts of introducing a digitally enabled care pathway for patients with acute kidney injury (AKI) at a tertiary referral hospital in the United Kingdom. A dedicated clinical response team—comprising existing nephrology and patient-at-risk and resuscitation teams—received AKI alerts in real time via Streams, a mobile app. Here, we present a qualitative evaluation of the experiences of users and other health care professionals whose work was affected by the implementation of the care pathway.

Objective

The aim of this study was to qualitatively evaluate the impact of mobile results viewing and automated alerting as part of a digitally enabled care pathway on the working practices of users and their interprofessional relationships.

Methods

A total of 19 semistructured interviews were conducted with members of the AKI response team and clinicians with whom they interacted across the hospital. Interviews were analyzed using inductive and deductive thematic analysis.

Results

The digitally enabled care pathway improved access to patient information and expedited early specialist care. Opportunities were identified for more constructive planning of end-of-life care due to the earlier detection and alerting of deterioration. However, the shift toward early detection also highlighted resource constraints and some clinical uncertainty about the value of intervening at this stage. The real-time availability of information altered communication flows within and between clinical teams and across professional groups.

Conclusions

Digital technologies allow early detection of adverse events and of patients at risk of deterioration, with the potential to improve outcomes. They may also increase the efficiency of health care professionals’ working practices. However, when planning and implementing digital information innovations in health care, the following factors should also be considered: the provision of clinical training to effectively manage early detection, resources to cope with additional workload, support to manage perceived information overload, and the optimization of algorithms to minimize unnecessary alerts.

Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs

Background

The development of acute kidney injury (AKI) in hospitalized patients is associated with adverse outcomes and increased health care costs. Simple automated e-alerts indicating its presence do not appear to improve outcomes, perhaps because of a lack of explicitly defined integration with a clinical response.

Objective

We sought to test this hypothesis by evaluating the impact of a digitally enabled intervention on clinical outcomes and health care costs associated with AKI in hospitalized patients.

Methods

We developed a care pathway comprising automated AKI detection, mobile clinician notification, in-app triage, and a protocolized specialist clinical response. We evaluated its impact by comparing data from pre- and postimplementation phases (May 2016 to January 2017 and May to September 2017, respectively) at the intervention site and another site not receiving the intervention. Clinical outcomes were analyzed using segmented regression analysis. The primary outcome was recovery of renal function to ≤120% of baseline by hospital discharge. Secondary clinical outcomes were mortality within 30 days of alert, progression of AKI stage, transfer to renal/intensive care units, hospital re-admission within 30 days of discharge, dependence on renal replacement therapy 30 days after discharge, and hospital-wide cardiac arrest rate. Time taken for specialist review of AKI alerts was measured. Impact on health care costs as defined by Patient-Level Information and Costing System data was evaluated using difference-in-differences (DID) analysis.

Results

The median time to AKI alert review by a specialist was 14.0 min (interquartile range 1.0-60.0 min). There was no impact on the primary outcome (estimated odds ratio [OR] 1.00, 95% CI 0.58-1.71; P=.99). Although the hospital-wide cardiac arrest rate fell significantly at the intervention site (OR 0.55, 95% CI 0.38-0.76; P<.001), DID analysis with the comparator site was not significant (OR 1.13, 95% CI 0.63-1.99; P=.69). There was no impact on other secondary clinical outcomes. Mean health care costs per patient were reduced by £2123 (95% CI −£4024 to −£222; P=.03), not including costs of providing the technology.

Conclusions

The digitally enabled clinical intervention to detect and treat AKI in hospitalized patients reduced health care costs and possibly reduced cardiac arrest rates. Its impact on other clinical outcomes and identification of the active components of the pathway requires clarification through evaluation across multiple sites.

Service evaluation of the implementation of a digitally-enabled care pathway for the recognition and management of acute kidney injury

Acute Kidney Injury (AKI), an abrupt deterioration in kidney function, is defined by changes in urine output or serum creatinine. AKI is common (affecting up to 20% of acute hospital admissions in the United Kingdom), associated with significant morbidity and mortality, and expensive (excess costs to the National Health Service in England alone may exceed £1 billion per year). NHS England has mandated the implementation of an automated algorithm to detect AKI based on changes in serum creatinine, and to alert clinicians. It is uncertain, however, whether ‘alerting’ alone improves care quality.

We have thus developed a digitally-enabled care pathway as a clinical service to inpatients in the Royal Free Hospital (RFH), a large London hospital. This pathway incorporates a mobile software application - the “Streams-AKI” app, developed by DeepMind Health - that applies the NHS AKI algorithm to routinely collected serum creatinine data in hospital inpatients. Streams-AKI alerts clinicians to potential AKI cases, furnishing them with a trend view of kidney function alongside other relevant data, in real-time, on a mobile device. A clinical response team comprising nephrologists and critical care nurses responds to these AKI alerts by reviewing individual patients and administering interventions according to existing clinical practice guidelines.

We propose a mixed methods service evaluation of the implementation of this care pathway. This evaluation will assess how the care pathway meets the health and care needs of service users (RFH inpatients), in terms of clinical outcome, processes of care, and NHS costs. It will also seek to assess acceptance of the pathway by members of the response team and wider hospital community. All analyses will be undertaken by the service evaluation team from UCL (Department of Applied Health Research) and St George’s, University of London (Population Health Research Institute).

Identifying women at increased risk of breast cancer: Can we use genotyping at low penetrance loci?

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Background: Most inherited predisposition to breast cancer is attributable to low penetrance susceptibility loci, a number of which have been identified through genome-wide association studies. Although individually each locus has a small effect, combining data from multiple loci would be expected to provide more risk information. We investigated the size of risk determination that can be achieved using genotyping at 18 loci. We then calculated its effect when combined with risk estimated from family history alone in terms of management under UK guidelines, where a woman who has a 10 year risk of 3% or greater requires additional breast screening from a younger age.

Methods: Genotyping for 18 loci was carried out in 253 women at increased risk of breast cancer due to a positive family history and 118 matched controls. The relative risks conferred by genotype at the 18 loci were combined under a log-additive model and transformed into a log-polygenic risk. The BOADICEA risk estimation tool was used to calculate breast cancer risk due to family history.

Results: Both the increased risk and control groups demonstrated a normal distribution of log-polygenic risk with similar variance. There was a significantly higher mean in the increased risk compared to the control group (mean = 0.1313 and 0.0874 respectively, p = 0.007). No significant correlation was found between polygenic risk calculated from genotype data and the family history risk estimated using BOADICEA. When polygenic risk was combined with family history risk there was significant reclassification of risk for those with a family history. 36.76% moved into a higher risk category while 3.68% moved into a lower risk category.

Conclusions: Our data suggests that genotyping will be clinically relevant for estimating breast cancer risk. Individuals with a family history overall have a higher genotype risk than the population. The lack of correlation of genotype risk with BOADICEA risk suggests that the two risk estimates can be considered independently. By combining genotype with family history data, we demonstrated a significant reclassification of risk for individuals with a family history, with better identification of women in this group requiring intervention.

Filling defects of the kidney pelvis

Filling defects in the renal pelvis should alert radiologists to the possibility of underlying tuberculosis. A case is presented with the major criteria used in differentiating tuberculosis from a neoplasm.

The effect of acute hyperglycemia on flow velocity in the macular capillaries

The effect of acute hyperglycemia on retinal blood flow was measured in 12 diabetic patients (mean blood glucose, 276 mg%) on continuous subcutaneous insulin infusion and six nondiabetic controls (mean blood glucose, 198 mg%). Flow velocity measurements in macular capillaries were made using the blue field entoptic method. Retinal artery and vein diameters were measured using red-free fundus photographs. No significant change in flow velocity or retinal vessel diameter was noted in either group.