Horizon scanning for Arden GEM CSU: long term conditions

Showing posts with label long term conditions. Show all posts

Monday, 22 February 2021

The NHSScotland COPD Support Service comprises a web app which is in use and machine learning predictive modelling which is still in development. The purpose of the modelling is to give real time predictions of how likely a patient is to have an exacerbation in the next 72 hours, their risk of readmission to hospital in the next 3 months and their 12-month mortality risk. As well as giving a measure of risk, the machine learning predictive modelling categorises the user of the web app as either high or low priority for these three outcomes, alerting a clinician to a negative change in condition and allowing them to act proactively.

Analysis of the association between the degree of risk factor control and cardiovascular disease (CVD) risk in type 2 diabetes (T2D) using data from English practices from CPRD GOLD (Clinical Practice Research Datalink) and the SCI-Diabetes dataset (Scottish Care Information-Diabetes). It was found that optimally managed people with T2D have a 21% higher CVD risk when compared with controls. People with T2D without cardio-renal disease would be predicted to benefit greatly from CVD risk factor intervention.

Our PHM approach to Long Term Conditions – Diabetes
Nottingham and Nottinghamshire Integrated Care System, February 2020

An outline of the health and care needs of those with diabetes including, demographics, socio-economic factors.
Part of the Nottingham and Nottinghamshire ICS PHM strategy.

Development and validation of the Cambridge Multimorbidity Score
CMAJ February 03, 2020 192 (5) E107-E114; DOI: https://doi.org/10.1503/cmaj.190757

Researchers at the NIHR School for Primary Care Research have developed a new score for measuring multiple long-term health conditions in patients in primary care.
The new Cambridge Multimorbidity Score is a transparent, simple measure of multimorbidity that can predict different outcomes in people with multiple conditions. The score is based on data obtained from the UK Clinical Practice Research Datalink (CPRD)
See NIHR news

This toolkit provides a focus for improving local health systems, tailored to the needs of the epilepsy population with expert practical advice and guidance on how to address these epilepsy-related challenges.
It includes guidance around identification and segmentation of a local epilepsy population and misdiagnosis.

Analysis of 22 studies - four studies reported on systolic blood pressure, 3 on low density lipoprotein cholesterol, 10 on CV risk management in patients with type II diabetes and 5 on guideline adherence. Analysis found no clear clinical benefit, but some features of CDSS were more promising than others.

This report focuses on dementia risk prediction as a tool to prevent future onset of the disease, although some of the issues associated with future risk prediction also apply to early detection. It provides an overview of current dementia risk prediction tools along with their uses and validity in different settings and populations.
Using a combination of literature review and informant interviews, the report describes the key issues and challenges around dementia risk prediction for policymakers, including identifying potential benefits, harms and uncertainties. It also outlines the research needed to enhance the utility of approaches to dementia risk prediction at population level.

Summary of findings