Risk Modelling for Quality Improvement in the Critically Ill
Dr David Harrison, Senior Statistician, Intensive Care and National Audit Research Centre, email@example.com
Start / End:
August 2015- June 2018
Dr David Harrison, Senior Statistician ICNARC, is leading this study linking with the UK Renal Registry.
Current risk prediction models (also known as risk adjustment or case mix adjustment models) use information from early in a patient’s illness to predict whether a patient will survive to leave hospital. They are useful for assessing the clinical services provided in hospital and for conducting research. However, leaving hospital is far from the end of the journey for patients that have been critically ill. The aim of the proposed study is to better understand the epidemiology of, risk factors for and consequences of critical illness. Using existing high quality clinical data collected for the Case Mix Programme (CMP) and National Cardiac Arrest Audit (NCAA) the national clinical audits for adult critical care and in-hospital cardiac arrest and linking together with the following national datasets so as to increase the available information on patients after leaving hospital:
- UK Renal Registry
- Hospital Episode Statistics inpatient data
- Office for National Statistics mortality data
- National Diabetes Audit
- National Adult Cardiac Surgery Audit
Data linkage will be undertaken by the NHS Digital, Data Access Request Service (DARS) acting as a trusted third party. Each audit will securely transfer patient identifiers (NHS number, date of birth, sex and postcode) to DARS who will perform the data linkage and will return a common key that can be used to link all records of the same patient across the datasets.
Please find below two documents explaining the project further.