Risk modelling for quality improvement in the critically ill

Making best use of routinely available data

The purpose of the study

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 this study is to better understand the epidemiology of, risk factors for and consequences of critical illness.

How the study will be conducted

In this study, we will utilise 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 link together with the following national datasets so as to increase the available information on patients after leaving hospital:

  • Hospital Episode Statistics inpatient data
  • Office for National Statistics mortality data
  • National Diabetes Audit
  • UK Renal Registry
  • 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 (see project data flows). 

We will use the information from this study to improve the risk prediction models that underpin the national clinical audits for adult general critical care, cardiothoracic critical care and in-hospital cardiac arrest.

If you have been treated in a hospital participating in the CMP or NCAA and do not wish data collected during your stay in hospital to be included in the study, please contact the health care team at the hospital where you were treated who will inform ICNARC.

ICNARC processes data for this study under the legitimate interest legal basis. This is because ICNARC is a registered charity and the data processing described here is to support scientific and statistical research. Information collected for the study is stored on secure servers which are owned by an authorised contractor called Exponential-e (https://www.exponential-e.com/). ICNARC takes steps to ensure this information is not lost and makes regular back-ups of the data. ICNARC also contract Babble Cloud who provide external desktop and network managed services, including end user and infrastructure support. Employees at Babble Cloud will not access the data, however, they do have remote access to ICNARC servers.

All patient identifiable data will be destroyed once data linkage is complete. The final linked and pseudonymised dataset will be stored for 10 years after the end of the study in accordance with the MRC Good Practice Principles for Sharing Individual Participant Data from Publicly Funded Clinical Trials.

When is it taking place?

The study was commissioned in August 2015 and due to complete in December 2022.

Who is leading the study?

Prof David Harrison, Head Statistician ICNARC

Further information

This study is funded by the National Institute for Health Research (NIHR) – Health Services and Delivery Research (HS&DR) Programme (Project: 14/19/06).

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