Title: Risk modelling for quality improvement in the critically ill: making best use of routinely available data
Funder: Department of Health and Social Care

Abstract: Aim: To better understand the epidemiology of, risk factors for and consequences of critical illness leading to improvements in risk models used to underpin national clinical audits for adult general critical care, cardiothoracic critical care and in-hospital cardiac arrest using data linkage with other routinely collected data sources Design: Risk modelling study using existing data Setting: Adult general critical care units, cardiothoracic critical care units and acute hospitals in England and Wales Data collection: The project will utilise 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. These data will be linked with data from the National Diabetes Audit, UK Renal Registry and National Adult Cardiac Surgery Audit, routine administrative data from Hospital Episode Statistics (HES) and death registrations from the Office for National Statistics (ONS). Data linkage will be undertaken by the HSCIC Data Linkage and Extract Service (DLES) acting as a trusted third party. Identifiers (with no associated clinical data) will be uploaded from each national clinical audit to secure servers at HSCIC. DLES 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. The three national clinical audits external to ICNARC will extract an agreed, pseudonymised dataset for linked records and DLES will extract data from HES and ONS and these datasets will be passed to ICNARC. ICNARC will produce pseudonymised data extracts from the CMP and NCAA and these will be linked to the datasets provided by the national clinical audits and DLES using the common key. In this way, only pseudonymised data will be linked between the multiple data sources. Data analysis: Methods of data analysis will be based on those used in the HS&DR-funded project (09/2000/65). Risk prediction models will be validated for their discrimination, calibration and overall fit using a panel of measures including: c index; plots of observed against predicted risk; Hosmer-Lemeshow goodness-of-fit statistic; Cox’s calibration regression; Shapiro’s R, Brier’s score and corresponding approximate R2 measures; and reclassification. Objective 1: For admissions to adult general critical care units, risk models will be developed for: (1a) mortality at fixed time-points and time-to-event outcomes (using data linkage with ONS); (1b) new diagnosis of diabetes post-critical care (National Diabetes Audit); (1c) new diagnosis of end-stage renal disease post-critical care (UK Renal Registry); and (1d) hospital resource use and costs post-critical care (HES). Objective 2: For admissions to cardiothoracic critical care units, risk models will be developed using enhanced risk factor data for pre- and intra-operative risk factors (National Adult Cardiac Surgery Audit) for: (2a) mortality at discharge from acute hospital; (2b) mortality at 30-days, 90-days and 1-year (ONS); and (2c) hospital resource use and costs post-critical care (HES). Objective 3: Risk models for in-hospital cardiac arrest will be developed using enhanced risk factor data for comorbidities (HES) for: (3a) return of spontaneous circulation (ROSC) for greater than 20 minutes and survival to hospital discharge; (3b) survival to 30 days, 90 days and 1 year (ONS); (3c) critical care resource use post-arrest (CMP); and (3d) subsequent hospital resource use and costs (HES).

Lay abstract: When patients are critically ill, they are rarely able to choose where they are treated. We must therefore ensure that all hospitals deliver high quality critical care. One way to do this is through clinical audit. The National Advisory Group for Clinical Audit and Enquiries describes clinical audit as, "the assessment of the process (using evidence-based criteria) and/or the outcome of care (by comparison with others)." When clinical audit is undertaken by “assessment of the outcome of care (by comparison with others)" it is essential that this comparison takes into account the differing types of patients treated in different hospitals. For example, if one hospital admits many more very sick or very elderly patients, then one might expect the death rate in that hospital to be higher. To do this, we use risk prediction models (also known as risk adjustment or case mix adjustment). These statistical models take information about the patient known before, or soon after, the start of their illness and make a prediction of their likely outcome based on many thousands of previous similar patients. Large amounts of information (data) are routinely collected about patients using NHS services, but often we do not make the best possible use of this data to improve patient care and outcomes. Data is held by different organisations in different databases. Joining up these different databases (data linkage) can give us a more complete picture of what has happened to a patient. The Intensive Care National Audit & Research Centre (ICNARC) is an independent charity that runs national clinical audits to monitor and improve care for critically ill patients. ICNARC co-ordinates two national clinical audits: the Case Mix Programme (CMP), a national clinical audit of adult critical care; and the National Cardiac Arrest Audit (NCAA), a national clinical audit of in-hospital cardiac arrest. Our proposed project aims to use data linkage to further develop and improve our risk prediction models underpinning these two audits, to ensure that they provide accurate and up-to-date information back to the hospitals to support quality improvement (and better patient care). The number of patients that survive to leave hospital after being admitted to an adult critical care unit has increased greatly over previous years. We therefore need to look beyond hospital mortality as an outcome of critical illness. By linking data from the CMP with death registrations we can monitor longer term survival. By linking with routine hospital data and two other national clinical audits – the National Diabetes Audit and the UK Renal Registry – we will develop risk models to enable us to monitor important problems that patients can experience after critical care. For cardiothoracic critical care – caring largely for patients who have had heart surgery – by linking data from the CMP with the National Adult Cardiac Surgery Audit we will get a more complete picture of how sick these patients were before they were admitted to critical care, helping us to improve our risk models to make fairer comparisons for these patients. Finally, it can be very difficult to record data about patients that have an in-hospital cardiac arrest as this can occur at any place and any time. By linking data from NCAA with routine hospital data, we can get a better picture of how sick these patients were before their arrest and by linking with death registrations we can monitor longer term survival, enabling us to develop risk models to make fairer comparisons for these patients.

Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: CARDIOVASCULAR