Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT02454257 |
| Other study ID # |
ICNARC/02/07/15 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
August 1, 2015 |
| Est. completion date |
December 23, 2022 |
Study information
| Verified date |
January 2023 |
| Source |
Intensive Care National Audit & Research Centre |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
The aim of the proposed study is to better understand the epidemiology of, risk factors for
and consequences of critical illness leading to improvements in the 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.
Description:
Aim: To improve 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.
Specific objectives are:
1. To improve risk models for adult general critical care by: (1a) developing risk models
for mortality at fixed time-points and time-to event outcomes (by data linkage between
the CMP and death registrations from ONS); developing risk models for longer term
chronic health outcomes of (1b) diabetes (by data linkage between the CMP and the
National Diabetes Audit) and (1c) end-stage renal disease (by data linkage between the
CMP and the UK Renal Registry); and (1d) developing risk models for subsequent health
care utilisation and costs (by data linkage between the CMP and HES)
2. To improve risk models for cardiothoracic critical care by: (2a) enhancing risk factor
data (by data linkage with the National Adult Cardiac Surgery Database); (2b) developing
risk models for longer term mortality (by data linkage between the CMP and death
registrations from ONS); and (2c) developing risk models for subsequent health care
utilisation and costs (by data linkage between the CMP and HES)
3. .To improve risk models for in-hospital cardiac arrest by: (3a) enhancing risk factor
data (by data linkage between NCAA and HES); (3b) developing risk models for longer term
mortality, health care utilisation and costs (by data linkage between NCAA and ONS);
(3c) developing risk models for subsequent critical care utilisation (by data linkage
between NCAA and CMP); and (3d) developing risk models for subsequent health care
utilisation and costs (by data linkage between NCAA, ONS and HES)
4. Immediate translation of the improved risk models into practice through: (4a) adoption
into routine comparative outcome reporting for the national clinical audits; and (4b)
communication of research output to providers, managers, commissioners, policy makers
and academics in critical care
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: The following approaches for model development will be applied depending on
the outcome and objectives of the analysis:
- Modelling of mortality at fixed time-points (30 days, 90 days, 1 year) using logistic
regression.
- Modelling of time-to-event outcomes using standard survival regression methods such as
Weibull and Cox regression.
- To handle interval-censored data: Cox proportional hazards models, complementary log-log
models using partial likelihood estimation (to permit interval censoring) and
discrete-time hazard models.
- To account for both interval censoring of the time-to-onset and competition with death:
Cause-specific Cox proportional hazards models and illness-death models will be
considered.
- For Objective 3, return of spontaneous circulation (ROSC) for greater than 20 minutes
and survival to hospital discharge outcome, multilevel logistic regression with random
effects of hospital will be applied.
- To model Hospital resource use and costs post-critical care: multilevel regression model
and Log linear regression model.
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.
