Can valid and practical risk-prediction or casemix adjustment models, including adjustment for comorbidity, be generated from English hospital administrative data (Hospital Episode Statistics)? A national observational study
Bottle A, Gaudoin R, Goudie R, Jones S, Aylin P
Record ID 32014001392
English
Authors' objectives:
To derive robust risk-adjustment models for various patient groups, including those admitted for heart failure (HF), acute myocardial infarction, colorectal and orthopaedic surgery, and outcomes adjusting for available patient factors such as comorbidity, using England's Hospital Episode Statistics (HES) data. To assess if more sophisticated statistical methods based on machine learning such as artificial neural networks (ANNs) outperform traditional logistic regression (LR) for risk prediction. To update and assess for the NHS the Charlson index for comorbidity. To assess the usefulness of outpatient data for these models.
Authors' recommendations:
Many practical risk-prediction or casemix adjustment models can be generated from HES data using LR, though an extra step is often required for accurate calibration. Including outpatient data in readmission models is useful. The three machine learning methods we assessed added little with these data. Readmission rates for HF patients should be divided by diagnosis on readmission when used for quality improvement.
Details
Project Status:
Completed
Year Published:
2014
URL for published report:
http://www.journalslibrary.nihr.ac.uk/hsdr/hsdr02400/#/abstract
English language abstract:
An English language summary is available
Publication Type:
Not Assigned
Country:
England, United Kingdom
MeSH Terms
- Humans
- Risk Assessment
- Prognosis
- Comorbidity
- Outcome Assessment, Health Care
Contact
Organisation Name:
NIHR Health Services and Delivery Research programme
Contact Address:
NIHR Journals Library, National Institute for Health and Care Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK
Contact Name:
journals.library@nihr.ac.uk
Contact Email:
journals.library@nihr.ac.uk
Copyright:
Queen's Printer and Controller of HMSO
This is a bibliographic record of a published health technology assessment from a member of INAHTA or other HTA producer. No evaluation of the quality of this assessment has been made for the HTA database.