Survival prediction algorithms miss significant opportunities for improvement if used for case selection in trauma quality improvement programs

Catherine Heim; Elaine Cole; Anita West; Nigel Tai; Karim Brohi

doi:10.1016/j.injury.2016.05.042

Survival prediction algorithms miss significant opportunities for improvement if used for case selection in trauma quality improvement programs

Injury. 2016 Sep;47(9):1960-5. doi: 10.1016/j.injury.2016.05.042. Epub 2016 Jun 1.

Authors

Catherine Heim¹, Elaine Cole², Anita West³, Nigel Tai⁴, Karim Brohi⁵

Affiliations

¹ Department of Anaesthesiology CHUV, 1011 Lausanne, Switzerland. Electronic address: Catherine.heim@chuv.ch.
² Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK. Electronic address: E.Cole@qmul.ac.uk.
³ Royal London Hospital, Barts and the London NHS Trust, London, UK. Electronic address: Anita.west@bartsandthelondon.nhs.uk.
⁴ Royal London Hospital, Barts and the London NHS Trust, London, UK. Electronic address: Nigel.tai@bartshealth.nhs.uk.
⁵ Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK. Electronic address: K.Brohi@qmul.ac.uk.

PMID: 27343135
DOI: 10.1016/j.injury.2016.05.042

Abstract

Background: Quality improvement (QI) programs have shown to reduce preventable mortality in trauma care. Detailed review of all trauma deaths is a time and resource consuming process and calculated probability of survival (Ps) has been proposed as audit filter. Review is limited on deaths that were 'expected to survive'. However no Ps-based algorithm has been validated and no study has examined elements of preventability associated with deaths classified as 'expected'. The objective of this study was to examine whether trauma performance review can be streamlined using existing mortality prediction tools without missing important areas for improvement.

Methods: We conducted a retrospective study of all trauma deaths reviewed by our trauma QI program. Deaths were classified into non-preventable, possibly preventable, probably preventable or preventable. Opportunities for improvement (OPIs) involve failure in the process of care and were classified into clinical and system deviations from standards of care. TRISS and PS were used for calculation of probability of survival. Peer-review charts were reviewed by a single investigator.

Results: Over 8 years, 626 patients were included. One third showed elements of preventability and 4% were preventable. Preventability occurred across the entire range of the calculated Ps band. Limiting review to unexpected deaths would have missed over 50% of all preventability issues and a third of preventable deaths. 37% of patients showed opportunities for improvement (OPIs). Neither TRISS nor PS allowed for reliable identification of OPIs and limiting peer-review to patients with unexpected deaths would have missed close to 60% of all issues in care.

Conclusions: TRISS and PS fail to identify a significant proportion of avoidable deaths and miss important opportunities for process and system improvement. Based on this, all trauma deaths should be subjected to expert panel review in order to aim at a maximal output of performance improvement programs.

Keywords: Audit filter; Mortality audit; Preventable death; Probability of survival; Quality improvement; TRISS; Trauma.

MeSH terms

Aged
Algorithms
Female
Humans
Injury Severity Score
Male
Middle Aged
Probability
Quality Improvement / organization & administration*
Retrospective Studies
Survival Analysis
Trauma Centers* / standards
Trauma Severity Indices
United Kingdom
Wounds and Injuries / mortality*