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Burns. 2015 Nov;41(7):1397-404. doi: 10.1016/j.burns.2015.06.018. Epub 2015 Jul 30.

Towards more efficient burn care: Identifying factors associated with good quality of life post-burn.

Author information

1
Burn Service of Western Australia at Fiona Stanley Hospital, Australia. Electronic address: vidya.finlay@health.wa.gov.au.
2
Burn Service of Western Australia at Fiona Stanley Hospital, Australia.
3
Burn Service of Western Australia at Fiona Stanley Hospital, Australia; Fiona Wood Foundation, Perth, Western Australia, Australia.
4
Burn Service of Western Australia at Fiona Stanley Hospital, Australia; Fiona Wood Foundation, Perth, Western Australia, Australia; State Adult Burn Unit, Fiona Stanley Hospital, Murdoch, Western Australia, Australia; Burn Injury Research Node, The University of Notre Dame Australia, Fremantle, Western Australia, Australia.

Abstract

BACKGROUND:

As minor burn patients constitute the vast majority of a developed nation case-mix, streamlining care for this group can promote efficiency from a service-wide perspective. This study tested the hypothesis that a predictive nomogram model that estimates likelihood of good long-term quality of life (QoL) post-burn is a valid way to optimise patient selection and risk management when applying a streamlined model of care.

METHOD:

A sample of 224 burn patients managed by the Burn Service of Western Australia who provided both short and long-term outcomes was used to estimate the probability of achieving a good QoL defined as 150 out of a possible 160 points on the Burn Specific Health Scale-Brief (BSHS-B) at least six months from injury. A multivariate logistic regression analysis produced a predictive model provisioned as a nomogram for clinical application. A second, independent cohort of consecutive patients (n=106) was used to validate the predictive merit of the nomogram.

RESULTS AND DISCUSSION:

Male gender (p=0.02), conservative management (p=0.03), upper limb burn (p=0.04) and high BSHS-B score within one month of burn (p<0.001) were significant predictors of good outcome at six months and beyond. A Receiver Operating Curve (ROC) analysis demonstrated excellent (90%) accuracy overall. At 80% probability of good outcome, the false positive risk was 14%. The nomogram was validated by running a second ROC analysis of the model in an independent cohort. The analysis confirmed high (86%) overall accuracy of the model, the risk of false positive was reduced to 10% at a lower (70%) probability. This affirms the stability of the nomogram model in different patient groups over time. An investigation of the effect of missing data on sample selection determined that a greater proportion of younger patients with smaller TBSA burns were excluded due to loss to follow up.

CONCLUSION:

For clinicians managing comparable burn populations, the BSWA burns nomogram is an effective tool to assist the selection of patients to a streamlined care pathway with the aim of improving efficiency of service delivery.

KEYWORDS:

BSHS-B; Burns; Efficiency; Health-care; Nomogram; Outcome; Prediction; Quality of life

PMID:
26233899
DOI:
10.1016/j.burns.2015.06.018
[Indexed for MEDLINE]

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