Abstract
Aims
Clinical management of open fractures is challenging and frequently requires complex reconstruction procedures. The Gustilo-Anderson classification lacks uniform interpretation, has poor interobserver reliability, and fails to account for injuries to musculotendinous units and bone. The Ganga Hospital Open Injury Severity Score (GHOISS) was designed to address these concerns. The major aim of this review was to ascertain the evidence available on accuracy of the GHOISS in predicting successful limb salvage in patients with mangled limbs.
Methods
We searched electronic data bases including PubMed, CENTRAL, EMBASE, CINAHL, Scopus, and Web of Science to identify studies that employed the GHOISS risk tool in managing complex limb injuries published from April 2006, when the score was introduced, until April 2021. Primary outcome was the measured sensitivity and specificity of the GHOISS risk tool for predicting amputation at a specified threshold score. Secondary outcomes included length of stay, need for plastic surgery, deep infection rate, time to fracture union, and functional outcome measures. Diagnostic test accuracy meta-analysis was performed using a random effects bivariate binomial model.
Results
We identified 1,304 records, of which six prospective cohort studies and two retrospective cohort studies evaluating a total of 788 patients were deemed eligible for inclusion. A diagnostic test meta-analysis conducted on five cohort studies, with 474 participants, showed that GHOISS at a threshold score of 14 has a pooled sensitivity of 93.4% (95% confidence interval (CI) 78.4 to 98.2) and a specificity of 95% (95% CI 88.7 to 97.9) for predicting primary or secondary amputations in people with complex lower limb injuries.
Conclusion
GHOISS is highly accurate in predicting success of limb salvage, and can inform management and predict secondary outcomes. However, there is a need for high-quality multicentre trials to confirm these findings and investigate the effectiveness of the score in children, and in predicting secondary amputations.
Cite this article: Bone Joint J 2023;105-B(1):21–28.
