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The Bone & Joint Journal
Vol. 105-B, Issue 1 | Pages 21 - 28
1 Jan 2023
Ndlovu S Naqshband M Masunda S Ndlovu K Chettiar K Anugraha A

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.


The Bone & Joint Journal
Vol. 100-B, Issue 6 | Pages 703 - 711
1 Jun 2018
Marson BA Deshmukh SR Grindlay DJC Scammell BE

Aims

The aim of this review was to evaluate the available literature and to calculate the pooled sensitivity and specificity for the different alpha-defensin test systems that may be used to diagnose prosthetic joint infection (PJI).

Materials and Methods

Studies using alpha-defensin or Synovasure (Zimmer Biomet, Warsaw, Indiana) to diagnose PJI were identified from systematic searches of electronic databases. The quality of the studies was evaluated using the Quality Assessment of Studies of Diagnostic Accuracy (QUADAS) tool. Meta-analysis was completed using a bivariate model.