The BACH classification of long bone osteomyelitis

Abstract

Objectives: The aim of this study was to assess the clinical application of, and optimize the variables used in, the BACH classification of long-bone osteomyelitis.

Methods: A total of 30 clinicians from a variety of specialities classified 20 anonymized cases of long-bone osteomyelitis using BACH. Cases were derived from patients who presented to specialist centres in the United Kingdom between October 2016 and April 2017. Accuracy and Fleiss' kappa (Fκ) were calculated for each variable. Bone involvement (B-variable) was assessed further by nine clinicians who classified ten additional cases of long bone osteomyelitis using a 3D clinical imaging package. Thresholds for defining multidrug-resistant (MDR) isolates were optimized using results from a further analysis of 253 long bone osteomyelitis cases.

Results: The B-variable had a classification accuracy of 77.0%, which improved to 95.7% when using a 3D clinical imaging package (p < 0.01). The A-variable demonstrated difficulty in the accuracy of classification for increasingly resistant isolates (A1 (non-resistant), 94.4%; A2 (MDR), 46.7%; A3 (extensively or pan-drug-resistant), 10.0%). Further analysis demonstrated that isolates with four or more resistant test results or less than 80% sensitive susceptibility test results had a 98.1% (95% confidence interval (CI) 96.6 to 99.6) and 98.8% (95% CI 98.1 to 100.0) correlation with MDR status, respectively. The coverage of the soft tissues (C-variable) and the host status (H-variable) both had a substantial agreement between users and a classification accuracy of 92.5% and 91.2%, respectively.

Conclusions: The BACH classification system can be applied accurately by users with a variety of clinical backgrounds. Accuracy of B-classification was improved using 3D imaging. The use of the A-variable has been optimized based on susceptibility testing results.Cite this article: A. J. Hotchen, M. Dudareva, J. Y. Ferguson, P. Sendi, M. A. McNally. The BACH classification of long bone osteomyelitis. Bone Joint Res 2019;8:459-468. DOI: 10.1302/2046-3758.810.BJR-2019-0050.R1.

Keywords: Bone and joint infection; Classification; Osteomyelitis.

Fig. 1

Flowchart presenting the development of the BACH classification of osteomyelitis. Here, the development of BACH version 2 to BACH version 3 is presented. The results of the retrospective assessment have been published previously.

Fig. 2

Chart showing the results of the classification of the bone involvement variable using a 3D web-based picture archiving and communication system (webPACS) system, as performed in clinical practice, versus using a screenshot of the images alone (2D group). The sample population demonstrated a significant improvement when completing the 3D analysis compared with both the total interuser population who completed the 2D analysis (95.7% (95% confidence interval (CI) 90.8 to 100.0) vs 77.0% (95% CI 71.2 to 82.8); p = 0.009, analysis of variance with Dunnett’s post hoc test and the sample population when completing the 2D analysis (79.3% (95% CI 62.3 to 96.3); p = 0.028, Student’s t-test).

Fig. 3

Results for the antimicrobial options variable in the interuser assessment. a) Bar chart showing the individual answers in each of the cases for the antimicrobial options variable, organized by the reference score. The dotted line is the mean score for the antimicrobial options category (92.2%). b) Heat map demonstrating the returned antimicrobial options score versus the reference antimicrobial options score.

Fig. 4

Summary of investigations into the development of the antimicrobial options classification. a) Flowchart of patients in the 2013 to 2017 cohort and the isolates grown on microbiology culture. b) Chart showing the proportion of European Society of Clinical Microbiology and Infectious Diseases (ESCMID) isolates compared between the early (2001 to 2004) and late (2013 to 2017) cohort. c) Receiver operating characteristic (ROC) curve of number of resistant susceptibility tests for ESCMID classifiable isolates in the 2013 to 2017 cohort (area under the curve 98.1% (95% confidence interval (CI) 96.6 to 99.6)). d) ROC curve demonstrating using the percentage of sensitive susceptibility tests for the ESCMID classifiable isolates in the 2013 to 2017 cohort (area under the curve 98.8% (95% CI 98.1 to 100.0)). e) Scatterplot of each isolate from the 2013 to 2017 cohort with percentage resistant susceptibility tests against the total number of susceptibility tests performed. The dotted line represents four resistant tests. Crosses represent multidrug-resistant (MDR) isolates and coloured dots represent non-MDR isolates, according to ESCMID. This substantiates the results of the ROC analysis where 98.3% of MDR isolates are situated on or above the dotted line (four resistant tests).

Fig. 5

The BACH classification system for long bone osteomyelitis. The four key variables are headers with the corresponding criteria for making the classification in each. The green band denotes ‘uncomplicated’ osteomyelitis that can be managed at a non-specialist centre, the amber band denotes ‘complex’ osteomyelitis that should be managed at a centre with specialist expertise, and the red band is ‘limited options available’. The overall complexity of the osteomyelitis is determined by the band of the most severely classified variable.