Retrospective Analysis of Candida-associated Osteoarticular Infections: Multicenter Data From Turkey

Osteoarticular infections caused by Candida species can cause serious morbidity if not diagnosed and treated early. There are two large reviews of osteomyelitis and arthritis cases in the literature, and other data on the disease are based on case reports or small case series. Treatment recommendations for rare infections, such as Candida-associated osteomyelitis, are based on small case series and case reports from randomized clinical trials designed on other clinical forms of the disease. Amphotericin B is the most commonly used treatment of choice; however, recent reports suggest that fluconazole or echinocandin may be used. There are case reports in Turkey, but no multicenter study data have been reported.

The aim of this retrospective observational study was to investigate the osteoarticular infections caused by Candida spp. in Turkey, to reveal the distribution of causative agents, demographic data, risk factors, clinical, radiologic and laboratory findings, and to contribute to the literature with the data to be obtained at the end of the study in the management of these infections by comparing successful and unsuccessful cases in treatment.

It was planned to share the Excel file prepared by the principal investigator containing the data to be collected via e-mail, and to evaluate all the data after collection via e-mail.

Statistical evaluation will be performed using IBM SPSS (version 29.0; IBM Corp., Armonk, NY, USA). Chi-square and Fisher's exact tests will be used to compare the categorical variables. The conformity of measurement variables to normal distribution will be examined by Kolmogorov-Smirnov test. Descriptive analyses will be presented using the mean, standard deviation, median, and minimum-maximum values. Risk factors for treatment failure or side effects will be evaluated using the Mann-Whitney U test or Student's t-test. Treatment failure will be considered the dependent variable and will be analyzed using univariate logistic regression with other variables. A P value < 0.05 will be considered significant. In a stepwise approach, the variables found to be significant in the univariate logistic regression test will be analyzed using multivariate logistic regression analysis.