Introduction : Transcutaneous osseointegration following amputation (TOFA) confers better mobility and quality of life for most patients versus socket prosthesis rehabilitation. Peri-TOFA infection remains the most frequent complication and lacks an evidence-based diagnostic algorithm. This study's objective was to investigate preoperative factors associated with positive intraoperative cultures among patients suspected of having peri-TOFA infection in order to create an evidence-based diagnostic algorithm. Methods : We conducted a retrospective study of 83 surgeries (70 patients) performed to manage suspected lower-extremity peri-TOFA infection at a specialty orthopedic practice and tertiary referral hospital in a major urban center. The diagnosis of infection was defined as positive intraoperative cultures. Preoperative patient history (fevers, subjective pain, increased drainage), physician examination findings (local cellulitis, purulent discharge, implant looseness), and laboratory data (white blood cell count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and external swab culture) were evaluated for association with subsequent positive intraoperative cultures using regression and area under receiver-operator curve (AUC) modeling. Results : Peri-implant limb pain (highly correlated with infection), ESR > 30 (highly correlated against infection), positive preoperative swab (moderately correlated with infection), gross implant motion (moderately correlated against infection), and erythema or cellulitis of the transcutaneous region (mildly correlated with infection) were variables included in the best AUC model, which achieved an 85 % positive predictive value. Other clinical findings and laboratory values (notably CRP and WBC) were non-predictive of infection. Conclusions : This seminal investigation to develop a preoperative diagnostic algorithm for peri-TOFA infection suggests that the clinical examination remains paramount. Further evaluation of a wider spectrum of clinical, laboratory, and imaging data, consistently and routinely collected with prospective data techniques in larger cohorts of patients, is necessary to create a robust predictive algorithm.