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Objective: Accurate determination of life expectancy becomes very important when determining the treatment of patients with pathologic fractures. We aimed to investigate the predictive role of the PATHFx model in Turkish patients by estimating the area under curve (AUC) of the receiver operator characteristic (ROC) and externally validating the results of PATHFx on the Turkish population. Methods: The data of 122 patients who presented to one of four orthopaedic oncology referral centres in Istanbul (2010-2017) and underwent surgical management of pathologic fractures were retrospectively collected. Patients were evaluated according to age, sex, type of pathologic fracture, presence of organ metastasis, presence of lymph node metastasis, haemoglobin concentration at presentation, primary oncologic diagnosis, number of bone metastases, and Eastern Cooperative Oncology Group (ECOG) status. Estimations of the PATHFx program by months were statistically evaluated using ROC analysis. Results: In our study population (122 patients), all survived the first month, 102 survived the third month, 89 were alive at 6 months, and 58 patients survived at 12 months. At 18 and 24 months, 39 and 27 patients were alive, respectively. The AUC value was 0.677 at 3 months, 0.695 at 6 months, 0.69 at 12 months, 0.674 at 18 months, and 0.693 at 24 months. The 3-, 6-, 12-, 18-, and 24-month survival rates were statistically significant (P < 0.01 and P < 0.05). ECOG performance status was 0-2 points in 33 patients (Memorial Sloan-Kettering Cancer Center (MSKCC) data set: 93 cases, our data set: 33 cases). ECOG performance status was 3-4 points in 89 patients (MSKCC data set: 96 cases, our data set: 89 cases). Conclusions: The objective data used by PATHFx for prediction provided statistically accurate estimates on Turkish patients, who are presumed to have mixed genomes through history from both Europe and Asia, and demonstrates its applicability to the Turkish population. Keywords: Expected survival, metastatic bone disease, PATHFx, pathologic fracture, Author(s): Korhan Ozkan [1]; Burak Ozturan [1]; Zilan Karadag [1]; Bulent Erol [2]; Bugra Alpan [3]; Harzem Ozger [4]; Volkan Gurkan [5]; Omer Sofulu [2]; Merter Yalcinkaya [6]; Erhan Okay [...]

Combat wound healing and resolution are highly affected by the resident microbial flora. We therefore sought to achieve comprehensive detection of microbial populations in wounds using novel genomic technologies and bioinformatics analyses. We employed a microarray capable of detecting all sequenced pathogens for interrogation of 124 wound samples from extremity injuries in combat-injured U.S. service members. A subset of samples was also processed via next-generation sequencing and metagenomic analysis. Array analysis detected microbial targets in 51% of all wound samples, with Acinetobacter baumannii being the most frequently detected species. Multiple Pseudomonas species were also detected in tissue biopsy specimens. Detection of the Acinetobacter plasmid pRAY correlated significantly with wound failure, while detection of enteric-associated bacteria was associated significantly with successful healing. Whole-genome sequencing revealed broad microbial biodiversity between samples. The total wound bioburden did not associate significantly with wound outcome, although temporal shifts were observed over the course of treatment. Given that standard microbiological methods do not detect the full range of microbes in each wound, these data emphasize the importance of supplementation with molecular techniques for thorough characterization of wound-associated microbes. Future application of genomic protocols for assessing microbial content could allow application of specialized care through early and rapid identification and management of critical patterns in wound bioburden.

Abstract Background The ability to forecast whether a wound will heal after closure without further debridement(s), would provide substantial benefits to patients with severe extremity trauma. Methods Wound effluent is a readily available material which can be collected without disturbing healthy tissue. For analysis of potential host response biomarkers, forty four serial combat wound effluent samples from 19 patients with either healing or failing traumatic- and other combat-related wounds were examined by 2-D DIGE. Spot map patterns were correlated to eventual wound outcome (healed or wound failure) and analyzed using DeCyder 7.0 and differential proteins identified via LC-MS/MS. Results This approach identified 52 protein spots that were differentially expressed and thus represent candidate biomarkers for this clinical application. Many of these proteins are intimately involved in inflammatory and immune responses. Furthermore, discriminate analysis further refined the 52 differential protein spots to a smaller subset of which successfully differentiate between wounds that will heal and those that will fail and require further surgical intervention with greater than 83% accuracy. Conclusion These results suggest candidates for a panel of protein biomarkers that may aid traumatic wound care prognosis and treatment. We recommend that this strategy be refined, and then externally validated, in future studies of traumatic wounds.