Predictors of Infection and Benefit of Antibiotics in Gunshot Wounds to the Face.

BACKGROUND: Gunshot wounds (GSWs) to the face are at high risk for infection due the extent of tissue injury and often-observed violation of oral and sinus cavities. Given the ambiguous data on antibiotic benefit in GSW to the face, the purpose of this study is to characterize antibiotic usage, infection details, and risk factors associated with higher infection rates in GSW to face. METHODS: We conducted a retrospective review of patients presenting with GSW to the face from 2009 to 2017. The primary outcome was to identify risk factors associated with infections in patients with facial GSWs. A stepwise multivariate linear regression analysis was performed to determine the impact of specific injury details. RESULTS: Two hundred sixty-nine patients qualified for the study. Demographic information and details of hospital stay are presented in tables. Most patients (88.8%) received admission antibiotics. Facial infections were observed in 36 patients (13.4%). The infected cohort required more antibiotic days (P < 0.001), higher percentage of invasive airway procedures (P = 0.01), longer length of stay (P < 0.001), greater number of surgeries (P < 0.022), and higher readmission rates (P < 0.001). Factors associated with head or neck infections included oral cavity (odds ratio, 1.23; P = 0.04) and sinus involvement (odds ratio, 1.10; P = 0.045). CONCLUSIONS: Bullet trajectories that violated the oral or maxillary sinus cavities were associated with higher head and neck infection rates. Patients without oral cavity or sinus involvement had a lower chance (4.1%) of developing an infection and therefore may have marginal benefit from antibiotics.

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning.

Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field.

Artificial Intelligence Modeling to Predict Periprosthetic Infection and Explantation following Implant-Based Reconstruction.

BACKGROUND: Despite improvements in prosthesis design and surgical techniques, periprosthetic infection and explantation rates following implant-based reconstruction (IBR) remain relatively high. Artificial intelligence is an extremely powerful predictive tool that involves machine learning (ML) algorithms. We sought to develop, validate, and evaluate the use of ML algorithms to predict complications of IBR. METHODS: A comprehensive review of patients who underwent IBR from January of 2018 to December of 2019 was conducted. Nine supervised ML algorithms were developed to predict periprosthetic infection and explantation. Patient data were randomly divided into training (80%) and testing (20%) sets. RESULTS: The authors identified 481 patients (694 reconstructions) with a mean ± SD age of 50.0 ± 11.5 years, mean ± SD body mass index of 26.7 ± 4.8 kg/m 2 , and median follow-up time of 16.1 months (range, 11.9 to 3.2 months). Periprosthetic infection developed in 113 of the reconstructions (16.3%), and explantation was required with 82 (11.8%) of them. ML demonstrated good discriminatory performance in predicting periprosthetic infection and explantation (area under the receiver operating characteristic curve, 0.73 and 0.78, respectively), and identified nine and 12 significant predictors of periprosthetic infection and explantation, respectively. CONCLUSIONS: ML algorithms trained using readily available perioperative clinical data accurately predict periprosthetic infection and explantation following IBR. The authors' findings support incorporating ML models into perioperative assessment of patients undergoing IBR to provide data-driven, patient-specific risk assessment to aid individualized patient counseling, shared decision-making, and presurgical optimization.