Drug-related problems (DRPs) are common among surgical patients, especially older patients with polypharmacy and underlying diseases. DRPs can potentially lead to morbidity, mortality, and increased treatment costs. The enhanced recovery after surgery (ERAS) system has shown great advantages in managing surgical patients. Medication therapy management for surgical patients (established as “surgical pharmacy” by Guangdong Province Pharmaceutical Association (GDPA)) is an important part of the ERAS system. Improper medication therapy management can lead to serious consequences and even death. In order to reduce DRPs further, and promote the rapid recovery of surgical patients, the need for pharmacists in the ERAS program is even more pressing. However, the medication therapy management services of surgical pharmacy and how surgical pharmacists should participate in ERAS programs are still unclear worldwide. Therefore, this article reviews the main perioperative medical management strategies and precautions from several aspects, including antimicrobial agents, antithrombotic agents, pain medication, nutritional therapy, blood glucose monitoring, blood pressure treatment, fluid management, treatment of nausea and vomiting, and management of postoperative delirium. Additionally, the way surgical pharmacists participate in perioperative medication management, and the relevant medication pathways are explored for optimizing medication therapy management services within the ERAS programs. This study will greatly assist surgical pharmacists’ work, contributing to surgeons accepting that pharmacists have an important role in the multidisciplinary team, benefitting medical workers in treating, counseling, and advocating for their patients, and further improving the effectiveness, safety and economy of medication therapy for patients and promoting patient recovery.

Background/Objectives: The development of anti-drug antibodies (ADA) significantly diminishes the clinical efficacy of infliximab (IFX) in Crohn’s disease (CD). This study aimed to develop and validate an interpretable machine learning (ML) framework for predicting ADA risk during IFX induction therapy using multidimensional clinical and laboratory data. Methods: We conducted a retrospective analysis of 606 CD patients who initiated IFX induction between January 2023 and August 2024 at the Sixth Affiliated Hospital of Sun Yat-sen University. Predictor selection was performed through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, with significant features further evaluated via multivariate logistic regression. Seven ML models were developed and evaluated mainly based on area under the curve (AUC), F1 score, and Brier score. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP). Results: Among the 606 CD patients, 145 (23.93%) developed ADA during IFX induction. Independent predictors included serum trough levels of IFX (TLI), erythrocyte sedimentation rate (ESR), history of delayed treatment, prior exposure to anti-TNF agents, and concomitant use of immunosuppressants (IMM). The XGBoost algorithm outperformed others, with an AUC of 0.899, accuracy of 0.851, F1 score of 0.640, and Brier score of 0.102 in validation. SHAP analysis identified TLI and ESR as the most influential predictors, with history of delayed treatment and prior exposure to anti-TNF agents showing moderate impact, while concomitant use of IMM was associated with a protective effect. Conclusions: We developed an interpretable ML model that effectively predicts ADA formation in CD patients undergoing IFX induction therapy, facilitating early risk stratification and personalized treatment planning. This approach integrates advanced analytics with clinical practice to support precision medicine in CD management.

Background Infliximab (IFX), a monoclonal antibody that neutralizes tumor necrosis factor-α, is widely used as a biologic treatment for Crohn’s disease (CD). Despite its established efficacy, a substantial proportion of patients develop subtherapeutic IFX trough concentrations (< 3 μg/mL), leading to diminished clinical response and treatment failure. Early identification of high-risk individuals remains challenging due to the multifactorial nature of IFX pharmacokinetics. Aim This study integrated Least Absolute Shrinkage and Selection Operator (LASSO)-based variable selection with multivariable logistic regression to identify CD patients at risk for subtherapeutic IFX trough levels during induction. Method A total of 347 patients diagnosed with CD who commenced IFX induction therapy at the Sixth Affiliated Hospital of Sun Yat-sen University from January to December 2023 were retrospectively reviewed in this study. Comprehensive demographic, clinical, and biochemical data were retrieved from electronic records. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO), after which a multivariable logistic model was developed. Model discrimination, calibration and clinical usefulness were assessed through the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA) and clinical impact curves (CIC). Results Of the 347 participants, 148 (42.7%) exhibited subtherapeutic IFX trough concentrations. LASSO and multivariable logistic analyses identified four independent predictors: older age at diagnosis (> 40 years), elevated anti-drug antibody levels, higher erythrocyte sedimentation rate and reduced albumin (P < 0.05). The model demonstrated an AUC of 0.737 (95% CI 0.684–0.790), with a bootstrap-adjusted AUC of 0.726 (95% CI 0.697–0.739) based on 1000 resamples. Calibration demonstrated close alignment with observed outcomes, validated by a non-significant Hosmer–Lemeshow test (χ² = 8.447, P = 0.391). DCA and CIC analyses indicated meaningful clinical utility. Conclusion The proposed LASSO-logistic regression model demonstrates promising predictive performance for identifying subtherapeutic IFX exposure in CD patients. By leveraging readily available clinical data, it enables early risk stratification and individualized therapeutic decision-making, thereby facilitating more effective treatment optimization.