Integrated evaluation of rapid diagnostic testing, genotypic-phenotypic resistance profiling, and AI-Assisted prediction models for antimicrobial stewardship and clinical outcomes in a resource-limited setting

Introduction: Antimicrobial resistance (AMR) remains a major global health concern, necessitating timely and accurate diagnostic approaches to guide appropriate therapy. Conventional antibiotic susceptibility testing (AST) is often associated with delays that may compromise clinical outcomes. Objective: To evaluate the impact of integrating rapid diagnostic testing, genotypic resistance profiling, and artificial intelligence (AI)-based prediction models on antimicrobial stewardship and clinical outcomes. Methods: A prospective observational study was conducted at Lady Reading Hospital, MTI, Peshawar, Pakistan from 13/02/2023–18/04/2025, including 410 adult patients with suspected bacterial infections. Participants underwent conventional AST, rapid diagnostic testing, and molecular detection of resistance genes. An AI-based model was developed using clinical and laboratory parameters to predict antimicrobial resistance. Key outcomes included time to effective therapy, antibiotic appropriateness, length of hospital stay, and mortality. Statistical analysis included comparative tests, logistic regression, and receiver operating characteristic (ROC) curve analysis and was performed using SPSS Version 26.0 and R version 4.5.2. Results: Rapid diagnostics significantly reduced time to pathogen identification (10.4 vs 48.6 hours, p