Articles

<b>Objective:</b> This integrative review aims to critically examine the clinical management of periprosthetic joint infections (PJI) in total hip arthroplasty (THA), emphasizing decision-making strategies, diagnostic advancements, and therapeutic innovations. The study focuses on the complexity of infection control, microbial resistance, and individualized treatment planning. <b>Methods:</b><b> </b>A systematic review of the literature was conducted using PubMed, Scopus, Web of Science, and Google Scholar, targeting studies published between 2015 and 2025. Articles were selected based on their contribution to understanding the clinical, microbiological, and surgical aspects of PJI in THA. Fifty-five studies met the inclusion criteria and were analyzed descriptively.<b> </b><b>Results:</b><b> </b>PJI in THA is influenced by multifactorial risk profiles, including obesity, diabetes, and immunosuppression. Staphylococcus aureus, particularly MRSA, remains the most frequently isolated pathogen, followed by Gram-negative organisms and fungal species. Diagnostic innovations such as next-generation sequencing have enhanced pathogen detection, while two-stage revision remains the gold standard for chronic infections. Emerging strategies—such as antimicrobial coatings, tailored antibiotic protocols, and multidisciplinary care models—demonstrate promise in improving clinical outcomes. <b>Conclusion:</b><b> </b>Managing PJI in THA necessitates a comprehensive and individualized approach, integrating early and accurate diagnosis, pathogen-specific treatment, and advanced preventive measures. The integration of emerging technologies and personalized care pathways is critical to optimizing outcomes and reducing the clinical and economic burden of PJI.

Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].

Healthcare is increasingly recognized as a data-intensive industry. Multi-hospital networks, among other organizations, face mounting operational and governance challenges because of rigid data-integration pipelines that support all data sources and destinations in the network. These pipelines have become difficult to modify, causing them to lag behind the changing needs of the clinical operation. Scalable data-pipeline architectures better support clinical decision making, optimize hospital operations, ease data quality and compliance concerns, and contribute to improved patient outcomes. Meeting scalability goals requires breaking up monolithic data-integration pipelines into smaller decoupled components and aligning service-level agreements of pipeline components and source systems. Parallelization and adoption of distributed data-warehouse technology mitigate the burden of ingesting data into a multi-hospital network. However, latency requirements still warrant the construction of separate pipelines for data ingress from clinical devices, electronic health records, and external laboratory-information systems. Healthcare associations recommend near real-time data availability for a growing list of clinical and operational applications. Mishandling the real-time ingestion of data from clinical devices, in particular, compromises availability and performance. Scalable architectural patterns for real-time streaming Ingestion from heterogeneous data sources, transport processes, and back-end processing structures are detailed.