Sandu Andreea M, Chifiriuc Mariana C, Vrancianu Corneliu O, Cristian Roxana-E, Alistar Cristina F, Constantin Marian, Paun Mihaela, Alistar Alexandru, Popa Loredana G, Popa Mircea I, Tantu Ana C, Sidoroff Manuela E, Mihai Mara M, Marcu Andreea, Popescu George, Tantu Monica M
Doctoral School, Carol Davila University of Medicine and Pharmacy, Eroii Sanitari 8, District 5, 050474, Bucharest, Romania.
The County Emergency Hospital, Aleea Spitalului 36, 110283, Pitești, Romania.
Infect Dis Ther. 2025 May;14(5):933-971. doi: 10.1007/s40121-025-01143-0. Epub 2025 Apr 10.
Healthcare-associated infections (HAIs), previously known as nosocomial infections, represent a significant threat to healthcare systems worldwide, prolonging patient hospital stays and the duration of antimicrobial therapy. One of the most serious consequences of HAIs is the increase in the rate of antibiotic resistance (AR) generated by the prolonged, frequent, and sometimes incorrect use of antibiotics, which leads to the selection of resistant bacteria, making treatment difficult and expensive, with direct consequences for the safety of patients and healthcare personnel. Therefore, timely and accurate diagnosis of HAIs is mandatory to develop appropriate infection prevention and control practices (IPC) and new therapeutic strategies. This review aimed to present the prevalence, risk factors, current diagnosis, including artificial intelligence (AI) and machine learning approaches, future perspectives in combating HAIs causative bacteria (phage therapy, microbiome-based interventions, and vaccination), and HAIs surveillance strategies. Also, we discussed the latest findings regarding the relationships of AR with climate change and environmental pollution in the context of the One Health approach. Phage therapy is an emerging option that can offer an alternative to ineffective antibiotic treatments for antibiotic-resistant bacteria causing HAIs. Clinical trials dealing with vaccine development for resistant bacteria have yielded conflicting results. Two promising strategies, fecal microbiota transplantation and probiotic therapy, proved highly effective against recurrent Clostridium difficile infections and have been shown to reduce HAI incidence in hospitalized patients undergoing antibiotic therapy. Artificial intelligence and machine learning systems offer promising predictive capabilities in processing large volumes of clinical, microbiological, and patient data but require robust data integration. Our paper argues that HAIs are still a global challenge, requiring stringent IPC policies, computer vision, and AI-powered tools. Despite promising avenues like integrated One Health approaches, optimized phage therapy, microbiome-based interventions, and targeted vaccine development, several knowledge gaps in clinical efficacy, standardization, and pathogen complexity remain to be answered.
医疗保健相关感染(HAIs),以前称为医院感染,对全球医疗保健系统构成重大威胁,会延长患者住院时间和抗菌治疗时长。HAIs最严重的后果之一是由于抗生素的长期、频繁使用,有时甚至是不当使用,导致抗生素耐药率(AR)上升,这会促使耐药菌的产生,使治疗变得困难且昂贵,直接影响患者和医护人员的安全。因此,及时、准确地诊断HAIs对于制定适当的感染预防和控制措施(IPC)以及新的治疗策略至关重要。本综述旨在介绍HAIs的患病率、危险因素、当前的诊断方法,包括人工智能(AI)和机器学习方法、对抗HAIs致病菌的未来前景(噬菌体疗法、基于微生物组的干预措施和疫苗接种)以及HAIs监测策略。此外,我们还在“同一个健康”方法的背景下讨论了关于AR与气候变化和环境污染关系的最新研究结果。噬菌体疗法是一种新兴的选择,可以为治疗导致HAIs的耐药菌提供一种替代无效抗生素治疗的方法。针对耐药菌疫苗研发的临床试验结果相互矛盾。两种有前景的策略,即粪便微生物群移植和益生菌疗法,已被证明对复发性艰难梭菌感染非常有效,并已显示可降低接受抗生素治疗住院患者的HAIs发生率。人工智能和机器学习系统在处理大量临床、微生物学和患者数据方面具有很有前景的预测能力,但需要强大的数据整合。我们的论文认为,HAIs仍然是一个全球性挑战,需要严格的IPC政策、计算机视觉和人工智能驱动的工具。尽管有像综合“同一个健康”方法、优化的噬菌体疗法、基于微生物组的干预措施和靶向疫苗研发等有前景的途径,但在临床疗效、标准化和病原体复杂性方面仍存在一些知识空白有待解答。