A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms

Staphylococcus aureus Endocarditis Immunothrombosis

Background: Infective endocarditis continues to represent a challenge for healthcare systems, requiring careful management and resources. Recent studies have indicated a shift in the predominant pathogens of concern, with Streptococcus sp. a being superseded by Staphylococcus sp. and Enterococcus sp. as the leading causes of concern. This shift is of concern as it is associated with Staphylococcus Aureus which has a high virulence rate and a tendency to form a biofilm, meaning that non-surgical therapy may not be effective. It is imperative to deliberate on the likelihood of platelet blood clot formation, which may be accompanied by bacterial infestation and the development of a biofilm. Methods: MEDLINE, Embase, and Pubmed were searched using terms relating to 'endocarditis' and 'Staphilococcus aureus', along with 'epidemiology', 'pathogenesis', 'coagulation', 'platelet', 'aggregation', and 'immunity'. The search focused on publications from the past 15 years, but excluded older, highly regarded articles. We also searched the reference lists of relevant articles. Recommended review articles are cited for more details. Results: An endocarditis lesion is believed to be a blood clot infected with bacteria that adheres to the heart valves. Infective endocarditis is a good example of immunothrombosis, where the coagulation system, innate immunity and the function of coagulation in isolating and eliminating pathogens interact. However, in the context of infective endocarditis, immunothrombosis unintentionally establishes an environment conducive to bacterial proliferation. The process of immunothrombosis impedes the immune system, enabling bacterial proliferation. The coagulation system plays a pivotal role in the progression of this condition. Conclusion: The coagulation system is key to how bacteria attach to the heart valves, how vegetations develop, and how complications like embolisation and valve dysfunction occur. Staphylococcus aureus, the main cause of infective endocarditis, can change blood clotting, growing well in the fibrin-rich environment of vegetation. The coagulation system is a good target for treating infective endocarditis because of its central role in the disease. But we must be careful, as using blood-thinning medicines in patients with endocarditis can often lead to an increased risk of bleeding.

Evaluating the impact of artificial intelligence in antimicrobial stewardship: a comparative meta-analysis with traditional risk scoring systems

OBJECTIVES

The growing challenge of antimicrobial resistance (AMR) has underscored the urgent need for robust antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to support enhanced decision-making in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and compare its effectiveness with traditional risk systems.

METHODS

PubMed/MEDLINE, Scopus, EMBASE, and Web of Science were searched to identify studies published up to July 2024. Any studies that evaluated the use of AI/ML in AMS compared with conventional decision-making approaches were eligible. Outcomes of interested were predictive performance metrics and diagnostic accuracy. The meta-estimate was performed pooling standardized mean difference, and effect size (ES) measured as Cohen's d with a 95% confidence interval (CI). The risk of bias was assessed using the QUADAS-AI tool.

RESULTS

Out of 3,458 studies, 27 were included, demonstrating that ML models outperform traditional methods in terms of sensitivity [1.93 (0.48-3.39) p = 0.009], and negative predictive value [1.66 (0.86-2.46), p < 0.001] but not in terms of area under the curve, accuracy, specificity, positive predictive value, when random effect models were applied.

CONCLUSIONS

Our results revealed that ML tools offer promising enhancements to traditional AMS strategies. However, high heterogeneity, inconsistent results between fixed and random effect models, and limited use of external validation in retrieved studies raise concerns about the generalizability of the findings. Furthermore, the lack of representation from outpatient and pediatric settings highlights a critical equity gap in the application of these technologies.

Artificial intelligence in hospital infection prevention: an integrative review

Background

Hospital-acquired infections (HAIs) represent a persistent challenge in healthcare, contributing to substantial morbidity, mortality, and economic burden. Artificial intelligence (AI) offers promising potential for improving HAIs prevention through advanced predictive capabilities.

Objective

To evaluate the effectiveness, usability, and challenges of AI models in preventing, detecting, and managing HAIs.

Methods

This integrative review synthesized findings from 42 studies, guided by the SPIDER framework for inclusion criteria. We assessed the quality of included studies by applying the TRIPOD checklist to individual predictive studies and the AMSTAR 2 tool for reviews.

Results

AI models demonstrated high predictive accuracy for the detection, surveillance, and prevention of multiple HAIs, with models for surgical site infections and urinary tract infections frequently achieving area-under-the-curve (AUC) scores exceeding 0.80, indicating strong reliability. Comparative data suggest that while both machine learning and deep learning approaches perform well, some deep learning models may offer slight advantages in complex data environments. Advanced algorithms, including neural networks, decision trees, and random forests, significantly improved detection rates when integrated with EHRs, enabling real-time surveillance and timely interventions. In resource-constrained settings, non-real-time AI models utilizing historical EHR data showed considerable scalability, facilitating broader implementation in infection surveillance and control. AI-supported surveillance systems outperformed traditional methods in accurately identifying infection rates and enhancing compliance with hand hygiene protocols. Furthermore, Explainable AI (XAI) frameworks and interpretability tools such as Shapley additive explanations (SHAP) values increased clinician trust and facilitated actionable insights. AI also played a pivotal role in antimicrobial stewardship by predicting the emergence of multidrug-resistant organisms and guiding optimal antibiotic usage, thereby reducing reliance on second-line treatments. However, challenges including the need for comprehensive clinician training, high integration costs, and ensuring compatibility with existing workflows were identified as barriers to widespread adoption.

Discussion

The integration of AI in HAI prevention and management represents a potentially transformative shift in enhancing predictive capabilities and supporting effective infection control measures. Successful implementation necessitates standardized validation protocols, transparent data reporting, and the development of user-friendly interfaces to ensure seamless adoption by healthcare professionals. Variability in data sources and model validations across studies underscores the necessity for multicenter collaborations and external validations to ensure consistent performance across diverse healthcare environments. Innovations in non-real-time AI frameworks offer viable solutions for scaling AI applications in low- and middle-income countries (LMICs), addressing the higher prevalence of HAIs in these regions.

Conclusions

Artificial Intelligence stands as a transformative tool in the fight against hospital-acquired infections, offering advanced solutions for prevention, surveillance, and management. To fully realize its potential, the healthcare sector must prioritize rigorous validation standards, comprehensive data quality reporting, and the incorporation of interpretability tools to build clinician confidence. By adopting scalable AI models and fostering interdisciplinary collaborations, healthcare systems can overcome existing barriers, integrating AI seamlessly into infection control policies and ultimately enhancing patient safety and care quality. Further research is needed to evaluate cost-effectiveness, real-world applications, and strategies (e.g., clinician training and the integration of explainable AI) to improve trust and broaden clinical adoption.

Artificial Intelligence in Bacterial Infections Control: A Scoping Review

Background/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI's role in infection control remains uncertain. This scoping review aims to understand the characteristics of AI applications in bacterial infection control. Results: This review examines the characteristics of AI applications in bacterial infection control, analyzing 54 eligible studies across 5 thematic scopes. The search from 3 databases yielded a total of 1165 articles, only 54 articles met the eligibility criteria and were extracted and analyzed. Five thematic scopes were synthesized from the extracted data; countries, aim, type of AI, advantages, and limitations of AI applications in bacterial infection prevention and control. The majority of articles were reported from high-income countries, mainly by the USA. The most common aims are pathogen identification and infection risk assessment. The most common AI used in infection control is machine learning. The commonest reported advantage is predictive modeling and risk assessment, and the commonest disadvantage is generalizability of the models. Methods: This scoping review was developed according to Arksey and O'Malley frameworks. A comprehensive search across PubMed, Embase, and Web of Science was conducted using broad search terms, with no restrictions. Publications focusing on AI in infection control and prevention were included. Citations were managed via EndNote, with initial title and abstract screening by two authors. Data underwent comprehensive narrative mapping and categorization, followed by the construction of thematic scopes. Conclusions: Artificial intelligence applications in infection control need to be strengthened for low-income countries. More efforts should be dedicated to investing in models that have proven their effectiveness in infection control, to maximize their utilization and tackle challenges.

Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy

The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-making and improve patient outcomes in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and to assess its predictive performance and diagnostic accuracy. We conducted a comprehensive literature search across PubMed/MEDLINE, Scopus, EMBASE, and Web of Science to identify studies published up to July 2024. Studies included were observational, cohort, or retrospective, focusing on the application of AI/ML in AMS. The outcomes assessed were the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We calculated the mean pooled effect size (ES) and its 95% confidence interval (CI) using a random-effects model. The risk of bias was assessed using the QUADAS-AI tool, and the protocol was registered in PROSPERO. Out of 3,458 retrieved articles, 80 studies met the inclusion criteria. Our meta-analysis demonstrated that ML models exhibited strong predictive performance and diagnostic accuracy, with the following results: AUC [ES: 72.28 (70.42-74.14)], accuracy [ES: 74.97 (73.35-76.58)], sensitivity [ES: 76.89; (71.90-81.89)], specificity [ES: 73.77; (67.87-79.67)], NPV [ES:79.92 (76.54-83.31)], and PPV [ES: 69.41 (60.19-78.63)] across various AMS settings. AI and ML tools offer promising enhancements due to their strong predictive performance. The integration of AI into AMS could lead to more precise antimicrobial prescribing, reduced antimicrobial resistance, and better resource utilization.

Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings

BACKGROUND

Antimicrobial resistance (AMR) poses a worldwide health threat; quick and accurate identification of AMR enhances patient outcomes and reduces inappropriate antibiotic usage. The objective of this systematic review is to evaluate the efficacy of machine learning (ML) approaches in predicting AMR in critical and high-priority pathogens (CHPP), considering antimicrobial susceptibility tests in real-world healthcare settings.

METHODS

The search methodology encompassed the examination of several databases, such as PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO. An extensive electronic database search was conducted from the inception of these databases until November 2024.

RESULTS

After completing the final step of the eligibility assessment, the systematic review ultimately included 21 papers. All included studies were cohort observational studies assessing 688,107 patients and 1,710,867 antimicrobial susceptibility tests. GBDT, Random Forest, and XGBoost were the top-performing ML models for predicting antibiotic resistance in CHPP infections. GBDT exhibited the highest AuROC values compared to Logistic Regression (LR), with a mean value of 0.80 (range 0.77-0.90) and 0.68 (range 0.50-0.83), respectively. Similarly, Random Forest generally showed better AuROC values compared to LR (mean value 0.75, range 0.58-0.98 versus mean value 0.71, range 0.61-0.83). However, some predictors selected by these algorithms align with those suggested by LR.

CONCLUSIONS

ML displays potential as a technology for predicting AMR, incorporating antimicrobial susceptibility tests in CHPP in real-world healthcare settings. However, limitations such as retrospective methodology for model development, nonstandard data processing, and lack of validation in randomized controlled trials must be considered before applying these models in clinical practice.

Prediction models for diagnosis and prognosis of the colonization or infection of multidrug-resistant organisms in adults: a systematic review, critical appraisal, and meta-analysis

BACKGROUND

Prediction models help to target patients at risk of multidrug-resistant organism (MDRO) colonization or infection and could serve as tools informing clinical practices to prevent MDRO transmission and inappropriate empiric antibiotic therapy. However, there is limited evidence to identify which among the available models are of low risk of bias and suitable for clinical application.

OBJECTIVES

To identify, describe, appraise, and summarise the performance of all prognostic and diagnostic models developed or validated for predicting MDRO colonization or infection.

DATA SOURCES

Six electronic literature databases and clinical registration databases were searched until April 2022.

STUDY ELIGIBILITY CRITERIA

Development and validation studies of any multivariable prognostic and diagnostic models to predict MDRO colonization or infection in adults.

PARTICIPANTS

Adults (≥ 18 years old) without MDRO colonization or infection (in prognostic models) or with unknown or suspected MDRO colonization or infection (in diagnostic models).

ASSESSMENT OF RISK OF BIAS

The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias. Evidence certainty was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach.

METHODS OF DATA SYNTHESIS

Meta-analyses were conducted to summarize the discrimination and calibration of the models' external validations conducted in at least two non-overlapping datasets.

RESULTS

We included 162 models (108 studies) developed for diagnosing (n = 135) and predicting (n = 27) MDRO colonization or infection. Models exhibited a high-risk of bias, especially in statistical analysis. High-frequency predictors were age, recent invasive procedures, antibiotic usage, and prior hospitalization. Less than 25% of the models underwent external validations, with only seven by independent teams. Meta-analyses for one diagnostic and two prognostic models only produced very low to low certainty of evidence.

CONCLUSIONS

The review comprehensively described the models for identifying patients at risk of MDRO colonization or infection. We cannot recommend which models are ready for application because of the high-risk of bias, limited validations, and low certainty of evidence from meta-analyses, indicating a clear need to improve the conducting and reporting of model development and external validation studies to facilitate clinical application.

The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review

BACKGROUND/OBJECTIVES

Carbapenem resistance poses a significant threat to public health by undermining the efficacy of one of the last lines of antibiotic defense. Addressing this challenge requires innovative approaches that can enhance our understanding and ability to combat resistant pathogens. This review aims to explore the integration of machine learning (ML) and epidemiological approaches to understand, predict, and combat carbapenem-resistant pathogens. It examines how leveraging large datasets and advanced computational techniques can identify patterns, predict outbreaks, and inform targeted intervention strategies.

METHODS

The review synthesizes current knowledge on the mechanisms of carbapenem resistance, highlights the strengths and limitations of traditional epidemiological methods, and evaluates the transformative potential of ML. Real-world applications and case studies are used to demonstrate the practical benefits of combining ML and epidemiology. Technical and ethical challenges, such as data quality, model interpretability, and biases, are also addressed, with recommendations provided for overcoming these obstacles.

RESULTS

By integrating ML with epidemiological analysis, significant improvements can be made in predictive accuracy, identifying novel patterns in disease transmission, and designing effective public health interventions. Case studies illustrate the benefits of interdisciplinary collaboration in tackling carbapenem resistance, though challenges such as model interpretability and data biases must be managed.

CONCLUSIONS

The combination of ML and epidemiology holds great promise for enhancing our capacity to predict and prevent carbapenem-resistant infections. Future research should focus on overcoming technical and ethical challenges to fully realize the potential of these approaches. Interdisciplinary collaboration is key to developing sustainable strategies to combat antimicrobial resistance (AMR), ultimately improving patient outcomes and safeguarding public health.

Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review

BACKGROUND

Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention.

METHODS

We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs.

RESULTS

We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%.

CONCLUSIONS

AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review

Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.

Concerning emergence of a new vancomycin-resistant Enterococcus faecium strain ST1299/CT1903/vanA at a tertiary university centre in South Germany

BACKGROUND

vanB-carrying vancomycin-resistant Enterococcus faecium (VREfm) of the sequence types 80 (ST80) and ST117 have dominated Germany in the past. In 2020, our hospital witnessed a sharp increase in the proportion of vanA-positive VREfm.

AIM

To attempt to understand these dynamics through whole-genome sequencing (WGS) and analysis of nosocomial transmissions.

METHODS

At our hospital, the first VREfm isolate per patient, treated during 2020, was analysed retrospectively using specific vanA/vanB PCR, WGS, multi-locus sequence typing (MLST), and core-genome (cg) MLST. Epidemiologic links between VRE-positive patients were assessed using hospital occupancy data.

FINDINGS

Isolates from 319 out of 356 VREfm patients were available for WGS, of which 181 (56.7%) fulfilled the ECDC definition for nosocomial transmission. The high load of nosocomial cases is reflected in the overall high clonality rate with only three dominating sequence (ST) and complex types (CT), respectively: the new emerging strain ST1299 (100% vanA, 77.4% CT1903), and the well-known ST80 (90.0% vanB, 81.0% CT1065) and ST117 (78.0% vanB, 65.0% CT71). The ST1299 isolates overall, and the subtype CT1903 in particular, showed high isolate clonality, which demonstrates impressively high spreading potential. Overall, 152 out of 319 isolates had an allelic cgMLST difference of ≤3 to another, including 91 (59.6%) ST1299. Occupancy data identified shared rooms (3.7%), shared departments (6.2%), and VRE-colonized prior room occupants (0.6%) within 30 days before diagnosis as solid epidemiological links.

CONCLUSION

A new emerging VREfm clone, ST1299/CT1903/vanA, dominated our institution in 2020 and has been an important driver of the increasing VREfm rates.

Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study

Background: While Enterobacteriaceae bacteria are commonly found in the healthy human gut, their colonization of other body parts can potentially evolve into serious infections and health threats. We investigate a graph-based machine learning model to predict risks of inpatient colonization by multidrug-resistant (MDR) Enterobacteriaceae. Methods: Colonization prediction was defined as a binary task, where the goal is to predict whether a patient is colonized by MDR Enterobacteriaceae in an undesirable body part during their hospital stay. To capture topological features, interactions among patients and healthcare workers were modeled using a graph structure, where patients are described by nodes and their interactions are described by edges. Then, a graph neural network (GNN) model was trained to learn colonization patterns from the patient network enriched with clinical and spatiotemporal features. Results: The GNN model achieves performance between 0.91 and 0.96 area under the receiver operating characteristic curve (AUROC) when trained in inductive and transductive settings, respectively, up to 8% above a logistic regression baseline (0.88). Comparing network topologies, the configuration considering ward-related edges (0.91 inductive, 0.96 transductive) outperforms the configurations considering caregiver-related edges (0.88, 0.89) and both types of edges (0.90, 0.94). For the top 3 most prevalent MDR Enterobacteriaceae, the AUROC varies from 0.94 for Citrobacter freundii up to 0.98 for Enterobacter cloacae using the best-performing GNN model. Conclusion: Topological features via graph modeling improve the performance of machine learning models for Enterobacteriaceae colonization prediction. GNNs could be used to support infection prevention and control programs to detect patients at risk of colonization by MDR Enterobacteriaceae and other bacteria families.

A deep learning model for predicting multidrug-resistant organism infection in critically ill patients

BACKGROUND

This study aimed to apply the backpropagation neural network (BPNN) to develop a model for predicting multidrug-resistant organism (MDRO) infection in critically ill patients.

METHODS

This study collected patient information admitted to the intensive care unit (ICU) of the Affiliated Hospital of Qingdao University from August 2021 to January 2022. All patients enrolled were divided randomly into a training set (80%) and a test set (20%). The least absolute shrinkage and selection operator and stepwise regression analysis were used to determine the independent risk factors for MDRO infection. A BPNN model was constructed based on these factors. Then, we externally validated this model in patients from May 2022 to July 2022 over the same center. The model performance was evaluated by the calibration curve, the area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

In the primary cohort, 688 patients were enrolled, including 109 (15.84%) MDRO infection patients. Risk factors for MDRO infection, as determined by the primary cohort, included length of hospitalization, length of ICU stay, long-term bed rest, antibiotics use before ICU, acute physiology and chronic health evaluation II, invasive operation before ICU, quantity of antibiotics, chronic lung disease, and hypoproteinemia. There were 238 patients in the validation set, including 31 (13.03%) MDRO infection patients. This BPNN model yielded good calibration. The AUC of the training set, the test set and the validation set were 0.889 (95% CI 0.852-0.925), 0.919 (95% CI 0.856-0.983), and 0.811 (95% CI 0.731-0.891), respectively.

CONCLUSIONS

This study confirmed nine independent risk factors for MDRO infection. The BPNN model performed well and was potentially used to predict MDRO infection in ICU patients.

Preventing Multidrug-Resistant Bacterial Transmission in the Intensive Care Unit with a Comprehensive Approach: A Policymaking Manual

Patients referred to intensive care units (ICU) commonly contract infections caused by multidrug-resistant (MDR) bacteria, which are typically linked to complications and high mortality. There are numerous independent factors that are associated with the transmission of these pathogens in the ICU. Preventive multilevel measures that target these factors are of great importance in order to break the chain of transmission. In this review, we aim to provide essential guidance for the development of robust prevention strategies, ultimately ensuring the safety and well-being of patients and healthcare workers in the ICU. We discuss the role of ICU personnel in cross-contamination, existing preventative measures, novel technologies, and strategies employed, along with antimicrobial surveillance and stewardship (AMSS) programs, to construct effective and thoroughly described policy recommendations. By adopting a multifaceted approach that combines targeted interventions with broader preventive strategies, healthcare facilities can create a more coherent line of defense against the spread of MDR pathogens. These recommendations are evidence-based, practical, and aligned with the needs and realities of the ICU setting. In conclusion, this comprehensive review offers a blueprint for mitigating the risk of MDR bacterial transmission in the ICU, advocating for an evidence-based, multifaceted approach.

Host-Bacterium Interaction Mechanisms in Staphylococcus aureus Endocarditis: A Systematic Review

Staphylococci sp. are the most commonly associated pathogens in infective endocarditis, especially within high-income nations. This along with the increasing burden of healthcare, aging populations, and the protracted infection courses, contribute to a significant challenge for healthcare systems. A systematic review was conducted using relevant search criteria from PubMed, Ovid's version of MEDLINE, and EMBASE, and data were tabulated from randomized controlled trials (RCT), observational cohort studies, meta-analysis, and basic research articles. The review was registered with the OSF register of systematic reviews and followed the PRISMA reporting guidelines. Thirty-five studies met the inclusion criteria and were included in the final systematic review. The role of Staphylococcus aureus and its interaction with the protective shield and host protection functions was identified and highlighted in several studies. The interaction between infective endocarditis pathogens, vascular endothelium, and blood constituents was also explored, giving rise to the potential use of antiplatelets as preventative and/or curative agents. Several factors allow Staphylococcus aureus infections to proliferate within the host with numerous promoting and perpetuating agents. The complex interaction with the hosts' innate immunity also potentiates its virulence. The goal of this study is to attain a better understanding on the molecular pathways involved in infective endocarditis supported by S. aureus and whether therapeutic avenues for the prevention and treatment of IE can be obtained. The use of antibiotic-treated allogeneic tissues have marked antibacterial action, thereby becoming the ideal substitute in native and prosthetic valvular infections. However, the development of effective vaccines against S. aureus still requires in-depth studies.

Infective Endocarditis in High-Income Countries

Infective endocarditis remains an illness that carries a significant burden to healthcare resources. In recent times, there has been a shift from Streptococcus sp. to Staphylococcus sp. as the primary organism of interest. This has significant consequences, given the virulence of Staphylococcus and its propensity to form a biofilm, rendering non-surgical therapy ineffective. In addition, antibiotic resistance has affected treatment of this organism. The cohorts at most risk for Staphylococcal endocarditis are elderly patients with multiple comorbidities. The innovation of transcatheter technologies alongside other cardiac interventions such as implantable devices has contributed to the increased risk attributable to this cohort. We examined the pathophysiology of infective endocarditis carefully. Inter alia, the determinants of Staphylococcus aureus virulence, interaction with host immunity, as well as the discovery and emergence of a potential vaccine, were investigated. Furthermore, the potential role of prophylactic antibiotics during dental procedures was also evaluated. As rates of transcatheter device implantation increase, endocarditis is expected to increase, especially in this high-risk group. A high level of suspicion is needed alongside early initiation of therapy and referral to the heart team to improve outcomes.