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

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.

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.