Influence of Microbiome Interactions on Antibiotic Resistance Development in the ICU Environment: Insights and Opportunities with Machine Learning

Abstract

Antibiotic resistance is a global health crisis exacerbated by the misuse of antibiotics in healthcare, agriculture, and the environment. In an intensive care unit (ICU), where high antibiotic usage, invasive procedures, and immunocompromised patients converge, resistance risks are amplified, leading to multidrug-resistant organisms (MDROs) and poor patient outcomes. The human microbiome plays a crucial role in the development and dissemination of antibiotic resistance genes (ARGs) through mechanisms like horizontal gene transfer, biofilm formation, and quorum sensing. Disruptions to the microbiome balance, or dysbiosis, further exacerbate resistance, particularly in high-risk ICU environments. This study explores microbiome interactions and antibiotic resistance in the ICU, highlighting machine learning (ML) as a transformative tool. Machine learning algorithms analyze high-dimensional microbiome data, predict resistance patterns, and identify novel therapeutic targets. By integrating genomic, microbiome, and clinical data, these models support personalized treatment strategies and enhance infection control measures. The results demonstrate the potential of machine learning to improve antibiotic stewardship and predict patient outcomes, emphasizing its utility in ICU-specific interventions. In conclusion, addressing antibiotic resistance in the ICU requires a multidisciplinary approach combining advanced computational methods, microbiome research, and clinical expertise. Enhanced surveillance, targeted interventions, and global collaboration are essential to mitigate antibiotic resistance and improve patient care.

1. Introduction

Antibiotic resistance is a significant global health challenge, contributing to increased morbidity, mortality, and healthcare costs. The World Health Organization (WHO) identifies it as one of the top ten public health threats worldwide [1]. Resistant infections lead to prolonged hospital stays, elevated medical expenses, and higher mortality rates [1]. Without effective interventions, antibiotic-resistant infections could result in as many as 10 million deaths annually by 2050 [1]. The misuse and overuse of antibiotics in healthcare accelerate the emergence of resistant bacterial strains [2]. Medication errors, including prescribing antibiotics for viral infections where they offer no clinical benefit, play a critical role [2]. Clinicians may prescribe antibiotics as a precautionary measure, and patients often fail to complete prescribed courses, leaving behind resistant bacteria [3]. Beyond healthcare, antibiotic use in agriculture and animal husbandry also contributes significantly to resistance, as antibiotics are used for growth promotion and disease prevention in livestock [3]. This promotes the selection of resistant bacteria, which can reach humans through the food chain [4]. Environmental contamination from agricultural antibiotics spreads resistance further [4].

Mechanisms of bacterial resistance include genetic mutations and horizontal gene transfer (HGT), which allow bacteria to rapidly acquire and disseminate resistance traits [5]. Mutations can alter antibiotic targets, reduce drug uptake, or enhance efflux mechanisms to expel antibiotics [2]. HGT enables bacteria to share resistance genes across species, exacerbating the problem [5]. Combating antibiotic resistance requires developing new antibiotics, improving diagnostic tools, enhancing surveillance, and optimizing antibiotic use through stewardship programs [6]. Public education and global collaboration among governments, healthcare providers, and industries are essential [7].

The human microbiome, a complex ecosystem of microorganisms residing in and on the body, is central to this issue [8]. Microbial interactions—competition, cooperation, and communication—shape the development and spread of antibiotic resistance genes (ARGs) [9]. Horizontal gene transfer facilitates the spread of ARGs within the microbiome, with commensal bacteria potentially transmitting resistance to pathogens [9]. Dysbiosis, often caused by antibiotic exposure, disrupts microbial balance, allowing resistant strains to proliferate [9]. Understanding these dynamics is crucial for mitigating resistance and promoting health [8,9].

ICUs are high-risk environments for antibiotic resistance due to factors like extensive antibiotic use, invasive procedures, and immunocompromised patients [10]. Broad-spectrum antibiotics used to prevent or treat infections in critically ill patients disrupt the microbiota, selecting for resistant organisms [11]. The high patient turnover and proximity in ICUs facilitate pathogen transmission [11]. ICU-specific challenges include dysbiosis, compromised immunity, and environmental contamination, all of which contribute to the spread of multidrug-resistant organisms (MDROs) like methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Enterobacteriaceae (CRE) [10,11]. Effective strategies, such as routine microbiome monitoring, antibiotic stewardship, and infection control measures, are essential to address these challenges [10,11]. These pathogens are of particular concern given their high transmissibility and limited treatment options. Carbapenem-resistant Acinetobacter baumannii similarly poses a major threat in the ICU due to its ability to survive on surfaces and form biofilms. These MDROs often harbor multiple resistance genes, leading to infections that are difficult to treat and associated with increased morbidity and mortality in critically ill patients.

Machine learning (ML) offers advanced tools to analyze microbiome data, predict resistance patterns, and develop targeted interventions [6,7,12,13,14]. High-throughput technologies generate vast datasets, which ML models can process to uncover patterns, predict risks, and support clinical decisions [13,14]. ML can identify patients at risk of resistant infections, guide personalized treatments, and uncover novel therapeutic targets by analyzing microbial interactions [13,14]. The integration of ML with electronic health records (EHRs) and microbiome profiles can revolutionize infection control and antimicrobial stewardship [13,14]. Collaboration among computational scientists, microbiologists, and clinicians is key to leveraging ML effectively [7].

Building on the findings of previous large-scale analyses [15,16,17,18], this review aims to integrate knowledge of ICU-specific microbiome disruptions with ML applications. By doing so, we highlight evidence gaps, offer potential routes for future research, and present a framework for personalized interventions in critical care.

Table 1. Overview of key factors, mechanisms, and interventions for antibiotic resistance in ICUs.

Category	Description	Examples/Mechanisms	Proposed Interventions
ICU-Specific Factors	Environmental and clinical elements unique to ICU settings that drive resistance.	High antibiotic usage, invasive devices, immunosuppression.	Antibiotic stewardship, device management, infection control.
Microbiome Interactions	Processes within the microbiome that influence resistance gene dissemination.	Horizontal gene transfer, biofilm formation, quorum sensing.	Probiotics, biofilm disruptors, quorum sensing inhibitors.
Antibiotic Resistance Mechanisms	Genetic and physiological strategies employed by bacteria to evade antibiotics.	Efflux pumps, enzymatic degradation, target modifications.	Target-specific inhibitors, combination therapies.
ML Applications	Computational approaches to predict, analyze, and mitigate antibiotic resistance.	Predicting resistance, identifying therapeutic targets, optimizing treatments.	Integrating genomic and clinical data, personalized medicine.

ICU—intensive care unit; ARGs—antibiotic resistance genes; MDROs—multidrug-resistant organisms; HGT—horizontal gene transfer; ML—machine learning.

summarizes the ICU-specific challenges, microbiome interactions, antibiotic resistance mechanisms, and potential ML-based interventions aimed at mitigating resistance and improving patient outcomes.

2. Microbiome Interactions in the ICU

2.1. Composition of the ICU Microbiome

Throughout this review, the term ‘ICU microbiome’ refers primarily to the microbial communities within ICU patients themselves, such as the gut, respiratory tract, and skin microbiomes, which undergo distinct shifts during critical illness. However, environmental microorganisms present in the ICU (on surfaces or equipment) also play a key role and can transfer resistance genes to patients’ endogenous flora. ICU patients often experience significant alterations in their microbiome, characterized by reduced diversity and the overgrowth of opportunistic pathogens [19]. The healthy human microbiome is a complex and balanced ecosystem that plays a crucial role in maintaining physiological functions, including digestion, immune modulation, and protection against pathogenic invasion [19]. In critically ill patients, this balance is disrupted due to various factors inherent to the ICU environment [20].

Factors such as antibiotic exposure are among the most significant contributors to microbiome alteration in ICU patients [21]. Broad-spectrum antibiotics, commonly administered to prevent or treat infections, can indiscriminately eliminate beneficial commensal bacteria along with pathogenic organisms [22]. This reduction in microbial diversity, known as dysbiosis, can compromise the gut barrier function and immune responses, making patients more susceptible to colonization by MDROs [21,22].

Mechanical ventilation is another factor influencing the ICU microbiome composition [23,24]. The use of ventilators can alter the respiratory tract microbiome by introducing external microbes and facilitating the growth of pathogens like Pseudomonas aeruginosa and Acinetobacter baumannii [24]. The endotracheal tube bypasses natural defense mechanisms, allowing microbes to access the lower respiratory tract, which can lead to ventilator-associated pneumonia (VAP) [25]. The biofilm formation on these devices further protects pathogens from antibiotics and the host immune system [26].

Nutritional support, including enteral and parenteral nutrition, can impact the microbiome by altering the availability of nutrients that support microbial growth [27]. Changes in diet composition, such as increased sugar or fat content, can favor the growth of certain bacteria over others, contributing to dysbiosis [28]. Additionally, fasting or reduced food intake, which are common in critically ill patients, can lead to mucosal atrophy and a decrease in beneficial microbial populations [29].

Other factors influencing the ICU microbiome include stress, hormonal changes, and the severity of illness [30,31]. The physiological stress of critical illness can lead to increased cortisol levels and altered immune responses, affecting microbial colonization and growth [28]. Surgical interventions and the use of invasive devices can introduce pathogens directly into sterile body sites, further disrupting the microbiome [32].

Recent evidence highlights the role of the gut–lung axis in ICU patients, especially in sepsis and acute respiratory distress syndrome (ARDS), which are among the leading causes of mortality [33,34,35]. These conditions are associated with significant alterations in the lung microbiome, with culture-independent methods identifying an enrichment of gut-associated bacteria in the lungs [33]. The lower gastrointestinal tract has been identified as the primary source of these bacteria, which traditional culture techniques fail to detect. In murine models and humans with ARDS, the presence of gut-specific bacteria, such as Bacteroides spp., correlates with systemic inflammation and alveolar TNF-α levels, a key driver of lung inflammation [33]. These findings suggest a shared gut–lung axis mechanism contributing to the pathogenesis of sepsis and ARDS [34,35]. Moreover, recent findings emphasize the role of the gut–lung axis in critically ill patients with VAP [36]. Evidence suggests that intestinal dysbiosis not only predisposes to systemic inflammation but may also promote translocation of gut-derived organisms to the lung, exacerbating respiratory infections. Integrating microbiome monitoring in both the gut and respiratory tract can help to identify potential VAP risk and guide targeted preventive measures [36].

The overgrowth of opportunistic pathogens within the ICU microbiome significantly elevates the risk of infection and sepsis [19]. Many of these pathogens harbor antibiotic resistance genes, complicating treatment and leading to poor patient outcomes [37]. Understanding the composition and dynamics of the ICU microbiome, including the gut–lung axis, is essential for developing strategies to prevent dysbiosis and reduce the incidence of MDRO infections, sepsis, and ARDS [38,39]. Multiple ICU-based cohort studies leveraging 16S rRNA and shotgun metagenomics have shown that dysbiosis—characterized by reduced commensal diversity and opportunistic overgrowth—correlates with increased colonization by MDROs [40,41]. For instance, prospective trials have linked gut dysbiosis with subsequent bloodstream infections by extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, suggesting that such shifts are not merely correlative but clinically impactful [42,43].

2.2. Factors Influencing Microbiome Dynamics

Environmental conditions, antibiotic regimens, patient genetics, and underlying diseases influence microbiome composition and interactions in the ICU [31,32]. The ICU environment is characterized by high microbial exposure due to frequent patient interactions, invasive procedures, and the presence of resistant organisms [33]. This environment creates a unique ecosystem where microbiome dynamics are constantly changing [34].

Antibiotic regimens play a pivotal role in shaping the microbiome [38]. The choice of antibiotic, dosage, duration, and combination therapies can selectively pressure microbial populations, leading to the emergence of resistant strains [39]. Prolonged antibiotic use can deplete beneficial bacteria, allowing pathogenic species to dominate [44]. Strategies to minimize unnecessary antibiotic exposure are critical in preserving microbiome integrity [45].

Patient genetics influence immune responses, mucosal barriers, and the susceptibility to infections, which in turn affect the microbiome composition [32]. Genetic variations can alter cytokine production, antimicrobial peptide expression, and other factors that regulate microbial colonization [46]. For example, polymorphisms in genes encoding Toll-like receptors can impact the recognition of microbial components and modulate immune responses [47].

Underlying diseases, such as diabetes, cancer, and chronic obstructive pulmonary disease (COPD), can disrupt the microbiome balance through metabolic changes, immune suppression, and altered physiological states [48]. These conditions may require treatments, like chemotherapy or corticosteroids, which further compromise immune function and affect microbial populations [39]. The presence of comorbidities increases the complexity of managing microbiome dynamics in ICU patients [31].

Cross-transmission between patients and healthcare workers is a significant factor in microbiome alterations [49]. Healthcare workers can inadvertently transfer pathogens through direct contact or via contaminated equipment and surfaces [50]. Inadequate hand hygiene and improper use of personal protective equipment contribute to the spread of MDROs [49]. The close proximity of patients and shared medical devices in the ICU facilitate microbial exchange [50].

Environmental factors, such as temperature, humidity, and surface materials, can influence microbial survival and transmission [50]. Biofilms formed on surfaces and medical devices act as reservoirs for pathogens, protecting them from cleaning agents and antibiotics [51]. Regular disinfection protocols and the use of antimicrobial materials are essential to mitigate environmental contamination [52].

Understanding the interplay of these factors is crucial for developing effective infection control measures [53]. Interventions may include antimicrobial stewardship programs to optimize antibiotic use, enhanced cleaning protocols, and staff education on hygiene practices [53]. Personalized medicine approaches that consider patient genetics and underlying conditions may also improve microbiome management in the ICU [54].

3. Mechanisms of Antibiotic Resistance Development via Microbiome Interactions

Antibiotic resistance development is a complex process influenced by various mechanisms that facilitate the acquisition and dissemination of resistance genes among bacterial populations. In the ICU environment, where patients are exposed to high antibiotic usage and invasive procedures, these mechanisms are amplified due to the dense and dynamic microbial communities present [45]. Clinical isolates from ICU patients frequently harbor mobile genetic elements carrying carbapenemase genes, underscoring HGT’s impact on resistance [55]. Likewise, ventilator tubing biofilms provide real-world evidence of biofilm-mediated resistance (e.g., Acinetobacter baumannii colonization), and quorum sensing inhibitors have shown efficacy in disrupting Pseudomonas aeruginosa virulence in animal models [56,57]. Understanding these mechanisms is crucial for developing strategies to mitigate the spread of antibiotic resistance.

3.1. Horizontal Gene Transfer

HGT is a primary mechanism by which bacteria acquire ARGs, involving the transfer of genetic material between organisms without reproduction [45,46]. This process allows for the rapid spread of resistance traits across different bacterial species and genera, significantly impacting microbial evolution and adaptation [46]. HGT occurs through three main mechanisms: transformation, transduction, and conjugation [44].

• Transformation involves the uptake of free DNA fragments from the environment by competent bacterial cells [45]. In the ICU setting, the frequent use of antibiotics leads to bacterial cell lysis, releasing DNA into the surroundings. The dense microbial populations increase the likelihood that other bacteria will encounter and incorporate this genetic material, including ARGs [46]. Environmental stressors, such as antibiotic pressure, can induce competence in bacteria, enhancing their ability to take up external DNA [45].
• Transduction is mediated by bacteriophages, which are viruses that infect bacteria [45]. During the infection process, bacteriophages may inadvertently package fragments of the host bacterial DNA, including ARGs, into new viral particles. When these phages infect other bacteria, they introduce the acquired DNA into the new host’s genome [45]. The high diversity of bacteriophages in the ICU microbiome facilitates the transduction of ARGs among different bacterial populations [46].
• Conjugation involves direct cell-to-cell contact through pilus formation, allowing the transfer of plasmids and transposons carrying ARGs between bacteria [46]. Plasmids are extrachromosomal DNA elements that often carry multiple resistance genes, and their transfer can confer multidrug resistance to recipient bacteria [45]. The selective pressure exerted by antibiotics in the ICU environment promotes the survival and proliferation of bacteria capable of conjugative gene transfer [46]. Mobile genetic elements, like integrons and insertion sequences, can also facilitate the integration and dissemination of ARGs within bacterial genomes [19].

Among the resistance genes most commonly transferred via plasmids are those encoding extended-spectrum β-lactamases (e.g., bla_TEM, bla_SHV, bla_CTX-M) and carbapenemases (e.g., bla_KPC, bla_NDM). These plasmid-borne genes typically confer resistance to critical β-lactams. Additionally, aminoglycoside-modifying enzymes (e.g., aac, ant, aph genes) frequently spread through conjugation, illustrating how HGT amplifies multidrug resistance in ICUs. The dense microbial populations in the ICU microbiome enhance opportunities for HGT [46]. Close physical proximity among bacteria in biofilms and on mucosal surfaces increases the chances of genetic exchange [46]. Moreover, the presence of invasive devices, such as catheters and ventilators, can create niches where bacterial communities thrive and interact [46]. The high turnover of patients and staff in the ICU also contributes to the introduction of diverse bacterial strains, further promoting genetic diversity and the potential for HGT [46].

HGT contributes significantly to the emergence and spread of MDROs in the ICU [45,46]. For instance, the rapid dissemination of carbapenemase-producing genes among Enterobacteriaceae is largely attributed to plasmid-mediated conjugation [45]. Understanding the factors that facilitate HGT in the ICU is essential for developing interventions aimed at disrupting gene transfer processes and reducing the spread of resistance [45,46].

Strategies to mitigate HGT include strict antibiotic stewardship to minimize selective pressure, enhanced infection control practices to reduce bacterial density and diversity, and the development of agents that can inhibit gene transfer mechanisms [45,46]. Ongoing research focuses on understanding the molecular basis of HGT and identifying potential targets for intervention [45].

3.2. Biofilm Formation

Biofilms are structured communities of bacteria embedded in a self-produced extracellular matrix, adhering to surfaces such as medical devices, host tissues, and mucosal linings [51]. The extracellular polymeric substance (EPS) matrix comprises polysaccharides, proteins, lipids, and extracellular DNA, providing structural integrity and protection to the bacterial community [51]. Biofilm-associated bacteria exhibit increased resistance to antibiotics and host immune responses due to several factors [52].

• Reduced antibiotic penetration: The EPS matrix acts as a physical barrier that impedes the diffusion of antibiotics into the deeper layers of the biofilm [51]. This barrier can result in sub-inhibitory concentrations of antibiotics reaching the bacterial cells, which not only fail to eradicate the bacteria but may also promote the development of resistance [52].
• Altered microenvironment: Within biofilms, gradients of nutrients, oxygen, and waste products create heterogeneous microenvironments [51]. Bacteria in different regions of the biofilm may exhibit varied metabolic activities, with cells in nutrient-deprived zones entering a slow-growing or dormant state [52]. Antibiotics targeting active cellular processes are less effective against these dormant cells, allowing them to survive treatment [52].
• Enhanced HGT: The close proximity of cells within biofilms facilitates HGT [51]. The accumulation of extracellular DNA within the EPS matrix serves as a source of genetic material for transformation [51]. Additionally, the high cell density promotes conjugation events, enabling the transfer of plasmids carrying ARGs [51]. Biofilms can thus act as reservoirs for resistance genes and hotspots for genetic exchange [51].
• Protection from immune responses: The EPS matrix shields bacteria from phagocytosis and the action of antimicrobial peptides produced by the host immune system [51]. Biofilm-associated infections often lead to chronic inflammation, which can cause tissue damage and further compromise the immune response [51].

In the ICU, biofilms are of particular concern due to their association with medical devices [53]. Catheters, endotracheal tubes, prosthetic joints, and other indwelling devices provide surfaces for biofilm formation [51,53]. These biofilm-associated infections are difficult to treat and are a major source of hospital-acquired infections (HAIs) [51]. For example, catheter-associated urinary tract infections and ventilator-associated pneumonia are often linked to biofilms on device surfaces [52].

The persistence of biofilms contributes to prolonged infections and increased antibiotic resistance [53]. Standard antibiotic therapies may be ineffective against biofilm-associated bacteria, necessitating higher doses or combination therapies that can have adverse side effects [54]. In some cases, removal of the infected device is required to resolve the infection [51].

Addressing biofilm-related resistance involves strategies to prevent biofilm formation and to disrupt established biofilms [53,54]. Preventive measures include the development of anti-fouling materials for medical devices, coating surfaces with antimicrobial agents, and implementing protocols to minimize device-associated infections [54]. Therapeutic approaches focus on agents that can penetrate the biofilm matrix, disrupt EPS components, or inhibit biofilm-specific gene expression [52]. Enzymes like DNase I, dispersin B, and agents targeting quorum sensing pathways are being investigated for their potential to break down biofilms and enhance antibiotic efficacy [52].

Understanding the mechanisms underlying biofilm formation and maintenance is critical for developing effective interventions [53]. Continued research in this area holds promise for reducing the impact of biofilms on antibiotic resistance and improving patient outcomes in the ICU [54,55,56].

3.3. Quorum Sensing

Quorum sensing is a cell-to-cell communication mechanism that regulates gene expression in response to population density through the production and detection of signaling molecules called autoinducers [57,58]. This process enables bacterial populations to coordinate collective behaviors that are advantageous under specific environmental conditions [58]. Quorum sensing controls various physiological activities, including:

• Virulence factor production: Bacteria can regulate the expression of toxins, enzymes, and other factors that contribute to pathogenicity [59]. Coordinated expression ensures that these factors are produced when the bacterial population reaches a threshold sufficient to overcome host defenses [59].
• Biofilm formation: Quorum sensing influences the expression of genes involved in adhesion, EPS production, and biofilm maturation [57]. By synchronizing biofilm development, bacteria enhance their collective resilience against environmental stresses, including antibiotics [58].
• Antibiotic resistance mechanisms: Quorum sensing can modulate the expression of efflux pumps, enzymes that degrade or modify antibiotics, and other resistance factors [59]. For instance, the overexpression of efflux pumps can reduce intracellular antibiotic concentrations, diminishing drug efficacy [59].
• Swarming and motility: Bacteria can regulate their movement to colonize new niches or escape hostile environments [58]. This collective behavior can contribute to the spread of infection and colonization of medical devices [58].

In the context of antibiotic resistance, quorum sensing plays a significant role by influencing behaviors that enhance bacterial survival under antibiotic pressure [59]. The activation of resistance genes through quorum sensing can lead to a rapid and coordinated response, increasing the likelihood of survival for the bacterial community [59].

The ICU environment, characterized by high bacterial densities and the frequent use of antibiotics, can intensify quorum sensing activities [58]. Antibiotics themselves can act as signaling molecules or interfere with quorum sensing pathways, inadvertently promoting resistance [59]. For example, sub-inhibitory concentrations of certain antibiotics may induce the expression of quorum sensing-regulated genes, leading to increased biofilm formation and virulence [59].

Targeting quorum sensing pathways offers a promising strategy to combat antibiotic resistance without exerting selective pressure that leads to the development of resistance [59]. Quorum sensing inhibitors (QSIs) aim to disrupt bacterial communication, preventing the coordinated expression of resistance mechanisms and virulence factors [59]. QSIs can be used in combination with antibiotics to enhance their effectiveness by rendering bacteria more susceptible to treatment [59].

Several approaches are being explored to inhibit quorum sensing:

• Enzymatic degradation of signaling molecules: Enzymes like lactonases and acylases can degrade autoinducers, interrupting the quorum sensing signal [60].
• Structural analogues of autoinducers: Molecules that mimic autoinducers can competitively inhibit receptor binding, blocking signal transduction [61].
• Natural compounds: Certain plant extracts and microbial metabolites have been identified with quorum sensing inhibitory activity [59]. These compounds offer potential as novel therapeutics with reduced risk of promoting resistance [59].
• Antagonists of quorum sensing receptors: Designing molecules that bind to quorum sensing receptors without activating them can prevent signal transduction [62].

Challenges in developing QSIs include ensuring specificity to target pathogens without affecting beneficial microbiota, minimizing toxicity to the host, and overcoming the potential for bacteria to develop resistance to QSIs themselves [61]. Additionally, understanding the complex interplay between quorum sensing and other regulatory networks is essential for the effective application of QSIs [60,61].

Research into quorum sensing continues to reveal insights into bacterial behavior and resistance mechanisms [62]. By exploiting vulnerabilities in bacterial communication systems, new therapeutic avenues may emerge to address the growing challenge of antibiotic resistance in the ICU and beyond [62].

Table 2. Mechanisms of antibiotic resistance development and mitigation strategies in ICU microbiomes.

Mechanism	Description	Key Features	Relevance in ICU	Mitigation Strategies
Horizontal Gene Transfer (HGT)	Transfer of genetic material between bacteria without reproduction. Includes transformation, transduction, and conjugation.	- Transformation: Uptake of free DNA - Transduction: DNA transfer via bacteriophages - Conjugation: Plasmid transfer via pili	Dense microbial populations and biofilms promote HGT. Antibiotic pressure in ICU increases selective survival of resistant bacteria.	Antibiotic stewardship, infection control, agents targeting gene transfer mechanisms.
Biofilm Formation	Structured bacterial communities embedded in extracellular polymeric substance (EPS) matrix.	- Reduced antibiotic penetration - Heterogeneous microenvironments - Enhanced HGT - Immune evasion	Biofilms form on medical devices (catheters, ventilators), leading to device-associated infections and resistance.	Anti-fouling materials, biofilm-disrupting agents, quorum sensing inhibitors, enzymatic degradation of biofilm components.
Quorum Sensing (QS)	Bacterial communication system regulating gene expression based on population density via autoinducers.	- Controls biofilm formation, virulence factors, and resistance mechanisms - Modulates efflux pumps and metabolic responses	High bacterial densities in ICU intensify quorum sensing activities, influencing resistance and virulence traits.	Quorum sensing inhibitors (QSIs), enzymatic degradation of signaling molecules, structural analogues of autoinducers, antagonists of quorum sensing receptors.

summarizes the mechanisms of resistance development, highlighting their characteristics, ICU relevance, and possible strategies for mitigation. Figure 1 illustrates the interconnected mechanisms contributing to antibiotic resistance development in ICU microbiomes.

4. The ICU Environment as a Catalyst for Resistance

4.1. High Antibiotic Usage

ICUs have the highest rates of antibiotic consumption within hospitals, often involving broad-spectrum agents [63]. Critically ill patients in ICUs are particularly vulnerable to infections due to their compromised health status, necessitating prompt and often aggressive antimicrobial therapy [63]. Broad-spectrum antibiotics are frequently used empirically to cover a wide range of potential pathogens while awaiting microbiological confirmation [63]. This practice, while lifesaving, can inadvertently promote the emergence and selection of antibiotic-resistant organisms [63].

This extensive use creates selective pressure favoring resistant organisms [64]. When antibiotics are administered, susceptible bacterial populations are eliminated, reducing competition and allowing resistant strains to flourish [65]. The repeated and prolonged use of antibiotics, especially broad-spectrum agents, intensifies this selective pressure [64]. In the ICU, where patients may receive multiple courses of antibiotics, the microbiome can become dominated by MDROs [65].

Moreover, the lack of rapid diagnostic tools often leads to the overprescription of antibiotics [63,64]. Clinicians may prescribe antibiotics preemptively to mitigate the risk of infection, contributing to unnecessary exposure [66,67]. The empirical use of antibiotics without specific pathogen identification can result in inappropriate therapy, further driving resistance [64]. Antibiotic stewardship programs aim to optimize antibiotic use by promoting appropriate selection, dosing, and duration of therapy, but their implementation in ICUs can be challenging due to the urgency of patient care [65].

The high density of antibiotic usage in ICUs also affects the environmental microbiota [66,67]. Antibiotics excreted by patients can persist in the environment, affecting microbial communities on surfaces and equipment [68]. This environmental reservoir of resistance can facilitate the transmission of resistant bacteria to other patients and healthcare workers [69]. The interconnectedness of patients in the ICU through shared spaces and equipment amplifies the risk of spreading resistant organisms [70].

Furthermore, antibiotic pressure can induce genetic changes in bacteria, such as mutations and the activation of mobile genetic elements carrying resistance genes [71]. Stress responses to antibiotics can trigger mechanisms like the SOS response, increasing mutation rates and promoting the acquisition of resistance [72]. HGT is also enhanced under antibiotic pressure, as bacteria seek to acquire genes that confer survival advantages [44].

Addressing high antibiotic usage in ICUs requires a multifaceted approach [49,50]. Implementing rapid diagnostic tests can help tailor antibiotic therapy more precisely, reducing unnecessary exposure [73]. Enhancing antibiotic stewardship programs with ICU-specific guidelines and involving multidisciplinary teams can improve prescribing practices [49,65]. Education of healthcare providers about the risks of overuse and the importance of antimicrobial stewardship is essential [65,74]. Monitoring antibiotic consumption and resistance patterns can inform policy changes and promote responsible use [64,74,75].

4.2. Patient Susceptibility

Critically ill patients are more susceptible to infections due to weakened immune systems, invasive devices, and prolonged hospital stays [76,77]. Underlying medical conditions, such as sepsis, organ failure, and trauma, can impair immune function, reducing the body’s ability to fight off infections [78]. Treatments administered in the ICU, including immunosuppressive drugs, further compromise immune defenses [76]. As a result, patients become more prone to colonization and infection by opportunistic and resistant bacteria [77,78].

Invasive devices are ubiquitous in the ICU setting and are essential for patient monitoring and support [79]. Devices, such as central venous catheters, urinary catheters, endotracheal tubes, and mechanical ventilators, provide direct access for bacteria to enter sterile body sites [80]. Biofilms can develop on these devices, sheltering bacteria from antibiotics and immune responses [50,51,52]. Biofilm-associated bacteria exhibit increased resistance and can serve as persistent sources of infection [52].

Prolonged hospital stays in the ICU increase exposure to the hospital environment and its microbial flora, including MDROs [80,81]. The longer a patient remains in the ICU, the higher the risk of acquiring a healthcare-associated infection (HAI) [79]. Frequent interactions with healthcare workers, exposure to other infected patients, and the need for multiple invasive procedures compound this risk [76,77]. Nutritional deficiencies, stress, and immobility further weaken patients, making them more vulnerable to infections [76].

These factors increase the risk of colonization and infection by resistant bacteria [66]. Colonization often precedes infection, with resistant bacteria residing on the skin, mucous membranes, or within the gastrointestinal tract [79]. Disruptions to the normal microbiota, such as those caused by antibiotic therapy, can facilitate colonization by resistant organisms [80]. Once colonized, patients are at increased risk of developing serious infections, including bloodstream infections, pneumonia, and surgical site infections caused by MDROs [80].

The management of infections in these patients is complicated by the limited therapeutic options due to resistance [73]. Treatment failures can occur when standard antibiotics are ineffective against resistant strains, leading to prolonged illness and increased mortality [66,67]. Additionally, the pharmacokinetics of antibiotics can be altered in critically ill patients, affecting drug distribution and efficacy [68].

Preventing infections in ICU patients involves strict adherence to infection control practices [76]. Hand hygiene, the use of personal protective equipment, and environmental cleaning are fundamental measures to reduce the transmission of resistant bacteria [75]. Careful management of invasive devices, including adherence to insertion and maintenance protocols, can minimize the risk of device-associated infections [49]. The early removal of unnecessary devices reduces exposure time [65].

Surveillance cultures and screening for colonization by resistant organisms can help identify at-risk patients [49,65,75]. Isolation precautions may be implemented to prevent the spread of MDROs [75]. Antimicrobial stewardship programs play a role in minimizing unnecessary antibiotic exposure, preserving the effectiveness of existing agents [64,66]. Multidisciplinary collaboration among healthcare providers is essential to address the complex needs of critically ill patients [75].

4.3. Environmental Factors

The ICU environment, including surfaces, equipment, and air quality, contributes to the persistence and transmission of resistant microbes [69]. High-touch surfaces, such as bed rails, doorknobs, keyboards, and medical equipment, can become contaminated with pathogens shed by patients or transferred by healthcare workers [80]. Environmental surfaces can harbor bacteria, like MRSA, Clostridioides difficile, and Acinetobacter baumannii, which can survive for extended periods [81].

Inadequate cleaning and disinfection practices can exacerbate this issue [80]. If cleaning protocols are insufficient or not properly followed, residual contamination can persist [80]. Factors contributing to inadequate cleaning include time constraints, a lack of training, and failure to adhere to recommended disinfectant contact times [81]. The use of shared equipment without proper disinfection between patients can facilitate cross-transmission of resistant organisms [80,81].

The ICU’s complex layout and the presence of numerous devices create challenges for effective environmental cleaning [80]. Areas that are difficult to reach or are overlooked can serve as reservoirs for bacteria [81]. Biofilms can form on surfaces and within equipment, like ventilator tubing and humidifiers, providing a protective niche for bacteria and increasing resistance to disinfectants [80]. Regular maintenance and replacement of equipment parts are necessary to mitigate this risk [81].

The air quality in the ICU can also influence the transmission of resistant microbes [82]. Aerosol-generating procedures, such as intubation and bronchoscopy, can disperse pathogens into the air [82]. Inadequate ventilation systems may fail to remove contaminated air effectively, increasing the potential for airborne transmission [80]. Airborne pathogens can settle on surfaces or be inhaled by patients and staff, leading to infections [81].

Addressing environmental factors requires a comprehensive approach [41]. Implementing strict cleaning and disinfection protocols, using effective disinfectants, and ensuring compliance through staff training and monitoring are essential steps [49]. The adoption of new technologies, such as ultraviolet (UV) light and hydrogen peroxide vapor systems, can enhance disinfection efforts [83]. These technologies can reduce environmental contamination by targeting areas that are difficult to clean manually [80,81,83].

Regular auditing of cleaning practices and feedback to staff can improve compliance and effectiveness [84]. Encouraging a culture of safety and accountability among healthcare workers promotes adherence to infection control measures [84]. The design of the ICU environment can also be optimized to facilitate cleaning, such as using materials that are easy to disinfect and reducing clutter [49].

Preventing the transmission of resistant microbes from the environment to patients involves not only environmental cleaning but also hand hygiene and the use of personal protective equipment [84,85]. Healthcare workers play a critical role in breaking the chain of transmission by following recommended practices [84,85]. Education and training programs can reinforce the importance of these measures and keep staff updated on best practices [80].

Table 3. Key factors influencing antibiotic resistance in ICUs and mitigation strategies.

Factor	Description	Mitigation Strategies
High Antibiotic Usage	Broad-spectrum antibiotics frequently used empirically; creates selective pressure favoring resistant organisms.	- Implement rapid diagnostic tests to tailor antibiotic therapy. - Enhance antibiotic stewardship programs. - Educate healthcare providers about antimicrobial stewardship. - Monitor antibiotic consumption and resistance patterns.
Patient Susceptibility	Critically ill patients have weakened immune systems, often requiring invasive devices, leading to higher infection risk.	- Adhere to strict infection control practices. - Carefully manage and remove invasive devices early. - Conduct surveillance cultures for colonization. - Implement antimicrobial stewardship programs.
Environmental Factors	Contaminated surfaces, inadequate cleaning, shared equipment, biofilm formation on devices, and poor air quality contribute to the spread of resistant microbes.	- Use effective cleaning protocols and disinfectants. - Adopt UV light and hydrogen peroxide vapor for disinfection. - Ensure regular maintenance of equipment. - Optimize ICU design for easier cleaning and reduce environmental risks.
Antibiotic Pressure	Promotes genetic changes (mutations, HGT) in bacteria and encourages MDRO proliferation.	- Minimize unnecessary antibiotic use. - Target antibiotic therapy based on pathogen identification. - Develop ICU-specific antibiotic guidelines.
Healthcare Worker Role	Cross-transmission due to inadequate hand hygiene and improper use of personal protective equipment.	- Reinforce hand hygiene and PPE use. - Provide regular training and education on infection control. - Encourage accountability and compliance with protocols.
Biofilm Formation	Persistent bacterial communities on medical devices shield bacteria from antibiotics and host defenses.	- Use anti-fouling materials on devices. - Develop biofilm-disrupting agents (e.g., enzymes, quorum sensing inhibitors). - Strictly follow device insertion and maintenance protocols.
Environmental Reservoir of Resistance	Antibiotics excreted by patients affect microbial communities on surfaces, enhancing resistance spread.	- Improve waste management and environmental hygiene. - Audit and refine cleaning protocols to prevent contamination.
Delayed Pathogen Identification	Empirical antibiotic therapy without confirmation risks inappropriate treatment.	- Deploy rapid diagnostic tools for timely pathogen detection. - Train clinicians in interpreting diagnostic results effectively.

ICU: intensive care unit, HGT: horizontal gene transfer, MDRO: multidrug resistant organisms, PPE: personal protective equipment.

summarizes the factors contributing to antibiotic resistance in ICUs and suggests comprehensive strategies to address them. Figure 2 provides a comprehensive framework of the interrelated factors that contribute to antibiotic resistance development and spread within the ICU environment. The diagram emphasizes the multifaceted nature of antibiotic resistance in the ICU setting and illustrates how patient vulnerability, healthcare practices, environmental conditions, and broader systemic factors interact to influence resistance patterns. This visualization supports a systems-thinking approach to understanding and addressing antibiotic resistance in critical care environments.

5. Machine Learning Applications

5.1. Predicting Antibiotic Resistance Patterns

Machine learning algorithms can analyze genomic and microbiome data to predict resistance patterns, aiding in the selection of effective antibiotics [13,14,86]. The growing challenge of antibiotic resistance necessitates innovative approaches to rapidly and accurately identify resistant organisms to guide appropriate therapy [7]. Traditional microbiological methods for detecting antibiotic resistance, such as culture and susceptibility testing, are time-consuming and may delay critical treatment decisions in the ICU setting [7,86]. Machine learning offers a promising solution by processing vast amounts of genomic and phenotypic data to predict resistance profiles efficiently [7,13,14,86].

Models such as random forests and neural networks have shown promise in this area [87,88,89]. Random forests are ensemble learning methods that construct multiple decision trees during training and output the mode of the classes for classification tasks [87,88,89]. They handle large datasets with high dimensionality and can model complex interactions between variables without overfitting [88]. In antibiotic resistance prediction, random forests can analyze patterns in genomic data, such as the presence of specific resistance genes or mutations, to classify bacterial isolates as resistant or susceptible [7,13].

Neural networks, particularly deep learning models, are also effective in predicting antibiotic resistance [88,89,90]. These models consist of multiple layers of interconnected nodes (neurons) that can learn hierarchical representations of data [91]. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are types of neural networks that have been applied to sequence data and time-series data, respectively [92]. By training on large datasets of bacterial genomes with known resistance profiles, neural networks can learn to recognize patterns associated with resistance mechanisms [91].

Machine learning models can integrate various types of data to enhance prediction accuracy [7,13]. For example, combining genomic data with clinical metadata, such as patient demographics, antibiotic usage history, and infection sources, can improve model performance [7,13,91,92]. Incorporating microbiome composition data can also provide insights into the microbial community’s role in resistance development and transmission [92].

Predictive models can assist clinicians in selecting the most effective antibiotics, thereby reducing the use of broad-spectrum agents and limiting the spread of resistance [87,88]. For instance, a ML model could predict that a patient’s infection is likely resistant to a particular antibiotic based on the genomic profile of the causative organism, prompting the selection of an alternative therapy [86]. This personalized approach to antibiotic selection aligns with the principles of precision medicine [86].

Several studies have demonstrated the utility of ML in predicting antibiotic resistance [87,88,89]. Beaudoin et al. developed a clinical prediction model using ML algorithms to identify patients at risk of bloodstream infections with resistant organisms [86]. Their model incorporated variables such as prior antibiotic use, comorbidities, and laboratory results, achieving high sensitivity and specificity [87]. Such models can support early intervention and improve patient outcomes [87]. Feretzakis et al. highlighted the potential of ML algorithms to predict antibiotic susceptibility using readily available microbiological data, offering a valuable tool to enhance empiric antibiotic selection and address AMR in critical care settings [88]. In a subsequent study, the authors further emphasized the utility of automated ML (AutoML) techniques, demonstrating their capability to predict antibiotic susceptibility with high accuracy using demographic and microbiological data without relying on clinical information. The application of stack ensemble methods achieved robust predictive performance, underscoring the potential of advanced computational tools to enhance empirical antibiotic selection and address AMR challenges, even in resource-limited settings [89]. In another study, ML algorithms were applied to EHRs of hospitalized patients to predict antibiotic resistance profiles. The ensemble model achieved area under the receiver operating characteristic (auROC) scores ranging from 0.73 to 0.79 without bacterial species information, and from 0.8 to 0.88 when such information was included [93]. These predictions can assist clinicians in selecting appropriate empiric antibiotic therapies, potentially reducing misuse and improving patient outcomes. A decision algorithm based on ML models predicted antibiotic susceptibility in patients with uncomplicated urinary tract infections (UTIs) [94]. When compared to clinician prescription recommendations, this approach reduced the use of second-line antibiotics, thereby promoting more effective and judicious antibiotic use. These real-world examples highlight the potential for ML to be successfully deployed to enhance antimicrobial stewardship, shorten hospital stay, and reduce treatment failure. However, systematic prospective validation across diverse settings remains a priority.

Common evaluation metrics for these predictive models include accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUROC). Where the focus is on rapid clinical decisions, balanced metrics, such as the F1-score or Matthews correlation coefficient (MCC), are crucial to account for the class imbalance in ICU settings where certain resistant phenotypes are relatively rare.

Despite the potential benefits, challenges exist in implementing ML models for resistance prediction [12,13,14,87,88,89,91,92]. Data quality and availability are critical, as models require large, diverse datasets for training [13]. Genomic data must be accurately annotated, and phenotypic resistance profiles need to be reliably determined [86]. Additionally, models must be validated across different populations and settings to ensure generalizability [92]. The models described in this review predominantly rely on publicly available datasets (e.g., NCBI Sequence Read Archive) and ICU-specific patient cohorts from prior studies [88,89]. Validation typically involves train/test splits and, when feasible, external validation against independent ICU cohorts. This approach aims to ensure reproducibility across varied clinical settings.

The interpretability of ML models is another important consideration [95,96]. Clinicians may be hesitant to rely on “black box” algorithms without understanding how predictions are made [96,97]. Efforts to develop explainable AI (XAI) aim to provide transparency by highlighting which features contribute most to predictions [89,97]. This can increase trust in the models and facilitate their integration into clinical workflows [91,97].

Ethical and legal issues related to patient data privacy must also be addressed [98,99,100]. Compliance with regulations, such as the General Data Protection Regulation (GDPR), is essential when handling sensitive genomic and health information [99]. Secure data storage and processing protocols are necessary to protect patient confidentiality [98,100].

In conclusion, ML holds significant promise for predicting antibiotic resistance patterns [91,92]. By leveraging advanced algorithms and integrating diverse data types, these models can support more effective antibiotic stewardship and improve patient care in the ICU and beyond [7,87,92]. Ongoing research and collaboration between data scientists, microbiologists, and clinicians are crucial to realize this potential fully [7,13,91].

5.2. Analyzing Microbiome Data

Advanced computational tools handle high-dimensional microbiome datasets, identifying key microbial species and interactions associated with resistance [101,102,103]. The human microbiome is a complex and dynamic ecosystem comprising trillions of microorganisms, including bacteria, viruses, fungi, and archaea [104,105]. Understanding the role of the microbiome in antibiotic resistance requires analyzing vast amounts of metagenomic sequencing data, which presents significant computational challenges [106].

Techniques like metagenomic sequencing and network analysis facilitate this process [107]. Metagenomic sequencing allows researchers to study genetic material recovered directly from environmental samples, providing insights into the microbial community’s composition and function without the need for culturing organisms [107]. However, the resulting datasets are enormous, often containing millions of sequences that need to be processed and interpreted [104].

Machine learning algorithms can process these high-dimensional datasets to identify patterns and associations that may not be apparent through traditional analysis [7,12]. Unsupervised learning methods, such as clustering algorithms, can group similar microbial communities or genes based on their characteristics, revealing underlying structures in the data [13]. Supervised learning methods can be used to predict outcomes, such as the presence of antibiotic resistance, based on microbiome features [92,108].

Network analysis is particularly useful in understanding the interactions between different microbial species and their collective impact on antibiotic resistance [107,108]. By constructing microbial interaction networks, researchers can identify keystone species that play crucial roles in maintaining community structure and function [109]. Disruptions to these keystone species, such as through antibiotic exposure, can have cascading effects on the microbiome, potentially leading to the proliferation of resistant organisms [16,110].

Techniques like co-occurrence analysis can reveal associations between microbial taxa and resistance genes [111]. For example, certain bacteria may consistently harbor specific ARGs, indicating a potential role in resistance dissemination [111]. Identifying these associations can inform targeted interventions to disrupt the spread of resistance [111].

Machine learning models can also predict functional profiles of microbial communities by analyzing gene abundance and expression data [112,113]. This functional metagenomics approach can uncover metabolic pathways and resistance mechanisms active within the microbiome [90]. For instance, an increase in genes related to efflux pumps or antibiotic-modifying enzymes may indicate an elevated risk of resistance [90].

Advanced models like deep learning neural networks can capture complex, nonlinear relationships in microbiome data [105]. These models can handle raw sequencing data, reducing the need for extensive preprocessing and feature engineering [105]. Deep learning has been applied to tasks such as taxonomic classification, resistance gene identification, and functional annotation [105,114].

Analyzing microbiome data with ML also supports the development of predictive models for patient outcomes [90,114]. By integrating microbiome profiles with clinical data, models can predict the susceptibility to infections, response to treatments, and risk of complications [90,115]. This personalized approach can enhance patient care by informing prophylactic strategies and optimizing therapy [115].

Challenges in this area include the need for large, high-quality datasets for model training and validation [90,104]. Variability in sample collection, sequencing methods, and data processing can introduce biases and limit comparability across studies [92]. Standardizing protocols and developing robust data sharing frameworks are essential to advance the field [90].

Computational resources and expertise are also critical, as analyzing microbiome data requires significant processing power and specialized knowledge in bioinformatics and ML [105]. Collaborations between microbiologists, data scientists, and clinicians can facilitate the translation of computational findings into practical applications [90,105].

Privacy concerns must be addressed when handling human microbiome data, as genetic information can be sensitive [90]. Ensuring compliance with ethical guidelines and regulations is necessary to protect participant confidentiality and maintain public trust [90].

In summary, ML plays a vital role in analyzing microbiome data to understand antibiotic resistance [90,105]. By identifying key microbial species and interactions, these computational tools can inform strategies to prevent and control resistance in the ICU and broader healthcare settings [90,105].

5.3. Identifying Novel Therapeutic Targets

Machine learning can uncover novel targets for therapeutic intervention by identifying critical nodes within microbial networks or pathways involved in resistance [116]. As antibiotic resistance continues to outpace the development of new antibiotics, innovative approaches are needed to discover effective treatments [116]. Machine learning offers a powerful means to analyze complex biological data and reveal insights that can guide drug discovery and development [103].

This approach supports the development of new antimicrobial agents or adjuvant therapies [103]. By analyzing genomic and proteomic data, ML models can predict essential genes and proteins in bacteria that are potential drug targets [116]. These targets are crucial for bacterial survival or pathogenicity, and inhibiting them could effectively eliminate the bacteria or reduce virulence [116].

For example, Maier et al. demonstrated the extensive impact of non-antibiotic drugs on human gut bacteria by analyzing interactions between drugs and microbial species [116]. Machine learning models can expand on such findings by predicting how different compounds affect microbial pathways, leading to the identification of repurposed drugs with antimicrobial properties [116].

Machine learning can also identify synergistic drug combinations that enhance efficacy or overcome resistance [103]. By modeling the interactions between various antibiotics and bacterial strains, algorithms can predict combinations that exhibit synergistic effects [103]. This can lead to the development of combination therapies that reduce the likelihood of resistance development and improve treatment outcomes [103].

Furthermore, ML can aid in the discovery of antimicrobial peptides (AMPs) and natural products with novel mechanisms of action [103]. By training models on databases of known AMPs, algorithms can predict new peptide sequences with antimicrobial activity [103]. These models can consider factors such as peptide structure, charge, hydrophobicity, and stability to optimize candidates for therapeutic use [103].

In addition to small molecules and peptides, ML can help identify phage therapy targets [116]. Bacteriophages, viruses that infect bacteria, offer a potential alternative to traditional antibiotics [116]. Machine learning models can analyze phage–host interactions to select phages effective against specific resistant bacteria [116]. This personalized approach can tailor treatments to individual patients’ infections.

Identifying critical nodes within microbial networks can reveal points of vulnerability in bacterial communication and biofilm formation [116]. Targeting quorum sensing pathways or biofilm-specific genes can disrupt bacterial coordination and enhance susceptibility to antibiotics [116]. Machine learning models can predict which genes or proteins are central to these processes, guiding the development of targeted inhibitors [116].

Challenges in using ML for therapeutic target identification include the complexity of biological systems and the need for comprehensive datasets [103,116]. Bacterial genomes and proteomes are vast, and interactions within microbial communities are intricate [117]. Accurate modeling requires high-quality data on gene function, expression patterns, and protein structures [117].

Ethical considerations also arise when manipulating microbial communities or developing therapies that could impact the microbiome [116]. Understanding the broader effects on human health and the environment is necessary to avoid unintended consequences [116].

In conclusion, ML provides valuable tools for identifying novel therapeutic targets to combat antibiotic resistance [103,116]. By uncovering critical components of bacterial survival and resistance mechanisms, these approaches can accelerate the development of new treatments and support efforts to manage resistant infections in the ICU and beyond.

Table 4. Machine learning applications in combating antibiotic resistance.

Application Area	Description	Techniques and Models Used	Challenges
Predicting Antibiotic Resistance	Analyze genomic and microbiome data to predict resistance patterns, aiding therapy selection.	Random forests, neural networks (CNNs, RNNs), stack ensembles, AutoML.	Data quality, model generalizability, interpretability, ethical concerns (data privacy, regulations).
Analyzing Microbiome Data	Process high-dimensional microbiome data to identify key species, interactions, and resistance mechanisms.	Clustering, network analysis, deep learning (taxonomic classification, resistance gene identification), functional metagenomics.	Standardization, computational demands, variability in sample collection, ensuring data privacy and ethical compliance.
Identifying Novel Therapeutic Targets	Discover critical nodes in microbial networks or resistance pathways for drug development.	Predictive modeling for essential genes/proteins, synergistic drug prediction, antimicrobial peptides (AMPs), phage therapy target identification, biofilm disruption pathways.	Complexity of biological systems, validation of predictions, need for extensive datasets, ethical concerns about environmental and health impacts.

CNNs: Convolutional Neural Networks, RNNs: Recurrent Neural Networks.

provides an overview of various ML applications used to combat antibiotic resistance.

6. Current Challenges and Limitations

6.1. Data Availability and Quality

High-quality, comprehensive datasets are essential for effective ML models, yet data scarcity and variability remain significant challenges [101,102]. Machine learning algorithms thrive on large volumes of accurate and diverse data to learn patterns and make reliable predictions. In the context of microbiome research and antibiotic resistance, acquiring such datasets is particularly challenging due to several factors.

Firstly, the complexity of microbiome data collection and analysis contributes to data scarcity. Collecting microbiome samples from ICU patients involves invasive procedures, strict ethical considerations, and coordination with critically ill individuals who may not be able to provide consent [27,70]. Additionally, longitudinal studies that track microbiome changes over time are resource-intensive and logistically difficult, leading to a limited availability of such datasets [117].

Secondly, variability in data arises from differences in sample collection methods, sequencing technologies, and data processing pipelines [118]. For instance, variations in DNA extraction protocols, primer selection for sequencing, and bioinformatics analyses can lead to discrepancies in microbial community profiles [117]. This heterogeneity makes it difficult to compare results across studies or to combine datasets for ML applications [118,119].

Thirdly, the human microbiome is highly individualized, influenced by genetics, the environment, diet, medications, and disease states [113]. ICU patients represent a particularly heterogeneous group due to diverse underlying conditions, treatments, and exposures [114]. Capturing this variability requires large, representative datasets that encompass different patient populations and clinical scenarios [119].

The standardization of data collection and reporting practices is needed to address these challenges. Establishing standardized protocols for sample collection, storage, sequencing, and data analysis can reduce variability and improve comparability across studies [117]. Initiatives like the Human Microbiome Project have highlighted the importance of such standardization, but widespread adoption remains limited [104]. Reproducibility is bolstered by standardized sampling and sequencing protocols, along with comprehensive metadata documentation (e.g., patient demographics, antibiotic regimens, and clinical outcomes). Additionally, the adoption of community-curated pipelines—such as QIIME or Mothur—and containerized workflows ensures consistent preprocessing across different laboratories, facilitating the cross-validation of results [120].

Moreover, there is a need for comprehensive metadata accompanying microbiome datasets [107]. Detailed clinical information, including patient demographics, antibiotic usage, comorbidities, and treatment outcomes, enhances the utility of the data for ML models [13]. However, collecting and sharing this information raises additional ethical and privacy concerns [13].

Data sharing platforms and collaborative networks can help overcome data scarcity [102,111]. By pooling resources and datasets, researchers can create larger, more diverse datasets suitable for ML applications [102,111]. However, issues related to data ownership, intellectual property, and confidentiality must be carefully managed [102].

Investment in data infrastructure and computational resources is also essential [102]. Handling large-scale microbiome and genomic data requires significant storage capacity and computational power [91]. Access to high-performance computing facilities and cloud-based platforms can support the processing and analysis of big data required for advanced ML models [91].

In summary, overcoming data availability and quality challenges is crucial for the successful application of ML in microbiome research and antibiotic resistance [104,121]. Collaborative efforts, standardization, and investment in data infrastructure are necessary to generate robust datasets that can drive meaningful insights and improvements in patient care [121,122].

6.2. Interpretability of Models

Complex models may lack transparency, making it difficult for clinicians to interpret results and apply them in practice. Machine learning algorithms, especially deep learning models, often operate as “black boxes,” providing accurate predictions without clear explanations of how decisions are made [96]. This opacity poses a significant barrier to clinical adoption, as clinicians are hesitant to rely on tools they do not fully understand or cannot explain to patients [97].

Developing interpretable models is crucial for clinical adoption [97]. Interpretability ensures that clinicians can comprehend the rationale behind a model’s predictions, increasing trust and facilitating integration into clinical decision-making [95,96,97].

Several approaches can enhance model interpretability:

• Use of explainable algorithms: Selecting algorithms that are inherently interpretable, such as decision trees or linear models, allows for a straightforward understanding of how input features contribute to the output [84]. While these models may be less powerful than complex neural networks, they offer transparency that is valuable in a clinical context [97].
• Model-agnostic explanation techniques: Methods like SHAP (Shapley Additive Explanations) values and LIME (Local Interpretable Model-Agnostic Explanations) provide insights into complex models by quantifying the contribution of each feature to a specific prediction [97]. These techniques enable clinicians to see which variables influenced the model’s decision, even in deep learning models [97].
• Visualization tools: Graphical representations of model outputs, such as heatmaps, feature importance plots, and decision pathways, can help clinicians grasp complex relationships within the data [97]. Visual tools make abstract concepts more tangible and can be integrated into user interfaces for clinical applications [97].
• Simplification of models: Distilling complex models into simpler approximations that retain key predictive capabilities can enhance interpretability [97]. For example, rule-based systems derived from ML models can provide actionable insights in a format familiar to clinicians [95].
• Education and training: Providing clinicians with training on ML concepts and the specific models used can bridge the knowledge gap [96]. Understanding the strengths, limitations, and appropriate contexts for using these tools empowers clinicians to make informed decisions [96].

Ensuring interpretability also aligns with ethical and regulatory considerations [96]. Healthcare providers have a duty to explain diagnostic and treatment decisions to patients, and relying on opaque algorithms undermines this responsibility [96]. Regulatory agencies, such as the Food and Drug Administration (FDA), increasingly emphasize the need for transparency and explainability in artificial intelligence applications in healthcare [96].

Balancing model complexity and interpretability is an ongoing challenge [97]. While complex models may offer higher accuracy, their lack of transparency can hinder clinical acceptance [97]. Research into developing models that are both accurate and interpretable is a critical area of focus. Techniques like attention mechanisms in neural networks aim to highlight relevant features, enhancing interpretability without sacrificing performance [97].

In conclusion, interpretability is a key factor in the successful implementation of ML models in clinical practice [95,96,97]. By prioritizing transparency and developing methods to elucidate model reasoning, researchers can build tools that clinicians trust and effectively integrate into patient care [95,96,97].

6.3. Ethical Considerations

Handling sensitive patient data raises ethical concerns regarding privacy, consent, and data security [98]. Microbiome and genomic data contain personal and potentially identifiable information, making the protection of patient confidentiality paramount [95]. Ethical considerations encompass the entire data lifecycle, from collection and storage to analysis and sharing [98].

Compliance with regulations like the General Data Protection Regulation (GDPR) is necessary to protect patient rights [99]. The GDPR in the European Union sets strict guidelines for the processing of personal data, emphasizing the need for lawful, fair, and transparent handling [99]. Key aspects include the following:

• Informed consent: Patients must provide explicit consent for their data to be collected and used for specific purposes. Consent forms should be clear and comprehensive, explaining the scope of data usage, potential risks, and the right to withdraw consent [99].
• Data minimization: Only data necessary for the intended purpose should be collected and processed [99]. Collecting excessive or irrelevant data increases the risk of breaches and infringes on privacy rights [99].
• Right to access and erasure: Patients have the right to access their data and request corrections or deletion under certain circumstances [99]. Systems must be in place to accommodate these requests promptly [99].
• Data security: Robust security measures are required to protect data from unauthorized access, alteration, or loss [99]. This includes technical safeguards, like encryption and secure servers, as well as organizational policies for data handling [99].
• Anonymization and pseudonymization: Techniques to remove or obscure personal identifiers reduce the risk of re-identification [98]. However, with genomic data, true anonymization is challenging, necessitating additional safeguards [98].

Data sharing policies must account for ethical obligations [98]. Agreements between institutions should specify the terms of data access, use, and protection. Oversight by ethics committees or institutional review boards ensures that data sharing complies with ethical standards and regulations [98].

Equity and justice are additional ethical considerations [98]. Research should strive to include diverse populations to ensure that findings are generalizable and benefits are distributed fairly. Excluding certain groups can exacerbate health disparities and limit the applicability of ML models [98].

Transparency and accountability are essential for maintaining public trust [99]. Open communication about data practices, including any breaches or incidents, reinforces a commitment to ethical standards [100]. Regular audits and compliance checks can verify that policies are being followed [100].

Table 5. Challenges and limitations in ML applications for antibiotic resistance.

Challenge	Details	Proposed Solutions
Data Availability and Quality	- Limited datasets due to invasive collection procedures, ethical considerations, and resource-intensive longitudinal studies	- Standardize protocols for data collection, storage, and analysis.
	- Variability in sample collection methods, sequencing technologies, and data processing	- Develop robust data-sharing frameworks while managing ownership and confidentiality.
	- High individual variability in microbiome profiles influenced by diverse factors like genetics and treatment conditions.	- Compile large, representative datasets across populations and clinical scenarios
Interpretability of Models	- Lack of transparency in complex models, particularly deep learning models (“black box” issue)	- Use inherently interpretable algorithms (e.g., decision trees, linear models).
	- Clinicians hesitant to trust tools they do not fully understand or cannot explain	- Provide education and training for clinicians on ML concepts and specific models
Ethical Considerations	- Privacy concerns with microbiome and genomic data due to potential identifiability	- Ensure compliance with GDPR and similar regulations
	- Risk of misuse or breaches when sharing data for scientific purposes	- Implement robust data security measures like encryption and secure servers
	- Inequities in data representation, leading to biased models and health disparities.	- Strive for inclusive data collection to ensure generalizability and equitable benefits.
Computational and Technical Barriers	- High resource demands for processing and storing large datasets.	- Invest in high-performance computing and cloud platforms.
	- Expertise required in bioinformatics and ML for microbiome data analysis	- Foster interdisciplinary collaboration among microbiologists, clinicians, and data scientists

summarizes the challenges and corresponding strategies to address limitations in applying ML to microbiome research and antibiotic resistance.

7. Future Perspectives

7.1. Integrating ML into Clinical Practice

As ML-driven predictions evolve, a key step is translating algorithms into real-time decision support tools that interface seamlessly with ICU electronic health records. Implementation challenges include data standardization across hospital systems, ensuring that model outputs are explainable (‘white-box’ approaches or interpretable AI), and establishing robust validation in multicenter prospective trials. When these barriers are overcome, ML could provide ICU staff with patient-specific antibiotic recommendations or alerts about high-risk microbiome shifts, facilitating timelier interventions. Bridging the gap between computational research and clinical application involves collaboration among clinicians, microbiologists, and data scientists [7]. Such interdisciplinary partnerships are crucial to ensure that ML tools address real-world clinical problems and are designed with end-user needs in mind [101]. Clinicians provide insights into patient care, workflows, and decision-making processes, while data scientists contribute expertise in algorithm development and data analysis [94]. Implementing user-friendly interfaces and decision-support systems can facilitate integration [13]. Tools must be accessible to clinicians who may not have advanced technical training [102].

Features that support integration include the following:

• Intuitive user interfaces: Simple and clear interfaces allow clinicians to interact with ML tools without extensive training [13,102]. Dashboards that display key information at a glance and allow for easy navigation enhance usability [102].
• EHR integration: Seamless integration with EHRs ensures that ML tools fit within existing workflows [101]. This reduces the need for duplicate data entry and minimizes disruption to clinical routines [101].
• Real-time analytics: Providing timely insights during patient care supports informed decision-making [91]. Real-time data processing and alerts can help clinicians respond promptly to emerging issues [91].
• Customization and flexibility: Allowing clinicians to adjust settings or parameters enables them to tailor tools to their specific needs and preferences [102]. Flexibility enhances adoption and satisfaction [102].

Training and support are essential components of successful integration [84]. Providing education on how to use ML tools, interpret outputs, and understand limitations empowers clinicians to leverage these technologies effectively [101]. Ongoing support and resources help address questions and facilitate continuous improvement [101].

Validation and regulation are critical for ensuring safety and efficacy [102]. Machine learning models must undergo rigorous testing, including clinical trials when appropriate, to demonstrate their value in patient care [102]. Regulatory bodies may require evidence of performance, risk assessments, and compliance with medical device regulations [102]. Meeting these standards is necessary for widespread adoption [100].

Addressing barriers to integration involves overcoming resistance to change [94]. Clinicians may be skeptical of new technologies, particularly if they perceive them as replacing clinical judgment or adding to workload [13,91,101]. Engaging clinicians early in the development process, demonstrating tangible benefits, and addressing concerns transparently can mitigate resistance [96].

Ethical and legal considerations must be addressed [100]. Ensuring patient privacy, data security, and compliance with regulations like GDPR is essential [95]. Policies and procedures should be in place to handle data responsibly and respond to any issues that arise [98,99].

7.2. Personalized Medicine Approaches

Leveraging individual microbiome profiles enables personalized antibiotic strategies, potentially improving outcomes and reducing resistance development [108,121,122]. Personalized medicine shapes treatment to the unique characteristics of each patient, considering genetic, environmental, and lifestyle factors [122,123,124]. In the context of antibiotic therapy, understanding a patient’s microbiome can inform the selection of antibiotics that are most effective and least disruptive to beneficial microbes [16,123]. Tools like the Microbiome Response Index (MiRIx) offer a systematic approach to predict microbiome susceptibility to specific antibiotics, aiding in the selection of targeted therapies while minimizing perturbations [125]. This index is a quantitative method developed to measure and predict the human microbiome’s response to specific antibiotics. It is designed to address the lack of systematic approaches to understanding how antibiotics perturb microbial communities. MiRIx integrates data on bacterial phenotypes and intrinsic antibiotic susceptibility, providing a numeric index for each antibiotic to quantify its impact on the microbiota [125].

Ongoing research aims to refine these approaches [114,125]. Studies are exploring how variations in the microbiome influence susceptibility to infections, response to antibiotics, and the risk of developing resistance [106,108,109]. For example, certain microbiome compositions may predispose patients to colonization by resistant organisms or impact the metabolism of antibiotics [126].

Machine learning models can analyze complex microbiome data to predict how patients might respond to specific interventions [112,114,121]. By identifying patterns and correlations within large datasets, these models can support clinicians in selecting the most appropriate therapy [112,114,121,127].

Implementing personalized medicine requires advancements in several areas:

• Rapid and accessible diagnostics: Developing point-of-care tests that can quickly profile a patient’s microbiome and resistance genes supports timely decision-making [128]. Technologies like metagenomic sequencing and microarray analysis are becoming more feasible for clinical use [113].
• Integration with clinical workflows: Personalized recommendations must fit within existing clinical practices [7]. Tools that seamlessly integrate with EHRs and provide actionable insights enhance adoption [13].
• Evidence-based guidelines: Clinical guidelines incorporating personalized medicine principles are needed to standardize practices. Research must demonstrate the effectiveness and safety of personalized approaches to gain acceptance [87].
• Cost-effectiveness: Assessing the economic impact of personalized medicine is important [129]. While personalized therapies may have higher upfront costs, they may reduce overall healthcare expenses by improving outcomes and reducing complications [129].

Ethical considerations include ensuring equitable access to personalized therapies [7,13,91,100]. There is a risk that such approaches could widen healthcare disparities if they are only accessible to certain populations [7,100]. Policies and initiatives that promote affordability and inclusion are necessary [100].

7.3. Policy Implications

Integrating microbiome insights and ML into ICU practices requires comprehensive policies that emphasize innovation, data sharing, stewardship, and global cooperation. These efforts can significantly mitigate the development and spread of antibiotic resistance in critical care settings.

The key policy implications include the following:

• Antimicrobial stewardship programs: Policies should encourage stewardship programs tailored to ICU settings [65]. These programs can leverage microbiome data and ML insights to ensure appropriate antibiotic use, minimize resistance, and optimize patient outcomes [91]. Guidelines for ICU-specific interventions can be developed to improve prescribing practices.
• Infection control standards: ICU-specific infection control policies should incorporate insights from microbiome studies to limit the spread of resistant organisms. This includes advanced sterilization protocols, isolation measures, and monitoring of microbiome composition in patients.
• Research and development incentives: Governments and funding institutions should incentivize research into microbiome–resistance interactions and the development of machine learning models [130,131]. Public–private partnerships could accelerate innovation in diagnostics and therapies.
• Data sharing and surveillance: Policies promoting data sharing across healthcare systems and research institutions are crucial. Establishing secure, standardized databases for microbiome and antibiotic resistance data can enhance machine learning applications and improve global surveillance of resistance trends [91].
• Global collaboration: Given the global nature of AMR, policies should align with international efforts such as those by the WHO [1,73,132,133]. Collaboration on research, funding, and standard-setting is necessary to combat resistance effectively.

8. Conclusions

Antibiotic resistance in ICU environments is heavily influenced by microbiome imbalances caused by frequent antibiotic use, invasive procedures, and underlying conditions. Microbial interactions, such as horizontal gene transfer and competition, play a key role in resistance development. ML provides powerful tools to analyze complex ICU microbiome data, uncovering patterns between microbial shifts, antibiotic exposure, and AMR. ML models can predict patient-specific microbiome responses to antibiotics, enabling targeted therapies that minimize disruptions to beneficial microbes and reduce AMR risks. Additionally, ML-driven insights can guide real-time decision-making and personalize antibiotic stewardship strategies. Future research should explore the role of specific taxa and resistance genes, leveraging longitudinal studies to track how ICU interventions impact resistance over time. Challenges such as dataset heterogeneity and clinical integration must be addressed to fully realize these tools’ potential. Our integrated approach details how ML can leverage specific ICU microbiome shifts and mechanistic insights (HGT, biofilms, quorum sensing) to inform targeted interventions. By linking computational predictions directly to clinically relevant outcomes, we propose a roadmap for personalized antibiotic stewardship in critical care settings.