Use of Artificial Intelligence in Device-Associated Infections in the Intensive Care Unit

INTRODUCTION

Intensive care units (ICUs) have evolved into technology-dense environments where invasive devices, such as central venous catheters (CVCs), urinary catheters, and mechanical ventilators, are essential for the survival of critically ill patients. However, the life-saving promise of these interventions is tempered by their recognized role as conduits for hospital-acquired infections (HAIs). Device-associated infections (DAIs) occur when a medical device is in place for at least 48 h and becomes the source or vector for infection, a risk amplified by the immune vulnerability of ICU patients and the frequent manipulation of devices. Ventilator-associated pneumonia (VAP) is pneumonia that develops in a patient who has been receiving invasive mechanical ventilation for more than 2 consecutive calendar days, with the day the ventilator was placed counted as day 1, and the ventilator still in place on the day of onset or the day before the infection event. The >2 days threshold is important because it distinguishes VAP from pneumonia that was present before ventilator use or that developed very early. Central line-associated bloodstream infection (CLABSI) is defined as a laboratory-confirmed bloodstream infection in a patient who had a central line in place for more than 2 calendar days. Catheter-associated urinary tract infection (CAUTI) is a urinary tract infection that occurs in a patient who had an indwelling urinary catheter in place for >48 h (2 calendar days).^[1-4]

The significance of DAIs is underscored by their clinical and economic impact: They account for a substantial proportion of hospital-acquired infections, prolonged ICU and hospital stays,increase costs, and fuel the emergence of multidrug-resistant organisms (MDROs). Regional and global trends indicate that DAIs remain a leading cause of avoidable morbidity and mortality in critically ill populations.^[1-3,5,6]

EPIDEMIOLOGY AND GLOBAL BURDEN

The prevalence and impact of DAIs vary internationally, reflecting differences in healthcare infrastructure, infection control practices, and resource availability. Recent data underscores the global footprint of the problem:

• In Europe, multicenter surveillance from 2021 revealed that 15.6% of ICU patients acquired at least one DAI during their stay. Pneumonia (10%), bloodstream infections (8%), and urinary tract infections (4%) were most prevalent^[1,2]

• Observational studies from India demonstrate rates of device-association as high as 95% among ICU-acquired infections, emphasizing the disproportionate burden in low- and middle-income countries (LMICs)^[2]

• Global multicenter data from the International Nosocomial Infection Control Consortium for 2013–2018 indicate pooled mean rates of 5.3 CLABSI per 1,000 central line-days, 11.5 VAP per 1,000 ventilator-days, and 3.2 CAUTI per 1,000 catheter-days. These rates are consistently higher in LMIC ICUs compared to high-income countries (HICs), reflecting differences in infrastructure, device handling practices, and infection prevention and control (IPC) program implementation.^[2]

Globally, out of every 100 patients in acute-care hospitals, seven in HICs and 15 in LMICs will acquire at least one healthcare-associated infection during their stay. The COVID-19 pandemic further exacerbated DAI rates, attributed to prolonged ICU admissions and resource constraints.^[2,6]

IMPACT ON OUTCOMES AND COSTS

DAIs unequivocally worsen patient outcomes:

• Mortality: Infected ICU patients have a mortality rate double that of non-infected counterparts (20–35% vs. <12%)^[2,3,5,6]

• Length of stay: Median ICU stay is consistently increased by 7–14 days, with total hospitalization extended by over 2 weeks in affected patients^[2,6]

• Costs: Each DAI episode increases healthcare costs by $15,000–$40,000 in HICs; ICU costs in India nearly double for affected patients.^[2]

These outcomes underscore a pressing mandate for effective prevention and management in every ICU environment.

MICROBIAL ETIOLOGY AND RESISTANCE PATTERNS

The pathogens responsible for DAIs are well characterized, although with some geographic variation. The microbial landscape of DAIs is dominated by a relatively narrow spectrum of pathogens – most notably Gram-negative bacilli and certain Gram-positive cocci – though fungi such as Candida spp. also play a role, particularly in CAUTI and CLABSI.^[1,7-9]

• VAP: Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae, and sometimes Staphylococcus aureus (including methicillin-resistant S. aureus [MRSA])^[1,10]

• CLABSI: Staphylococcus epidermidis and other coagulase-negative staphylococci, S. aureus, Enterococcus spp., and Candida spp.

• CAUTI: Escherichia coli, K. pneumoniae, Proteus spp., Enterococcus spp., and Candida spp.^[8,11]

Multidrug resistance (MDR): A mounting threat.

MDROs are the rule rather than the exception in ICUs.

Resistance patterns are alarming and worsening. Recent Indian ICU surveillance found carbapenem resistance in 85% of A. baumannii isolates and third-generation cephalosporin resistance in up to 60% of Klebsiella spp. P. aeruginosa is increasingly exhibiting resistance to both carbapenems and aminoglycosides. Over 80% of DAI pathogens in some settings now meet criteria for MDR.^[9,10,12,13]

The prevalence of resistance to first-line antimicrobials is substantial: resistance to ceftriaxone (88%), ceftazidime (80%), and levofloxacin (72%) is now common in Asia, leading to more complicated management and poorer patient outcomes.^[9,10,12-14] The overall MDR rate in ICUs can reach 85–96%, as documented in recent studies from both developed and developing regions.^[9,13,14]

The dissemination of mobile genetic elements carrying carbapenemase genes, such as NDM, KPC, and VIM, is accelerating the spread of resistance. Moreover, the ICU environment acts as a reservoir where antimicrobial pressure promotes selection of hardier, extensively drug-resistant, or even pan-resistant strains. These epidemiologic shifts complicate empiric therapy and underscore the importance of local antibiogram-guided treatment. The drivers for such resistance patterns include high baseline colonization pressures, frequent antibiotic use, lapses in hand hygiene, and the biofilm phenotype, which further impedes antimicrobial effectiveness.

Furthermore, there has been a rising fungal DAIs, particularly with Candida species, some of which are azole- and echinocandin resistant. Candida species is the most common cause of fungal HAI. Candida bloodstream infections followed by urinary tract infections are very common. True fungal VAP is uncommon compared with bacterial VAP. Most fungal isolates from endotracheal (ET) aspirates represent colonization and not invasive infection. However, in high-risk ICU patients, invasive fungal pneumonia can occur. Candida species remain the most common cause of fungal sepsis, but emerging pathogens such as Candida auris present a growing challenge due to MDR. Among other etiologies, Aspergillus fumigatus, Cryptococcus, and Mucorales can also lead to fungal sepsis. Blood culture, beta-D-glucan assay, galactomannan (for Aspergillus), polymerase chain reaction (PCR)-based techniques, imaging (computed tomography chest), and tissue biopsy (gold standard in many cases) are required to diagnose infections due to fungal species. Removal of CVC in candidemia and minimizing catheter duration is recommended. Early diagnosis, prompt antifungal therapy, source control, and strict infection prevention measures are essential to reduce mortality.^[15,16]

Viral infections, although less common than bacterial infections, may also cause VAP. Some common causes include Influenza virus(A), severe acute respiratory syndrome coronavirus 2, respiratory syncytial virus, human metapneumovirus, and herpesviruses (reactivation in ICU). They are often seen in immunocompromised patients, severe acute respiratory distress syndrome, and prolonged mechanical ventilation. Parvovirus B19 (B19V) plays a role as an indirect cause of morbidity in patients with medical devices. Rather than directly infecting the device, it complicates the postoperative course through severe systemic and hematological effects, often mimicking device malfunction. It may manifest in post-mechanical heart valve replacement as postoperative pancytopenia, recurrent severe aplastic anemia, and reticulocytopenia.^[17,18]

RISK FACTORS AND PATHOGENESIS

THE CENTRAL ROLE OF BIOFILMS

Biofilm formation is now recognized as the cornerstone of DAI pathogenesis and treatment difficulty.^{[8,9,15,21-23]}Biofilms are structured microbial communities encased in self-produced extracellular polymeric substances, adherent to device surfaces. Within hours of device insertion, microorganisms adhere to surfaces and begin producing extracellular polymeric substances, creating a matrix that anchors the biofilm to the device.

Biofilms also confer immune evasion advantages, impairing neutrophil penetration and complement activity. Clinically, this means that even prolonged antibiotic therapy often fails to eradicate infection unless the colonized device is removed. Common biofilm-forming DAI pathogens include S. epidermidis, P. aeruginosa, and Candida spp. Within biofilms, pathogens benefit from remarkable protection:

• Antibiotic resistance: Bacteria in biofilms exhibit resistance to antibiotics up to 1,000-fold greater than their planktonic counterparts. Even antibiotics effective in standard laboratory testing may be powerless against biofilm-residing organisms^[9,21-23]

• Immune evasion: This structure impedes antibiotic penetration, creates microenvironments with altered pH and oxygen levels, and harbors metabolically dormant “persister” cells with extraordinary tolerance to antimicrobial agents. The biofilm matrix blocks immune cell penetration and complement activation, allowing chronic infection and immune escape^[7,8,21]

• Recurrent and chronic infection: These features explain the notorious tendency of DAI pathogens to persist and relapse despite what appears to be adequate therapy – a challenge compounded by the often necessary step of device removal.^[7,22]

Biofilm-related infection has been most clearly demonstrated in CLABSI and VAP, where both Gram-positive (e.g., S. epidermidis, S. aureus) and Gram-negative (e.g., P. aeruginosa, K. pneumoniae) pathogens, as well as fungi (Candida spp.), are implicated.^[9,21-23]

CLINICAL IMPACT AND OUTCOMES

DAIs are not merely laboratory-defined complications; their full impact is reflected in clinical deterioration, adverse outcomes, and resource escalation.^{[2,3,5,6,9,13]}

• Increased mortality: Studies consistently report 20– 35% higher mortality in ICU patients who develop DAIs compared to those who do not. Advanced age, higher illness severity scores, and the presence of MDR pathogens exacerbate this risk^{[2,3,5,6,9,19]}

• Prolonged hospitalization: DAIs increase ICU stays by 7–14 days and total hospital stay by over 2 weeks; some studies in India demonstrate even greater lengthening of stay, bearing a direct relationship to infection duration and resistance pattern^[2,3,9]

• Economic burden: DAIs double hospital costs. In the United States, each CLABSI can incur over $45,000 in costs; In India, infected cases can see costs rise from INR 92,893 to INR 180,469. These expenses reflect prolonged device use, more complex antimicrobial regimens, repeat interventions, and management of complications^[2,3,9]

• Societal and systemic impact: DAIs reduce ICU bed availability, delay other critical admissions, and strain institutional budgets, compounding harm in health systems already under duress from pandemics or resource scarcity.^[6,13,23]

DIAGNOSTIC CHALLENGES

Diagnosing DAIs in ICU populations remains fraught with difficulty:

• Non-specific signs: Fever, leukocytosis, and elevated markers of inflammation overlap with other ICU illnesses such as trauma, acute pancreatitis, or the systemic inflammatory response to surgery^[2,7,8]

• Laboratory limitations: Conventional cultures require 24–72 h and have a significant false-negative rate – particularly in pre-treated patients or those with biofilm-centered infection^[7,8,21]

• Biofilm protection: Pathogens may remain sequestered in biofilms, escaping detection in blood or fluid cultures^[21-23]

• Rapid diagnostics: Newer molecular tools (multiplex PCR panels) and biomarkers (procalcitonin) can distinguish infection from colonization and hasten targeted therapy, but their accessibility remains limited outside major referral centers or high-income settings.^[20,22,23]

PREVENTION STRATEGIES AND BEST PRACTICES

Effective prevention of DAIs is possible through coordinated, multifaceted approaches: ^{[1,2,13,23,24]} Evidence-based prevention strategies for DAIs are well established and revolve around three pillars: Minimizing device use, ensuring aseptic insertion, and maintaining meticulous device care.

Educational initiatives have demonstrated significant improvements in IPC protocol adherence. Structured training programs targeting all ICU staff – medical, nursing, and ancillary – reduce infection rates and improve patient outcomes. Frequent, structured training of ICU staff – emphasizing hand hygiene, device handling, and infection control protocols – has proven to significantly increase compliance, decrease DAIs, and improve outcomes.^[1,2,24]

EVIDENCE-BASED BUNDLES

Standardized care bundles, such as those advocated by the Centers for Disease Control and Prevention and the World Health Organization, have proven efficacy in reducing CLABSI, CAUTI, and VAP rates. These bundles integrate practical, evidence-based interventions tailored to each device:

• CLABSI bundles: Meticulous hand hygiene, maximal sterile barrier precautions, chlorhexidine skin antisepsis, preference for subclavian access, and daily review for device removal

• CAUTI bundles: Limiting device use, maintaining closed systems, daily review for necessity, and strict aseptic technique

• VAP bundles: ^[1-6,23,25]

Components of the VAP bundle are,

• Elevation of the head of bed 30–45°

• Strict hand hygiene and ICU infection control

• Oral care with tooth-brushing and chlorhexidine

• Subglottic secretion drainage

• Minimize sedation and daily weaning trials

• Early enteral nutrition

• Change ventilator circuits when soiled

• Implement ICU surveillance and staff education.

Above reduces VAP rates more than individual measures alone. Implementation of bundles is associated with substantial reductions in infection rates, even in LMIC settings.

DAIs in the ICU have been summarized in Table 1^{.[1,2,4,8,10,15,19,25,26]}

SURVEILLANCE AND FEEDBACK

Real-time infection surveillance (active and passive) and regular reporting to ICU staff – alongside benchmarking against regional and international data – are essential for ongoing reduction in DAIs. Root cause analysis helps address outbreaks or persistent problem areas.^[2,6,13,23]

ANTIMICROBIAL STEWARDSHIP

Antimicrobial Stewardship Programs are crucial, aiming to limit unnecessary or prolonged antibiotic use. Early de-escalation, use of narrow-spectrum agents, and shortened courses when feasible limit the emergence and transmission of MDR organisms.^[13,23,24]

DEVICE INNOVATION

Technological advances have yielded silver-alloy-coated urinary catheters (reducing CAUTI by 32%), chlorhexidine/silver-sulfadiazine-coated central lines (reducing CLABSI by up to 44%), and antibiotic-impregnated ET tubes (reducing VAP incidence). Cost barriers limit widespread use in LMICs, but these tools are becoming more accessible over time.^[7,20,22,23]

CHALLENGES IN RESOURCE-LIMITED SETTINGS

ICUs in LMICs confront unique and intensified barriers:

• Hand hygiene compliance may be below 50% due to insufficient supplies or staffing^[2,6]

• Surveillance gaps: Only a minority of ICUs in LMICs maintain active tracking of HAIs, delaying response to clusters or outbreaks^[2,6,23]

• Lack of access to novel diagnostics and device coatings further undermines preventive efforts

• Inconsistent training and high staff turnover lead to protocol lapses.

Despite these challenges, the basics still drive success: investing in fundamental measures – hand hygiene, aseptic technique, regular device audits, surveillance, and patient selection – remains the most effective, scalable solution.^[2,13,24]

FUTURE DIRECTIONS

Future progress in DAI prevention and management will hinge on technological innovation, global collaboration, and sustained investment in IPC. Promising developments include affordable antimicrobial and antibiofilm device coatings, point-of-care rapid diagnostics, and artificial intelligence (AI)-driven predictive analytics capable of identifying infection risk before clinical onset. On a systems level, harmonization of surveillance methodologies and expansion of infection control networks in LMICs will be vital.

RAPID DIAGNOSTICS AND BIOMARKERS

Wider clinical use of multiplex PCR panels and validated biomarkers (procalcitonin, CRP, sTREM-1) holds promise for much faster diagnosis, prompting earlier, more targeted therapy and improved outcomes.^[20,22,23]

AI IN DAI

AI is getting attention as a tool to help prevent, find early, and manage DAIs in the ICU. Machine-learning algorithms can take kinds of data – the patient’s age, gender, other health problems, laboratory test results over time, and the length and type of invasive device used – and turn the data into a risk prediction for each patient. AI can give a risk prediction for VAP. AI can give a risk prediction for catheter-related bloodstream infections. Researchers built VAP-prediction models using large critical-care databases such as MIMIC. The VAP-prediction models show test performance with an AUC around 0.8–0.85. The VAP-prediction models also have sensitivity and higher specificity than the clinical pulmonary infection scores. The VAP-prediction models let clinicians check risk in a changing, updated-over-time way, rather than using a static look-back view. Recently, researchers used deep learning models such as the long short-term memory networks to build the time series models for the early VAP prediction. The PREDICT tool is an example of the deep learning model. In the deep learning models, the changes in the signs and the oxygenation parameters give the predictive signals. The deep learning models beat the machine learning methods, over the longer prediction times.

AI has used predictive capabilities to help build time clinical decision support systems. The time clinical decision support systems can prompt earlier preventive actions or diagnostic actions. The time clinical decision support systems can ask clinicians to assess device necessity. The time clinical decision support systems can ask clinicians to reinforce care bundles. The time clinical decision support systems can ask clinicians to order targeted microbiological investigations. The time clinical decision support systems can reduce delays in infection recognition.

In adult settings, automated electronic surveillance systems examine electronic medical records for predefined triggers. The automated electronic surveillance systems look for triggers related to CLABSI, CAUTI, and clinical deterioration. The automated electronic surveillance systems generate alerts. The automated electronic surveillance systems support bedside interventions. This shows that algorithmic surveillance can complement human monitoring efforts. Parallel advances in AI-based surveillance platforms extend the benefits to the infection-prevention domain. The AI-based surveillance platforms use data to watch for infections. Automated HAI surveillance systems use data and show high sensitivity and high specificity for CLABSI detection and, for CAUTI detection, compared with manual chart review. Automated HAI surveillance systems may thus lower the reporting burden and reduce the observer variability. AI can apply surveillance definitions such as NHSN criteria for CLABSI and CAUTI. AI does this with high accuracy when the inputs are clearly structured. AI can help make infection control workflows simpler, as long as humans keep oversight.

AI may also help to make prevention plans for DAIs. Following AI uses are being explored, i.e., AI using computer-vision and sensor-based systems to watch the hand hygiene opportunities, to watch the protective equipment use, and to watch if staff follow the insertion and maintenance bundles for the central lines and the ventilators, and AI giving feedback. Feedback from AI can cut down differences in how the processes are done and lower HAI risk. It has also been observed that AI-enhanced laboratory tools – such as convolutional neural networks applied to digital microscopy – reach over 90% accuracy. Though still under study, AI-enhanced laboratory tools may classify bacteria in Gram-stained blood culture specimens. AI-enhanced laboratory tools can cut the time from specimen positivity to organism identification. AI-enhanced laboratory tools may let doctors start therapy sooner for patients with suspected device-related sepsis. AI may also help in antimicrobial stewardship by bringing together the real-time infection risk estimates, the local antibiograms, and the patient-specific factors. AI can suggest focused first treatments and can find chances to step down treatment when the laboratory data become available. AI tools are still mostly being studied in the ICU work.

Clinical use of AI for DAI management is still limited. Researchers have built models and have tested those models in a single center or on old data sets. It has not been shown yet that AI for DAI management works well in ICUs with different patient mixes or with other data systems. The main barriers are that data are often missing or low quality in electronic health record structures. Underestimation of risk can cause recognition of only severe sepsis. Explainable AI approaches and close alignment with stewardship principles are essential. The stakes are high. Going forward, robust multicenter, external validation, prospective impact evaluations, and transparent reporting of model development and performance will be crucial to ensure that AI for DAIs in the ICU actually translates from promising proof-of-concept into safe, effective tools in the real world that genuinely improve patient outcomes and support ICU teams rather than just replacing clinical judgment.^[27-30]

CONCLUSION

DAIs in ICUs represent a preventable yet persistent threat despite notable advances in care. They are a powerful driver of patient harm, economic waste, and antimicrobial resistance. The solution lies in relentless adherence to detailed protocols, surveillance and feedback, regular staff education, antimicrobial stewardship, and, when possible, adoption of device innovations and rapid diagnostics. For ICUs in LMICs, robust implementation of the basics – hand hygiene, device checklists, and culture of quality improvement – offers the greatest dividends. As global collaboration grows, new technological advances and enhanced awareness offer hope that the burden of DAIs can be markedly reduced in the years ahead.