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Review

Polymicrobial Infections: A Comprehensive Review on Current Context, Diagnostic Bottlenecks and Future Directions

1
Molecular Biotechnology Laboratory, Department of Biotechnology, NIST University, Institute Park, Berhampur 761008, Odisha, India
2
Department of Microbiology, Indira Gandhi Government Medical College and Hospital (IGGMCH), Nagpur 440018, Maharashtra, India
3
Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Acta Microbiol. Hell. 2025, 70(4), 39; https://doi.org/10.3390/amh70040039
Submission received: 2 August 2025 / Revised: 22 September 2025 / Accepted: 9 October 2025 / Published: 14 October 2025

Abstract

Worldwide, polymicrobial infections (PMIs) account for an estimated 20–50% of severe clinical infection cases, with biofilm-associated and device-related infections reaching 60–80% in hospitalized patients. This review discusses the clinical burden of major infections in which PMIs are almost inevitable, such as diabetic foot infections, intra-abdominal infections, pneumonia, and biofilm-associated device infections. Globally, the PMI landscape is diverse; however, the Indian subcontinent is a PMI hotspot where high comorbidities, endemic antimicrobial resistance, and underdeveloped diagnostic capacity elevate the risks of poor outcomes. Existing diagnostic like culture-based methods, PCR panels, sequencing, and biomarker-based assays are constrained by sensitivity, turnaround times (TATs), and high costs. Vulnerable populations, particularly neonates, the elderly, immunocompromised patients, and socioeconomically marginalized groups, show case-fatality rates 2-fold higher than monomicrobial infections in similar settings. Emerging diagnostic solutions include CRISPR-based multiplex assays, artificial intelligence-based metagenomic platforms, and sensitive biosensors with point-of-care applicability. These technologies show potential in reducing the TAT (<2 h) with high accuracy (>95%). However, their translation to real-world settings depends critically on affordability, integration into healthcare pathways, and supportive policy. This will provide equitable diagnostic access, particularly in low- and middle-income countries (LMICs).

1. Introduction

Polymicrobial infections, characterized by the simultaneous presence of multiple microbial species at the site of infection, present significant challenges in clinical diagnostics and treatment [1,2]. These infections are increasingly recognized as critical contributors to morbidity and mortality globally, aggravated by their complex microbial interactions and the rising prevalence of antimicrobial resistance (AMR). Current diagnostic strategies often fall short in accurately identifying all pathogens involved, leading to diagnostic gaps that compromise clinical outcomes [3]. Recent epidemiological data underscore the clinical significance of polymicrobial infections and their impact on patient health worldwide. For example, hospital-acquired infections (HAIs), many of which are polymicrobial, affect nearly 2 million Americans annually with approximately 99,000 deaths, highlighting the grave consequences of these infections if not adequately diagnosed and treated [4]. AMR often goes together with PMIs, making it even harder to treat these diseases. Each year, more than a million people die from drug-resistant bacteria, and this number could rise to two million by 2050 if action is not taken [5]. These figures illustrate the urgent need for improved diagnostic approaches to support effective treatment decisions, minimize resistance development, and reduce mortality.
Traditional culture-dependent diagnostic methods, though foundational, exhibit critical limitations that contribute to diagnostic gaps in PMIs. These methods often suffer from low sensitivity, particularly for slow-growing, low-abundance, or unculturable pathogens, resulting in false negatives and incomplete pathogen profiles [3,6]. Culture-based techniques typically focus on a narrow spectrum of anticipated pathogens, overlooking potentially significant co-infecting organisms and their contributions to disease pathogenesis [6]. Furthermore, conventional antibiotic susceptibility testing (AST) may not accurately predict clinical outcomes in polymicrobial contexts due to complex microbial interactions and biofilm formation, complicating treatment strategies [6]. These shortcomings have propelled the exploration of advanced molecular and metagenomic techniques that promise greater sensitivity, specificity, and rapid turnaround times (TAT).
Emerging technologies such as metagenomic next-generation sequencing (mNGS) and broad-range polymerase chain reaction (BRPCR) offer promising solutions to overcome these diagnostic challenges. Unlike culture-based methods, metagenomics allows for unbiased, culture-independent identification of entire microbial communities, including bacteria, viruses, fungi, and parasites, within clinical samples [3,7]. This high-throughput approach can detect pathogens missed by conventional diagnostics, provide detailed taxonomic and resistance gene profiles, and deliver results within 24 h in some contexts [3]. However, these advanced diagnostics face hurdles such as variable sensitivity for certain pathogens, cost considerations, and the need for clinical validation and integration into existing workflows [7,8]. The future of diagnosing PMIs lies in refining these technologies, implementing diagnostic stewardship, and fostering multidisciplinary collaboration to translate molecular insights into improved patient care.

2. Materials and Methods

The information presented in the review has been acquired through literature search across Pubmed, Pubmed Central, Google Scholar and Scopus. The timeframe was restricted to last 05 years (2020–2025); in case of insufficient information, the timeframe was subsequently extended beyond last 5–10 years. The literature search was performed using combinations of the following keywords and Boolean operators to maximize relevance “polymicrobial infection” OR “co-infection” OR “mixed infection”, “biofilm-associated infections”, “antimicrobial resistance” OR “AMR”, “diagnostic challenges”, “CRISPR diagnostics”, “metagenomic sequencing”, “AI microbiology”, “microfluidics infection diagnosis”, “Indian subcontinent infections”, “low- and middle-income countries (LMICs)”, “hospital-acquired infections”, “co-infection and public health”, “diagnosis in microbiology and cost” and “emerging diagnostic technologies”. These terms were adapted for each database with slight modifications as necessary, alongside additional filters for human studies, and clinical studies.

3. Results

3.1. Status of PMIs: Brief Global Context and Indian Subcontinent Scenario

Epidemiological data show that PMIs account for over 50% of bacterial infections worldwide, which are often under-diagnosed. Conventional culture-based diagnostic methods tend to detect only fast-growing, dominant microbes, often missing other slow-growing, anaerobic, or hard-to-culture organisms [9,10]. The complex interplay between co-infecting microbes substantially alters disease pathophysiology, severity, and therapeutic response, heightening the risk of morbidity, prolonging hospitalization, and inflating healthcare costs.

3.1.1. The Global Context: A Ubiquitous and Underappreciated Burden

Globally, PMIs are an etiological cornerstone of both community-acquired and healthcare-associated infections (HAIs), presenting grave challenges for patient management and antimicrobial stewardship. The range/ proportions of infecting microbial groups vary based on the geographical region, demography and socioeconomic status. However, infection types are broadly similar. The most common ones are discussed below.
Intra-Abdominal Infections (IAIs)
PMIs account for over 80% of IAIs following gastrointestinal perforation, with Escherichia coli present in 60–70% and Bacteroides fragilis in 30–40% of cases. These combinations promote greater virulence, facilitate abscess formation, and complicate effective antimicrobial penetration making targeted therapy difficult and increasing failure rates [11,12].
Diabetic Foot Infections (DFIs)
DFIs are paradigmatic for chronic PMIs, with 60–80% involving mixed infections: Gram-positive cocci such as Staphylococcus aureus and Streptococcus spp., Gram-negative bacilli like Pseudomonas aeruginosa and Enterobacteriaceae, and obligate anaerobes. Polymicrobial biofilm formation leads to AMR, contributing to treatment failure rates that reach 30%, which in turn doubles the risk of amputation and results in recurring healthcare episodes [13,14].
Pneumonia/Respiratory Infections
Both community-acquired pneumonia (CAP) and ventilator-associated pneumonia (VAP) frequently have polymicrobial etiologies. In CAP, Streptococcus pneumoniae as a primary pathogen is often joined by Haemophilus influenzae, Moraxella catarrhalis, and S. aureus. VAP, particularly in the ICU, demonstrates even greater pathogen diversity: 40–70% of cases involve multidrug-resistant (MDR) Gram-negative organisms such as Acinetobacter baumannii and P. aeruginosa impose major threats in VAPs [15]. PMIs associated with them have a 15–25% higher ICU mortality compared to monomicrobial infections [16,17,18]. COVID-19 mortality increased over 50% owing to PMIs caused by an array of co-pathogens [19]. These co-pathogens included bacteria (S. pneumoniae, S. aureus, Klebsiella pneumoniae, Mycoplasma pneumoniae, Chlamydia pneumonia, Legionella pneumophila and A. baumannii); fungi (Candida species and Aspergillus flavus); and viruses [influenza, coronavirus, rhinovirus/enterovirus, parainfluenza, metapneumovirus, influenza B virus, poxvirus and human immunodeficiency virus (HIV)] [20,21].
Biofilm-Associated Infections
Infections from indwelling medical devices (catheters, prosthetic joints, heart valves) are fundamentally polymicrobial. Biofilm-embedded communities exhibit markedly increased AMR—as much as 10- to 1000-fold reduction in antibiotic effectiveness. K. pneumoniae, especially the hypervirulent strains, influences the mortality significantly in biofilm-associated PMIs [22]. Such infections frequently recur, with rates exceeding 20% despite aggressive treatment and often necessitate complex surgical management [13,23,24].
Cystic Fibrosis (CF) Lung Infections
In adults with CF, over 70% harbor chronic polymicrobial lung infections, where S. aureus and H. influenzae precede P. aeruginosa and other opportunists like Burkholderia cepacia complex and fungi (Aspergillus fumigatus). Such microbial consortia are closely linked to accelerated lung function decline, increased hospitalizations, and the emergence of difficult-to-treat resistant strains [25].
The most pressing challenges in global PMI management are the comprehensive and accurate identification of all causative pathogens and interpretation of their synergistic or antagonistic interactions in vivo. Traditional culture-based approaches can miss up to 30–40% of co-pathogens in polymicrobial samples, leading to suboptimal empiric therapy and worsened clinical outcomes [26]. The rise of AMR within PMIs is particularly alarming: polymicrobial biofilms accelerate horizontal gene transfer—of carbapenemases, ESBLs, and other resistance determinants—across taxa, complicating therapy and infection [27]. Recent studies show MDR pathogen prevalence in polymicrobial bloodstream infections surpasses 50% in ICU settings, emphasizing an urgent global need for rapid, multiplexed diagnostics and effective stewardship to improve outcomes [28].

3.1.2. The Indian Subcontinent: A “Hub” for PMIs

The Indian subcontinent (comprising India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan, and the Maldives) presents a unique and more complex scenario. A confluence of factors, including high population density, a tropical climate, variable sanitation standards, a high burden of malnutrition and co-morbidities, and an unprecedented level of AMR, magnify the PMI challenge.
High Burden of Community-Acquired Syndromes
Skin and soft tissue infections (SSTIs) are very common in India, with S. aureus causing about 62% of community-acquired cases. Of these, nearly 24% are due to community-associated methicillin-resistant S. aureus (CA-MRSA), which is multi-drug resistant [29]. Gram-negative bacteria, such as E. coli and Klebsiella pneumoniae, have a rising role in SSTIs, particularly in diabetic and immunocompromised populations [30,31]. Epidemiological studies show that the overall prevalence of purulent SSTIs ranges from 19% to 38% in major Indian cities [32]. Tropical fevers with secondary bacterial sepsis are a major challenge. Among children with acute febrile illnesses in India, 19.4% had laboratory-confirmed co-infections, most commonly scrub typhus and dengue, with bacterial sepsis (notably E. coli and K. pneumoniae) occurring in 5.6% and 0.6% of febrile cases, respectively [33,34]. The sepsis burden is epidemiologically significant: Gram-negative organisms accounted for 43.9% of isolates in septic patients, with high rates of third-generation cephalosporin and carbapenem resistance (e.g., E. coli 75.8% resistance to cephalosporins, 10.1% to carbapenems) [35].
Epicenter of AMR in HAIs
Hospital-acquired infections (HAIs) affect 10–20% of Indian inpatients, with ICU infection rates reaching 25% [36]. VAP, catheter-associated urinary tract infections (CAUTIs), and surgical site infections (SSIs) are predominantly caused by polymicrobial biofilms featuring carbapenem-resistant Enterobacteriaceae (CRE), A. baumannii, and P. aeruginosa. Recent surveillance found >50% prevalence of carbapenem-resistant Gram-negative bacilli in Indian ICUs, and pan-drug resistant strains in over 10% of critical care cases [17,37,38]. The New Delhi metallo-beta-lactamase (blaNDM-1) gene confers high resistance to almost all β-lactam antibiotics. It is found in 30–40% of clinical Enterobacteriaceae isolates in India—the highest rate globally. This gene can spread quickly between different bacterial species through plasmid exchange [39,40].
Chronic and Co-Morbid Infections
DFIs are a particular concern, given India’s unmatched diabetes burden. Gram-negative bacteria such as P. aeruginosa, A. baumannii are the most common pathogens, alongside various fungi. Alarmingly, about 14% of these isolates complicates treatment by showing resistance to powerful antibiotics like carbapenems and polymyxins [30,31,41]. These microbiological distributions are more complex than in most Western cohorts. Tuberculosis (TB) with co-infections is another salient issue. India holds the world’s highest TB caseload, and secondary infections in TB patients are widely underdiagnosed. In cases of chronic lung cavities, co-infections with P. aeruginosa, K. pneumoniae, and invasive Aspergillus species are frequently encountered, with Aspergillus-related chronic pulmonary aspergillosis estimated to affect tens of thousands of TB survivors annually [35]. These polymicrobial interactions worsen morbidity and mortality yet are rarely detected by standard diagnostic algorithms. Quantitative epidemiology reinforces the need for advanced, rapid, and context-specific diagnostics and stewardship policies to address this unique challenge [32,35,42]. Table 1 summarizes diagnostic implications of PMIs.

3.2. Current Diagnostic Approaches and Major Gaps

The accurate and timely diagnosis of PMIs remains challenging because most diagnostic tools were initially designed for monomicrobial infections, causing significant gaps in managing the complex microbial communities of PMIs. These gaps can be analyzed across key performance metrics: sampling, yield, TAT, operational complexity, and cost.

3.2.1. Culture-Based Methods

Microbial culture remains the gold standard in clinical microbiology, involving inoculation of clinical specimens onto selective and nutritive media, incubation, colony isolation, and identification via biochemical tests or mass spectrometry methods (e.g., MALDI-TOF MS). Phenotypic AST further informs targeted therapy [3,49].
Sample quality critically affects detection. Superficial swabs often reflect surface colonizers and fail to capture deep tissue anaerobes or Gram-negative pathogens in hypoxic environments such as diabetic foot ulcers [49,50]. Many pathogens are fastidious or obligate anaerobes and thus may not grow under standard culture conditions. The viable but non-culturable (VBNC) state of some bacteria and dominance of fast-growing organisms (e.g., P. aeruginosa) can mask co-infecting microbes, resulting in underrepresentation of true polymicrobial diversity [9,25,51]. Culture-based diagnosis typically requires at least 48–72 h for identification, with an additional 24–48 h for AST. This delay necessitates empirical broad-spectrum therapy, potentially promoting AMR [52,53]. Interpretation of polymicrobial cultures requires considerable expertise to distinguish pathogens from commensals and contaminants, making it labor-intensive and prone to variability. Direct costs per test are low, making culture accessible globally. However, indirect costs, including delayed diagnosis, prolonged hospitalization, and inappropriate therapy, are substantial [3,49].

3.2.2. Molecular and Immunological Techniques

Multiplex real-time PCR panels test for predefined pathogens (bacteria, viruses, fungi) rapidly, often within hours. 16S rRNA gene sequencing identifies a broad range of bacteria, including non-culturable species [49]. Unbiased sequencing reveals entire microbial communities, detecting bacteria, fungi, viruses, and resistance genes, offering comprehensive etiological insights [3]. Detection of DNA from dead microbes or colonizers complicates differentiating active infection from harmless presence, termed the “DNA ≠ life” problem. PCR panels yield results within 1–3 h; mNGS requires at least 24 h, though improvements are underway. PCR panels are increasingly automated; mNGS requires advanced bioinformatics expertise and complex workflows, limiting widespread use. High per-test costs restrict use in routine clinical practice, especially in LMICs [3,49,52].

3.2.3. Biomarkers and Their Limitations

Host-response biomarkers such as C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) indicate presence of infection by reflecting host inflammation but do not identify causative microorganisms [54]. Biomarkers cannot distinguish monomicrobial from PMIs or identify specific pathogens, limiting guidance for targeted therapy. Elevated biomarkers also occur in non-infectious inflammatory conditions (e.g., trauma, surgery), and early or localized infections may yield low levels, reducing diagnostic accuracy. Typically, rapid (under 1 h) on automated platforms, facilitating clinical decision-making, but biomarkers alone are insufficient for diagnosis [55,56].

3.3. Diagnostic Challenges in Vulnerable Populations

The diagnosis of PMIs is universally challenging; however, this complexity is markedly intensified in vulnerable populations due to unique physiological, immunological, socioeconomic, and healthcare system factors.

3.3.1. Children (Neonates and Pediatrics)

PMIs may present with non-specific and subtle symptoms including lethargy, poor feeding, irritability, or hypothermia, which complicates early clinical suspicion and delays diagnostic workup and treatment initiation. Practical limitations include inability to obtain sputum from infants, restricted blood volumes per draw due to safety and stress considerations, and the higher risk associated with invasive procedures like lumbar punctures or tissue biopsies [57,58]. These constraints limit the number and type of microbiological tests that can be performed, often forcing clinicians to prioritize tests, potentially compromising comprehensive detection of polymicrobial etiologies. Initial infections are frequently polymicrobial, often involving pathogens acquired from the maternal genital tract such as Group B Streptococcus and E. coli, or nosocomial organisms especially in neonatal intensive care settings. Viral infections, notably Respiratory Syncytial Virus (RSV), are common and predispose infants to secondary bacterial pneumonia. This creates complex viral-bacterial PMIs which are challenging to detect and differentiate using conventional diagnostic methods that are often optimized for monomicrobial infections [3,57,58,59].

3.3.2. The Elderly

The aging process is accompanied with immunosenescence, which adversely impacts every stage of the diagnostic pathway for PMIs. Similar to neonates, elderly patients frequently fail to manifest classical infection signs such as fever and leukocytosis. Immunosenescence diminishes febrile responses and reduces white blood cell activation. Severe PMIs like aspiration pneumonia or infected pressure ulcers may result in delirium, falls, functional deficits with movement or continence. These symptoms are not specific to infection and are often mistaken for issues like dementia or chronic illness. As a result, diagnosis and treatment are often delayed [60,61]. The elderly harbor high rates of chronic conditions such as diabetes mellitus, chronic obstructive pulmonary disease (COPD), congestive heart failure, and chronic kidney disease, which not only predispose to PMIs but also complicate clinical assessment as their symptoms overlap with infectious processes [61]. Polypharmacy is pervasive in this group, with many elderly patients on medications including corticosteroids, nonsteroidal anti-inflammatory drugs (NSAIDs), and other immunomodulators that blunt infection symptoms, notably fever [62,63]. This pharmacological masking further obscures timely recognition. The elderly disproportionately suffer from infections inherently polymicrobial in nature, such as aspiration pneumonia involving oral anaerobes, infected pressure ulcers, and catheter-associated urinary tract infections. Accurate diagnosis in these cases is particularly challenging but critical given the associated morbidity and mortality [60,61,62].

3.3.3. The Immunocompromised

Immunocompromised individuals—including those with HIV/AIDS, cancer patients undergoing chemotherapy, organ transplant recipients maintained on immunosuppressant regimens, and patients on long-term corticosteroids—face profound diagnostic challenges in PMIs due to severely impaired host defences [64]. Neutropenic patients, for example, may manifest disseminated, life-threatening PMIs with minimal clinical signs, complicating prompt diagnosis. Vulnerable hosts are susceptible to a broad range of pathogens, extending beyond typical bacteria to include fungi (e.g., Aspergillus, Candida, Pneumocystis jirovecii), viruses (cytomegalovirus [CMV], Epstein–Barr virus [EBV]), and parasites that are normally non-pathogenic in immunocompetent individuals. These infections are frequently polymicrobial, with unusual pathogen combinations occurring simultaneously [65,66]. Serologic methods reliant on host antibody responses often yield false negatives or ambiguous results due to impaired humoral immunity. This elevates the importance of direct pathogen detection methods such as PCR, antigen detection, and NGS, which must possess broad detection ranges and high sensitivity to identify unexpected or rare organisms [64].

3.3.4. Marginalized Populations and LMICs

The scarcity of advanced diagnostic platforms such as MALDI-TOF mass spectrometry and molecular sequencing confines these capabilities to a handful of central reference laboratories, rarely accessible to patients. Basic microbiology cultures are often impaired by intermittent electricity, unreliable reagent supply chains, and inadequate laboratory facilities. Thus, diagnostics appropriate for LMICs must be low-cost, robust to power fluctuations (battery-powered or electricity-free), temperature stable, and operationally simple [67,68,69]. There is a critical shortage of trained laboratory personnel, including microbiologists, pathologists, and skilled technicians, who are essential for performing and interpreting microbiological tests accurately. Consequently, tests requiring minimal hands-on time and providing clear, unambiguous results are urgently needed. Economic hardship, geographic barriers, and low health literacy often cause patients to present late during the infectious process when infections may be advanced or disseminated. These barriers also inhibit adherence to multi-step diagnostic and complex treatment regimens, limiting the feasibility of protracted laboratory workflows [70]. The pathogen spectrum and AMR profiles in LMICs vary markedly from those in high-income countries, where most diagnostics are developed and validated. Commercial syndromic PCR panels designed for North American or European pathogen spectra often overlook regionally endemic tropical pathogens and plausible co-infections common in Sub-Saharan Africa, South Asia, and Southeast Asia [71].

3.4. Emerging Methods to Detect Polymicrobial Infection

3.4.1. Molecular Assays (CRISPR-Based)

CRISPR-based molecular diagnostics are now demonstrating markedly improved sensitivity and TAT over conventional methods for the detection of PMIs [72]. A recent multicenter study from 2024 reported that multiplexed CRISPR-Cas assays, when applied to complex clinical samples, identified 19.4% of PMIs that were missed by traditional culture methods, which detected only 3.5%, representing a greater than fivefold increase in diagnostic yield [73]. Moreover, these CRISPR platforms—employing systems such as Cas12a and Cas13a—can detect pathogen DNA/RNA at concentrations as low as 10 copies/μL with reported assay times of 30–60 min, enabling almost real-time actionable results at the point of care and outpacing mNGS workflows that typically require 24–48 h [72,74]. The high throughput multiplexing capability allows simultaneous identification of diverse bacteria, fungi, and even resistance genes, supporting comprehensive diagnosis in scenarios with mixed microbial etiologies [75].

3.4.2. Microfluidic Chip-Based Approaches

Microfluidic chip-based diagnostic platforms have revolutionized the handling and analysis of clinical specimens for polymicrobial infection, enabling rapid, sensitive, and automated detection. State-of-the-art microfluidic systems can reliably detect bacterial DNA at concentrations as low as 10 copies/mL, with some devices delivering fully automated, multiplexed results from raw sample to species identification in 15–30 min [76,77]. In a groundbreaking 2024 proof-of-concept study, a single microfluidic assay was able to simultaneously analyze up to 20 different bacterial and fungal species, offering precise quantitative counts for each pathogen [78]. These devices increasingly incorporate real-time fluorescence or colorimetric output, facilitating both broad pathogen identification and immediate AST, which is crucial for timely therapy adjustments in polymicrobial cases [79].

3.4.3. Culturomics

Culturomics is a high-throughput culture-based technique that enables identification of bacterial species by optimizing diverse growth conditions, often revealing taxa missed by sequencing-based methods. When integrated with MALDI-TOF mass spectrometry, culturomics has accelerated discovery of clinically relevant bacteria, expanding mass spectrometer databases and allowing rapid species-level diagnosis for PMIs. In a large retrospective study, MALDI-TOF precisely identified 95.2% of isolates from specimens, underscoring how culturomics enlarges the known spectrum of pathogenic bacteria involved in infection [80].

3.4.4. Sequencing and AI/ML-Based Approaches

NGS, especially when combined with artificial intelligence (AI) and machine learning (ML), provides an unparalleled diagnostic window into the complexity of PMIs [81]. Recent benchmarking studies show that mNGS—analyzing millions of reads per sample—can reliably identify over 95% of clinically relevant pathogens present at >100 reads per million (rpm), even within heavily contaminated or mixed-microbe samples [82]. AI/ML-based algorithms are now able to process sequencing data and provide pathogen identification reports in under 10 min per patient sample (versus several hours for manual bioinformatic review), while delivering up to 10–15% greater precision in detecting low-abundance or co-infecting organisms. Furthermore, ML models exhibit >90% accuracy in predicting AMR profiles from genomic data, informing targeted therapy particularly in settings where multiple resistant species may coexist [81,82]. Table 2 consolidates some of the computational pipelines that can facilitate PMI diagnostics.

4. Discussion

Despite the persistent challenges posed by emerging pathogens causing PMIs, recent innovations in diagnostic technologies demonstrate significant promise. AI-driven mNGS in intensive care units has shown exceptional performance, with studies reporting sensitivity as high as 96.6% and specificity around 88% in pathogen identification for lower respiratory infections. This approach detects a broader range of pathogens, including anaerobes missed by conventional culture, enhancing diagnostic yield by up to 20% compared to traditional methods. Importantly, these rapid results (within 6–7 h) guided antimicrobial therapy changes in nearly 50% of cases, preventing complications and generating cost savings greater than $695,000 in single-centre evaluations despite per-test costs ranging from $100 to $300 [6,42,50,88].
Point-of-care microfluidic technologies have also made substantial strides, offering multiplex detection of up to 24 pathogens simultaneously within 20–30 min with detection limits as low as 10 copies/mL. These tests cost substantially less—between $10 and $50 per test—making them more viable for resource-limited settings. Several field studies demonstrated their feasibility and impact in improving clinical management where rapid decision-making is critical [50]. Complementarily, CRISPR-based assays enable ultra-rapid (<1 h) identification of MDR pathogens with minimal equipment, showing promise for decentralized deployment. However, challenges remain regarding reagent supply chains and training for sustainable implementation. From therapeutic perspective, especially for PMIs coupled with AMR, innovative strategies are the need of the hour. New strategies such as targeting novel bacterial ultrastructures like metallophores, which are critical for nutrient acquisition, and employing the ‘Trojan horse’ approach that exploits bacterial uptake systems to deliver antimicrobial agents selectively holds immense potential [89,90].
Nevertheless, widespread uptake of these advanced diagnostics, especially in LMICs, depends heavily on economic and infrastructural considerations. High capital and operational costs for NGS setups risk worsening healthcare inequities without policy support [3,53]. Meta-analyses suggest that integrating standardized sample processing and diagnostic thresholds can reduce false positives and negatives by 10–15%, translating into fewer unnecessary treatments and shorter hospital stays, which help offset initial expenditures over time [57]. Nevertheless, these state-of-the-art targeted solutions, especially involving MDR pathogens cost several hundred to over $1000 per treatment, with regulatory pathways undeveloped or inconsistent across regions. Sophisticated infrastructure and trained personnel are scarce, limiting deployment. Furthermore, ethical concerns around gene editing and data privacy remain unresolved. Addressing these requires streamlined regulatory frameworks, affordable scalable technologies, and intensive workforce training to enable safe, responsible use in resource-constrained settings [91]. Looking ahead, the future of PMI diagnostics hinges on achieving rapid (<1 h), and highly accurate (>95% sensitivity and specificity) testing integrated into diverse healthcare systems. Sustained investments in scalable AI-enhanced bioinformatics, cost-efficient, and user-friendly assays must be supported by clear regulatory pathways and equitable policies. This will ultimately reduce the burden of PMIs on patients and health systems alike [3,6,52,92].

5. Conclusions

PMIs represent a complex and underappreciated frontier in infectious disease management, with profound clinical, diagnostic, and public health implications. The present study focused on understanding the PMI landscape of the Indian subcontinent as compared to high-income countries. It was revealed how conventional diagnostics fall short in capturing their true diversity and impact—particularly in resource-limited settings where AMR, comorbidities, and health disparities converge. The impact on vulnerable populations was specifically discussed. The review also highlights potential solutions offered by emerging CRISPR-based assays, microfluidics, computational-platforms and AI-driven metagenomics. However, the same must be made accessible, affordable, and adaptable to regional epidemiology. A global shift toward integrated surveillance is essential to guide antimicrobial stewardship and mitigate the escalating burden of PMIs. This integration comprises molecular diagnosis, AMR monitoring, and local epidemiological data, enabling the early PMI detection, the best antimicrobial choice, and coordination of global response. This convergence will not only improve patient outcomes but also reduce the increasing global burden of AMR-related PMIs by encouraging evidence-based stewardship policies, reducing inappropriate use of antibiotics, and facilitating sustainable infection control practices.

Author Contributions

A.P. and T.K.: Data Curation, Writing—Original Draft Preparation. S.B.: Compilation, Visualization, Supervision, Writing—Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Comparative Analysis and Diagnostic Implications in polymicrobial infection globally and in Indian subcontinent.
Table 1. Comparative Analysis and Diagnostic Implications in polymicrobial infection globally and in Indian subcontinent.
FeatureGlobal Context (High-Income Countries)Indian Subcontinent ContextDiagnostic ImplicationReferences
Pathogen SpectrumPMIs: 15–25% with anaerobes/atypicals >30% polymicrobial complexityDiagnostics require better multiplex panels including tropical, resistant pathogens[3,35]
AMRMRSA prevalence: 1–5% community-acquiredCarbapenem-resistant Gram-negative pathogen in ICUs Rapid multiplex genotypic tests
(include beta-lactamase genes)
[3,35,43]
VRE prevalence: 3–7% in hospital settingsMDR strains in critical care; blaNDM-1 plasmid in Enterobacterales
Healthcare InfrastructureMALDI-TOF in most clinical labsCulture reliance in ~60% healthcare facilitiesNeed point-of-care testing: <$50/test, <1 h, multiplexed detection tailored to local epidemiology[5,6,44,45]
mNGS TAT: 24–48 h; high cost: (>$80/test)Limited accessibility to advanced diagnostics
Host FactorsObesity: ~30% adults; controlled diabetes: ~10%Malnutrition: up to 40% (rural)Diagnostics must incorporate host biomarkers to distinguish infection vs. colonization[13,46,47,48]
Uncontrolled diabetes: 15–20%; TB incidence
HIV prevalence: ~0.3%
Table 2. Computational pipelines (AI/ non-AI) to expedite polymicrobial infection detection.
Table 2. Computational pipelines (AI/ non-AI) to expedite polymicrobial infection detection.
Pipeline AI/ML BasedMain FeaturesSensitivitySource
IDseqNoCloud-based, mNGS data processing pipeline with host filtering, assembly-based alignment, taxonomic classification, reporting, and visualization.High sensitivity for microbial pathogen detection; supports divergent/novel virus detection.[83]
MetaCherchant/TBwDM PipelinePartiallyBioinformatics pipeline for detection of pathogens and AMR genes (ARGs) from polymicrobial simulated reads; marker-based confirmationHigh precision and recall (>97% for monomicrobial; ~78% overall in polymicrobial)[84]
BacPipeNoUser-friendly whole-genome sequencing pipeline for bacterial genome assembly, annotation, and outbreak detection with clinical applicationSupports rapid clinical bacterial diagnostics; accuracy clinically validatedhttps://github.com/wholeGenomeSequencingAnalysisPipeline/BacPipe, accessed on 1 August 2025
DeepColonyYesAI-based hierarchical multi-network for automated culture plate interpretation, species ID, quantitation>99% agreement for negative, >95% for positive cultures[85]
PCR.AiYesMachine learning-based automation of PCR data analysis; auto-interpretation and quality control100% concordance with manual interpretation; faster TAT[86]
NEKSUS (Oxford Ont Hybrid Assembly Tool)NoNanopore long-read hybrid assembler for genome surveillance in bloodstream infectionsHigh completeness and accuracyhttps://github.com/oxfordmmm/NEKSUS_ont_hybrid_assembly_comparison, accessed on 1 August 2025
ASA3PNoAutomatic and scalable pipeline for bacterial genome assembly, annotation, and analysisFully automatic, locally executable; suited for clinical bacterial genomics[87]
DMP (Dutch Microbiome Project pipeline)PartiallyPipeline and workflows for microbiome profiling and analysisPublished in microbiome studies; quality-controlled profilinghttps://github.com/GRONINGEN-MICROBIOME-CENTRE/DMP, accessed on 1 August 2025
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Patnaik, A.; Kayal, T.; Basu, S. Polymicrobial Infections: A Comprehensive Review on Current Context, Diagnostic Bottlenecks and Future Directions. Acta Microbiol. Hell. 2025, 70, 39. https://doi.org/10.3390/amh70040039

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Patnaik A, Kayal T, Basu S. Polymicrobial Infections: A Comprehensive Review on Current Context, Diagnostic Bottlenecks and Future Directions. Acta Microbiologica Hellenica. 2025; 70(4):39. https://doi.org/10.3390/amh70040039

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Patnaik, Amit, Titirsha Kayal, and Soumya Basu. 2025. "Polymicrobial Infections: A Comprehensive Review on Current Context, Diagnostic Bottlenecks and Future Directions" Acta Microbiologica Hellenica 70, no. 4: 39. https://doi.org/10.3390/amh70040039

APA Style

Patnaik, A., Kayal, T., & Basu, S. (2025). Polymicrobial Infections: A Comprehensive Review on Current Context, Diagnostic Bottlenecks and Future Directions. Acta Microbiologica Hellenica, 70(4), 39. https://doi.org/10.3390/amh70040039

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