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Original Article
Pulmonary Real-world experience using multiplex polymerase chain reaction in intensive care unit patients with hospital-acquired and ventilator-associated pneumonia in South Korea
Acute and Critical Care 2026;41(1):87-96.
DOI: https://doi.org/10.4266/acc.005100
Published online: February 27, 2026

1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Soonchunhyang University Seoul Hospital, Seoul, Korea

2Department of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

3Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

4Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

5Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

Corresponding author: Sang-Bum Hong Department of Pulmonary and Critical Care Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea Tel: +82-2-3010-3893, Fax: +82-2-3010-6968, Email: sbhong@amc.seoul.kr
• Received: October 23, 2025   • Revised: December 7, 2025   • Accepted: December 11, 2025

© 2026 The Korean Society of Critical Care Medicine

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Background:
    Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are common among critically ill patients, leading to increased morbidity and mortality rates. Conventional culture-based diagnostics require 48–72 hours, which delays pathogen identification and prolongs the use of broad-spectrum antibiotics. Multiplex polymerase chain reaction (mPCR) enables the rapid detection of pathogens and resistance genes, but its effects on real-world antibiotic decision-making remain unclear.
  • Methods:
    This retrospective study included patients in the intensive care unit who were diagnosed with HAP or VAP at a tertiary medical center between July 2023 and June 2024. All patients underwent both mPCR and respiratory culture. The primary outcome was the time to the first antibiotic modification based on mPCR or respiratory culture. The secondary outcome was the rate of antibiotic de-escalation from carbapenem or teicoplanin/vancomycin based on mPCR findings.
  • Results:
    In total, 75 patients were included (median age, 68 years; male, 61.3%). mPCR identified bacterial pathogens in 45.3% of cases, with a median turnaround time of 281 minutes. The median time to antibiotic modification was 5.8 hours for mPCR versus 122.3 hours for culture (P<0.01). Despite negative mPCR results for gram-negative bacilli, carbapenem therapy was discontinued in only 1 of 24 cases (4.2%). Among 39 patients with negative results for Staphylococcus aureus, vancomycin or teicoplanin was discontinued in only 3 cases (7.7%).
  • Conclusions:
    mPCR provided faster pathogen identification and earlier antibiotic modifications than conventional respiratory culture. However, antibiotic discontinuation remained uncommon despite negative mPCR results, highlighting challenges in real-world antimicrobial stewardship.
  • Key Words: anti-bacterial agents; multiplex polymerase chain reaction; pneumonia
Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are common infections in critically ill patients that lead to increased morbidity, prolonged intensive care unit (ICU) stays, and higher healthcare costs [1,2]. Timely and appropriate antimicrobial therapy is essential for improving the outcomes of critically ill patients [3]. Conventional culture methods typically require 48–72 hours to yield results, and their limited sensitivity in detecting causative pathogens hinders early pathogen identification [4-6]. Delays in obtaining culture results often lead to the prolonged use of broad-spectrum antibiotics, which can increase the risk of acquiring multidrug-resistant organisms [7,8].
Recent advances in multiplex polymerase chain reaction (mPCR) technology have enabled the rapid detection of pneumonia pathogens and antimicrobial resistance genes, offering an alternative to traditional culture-based diagnostics [9]. Unlike traditional methods, which rely on bacterial growth, mPCR directly identifies nucleic acids, providing results within hours [10,11]. Recent studies have examined the potential effects of mPCR testing on pneumonia management by means of earlier and more precise antibiotic modifications [12-14]. However, the broad use of mPCR remains limited by challenges such as false-positive results, difficulty in distinguishing colonization from true infection, and discrepancies between genotypic resistance markers and phenotypic antimicrobial susceptibility [4,15]. An evaluation of its role in real-world clinical settings is needed to determine its effects on clinical decision-making and antibiotic optimization in ICU patients.
This study evaluated the clinical utility of mPCR-based pneumonia diagnostics in ICU patients with HAP or VAP. Specifically, we evaluated the time to antibiotic modification and the potential for antibiotic de-escalation. By examining the effectiveness of mPCR in antimicrobial decision-making, we aimed to clarify its role in optimizing antimicrobial stewardship and guiding the management of critically ill patients with HAP or VAP.
The Institutional Review Board of Asan Medical Center approved this study (No. 2010-0079) and waived the requirement for informed consent because this study was retrospective.
Study Design
We conducted a retrospective observational study as part of an ongoing cohort study of severe pneumonia in adult patients admitted to the medical ICU of a 2,700-bed tertiary referral hospital in Seoul, South Korea. The parent cohort was established in March 2010 and continued through July 2024. The present analysis included patients admitted between July 2023 and June 2024, following the implementation of mPCR testing for pneumonia.
Diagnosis of Pneumonia
HAP was defined as pneumonia that developed ≥48 hours after hospital admission without prior mechanical ventilation, and VAP was defined as pneumonia occurring ≥48 hours after endotracheal intubation [2]. The diagnosis was based on a combination of clinical, laboratory, and radiological findings, including new or progressive pulmonary infiltrates on chest imaging, leukocytosis or leukopenia, fever, purulent respiratory secretions, and worsening oxygenation.
Patient Selection
Patients with clinically and radiologically confirmed HAP or VAP were included. Patients who did not undergo microbiological testing with both mPCR and conventional respiratory culture during hospitalization were excluded.
Microbiological Performance
Data from routine respiratory culture and the FilmArray Pneumonia Plus Panel (BioFire Diagnostics) were collected for all patients. Respiratory cultures were processed using standard microbiological methods, including gram staining, aerobic bacterial culture, and antimicrobial susceptibility testing. The FilmArray Pneumonia Plus Panel, an mPCR assay, detects 34 bacterial and viral pathogens and 7 antimicrobial resistance genes, with results available within approximately 1 hour [16,17]. In this study, a semi-quantitative threshold of 10⁵ copies/mL was considered significant for bacterial detection [4]. To evaluate the diagnostic performance and effects on antibiotic management, turnaround time (TAT) was defined as the interval from the test order to result availability. Additionally, the time to the first antibiotic modification, defined as the interval from the test order to the first documented change in antibiotic therapy, was analyzed. Concordance was calculated only among patients with positive bacterial results on PCR, using the mPCR result as the denominator.
Primary and Secondary Outcomes
The primary outcome was the time to the first antibiotic modification based on either mPCR results or respiratory culture results. Antibiotic modification based on mPCR or respiratory culture results was defined as a change in antimicrobial therapy that met the following criteria: (1) clinician documentation indicating that the modification was made in response to the corresponding microbiological result (mPCR or respiratory culture); (2) modification occurring after the availability of the result; and (3) discontinuation or escalation (initiation of a new agent to expand antimicrobial coverage) consistent with the organism or resistance profile reported by the test. When both mPCR and respiratory culture revealed the need for antibiotic changes in the same patient, the event was classified as mPCR-guided because the mPCR results were available earlier. The secondary outcome was the rate of antibiotic de-escalation according to mPCR findings. Antibiotic de-escalation was defined as the discontinuation of carbapenem or anti-methicillin-resistant Staphylococcus aureus (MRSA) treatment (vancomycin or teicoplanin). Replacing carbapenem with ceftazidime/avibactam or ceftolozane/tazobactam for targeted coverage of carbapenem-resistant Enterobacteriaceae (CRE)/carbapenemase-producing Enterobacteriaceae (CPE) or carbapenemase-resistant Pseudomonas aeruginosa (CRPA) was not considered de-escalation.
Data Analysis
The following demographic and clinical data were collected from electronic medical records: age, sex, comorbidities, type of pneumonia (HAP or VAP), disease severity according to the Sequential Organ Failure Assessment (SOFA) score and Acute Physiology and Chronic Health Evaluation (APACHE) II score, and presence of shock or mechanical ventilation. The microbiological data were mPCR and respiratory culture results, identified pathogens, and antimicrobial resistance genes. Information on prior colonization by multidrug-resistant organisms and antibiotic use before and after mPCR testing was also collected. Categorical variables are presented as numbers (percentages), and continuous variables are reported as medians with interquartile ranges. For group comparisons, the Mann-Whitney U-test was used for continuous variables and the chi-square or Fisher’s exact test for categorical variables, as appropriate. A post-hoc power analysis was performed for the primary outcome (time to first antibiotic modification) using the observed effect size (Cohen’s d=1.88) and actual group sizes. This analysis was conducted using a two-sided t-test with α=0.05. A supplementary comparison was performed between patients who met the criteria for HAP/VAP but did not undergo mPCR and those who underwent mPCR to evaluate potential selection bias. The SOFA and APACHE II scores were compared between those two groups. Statistical analyses were performed using IBM SPSS software version 27.0 (IBM Corp.).
Subgroup Analyses
Subgroup analyses were performed to compare patients who did and did not undergo mPCR-guided antibiotic modification, and pathogen-concordant and pathogen-discordant groups among patients with positive mPCR results.
Exploratory Analysis
To assess whether mPCR independently influenced antibiotic modification, a logistic regression analysis was conducted using mPCR-guided modification as the dependent variable. Variables associated with the outcome (P<0.1 in the univariable analyses) were included in the multivariable analysis. The pneumonia type was included as a dichotomous variable (reference: HAP). Given the limited number of events, model complexity was restricted to prevent overfitting. To evaluate whether mPCR-guided antibiotic modification was associated with clinically meaningful outcomes, multivariable logistic regression analyses were conducted for overall in-hospital mortality, 90-day mortality, and the acquisition of new multidrug-resistant organisms (CRE/CPE and CRPA). The acquisition of carbapenem-resistant Acinetobacter baumannii (CRAB) was excluded from the outcome because of the small number of events (n=6). For the length of stay outcomes (hospital and ICU stays), multivariable linear regression models were used. ICU and hospital lengths of stay were log-transformed because of their non-normal distribution, and the percentage change after back-transformation was calculated as (eβ–1)×100. Only variables with P<0.1 in the univariable analyses were included in the multivariable models to minimize the risk of overfitting.
Baseline Characteristics
In total, 75 patients met the inclusion criteria (Figure 1). The median age of the study population was 68 years, and 61.3% of the patients were male. HAP accounted for 81.3% cases, and VAP occurred in 18.7% cases. Mechanical ventilation was required in 97.3% of the patients, and 28.0% presented with shock at the time of pneumonia diagnosis. The median SOFA score was 10, and the median APACHE II score was 25, indicating a high severity of illness in the study population (Table 1). Among the 87 ICU patients in the cohort who were diagnosed with HAP/VAP¸ 12 did not undergo mPCR. The median SOFA and APACHE II scores of those 12 patients were comparable to those of patients who underwent mPCR (Supplementary Table 1).
Results of mPCR
Among the 75 patients in the study population, mPCR identified bacterial pathogens in 34 (45.3%). Klebsiella pneumoniae (17.3%) was the most frequently detected bacterium, followed by P. aeruginosa (16.0%) and A. baumannii (10.7%) (Table 2). The most frequently detected resistance gene was cefotaximase-Munich (13.3%), followed by Klebsiella pneumoniae carbapenemase (9.0%) and New Delhi metallo-β-lactamase (6.7%) (Supplementary Table 2). Additionally, viral pathogens were identified in 13 cases (17.3%), with the parainfluenza virus (6.7%) being the most common, followed by the respiratory syncytial virus (5.3%) and influenza A virus (4.0%).
Performance of mPCR
In the study population, mPCR specimens were obtained using endotracheal suction (62.7%), bronchoalveolar lavage (BAL; 16.0%), or sputum collection (21.3%). The median TAT for mPCR results was 281.0 minutes, with same-day reporting achieved in 74.7% cases. The concordance between mPCR and respiratory culture for bacterial identification was 70.6% (24/34 cases), and agreement for both bacterial identification and resistance genes was 47.1% (16/34 cases) (Table 3).
Antibiotic Modification Based on mPCR
Among the 34 patients with positive bacterial findings on mPCR, 18 (52.9%) underwent antibiotic modification. The median time to first antibiotic modification was 5.8 hours with mPCR, compared with 122.3 hours with respiratory culture (Table 4). The estimated mean difference was 115.9 hours (95% CI, 91.4–140.3; P<0.01). In a post-hoc power analysis using the observed effect size and group sizes, the power required to detect this difference was 0.99.
Among the 41 patients receiving carbapenem therapy, 24 had negative mPCR results for gram-negative bacilli, but carbapenem was discontinued in only one of those 24 patients. Furthermore, among the 24 carbapenem-treated patients with negative mPCR results for gram-negative bacilli, only one patient had a culture-confirmed indication. Among 34 patients who were not receiving carbapenems, mPCR detected gram-negative bacilli in 15 cases, which led to carbapenem initiation in eight cases. However, only two of those cases had culture-confirmed indications for carbapenem therapy (Supplementary Table 3).
Of the 40 patients receiving vancomycin or teicoplanin, 39 had negative mPCR results for S. aureus, and vancomycin or teicoplanin was discontinued in only three of those 39 patients. Respiratory cultures did not identify S. aureus in the remaining 36 patients. In 35 patients not receiving vancomycin or teicoplanin, mPCR detected S. aureus in three cases, which led to antibiotic initiation in two patients, although the culture results did not confirm the indication for the use of vancomycin or teicoplanin in either of them. In the remaining 32 patients with negative mPCR results for S. aureus, none required vancomycin or teicoplanin based on the culture results, except for one case with Corynebacterium striatum (Supplementary Table 4).
Subgroup Analyses
The subgroup analyses showed no significant differences in baseline clinical or microbiological characteristics or clinical outcomes between patients who did and did not undergo mPCR-guided antibiotic modification (Supplementary Table 5). Another subgroup analysis among patients with positive mPCR findings demonstrated that the rate of antibiotic modification and clinical outcomes did not differ between the pathogen-concordant and -discordant groups (Supplementary Table 6).
Exploratory Analysis
Based on the univariable logistic regression analyses, pneumonia type (VAP vs. HAP) and APACHE II score (P<0.1) were included in the multivariable model. However, in the multivariable analysis, no significant association with mPCR-guided modification was observed (Supplementary Table 7). Furthermore, mPCR-guided antibiotic modification was not independently associated with overall in-hospital mortality, 90-day mortality, hospital length of stay, ICU length of stay, or the acquisition of new multidrug-resistant organisms in the adjusted analyses (Supplementary Tables 8-13).
This study evaluated the clinical impact of mPCR in ICU patients with HAP and VAP, particularly focusing on its value for real-world antimicrobial decision-making. In our study, mPCR provided faster pathogen detection, with a median TAT of 281 minutes and a median time to antibiotic modification of 5.8 hours, compared with 122.3 hours based on respiratory culture. These findings are consistent with those of prior studies, which demonstrated reductions of approximately 2.5 days in TAT [18] and earlier initiation of targeted therapy (4.3 vs. 26.4 hours, P=0.0376) in critically ill patients with pneumonia [19].
Despite its rapidity, mPCR had only a modest effect on antibiotic de-escalation in real-world ICU practice. Among the patients with negative mPCR results in this study, carbapenem was discontinued in only one of 24 eligible cases (4.2%), and vancomycin or teicoplanin was discontinued in three of 39 cases (7.7%), even though only one patient had a culture-confirmed indication for continuing broad-spectrum antibiotic therapy. That patient had a prior history of extended-spectrum β-lactamase (ESBL)-producing K. pneumoniae pneumonia and demonstrated ESBL K. pneumoniae on the respiratory culture despite a negative mPCR result. Prior randomized trials have reported heterogeneous effects of mPCR on antibiotic decisions [12,13,20]. The Flagship II trial showed that BAL-based Gram-negative-targeted mPCR reduced inappropriate antibiotic therapy by 38.6 hours (47.1 hours vs. 85.7 hours) and produced no differences in clinical outcomes [12]. In contrast, a randomized controlled trial of 1,181 hospitalized patients found no significant difference in the overall time to first antibiotic modification between mPCR and standard culture (20.4 hours vs. 25.8 hours, P=0.076), although mPCR enabled faster escalation for gram-positive bacteria (10.3 hours vs. 24.6 hours, P=0.044) and gram-negative organisms (17.3 hours vs. 27.2 hours, P=0.010) [13]. More recently, the INHALE WP3 trial (the Impact of using FilmArray pneumonia panel moLecular diagnostics for Hospital-Acquired and ventilator-associated pneumonia on antimicrobiaL stewardship and patiEnt outcomes work package 3 trial) evaluated mPCR in ICU patients with HAP or VAP and demonstrated a higher rate of appropriate and proportionate antibiotic therapy at 24 hours in the PCR group than in the standard-care group (76.5% vs. 55.9%; absolute difference, 21%; 95% CI, 13%–28%), but no difference in clinical cure at 14 days (56.7% vs. 64.5%; estimated difference, –6%; 95% CI, –15% to 2%) [14]. Collectively, these results indicate a significant gap between the diagnostic potential of mPCR and its actual influence on clinical practice. This is likely explained by key differences between prior randomized trials and our real-world ICU cohort: previous studies used protocolized antibiotic recommendations and patient characteristics differed. Our findings highlight that the implementation of mPCR alone does not ensure optimized antimicrobial stewardship in high-acuity ICU settings unless it is accompanied by structured decision support [21]. Furthermore, practical barriers also limit real-world application. The high cost of mPCR and reimbursement restrictions requiring early testing after ICU admission limit its routine use, especially in critically ill patients whose diagnostic evaluation is delayed.
Clinicians managing critically ill patients tend to adopt a conservative approach when narrowing antibiotic therapy, a tendency associated with concerns about disease severity and diagnostic uncertainty, including the fear of missing a pathogen [22,23]. This reluctance could be influenced by limited clinician confidence in applying a relatively new diagnostic test such as mPCR, particularly in ICU settings with a high burden of multidrug-resistant organisms. In our study, a substantial proportion of patients had hematological malignancies (32.00%) or cancer (17.33%), along with prevalent colonization with CRE/CPE, CRPA, and CRAB. These factors, combined with uncertainty about the reliability of negative mPCR findings in such complex clinical contexts, contributed to hesitancy in discontinuing broad-spectrum antibiotic therapy.
However, maintaining the unnecessary use of broad-spectrum antibiotics poses specific risks to critically ill patients. In our study, newly acquired resistant organisms, including CRE/CPE (24.0%), CRPA (13.3%) and CRAB (8.0%), were detected after ICU admission. Previous studies have demonstrated that empirical broad-spectrum antibiotic use in critically ill patients is associated with an increased risk of resistant pathogen acquisition and overall mortality [24-27]. Sakata et al. [26] reported a positive correlation between meropenem use and meropenem-non-susceptible Acinetobacter spp. (r=0.43, P=0.001) and between cefepime use and ESBL-producing gram-negative bacilli (r=0.51, P<0.001) [26]. A cohort study of 17,430 patients further demonstrated that higher in-hospital mortality was associated with unnecessary broad-spectrum antibiotic therapy (adjusted odds ratio, 1.22; 95% CI, 1.06–1.40; P=0.007) [27]. Together, these findings underscore the clinical importance of avoiding unwarranted broad-spectrum coverage, especially when mPCR results are negative.
Despite the substantial reduction in time to antibiotic modification, our exploratory analyses did not demonstrate an independent association between mPCR-guided modification and in-hospital or 90-day mortality, length of stay, or the emergence of new resistant organisms. The high mortality rate in our cohort likely reflects the underlying clinical complexity of our population, rather than the effect of mPCR-guided antibiotic modification itself. Specifically, our cohort exhibited a substantially higher burden of colonization with multidrug resistance and immunocompromising conditions—including hematologic malignancies—than previous studies. The INHALE WP3 trial reported that known MRSA, ESBL, or CPE colonization each accounted for only 0.5% of enrolled patients, and the Flagship II trial included 25% immunocompromised hosts, whereas both those values were considerably higher in our population [12,14]. These contextual differences could help explain both the high prevalence of multidrug resistance pathogens and the high mortality rate observed in this cohort.
The overall bacterial detection rate of mPCR in this study was 45.33%, with a concordance of 70.6% for bacterial identification and a detection rate of 47.1% when both bacterial identification and resistance gene detection were considered. These results are consistent with prior studies reporting that mPCR offered high sensitivity and negative predictive value but that its positive predictive value remained lower than that of culture [9,28-31]. Several factors contribute to this incomplete concordance. One limitation is that mPCR detects microbial DNA regardless of organism viability and can yield positive molecular results even when viable bacteria are no longer present. Fratoni et al. demonstrated the persistent detection of bacterial DNA in BAL specimens, even after antibiotic exposure had suppressed culture growth [32]. Additionally, syndromic panels can identify low-level signals (≤10⁵ copies/ml) or organisms that represent colonization rather than true infection, consistent with findings from Kolenda et al. [33] in which 60.5% of mPCR detections were culture negative. Distinguishing colonization from true infection and genotypic versus phenotypic resistance remain notable limitations of mPCR, and specimen quality, particularly in non-BAL samples, further affects diagnostic agreement [34]. The higher concordance observed at greater semi-quantitative loads supports the clinical relevance of bacterial burden, as reported by Gong et al. [34]. Although we applied a semi-quantitative threshold of 10⁵ copies/ml to define a positive result, no clear consensus currently exists about which semi-quantitative values reflect clinically significant infection. Emerging innovations, such as refined quantitative modeling and gene–phenotype correlation frameworks could help to establish evidence-based thresholds and enhance diagnostic concordance.
This study has several limitations. First, the small sample size and single-center design limited generalizability, especially when considering local antibiograms. Second, because this was a retrospective analysis, no real-time intervention or stewardship input was applied during the study period. Third, due to sampling differences, the adequacy of specimens for mPCR versus culture could not be assessed consistently. Fourth, 12 patients who met the criteria for HAP/VAP did not undergo mPCR, and the reason for non-testing could not be reliably determined from the retrospective records. Although their disease severity (SOFA and APACHE II scores) was comparable to that of the tested population, this uncertainty introduces potential selection bias. Despite those limitations, this study provides real-world evidence for the clinical effects of mPCR in ICU patients diagnosed with HAP or VAP. Although mPCR enabled faster pathogen identification and earlier antibiotic modification, the overall effect on antibiotic de-escalation was limited, with many clinicians continuing broad-spectrum therapy despite negative mPCR results. Future studies should focus on developing strategies to improve clinician confidence in de-escalation decisions and determining the long-term effects of mPCR-guided therapy on patient outcomes.
▪ Multiplex polymerase chain reaction (mPCR) enables faster pathogen identification and antibiotic modification than conventional respiratory culture.
▪ The de-escalation of carbapenem or teicoplanin/vancomycin was uncommon, even with negative mPCR results.

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

FUNDING

This study was funded by the Soonchunhyang Research Fund.

ACKNOWLEDGMENTS

None.

AUTHOR CONTRIBUTIONS

Conceptualization: SBH. Methodology: SBH. Data curation: ECY. Formal analysis: ECY. Investigation: ECY, SHC, HS, YPC, EC, KHD, SYL, DH, JHA, JWH, CML. Validation: SBH, ECY. Writing – original draft preparation: ECY. Writing - review & editing: SBH, ECY, SHC, HS, YPC, EC, KHD, SYL, DH, JHA, JWH, CML. All authors read and agreed to the published version of the manuscript.

Supplementary materials can be found via https://doi.org/10.4266/acc.005100.
Supplementary Table 1.
Severity scores of mPCR-tested and non-tested HAP/VAP patients
acc-005100-Supplementary-Table-1.pdf
Supplementary Table 2.
Results of mPCR on resistance gene
acc-005100-Supplementary-Table-2.pdf
Supplementary Table 3.
Antibiotic modification before and after mPCR based on prior carbapenem use
acc-005100-Supplementary-Table-3.pdf
Supplementary Table 4.
Antibiotics modification before and after mPCR based on prior teicoplanin or vancomycin use
acc-005100-Supplementary-Table-4.pdf
Supplementary Table 5.
Baseline characteristics and clinical outcomes according to mPCR-guided antibiotic modification
acc-005100-Supplementary-Table-5.pdf
Supplementary Table 6.
Baseline characteristics and clinical outcomes according to pathogen concordance between mPCR and respiratory culture
acc-005100-Supplementary-Table-6.pdf
Supplementary Table 7.
Multivariable logistic regression analysis for antibiotic modification based on mPCR results
acc-005100-Supplementary-Table-7.pdf
Supplementary Table 8.
Multivariable logistic regression analysis for overall mortality during hospitalization
acc-005100-Supplementary-Table-8.pdf
Supplementary Table 9.
Multivariable logistic regression analysis for 90-day mortality
acc-005100-Supplementary-Table-9.pdf
Supplementary Table 10.
Multivariable logistic regression analysis for CRE/CPE acquisition after ICU admission
acc-005100-Supplementary-Table-10.pdf
Supplementary Table 11.
Multivariable logistic regression analysis for CRPA acquisition after ICU admission
acc-005100-Supplementary-Table-11.pdf
Supplementary Table 12.
Multivariable linear regression analysis for hospital length of stay
acc-005100-Supplementary-Table-12.pdf
Supplementary Table 13.
Multivariable linear regression analysis for ICU admission day
acc-005100-Supplementary-Table-13.pdf
Figure 1.
Patient selection flowchart for intensive care unit (ICU) cases of hospital-acquired pneumonia (HAP) or ventilator-associated pneumonia (VAP) diagnosed with multiplex polymerase chain reaction (mPCR) results.
acc-005100f1.jpg
Table 1.
Baseline characteristics of the study population
Variable Value
Number 75
Age (yr) 68 (59–74)
Male 46 (61.3)
Comorbid condition
 Diabetes mellitus 15 (20.0)
 End stage renal disease 4 (5.3)
 Liver cirrhosis 6 (8.0)
 Chronic obstructive pulmonary disease 7 (9.3)
 Interstitial lung disease 4 (5.3)
 Cancer 13 (17.3)
 Hematologic malignancy 24 (32.0)
 Bone marrow transplantation 2 (2.7)
 Solid organ transplantation 6 (8.0)
Pneumonia
 Hospital-acquired pneumonia 61 (81.3)
 Ventilator-associated pneumonia 14 (18.7)
Severity of disease
 Mechanical ventilation 73 (97.3)
 Shock 21 (28.0)
 SOFA score 10.0 (7.0–13.0)
 APACHE II score 25.0 (20.0–29.0)
Prior resistance
 CRE or CPE 14 (18.7)
 CRPA 10 (13.3)
 CRAB 2 (2.7)

Values are presented as median (interquartile range) or number (%).

SOFA: Sequential Organ Failure Assessment; APACHE: Acute Physiology and Chronic Health Evaluation; CRE: carbapenem-resistant Enterobacteriaceae; CPE: carbapenemase-producing Enterobacteriaceae; CRPA: carbapenemase-resistant Pseudomonas aeruginosa; CRAB: carbapenem-resistant Acinetobacter baumannii.

Table 2.
Results of mPCR
Identified bacteria Resistant gene Number (%)
Total 34 (45.3)
Acinetobacter baumannii CTX-M (n=2), KPC (n=1), NDM (n=1) 8 (10.7)
Enterobacter cloacae IMP (n=1) 1 (1.3)
Escherichia coli CTX-M (n=2), KPC (n=1), NDM (n=1) 4 (5.3)
Klebsiella pneumoniae CTX-M (n=5), KPC (n=6), NDMa) (n=2) 13 (17.3)
Pseudomonas aeruginosa CTX-M (n=2), KPC (n=1), IMP (n=1), NDMa) (n=4) 12 (16.0)
Serratia marcescens KPC (n=1) 2 (2.7)
Staphylococcus aureus mecA/C and MREJ (n=2) 4 (5.3)

mPCR: multiplex polymerase chain reaction; CTX-M: cefotaximase-Munich; KPC: Klebsiella pneumoniae carbapenemase; NDM: New Delhi metallo-β-lactamase; IMP: imipenemase; mecA/C: methicillin resistance gene A/C; MREJ: mec right extremity junction.

a)One K. pneumoniae isolate and one P. aeruginosa isolate carried both KPC and NDM.

Table 3.
Performance of mPCR
Value
Sampling methods
 Sputum 16 (21.3)
 Endotracheal suction 47 (62.7)
 Bronchoalveolar lavage 12 (16.0)
Timing of mPCR from ICU admission
 Within 48 hr 50 (66.7)
Turnaround time for mPCR results (min) 281.0 (146.0–572.0)
Same-day turnaround for mPCR 56 (74.7)
Positive mPCR bacterial results 34 (45.3)
Concordance with respiratory culturea) (n=34)
 Bacteria 24 (70.6)
 Bacteria and resistance gene 16 (47.1)

Values are presented as median (interquartile range) or number (%).

mPCR: multiplex polymerase chain reaction; ICU: intensive care unit.

a)Percentages for concordance were calculated using the number of mPCR-positive bacterial cases (n=34) as the denominator.

Table 4.
Clinical outcomes
Value
Antibiotic modifications
 Based on positive mPCR results (n=34) 18 (52.9)
Time to antibiotic change
 Based on mPCR results (n=18, hr) 5.8 (3.0–9.9)
 Based on respiratory culture (n=43, hr) 122.3 (73.5–173.8)
Acquired resistance after ICU admission
 CRE/CPE 18 (24.0)
 CRPA 10 (13.3)
 CRAB 6 (8.0)
Admission days
 Hospital length of stay 53.0 (33.5–89.0)
 ICU admission days 22.0 (12.0–34.0)
Mortality
 Overall mortality during hospitalization 47 (62.7)
 28-Day mortality 9 (12.0)
 90-Day mortality 36 (48.0)

Values are presented as number (%) or median (interquartile range). The percentages were calculated using row-specific denominators. For antibiotic modification outcomes, the denominators were mPCR-positive cases (n=34), mPCR-guided modifications (n=18), and culture-guided modifications (n=43). All other percentages were calculated using the total study population (n=75).

mPCR: multiplex polymerase chain reaction; ICU: intensive care unit; CRE: carbapenem-resistant Enterobacteriaceae; CPE: carbapenemase-producing Enterobacteriaceae; CRPA: carbapenemase-resistant Pseudomonas aeruginosa; CRAB: carbapenem-resistant Acinetobacter baumannii.

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        Real-world experience using multiplex polymerase chain reaction in intensive care unit patients with hospital-acquired and ventilator-associated pneumonia in South Korea
        Acute Crit Care. 2026;41(1):87-96.   Published online February 27, 2026
        DOI: https://doi.org/10.4266/acc.005100
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      Real-world experience using multiplex polymerase chain reaction in intensive care unit patients with hospital-acquired and ventilator-associated pneumonia in South Korea
      Image
      Figure 1. Patient selection flowchart for intensive care unit (ICU) cases of hospital-acquired pneumonia (HAP) or ventilator-associated pneumonia (VAP) diagnosed with multiplex polymerase chain reaction (mPCR) results.

      Figure 1.

      Real-world experience using multiplex polymerase chain reaction in intensive care unit patients with hospital-acquired and ventilator-associated pneumonia in South Korea
      Variable Value
      Number 75
      Age (yr) 68 (59–74)
      Male 46 (61.3)
      Comorbid condition
       Diabetes mellitus 15 (20.0)
       End stage renal disease 4 (5.3)
       Liver cirrhosis 6 (8.0)
       Chronic obstructive pulmonary disease 7 (9.3)
       Interstitial lung disease 4 (5.3)
       Cancer 13 (17.3)
       Hematologic malignancy 24 (32.0)
       Bone marrow transplantation 2 (2.7)
       Solid organ transplantation 6 (8.0)
      Pneumonia
       Hospital-acquired pneumonia 61 (81.3)
       Ventilator-associated pneumonia 14 (18.7)
      Severity of disease
       Mechanical ventilation 73 (97.3)
       Shock 21 (28.0)
       SOFA score 10.0 (7.0–13.0)
       APACHE II score 25.0 (20.0–29.0)
      Prior resistance
       CRE or CPE 14 (18.7)
       CRPA 10 (13.3)
       CRAB 2 (2.7)
      Identified bacteria Resistant gene Number (%)
      Total 34 (45.3)
      Acinetobacter baumannii CTX-M (n=2), KPC (n=1), NDM (n=1) 8 (10.7)
      Enterobacter cloacae IMP (n=1) 1 (1.3)
      Escherichia coli CTX-M (n=2), KPC (n=1), NDM (n=1) 4 (5.3)
      Klebsiella pneumoniae CTX-M (n=5), KPC (n=6), NDMa) (n=2) 13 (17.3)
      Pseudomonas aeruginosa CTX-M (n=2), KPC (n=1), IMP (n=1), NDMa) (n=4) 12 (16.0)
      Serratia marcescens KPC (n=1) 2 (2.7)
      Staphylococcus aureus mecA/C and MREJ (n=2) 4 (5.3)
      Value
      Sampling methods
       Sputum 16 (21.3)
       Endotracheal suction 47 (62.7)
       Bronchoalveolar lavage 12 (16.0)
      Timing of mPCR from ICU admission
       Within 48 hr 50 (66.7)
      Turnaround time for mPCR results (min) 281.0 (146.0–572.0)
      Same-day turnaround for mPCR 56 (74.7)
      Positive mPCR bacterial results 34 (45.3)
      Concordance with respiratory culturea) (n=34)
       Bacteria 24 (70.6)
       Bacteria and resistance gene 16 (47.1)
      Value
      Antibiotic modifications
       Based on positive mPCR results (n=34) 18 (52.9)
      Time to antibiotic change
       Based on mPCR results (n=18, hr) 5.8 (3.0–9.9)
       Based on respiratory culture (n=43, hr) 122.3 (73.5–173.8)
      Acquired resistance after ICU admission
       CRE/CPE 18 (24.0)
       CRPA 10 (13.3)
       CRAB 6 (8.0)
      Admission days
       Hospital length of stay 53.0 (33.5–89.0)
       ICU admission days 22.0 (12.0–34.0)
      Mortality
       Overall mortality during hospitalization 47 (62.7)
       28-Day mortality 9 (12.0)
       90-Day mortality 36 (48.0)
      Table 1. Baseline characteristics of the study population

      Values are presented as median (interquartile range) or number (%).

      SOFA: Sequential Organ Failure Assessment; APACHE: Acute Physiology and Chronic Health Evaluation; CRE: carbapenem-resistant Enterobacteriaceae; CPE: carbapenemase-producing Enterobacteriaceae; CRPA: carbapenemase-resistant Pseudomonas aeruginosa; CRAB: carbapenem-resistant Acinetobacter baumannii.

      Table 2. Results of mPCR

      mPCR: multiplex polymerase chain reaction; CTX-M: cefotaximase-Munich; KPC: Klebsiella pneumoniae carbapenemase; NDM: New Delhi metallo-β-lactamase; IMP: imipenemase; mecA/C: methicillin resistance gene A/C; MREJ: mec right extremity junction.

      One K. pneumoniae isolate and one P. aeruginosa isolate carried both KPC and NDM.

      Table 3. Performance of mPCR

      Values are presented as median (interquartile range) or number (%).

      mPCR: multiplex polymerase chain reaction; ICU: intensive care unit.

      Percentages for concordance were calculated using the number of mPCR-positive bacterial cases (n=34) as the denominator.

      Table 4. Clinical outcomes

      Values are presented as number (%) or median (interquartile range). The percentages were calculated using row-specific denominators. For antibiotic modification outcomes, the denominators were mPCR-positive cases (n=34), mPCR-guided modifications (n=18), and culture-guided modifications (n=43). All other percentages were calculated using the total study population (n=75).

      mPCR: multiplex polymerase chain reaction; ICU: intensive care unit; CRE: carbapenem-resistant Enterobacteriaceae; CPE: carbapenemase-producing Enterobacteriaceae; CRPA: carbapenemase-resistant Pseudomonas aeruginosa; CRAB: carbapenem-resistant Acinetobacter baumannii.

      Table 1.

      Table 2.

      Table 3.

      Table 4.

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