Characteristics of pathogenic microorganisms in COPD-related infections: prognostic correlations and implications

Characteristics of pathogenic microorganisms in COPD-related infections: prognostic correlations and implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characteristics of pathogenic microorganisms in COPD-related infections: prognostic correlations and implications Chaoying Liu, Caihong Liu, Zhuo Gao, Shan Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7689813/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic obstructive pulmonary disease (COPD) significantly impacts global health, primarily due to frequent acute exacerbations caused by respiratory infections. Precise microbial characterization may inform prognostic insights and optimize clinical management. Methods We conducted a prospective observational study from December 2023 to February 2025 involving 1146 patients (259 COPD; 887 non-COPD) with suspected respiratory infections. Bronchoalveolar lavage fluid samples underwent next-generation sequencing (NGS) and conventional microbiological testing. Multivariate logistic regression identified COPD predictors, and machine learning modeled prognostic outcomes based on microbial profiles. Results Distinct pathogen distributions emerged between COPD and non-COPD groups, with COPD patients exhibiting higher prevalence of gram-negative bacteria, particularly Pseudomonas aeruginosa and Haemophilus influenzae , and fungal pathogens. Non-COPD patients demonstrated increased occurrence of atypical pathogens, notably Mycoplasma pneumoniae . COPD patients also presented higher loads of traditionally commensal microorganisms, such as Veillonella parvula and Schaalia odontolytica . Age, dyspnea, smoking duration, elevated leukocyte and neutrophil counts, and decreased lymphocyte levels were significantly associated with COPD presence. Machine learning identified specific microorganisms as strong predictors of adverse outcomes, such as SARS-CoV-2, Veillonella parvula , and Achromobacter xylosoxidans , achieving an area under the receiver operating characteristic curve of 0.9998. Conclusions Comprehensive microbial profiling using NGS effectively distinguishes pathogen differences between COPD and non-COPD patients, revealing key associations with clinical prognosis. These insights can inform tailored clinical interventions aimed at mitigating COPD exacerbations and improving patient outcomes. COPD pathogenic microorganism infection prognosis next-generation sequencing machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background Chronic obstructive pulmonary disease (COPD) represents a major global health burden, characterized by persistent respiratory symptoms and progressive airflow limitation, resulting from chronic inflammation of the airways and lung parenchyma ( 1 , 2 ). As the third leading cause of death worldwide, COPD significantly impairs patients' quality of life, incurs substantial healthcare expenditures, and imposes a considerable socioeconomic burden ( 3 , 4 ). Among COPD-related complications, acute exacerbations are particularly critical, contributing substantially to disease progression, increased hospitalization rates, accelerated lung function decline, and heightened mortality ( 5 , 6 ). Respiratory infections are established as predominant precipitants of acute exacerbations in COPD, with bacterial, viral, and atypical pathogens frequently implicated ( 7 – 10 ). However, the spectrum and distribution of infectious microorganisms among COPD patients compared with non-COPD individuals, as well as their potential prognostic significance, remain inadequately elucidated. Recent technological advancements in pathogen identification, particularly next-generation sequencing (NGS), offer unprecedented sensitivity and specificity in the detection and characterization of microbial pathogens ( 11 , 12 ). Compared to traditional culture-based methods, NGS provides comprehensive microbiome profiles, enabling more accurate identification of pathogenic organisms, thereby deepening our understanding of the microbial landscape in COPD patients, informing targeted antimicrobial therapies, and potentially improving clinical management strategies aimed at reducing exacerbation frequency and severity. Therefore, this study aims to systematically investigate the differences in infectious pathogen profiles between COPD and non-COPD patients, and further explore the associations between pathogen-specific infections and adverse clinical outcomes in COPD patients. Our findings may provide critical insights into disease mechanisms underlying COPD exacerbations and facilitate the development of precise clinical interventions aimed at optimizing patient prognosis. 2. Methods 2.1 Study design and participants This study was conducted at the Department of Respiratory Medicine, the First Hospital of Jilin University. Patients with suspected respiratory infection who were admitted to our department between December 2023 and February 2025 were consecutively included. Inclusion criteria encompassed ( 1 ) suspected lower respiratory infection (defined as a new-onset radiological findings on chest images or a hematologic parameter abnormality combined with at least one compatible symptom, such as fever, cough, or dyspnea); ( 2 ) need for bronchoalveolar lavage according to clinical standard procedure; ( 3 ) sufficient bronchoalveolar lavage fluid (BALF) samples for the NGS and conventional microbiological tests (CMTs, including BALF culture, PCR, and serological tests). Patients with insufficient clinical information or inavailable BALF sample were excluded. Enrolled patients were divided into two groups based on whether they had COPD status or not. The diagnosis of COPD status was according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines ( 13 ). Fluid from the middle segment of bronchoalveolar lavage was collected as the study sample. All BALF samples underwent conventional microbiological testing, and remaining aliquots were preserved for NGS. This study design received approval from the Ethics Committee of the First Hospital of Jilin University in accordance with the Declaration of Helsinki (2025 − 396). The study was conducted with the consent of every human participant. 2.2 Clinical data collection Detailed demographic and clinical data were extracted from electronic medical records, including age, gender, immune status (immunocompetent, immunocompromised by cancer chemotherapy, and immunocompromised by diabetes), dyspnea, smoking history, hematologic parameters (white blood cell [WBC], neutrophil [NE], lymphocyte [LYM], C-reactive protein [CRP], and procalcitonin [PCT]), and prognosis parameters (days of hospitalization, intensive care unit [ICU] admission, mechanical ventilation, and outcome [survival and death]). Poor prognosis was defined as the presence of any of ICU admission, mechanical ventilation, and death. 2.3 Next-generation sequencing Before extracting the nucleic acid, host DNA was first depleted from BALF samples using the MolYsis™ Basic 5 Kit (Molzym GmbH & Co. KG, Bremen, Germany). Nucleic acids were then extracted using the Magnetic Pathogen DNA/RNA Kit (Tiangen Biotech [Beijing] Co., Ltd, Beijing, China). Following this, the RNA was reverse-transcribed into cDNA using the Hieff NGS Double Stranded cDNA Synthesis Kit (Yeasen, Shanghai, China). The concentration of the total DNA was quantified using the Qubit™ Double-Stranded DNA High Sensitivity Assay Kit (Thermo Fisher Scientific Inc., Waltham, MA, USA). Then DNA libraries were prepared using the VAHTS Universal Plus DNA Library Prep Kit for MGI (Vazyme, Nanjing, China) with an initial input of 2 ng. Meanwhile, library quality control was performed with the Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA) to evaluate DNA concentration and fragment size distribution. Libraries exhibiting a dominant fragment peak between 240 bp and 350 bp and a concentration exceeding 1 ng/µL were deemed qualified. Approved libraries were pooled, denatured, and circularized to form single-stranded DNA circles. DNA nanoballs (DNBs) were subsequently generated through rolling circle amplification. The resulting DNBs were loaded onto sequencing chips and subjected to single-end 50-bp sequencing on the BGISEQ-500 (BGI, Shenzhen, China) platform, yielding approximately 10 to 20 million reads per library. Following sequencing, low-quality reads, short reads, and adapter were removed using Fastp (version 0.23.4) to generate high-quality data for downstream analysis. Clean reads were aligned to three human reference genomes (hg38, T2T-CHM13, and YH1) using Burrows-Wheeler Aligner (BWA, version 0.7.17-r1188). Human-derived reads were subsequently excluded using Samtools (version 1.6). Then the remaining reads were aligned against a custom microbial database using BWA to derive annotations ( Supplementary Table 1 ). After that, the annotation results were further validated by BLAST (version 2.12.0) to ensure accuracy. 2.4 Clinical diagnosis Microorganisms identified by NGS were independently evaluated by a minimum of three clinicians holding at least an associate senior professional title, and classified into four levels, including causative pathogen, possibly causative pathogen, microorganism without pathogenic role, and not causative pathogen ( 12 ). The clinical diagnosis was established by clinicians following a comprehensive assessment that integrated patient age, gender, immune status, medical history, symptoms, hematologic parameters, radiological findings, CMT results, prognosis, and other relevant clinical data ( Supplementary Fig. 1 ). 2.5 Statistical analysis For descriptive statistics, continuous variables were presented as medians and interquartile ranges (IQR), whereas categorical variables were reported as frequencies and percentages. Multivariate logistic regression was used to compute the odds ratio (OR) and 95% confidence interval (CI) for the associations of gender, age, immune status, symptoms, smoking history, hematologic and prognosis parameters with the presence of COPD. Data normality was assessed by the Shapiro-Wilk test. Nonparametric variables were compared using Mann-Whitney test. All tests were two-tailed and significance threshold was set at p value ≤ 0.05. All statistical analyses were performed using SPSS Statistics (version 26.0, IBM Corp., Armonk, NY, USA). All figures were drawn using R software (version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria), Python (version 3.11, Python Software Foundation, Wilmington, DE, USA), and GraphPad Prism (version 9.5.0, GraphPad Software LLC., San Diego, CA, USA). 3. Results 3.1 Study participants In this study, a total of 1292 patients with suspected respiratory infection were screened between December 2023 and February 2025 ( Figure 1A ). Among this, 146 cases were excluded due to insufficient clinical information (n = 137) and inability to collect BALF samples (n = 9). BALF samples of 1146 patients were analyzed using NGS. In patients with COPD (22.6%, 259/1146), NGS detected causative pathogens in 221 samples, microorganisms without pathogenic role in 34 samples, and no microorganisms in 4 samples. Whereas in patients without COPD (77.4%, 887/1146), NGS detected causative pathogens in 745 samples, possibly causative pathogens in 10 samples, microorganisms without pathogenic role in 111 samples, and no microorganisms in 21 samples. The monthly incidence of respiratory infection episodes (median 87 [IQR 67 - 116] cases) remained relatively consistent throughout the study period, with modest increases observed in April (n = 134), May (n = 116), July (n = 138), and December (n = 139) ( Figure 1B ). The proportion of cases involving patients with COPD among those presenting with respiratory infections also demonstrated temporal stability, with a median prevalence of 22.99% (IQR 17.07% - 25.00%). The demographic and clinical characteristics of patients in this study were shown in Table 1 . The median age of the overall cohort was 66 (IQR 55 - 73) years, with patients in COPD group being older (median 70 [IQR 66 - 78] years) compared to those without COPD (median 63 [IQR 52 - 71] years; p < 0.0001). Regarding gender distribution, females accounted for 44.1% (505/1146) of the total population, with a slightly higher proportion in COPD group (48.3%, 125/259) compared to non-COPD group (42.8%, 380/887; p > 0.05). In terms of immune status, the majority of patients were immunocompetent (77.9%, 893/1146), with comparable rates between COPD (81.9%, 212/259) and non-COPD (76.8%, 681/887) groups (p > 0.05). The immunocompromised cohort primarily comprised patients with diabetes mellitus (16.5%, 189/1146) and patients undergoing chemotherapy for cancer (5.6%, 64/1146). Among patients with COPD, the proportion of smoking was higher (49.4% vs. 32.8%; p < 0.0001), and the duration of smoking history was longer (median 10 [IQR 0 - 37.5] vs. 0 [IQR 0 - 20] years; p < 0.0001), compared to that in patients without COPD. For hematologic parameters, the WBC count (median 10.3 [IQR 7.3 - 13.7] vs. 8.9 [IQR 5.4 - 13.0] 10 9 /L; p = 0.0068) and NE percentage (median 83.2% [IQR 71.6% - 90.3%] vs. 78.8% [IQR 62.5% - 88.6%]; p < 0.0001) were both higher in COPD group than in non-COPD group. The median CRP and PCT were 45.8 (IQR 9.7 - 110.0) mg/L and 0.116 (IQR 0.057 - 0.412) ng/mL, respectively, with comparable values between COPD (median 44.5 [IQR 9.6 - 121.9] mg/L for CRP; median 0.108 [IQR 0.054 - 0.349] ng/mL for PCT) and non-COPD (median 46.3 [IQR 9.9 - 107.7] mg/L for CRP; median 0.117 [IQR 0.058 - 0.425] ng/mL for PCT) groups (p > 0.05). With respect to duration of hospitalization and mortality, no significant differences were observed between COPD and non-COPD groups (p > 0.05). In the overall cohort, the median length of hospital stay was 8 (IQR 6 - 11) days, and the mortality rate was 4.8% (55/1146). However, the ICU occupancy (15.44% vs. 10.94%, p = 0.04901) and mechanical ventilation utilization (12.36% vs. 4.4%, p < 0.0001) of COPD group were significantly higher than that of non-COPD group. 3.2 Significant correlation between COPD and poor prognosis Multivariate logistic regression analysis was performed to clarify the associations of demographic and clinical characteristics of patients with the presence of COPD among those with respiratory infections ( Figure 2 ). Age was a strong predictor: compared with patients younger than 60 years, those aged 60 - 70 years had nearly a four-fold higher odds of COPD (OR 3.9, 95% CI 2.6 - 6.1, p value < 0.0001), and those aged 70 - 90 years had almost six-times the odds (OR 5.8, 95% CI 3.9 - 8.8, p value < 0.0001). Dyspnea was significantly associated with COPD (OR 3.8, 95% CI 2.6 - 5.5, p value < 0.0001). A longer history of smoking was also independently associated: compared with never-smokers, those with 20 - 40 years of smoking had more than double the odds (OR 2.2, 95% CI 1.5 - 3.1, p value < 0.0001), and those with 40 - 70 years had a similar increase (OR 2.3, 95% CI 1.4 - 3.6, p value = 0.0005). Laboratory markers and prognosis parameters differed significantly in COPD patients with respiratory infections ( Figure 2 ). These patients demonstrated significantly higher odds of elevated WBC counts (10 - 50 10 9 /L, OR 1.6, 95% CI 1.2 - 2.2, p value = 0.0011), as well as increased NE (75% - 100%, OR 1.7, 95% CI 1.3 - 2.3, p value = 0.0004) and decreased LYM (0% - 20%, OR 2.2, 95% CI 1.5 - 3.1, p value < 0.0001) levels. The correlation between ICU admission and COPD approaches significance (p = 0.0513). Notably, COPD patients were three times more likely to require mechanical ventilation (OR 3, 95% CI 1.8 - 4.8, p value < 0.0001). 3.3 Characterization of causative pathogens Based on the complete spectrum of microorganisms identified by NGS, the distribution of clinical diagnostic classifications was broadly comparable between patients with and without COPD ( Figure 3A ). In both cohorts, "microorganisms without pathogenic role" comprised the single largest category (51.22% for COPD; 52.90% for non-COPD), followed by "possibly causative pathogens" (17.99% for COPD; 17.78% for non-COPD), "causative pathogens" (14.65% for COPD; 17.07% for non-COPD), and "not causative pathogens" (16.13% for COPD; 12.26% for non-COPD). In addition, causative pathogens were identified in the majority of samples from both subgroups (85.33% for COPD; 83.99% for non-COPD) ( Supplementary Figure 2 ). For causative or possibly causative pathogens, monthly trends over a period of more than one year (January 2024 - January 2025) demonstrated that gram-negative bacteria (GNB) consistently accounted for the largest share of detections - ranging from 30.92% to 61.45% of cases - with peaks in late spring (June) and autumn (November) ( Figure 3B ). Gram-positive bacteria (GPB) remained relatively stable at 10.29% - 34.30%. fungus comprised 5.49% - 15.52% of identifications and showed a gradual upward trajectory toward winter. Virus detections (both DNA and RNA viruses) exhibited clear seasonal oscillations, with RNA virus frequency rising 6.30% in November 2024 - January 2025. Atypical pathogens remained rare (2.69% - 14.71%) throughout. When restricted to causative or possibly causative pathogens, GNB dominated both groups but significantly more so in COPD than in non-COPD patients (55.91% versus 45.53%) ( Figure 3C&D ). Furthermore, the prevalence of fungal infections was higher among patients with COPD compared to those without COPD (11.02% vs. 9.98%). In contrast, infections caused by gram-positive bacteria (21.69% vs. 19.09%), DNA viruses (8.19% vs. 5.12%), RNA viruses (7.33% vs. 6.89%), and atypical pathogens (7.27% vs. 1.97%) were more frequently observed in non-COPD patients. At the species level, Streptococcus pneumoniae (21.62% for COPD; 18.60% for non-COPD) and Klebsiella pneumoniae (16.22% for COPD; 18.15% for non-COPD) were the two most frequently detected pathogens in both cohorts ( Figure 3E ). In COPD patients, Pseudomonas aeruginosa (21.62% versus 10.15%) and Haemophilus influenzae (15.44% versus 9.02%) were relatively enriched, whereas non-COPD patients exhibited higher detection rates of atypical (12.63% versus 2.70%) and viral (14.43% versus 10.04% for DNA virus; 12.40% versus 11.58% for RNA virus) pathogens such as Mycoplasma pneumoniae (11.39% versus 2.32%) and Human gammaherpesvirus 4 (7.89% versus 6.18%). Through a more detailed analysis, the extensive inter-patient heterogeneity was further proved. The heatmap in Supplementary Figure 3 further highlighted extensive inter-patient heterogeneity: Streptococcus pneumoniae and Klebsiella pneumoniae maintained high occurrence across samples, while fungal and viral detections were more sporadic (distribution of all microorganisms was shown in Supplementary Figure 4 ). The nucleic acid analysis against the causative or possibly causative pathogens showed no significant differences between COPD and non-COPD groups for total pathogens (p value = 0.8922), GPB (p value = 0.3514), GNB (p value = 0.0578), fungi (p value = 0.1056), DNA viruses (p value = 0.0843), or RNA viruses (p value = 0.9342) ( Figure 3F ). By contrast, atypical pathogens exhibited a significantly higher read count in non-COPD patients (p value = 0.0009). In 259 COPD patients with respiratory infections, bacterial pathogens were the most frequently detected etiology, accounting for 71.43% (185/259) of samples ( Supplementary Figure 5A ). Pathogen-negative specimens comprised 14.67% (38/259), fungal infections 5.79% (15/259), viral infections 4.63% (12/259), atypical infections 1.16% (3/259), and mixed infections 2.32% (6/259). Among the six mixed infection cases, dual bacterial-viral co-infections predominated (66.67%, 4/6), while viral-atypical and triple bacterial-fungal-viral combinations each occurred in one patient (16.67%, 1/6, each). Human gammaherpesvirus 4 (n = 3) and Streptococcus pneumoniae (n = 2) were the most two common co-pathogens ( Supplementary Figure 5B ). In 887 non-COPD patients, bacterial infection remained the dominant infection type (56.03%, 497/887), followed by atypical infections 10.03% (89/887) and viral infections 8.34% (74/887) ( Supplementary Figure 5C ). Mixed infections accounted for 4.74% (42/887) of samples and were predominantly bacterial-fungal (35.71%, 15/42), bacterial-viral (26.19%, 11/42), and bacterial-fungal-viral (11.90%, 5/42) combinations. Common co-pathogens included Aspergillus fumigatus (n = 13), Streptococcus pneumoniae (n = 11), and Pneumocystis jirovecii (n = 11) ( Supplementary Figure 5D ). 3.4 Characterization of non-pathogenic microorganisms A total of 42 non-pathogenic microbial species were detected across both cohorts: only one species was unique to COPD group, 16 species were unique to non-COPD group, and 25 species were shared by both ( Figure 4A ). Loads of these microorganisms were significantly higher in COPD patients (median reads count 1.31×10 5 ) than in non-COPD patients (median reads count 8.55×10 4 ; p value = 0.004) ( Figure 4B ). Principal coordinate analysis based on all non-pathogenic microbial detection data revealed clear separation along the first axis: PC1 accounted for 14.2% of the variance and differed markedly between COPD and non-COPD groups (p value < 0.0001), whereas PC2 (7.2% of variance) showed no significant difference (p value = 0.7957). PERMANOVA confirmed that group membership explained a small but statistically significant fraction of the overall variance (R 2 = 0.0045, p value = 0.001) ( Figure 4C ). The overall distribution of microbial species was comparable between COPD and non-COPD groups, with a predominance of GNB observed in both cohorts (59% for COPD; 54% for non-COPD), followed by GPB (32% for COPD; 34% for non-COPD), fungi (7% for COPD; 11% for non-COPD), and viruses (1% in both groups) ( Figure 4D&E ). However, species-level differences were noted between the groups. Veillonella parvula and Schaalia odontolytica , the predominant microorganisms, accounted for a higher proportion of isolates in COPD patients (43% and 22%, respectively) compared to non-COPD patients (35% and 16%, respectively). In contrast, several GPB species were more frequently detected in non-COPD group, including Staphylococcus epidermidis (5% versus 0.7%), Enterococcus faecium (4% versus 1%), Streptococcus intermedius (3% versus 2%), and Enterococcus faecalis (1% versus 0.5%). 3.5 Machine learning model predicts poor prognosis In this study, a machine learning model was developed using comprehensive microbial detection data to identify key microbial factors associated with poor clinical outcomes. The random forest classifier was first tuned by Leave-One-Out Cross-Validation to determine the optimal number of predictors to include ( Figure 5A ). Model accuracy steadily increased with the number of predictors and plateaued at 12 features, which was chosen for the final model. In a bootstrap-resampled test set, the model exhibited near-perfect discrimination: area under the receiver operating characteristic curve (AUC) = 0.9998, sensitivity = 100.00%, specificity = 97.62%, positive predictive value (PPV) = 99.54%, negative predictive value (NPV) = 100.00%, and overall accuracy = 99.61% ( Figure 5B ). Feature-importance analysis (mean decrease in Gini) revealed that SARS-CoV-2 abundance was the single strongest predictor of poor prognosis (0.0201), followed in descending order by Veillonella parvula (0.0056), Achromobacter xylosoxidans (0.0045), and Klebsiella pneumoniae (0.0042) ( Figure 5C ). Several non-pathogenic microorganisms - including Actinomyces naeslundii (-0.0051), Schaalia odontolytica (-0.0047), and Candida albicans (-0.0043) - exhibited negative importance scores, suggesting a relative protective association. Partial decision-tree excerpts ( Supplementary Figure 6 ) illustrate how high SARS-CoV-2 loads and elevated levels of key microorganisms drive the top-level splits that distinguish patients with poor versus good prognosis, underscoring the complex, hierarchical interplay of microbial signatures in prognostication. 4. Discussion In this comprehensive investigation, we characterized the microbial profiles in COPD patients compared with non-COPD individuals, demonstrating notable differences in pathogenic and non-pathogenic microorganisms and their correlation with clinical outcomes. Utilizing NGS, we highlighted distinct microbiological landscapes and identified key pathogens significantly associated with adverse clinical outcomes in COPD patients. Consistent with prior studies, bacterial pathogens predominated in respiratory infections among both COPD and non-COPD groups ( 14 – 16 ). Importantly, our findings demonstrate a marked enrichment of GNB, particularly Pseudomonas aeruginosa and Haemophilus influenzae , among COPD patients. These pathogens have previously been implicated in recurrent exacerbations, rapid disease progression, and impaired lung function, potentially due to chronic colonization facilitated by altered airway immunity and compromised mucociliary clearance characteristic of COPD ( 17 – 20 ). Interestingly, the incidence of fungal infections was higher among COPD patients. This aligns with earlier evidence suggesting increased susceptibility of COPD patients to fungal colonization and infection, possibly attributable to prolonged corticosteroid therapy, frequent antibiotic exposure, and impaired local immunity ( 21 – 24 ). The presence of fungi such as Aspergillus has previously been associated with worse clinical outcomes, indicating the necessity for clinicians to maintain heightened vigilance for fungal pathogens in managing COPD exacerbations. In contrast, atypical pathogens, including Mycoplasma pneumoniae , were significantly more prevalent in non-COPD patients. This discrepancy could stem from differences in host immunity or microbiome composition, warranting further exploration into the immunopathological mechanisms underpinning susceptibility to atypical pathogens in the non-COPD population ( 25 – 27 ). Additionally, the seasonal trends observed in viral infections underscore the importance of preventive strategies, including targeted vaccination programs, to mitigate COPD exacerbation risks during peak viral transmission periods. Another critical aspect of our study was the characterization of microorganisms initially deemed non-pathogenic. The significant enrichment of Veillonella parvula and Schaalia odontolytica in COPD patients may reflect alterations in the respiratory microbiome associated with chronic airway inflammation. Though traditionally considered commensal organisms, their heightened presence suggests potential roles in modulating airway inflammation or interacting synergistically with pathogenic bacteria, thereby influencing disease severity and exacerbation frequency. Further studies employing longitudinal designs and functional microbiomics are required to clarify their precise clinical significance. Remarkably, our advanced machine learning model demonstrated robust predictive capabilities for identifying patients at risk of poor clinical outcomes, with SARS-CoV-2 being the strongest single predictor. This observation highlights the persistent threat posed by SARS-CoV-2 infections, particularly in vulnerable COPD populations. The prognostic implications of other microbial factors identified by the model, such as Achromobacter xylosoxidans and Klebsiella pneumoniae , suggest opportunities for developing pathogen-specific therapeutic strategies and enhanced monitoring protocols to prevent progression to severe outcomes. Our study has several notable strengths, including a large cohort size, utilization of highly sensitive and specific NGS technology, and comprehensive analysis encompassing both pathogenic and traditionally non-pathogenic microorganisms. Nonetheless, certain limitations should be acknowledged. Firstly, our study was conducted at a single institution, potentially limiting generalizability. Secondly, the observational design precludes establishing causal relationships between microbial profiles and clinical outcomes. Prospective, multicenter studies with extended follow-up periods are warranted to validate our findings and further elucidate the mechanistic pathways linking microbial communities to COPD exacerbations and prognosis. 5. Conclusions In conclusion, our findings provide critical insights into the microbiological underpinnings of COPD-related infections, highlighting pathogen-specific associations with clinical prognosis. These results have significant implications for clinical practice, suggesting that precise microbial identification via NGS could facilitate personalized therapeutic strategies, ultimately improving patient outcomes in COPD. Abbreviations AUC, area under the receiver operating characteristic curve BALF, bronchoalveolar lavage fluid BWA, Burrows-Wheeler Aligner CI, confidence interval CMT, conventional microbiological test COPD, chronic obstructive pulmonary disease CRP, C-reactive protein DNB, DNA nanoball GNB, gram-negative bacteria GOLD, Chronic Obstructive Lung Disease GPB, gram-positive bacteria ICU, intensive care unit IQR, interquartile range LYM, lymphocyte NE, neutrophil NGS, next-generation sequencing NPV, negative predictive value OR, odds ratio PCT, procalcitonin PPV, positive predictive value Ref, reference WBC, white blood cell Declarations Ethics approval and consent to participate This study design received approval from the Ethics Committee of the First Hospital of Jilin University in accordance with the Declaration of Helsinki (2025-396). Consent for publication The study was conducted with the consent of every human participant. Availability of data and materials The raw sequencing data and relevant materials of the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the Specialized Program for Healthcare Talents in Jilin Province (JlSRCZX2025-047) from the Finance Department of Jilin Province. Authors' contributions All authors contributed to conceptualization and writing. CyL coordinated the entire study and performed the data curation and analysis. ChL generated all figures and tables. ZG was responsible for the investigation and methodology. SL supervised this study and reviewed the original draft. All authors have read and agreed to the published version of the manuscript. Acknowledgements We appreciate it for the support of the hospital staff and the understanding of the patients and their families. References Wang L, Xie J, Hu Y-h, Tian Y. Air pollution and risk of chronic obstructed pulmonary disease: The modifying effect of genetic susceptibility and lifestyle. EBioMedicine. 2022;79. Bigna JJR, Kenne AM, Asangbeh SL, Sibetcheu AT. Prevalence of chronic obstructive pulmonary disease in the global population with HIV: a systematic review and meta-analysis. The Lancet Global health. 2017;6 2:e193-e202. Florman KE, Siddharthan T, Pollard SL, Alupo P, Barber J, Chandyo RK, et al. Unmet Diagnostic and Therapeutic Opportunities for Chronic Obstructive Pulmonary Disease in Low- and Middle-Income Countries. American Journal of Respiratory and Critical Care Medicine. 2023;208:442 - 50. Lytsy P, Engström S, Ekstedt M, Engström I, Hansson L, Ali L, et al. Outcomes associated with higher relational continuity in the treatment of persons with asthma or chronic obstructive pulmonary disease: A systematic review. eClinicalMedicine. 2022;49. Jain PK, Seval M, Labana R, Sarla. A Cross-sectional Study of Correlation of Neutrophil-to-lymphocyte Ratio in Acute Exacerbation of Chronic Obstructive Pulmonary Disease. APIK Journal of Internal Medicine. 2023. Puhan MA, Gimeno-Santos E, Cates CJ, Troosters T. Pulmonary rehabilitation following exacerbations of chronic obstructive pulmonary disease. The Cochrane database of systematic reviews. 2016;12:CD005305. Tian Y, Zeng T, Tan L, Wu Y, Yu J, Huang J, et al. BPI-ANCA in chronic obstructive pulmonary disease with pulmonary Pseudomonas aeruginosa colonisation: a novel indicator of poor prognosis. British Journal of Biomedical Science. 2018;75:206 - 8. Sonowal T, Shariff M. Direct Detection of Atypical Bacteria in Clinical Samples from Patients with Chronic Obstructive Pulmonary Disease (COPD) by PCR and RT-PCR. International Journal of Current Microbiology and Applied Sciences. 2024. Liao K-M, Chen Y-J, Shen C-W, Ou S-K, Chen C-Y. The Influence of Influenza Virus Infections in Patients with Chronic Obstructive Pulmonary Disease. International Journal of Chronic Obstructive Pulmonary Disease. 2022;17:2253 - 61. Linden D, Guo-Parke H, Coyle PV, Fairley DJ, McAuley DF, Taggart CC, et al. Respiratory viral infection: a potential “missing link” in the pathogenesis of COPD. European Respiratory Review. 2019;28. Yin Y, Zhu P, Guo Y, Li Y, Chen H, Liu J, et al. Enhancing lower respiratory tract infection diagnosis: implementation and clinical assessment of multiplex PCR-based and hybrid capture-based targeted next-generation sequencing. eBioMedicine. 2024;107. Fourgeaud J, Régnault B, Ok V, Da Rocha N, Sitterlé É, Mekouar M, et al. Performance of clinical metagenomics in France: a prospective observational study. The Lancet Microbe. 2023. GOLD. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: 2025 Report. 2025. Li Z-J, Zhang H, Ren L, Lu Q, Ren X, Zhang C, et al. Etiological and epidemiological features of acute respiratory infections in China. Nature Communications. 2021;12. Ieven M, Coenen S, Loens K, Lammens C, Coenjaerts F, Vanderstraeten A, et al. Aetiology of lower respiratory tract infection in adults in primary care: a prospective study in 11 European countries. Clinical Microbiology and Infection. 2018;24:1158-63. Shmoury A, Zakhour J, Sawma T, Haddad S, Zahreddine N, Tannous J, et al. Bacterial respiratory infections in patients with COVID-19: A retrospective study from a tertiary care center in Lebanon. Journal of infection and public health. 2023. McDonnell M, Jary H, Perry A, Macfarlane J, Hester K, Small T, et al. Non cystic fibrosis bronchiectasis: A longitudinal retrospective observational cohort study of Pseudomonas persistence and resistance. Respiratory medicine. 2015;109 6:716-26. Eklöf J, Sørensen R, Ingebrigtsen T, Sivapalan P, Achir I, Boel J, et al. Pseudomonas aeruginosa and risk of death and exacerbations in patients with chronic obstructive pulmonary disease: an observational cohort study of 22.053 patients. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases. 2020. Araújo D, Shteinberg M, Aliberti S, Goeminne P, Hill A, Fardon T, et al. The independent contribution of Pseudomonas aeruginosa infection to long-term clinical outcomes in bronchiectasis. European Respiratory Journal. 2018;51. Lindgren N, Novak L, Hunt B, McDaniel M, Swords W. Nontypeable Haemophilus influenzae Infection Impedes Pseudomonas aeruginosa Colonization and Persistence in Mouse Respiratory Tract. Infection and Immunity. 2021;90. Tiew P, Ko F, Pang SL, Matta S, Sio Y, Poh M, et al. Environmental fungal sensitisation associates with poorer clinical outcomes in COPD. The European Respiratory Journal. 2020;56. Wu Y-X, Zuo Y, Cheng Q, Huang Y, Bao Z-Y, Jin X-Y, et al. Respiratory Aspergillus Colonization Was Associated With Relapse of Acute Exacerbation in Patients With Chronic Obstructive Pulmonary Disease: Analysis of Data From A Retrospective Cohort Study. Frontiers in Medicine. 2021;8. Waqas S, Dunne K, Talento A, Wilson G, Martín-Loeches I, Keane J, et al. Prospective observational study of respiratory Aspergillus colonization or disease in patients with various stages of chronic obstructive pulmonary disease utilizing culture versus nonculture techniques. Medical mycology. 2020. Tiew P, Thng K, Chotirmall S. Clinical Aspergillus Signatures in COPD and Bronchiectasis. Journal of Fungi. 2022;8. Georgakopoulou V, Lempesis I, Sklapani P, Trakas N, Spandidos D. Exploring the pathogenetic mechanisms of Mycoplasma pneumoniae (Review). Experimental and Therapeutic Medicine. 2024;28. Fan L, Xu N, Guo Y, Li L. Enhanced insights into the neutrophil-driven immune mechanisms during Mycoplasma pneumoniae infection. Heliyon. 2024;10. Narita M. Classification of Extrapulmonary Manifestations Due to Mycoplasma pneumoniae Infection on the Basis of Possible Pathogenesis. Frontiers in Microbiology. 2016;7. Table Table 1 . Demographic and clinical characteristics of patients across COPD and Non-COPD groups. Overall (n = 1146) COPD (n = 259) Non-COPD (n = 887) p value Age , median (IQR) (years) 66 (55 - 73) 70 (66 - 78) 63 (52 - 71) < 0.0001 Gender Female, n (%) 505 (44.1%) 125 (48.3%) 380 (42.8%) 0.1221 Male, n (%) 641 (55.9%) 134 (51.7%) 507 (57.2%) 0.1221 Immune status Immunocompetent, n (%) 893 (77.9%) 212 (81.9%) 681 (76.8%) 0.0830 Cancer, n (%) 64 (5.6%) 12 (4.6%) 52 (5.9%) 0.4485 Diabetes, n (%) 189 (16.5%) 35 (13.5%) 154 (17.4%) 0.1420 Dyspnea , n (%) 411 (35.9%) 198 (76.4%) 213 (24.0%) < 0.0001 Smoking history Smoking, n (%) 419 (36.6%) 128 (49.4%) 291 (32.8%) < 0.0001 Smoking, median (IQR) (years) 0 (0 - 30) 10 (0 - 37.5) 0 (0 - 20) < 0.0001 Hematologic parameters WBC, median (IQR) (10^9/L) 9.2 (5.5 - 13.2) 10.3 (7.3 - 13.7) 8.9 (5.4 - 13.0) 0.0068 NE, median (IQR) (%) 79.8 (65.1 - 89.1) 83.2 (71.6 - 90.3) 78.8 (62.5 - 88.6) < 0.0001 LYM, median (IQR) (%) 12.0 (6.0 - 20.9) 9.7 (5.3 - 16.3) 12.8 (6.4 - 22.6) < 0.0001 CRP, median (IQR) (mg/L) 45.8 (9.7 - 110.0) 44.5 (9.6 - 121.9) 46.3 (9.9 - 107.7) 0.3306 PCT, median (IQR) (ng/mL) 0.116 (0.057 - 0.412) 0.108 (0.054 - 0.349) 0.117 (0.058 - 0.425) 0.9278 Prognosis After admission, median (IQR) (days) 8 (6 - 11) 8 (6 - 10) 8 (6 - 11) 0.2008 ICU, n (%) 137 (11.95%) 40 (15.44%) 97 (10.94%) 0.0491 Mechanical ventilation, n (%) 71 (6.2%) 32 (12.36%) 39 (4.4%) < 0.0001 Dead, n (%) 55 (4.8%) 9 (3.5%) 46 (5.2%) 0.2570 Abbreviations: COPD, chronic obstructive pulmonary disease; IQR, interquartile range; WBC, white blood cell; NE, neutrophil; LYM, lymphocyte; CRP, C-reactive protein; PCT, procalcitonin; ICU, intensive care unit. Supplementary Files SupplementaryFigure1.tif Supplementary Figure 1.tif Workflow of clinical diagnosis. NGS, next-generation sequencing; BALF, bronchoalveolar lavage fluid; CMT, conventional microbiological test. SupplementaryFigure2.tiff Supplementary Figure 2.tiff All samples from patients with and without COPD, along with the proportional distribution across each corresponding level of clinical diagnosis. COPD, chronic obstructive pulmonary disease. SupplementaryFigure3.tiff Supplementary Figure 3.tiff Distribution of each species of causative or possibly causative pathogens detected in all samples. Bars are the frequency statistics. SupplementaryFIgure4.tiff Supplementary Figure 4.tiff Distribution of each species of microorganisms detected in all samples. Bars were the frequency statistics. COPD, chronic obstructive pulmonary disease. SupplementaryFigure5.tif Supplementary Figure 5.tif Analysis of infection types in COPD and non-COPD patients. (A) Distribution of infection types in COPD patients. The left pie represents the types of causative or possibly causative pathogen detected in all samples from COPD patients. The right pie represents the types of mixed infection. (B) Frequency of causative or possibly causative pathogens in mixed infections of COPD patients. (C) Distribution of infection types in non-COPD patients. The left pie represents the types of causative or possibly causative pathogen detected in all samples from non-COPD patients. The right pie represents the types of mixed infection. (D) Frequency of causative or possibly causative pathogens in mixed infections of non-COPD patients. SupplementaryFigure6.tif Supplementary Figure 6.tif Partial decision trees in the random forest model for predicting poor prognosis of respiratory infections in COPD patients. SupplementaryTable.xlsx Supplemental Table 1.xlsx Microorganisms list in the microbial genome database. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7689813","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521442697,"identity":"002512d8-2f5e-4597-ad92-2ddbabe82b9c","order_by":0,"name":"Chaoying Liu","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Chaoying","middleName":"","lastName":"Liu","suffix":""},{"id":521442698,"identity":"0634666a-811c-4b5e-ad46-97643f8f9be4","order_by":1,"name":"Caihong Liu","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Caihong","middleName":"","lastName":"Liu","suffix":""},{"id":521442699,"identity":"84f2ffb9-7a43-4559-b4ba-561a5b4b9b79","order_by":2,"name":"Zhuo Gao","email":"","orcid":"","institution":"Changchun Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"","lastName":"Gao","suffix":""},{"id":521442700,"identity":"bd7fd75b-2763-456e-b623-ba5df1f24adc","order_by":3,"name":"Shan Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACPmYGBiBi4OFnbz744AOPBUjQAK8WNqgWGcmeY8mGM3gkiNDCANFiY3Ajx0yah4EYLey8hz8X1NzhYbiRlmxsIyOR2MDevE2CoeYOHofxpUnPOPaMh7Hn8cHHOTxALTzHyiQYjj3Do4XHjJmH7TAPMzvQFrAWiRwzCcaGw/i0GH/m+XeYh40B6BcLkBb5NwS1GEjzth3m4eEAamEA28JDUIuZNG/fYR4JHmAg9/BIGLfxpBVbJBzDrYWf/wzQYd8O29sfB0blzx4b2X72wxtvfKjBrQUVMPZAYoohgUgNQPCDeKWjYBSMglEwcgAAgnVHspa6A/AAAAAASUVORK5CYII=","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Shan","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-09-23 05:21:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7689813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7689813/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93244414,"identity":"ba95b1b3-75d1-4cd1-b762-cd821aa615b0","added_by":"auto","created_at":"2025-10-10 15:07:05","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":738632,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/5fb9acc5911dcc072062adaa.tif"},{"id":93243479,"identity":"4910c8eb-33be-450f-b214-567c63800170","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":427500,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/58ce5fa5aaced31902ca31a4.tiff"},{"id":93244415,"identity":"ca78326f-e954-47c0-8676-9fba5556bc12","added_by":"auto","created_at":"2025-10-10 15:07:05","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1632704,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/eb8d5e8ba25c4745f7c691fb.tif"},{"id":93243487,"identity":"7ba48284-1423-443c-8a33-ea5d197f112c","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5181372,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/0ad5d2646e529357aac5a261.tif"},{"id":93243502,"identity":"5ece69a6-eb37-488b-ac47-d58dd0c48e1d","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3102724,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/30536e023ea10602ee99f04c.tif"},{"id":93243482,"identity":"e32f3801-6757-4df7-985a-f3b173b50373","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10584,"visible":true,"origin":"","legend":"","description":"","filename":"jtrmJTRMD2516696.xml","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/66b007ac63f2511b6d230ad7.xml"},{"id":93247030,"identity":"b9b7d0e6-e341-4339-bffa-bfd8832a5cd6","added_by":"auto","created_at":"2025-10-10 15:23:06","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1435,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD2516696147670.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/53e07f49522b245c10b8fc54.xml"},{"id":93244418,"identity":"3f0c2eaf-e041-469e-8ddd-0dff96592961","added_by":"auto","created_at":"2025-10-10 15:07:05","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":837,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD2516696Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/ad616bea194f1e5a3eba65dd.xml"},{"id":93243503,"identity":"d2dcda05-c516-4f0e-adf5-370cf6f8c496","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102838,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD25166960enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/95b70ff366de370d80b29068.xml"},{"id":93246568,"identity":"bee093be-a7e8-4f81-8815-f00703aad7f0","added_by":"auto","created_at":"2025-10-10 15:15:06","extension":"tif","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":738632,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/d910a6d7a3dc688c39280a65.tif"},{"id":93243495,"identity":"c8ea8522-cf77-4346-ae3f-5d33334e524c","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"tiff","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":427500,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/bd6e5f12d58a10441b648db5.tiff"},{"id":93243498,"identity":"3183b6c0-1b14-4514-b45f-a5a01f1f4ca7","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"tif","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1632704,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/abb36713dec051d21f1a1200.tif"},{"id":93244421,"identity":"88a80062-9cb8-4f95-8c82-e53dca4acade","added_by":"auto","created_at":"2025-10-10 15:07:06","extension":"tif","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5181372,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/a2dac1fd0371fca11f4c3c7c.tif"},{"id":93244419,"identity":"9442eaaf-a7b6-406a-96ef-c17fb89da84b","added_by":"auto","created_at":"2025-10-10 15:07:06","extension":"tif","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3102724,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/5d14a5267504220be6e051dd.tif"},{"id":93243489,"identity":"3ca6e3f4-6daa-486c-bdc8-85997957ce9d","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147683,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/ad7a3312889c6af0e505c241.png"},{"id":93246569,"identity":"a2a10307-b210-4605-96f5-11edecfd65f0","added_by":"auto","created_at":"2025-10-10 15:15:06","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137801,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/7bbee33994fc94390d6495c1.png"},{"id":93244424,"identity":"dfc7d992-5404-4e92-9043-68375e7189b0","added_by":"auto","created_at":"2025-10-10 15:07:06","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":264464,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/96001ad6e0dad7741471421a.png"},{"id":93243496,"identity":"8634b47c-6868-4d5c-9a7e-a55bfb9eedc9","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":965875,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/39abff9ffc8d561866983bd7.png"},{"id":93244425,"identity":"54064f62-1663-458c-9c49-9fad4f4936f1","added_by":"auto","created_at":"2025-10-10 15:07:06","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":586331,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/91adddedfcd82b5ee0bf2062.png"},{"id":93243499,"identity":"53d9b686-1f9a-4623-b15d-4dc14210fa9b","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"xml","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100795,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD25166960structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/70c1cdd10127a1a3b148726f.xml"},{"id":93243492,"identity":"363647db-aa19-4739-a47f-f17e8fb33f64","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109662,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/e1f2e5912a054f70f5365f9a.html"},{"id":93243476,"identity":"8b09d39d-5841-4004-80f0-18ebcd51d2f3","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1405194,"visible":true,"origin":"","legend":"\u003cp\u003ePatients included in this study. \u003cstrong\u003e(A) \u003c/strong\u003eFlowchart of samples analyzed by NGS. BALF, bronchoalveolar lavage fluid; NGS, next-generation sequencing; COPD, chronic obstructive pulmonary disease. \u003cstrong\u003e(B) \u003c/strong\u003ePatients with respiratory infections enrolled between January 2024 and January 2025 and the percentage of COPD patients among them.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/a90d892bb38ae1c82fdbe17f.png"},{"id":93244413,"identity":"ba78bb54-49b4-4bf4-a4b9-3d3a6b807a60","added_by":"auto","created_at":"2025-10-10 15:07:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1258424,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate regression analysis of COPD and clinical parameters. Only p values less than 0.05 were considered significant and are indicated by red dots. COPD, chronic obstructive pulmonary disease; OR, odds ratio; CI, confidence interval; Ref, reference; WBC, white blood cell; NE, neutrophil; LYM, lymphocyte; ICU, intensive care unit.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/c083ab3f132bea84bcff73d4.png"},{"id":93246567,"identity":"06054d22-afcd-49ab-9299-adceceb00f21","added_by":"auto","created_at":"2025-10-10 15:15:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2760491,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of causative or possibly causative pathogens. \u003cstrong\u003e(A) \u003c/strong\u003eAll microorganisms identified by NGS in patients with and without COPD, along with the proportional distribution across each corresponding level of clinical diagnosis. \u003cstrong\u003e(B) \u003c/strong\u003eProportional distribution of each species type of causative or possibly causative pathogens detected between January 2024 and January 2025. GPB, gram-positive bacteria; GNB, gram-negative bacteria. \u003cstrong\u003e(C-D) \u003c/strong\u003eProportional distribution of each species type of causative or possibly causative pathogens detected in patients with and without COPD. \u003cstrong\u003e(E) \u003c/strong\u003eOccurrence of each species of causative or possibly causative pathogens detected in patients with and without COPD. Circles represent the occurrence of each species type of causative or possibly causative pathogens detected in patients with and without COPD. \u003cstrong\u003e(F) \u003c/strong\u003eDistribution of reads counts of causative or possibly causative pathogens detected by NGS in COPD versus non-COPD groups, overall and across each species type.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/f8e9c0fea8a1b4ece5a84204.png"},{"id":93243484,"identity":"b53e93d8-fb4d-4e75-911c-f0569be11d53","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16440129,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of non-pathogenic microorganisms. \u003cstrong\u003e(A) \u003c/strong\u003eNumber of species of microorganisms identified in COPD versus non-COPD groups. The intersection is the number of species identified in both groups. \u003cstrong\u003e(B) \u003c/strong\u003eDistribution of reads counts of microorganisms detected by NGS in COPD versus non-COPD groups. \u003cstrong\u003e(C) \u003c/strong\u003ePrincipal coordinate analysis based on all non-pathogenic microbial detection information in COPD and non-COPD groups. PC1, principal coordinate one, the largest source of variance; PC2, principal coordinate two, the second largest source of variance. Percentages in parentheses are the degree of explanation of variance for the corresponding principal coordinate. \u003cstrong\u003e(D-E) \u003c/strong\u003eDistribution of microbial species and the types they belong to in COPD and non-COPD groups. GPB, gram-positive bacteria; GNB, gram-negative bacteria.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/8c8696ee0c895547415908bc.png"},{"id":93243486,"identity":"93bb228f-6e47-41d6-a7ed-4aaebf8d0340","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7872420,"visible":true,"origin":"","legend":"\u003cp\u003eMachine learning model for predicting poor prognosis of respiratory infections in COPD patients. \u003cstrong\u003e(A) \u003c/strong\u003eCross-validation of hyperparameter screening for the random forest model. \u003cstrong\u003e(B) \u003c/strong\u003ePerformance of the random forest model in bootstrap sampling-based test set. AUC, area under the receiver operating characteristic curve; PPV, positive predictive value; NPV, negative predictive value. \u003cstrong\u003e(C) \u003c/strong\u003eImportance of factors extracted from the random forest model.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/e3534a840f4fd854b6ae0699.png"},{"id":95222994,"identity":"8b43ff9a-8859-444b-85e9-085338fc3af2","added_by":"auto","created_at":"2025-11-05 16:21:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28603559,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/1c19431f-c9a0-4e79-ba13-56682c4f48a1.pdf"},{"id":93243474,"identity":"8cc38f7b-5a3b-4641-8e6d-766f8f1f25a1","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":327688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e.tif Workflow of clinical diagnosis. NGS, next-generation sequencing; BALF, bronchoalveolar lavage fluid; CMT, conventional microbiological test.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/95188a9cd188a633b9ea032d.tif"},{"id":93243473,"identity":"03e08699-d624-4607-87bd-6c867610852d","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":134962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e.tiff All samples from patients with and without COPD, along with the proportional distribution across each corresponding level of clinical diagnosis. COPD, chronic obstructive pulmonary disease.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/c258472b3b560c33ab267d98.tiff"},{"id":93243480,"identity":"c1c92f15-94f2-4610-8069-5036944225b7","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1591436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3\u003c/strong\u003e.tiff Distribution of each species of causative or possibly causative pathogens detected in all samples. Bars are the frequency statistics.\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/356ac6054bfff94ee4dc856e.tiff"},{"id":93243477,"identity":"1a3b8d4e-f88f-4433-8a3c-b53e1eb05d62","added_by":"auto","created_at":"2025-10-10 14:59:05","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1961072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4\u003c/strong\u003e.tiff Distribution of each species of microorganisms detected in all samples. Bars were the frequency statistics. COPD, chronic obstructive pulmonary disease.\u003c/p\u003e","description":"","filename":"SupplementaryFIgure4.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/136bec243bb6439bccc0b8cd.tiff"},{"id":93244417,"identity":"99ba0183-9b98-457f-9917-02913cbb0eb6","added_by":"auto","created_at":"2025-10-10 15:07:05","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":906328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5\u003c/strong\u003e.tif Analysis of infection types in COPD and non-COPD patients. (A) Distribution of infection types in COPD patients. The left pie represents the types of causative or possibly causative pathogen detected in all samples from COPD patients. The right pie represents the types of mixed infection. (B) Frequency of causative or possibly causative pathogens in mixed infections of COPD patients. (C) Distribution of infection types in non-COPD patients. The left pie represents the types of causative or possibly causative pathogen detected in all samples from non-COPD patients. The right pie represents the types of mixed infection. (D) Frequency of causative or possibly causative pathogens in mixed infections of non-COPD patients.\u003c/p\u003e","description":"","filename":"SupplementaryFigure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/1ee3e3757f743a3d57ae2674.tif"},{"id":93243504,"identity":"37dba1d5-724d-4f67-bb15-802abd0472e6","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":4655388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 6\u003c/strong\u003e.tif Partial decision trees in the random forest model for predicting poor prognosis of respiratory infections in COPD patients.\u003c/p\u003e","description":"","filename":"SupplementaryFigure6.tif","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/41375672dbad7defb64b475e.tif"},{"id":93243501,"identity":"9b8add21-d116-44be-98de-72b96dab8cb9","added_by":"auto","created_at":"2025-10-10 14:59:06","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":297583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e.xlsx Microorganisms list in the microbial genome database.\u003c/p\u003e","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7689813/v1/70232eee4009c09a8c4d173b.xlsx"}],"financialInterests":"","formattedTitle":"Characteristics of pathogenic microorganisms in COPD-related infections: prognostic correlations and implications","fulltext":[{"header":"1. Background","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) represents a major global health burden, characterized by persistent respiratory symptoms and progressive airflow limitation, resulting from chronic inflammation of the airways and lung parenchyma (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). As the third leading cause of death worldwide, COPD significantly impairs patients' quality of life, incurs substantial healthcare expenditures, and imposes a considerable socioeconomic burden (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Among COPD-related complications, acute exacerbations are particularly critical, contributing substantially to disease progression, increased hospitalization rates, accelerated lung function decline, and heightened mortality (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Respiratory infections are established as predominant precipitants of acute exacerbations in COPD, with bacterial, viral, and atypical pathogens frequently implicated (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, the spectrum and distribution of infectious microorganisms among COPD patients compared with non-COPD individuals, as well as their potential prognostic significance, remain inadequately elucidated.\u003c/p\u003e\u003cp\u003eRecent technological advancements in pathogen identification, particularly next-generation sequencing (NGS), offer unprecedented sensitivity and specificity in the detection and characterization of microbial pathogens (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Compared to traditional culture-based methods, NGS provides comprehensive microbiome profiles, enabling more accurate identification of pathogenic organisms, thereby deepening our understanding of the microbial landscape in COPD patients, informing targeted antimicrobial therapies, and potentially improving clinical management strategies aimed at reducing exacerbation frequency and severity.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to systematically investigate the differences in infectious pathogen profiles between COPD and non-COPD patients, and further explore the associations between pathogen-specific infections and adverse clinical outcomes in COPD patients. Our findings may provide critical insights into disease mechanisms underlying COPD exacerbations and facilitate the development of precise clinical interventions aimed at optimizing patient prognosis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e\u003cp\u003eThis study was conducted at the Department of Respiratory Medicine, the First Hospital of Jilin University. Patients with suspected respiratory infection who were admitted to our department between December 2023 and February 2025 were consecutively included. Inclusion criteria encompassed (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) suspected lower respiratory infection (defined as a new-onset radiological findings on chest images or a hematologic parameter abnormality combined with at least one compatible symptom, such as fever, cough, or dyspnea); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) need for bronchoalveolar lavage according to clinical standard procedure; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) sufficient bronchoalveolar lavage fluid (BALF) samples for the NGS and conventional microbiological tests (CMTs, including BALF culture, PCR, and serological tests). Patients with insufficient clinical information or inavailable BALF sample were excluded. Enrolled patients were divided into two groups based on whether they had COPD status or not. The diagnosis of COPD status was according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Fluid from the middle segment of bronchoalveolar lavage was collected as the study sample. All BALF samples underwent conventional microbiological testing, and remaining aliquots were preserved for NGS.\u003c/p\u003e\u003cp\u003e This study design received approval from the Ethics Committee of the First Hospital of Jilin University in accordance with the Declaration of Helsinki (2025\u0026thinsp;\u0026minus;\u0026thinsp;396). The study was conducted with the consent of every human participant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Clinical data collection\u003c/h2\u003e\u003cp\u003eDetailed demographic and clinical data were extracted from electronic medical records, including age, gender, immune status (immunocompetent, immunocompromised by cancer chemotherapy, and immunocompromised by diabetes), dyspnea, smoking history, hematologic parameters (white blood cell [WBC], neutrophil [NE], lymphocyte [LYM], C-reactive protein [CRP], and procalcitonin [PCT]), and prognosis parameters (days of hospitalization, intensive care unit [ICU] admission, mechanical ventilation, and outcome [survival and death]). Poor prognosis was defined as the presence of any of ICU admission, mechanical ventilation, and death.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Next-generation sequencing\u003c/h2\u003e\u003cp\u003eBefore extracting the nucleic acid, host DNA was first depleted from BALF samples using the MolYsis\u0026trade; Basic 5 Kit (Molzym GmbH \u0026amp; Co. KG, Bremen, Germany). Nucleic acids were then extracted using the Magnetic Pathogen DNA/RNA Kit (Tiangen Biotech [Beijing] Co., Ltd, Beijing, China). Following this, the RNA was reverse-transcribed into cDNA using the Hieff NGS Double Stranded cDNA Synthesis Kit (Yeasen, Shanghai, China). The concentration of the total DNA was quantified using the Qubit\u0026trade; Double-Stranded DNA High Sensitivity Assay Kit (Thermo Fisher Scientific Inc., Waltham, MA, USA). Then DNA libraries were prepared using the VAHTS Universal Plus DNA Library Prep Kit for MGI (Vazyme, Nanjing, China) with an initial input of 2 ng. Meanwhile, library quality control was performed with the Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA) to evaluate DNA concentration and fragment size distribution. Libraries exhibiting a dominant fragment peak between 240 bp and 350 bp and a concentration exceeding 1 ng/\u0026micro;L were deemed qualified. Approved libraries were pooled, denatured, and circularized to form single-stranded DNA circles. DNA nanoballs (DNBs) were subsequently generated through rolling circle amplification. The resulting DNBs were loaded onto sequencing chips and subjected to single-end 50-bp sequencing on the BGISEQ-500 (BGI, Shenzhen, China) platform, yielding approximately 10 to 20\u0026nbsp;million reads per library.\u003c/p\u003e\u003cp\u003eFollowing sequencing, low-quality reads, short reads, and adapter were removed using Fastp (version 0.23.4) to generate high-quality data for downstream analysis. Clean reads were aligned to three human reference genomes (hg38, T2T-CHM13, and YH1) using Burrows-Wheeler Aligner (BWA, version 0.7.17-r1188). Human-derived reads were subsequently excluded using Samtools (version 1.6). Then the remaining reads were aligned against a custom microbial database using BWA to derive annotations (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). After that, the annotation results were further validated by BLAST (version 2.12.0) to ensure accuracy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Clinical diagnosis\u003c/h2\u003e\u003cp\u003eMicroorganisms identified by NGS were independently evaluated by a minimum of three clinicians holding at least an associate senior professional title, and classified into four levels, including causative pathogen, possibly causative pathogen, microorganism without pathogenic role, and not causative pathogen (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The clinical diagnosis was established by clinicians following a comprehensive assessment that integrated patient age, gender, immune status, medical history, symptoms, hematologic parameters, radiological findings, CMT results, prognosis, and other relevant clinical data (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eFor descriptive statistics, continuous variables were presented as medians and interquartile ranges (IQR), whereas categorical variables were reported as frequencies and percentages. Multivariate logistic regression was used to compute the odds ratio (OR) and 95% confidence interval (CI) for the associations of gender, age, immune status, symptoms, smoking history, hematologic and prognosis parameters with the presence of COPD. Data normality was assessed by the Shapiro-Wilk test. Nonparametric variables were compared using Mann-Whitney test. All tests were two-tailed and significance threshold was set at p value\u0026thinsp;\u0026le;\u0026thinsp;0.05. All statistical analyses were performed using SPSS Statistics (version 26.0, IBM Corp., Armonk, NY, USA). All figures were drawn using R software (version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria), Python (version 3.11, Python Software Foundation, Wilmington, DE, USA), and GraphPad Prism (version 9.5.0, GraphPad Software LLC., San Diego, CA, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Study participants\u003c/h2\u003e\n\u003cp\u003eIn this study, a total of 1292 patients with suspected respiratory infection were screened between December 2023 and February 2025 (\u003cstrong\u003eFigure 1A\u003c/strong\u003e). Among this, 146 cases were excluded due to insufficient clinical information (n = 137) and inability to collect BALF samples (n = 9). BALF samples of 1146 patients were analyzed using NGS. In patients with COPD (22.6%, 259/1146), NGS detected causative pathogens in 221 samples, microorganisms without pathogenic role in 34 samples, and no microorganisms in 4 samples. Whereas in patients without COPD (77.4%, 887/1146), NGS detected causative pathogens in 745 samples, possibly causative pathogens in 10 samples, microorganisms without pathogenic role in 111 samples, and no microorganisms in 21 samples. The monthly incidence of respiratory infection episodes (median 87 [IQR 67 - 116] cases) remained relatively consistent throughout the study period, with modest increases observed in April (n = 134), May (n = 116), July (n = 138), and December (n = 139) (\u003cstrong\u003eFigure 1B\u003c/strong\u003e). The proportion of cases involving patients with COPD among those presenting with respiratory infections also demonstrated temporal stability, with a median prevalence of 22.99% (IQR 17.07% - 25.00%).\u003c/p\u003e\n\u003cp\u003eThe demographic and clinical characteristics of patients in this study were shown in \u003cstrong\u003eTable 1\u003c/strong\u003e. The median age of the overall cohort was 66 (IQR 55 - 73) years, with patients in COPD group being older (median 70 [IQR 66 - 78] years) compared to those without COPD (median 63 [IQR 52 - 71] years; p \u0026lt; 0.0001). Regarding gender distribution, females accounted for 44.1% (505/1146) of the total population, with a slightly higher proportion in COPD group (48.3%, 125/259) compared to non-COPD group (42.8%, 380/887; p \u0026gt; 0.05). In terms of immune status, the majority of patients were immunocompetent (77.9%, 893/1146), with comparable rates between COPD (81.9%, 212/259) and non-COPD (76.8%, 681/887) groups (p \u0026gt; 0.05). The immunocompromised cohort primarily comprised patients with diabetes mellitus (16.5%, 189/1146) and patients undergoing chemotherapy for cancer (5.6%, 64/1146). Among patients with COPD, the proportion of smoking was higher (49.4% vs. 32.8%; p \u0026lt; 0.0001), and the duration of smoking history was longer (median 10 [IQR 0 - 37.5] vs. 0 [IQR 0 - 20] years; p \u0026lt; 0.0001), compared to that in patients without COPD. For hematologic parameters, the WBC count (median 10.3 [IQR 7.3 - 13.7] vs. 8.9 [IQR 5.4 - 13.0] 10\u003csup\u003e9\u003c/sup\u003e/L; p = 0.0068) and NE percentage (median 83.2% [IQR 71.6% - 90.3%] vs. 78.8% [IQR 62.5% - 88.6%]; p \u0026lt; 0.0001) were both higher in COPD group than in non-COPD group. The median CRP and PCT were 45.8 (IQR 9.7 - 110.0) mg/L and 0.116 (IQR 0.057 - 0.412) ng/mL, respectively, with comparable values between COPD (median 44.5 [IQR 9.6 - 121.9] mg/L for CRP; median 0.108 [IQR 0.054 - 0.349] ng/mL for PCT) and non-COPD (median 46.3 [IQR 9.9 - 107.7] mg/L for CRP; median 0.117 [IQR 0.058 - 0.425] ng/mL for PCT) groups (p \u0026gt; 0.05). With respect to duration of hospitalization and mortality, no significant differences were observed between COPD and non-COPD groups (p \u0026gt; 0.05). In the overall cohort, the median length of hospital stay was 8 (IQR 6 - 11) days, and the mortality rate was 4.8% (55/1146). However, the ICU occupancy (15.44% vs. 10.94%, p = 0.04901) and mechanical ventilation utilization (12.36% vs. 4.4%, p \u0026lt; 0.0001) of COPD group were significantly higher than that of non-COPD group.\u003c/p\u003e\n\u003ch2\u003e3.2 Significant correlation between COPD and poor prognosis\u003c/h2\u003e\n\u003cp\u003eMultivariate logistic regression analysis was performed to clarify the associations of demographic and clinical characteristics of patients with the presence of COPD among those with respiratory infections (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Age was a strong predictor: compared with patients younger than 60 years, those aged 60 - 70 years had nearly a four-fold higher odds of COPD (OR 3.9, 95% CI 2.6 - 6.1, p value \u0026lt; 0.0001), and those aged 70 - 90 years had almost six-times the odds (OR 5.8, 95% CI 3.9 - 8.8, p value \u0026lt; 0.0001). Dyspnea was significantly associated with COPD (OR 3.8, 95% CI 2.6 - 5.5, p value \u0026lt; 0.0001). A longer history of smoking was also independently associated: compared with never-smokers, those with 20 - 40 years of smoking had more than double the odds (OR 2.2, 95% CI 1.5 - 3.1, p value \u0026lt; 0.0001), and those with 40 - 70 years had a similar increase (OR 2.3, 95% CI 1.4 - 3.6, p value = 0.0005). Laboratory markers and prognosis parameters differed significantly in COPD patients with respiratory infections (\u003cstrong\u003eFigure 2\u003c/strong\u003e). These patients demonstrated significantly higher odds of elevated WBC counts (10 - 50 10\u003csup\u003e9\u003c/sup\u003e/L, OR 1.6, 95% CI 1.2 - 2.2, p value = 0.0011), as well as increased NE (75% - 100%, OR 1.7, 95% CI 1.3 - 2.3, p value = 0.0004) and decreased LYM (0% - 20%, OR 2.2, 95% CI 1.5 - 3.1, p value \u0026lt; 0.0001) levels. The correlation between ICU admission and COPD approaches significance (p = 0.0513). Notably, COPD patients were three times more likely to require mechanical ventilation (OR 3, 95% CI 1.8 - 4.8, p value \u0026lt; 0.0001).\u003c/p\u003e\n\u003ch2\u003e3.3 Characterization of causative pathogens\u003c/h2\u003e\n\u003cp\u003eBased on the complete spectrum of microorganisms identified by NGS, the distribution of clinical diagnostic classifications was broadly comparable between patients with and without COPD (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). In both cohorts, \"microorganisms without pathogenic role\" comprised the single largest category (51.22% for COPD; 52.90% for non-COPD), followed by \"possibly causative pathogens\" (17.99% for COPD; 17.78% for non-COPD), \"causative pathogens\" (14.65% for COPD; 17.07% for non-COPD), and \"not causative pathogens\" (16.13% for COPD; 12.26% for non-COPD). In addition, causative pathogens were identified in the majority of samples from both subgroups (85.33% for COPD; 83.99% for non-COPD) (\u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eFor causative or possibly causative pathogens, monthly trends over a period of more than one year (January 2024 - January 2025) demonstrated that gram-negative bacteria (GNB) consistently accounted for the largest share of detections - ranging from 30.92% to 61.45% of cases - with peaks in late spring (June) and autumn (November) (\u003cstrong\u003eFigure 3B\u003c/strong\u003e). Gram-positive bacteria (GPB) remained relatively stable at 10.29% - 34.30%. fungus comprised 5.49% - 15.52% of identifications and showed a gradual upward trajectory toward winter. Virus detections (both DNA and RNA viruses) exhibited clear seasonal oscillations, with RNA virus frequency rising 6.30% in November 2024 - January 2025. Atypical pathogens remained rare (2.69% - 14.71%) throughout.\u003c/p\u003e\n\u003cp\u003eWhen restricted to causative or possibly causative pathogens, GNB dominated both groups but significantly more so in COPD than in non-COPD patients (55.91% versus 45.53%) (\u003cstrong\u003eFigure 3C\u0026amp;D\u003c/strong\u003e). Furthermore, the prevalence of fungal infections was higher among patients with COPD compared to those without COPD (11.02% vs. 9.98%). In contrast, infections caused by gram-positive bacteria (21.69% vs. 19.09%), DNA viruses (8.19% vs. 5.12%), RNA viruses (7.33% vs. 6.89%), and atypical pathogens (7.27% vs. 1.97%) were more frequently observed in non-COPD patients.\u003c/p\u003e\n\u003cp\u003eAt the species level, \u003cem\u003eStreptococcus pneumoniae\u0026nbsp;\u003c/em\u003e(21.62% for COPD; 18.60% for non-COPD) and \u003cem\u003eKlebsiella pneumoniae\u0026nbsp;\u003c/em\u003e(16.22% for COPD; 18.15% for non-COPD) were the two most frequently detected pathogens in both cohorts (\u003cstrong\u003eFigure 3E\u003c/strong\u003e). In COPD patients, \u003cem\u003ePseudomonas aeruginosa\u0026nbsp;\u003c/em\u003e(21.62% versus 10.15%) and \u003cem\u003eHaemophilus influenzae\u0026nbsp;\u003c/em\u003e(15.44% versus 9.02%) were relatively enriched, whereas non-COPD patients exhibited higher detection rates of atypical (12.63% versus 2.70%) and viral (14.43% versus 10.04% for DNA virus; 12.40% versus 11.58% for RNA virus) pathogens such as \u003cem\u003eMycoplasma pneumoniae\u0026nbsp;\u003c/em\u003e(11.39% versus 2.32%) and Human gammaherpesvirus 4 (7.89% versus 6.18%). Through a more detailed analysis, the extensive inter-patient heterogeneity was further proved. The heatmap in \u003cstrong\u003eSupplementary Figure 3\u0026nbsp;\u003c/strong\u003efurther highlighted extensive inter-patient heterogeneity: \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e maintained high occurrence across samples, while fungal and viral detections were more sporadic (distribution of all microorganisms was shown in \u003cstrong\u003eSupplementary Figure 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe nucleic acid analysis against the causative or possibly causative pathogens showed no significant differences between COPD and non-COPD groups for total pathogens (p value = 0.8922), GPB (p value = 0.3514), GNB (p value = 0.0578), fungi (p value = 0.1056), DNA viruses (p value = 0.0843), or RNA viruses (p value = 0.9342) (\u003cstrong\u003eFigure 3F\u003c/strong\u003e). By contrast, atypical pathogens exhibited a significantly higher read count in non-COPD patients (p value = 0.0009).\u003c/p\u003e\n\u003cp\u003eIn 259 COPD patients with respiratory infections, bacterial pathogens were the most frequently detected etiology, accounting for 71.43% (185/259) of samples (\u003cstrong\u003eSupplementary Figure 5A\u003c/strong\u003e). Pathogen-negative specimens comprised 14.67% (38/259), fungal infections 5.79% (15/259), viral infections 4.63% (12/259), atypical infections 1.16% (3/259), and mixed infections 2.32% (6/259). Among the six mixed infection cases, dual bacterial-viral co-infections predominated (66.67%, 4/6), while viral-atypical and triple bacterial-fungal-viral combinations each occurred in one patient (16.67%, 1/6, each). Human gammaherpesvirus 4 (n = 3) and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (n = 2) were the most two common co-pathogens (\u003cstrong\u003eSupplementary Figure 5B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn 887 non-COPD patients, bacterial infection remained the dominant infection type (56.03%, 497/887), followed by atypical infections 10.03% (89/887) and viral infections 8.34% (74/887) (\u003cstrong\u003eSupplementary Figure 5C\u003c/strong\u003e). Mixed infections accounted for 4.74% (42/887) of samples and were predominantly bacterial-fungal (35.71%, 15/42), bacterial-viral (26.19%, 11/42), and bacterial-fungal-viral (11.90%, 5/42) combinations. Common co-pathogens included \u003cem\u003eAspergillus fumigatus\u0026nbsp;\u003c/em\u003e(n = 13), \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (n = 11), and \u003cem\u003ePneumocystis jirovecii\u0026nbsp;\u003c/em\u003e(n = 11) (\u003cstrong\u003eSupplementary Figure 5D\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003e3.4 Characterization of non-pathogenic microorganisms\u003c/h2\u003e\n\u003cp\u003eA total of 42 non-pathogenic microbial species were detected across both cohorts: only one species was unique to COPD group, 16 species were unique to non-COPD group, and 25 species were shared by both (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). Loads of these microorganisms were significantly higher in COPD patients (median reads count 1.31×10\u003csup\u003e5\u003c/sup\u003e) than in non-COPD patients (median reads count 8.55×10\u003csup\u003e4\u003c/sup\u003e; p value = 0.004) (\u003cstrong\u003eFigure 4B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003ePrincipal coordinate analysis based on all non-pathogenic microbial detection data revealed clear separation along the first axis: PC1 accounted for 14.2% of the variance and differed markedly between COPD and non-COPD groups (p value \u0026lt; 0.0001), whereas PC2 (7.2% of variance) showed no significant difference (p value = 0.7957). PERMANOVA confirmed that group membership explained a small but statistically significant fraction of the overall variance (R\u003csup\u003e2\u003c/sup\u003e = 0.0045, p value = 0.001) (\u003cstrong\u003eFigure 4C\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe overall distribution of microbial species was comparable between COPD and non-COPD groups, with a predominance of GNB observed in both cohorts (59% for COPD; 54% for non-COPD), followed by GPB (32% for COPD; 34% for non-COPD), fungi (7% for COPD; 11% for non-COPD), and viruses (1% in both groups) (\u003cstrong\u003eFigure 4D\u0026amp;E\u003c/strong\u003e). However, species-level differences were noted between the groups. \u003cem\u003eVeillonella parvula\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSchaalia odontolytica\u003c/em\u003e, the predominant microorganisms, accounted for a higher proportion of isolates in COPD patients (43% and 22%, respectively) compared to non-COPD patients (35% and 16%, respectively). In contrast, several GPB species were more frequently detected in non-COPD group, including \u003cem\u003eStaphylococcus epidermidis\u0026nbsp;\u003c/em\u003e(5% versus 0.7%), \u003cem\u003eEnterococcus faecium\u0026nbsp;\u003c/em\u003e(4% versus 1%), \u003cem\u003eStreptococcus intermedius\u0026nbsp;\u003c/em\u003e(3% versus 2%), and \u003cem\u003eEnterococcus faecalis\u0026nbsp;\u003c/em\u003e(1% versus 0.5%).\u003c/p\u003e\n\u003ch2\u003e3.5 Machine learning model predicts poor prognosis\u003c/h2\u003e\n\u003cp\u003eIn this study, a machine learning model was developed using comprehensive microbial detection data to identify key microbial factors associated with poor clinical outcomes. The random forest classifier was first tuned by Leave-One-Out Cross-Validation to determine the optimal number of predictors to include (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). Model accuracy steadily increased with the number of predictors and plateaued at 12 features, which was chosen for the final model. In a bootstrap-resampled test set, the model exhibited near-perfect discrimination: area under the receiver operating characteristic curve (AUC) = 0.9998, sensitivity = 100.00%, specificity = 97.62%, positive predictive value (PPV) = 99.54%, negative predictive value (NPV) = 100.00%, and overall accuracy = 99.61% (\u003cstrong\u003eFigure 5B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eFeature-importance analysis (mean decrease in Gini) revealed that SARS-CoV-2 abundance was the single strongest predictor of poor prognosis (0.0201), followed in descending order by \u003cem\u003eVeillonella parvula\u003c/em\u003e (0.0056), \u003cem\u003eAchromobacter xylosoxidans\u003c/em\u003e (0.0045), and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (0.0042) (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). Several non-pathogenic microorganisms - including \u003cem\u003eActinomyces naeslundii\u0026nbsp;\u003c/em\u003e(-0.0051), \u003cem\u003eSchaalia odontolytica\u0026nbsp;\u003c/em\u003e(-0.0047), and \u003cem\u003eCandida albicans\u0026nbsp;\u003c/em\u003e(-0.0043) - exhibited negative importance scores, suggesting a relative protective association. Partial decision-tree excerpts (\u003cstrong\u003eSupplementary Figure 6\u003c/strong\u003e) illustrate how high SARS-CoV-2 loads and elevated levels of key microorganisms drive the top-level splits that distinguish patients with poor versus good prognosis, underscoring the complex, hierarchical interplay of microbial signatures in prognostication.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this comprehensive investigation, we characterized the microbial profiles in COPD patients compared with non-COPD individuals, demonstrating notable differences in pathogenic and non-pathogenic microorganisms and their correlation with clinical outcomes. Utilizing NGS, we highlighted distinct microbiological landscapes and identified key pathogens significantly associated with adverse clinical outcomes in COPD patients.\u003c/p\u003e\u003cp\u003eConsistent with prior studies, bacterial pathogens predominated in respiratory infections among both COPD and non-COPD groups (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Importantly, our findings demonstrate a marked enrichment of GNB, particularly \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e and \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, among COPD patients. These pathogens have previously been implicated in recurrent exacerbations, rapid disease progression, and impaired lung function, potentially due to chronic colonization facilitated by altered airway immunity and compromised mucociliary clearance characteristic of COPD (\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, the incidence of fungal infections was higher among COPD patients. This aligns with earlier evidence suggesting increased susceptibility of COPD patients to fungal colonization and infection, possibly attributable to prolonged corticosteroid therapy, frequent antibiotic exposure, and impaired local immunity (\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The presence of fungi such as \u003cem\u003eAspergillus\u003c/em\u003e has previously been associated with worse clinical outcomes, indicating the necessity for clinicians to maintain heightened vigilance for fungal pathogens in managing COPD exacerbations.\u003c/p\u003e\u003cp\u003eIn contrast, atypical pathogens, including \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, were significantly more prevalent in non-COPD patients. This discrepancy could stem from differences in host immunity or microbiome composition, warranting further exploration into the immunopathological mechanisms underpinning susceptibility to atypical pathogens in the non-COPD population (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, the seasonal trends observed in viral infections underscore the importance of preventive strategies, including targeted vaccination programs, to mitigate COPD exacerbation risks during peak viral transmission periods.\u003c/p\u003e\u003cp\u003eAnother critical aspect of our study was the characterization of microorganisms initially deemed non-pathogenic. The significant enrichment of \u003cem\u003eVeillonella parvula\u003c/em\u003e and \u003cem\u003eSchaalia odontolytica\u003c/em\u003e in COPD patients may reflect alterations in the respiratory microbiome associated with chronic airway inflammation. Though traditionally considered commensal organisms, their heightened presence suggests potential roles in modulating airway inflammation or interacting synergistically with pathogenic bacteria, thereby influencing disease severity and exacerbation frequency. Further studies employing longitudinal designs and functional microbiomics are required to clarify their precise clinical significance.\u003c/p\u003e\u003cp\u003eRemarkably, our advanced machine learning model demonstrated robust predictive capabilities for identifying patients at risk of poor clinical outcomes, with SARS-CoV-2 being the strongest single predictor. This observation highlights the persistent threat posed by SARS-CoV-2 infections, particularly in vulnerable COPD populations. The prognostic implications of other microbial factors identified by the model, such as \u003cem\u003eAchromobacter xylosoxidans\u003c/em\u003e and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, suggest opportunities for developing pathogen-specific therapeutic strategies and enhanced monitoring protocols to prevent progression to severe outcomes.\u003c/p\u003e\u003cp\u003eOur study has several notable strengths, including a large cohort size, utilization of highly sensitive and specific NGS technology, and comprehensive analysis encompassing both pathogenic and traditionally non-pathogenic microorganisms. Nonetheless, certain limitations should be acknowledged. Firstly, our study was conducted at a single institution, potentially limiting generalizability. Secondly, the observational design precludes establishing causal relationships between microbial profiles and clinical outcomes. Prospective, multicenter studies with extended follow-up periods are warranted to validate our findings and further elucidate the mechanistic pathways linking microbial communities to COPD exacerbations and prognosis.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, our findings provide critical insights into the microbiological underpinnings of COPD-related infections, highlighting pathogen-specific associations with clinical prognosis. These results have significant implications for clinical practice, suggesting that precise microbial identification via NGS could facilitate personalized therapeutic strategies, ultimately improving patient outcomes in COPD.\u003c/p\u003e"},{"header":" Abbreviations","content":"\u003cp\u003eAUC, area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eBALF, bronchoalveolar lavage fluid\u003c/p\u003e\n\u003cp\u003eBWA, Burrows-Wheeler Aligner\u003c/p\u003e\n\u003cp\u003eCI, confidence interval\u003c/p\u003e\n\u003cp\u003eCMT, conventional microbiological test\u003c/p\u003e\n\u003cp\u003eCOPD, chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eCRP, C-reactive protein\u003c/p\u003e\n\u003cp\u003eDNB, DNA nanoball\u003c/p\u003e\n\u003cp\u003eGNB, gram-negative bacteria\u003c/p\u003e\n\u003cp\u003eGOLD, Chronic Obstructive Lung Disease\u003c/p\u003e\n\u003cp\u003eGPB, gram-positive bacteria\u003c/p\u003e\n\u003cp\u003eICU, intensive care unit\u003c/p\u003e\n\u003cp\u003eIQR, interquartile range\u003c/p\u003e\n\u003cp\u003eLYM, lymphocyte\u003c/p\u003e\n\u003cp\u003eNE, neutrophil\u003c/p\u003e\n\u003cp\u003eNGS, next-generation sequencing\u003c/p\u003e\n\u003cp\u003eNPV, negative predictive value\u003c/p\u003e\n\u003cp\u003eOR, odds ratio\u003c/p\u003e\n\u003cp\u003ePCT, procalcitonin\u003c/p\u003e\n\u003cp\u003ePPV, positive predictive value\u003c/p\u003e\n\u003cp\u003eRef, reference\u003c/p\u003e\n\u003cp\u003eWBC, white blood cell\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study design received approval from the Ethics Committee of the First Hospital of Jilin University in accordance with the Declaration of Helsinki (2025-396).\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eThe study was conducted with the consent of every human participant.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe raw sequencing data and relevant materials of the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Specialized Program for Healthcare Talents in Jilin Province (JlSRCZX2025-047) from the Finance Department of Jilin Province.\u003c/p\u003e\n\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to conceptualization and writing. CyL coordinated the entire study and performed the data curation and analysis. ChL generated all figures and tables. ZG was responsible for the investigation and methodology. SL supervised this study and reviewed the original draft. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe appreciate it for the support of the hospital staff and the understanding of the patients and their families.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang L, Xie J, Hu Y-h, Tian Y. Air pollution and risk of chronic obstructed pulmonary disease: The modifying effect of genetic susceptibility and lifestyle. EBioMedicine. 2022;79.\u003c/li\u003e\n\u003cli\u003eBigna JJR, Kenne AM, Asangbeh SL, Sibetcheu AT. Prevalence of chronic obstructive pulmonary disease in the global population with HIV: a systematic review and meta-analysis. The Lancet Global health. 2017;6 2:e193-e202.\u003c/li\u003e\n\u003cli\u003eFlorman KE, Siddharthan T, Pollard SL, Alupo P, Barber J, Chandyo RK, et al. Unmet Diagnostic and Therapeutic Opportunities for Chronic Obstructive Pulmonary Disease in Low- and Middle-Income Countries. American Journal of Respiratory and Critical Care Medicine. 2023;208:442 - 50.\u003c/li\u003e\n\u003cli\u003eLytsy P, Engstr\u0026ouml;m S, Ekstedt M, Engstr\u0026ouml;m I, Hansson L, Ali L, et al. Outcomes associated with higher relational continuity in the treatment of persons with asthma or chronic obstructive pulmonary disease: A systematic review. eClinicalMedicine. 2022;49.\u003c/li\u003e\n\u003cli\u003eJain PK, Seval M, Labana R, Sarla. A Cross-sectional Study of Correlation of Neutrophil-to-lymphocyte Ratio in Acute Exacerbation of Chronic Obstructive Pulmonary Disease. APIK Journal of Internal Medicine. 2023.\u003c/li\u003e\n\u003cli\u003ePuhan MA, Gimeno-Santos E, Cates CJ, Troosters T. Pulmonary rehabilitation following exacerbations of chronic obstructive pulmonary disease. The Cochrane database of systematic reviews. 2016;12:CD005305.\u003c/li\u003e\n\u003cli\u003eTian Y, Zeng T, Tan L, Wu Y, Yu J, Huang J, et al. BPI-ANCA in chronic obstructive pulmonary disease with pulmonary Pseudomonas aeruginosa colonisation: a novel indicator of poor prognosis. British Journal of Biomedical Science. 2018;75:206 - 8.\u003c/li\u003e\n\u003cli\u003eSonowal T, Shariff M. Direct Detection of Atypical Bacteria in Clinical Samples from Patients with Chronic Obstructive Pulmonary Disease (COPD) by PCR and RT-PCR. International Journal of Current Microbiology and Applied Sciences. 2024.\u003c/li\u003e\n\u003cli\u003eLiao K-M, Chen Y-J, Shen C-W, Ou S-K, Chen C-Y. The Influence of Influenza Virus Infections in Patients with Chronic Obstructive Pulmonary Disease. International Journal of Chronic Obstructive Pulmonary Disease. 2022;17:2253 - 61.\u003c/li\u003e\n\u003cli\u003eLinden D, Guo-Parke H, Coyle PV, Fairley DJ, McAuley DF, Taggart CC, et al. Respiratory viral infection: a potential \u0026ldquo;missing link\u0026rdquo; in the pathogenesis of COPD. European Respiratory Review. 2019;28.\u003c/li\u003e\n\u003cli\u003eYin Y, Zhu P, Guo Y, Li Y, Chen H, Liu J, et al. Enhancing lower respiratory tract infection diagnosis: implementation and clinical assessment of multiplex PCR-based and hybrid capture-based targeted next-generation sequencing. eBioMedicine. 2024;107.\u003c/li\u003e\n\u003cli\u003eFourgeaud J, R\u0026eacute;gnault B, Ok V, Da Rocha N, Sitterl\u0026eacute; \u0026Eacute;, Mekouar M, et al. Performance of clinical metagenomics in France: a prospective observational study. The Lancet Microbe. 2023.\u003c/li\u003e\n\u003cli\u003eGOLD. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: 2025 Report. 2025.\u003c/li\u003e\n\u003cli\u003eLi Z-J, Zhang H, Ren L, Lu Q, Ren X, Zhang C, et al. Etiological and epidemiological features of acute respiratory infections in China. Nature Communications. 2021;12.\u003c/li\u003e\n\u003cli\u003eIeven M, Coenen S, Loens K, Lammens C, Coenjaerts F, Vanderstraeten A, et al. Aetiology of lower respiratory tract infection in adults in primary care: a prospective study in 11 European countries. Clinical Microbiology and Infection. 2018;24:1158-63.\u003c/li\u003e\n\u003cli\u003eShmoury A, Zakhour J, Sawma T, Haddad S, Zahreddine N, Tannous J, et al. Bacterial respiratory infections in patients with COVID-19: A retrospective study from a tertiary care center in Lebanon. Journal of infection and public health. 2023.\u003c/li\u003e\n\u003cli\u003eMcDonnell M, Jary H, Perry A, Macfarlane J, Hester K, Small T, et al. Non cystic fibrosis bronchiectasis: A longitudinal retrospective observational cohort study of Pseudomonas persistence and resistance. Respiratory medicine. 2015;109 6:716-26.\u003c/li\u003e\n\u003cli\u003eEkl\u0026ouml;f J, S\u0026oslash;rensen R, Ingebrigtsen T, Sivapalan P, Achir I, Boel J, et al. Pseudomonas aeruginosa and risk of death and exacerbations in patients with chronic obstructive pulmonary disease: an observational cohort study of 22.053 patients. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases. 2020.\u003c/li\u003e\n\u003cli\u003eAra\u0026uacute;jo D, Shteinberg M, Aliberti S, Goeminne P, Hill A, Fardon T, et al. The independent contribution of Pseudomonas aeruginosa infection to long-term clinical outcomes in bronchiectasis. European Respiratory Journal. 2018;51.\u003c/li\u003e\n\u003cli\u003eLindgren N, Novak L, Hunt B, McDaniel M, Swords W. Nontypeable Haemophilus influenzae Infection Impedes Pseudomonas aeruginosa Colonization and Persistence in Mouse Respiratory Tract. Infection and Immunity. 2021;90.\u003c/li\u003e\n\u003cli\u003eTiew P, Ko F, Pang SL, Matta S, Sio Y, Poh M, et al. Environmental fungal sensitisation associates with poorer clinical outcomes in COPD. The European Respiratory Journal. 2020;56.\u003c/li\u003e\n\u003cli\u003eWu Y-X, Zuo Y, Cheng Q, Huang Y, Bao Z-Y, Jin X-Y, et al. Respiratory Aspergillus Colonization Was Associated With Relapse of Acute Exacerbation in Patients With Chronic Obstructive Pulmonary Disease: Analysis of Data From A Retrospective Cohort Study. Frontiers in Medicine. 2021;8.\u003c/li\u003e\n\u003cli\u003eWaqas S, Dunne K, Talento A, Wilson G, Mart\u0026iacute;n-Loeches I, Keane J, et al. Prospective observational study of respiratory Aspergillus colonization or disease in patients with various stages of chronic obstructive pulmonary disease utilizing culture versus nonculture techniques. Medical mycology. 2020.\u003c/li\u003e\n\u003cli\u003eTiew P, Thng K, Chotirmall S. Clinical Aspergillus Signatures in COPD and Bronchiectasis. Journal of Fungi. 2022;8.\u003c/li\u003e\n\u003cli\u003eGeorgakopoulou V, Lempesis I, Sklapani P, Trakas N, Spandidos D. Exploring the pathogenetic mechanisms of Mycoplasma pneumoniae (Review). Experimental and Therapeutic Medicine. 2024;28.\u003c/li\u003e\n\u003cli\u003eFan L, Xu N, Guo Y, Li L. Enhanced insights into the neutrophil-driven immune mechanisms during Mycoplasma pneumoniae infection. Heliyon. 2024;10.\u003c/li\u003e\n\u003cli\u003eNarita M. Classification of Extrapulmonary Manifestations Due to Mycoplasma pneumoniae Infection on the Basis of Possible Pathogenesis. Frontiers in Microbiology. 2016;7.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Demographic and clinical characteristics of patients across COPD and Non-COPD groups.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e(n = 1146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u0026nbsp;\u003c/strong\u003e(n = 259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-COPD\u0026nbsp;\u003c/strong\u003e(n = 887)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e, median (IQR) (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e66 (55 - 73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e70 (66 - 78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e63 (52 - 71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Female, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e505 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e125 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e380 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.1221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Male, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e641 (55.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e134 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e507 (57.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.1221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmune status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Immunocompetent, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e893 (77.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e212 (81.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e681 (76.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Cancer, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e64 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e52 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.4485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Diabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e189 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e35 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e154 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.1420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyspnea\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e411 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e198 (76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e213 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e419 (36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e128 (49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e291 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Smoking, median (IQR) (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0 (0 - 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e10 (0 - 37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0 (0 - 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematologic parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; WBC, median (IQR) (10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9.2 (5.5 - 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e10.3 (7.3 - 13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e8.9 (5.4 - 13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; NE, median (IQR) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e79.8 (65.1 - 89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e83.2 (71.6 - 90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e78.8 (62.5 - 88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; LYM, median (IQR) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12.0 (6.0 - 20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9.7 (5.3 - 16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12.8 (6.4 - 22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; CRP, median (IQR) (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e45.8 (9.7 - 110.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e44.5 (9.6 - 121.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e46.3 (9.9 - 107.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.3306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; PCT, median (IQR) (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.116 (0.057 - 0.412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.108 (0.054 - 0.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.117 (0.058 - 0.425)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.9278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrognosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; After admission, median (IQR) (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e8 (6 - 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e8 (6 - 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e8 (6 - 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.2008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; ICU, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e137 (11.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e40 (15.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e97 (10.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Mechanical ventilation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e71 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e32 (12.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e39 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp; Dead, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e55 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e46 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.2570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: COPD, chronic obstructive pulmonary disease; IQR, interquartile range; WBC, white blood cell; NE, neutrophil; LYM, lymphocyte; CRP, C-reactive protein; PCT, procalcitonin; ICU, intensive care unit.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COPD, pathogenic microorganism, infection, prognosis, next-generation sequencing, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7689813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7689813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eChronic obstructive pulmonary disease (COPD) significantly impacts global health, primarily due to frequent acute exacerbations caused by respiratory infections. Precise microbial characterization may inform prognostic insights and optimize clinical management.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a prospective observational study from December 2023 to February 2025 involving 1146 patients (259 COPD; 887 non-COPD) with suspected respiratory infections. Bronchoalveolar lavage fluid samples underwent next-generation sequencing (NGS) and conventional microbiological testing. Multivariate logistic regression identified COPD predictors, and machine learning modeled prognostic outcomes based on microbial profiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDistinct pathogen distributions emerged between COPD and non-COPD groups, with COPD patients exhibiting higher prevalence of gram-negative bacteria, particularly \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e and \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, and fungal pathogens. Non-COPD patients demonstrated increased occurrence of atypical pathogens, notably \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e. COPD patients also presented higher loads of traditionally commensal microorganisms, such as \u003cem\u003eVeillonella parvula\u003c/em\u003e and \u003cem\u003eSchaalia odontolytica\u003c/em\u003e. Age, dyspnea, smoking duration, elevated leukocyte and neutrophil counts, and decreased lymphocyte levels were significantly associated with COPD presence. Machine learning identified specific microorganisms as strong predictors of adverse outcomes, such as SARS-CoV-2, \u003cem\u003eVeillonella parvula\u003c/em\u003e, and \u003cem\u003eAchromobacter xylosoxidans\u003c/em\u003e, achieving an area under the receiver operating characteristic curve of 0.9998.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eComprehensive microbial profiling using NGS effectively distinguishes pathogen differences between COPD and non-COPD patients, revealing key associations with clinical prognosis. These insights can inform tailored clinical interventions aimed at mitigating COPD exacerbations and improving patient outcomes.\u003c/p\u003e","manuscriptTitle":"Characteristics of pathogenic microorganisms in COPD-related infections: prognostic correlations and implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 14:59:00","doi":"10.21203/rs.3.rs-7689813/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46eecfbb-64de-4a34-82fc-4b5e94c35c53","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T18:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 14:59:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7689813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7689813","identity":"rs-7689813","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}