This study is a retrospective single-center observational cohort study. We consecutively screened adult patients (age ≥ 18 years) who were admitted for severe pneumonia and underwent BALF-tNGS between January 1, 2024, and December 31, 2024.

Inclusion criteria for this study were: (1) clinical diagnosis of severe pneumonia; (2) successful bronchoscopy with acquisition of qualified BALF samples; (3) availability of complete peripheral blood lymphocyte subset test results (including CD4⁺ T cells count). Exclusion criteria were: (1) missing CD4⁺ T cells count data; (2) incomplete clinical medical records preventing the acquisition of key baseline characteristics or outcome data.

The study protocol was approved by the Institutional Ethics Review Board of our hospital, and the requirement for informed consent was waived by the ethics committee because this retrospective study analyzed anonymized clinical data without any additional intervention.

Two trained researchers independently extracted data from the electronic medical record system and performed cross-verification to ensure accuracy. The collected data included:

Demographics: age and sex.

Clinical symptoms and signs: fatigue, headache, shortness of breath, etc., upon admission.

Comorbidities and immune status: hypertension, diabetes, chronic kidney disease, malignancy, autoimmune diseases, hematological diseases, etc.

Laboratory parameters: The most recent peripheral blood test results prior to BALF-tNGS submission were collected, including: white blood cell count, neutrophil percentage, lymphocyte percentage, C-reactive protein, procalcitonin, interleukin-6, and key lymphocyte subset absolute count.

Etiological test results: The BALF-tNGS results served as the primary basis for pathogen identification. Results from conventional microbiological tests, such as blood culture, sputum culture, fungal (1,3)-β-D-glucan test (G test), and galactomannan test (GM test), were also recorded to aid clinical interpretation.

Treatment and outcomes: Initial and subsequent antimicrobial therapy, length of hospital stay, and discharge status.

Strata based on established immunological stratification criteria in infectious diseases, patients were categorized into four ordinal immunological strata according to their baseline CD4⁺ T cells count:

Stratum 1 (Severe immunosuppression): CD4⁺ T cells < 100 cells/μL

Stratum 2 (Moderate immunosuppression): 100 ≤ CD4⁺ T cells < 200 cells/μL

Stratum 3 (Low-normal immunity): 200 ≤ CD4⁺ T cells < 500 cells/μL

Stratum 4 (Normal immunity): CD4⁺ T cells ≥ 500 cells/μL

All pathogens detected (including bacteria, viruses, fungi, and atypical pathogens) were extracted from the BALF-tNGS reports. During data analysis, the sequenced reads are aligned against the authoritative NCBI RefSeq database for pathogen identification. For result interpretation, specific positive thresholds are applied, provided the corresponding pathogen is not detected in the negative control. Specifically, the required number of unique reads is >50 for bacteria, >3 for fungi, >10 for viruses, and >100 for parasites. Establishing these thresholds effectively distinguishes true pathogen infection from background noise, thereby ensuring the specificity of the results and the reliability of clinical guidance. Pathogens were categorized to facilitate the analysis. The analysis focused on opportunistic pathogens associated with immune status.

All statistical analyses were performed using the R language(version 4.5.1), with the significance level set at a two-sided α = 0.05. Descriptive Statistics: Continuous variables conforming to a normal distribution are presented as mean ± standard deviation and were compared between groups using analysis of variance. Non-normally distributed continuous variables are presented as median (interquartile range) and were compared using the Kruskal-Wallis H test. Categorical variables are presented as frequencies and percentages and were compared using the chi-square test or Fisher’s exact test, as appropriate. Trend Analysis: The Cochran-Armitage trend test was employed to analyze the changes in the detection rates of specific pathogens across the CD4⁺ T cells strata, assessing for the presence of a significant linear trend. Analysis of Co-infection Complexity: The number of pathogen species detected via BALF-tNGS for each patient was calculated as an indicator of co-infection complexity. Differences in the number of pathogen species among the CD4⁺ T cells strata were compared using the Kruskal-Wallis H test.

According to the inclusion and exclusion criteria, a total of 286 patients clinically diagnosed with severe pneumonia and undergoing BALF-tNGS were initially screened. As shown in Figure 1, after applying the exclusion criteria (missing CD4⁺ T cells count data, n = 51; incomplete key clinical data, n = 14), 221 patients were included in the final analytical cohort. The cohort was stratified into four distinct groups based on baseline CD4⁺ T cells count: Stratum 1 (Severe Immunosuppression, CD4⁺ T cells < 100 cells/μL, n = 48), Stratum 2 (Moderate Immunosuppression, 100 ≤ CD4⁺ T cells < 200 cells/μL, n = 41), Stratum 3 (Low-Normal Immunity, 200 ≤ CD4⁺ T cells < 500 cells/μL, n = 80), and Stratum 4 (Immunocompetent, CD4⁺ T cells ≥ 500 cells/μL, n = 52).

No significant differences were observed in age, gender, or the distribution of major underlying comorbidities among the four groups, indicating good comparability. However, a statistically significant difference in IL-6 levels was found across the groups (p = 0.008), with the Normal immunity stratum (Stratum 4) exhibiting the highest IL-6 concentration (Table 1). This paradoxical finding suggests a complex interplay between cellular immunity and the inflammatory response, where immunocompetent patients mount a more robust inflammatory reaction to infection. In contrast, immunosuppressed patients may exhibit blunted inflammatory responses despite having a severe infection.

BALF-tNGS detected a variety of pathogens. Trend analysis was performed for several important opportunistic pathogens. As shown in Figure 2, the detection rates of Pneumocystis jirovecii (PJP) and Cytomegalovirus (CMV) demonstrated a significant increasing trend with decreasing CD4⁺ T cells strata(Cochran-Armitage trend test, PJP, p < 0.05; CMV, p < 0.05). In Stratum 1 (CD4⁺ T cells < 100 cells/μL), the detection rate of PJP was as high as 39.6%, while that of CMV was 35.4%. In Stratum 4 (CD4⁺ T cells ≥ 500 cells/μL), the detection rates of PJP and CMV decreased to 11.5% and 5.8%, respectively. In contrast, no significant trend variations in detection rates were observed for Epstein-Barr virus (EBV), Aspergillus, or Candida across the different CD4⁺ T cells strata.

To provide a global view of pathogen distribution, a heatmap was generated (Figure 3). The heatmap clearly demonstrates that opportunistic pathogens, represented by PJP and CMV, formed distinct enrichment clusters predominantly in Stratum 1 and Stratum 2 (left side). In contrast, common bacteria such as Pseudomonas aeruginosa and Klebsiella pneumoniae were distributed relatively evenly across all strata. Furthermore, the visualization intuitively suggests a more complex pathogen profile, indicative of greater co-infection complexity, among patients in the lower CD4⁺ T cells strata.

We further quantified the co-infection complexity for each patient, defined as the number of distinct pathogen species detected. The analysis revealed a significant increase in co-infection complexity with the severity of immunosuppression (p < 0.05). The median number of pathogen species was 4 in Stratum 1, which was significantly higher than the median of 2 species observed in Stratum 4 (Figure 4).

Regarding clinical outcomes, the mortality/treatment withdrawal rate in Stratum 1 (severe immunosuppression) was 10.4%, which was higher than that in the other strata. The observed rates were 4.8% in Stratum 2, 6.3% in Stratum 3, and 3.8% in Stratum 4. Although Stratum 1 showed the highest rate, the differences among groups were not statistically significant (Chi-square test, p = 0.166; Fisher’s exact test, p = 0.198) (Figure 5).

This study found that the detection rates of PJP and CMV increased sharply in the group with CD4⁺ T cells count < 100 cells/μL, a finding with significant clinical relevance. While this phenomenon is well-established in people living with HIV (Tang et al., 2025), our findings demonstrate that the same strong association exists in a broader cohort of patients with severe pneumonia, irrespective of HIV status. This association is equally applicable to non-HIV immunosuppressed patients, such as those with malignancies or autoimmune diseases. A CD4⁺ T cells count < 100 cells/μL can be regarded as a critical “risk threshold.” When a patient falls below this level, clinicians should maintain a high index of suspicion for PJP and CMV infection, even in the absence of a typical HIV history. This finding advances the traditional qualitative concept of immunodeficiency into a new phase of risk quantification centered on CD4⁺ T cells, providing precise guidance for empirical antimicrobial therapy. In contrast, pathogens such as EBV and Aspergillus did not demonstrate a significant trend. This may be related to their complex characteristics as colonizing organisms or reactivated latent infections, whose clinical significance requires comprehensive interpretation in conjunction with host factors and other diagnostic findings.

Another key finding of this study is the significant increase in the number of pathogen species detected in BALF as the CD4⁺ T cell stratum decreases. This provides direct visual evidence, from a microbiological perspective, of the progressive loss of immune control in the host. In immunocompetent individuals, the immune system can effectively suppress most opportunistic pathogens, and infections are typically dominated by a single or a limited number of pathogens. In contrast, the significantly higher median number of pathogen species observed in patients with severe immunosuppression illustrates a state of “microbiological chaos” resulting from the failure of immune surveillance. This metric of co-infection complexity serves as a quantifiable biomarker for the degree of underlying immune dysfunction. Conversely, in a state of severe immunosuppression, the collapse of immune surveillance and clearance mechanisms creates a favorable environment for the proliferation of multiple pathogens (Handley and Hand, 2022). This finding underscores that for patients with low CD4⁺ T cells count, anti-infective regimens must possess sufficient breadth to cover bacterial, fungal, and viral possibilities. Therapy targeting a single pathogen is often insufficient. The BALF-tNGS technique demonstrates its unique value in such patients; its ability to rapidly uncover the complex landscape of co-infections provides a critical evidence base for formulating comprehensive treatment strategies.

This study found that the immunocompetent patient group (Stratum 4) exhibited the highest levels of IL-6. We speculate that this is not coincidental but rather a reflection of their intact immune function. Previous mechanistic studies have demonstrated that in immunocompetent hosts, fully functional immune cells such as T cells and macrophages, upon recognizing pathogens, can be rapidly activated and produce large quantities of pro-inflammatory cytokines (including IL-6), thereby mounting a robust inflammatory response to clear the pathogen (O’Neill and Pearce, 2016). In contrast, immunocompromised patients have impaired immune cell function, which may lead to a delayed initiation or diminished intensity of the inflammatory response (Dilloo et al., 1995). Therefore, our observations reveal a potential profound link between the level of inflammation and immune competence, and a strong inflammatory response may indeed be a marker of intact immune function. This dissociation between immune competence and inflammatory response has important clinical implications. In immunocompromised patients, the absence of marked inflammation does not exclude serious infection, and conversely, elevated inflammatory markers in immunocompetent patients may represent appropriate immune mobilization rather than uncontrolled cytokine storm (Silva et al., 2023). This insight alerts us that when assessing infections in immunosuppressed patients, we cannot rely solely on the level of inflammatory markers to gauge disease severity. A low inflammatory response may indicate more profound immune paralysis, while a heightened inflammatory response could signify uncontrolled infection and inflammation. Future research should incorporate additional immunological parameters (e.g., monocyte and NK cell function) to enable more refined immune phenotyping.

Based on our findings, we propose a CD4⁺ T cells count-guided diagnostic and therapeutic pathway for severe pneumonia (Figure 6). We recommend that lymphocyte subset analysis, including CD4⁺ T cells count, be routinely incorporated into the initial evaluation of patients with severe pneumonia, especially when immune dysfunction is suspected. Clinical management should then be stratified according to the CD4⁺ T cells count as follows. In patients with CD4⁺ T cells count ≥ 500 cells/μL, management may adhere to standard guidelines for community-acquired pneumonia, as their immune competence is largely preserved. For those with a count between 200 and 500 cells/μL, clinicians should maintain a higher index of suspicion for underlying immunocompromise and consider expanding the spectrum of pathogen detection in diagnostic evaluations. In patients with CD4⁺ T cells count below 200 cells/μL—particularly under 100 cells/μL—imemptive prophylaxis against PJP pneumonia (e.g., trimethoprim-sulfamethoxazole) is strongly indicated, accompanied by active screening for cytomegalovirus and other opportunistic infections. In this high-risk group, BALF-tNGS may serve as a valuable complementary diagnostic tool. However, its results must be interpreted in conjunction with clinical presentation and ancillary microbiologic data to accurately distinguish true infection from colonization or latent viral shedding.

This study has several limitations. First, as a single-center retrospective study, it is susceptible to selection bias, and the findings are inherently susceptible to selection bias and may lack generalizability to broader populations. To strengthen the validity of our conclusions, future multicenter prospective studies are warranted to validate the association between CD4⁺ T cells stratification and pathogen profiles across diverse clinical settings. Additionally, while BALF-tNGS excels at deep sequencing of specific targets, it has inherent drawbacks. The primary and most fundamental limitation is its finite amount of information. BALF-tNGS can only detect genes or regions that are pre-designed on its panel, making it incapable of discovering novel targets outside the panel, such as entirely new pathogens or unknown disease-causing genes. This renders it ineffective when confronted with unknown scenarios (Chen et al., 2024). Moreover, careful interpretation of BALF-tNGS results is crucial to avoid misdiagnosis. For negative results, clinicians should consider the possibility of pathogens not covered by the panel or below the detection threshold. For detected pathogens, especially those with known colonization potential (e.g., Candida, EBV), clinical correlation is essential. Results should be interpreted in conjunction with the host’s immune status, radiographic findings, and other laboratory tests to distinguish between true infection and colonization or latency. Secondly, the lack of uniform standards for panels, procedures, and databases across different laboratories makes it difficult to compare and standardize results directly. These factors collectively limit the broader application of BALF-tNGS (Society of Clinical Microbiology of China International E, Promotion Association for M, Healthcare, 2024).

In conclusion, this study confirms that the CD4⁺ T cells count is a key variable for the pathogen profile in severe pneumonia. A descending CD4⁺ T cells gradient is associated with a significantly increased risk of opportunistic pathogens and greater infection complexity. Integrating CD4⁺ T cells counting into the initial assessment framework for severe pneumonia could facilitate more precise and earlier targeted therapy, ultimately improving patient outcomes.