Abstract
Background
Pneumocystis jirovecii pneumonia (PJP) remains a life-threatening opportunistic infection with high mortality, particularly among non-HIV immunocompromised patients. Identifying accessible and reliable prognostic biomarkers is of major clinical importance.
Objectives
To investigate the prognostic value of dynamic changes in the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) among patients with PJP.
Methods
A retrospective study of 165 PJP patients was conducted at two tertiary hospitals. Post-diagnostic trajectories of NLR, MLR, and PLR were analyzed using group-based trajectory modeling (GBTM). Associations between these trajectories and 28-day survival were assessed by Cox proportional hazards regression and Kaplan–Meier survival analysis.
Results
Three distinct NLR trajectories were identified: continuously decreasing (15%), stable (68%), and continuously increasing (17%). Patients with continuously decreasing NLR had significantly lower 28-day survival (P < 0.05). The log-transformed NLR (logNLR) trajectory was an independent prognostic factor, whereas logMLR and logPLR were not significantly associated with outcomes.
Conclusion
The temporal trajectory of logNLR is strongly associated with 28-day survival in PJP. A persistently declining logNLR predicts poor prognosis, suggesting its utility in early risk stratification.
Keywords: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, Pneumocystis jirovecii pneumonia, prognosis, trajectory analysis
Introduction
Pneumocystis jirovecii pneumonia (PJP) is a major opportunistic and life-threatening infection that primarily affects immunocompromised individuals or those undergoing long-term immunosuppressive or corticosteroid therapy (1–3). Although prophylactic treatment has reduced AIDS-related PJP, the incidence among non-HIV immunocompromised hosts continues to rise, accompanied by high mortality rates (4–6).
Inflammatory responses and immune function play crucial roles in PJP pathogenesis (7, 8). However, no specific biomarkers have been consistently associated with prognosis. Simple hematological ratios—such as neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR)—offer easily accessible, cost-effective, and repeatable markers reflecting the balance between innate and adaptive immunity. Nevertheless, few studies have examined their temporal evolution in PJP (9, 10).
To address this gap, we applied group-based trajectory modeling (GBTM) to analyze longitudinal logNLR, logMLR, and logPLR data from PJP patients, identifying trajectory groups and their association with 28-day survival (11, 12).
Materials and methods
Study design and population
A retrospective cohort of 165 patients diagnosed with Pneumocystis jirovecii pneumonia (PJP) between October 2020 and August 2025 was included. Diagnostic criteria: clinical symptoms (fever, cough, dyspnea), radiologic findings of pulmonary infiltrates, and positive P. jirovecii detection in bronchoalveolar lavage fluid via metagenomic next-generation sequencing (mNGS) (13). Exclusion: patients <18 years, HIV-positive status, and pregnancy or lactation.
Ethical approval was granted by both hospital committees. Written informed consent was obtained from all participants.
Data collection
Clinical variables included demographics, comorbidities, hospital and ICU stays, cute physiology and chronic health evaluation II (APACHE II) scores and sequential Organ Failure Assessment (SOFA) scores, treatments, and laboratory indices (WBC, NEUT, LYM, MONO, PLT, CRP, PT, FIB, LDH, etc.). Dynamic values of neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) were recorded up to 28 days post-diagnosis.
Outcome measures
The primary endpoint was 28-day survival after PJP diagnosis.
Statistical analysis
Continuous variables were assessed for normality using the Shapiro–Wilk test. Between-group comparisons used t-test or Mann–Whitney U-test. Categorical variables were compared by chi-square or Fisher’s exact test.
Group-based trajectory modeling was used to model trajectories for logNLR, logMLR, and logPLR in R (v4.3.2). The best-fitting model was selected based on AIC/BIC. Associations with mortality were analyzed via Cox regression and Kaplan–Meier analysis. Statistical significance was defined as two-sided P < 0.05.
Results
Baseline characteristics
Among 165 Pneumocystis jirovecii pneumonia (PJP) patients (101 male, 64 female), 128 survived and 37 died within 28 days. Non-survivors had longer Intensive Care Unit (ICU) stays, higher cute physiology and chronic health evaluation II (APACHE II) scores and sequential Organ Failure Assessment (SOFA) scores, and were more likely to receive vasoactive therapy. Laboratory markers including PT, D-dimer, CRP, LDH, and PCT were elevated in non-survivors, whereas lymphocyte and monocyte counts were lower (Table 1).
TABLE 1.
Baseline characteristics of survivors and non-survivors with Pneumocystis jirovecii pneumonia (PJP).
| Characteristics | Category | Group D | Group L | P |
|---|---|---|---|---|
| n | – | 37 | 128 | – |
| Male (%) | M | 17 (45.9) | 84 (65.6) | 0.049 |
| W | 20 (54.1) | 44 (34.4) | – | |
| Age (median [IQR]) | – | 67.00 [54.00, 73.00] | 61.00 [53.00, 72.00] | 0.297 |
| LOS1 (median [IQR]) | – | 12.00 [8.00, 16.00] | 12.77 [8.00, 24.00] | 0.177 |
| LOS2 (median [IQR]) | – | 6.00 [2.00, 10.00] | 0.00 [0.00, 10.88] | 0.013 |
| Health (%) | 0 | 5 (13.5) | 30 (23.4) | 0.541 |
| 1 | 3 (8.1) | 15 (11.7) | – | |
| 2 | 9 (24.3) | 21 (16.4) | – | |
| 3 | 3 (8.1) | 5 (3.9) | – | |
| 4 | 4 (10.8) | 16 (12.5) | – | |
| 5 | 10 (27.0) | 25 (19.5) | – | |
| 6 | 3 (8.1) | 16 (12.5) | – | |
| APACHE II (median [IQR]) | – | 20.00 [15.00, 24.00] | 8.00 [6.00, 17.00] | <0.001 |
| SOFA (median [IQR]) | – | 4.00 [3.00, 6.00] | 2.00 [0.00, 4.00] | <0.001 |
| CT (%) | 0 | 0 (0.0) | 1 (0.8) | 1 |
| 1 | 37 (100.0) | 127 (99.2) | – | |
| Therapy1 (%) | 0 | 16 (43.2) | 71 (55.5) | 0.261 |
| 1 | 21 (56.8) | 57 (44.5) | – | |
| Therapy3 (%) | 0 | 14 (37.8) | 60 (46.9) | 0.432 |
| 1 | 23 (62.2) | 68 (53.1) | – | |
| Therapy4 (%) | 0 | 14 (37.8) | 86 (67.2) | 0.002 |
| 1 | 23 (62.2) | 42 (32.8) | – | |
| Therapy5 (%) | 0 | 4 (10.8) | 48 (37.5) | 0.004 |
| 1 | 33 (89.2) | 80 (62.5) | – | |
| Therapy6 (median [IQR]) | – | 6.00 [4.00, 10.00] | 4.00 [0.00, 10.25] | 0.105 |
| Therapy7 (%) | 0 | 5 (13.5) | 71 (55.5) | <0.001 |
| 1 | 32 (86.5) | 57 (44.5) | – | |
| Therapy8 (median [IQR]) | – | 0.00 [0.00, 14.00] | 0.00 [0.00, 0.00] | 0.002 |
| Therapy9 (median [IQR]) | – | 93.00 [21.00, 193.00] | 0.00 [0.00, 0.00] | <0.001 |
| Therapy10 (%) | 0 | 30 (81.1) | 114 (89.1) | 0.316 |
| 1 | 7 (18.9) | 14 (10.9) | – | |
| Therapy11 (%) | 0 | 33 (89.2) | 122 (95.3) | 0.325 |
| 1 | 4 (10.8) | 6 (4.7) | – | |
| Therapy12 (%) | 0 | 34 (91.9) | 118 (92.2) | 1 |
| 1 | 3 (8.1) | 10 (7.8) | – | |
| Therapy13 (%) | 0 | 32 (86.5) | 101 (78.9) | 0.549 |
| 1 | 5 (13.5) | 27 (21.1) | – | |
| APTT (median [IQR]) | – | 28.35 [26.37, 38.83] | 29.35 [26.64, 34.92] | 0.911 |
| FIB (median [IQR]) | – | 4.80 [3.47, 5.44] | 4.36 [3.41, 5.62] | 0.872 |
| PT (median [IQR]) | – | 13.60 [11.95, 15.20] | 12.70 [11.74, 13.50] | 0.033 |
| PCT (median [IQR]) | – | 0.89 [0.15, 1.78] | 0.13 [0.05, 0.30] | 0.002 |
| LYMPH (median [IQR]) | – | 0.45 [0.31, 0.60] | 0.74 [0.40, 1.25] | 0.011 |
| MONO (median [IQR]) | – | 0.23 [0.11, 0.33] | 0.41 [0.22, 0.60] | 0.001 |
| NEUT (median [IQR]) | – | 6.82 [5.53, 9.93] | 6.11 [3.99, 8.66] | 0.161 |
| WBC (median [IQR]) | – | 7.78 [6.32, 10.55] | 7.22 [5.95, 10.20] | 0.566 |
| LAC (median [IQR]) | – | 2.00 [1.33, 3.32] | 1.45 [1.12, 1.80] | 0.084 |
| PO2 (median [IQR]) | – | 64.55 [48.50, 89.95] | 81.00 [70.00, 95.20] | 0.124 |
| FIO2 (median [IQR]) | – | 0.50 [0.50, 0.50] | 0.21 [0.21, 0.23] | 0.050 |
| PO2/FIO2 (median [IQR]) | – | 153.60 [153.60, 153.60] | 359.52 [284.66, 407.14] | 0.181 |
| ALB (median [IQR]) | – | 29.70 [25.57, 33.25] | 32.80 [28.65, 36.00] | 0.015 |
| ALT (median [IQR]) | – | 21.00 [16.08, 30.25] | 22.00 [14.00, 38.00] | 0.742 |
| DD (median [IQR]) | – | 4.40 [1.54, 9.81] | 1.15 [0.50, 2.74] | 0.001 |
| TBIL (median [IQR]) | – | 12.25 [9.38, 17.11] | 9.15 [6.60, 12.60] | 0.05 |
| CRP (median [IQR]) | – | 91.16 [79.23, 151.04] | 29.26 [13.28, 61.19] | <0.001 |
| LDH (median [IQR]) | – | 625.55 [497.75, 1092.88] | 400.00 [221.50, 548.49] | <0.001 |
| BUN/CREA (median [IQR]) | – | 32.07 [22.74, 56.85] | 21.47 [15.45, 29.78] | 0.003 |
| CREA (median [IQR]) | – | 67.00 [47.35, 118.00] | 68.00 [50.08, 101.30] | 0.817 |
| PLT (median [IQR]) | – | 146.00 [78.75, 206.50] | 207.00 [141.00, 279.75] | 0.013 |
| ESR (median [IQR]) | – | 33.00 [26.00, 52.00] | 46.00 [22.50, 67.50] | 0.368 |
| HCO3– (median [IQR]) | – | 19.30 [17.05, 21.45] | 25.32 [22.64, 27.66] | 0.066 |
| NLR (median [IQR]) | – | 13.29 [9.26, 23.77] | 8.20 [3.38, 18.76] | 0.012 |
| MLR (median [IQR]) | – | 0.47 [0.27, 0.64] | 0.51 [0.30, 0.83] | 0.239 |
| PLR (median [IQR]) | – | 294.01 [167.62, 437.08] | 262.00 [168.60, 467.53] | 0.966 |
LOS1, total hospitalization duration; LOS2, ICU stay duration; Health, baseline conditions (absent/graft/tears/hematologic diseases/pulmonary conditions/connected tissue diseases/diabetes: 0/1/2/3/4/5/6); CT, chest CT scan showing ground-glass opacity (present/absent: 1/0); Therapy1, antibiotic use within 1 week before admission (present/absent: 1/0); Therapy2, trimethoprim-sulfamethoxazole tablet dosage; Therapy3, carbenicin (present/absent: 1/0); Therapy4, antiviral drugs (present/absent: 1/0); Therapy5, glucocorticoid use (present/absent: 1/0); Therapy6, glucocorticoid treatment duration (days); Therapy7, vasopressors (present/absent: 1/0); Therapy8, non-invasive mechanical ventilation duration (hours); Therapy9, invasive mechanical ventilation duration (hours); Therapy10, CRRT (present/absent: 1/0); Therapy11, ECMO (present/absent: 1/0); Therapy12, fungal infection (present/absent: 1/0); Therapy13, cytomegalovirus (present/absent: 1/0); M, male; W, female; L, survived; D, died; WBC, white blood cell count; NEUT, neutrophil absolute count; LYM, lymphocyte absolute count; MONO, monocyte absolute count; PLT, platelet count; CRP, C-reactive protein (RRT); APTT, activated partial thromboplastin time; FIB, fibrinogen; PT, prothrombin time; D-D, D-dimer; PCT, procalcitonin; ESR, erythrocyte sedimentation rate; CREA, creatinine; BUN/CREA, blood urea nitrogen/creatinine; TBIL, total bilirubin; ALT, alanine aminotransferase; ALB, albumin; LDH, lactate dehydrogenase; HCO3–, carbon dioxide partial pressure; LAC, Lactic acid; PaO2, Partial oxygen pressure; FIO2, Fraction of oxygen administered; PO2/FIO2, Oxygenation index; NLR, Neutrophil/lymphocyte ratio; MLR, Monocyte/lymphocyte ratio; PLR, Platelet/lymphocyte ratio.
LogNLR trajectories
Three logNLR trajectories were identified: continuously decreasing (15%), stable (68%), and increasing (17%). Kaplan–Meier survival analysis revealed significantly lower survival in the decreasing group (P = 0.014) (Figures 1, 2).
FIGURE 1.
LogNLR trajectory comparison among clusters.
FIGURE 2.
Kaplan–Meier survival curves by logNLR trajectory.
LogMLR and logPLR trajectories
Distinct logMLR and logPLR patterns were observed but showed no significant association with survival (logMLR P = 0.84; logPLR P = 0.29) (Figures 3–6).
FIGURE 3.
LogMLR trajectory comparison among clusters.
FIGURE 6.
Survival analysis by logPLR trajectories.
FIGURE 4.
Survival analysis by logMLR trajectories.
FIGURE 5.
LogPLR trajectory comparison among clusters.
Clinical correlates
Patients in the decreasing logNLR group had higher SOFA scores and different corticosteroid usage patterns (P < 0.05) (Table 2).
TABLE 2.
Baseline characteristics according to logNLR trajectory groups.
| Characteristics | Category | Group A | Group B | Group C | P |
|---|---|---|---|---|---|
| n | – | 18 | 79 | 20 | – |
| Male (%) | M | 10 (55.6) | 54 (68.4) | 9 (45.0) | 0.127 |
| W | 8 (44.4) | 25 (31.6) | 11 (55.0) | ||
| Age (median [IQR]) | – | 55.50 [36.00, 67.00] | 64.00 [53.50, 72.50] | 59.00 [52.75, 68.25] | 0.064 |
| LOS1 (median [IQR]) | – | 18.00 [12.00, 37.25] | 14.00 [9.00, 22.50] | 12.77 [10.12, 20.75] | 0.294 |
| LOS2 (median [IQR]) | – | 4.82 [0.23, 17.70] | 3.00 [0.00, 12.00] | 6.50 [0.00, 12.16] | 0.57 |
| Health (%) | 0 | 4 (22.2) | 16 (20.3) | 3 (15.0) | 0.63 |
| 1 | 4 (22.2) | 8 (10.1) | 1 (5.0) | – | |
| 2 | 1 (5.6) | 15 (19.0) | 3 (15.0) | – | |
| 3 | 2 (11.1) | 2 (2.5) | 2 (10.0) | – | |
| 4 | 3 (16.7) | 9 (11.4) | 2 (10.0) | – | |
| 5 | 3 (16.7) | 19 (24.1) | 6 (30.0) | – | |
| 6 | 1 (5.6) | 10 (12.7) | 3 (15.0) | – | |
| APACHE II (median [IQR]) | – | 16.00 [13.50, 23.00] | 10.00 [6.00, 20.50] | 15.50 [10.75, 21.00] | 0.11 |
| SOFA (median [IQR]) | – | 6.00 [4.00, 8.75] | 2.00 [0.00, 5.00] | 2.50 [1.50, 6.00] | 0.002 |
| CT (median [IQR]) | – | 1.00 [1.00, 1.00] | 1.00 [1.00, 1.00] | 1.00 [1.00, 1.00] | 0.786 |
| Therapy1 (%) | 0 | 9 (50.0) | 44 (55.7) | 10 (50.0) | 0.846 |
| 1 | 9 (50.0) | 35 (44.3) | 10 (50.0) | – | |
| Therapy3 (%) | 0 | 4 (22.2) | 33 (41.8) | 6 (30.0) | 0.237 |
| 1 | 14 (77.8) | 46 (58.2) | 14 (70.0) | – | |
| Therapy4 (%) | 0 | 8 (44.4) | 49 (62.0) | 8 (40.0) | 0.123 |
| 1 | 10 (55.6) | 30 (38.0) | 12 (60.0) | – | |
| Therapy5 (%) | 0 | 3 (16.7) | 26 (32.9) | 1 (5.0) | 0.024 |
| 1 | 15 (83.3) | 53 (67.1) | 19 (95.0) | – | |
| Therapy6 (median [IQR]) | – | 11.00 [4.50, 17.00] | 6.00 [0.00, 12.00] | 7.50 [3.75, 10.25] | 0.189 |
| Therapy7 (%) | 0 | 4 (22.2) | 33 (41.8) | 7 (35.0) | 0.293 |
| 1 | 14 (77.8) | 46 (58.2) | 13 (65.0) | – | |
| Therapy8 (median [IQR]) | – | 0.00 [0.00, 0.00] | 0.00 [0.00, 2.50] | 0.00 [0.00, 0.00] | 0.177 |
| Therapy9 (median [IQR]) | – | 89.50 [0.00, 245.25] | 0.00 [0.00, 88.00] | 0.00 [0.00, 99.00] | 0.141 |
| Therapy10 (%) | 0 | 13 (72.2) | 68 (86.1) | 17 (85.0) | 0.351 |
| 1 | 5 (27.8) | 11 (13.9) | 3 (15.0) | – | |
| Therapy11 (%) | 0 | 16 (88.9) | 71 (89.9) | 20 (100.0) | 0.321 |
| 1 | 2 (11.1) | 8 (10.1) | 0 (0.0) | – | |
| Therapy12 (%) | 0 | 16 (88.9) | 73 (92.4) | 20 (100.0) | 0.358 |
| 1 | 2 (11.1) | 6 (7.6) | 0 (0.0) | – | |
| Therapy13 (%) | 0 | 14 (77.8) | 63 (79.7) | 19 (95.0) | 0.233 |
| 1 | 4 (22.2) | 16 (20.3) | 1 (5.0) | – | |
| APTT (median [IQR]) | – | 33.90 [28.80, 38.70] | 30.20 [26.28, 35.65] | 27.15 [26.40, 28.30] | 0.172 |
| FIB (median [IQR]) | – | 4.28 [3.39, 5.48] | 4.44 [3.47, 5.65] | 5.02 [3.41, 5.56] | 0.935 |
| PT (median [IQR]) | – | 13.40 [12.20, 15.50] | 12.85 [11.74, 13.83] | 12.45 [12.00, 13.30] | 0.226 |
| PCT (median [IQR]) | – | 0.32 [0.12, 1.04] | 0.18 [0.07, 1.02] | 0.13 [0.10, 0.30] | 0.681 |
| LYMPH (median [IQR]) | – | 0.44 [0.19, 0.78] | 0.60 [0.36, 1.11] | 0.49 [0.29, 0.67] | 0.217 |
| MONO (median [IQR]) | – | 0.17 [0.12, 0.46] | 0.34 [0.14, 0.68] | 0.28 [0.16, 0.36] | 0.29 |
| NEUT (median [IQR]) | – | 7.27 [5.81, 9.35] | 6.68 [4.39, 9.66] | 6.97 [4.73, 8.18] | 0.62 |
| WBC (median [IQR]) | – | 7.81 [6.33, 10.57] | 7.65 [6.23, 10.66] | 7.83 [5.29, 9.35] | 0.538 |
| LAC (median [IQR]) | – | 1.52 [1.23, 2.07] | 1.50 [1.35, 2.64] | 1.80 [1.80, 2.20] | 0.653 |
| PO2 (median [IQR]) | – | 84.65 [71.05, 112.25] | 73.83 [49.08, 90.70] | 78.70 [63.98, 82.32] | 0.501 |
| ALB (median [IQR]) | – | 29.15 [24.60, 33.81] | 32.35 [27.91, 34.88] | 28.50 [24.22, 32.76] | 0.157 |
| ALT (median [IQR]) | – | 16.50 [11.03, 30.50] | 22.90 [16.95, 30.55] | 20.50 [15.00, 30.50] | 0.525 |
| DD (median [IQR]) | – | 3.38 [1.64, 9.45] | 1.60 [0.68, 4.40] | 1.38 [0.65, 3.41] | 0.324 |
| TBIL (median [IQR]) | – | 10.45 [5.99, 15.86] | 10.20 [7.48, 13.50] | 6.80 [6.20, 11.00] | 0.201 |
| CRP (median [IQR]) | – | 88.18 [60.84, 127.95] | 45.80 [17.75, 95.72] | 80.77 [30.53, 111.48] | 0.252 |
| LDH (median [IQR]) | – | 537.00 [402.50, 625.55] | 464.25 [282.58, 773.55] | 593.50 [457.30, 1092.75] | 0.171 |
| BUN/CREA (median [IQR]) | – | 21.95 [12.87, 44.31] | 28.97 [20.66, 36.43] | 24.44 [14.38, 42.74] | 0.519 |
| CREA (median [IQR]) | – | 68.50 [43.10, 144.25] | 69.00 [51.45, 110.00] | 55.00 [40.00, 109.58] | 0.566 |
| HCO3– (median [IQR]) | – | 22.75 [20.31, 23.45] | 24.90 [20.52, 28.82] | 22.25 [19.90, 25.95] | 0.569 |
| PLT (median [IQR]) | – | 148.00 [50.25, 233.75] | 204.00 [110.25, 280.00] | 178.50 [138.75, 209.25] | 0.229 |
| ESR (median [IQR]) | – | 26.00 [18.00, 28.50] | 42.50 [22.25, 67.75] | 52.00 [46.00, 59.00] | 0.066 |
| FIO2 (median [IQR]) | – | 0.21 [0.21, 0.21] | 0.21 [0.21, 0.29] | 0.45 [0.45, 0.45] | 0.285 |
LOS1, Total hospitalization duration; LOS2, ICU stay duration; Health, baseline conditions (absent/graft/tears/hematologic diseases/pulmonary conditions/connected tissue diseases/diabetes: 0/1/2/3/4/5/6); CT, chest CT scan showing ground-glass opacity (present/absent: 1/0); Therapy1, antibiotic use within 1 week before admission (present/absent: 1/0); Therapy2, trimethoprim-sulfamethoxazole tablet dosage; Therapy3, carbenicin (present/absent: 1/0); Therapy4, antiviral drugs (present/absent: 1/0); Therapy5, glucocorticoid use (present/absent: 1/0); Therapy6, glucocorticoid treatment duration (days); Therapy7, vasopressors (present/absent: 1/0); Therapy8, non-invasive mechanical ventilation duration (hours); Therapy9, invasive mechanical ventilation duration (hours); Therapy10, CRRT (present/absent: 1/0); Therapy11, ECMO (present/absent: 1/0); Therapy12, fungal infection (present/absent: 1/0); Therapy13, cytomegalovirus (present/absent: 1/0); M, male; W, female; L, survived; D, died; WBC, white blood cell count; NEUT, neutrophil absolute count; LYM, lymphocyte absolute count; MONO, monocyte absolute count; PLT, platelet count; CRP, C-reactive protein (RRT); APTT, activated partial thromboplastin time; FIB, fibrinogen; PT, prothrombin time; D-D, D-dimer; PCT, procalcitonin; ESR, erythrocyte sedimentation rate; CREA, creatinine; BUN/CREA, blood urea nitrogen/creatinine; TBIL, total bilirubin; ALT, alanine aminotransferase; ALB, albumin; LDH, lactate dehydrogenase; HCO3–, carbon dioxide partial pressure; LAC, lactic acid; PaO2, partial oxygen pressure; FIO2, fraction of oxygen administered.
Discussion
Pneumocystis jirovecii pneumonia (PJP) remains a potentially fatal opportunistic infection, particularly among patients with non-acquired immunodeficiency syndrome (NADIS) (14–16). Compared with AIDS-related PJP, NADIS-associated PJP is characterized by a more acute onset, rapid clinical deterioration, and substantially higher mortality rates, which are often attributed to delayed diagnosis and insufficient targeted antimicrobial therapy (6, 17–19). Previous studies have demonstrated that PJP in non-HIV populations is frequently accompanied by excessive inflammatory responses within the alveolar microenvironment, leading to impaired gas exchange and subsequent multi-organ dysfunction (20–23). In this context, identifying reliable and easily accessible biomarkers for dynamic assessment of inflammatory and immune status is of critical importance. Although molecular diagnostic technologies have significantly improved the detection of P. jirovecii (24, 25), robust indicators for treatment monitoring and prognostic stratification are still lacking (19, 26–28).
Recently, complete blood count (CBC)-derived inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), have attracted increasing attention due to their cost-effectiveness and clinical feasibility (29–34). These indices reflect the balance between innate and adaptive immunity and have shown prognostic value in various inflammatory and infectious diseases. However, their role in PJP, especially from a longitudinal perspective, remains insufficiently explored (35).
Our study demonstrated that baseline NLR was significantly higher in non-survivors than in survivors, indicating that an excessive inflammatory burden at admission is associated with poor short-term outcomes. More importantly, through group-based trajectory modeling (GBTM) of log-transformed NLR values, we identified three distinct dynamic patterns: sustained decline, stable trajectory, and sustained increase. Unexpectedly, patients in the sustained decline trajectory group exhibited the worst survival outcomes, whereas those with sustained increase trajectories showed the most favorable prognosis. This paradoxical phenomenon highlights that not only the absolute value of NLR, but also its temporal evolution, is crucial for outcome prediction.
The biological explanation for this finding may be multifactorial. Neutrophils play a central role in the early innate immune response against pathogens, while lymphocytes, particularly CD4+ T cells, are indispensable for effective clearance of P. jirovecii. An elevated NLR at disease onset likely reflects a hyperinflammatory state combined with impaired adaptive immunity (36, 37). However, a rapid decline in NLR during disease progression may represent immune exhaustion, bone marrow suppression, extensive lymphocyte apoptosis, or immune reconstitution inflammatory syndrome (IRIS) (28). These processes may result in functional immune collapse, impaired pathogen clearance, and an increased risk of fatal outcomes.
In addition, patients in the sustained decline group exhibited significantly higher sequential Organ Failure Assessment (SOFA) scores, suggesting more severe organ dysfunction and systemic involvement. Differences in glucocorticoid use among trajectory groups further reflect the complex immunomodulatory effects of corticosteroids in PJP, which may exert both beneficial and detrimental effects on immune homeostasis, depending on timing, dose, and patient-specific immune status.
Of note, MLR and PLR, either at baseline or during dynamic follow-up, were not significantly associated with 28-day mortality in our cohort. This finding suggests that NLR, integrating signals from both neutrophils and lymphocytes, may serve as a more sensitive and disease-specific marker for immunoinflammatory imbalance in PJP.
To the best of our knowledge, this is the first study to systematically evaluate the prognostic value of dynamic NLR trajectories in patients with PJP. Our findings provide a novel framework for early risk stratification and individualized clinical management. Dynamic monitoring of NLR trajectories may help clinicians to identify high-risk patients at an earlier stage, optimize immunomodulatory strategies, and allocate critical care resources more precisely.
Several limitations should be acknowledged. First, this was a retrospective with a relatively limited sample size, which may introduce selection bias. Second, follow-up was restricted to 28 days, and long-term outcomes were not assessed. Third, laboratory testing intervals were not completely standardized, although this limitation was partially mitigated by trajectory modeling techniques. Finally, external validation in large-scale, prospective, multicenter cohorts is required before these findings can be translated into routine clinical practice.
Conclusion
In patients with Pneumocystis jirovecii pneumonia, the dynamic trajectory of logNLR is strongly associated with short-term survival. A persistently decreasing logNLR indicates poor prognosis and warrants closer clinical monitoring and supportive interventions.
Funding Statement
The author(s) declared that financial support was not received for this work and/or its publication.
Footnotes
Edited by: Guoxing Wang, Sanofi, Immunology & Inflammation Research Therapeutic Area, United States
Reviewed by: Mihrican Yeşildağ, Konya Meram State Hospital, Türkiye
Wei Zhang, Fable Therapeutics, United States
Data availability statement
The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by The First Affiliated Hospital of University of Science and Technology of China, Hefei, China. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.
Author contributions
FY: Investigation, Formal analysis, Writing – review & editing, Methodology, Writing – original draft, Data curation, Resources, Project administration. YY: Writing – review & editing, Supervision, Project administration, Methodology, Validation, Data curation, Investigation, Formal analysis, Software. MS: Resources, Software, Data curation, Project administration, Writing – review & editing, Formal analysis, Validation, Methodology, Supervision.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Correction note
This article has been corrected with minor changes. These changes do not impact the scientific content of the article.
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References
- 1.Rhoads S, Maloney J, Mantha A, Van Hook R, Henao-Martínez AF. Pneumocystis jirovecii pneumonia in HIV-negative, non-transplant patients: epidemiology, clinical manifestations, diagnosis, treatment, and prevention. Curr Fungal Infect Rep. (2024). 18:125–35. 10.1007/s12281-024-00482-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Xue T, Kong X, Ma L. Trends in the epidemiology of pneumocystis pneumonia in immunocompromised patients without HIV infection. J Fungi (Basel). (2023) 9:812. 10.3390/jof9080812 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Vargas Barahona L, Molina KC, Pedraza-Arévalo LC, Sillau S, Tagawa A, Scherger S, et al. Previous corticosteroid exposure associates with an increased Pneumocystis jirovecii pneumonia mortality among HIV-negative patients: a global research network with a follow-up multicenter case-control study. Ther Adv Infect Dis. (2023) 10:20499361231159481. 10.1177/20499361231159481 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Ibrahim A, Chattaraj A, Iqbal Q, Anjum A, Ur Rehman ME, Aijaz Z, et al. Pneumocystis jiroveci pneumonia: a review of management in human immunodeficiency virus (HIV) and non-HIV immunocompromised patients. Avicenna J Med. (2023) 13:23–34. 10.1055/s-0043-1764375 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Taniguchi J, Aso S, Jo T, Matsui H, Fushimi K, Yasunaga H. Prognostic factors of in-hospital mortality in patients without human immunodeficiency virus infection with pneumocystis pneumonia: a retrospective cohort study. Intern Med. (2025) 64:651–7. 10.2169/internalmedicine.4090-24 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Lee J, Eom JS, Shi HJ, Hong SH, Park Y. Comparison of clinical characteristics of pneumocystis pneumonia with or without human immunodeficiency virus: a prognosis contrary to initial clinical features. Yonsei Med J. (2025) 66:722–30. 10.3349/ymj.2025.0058 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Peng Y, Wu Q, Zhou Q, Yang Z, Yin F, Wang L, et al. Identification of immune-related genes concurrently involved in critical illnesses across different etiologies: a data-driven analysis. Front Immunol (2022) 13:858864. 10.3389/fimmu.2022.858864 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Yeşildağ M, Özkan Bakdık B, Balasar B, Eroğlu E. Evaluation of the relationship between biomarkers and disease severity in patients with community-acquired pneumonia. Eur J Therap. (2024) 30:354–61. 10.58600/eurjther1976 [DOI] [Google Scholar]
- 9.Wang D, Guan L, Li X, Tong Z. A combined immune and inflammatory indicator predict the prognosis of severe Pneumocystis jirovecii pneumonia patients: a 12-year, retrospective, observational cohort. BMC Pulm Med. (2024) 24:285. 10.1186/s12890-024-03093-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Aljama C, Núñez A, Esquinas C, Tanash H, Bartošovská E, Torres-Duran M, et al. Routine blood biomarkers and lung disease in patients with Alpha-1 antitrypsin deficiency from the EARCO registry. Respiration. (2025). 10.1159/000548597 [Epub ahead of print]. [DOI] [PubMed] [Google Scholar]
- 11.Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. (2010) 6:109–38. 10.1146/annurev.clinpsy.121208.131413 [DOI] [PubMed] [Google Scholar]
- 12.Twisk J, Hoekstra T. Classifying developmental trajectories over time should be done with great caution: a comparison between methods. J Clin Epidemiol. (2012) 65:1078–87. 10.1016/j.jclinepi.2012.04.010 [DOI] [PubMed] [Google Scholar]
- 13.Maschmeyer G, Helweg-Larsen J, Pagano L, Robin C, Cordonnier C, Schellongowski P. ECIL guidelines for treatment of Pneumocystis jirovecii pneumonia in non-HIV-infected haematology patients. J Antimicrob Chemother. (2016) 71:2405–13. 10.1093/jac/dkw158 [DOI] [PubMed] [Google Scholar]
- 14.Mendoza MA, Friedman DZP, Johnson BK, Kremers WK, Razonable RR, Vergidis P. Risk factors and outcomes of Pneumocystis jirovecii pneumonia in kidney transplant recipients: a case-control study of the United States renal data system. Am J Transplant. (2025). 10.1016/j.ajt.2025.12.012 [Epub ahead of print]. [DOI] [PubMed] [Google Scholar]
- 15.Wang Y, Lou L, Cong Y, Huang C, Zhou Y, Xu S, et al. The development of a prediction model for in-hospital mortality of Pneumocystis jirovecii pneumonia among kidney transplant recipients based on the trajectory of absolute lymphocyte count. Mycopathologia. (2025) 190:106. 10.1007/s11046-025-01017-6 [DOI] [PubMed] [Google Scholar]
- 16.Wang Y, Liu K, Huang C, Lou L, Zhou Y, Li S. Development of a prognostic model for in-hospital mortality in idiopathic membranous nephropathy patients with Pneumocystis jirovecii pneumonia based on persistent lymphopenia. BMC Infect Dis. (2025) 25:1435. 10.1186/s12879-025-11932-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Pates K, Periselneris J, Russell MD, Mehra V, Schelenz S, Galloway JB. Rising incidence of pneumocystis pneumonia: a population-level descriptive ecological study in England. J Infect. (2023) 86:385–90. 10.1016/j.jinf.2023.02.014 [DOI] [PubMed] [Google Scholar]
- 18.Franceschini E, Dolci G, Santoro A, Meschiari M, Riccò A, Menozzi M, et al. Pneumocystis jirovecii pneumonia in patients with decompensated cirrhosis: a case series. Int J Infect Dis. (2023) 128:254–6. 10.1016/j.ijid.2022.12.027 [DOI] [PubMed] [Google Scholar]
- 19.Peghin M, Fishman JA, Grossi PA. Pneumocystis jiroveci: still troublesome to diagnose and treat. Curr Opin Infect Dis (2025) 38:522–9. 10.1097/QCO.0000000000001155 [DOI] [PubMed] [Google Scholar]
- 20.Shi Y, Chen R, Sun H, Xu K, Li Z, Wang M, et al. Prognostic analysis of concurrent Pneumocystis jirovecii pneumonia in patients with systemic lupus erythematosus: a retrospective study. BMC Infect Dis. (2024) 24:874. 10.1186/s12879-024-09757-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhang CR, Wang M, Wang L, Liu L, Shen Y-D, Chen R-F, et al. Differential composition of the pulmonary microbiome in HIV-positive versus HIV-negative patients with Pneumocystis jirovecii. PLoS One. (2025) 20:e0334220. 10.1371/journal.pone.0334220 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Tekin A, Truong HH, Rovati L, Lal A, Gerberi DJ, Gajic O, et al. The diagnostic accuracy of metagenomic next-generation sequencing in diagnosing pneumocystis pneumonia: a systemic review and meta-analysis. Open Forum Infect Dis. (2023) 10:ofad442. 10.1093/ofid/ofad442 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Shi Y, Chen R, Xu K, Shao C, Liu Y, Huang H. Clinical characteristics and prognostic analysis of concurrent Pneumocystis jirovecii pneumonia in patients with malignancies: a retrospective study. BMC Infect Dis. (2025) 25:1408. 10.1186/s12879-025-11858-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Xin L, Jiao X, Gong X, Yu J, Zhao J, Lv J, et al. Diagnostic value of metagenomic next generation sequencing of bronchoalveolar lavage fluid in immunocompromised patients with pneumonia. Front Cell Infect Microbiol. (2025) 15:1602636. 10.3389/fcimb.2025.1602636 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Koehler P, Prattes J, Simon M, Haensel L, Hellmich M, Cornely OA. Which trial do we need? Combination treatment of Pneumocystis jirovecii pneumonia in non-HIV infected patients. Clin Microbiol Infect. (2023) 29:1225–8. 10.1016/j.cmi.2023.05.004 [DOI] [PubMed] [Google Scholar]
- 26.Li S, Han X, Ma J, Huang GH, Yang ST, Wang CM. Study on mNGS technique in diagnosing Pneumocystis jirovecii pneumonia in non-HIV-infected patients. Infect Drug Resist. (2024) 17:1397–405. 10.2147/IDR.S450878 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Giacobbe DR, Dettori S, Di Pilato V, Asperges E, Ball L, Berti E, et al. Pneumocystis jirovecii pneumonia in intensive care units: a multicenter study by ESGCIP and EFISG. Crit Care. (2023) 27:323. 10.1186/s13054-023-04608-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Zeng H, Zheng L, Wu D, Yu X, Zhang G, Du J. Exploring immunological determinants and constructing a predictive model for diagnosis of gout: insights into immune biomarkers. J Inflamm Res. (2025) 18:14629–47. 10.2147/JIR.S549357 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Yende S, Kellum JA, Talisa VB, Peck Palmer OM, Chang CCH, Filbin MR, et al. Long-term host immune response trajectories among hospitalized patients with sepsis. JAMA Netw Open. (2019) 2:e198686. 10.1001/jamanetworkopen.2019.8686 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Xu H, Wang Y, Sa Q, Zhou Y, Ma F, Zhang P, et al. Associations between dynamic hematological indicators and outcomes in HR-positive, HER2-negative metastatic breast cancer treated with bireociclib or placebo plus fulvestrant: findings from the BRIGHT-2 trial. Breast. (2025) 84:104598. 10.1016/j.breast.2025.104598 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Lu R, Gao Y, Sun C, Wang M, Huang T, Yang G, et al. The correlation between systemic inflammatory index and rebleeding after bronchial artery embolization: a retrospective cohort study. Int J Emerg Med. (2025) 18:225. 10.1186/s12245-025-00981-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Sun M, Kang H, Luo Y, Zhou Y, Wang Y, Tang F, et al. Enhancing prognostic precision: exploring the role of lung immune prognostic index in immunotherapy-treated soft tissue sarcoma patients-a retrospective study. J Orthop Surg Res. (2025) 20:941. 10.1186/s13018-025-06344-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Tong Y, Li H, Cheng Y, Wang H, Mao M. Novel inflammatory-related indices for screening sarcopenia: insights from a population-based study. Nutrition. (2025) 142:112976. 10.1016/j.nut.2025.112976 [DOI] [PubMed] [Google Scholar]
- 34.Zeng H, Gou Y, Qi C, Tan S, Agrafiotis AC, Hong G, et al. Association of peripheral-blood neutrophil-to-lymphocyte ratio with Pneumocystis jirovecii pneumonia in patients with solid tumors: a case-control study. J Thorac Dis. (2025) 17:6099–111. 10.21037/jtd-2025-1295 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Pan X, Zheng J, Xu J, Pan L, Wang C, Huang X, et al. Clinical characteristics and mortality risk factors in patients with tuberculosis and coincident Pneumocystis jirovecii pneumonia: a retrospective single-center study. Infect Drug Resist. (2025) 18:3535–42. 10.2147/IDR.S530186 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Srikumar A, Kaltchenko M, Niknejad K, Bao A, McCaffrey T, Kang J. Active rash, interstitial lung disease, and neutrophil to lymphocyte ratio and mortality in dermatomyositis. JAMA Dermatol. (2025) 161:1252–7. 10.1001/jamadermatol.2025.4161 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Xie J, Gao A, Zhang W, Zhao D, Liu W. Efficacy and safety of echinocandins combined with Trimethoprim-Sulfamethoxazole as first-line treatment for pneumocystis pneumonia in HIV-infected and non-HIV-infected patients. BMC Infect Dis. (2025) 25:1443. 10.1186/s12879-025-11857-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding authors.