Preamble

Study on the Predictive Value of a Multi-parameter Model Based on Lymphocyte Count for the Prognosis of Patients with Acute Exacerbation of Interstitial Lung Disease Complicated with Pulmonary Infection

YAN Yi¹, JIANG Yu¹, CHEN Bi¹, ZHANG Cantang¹, WANG Jing²*

¹Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China
²Department of Respiratory and Critical Care Medicine, The Second People's Hospital of Huai'an, Huai'an 223002, China

Corresponding author: WANG Jing, Chief Physician; E-mail: 1937174876@qq.com

Abstract

Background: Patients with interstitial lung disease (ILD) are prone to acute exacerbation (AE-ILD), with infection being a significant trigger. AE-ILD patients exhibit high mortality rates and poor prognoses, yet domestic research on this population remains limited.

Objective: To investigate the clinical predictive value of dynamic changes in peripheral blood lymphocyte count (LYM) for 28-day prognosis in AE-ILD patients with pulmonary infection and to establish a corresponding prognostic prediction model.

Methods: A retrospective cohort study included AE-ILD patients hospitalized in the Department of Respiratory Medicine at the Affiliated Hospital of Xuzhou Medical University from January 2022 to June 2024. Patients were stratified into survival (n=37) and non-survival (n=65) groups based on 28-day outcomes. Data collected included demographics (sex, age, diagnosis, ILD subtype, comorbidities), disease severity scores (APACHE II, SOFA), and laboratory parameters: white blood cell count (WBC), neutrophil count (NEU), lymphocyte count on days 1, 3, and 5 (d1 LYM, d3 LYM, d5 LYM), hemoglobin (Hb), platelet count (PLT), procalcitonin (PCT), C-reactive protein (CRP), albumin (ALb), total bilirubin (T-bil), lactate dehydrogenase (LDH), creatinine (Scr), activated partial thromboplastin time (APTT), partial pressure of oxygen (PaO2), partial pressure of carbon dioxide (PaCO2), fraction of inspired oxygen (FiO2), PaO2/FiO2 ratio (P/F), and lactate (Lac). Intergroup differences were analyzed, and statistically significant variables were identified. Receiver operating characteristic (ROC) curves evaluated prognostic predictive capacity. Univariate and multivariate Cox proportional hazards regression analyses were conducted using R software. Scores were assigned to each indicator based on hazard ratios (HR). A nomogram prediction model was constructed, and risk stratification was established after calculating the total score of each indicator. The ROC curve of the prediction model was drawn to evaluate its predictive value. Survival curves for 28-day prognosis of AE-ILD patients with different risk stratifications were plotted using R software, and 28-day survival rates were compared between groups.

Results: The non-survival group exhibited higher APACHE II scores, SOFA scores, PCT, CRP, and LDH (P<0.05) but lower d3 LYM, d5 LYM, ALb, and P/F (P<0.05). Dynamic LYM trends diverged: non-survivors showed progressive lymphopenia, while survivors demonstrated lymphocyte recovery. ROC analysis showed that d3 LYM, d5 LYM, APACHE II score, and SOFA score predicted 28-day prognosis with AUCs of 0.723, 0.764, 0.733, and 0.704, respectively. Multivariate Cox regression identified P/F (HR=2.01, 95%CI=1.08-3.75), PCT (HR=2.14, 95%CI=1.02-4.49), Hb (HR=2.34, 95%CI=1.22-4.48), and d5 LYM (HR=2.40, 95%CI=1.01-5.70) as independent predictors of 28-day mortality. A nomogram model based on d5 LYM, P/F, PCT, and Hb predicted 28-day mortality with an AUC of 0.853 (95%CI=0.781-0.925), optimal cut-off value of 2, sensitivity of 88.24%, and specificity of 82.35%. Risk stratification based on the optimal cut-off classified 0-2 points as low-risk and 3-6 points as high-risk, with significantly different 28-day survival rates between groups (χ²=51, P<0.001).

Conclusion: Lymphopenia is associated with increased 28-day mortality in AE-ILD patients with pulmonary infection. The nomogram model incorporating d5 LYM, P/F, PCT, and Hb provides a clinically practical tool for risk stratification and prognostic assessment.

Keywords: Lymphocyte count; Dynamic change; Infection; Acute exacerbation of interstitial lung disease; Prognosis

Introduction

Interstitial lung disease (ILD) comprises a heterogeneous group of disorders affecting the lung interstitium, encompassing over 200 distinct conditions with complex and not fully elucidated etiologies and pathogeneses [1]. Acute exacerbation of interstitial lung disease (AE-ILD) represents a major cause of mortality in ILD patients [2], characterized by complex etiology and high fatality rates [3]. The concept of acute exacerbation was initially described in idiopathic pulmonary fibrosis (IPF) [4], but with deepening understanding of ILD, acute exacerbations are increasingly recognized across other ILD subtypes [5-7]. While earlier diagnostic criteria required exclusion of infection, emerging research has identified infection as the primary trigger for acute exacerbation [8-9].

Lymphocyte count (LYM) serves as a crucial indicator of immune function and is a readily accessible and inexpensive clinical parameter. Severe infection induces massive lymphocyte apoptosis [10-11], and studies have demonstrated lymphocyte involvement in the regulation of fibrotic processes [12-14], suggesting a significant role for LYM in AE-ILD pathogenesis. Previous research [15] revealed that low peripheral blood LYM levels correlate with poor short-term prognosis in AE-ILD patients. Further analysis showed that baseline LYM did not differ significantly between infected and non-infected AE-ILD patients, yet prognoses differed markedly, suggesting that delayed LYM reduction during inflammation may limit its utility in assessing disease progression [16]. Given that AE-ILD patients are characterized by prolonged hospitalization, advanced age, and severe illness—distinct from typical acute pulmonary infection populations—this study employed Cox regression analysis to explore the relationship between early dynamic LYM changes and prognosis in AE-ILD patients with infection, aiming to establish a predictive model and provide novel insights for clinical management.

Methods

1.1 Study Population

This retrospective cohort study included patients hospitalized in the Department of Respiratory and Critical Care Medicine at the Affiliated Hospital of Xuzhou Medical University from January 2022 to June 2024. Patients were divided into survival and death groups based on 28-day survival status. All treatment protocols adhered to the Expert Consensus on Diagnosis and Treatment of AE-ILD [17]. The study was approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (Approval No: XYFY2024-KL214-01), with strict adherence to ethical requirements in medical clinical research and informed consent waived.

1.2 AE-ILD Diagnostic Criteria

Diagnosis followed the latest AE-ILD criteria [17-18]: (1) prior or current ILD diagnosis; (2) acute worsening or progression of dyspnea typically within one month; (3) new bilateral diffuse ground-glass opacities and/or consolidation on high-resolution CT superimposed on the underlying ILD pattern; and (4) exclusion of heart failure or fluid overload.

1.3 Inclusion and Exclusion Criteria

Inclusion criteria: (1) met AE-ILD diagnostic criteria; (2) confirmed infection (presence of infectious symptoms, elevated inflammatory markers, imaging findings, and/or microbiological evidence); (3) age 18-80 years; and (4) complete follow-up data.

Exclusion criteria: (1) history of organ transplantation; (2) history of acquired immunodeficiency; (3) malignant tumor history; (4) immunotherapy during treatment; (5) hospitalization <72 hours; (6) pregnancy or lactation; and (7) incomplete medical records.

1.4 General Data Collection

Patient demographics including sex, age, diagnosis, ILD subtype, and comorbidities (diabetes, cardiovascular disease, renal disease) were collected.

1.5 Severity Scoring

Sequential Organ Failure Assessment (SOFA) Score: Based on oxygenation index, platelet count, serum total bilirubin, mean arterial pressure, vasopressor dosage, Glasgow Coma Scale, and creatinine level, with each system scored 0-4 points (total range 0-24).

Acute Physiology and Chronic Health Evaluation II (APACHE II) Score: Based on acute physiology parameters (temperature, mean arterial pressure, heart rate, respiratory rate, etc.), age, and chronic health status (presence of severe organ dysfunction or immunosuppression, scored based on surgery status), with total scores ranging from 0-71, where higher values indicate more severe illness and poorer prognosis.

1.6 Laboratory Parameters

Laboratory data collected at admission included: white blood cell count (WBC), neutrophil count (NEU), lymphocyte count on days 1, 3, and 5 (LYM), hemoglobin (Hb), platelet count (PLT), procalcitonin (PCT), C-reactive protein (CRP), albumin (ALb), total bilirubin (T-bil), lactate dehydrogenase (LDH), creatinine (Scr), activated partial thromboplastin time (APTT), partial pressure of oxygen (PO2), partial pressure of carbon dioxide (PCO2), fraction of inspired oxygen (FIO2), oxygenation index (P/F), and lactate (Lac).

1.7 Statistical Analysis

Statistical analysis was performed using SPSS 25.0 software. Normally distributed data were expressed as mean ± standard deviation (x̄±s) and compared between groups using independent samples t-tests. Non-normally distributed data were expressed as median (interquartile range) [M(P25, P75)] and compared using Mann-Whitney U tests. Categorical data were expressed as frequencies and percentages and compared using χ² tests. ROC curves were plotted to evaluate the predictive value of various indicators for 28-day prognosis in infection-related AE-ILD patients, with area under the curve (AUC) calculated. Univariate and multivariate Cox regression analyses were conducted using R software ("ezcox" package) to establish proportional hazards assumptions and visualize covariate effects, with multicollinearity assessed via variance inflation factor (VIF) values. A nomogram prediction model was constructed based on multivariate Cox regression results, with risk stratification established after calculating total scores. Model performance was evaluated using ROC curves ("pROC" package) and survival curves ("survminer" package). Statistical significance was defined as P<0.05.

Results

2.1 Baseline Characteristics and Comparison Between Groups

Among 523 ILD patients screened, 351 met AE-ILD diagnostic criteria. After applying exclusion criteria (87 non-infection AE-ILD, 51 with incomplete data, 3 with organ transplant history, 6 with malignant tumor history, 21 receiving immunotherapy, 2 with hospitalization <72 hours, and 0 pregnant/lactating), 102 patients were included with complete data without imputation. The cohort comprised 62 males (60.8%) and 40 females (39.2%) with a median age of 69 (57-83) years. The 28-day mortality rate was 63.7% (65 deaths, 37 survivors). The death group had significantly higher rates of cardiovascular disease and renal insufficiency, as well as higher APACHE II scores, SOFA scores, PCT, CRP, and LDH (P<0.05). The death group also had significantly lower LYM on days 3 and 5, albumin, and P/F ratio (P<0.05). No significant differences were observed between groups in sex, age, diabetes prevalence, ILD subtype, PLT, T-bil, or Scr (P>0.05) [TABLE:1]. Multicollinearity testing revealed VIF values <5 for all variables, indicating no significant collinearity.

2.2 ROC Curve Analysis of Prognostic Indicators

ROC curves were plotted for LYM levels at different time points, disease severity scores, and other clinical indicators. Day 3 LYM predicted 28-day mortality with an AUC of 0.723 (95%CI=0.626-0.820), optimal cut-off 0.55×10⁹/L. Day 5 LYM showed superior predictive performance with an AUC of 0.764 (95%CI=0.672-0.856), optimal cut-off 0.7×10⁹/L. DeLong test confirmed day 5 LYM had significantly higher AUC than day 3 LYM (Z=1.86, P=0.04). Other indicators including PCT, CRP, Lac, P/F, Hb, Alb, and LDH had AUCs <0.7, indicating limited individual predictive value [FIGURE:1].

2.3 Univariate and Multivariate Cox Regression Analysis

Based on intergroup comparisons, potential prognostic indicators were selected. Univariate Cox regression analysis with 28-day prognosis as the dependent variable (death=1, survival=0) and clinical indicators (SOFA score, APACHE II score, Lac, P/F, PCT, LDH, Alb, CRP, Hb, day 3 LYM, day 5 LYM, all as continuous variables) as independent variables revealed that SOFA score (HR=2.20, 95%CI=1.27-3.81), APACHE II score (HR=3.15, 95%CI=1.78-5.58), Lac (HR=2.06, 95%CI=1.14-3.73), P/F (HR=2.57, 95%CI=1.47-4.47), PCT (HR=4.23, 95%CI=2.16-8.27), LDH (HR=4.12, 95%CI=1.28-13.2), Alb (HR=2.16, 95%CI=1.22-3.82), CRP (HR=2.20, 95%CI=1.26-3.84), Hb (HR=3.52, 95%CI=1.98-6.28), day 3 LYM (HR=3.56, 95%CI=2.02-6.27), and day 5 LYM (HR=3.93, 95%CI=1.96-7.88) were associated with 28-day mortality [TABLE:3].

Multivariate Cox regression analysis incorporating factors with P<0.01 from univariate analysis identified P/F (HR=2.01, 95%CI=1.08-3.75), PCT (HR=2.14, 95%CI=1.02-4.49), Hb (HR=2.34, 95%CI=1.22-4.48), and day 5 LYM (HR=2.40, 95%CI=1.01-5.70) as independent risk factors for 28-day mortality in infection-related AE-ILD [TABLE:4].

2.4 Construction and Validation of the Multi-parameter Prediction Model

A nomogram prediction model was constructed using R software based on the independent risk factors. For clinical convenience, scores were assigned as follows: day 5 LYM (>0.7×10⁹/L=0 points, <0.7×10⁹/L=1 point), P/F (>132 mmHg=0 points, <132 mmHg=1 point), PCT (<0.12 μg/L=0 points, >0.12 μg/L=2 points), and Hb (>100 g/L=0 points, <100 g/L=1 point) [FIGURE:2]. Using X-tile software, optimal risk stratification was determined: 0-2 points as low-risk and 3-6 points as high-risk.

The nomogram model predicted 28-day mortality with an AUC of 0.853 (95%CI=0.781-0.925), optimal cut-off value of 2, sensitivity of 88.24%, and specificity of 82.35% [FIGURE:3].

2.5 Comparison of 28-Day Survival Curves Between Risk Groups

Based on the nomogram model, survival curves were plotted for high-risk and low-risk groups. The 28-day survival rates differed significantly between groups (χ²=51, P<0.001) [FIGURE:4].

Discussion

Acute exacerbation is a common event in the natural course of ILD with persistently high mortality [2]. Infection is a confirmed major external trigger for AE-ILD [19-20], occurring in approximately 30% of AE-ILD patients [21]. The mechanism by which infection exacerbates ILD may involve pathogen-triggered immune responses; for example, CHO et al. [22] demonstrated that Streptococcus pneumoniae infection accelerates fibrosis progression via AIM2 inflammasome activation. AE-ILD patients with infection frequently develop lymphopenia [23-24]. Given the close association between LYM, infection, and ILD progression, this study focused on the prognostic value of dynamic LYM monitoring in AE-ILD patients with infection to construct a practical clinical model.

Baseline analysis revealed that the death group had significantly higher rates of cardiovascular disease and renal insufficiency, along with elevated APACHE II scores, SOFA scores, PCT, CRP, and LDH. These findings suggest that systemic inflammatory response, organ dysfunction, and immunosuppression are closely related to short-term prognosis in AE-ILD patients. Over time, LYM decreased in the death group while increasing in the survivors, with both day 3 and day 5 LYM associated with prognosis. The mechanism of LYM reduction in AE-ILD may involve pulmonary inflammation releasing chemokines such as CXCL9/CXCL10, which drive peripheral blood T lymphocyte migration to lung tissue, resulting in peripheral lymphopenia [25]. This phenomenon is closely associated with disease deterioration and poor prognosis.

ROC analysis demonstrated that day 3 LYM, day 5 LYM, APACHE II score, and SOFA score all had predictive value for 28-day prognosis, with day 5 LYM showing the highest AUC (0.764). This suggests that dynamic LYM monitoring, particularly on day 5, may be more effective for early identification of high-risk patients, while single inflammatory markers (e.g., PCT, CRP) have limited predictive capacity. Similar findings in septic shock patients have shown that day 4 lymphocyte count can predict 28-day mortality [26]. Cox regression analysis ultimately identified day 5 LYM, P/F, PCT, and Hb as independent risk factors for 28-day mortality, indicating that oxygenation impairment, inflammatory response, anemia, and immunosuppression are core drivers of short-term mortality in infection-related AE-ILD. The nomogram model constructed from these indicators (AUC=0.853, sensitivity 88.24%, specificity 82.35%) demonstrated effective risk stratification after survival analysis validation, with a cut-off of 2 points enabling efficient classification (low-risk 0-2 points, high-risk 3-6 points), suggesting the model can provide quantitative guidance for clinical intervention.

To enable rapid prognostic assessment and timely intervention in infection-related AE-ILD patients, this study developed a nomogram model using four readily accessible clinical indicators (day 5 LYM, P/F, PCT, Hb). While commonly used severity scoring systems (e.g., APACHE II, SOFA) assess overall organ status, they are operationally complex and may underestimate respiratory-specific risks. Our model, focused specifically on infection-related AE-ILD, offers high sensitivity and specificity with advantages of convenience, low cost, and broad applicability across medical institutions, providing a practical tool for rapid prognostic judgment and stratified management.

This study has several limitations: (1) it is a single-center study lacking external validation; (2) the sample size is relatively small, requiring expansion to reduce bias; and (3) only 28-day survival was tracked, without longer-term follow-up.

In conclusion, dynamic LYM decline is associated with increased 28-day mortality in AE-ILD patients with pulmonary infection. The nomogram model incorporating day 5 LYM, P/F, PCT, and Hb provides a simple method for prognostic assessment. This model can help clinicians identify severe AE-ILD patients early, guide timely interventions, and improve patient outcomes. Future research will involve multicenter, large-scale studies to explore the prognostic value of additional clinical indicators and conduct long-term follow-up investigations.

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Author Contributions: YAN Yi contributed to study conception/design, implementation, data collection, and manuscript writing. JIANG Yu contributed to data collection/organization, statistical analysis, and figure/table preparation. CHEN Bi and ZHANG Cantang contributed to feasibility analysis and manuscript revision. WANG Jing contributed to quality control, manuscript review, and overall supervision.

Conflict of Interest Statement: The authors declare no conflicts of interest.

Funding: This study was supported by the Scientific Research Project of Jiangsu Provincial Health Commission (H2023005).

Citation: YAN Y, JIANG Y, CHEN B, et al. Study on the predictive value of a multi-parameter model based on lymphocyte count for the prognosis of patients with acute exacerbation of interstitial lung disease complicated with pulmonary infection [J]. Chinese General Practice, 2025. DOI:10.12114/j.issn.1007-9572.2025.0105. [Epub ahead of print].

Editor: LI Weixia

Received: 2025-02-10; Revised: 2025-05-06