Distribution characteristics and influencing factors of active cytomegalovirus infection in patients with hematological malignancies complicated with sepsis

Abstract

Background

Cytomegalovirus (CMV) tends to reactivate in immunocompromised individuals. Patients with hematological malignancies are at high risk of CMV infection due to treatment-related immunosuppression, and sepsis may further promote latent CMV reactivation via inflammatory disorders. However, research on active CMV infection in patients with both conditions remains limited.

Methods

A single-center retrospective cohort study included 119 patients with hematological malignancies complicated by sepsis (Ningbo Medical Center Lihuili Hospital, June 2022–June 2025). Patients were divided into active CMV infection group (plasma CMV DNAemia ≥ 500 copies/mL, 41 cases) and non-active group (< 500 copies/mL, 78 cases) via qPCR. Clinical features, laboratory indicators, treatment, and outcomes were compared; regression analysis screened risk factors, and ROC curves assessed predictive value of indicators.

Results

Active CMV infection incidence was 34.45%, with 92.68% occurring within 0–7 days of hospitalization. CMV was detected in lower respiratory tract specimens of 21.84% patients, and plasma CMV load correlated positively with that in these specimens (r = 0.558, P < 0.001). The active group had lower weight, BMI, hemoglobin, albumin, white blood cell and platelet counts, but higher SOFA scores and body temperature (all P < 0.05). The active group had higher rates of blood transfusion and immunosuppressant use, longer hospitalization and fever duration, and higher 28-day all-cause mortality (all P < 0.05); no difference in complication incidence was observed. Decreased white blood cell count was an independent risk factor (β=-0.209, OR = 0.811, P = 0.021) with optimal predictive value (AUC = 0.746); hemoglobin and platelet count also had good predictive value.

Conclusions

Patients with hematological malignancies complicated by sepsis have high active CMV infection incidence (mostly early hospitalization), associated with poor prognosis. Decreased white blood cell count is a useful early screening indicator, requiring early clinical monitoring and intervention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10096-026-05523-3.

Introduction

Cytomegalovirus (CMV) is a widely prevalent herpesvirus that typically exists in a latent state in healthy individuals but can reactivate and cause active infections in immunocompromised populations [1]. In recent years, advancements in diagnostic and therapeutic techniques for hematological malignancies have significantly prolonged patient survival; however, treatments such as chemotherapy, radiotherapy, and hematopoietic stem cell transplantation often lead to immunosuppressive states that make these patients high-risk for CMV infections [2]. Studies have shown that the reactivation rate of CMV among patients with hematological malignancies can reach 30%−50%, often accompanied by severe complications that increase treatment difficulty and mortality risk [3].

Sepsis is a systemic inflammatory response syndrome triggered by infection and is one of the most common fatal complications among patients with hematological malignancies. Due to immune dysfunctions caused by their underlying disease—such as mucosal barrier damage or prolonged exposure to broad-spectrum antibiotics—the incidence rate of sepsis among these individuals is significantly higher than that observed in the general population. Once it occurs, disease progression tends to be rapid with high rates of multi-organ dysfunction development. Furthermore, sepsis itself may disrupt immune balance through immune dysregulation or cytokine storms induced by inflammation processes—potentially serving as a “catalyst” for reactivating latent CMV infections [4]. Previous studies have confirmed an approximately 20% occurrence rate for active CMV infections among mechanically ventilated septic patients; this condition has been closely associated with extended mechanical ventilation durations or prolonged ICU stays [5]. However specific data regarding patterns surrounding active CMVs within unique cohorts like those suffering simultaneously from both conditions remain insufficiently elucidated thus far.

Patients diagnosed concurrently alongside both diseases exhibit distinct complexities clinically speaking: On one hand immunodeficiencies stemming directly out-of-treatment regimens themselves (e.g., neutropenia depletion exhaustion T-cell functionality); while conversely heightened inflammatory statuses provoked via septic responses might accelerate viral replication cycles indirectly mediated via pro-inflammatory cytokines upregulated pathways IL-6/TNF-alpha mechanisms forming vicious feedback loops perpetuating “immune suppression-inflammatory storm-virus activation“ [6, 7]. In addition, such patients often undergo multiple invasive procedures (e.g., central venous catheterization, mechanical ventilation) and receive broad-spectrum anti-infective treatments, further increasing the diagnostic difficulty and therapeutic challenges of CMV infection. For example, the clinical manifestations of CMV pneumonia overlap with those of bacterial pneumonia, and interactions between antiviral drugs and chemotherapeutic agents may exacerbate adverse effects such as bone marrow suppression, thereby affecting anti-infective treatment decisions.

Currently, research on CMV infection has mostly focused on single-disease populations (e.g., patients with sepsis alone or hematologic malignancies alone), with limited studies addressing patients who have both conditions simultaneously. Existing evidence suggests that in patients with simple sepsis, low hemoglobin levels are an independent risk factor for CMV activation, and active infection can prolong the duration of mechanical ventilation and hospitalization [8]. In patients with hematologic malignancies, CMV infection is associated with more severe hematopoietic dysfunction and higher transplant-related mortality [9]. However, it remains unclear whether the incidence of CMV infection is further increased in patients with both hematologic malignancies and sepsis, and whether CMV infection exacerbates sepsis-related organ injury or prolongs hospitalization.

Elucidating the clinical characteristics of active CMV infection in patients with hematologic malignancies complicated by sepsis is of great significance for optimizing diagnostic and therapeutic strategies. Identifying high-risk populations and implementing targeted monitoring may help facilitate early infection detection and preemptive treatment, potentially reducing complications, shortening hospital stays, and improving outcomes. For instance, if specific laboratory markers (e.g., hemoglobin levels, inflammatory factors) are confirmed to predict CMV activation, they could provide convenient tools for clinical screening. Furthermore, understanding the impact of CMV infection on clinical outcomes (e.g., incidence of septic shock) may inform decisions regarding the optimal timing of antiviral therapy.

Based on the above background, this study aims to systematically investigate the incidence of active CMV infection in patients with hematologic malignancies complicated by sepsis, analyze its clinical features and risk factors, and explore the impact of the infection on treatment and prognosis. The study will enroll septic patients treated in the Department of Hematology at Ningbo Medical Center Lihuili Hospital between June 2022 and June 2025. Active and non-active CMV infection groups will be defined based on CMV DNAemia levels during hospitalization. Clinical data and outcome differences between the two groups will be compared. The findings of this study are expected to provide evidence-based guidance for CMV infection management in this patient population and contribute to improved clinical care.

Materials and methods

Study design and ethics

This single-center retrospective cohort study included 119 patients hospitalized for hematological malignancies complicated by sepsis at Ningbo Medical Center Lihuili Hospital between June 2022 and June 2025. The study protocol was approved by the hospital’s ethics committee (KY2025SL306-01). As this is a retrospective study, patient informed consent was waived. All data collection and analysis adhered to the Declaration of Helsinki and relevant ethical guidelines.

Participants and defnitions

Inclusion Criteria: (1) Age ≥ 18 years; (2) Diagnosis consistent with hematological malignancies (including leukemia, lymphoma, multiple myeloma based on WHO classification standards for tumors of hematopoietic and lymphoid tissues [10]). Detailed subtypes of the included diseases are presented in Table 1 and Figure E2, including Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL), B-cell-derived lymphoma, T-cell-derived lymphoma, Other types of lymphoma, Newly diagnosed multiple myeloma (NDMM), and Relapsed and refractory multiple myeloma (RRMM); (3) Sepsis diagnosis meeting SEPSIS-3 criteria (Sequential Organ Failure Assessment [SOFA] score ≥ 2 due to infection [11]); Sepsis diagnosis meeting SEPSIS-3 criteria (Sequential Organ Failure Assessment [SOFA] score ≥ 2 due to infection [11]). For patients with suspected respiratory tract infection (including sCAP), pathogenic microorganism detection was conducted using sputum culture, blood culture, and/or nucleic acid amplification test(NAAT) to identify specific pathogens. Detailed classification of sepsis infection sources was based on clinical manifestations, imaging findings, and laboratory test results. (4) Received anti-infective treatment during hospitalization with at least one CMV DNAemia test performed; (5) Complete clinical data available (including laboratory tests, treatment measures, outcome data). Exclusion Criteria: (1) Concurrent other malignant tumors (e.g., solid tumors); (2) Hematopoietic stem cell transplantation within three months prior to enrollment; (3) Prior antiviral therapy before enrollment (e.g., ganciclovir, valganciclovir or foscarnet); (4) Pregnant or lactating women; (5) Hospitalization duration < 72 h or severely incomplete clinical data; (6) Coexisting HIV infection or congenital immunodeficiency syndromes causing severe immune dysfunction unrelated to hematological malignancy-induced immunosuppression.

Table 1.

Distribution of CMV infection among different types of hematological malignancies

Disease Type	Total Population (n = 119)	Active CMV4 infection(n = 41, 34.45%)	Non-Active CMV infection(n = 78, 65.54%)	CMV Infection Rate	Intergroup Comparison P-Value (vs. MM)
AL1	53 (44.5%)	22 (53.7%)	31 (39.7%)	53.7%	0.028
NHL2	57 (47.9%)	19 (46.3%)	38 (48.7%)	46.3%	0.042
MM3	9 (7.6%)	0 (0.0%)	9 (11.5%)	0.0%	-

Grouping criteria

Patients were divided into two groups based on plasma CMV DNAemia results during hospitalization: Active CMV Infection Group: At least one test showing plasma CMV DNAemia ≥ 500 copies/mL (based on commonly used diagnostic thresholds in clinical practice). Non-active CMV Infection Group: Plasma CMV DNAemia < 500 copies/mL across all tests performed during hospitalization (including values as low as zero copies/mL).

CMV DNAemia testing utilized real-time quantitative polymerase chain reaction (qPCR), using peripheral venous plasma samples collected routinely every seven days. “Disease progression” was specifically defined as the occurrence of any of the following conditions: (1) Persistent or recurrent fever (body temperature ≥ 38.5 °C lasting more than 48 h despite anti-infective treatment); (2) Newly developed or worsening organ dysfunction (e.g., increase in SOFA score by ≥ 2 points, significant elevation of serum creatinine or total bilirubin, or decrease in arterial partial pressure of oxygen/fraction of inspired oxygen); (3) Significant decline in hematological parameters (e.g., white blood cell count < 1.0 × 10⁹/L or platelet count < 20 × 10⁹/L). For patients with disease progression, the collection frequency was increased to once every 2–3 days until clinical stability or viral load clearance was achieved.

Data collection

Data extraction was conducted via electronic medical records by two trained researchers independently entering information (including details of CMV DNAemia testing timing and frequency based on clinical conditions) followed by cross-verification; discrepancies were resolved through arbitration by a senior clinician.

Baseline Clinical Characteristics: Age; gender; body mass index (BMI); type(s) of hematological malignancy; comorbidities such as hypertension/diabetes/chronic kidney disease; triggers for sepsis including pneumonia/urinary tract infections/catheter-related infections etc.; SOFA scores upon admission.

Laboratory Results: Blood routine tests, immune function indicators, and biochemical markers were detected within the first day post-admission. Immune function indicators included CD4⁺T cell count, CD8⁺T cell count, Natural Killer (NK) cell count, and B cell count, which were measured by flow cytometry. Blood routine tests included hemoglobin counts, neutrophil counts, and platelet levels. Biochemical markers included Aspartate Aminotransferase (AST), Serum Creatinine (Scr), inflammatory biomarkers (CRP, procalcitonin), coagulation parameters (Prothrombin Time [PT], Activated Partial Thromboplastin Time [APTT]), and dynamic trends of viral loads detected via qPCR assays. Additionally, serum CMV IgM and IgG antibody detection was performed using enzyme-linked immunosorbent assay (ELISA) for 68 of the 119 patients (57.1%) with complete clinical records. The detection was conducted simultaneously with other laboratory tests within 24 h after admission to ensure consistency of baseline data.

Treatment Measures: Anti-infective treatment regimens (including types and courses of antibacterial and antiviral drugs), use of glucocorticoids, blood transfusion (red blood cells, platelets) and intravenous immunoglobulin infusion, invasive procedures (such as central venous catheterization and mechanical ventilation), etc.

Clinical Outcomes: Incidence of septic shock, length of hospital stay, all-cause mortality during hospitalization, 28-day all-cause mortality.

The process of this study is shown in Figure S1.

Data analysis

Data analysis was performed using SPSS 26.0 software. Measurement data conforming to a normal distribution were expressed as mean ± standard deviation (± s), with intergroup comparisons conducted using independent sample t-tests; for data not conforming to a normal distribution, the median (interquartile range) [M(P25,P75)] was used for expression, with intergroup comparisons conducted using the Wilcoxon rank-sum test. Count data were expressed as frequency (percentage) [n(%)], with intergroup comparisons conducted using χ² tests or Fisher’s exact probability method (when theoretical frequencies < 5). Univariate logistic regression analysis was used to identify potential risk factors for active CMV infection (variables with P < 0.1 were included in multivariate analysis), followed by multivariate logistic regression models to determine independent risk factors and calculate odds ratios (ORs) along with 95% confidence intervals (CIs). Receiver operating characteristic curves (ROC) were used to evaluate the predictive value of continuous variables such as hemoglobin for active CMV infection by calculating the area under the curve (AUC), optimal cutoff values, sensitivity, and specificity. Kaplan-Meier methods were employed to analyze survival curves between two groups of patients regarding outcomes such as length of hospital stay; differences between groups were assessed using log-rank tests. All statistical tests were two-sided, with P < 0.05 considered statistically significant.

Results

Incidence of active CMV infection

The distribution of specific subtypes of hematological malignancies in the study population is summarized in Table 1. There were significant differences in the active CMV infection rate among different subtypes of hematological malignancies (χ²=6.735, P = 0.034). Specifically, the infection rate was significantly higher in patients with AML (53.7%) and ALL (19.5%) under AL, as well as B-cell-derived lymphoma (14.6%) and T-cell-derived lymphoma (24.4%) under NHL, compared with NDMM (0.0%) and RRMM (0.0%) under MM (all P < 0.05). No significant difference in CMV infection rate was observed between different subtypes within AL or NHL (P > 0.05). The detailed distribution of active/non-active CMV infection across each subtype is visualized in Figure S2.

Data are presented as n (percentage). ¹AL = Acute Leukemia; ²NHL = Non-Hodgkin Lymphoma; ³MM = Multiple Myeloma; ⁴CMV = Cytomegalovirus. The CMV infection rate was calculated as (number of CMV-positive patients/total number of patients with the corresponding disease) × 100%. Intergroup comparisons were performed using Pearson’s chi-square test, with P < 0.05 considered statistically significant. “-” indicates no comparison applicable.

A total of 119 septic patients who received empirical anti-infective therapy in the hematology department were enrolled in this study. During their hospitalization, cytomegalovirus (CMV) DNA was detected in peripheral blood samples using quantitative polymerase chain reaction (qPCR). Among these patients, 41 were diagnosed with CMV DNAemia (i.e., active CMV infection), yielding an infection rate of 34.45%; the remaining 78 patients tested negative for active CMV infection, accounting for 65.54% (Fig. 1A, Figure S3A). Stratified analysis of CMV load among the 41 patients with CMV DNAemia revealed distribution across all defined load intervals (500–1000 copies/mL, 1001–5000 copies/mL, 5001–10000 copies/mL, and > 10,000 copies/mL), reflecting variations in CMV replication levels within this cohort (Fig. 1B).

Fig. 1.

Incidence of Active CMV Infection and Level of CMV DNAemia Load. A Incidence of CMV infection within 28-day Department of Hematology admission; B CMV DNAemia load within 28-day Department of Hematology admission

Regarding the timing of infection detection (Table S1.docx), among the 41 patients with CMV DNAemia, 31 cases (75.61%) tested positive between days 4 and 7 after admission to the hematology department, 7 cases (17.07%) tested positive between days 0 and 3 after admission, while only 2 cases (4.88%) and 1 case (2.44%) tested positive between days 8–11 and days 12–15 after admission, respectively. These results indicate that CMV infection in this patient population primarily occurs during the early phase of hospitalization in the hematology department (days 0–7). Additionally, CMV was simultaneously detected in endotracheal aspirate (ETA) specimens (lower respiratory tract samples) from all enrolled patients. The results demonstrated that CMV was detected in ETA specimens from 26 patients (21.84%), whereas no CMV was detected in 93 patients (78.15%) (Figure S3B). Further Pearson correlation analysis revealed a statistically significant positive correlation between plasma CMV load and CMV load in ETA specimens (correlation coefficient r = 0.558, P < 0.001) (Table S2.docx). These findings suggest that plasma CMV testing may partially reflect local CMV replication in the lower respiratory tract, offering a potential non-invasive method for monitoring respiratory CMV infection in such patients.

Patient clinical characteristics

A total of 119 septic patients who received empirical anti-infective therapy in the Department of Hematology were enrolled in this study, including 70 males (58.82%) and 49 females (41.17%). The median age of all participants was 64 years (interquartile range, IQR: 50–72 years), and the median Sequential Organ Failure Assessment (SOFA) score was 7 points (IQR: 5–9 points). Regarding the etiology of sepsis, severe community-acquired pneumonia (sCAP) was the primary cause, accounting for 52.94% (63/119), while other etiologies accounted for 47.05% (56/119). For the 63 patients with sCAP, pathogenic microorganism detection was performed via sputum culture, blood culture, and/or nucleic acid amplification testing (NAAT). The detection results showed that 38 cases (60.32%) were positive for specific pathogenic organisms, including 16 cases (25.40%) of Streptococcus pneumoniae, 10 cases (15.87%) of Haemophilus influenzae, 7 cases (11.11%) of Klebsiella pneumoniae, 3 cases (4.76%) of Pseudomonas aeruginosa, and 2 cases (3.17%) of Legionella pneumophila. The remaining 25 cases (39.68%) were negative for pathogenic microorganism detection, possibly due to prior empirical antibiotic use or viral infection (e.g., influenza virus, respiratory syncytial virus) not covered by the current detection panel. Detailed classification of sepsis infection sources is shown in Table S3. Specifically, the infection sources included respiratory tract infection (n = 79, 66.39%), urinary tract infection (n = 18, 15.13%), intra-abdominal infection (n = 10, 8.40%), skin and soft tissue infection (n = 6, 5.04%), catheter-related bloodstream infection (n = 4, 3.36%), and other unspecified infections (n = 2, 1.68%). Among respiratory tract infections, sCAP accounted for 63 cases (79.75%), and hospital-acquired pneumonia (HAP) accounted for 16 cases (20.25%). Concerning comorbidities, hypertension (15.12%, 18/119), diabetes mellitus (23.52%, 28/119), and chronic obstructive pulmonary disease (COPD, 10.92%, 13/119) were relatively common, whereas the prevalence of cardiovascular diseases (6.72%, 8/119), chronic kidney disease (CKD, 5.88%, 7/119), interstitial lung disease (ILD, 7.56%, 9/119), and cerebrovascular accident (CVA, 3.36%, 4/119) was relatively low.

A further comparison of clinical characteristics between the CMV DNAemia group (41 cases, 34.45%) and the non-CMV DNAemia group (78 cases, 65.54%) revealed no statistically significant differences in age (median age: 65 years vs. 64 years, P = 0.573), gender (proportion of males: 68.29% vs. 53.84%, P = 0.130), sepsis etiology (proportion of sCAP: 63.41% vs. 47.43%, P = 0.074), or most comorbidities (hypertension, diabetes mellitus, COPD, etc., all P > 0.05). However, patients in the CMV DNAemia group had significantly lower body weight (57.97 ± 5.89 kg vs. 65.34 ± 6.72 kg, P < 0.001) and body mass index (BMI, 20.78 ± 1.50 kg/m² vs. 22.52 ± 1.50 kg/m², P < 0.001), as well as significantly higher SOFA scores (median score: 10 points vs. 6 points, P < 0.001) and body temperature (median temperature: 38.8 °C vs. 37.9 °C, P < 0.001) compared with the non-DNAemia group. Additionally, no patients in the CMV DNAemia group had comorbid CKD, which was significantly different from the non-DNAemia group (8.97%, 7/78, P = 0.027) (Table 2).

Table 2.

Patients clinical features at Department of Hematology admission

Age(yrs)	Total N = 119	CMV1 DNAemia Yes(n = 41, 34.45%)	CMV DNAemia2 No(n = 78, 65.54%)	P #
	64(50,72)	65(50,72)	64(50,71)	0.573
Sex, n(%)				0.130
Male	70(58.82%)	28(68.29%)	42(53.84%)
Female	49(41.17%)	13(31.70%)	36(46.15%)
Weight(Kg)	62.80 ± 7.32	57.97 ± 5.89	65.34 ± 6.72	<0.001
BMI3(kg/m2)	21.92 ± 1.71	20.778 ± 1.50	22.521 ± 1.50	<0.001
SOFA4	7(5,9)	10(7,12)	6(5,7)	<0.001
Causes of sepsis, n(%)				0.074
sCAP5	63(52.94%)	26(63.41%)	37(47.43%)
Other	56(47.05%)	15(36.58%)	41(52.56%)
Vital signs
Average blood pressure (mmHg)	91.60 ± 15.33	93.43 ± 13.92	90.64 ± 16.03	0.346
Heart rate (bpm)	87(72,97)	90(73,100)	85(71,97)	0.709
Temperature (℃)	38.2(37.7,38.8)	38.8(38.2,38.8)	37.9(37.7,38.3)	<0.001
Comorbidities, n (%)
Hypertension	18(15.12%)	6(14.63%)	12(15.38%)	0.949
Diabetes	28(23.52%)	11(26.82%)	17(21.79%)	0.538
Cardiovascular diseases	8(6.72%)	2(4.87%)	6(7.69%)	0.560
COPD6	13(10.92%)	4(9.75%)	9(11.53%)	0.765
CVA7	4(3.36%)	1(2.43%)	3(3.84%)	0.688
CKD8	7(5.88%)	0(0%)	7(8.97%)	0.027
ILD9	9(7.56%)	3(7.31%)	6(7.69%)	0.952

¹CMV Cytomegalovirus, ²DNAemia DNA detection by quantitative polymerase chain reaction (qPCR) on peripheral blood samples, ³BMI Body Mass Index, ⁴SOFA Sequential Organ Failure Assessment, ⁵sCAP Severe Community-acquired Pneumonia, ⁶COPD Chronic Obstructive Pulmonary Disease, ⁷CVA Cerebrovascular Accident, ⁸CKD Chronic Kidney Disease, ⁹ILD Interstitial Lung Disease

# P < 0.05; Categorical variables were expressed as n (%), Continuous variables were expressed as Mean ± SD or Median (IQRs); Bold font indicates the diference was statistically significant

Comparison of laboratory indicators showed that patients in the CMV DNAemia group had significantly lower hemoglobin (Hb, median level: 80 g/L vs. 119 g/L, P < 0.001), albumin (median level: 33 g/L vs. 39 g/L, P = 0.002), white blood cell count (median level: 3.10 × 10⁹/L vs. 8.25 × 10⁹/L, P < 0.001), and platelet count (median level: 35 × 10⁹/L vs. 171 × 10⁹/L, P < 0.001), while significantly higher arterial partial pressure of oxygen/fraction of inspired oxygen (PaO₂/FiO₂, median level: 417 vs. 399, P = 0.035) and total bilirubin (T-BIL, median level: 13.30 µmol/L vs. 11.20 µmol/L, P = 0.034) compared with the non-DNAemia group. No statistically significant differences were observed between the two groups in indicators such as procalcitonin (PCT), C-reactive protein (CRP), coagulation function parameters (PT, APTT), B-type natriuretic peptide (BNP), aspartate aminotransferase (AST), and serum creatinine (Scr) (all P > 0.05) (Table 3).

Table 3.

Laboratory findings at Department of Hematology admission

PaO2/FiO23	Total N = 119	CMV1 DNAemia Yes(n = 41, 34.45%)	CMV DNAemia 2 No(n = 78, 65.54%)	P #
	405(356,435)	417(372,336)	399(327,427)	0.035
White blood cells (109/L)	7.1(3.9,10.7)	3.10(1.00,8.60)	8.25(6.30,11.20)	<0.001
Hemoglobin (g/L)	108(80,121)	80(60,115)	119(100,121)	<0.001
Platelet (109/L)	165(62,183)	35(16,172)	171(151,186)	<0.001
Procalcitonin (ng/mL)	0.48(0.13,2.00)	1.16(0.26,2.62)	0.32(0.13,2.00)	0.421
C-reactive protein (mg/L)	60.90(34.60,130.20)	63.00(34.65,138.40)	60.80(33.20,129.95)	0.837
PT4 (s)	11.20(10.20,12.10)	11.20(10.50,11.90)	11.15(9.90,12.25)	0.766
APTT5(s)	27.80(26.30,29.80)	28.30(26.60,30.10)	27.75(26.25,29.27)	0.262
BNP6 (pg/mL)	122(72,172)	121(77,172)	122.50(72.75,171.25)	0.832
AST7 (U/L)	49(39,69)	52(39,72)	46(39,63)	0.333
Albumin (g/L)	38(31,43)	33(29,42)	39(35,43)	0.002
T-BIL8 (µmol/L)	11.30(9.3,17.20)	13.30(9.65,23.65)	11.20(9.20,15.20)	0.034
Scr9 (µmol/L)	63(52,88)	72(52,93)	63(50,85)	0.080

¹CMV Cytomegalovirus, ²DNAemia DNA detection by quantitative polymerase chain reaction (qPCR) on peripheral blood samples, ³PaO2/FiO2 Partial Pressure of Arterial Oxygen/Fraction of Inspired Oxygen, ⁴PT Prothrombin Time, ⁵APTT Activated Partial Thromboplastin Time, ⁶BNP B-Type Natriuretic Peptide, ⁷AST Aspartate Aminotransferase, ⁸T-BIL Total Bilirubin, ⁹Scr Serum Creatinine

# P < 0.05; Continuous variables were expressed as Mean ± SD or Median (IQRs); Bold font indicates the diference was statistically signifcant

Treatment measures and complications

Among the 119 septic patients enrolled in this study who were treated in the Department of Hematology, there were significant differences in the utilization of certain therapeutic interventions between the CMV DNAemia group (41 cases, 34.45%) and the non-CMV DNAemia group (78 cases, 65.54%) (Table 3). Specifically, the proportion of patients receiving blood transfusions in the CMV DNAemia group (39.02%, 16/41) was significantly higher compared to the non-CMV DNAemia group (5.12%, 4/78), with a statistically significant difference (P < 0.001). Additionally, the proportion of patients using immunosuppressive agents in the CMV DNAemia group (60.97%, 25/41) was significantly greater than in the non-CMV DNAemia group (30.76%, 24/78), and this difference was also statistically significant (P = 0.015). However, no statistically significant differences were observed between the two groups regarding the use of glucocorticoids (31.70% vs. 21.94%, P = 0.240) or gamma globulin infusions (31.70% vs. 17.94%, P = 0.088).

Additionally, comparison of immune function indicators showed significant differences between the two groups (Table 4). The median CD4⁺T cell count in the active CMV infection group was 3 cells/µL (IQR: 0, 2089), which was significantly lower than that in the non-active CMV infection group [1523 cells/µL (IQR: 0, 2138)] (P < 0.001). Similarly, the median Natural Killer (NK) cell count in the active CMV infection group [180 cells/µL (IQR: 0, 568)] was significantly lower than that in the non-active group [331 cells/µL (IQR: 0, 587)] (P < 0.001). The median B cell count in the active CMV infection group [180 cells/µL (IQR: 0, 568)] was also significantly lower than that in the non-active group [331 cells/µL (IQR: 0, 599)] (P < 0.001). These results indicate that patients with active CMV infection have comprehensive impairment of key immune cell subsets (CD4⁺T cells, NK cells, and B cells) at admission, which may weaken the body’s antiviral defense capacity and be closely associated with CMV reactivation.

Table 4.

Comparison of immune function indicators between the two groups

Immune Function Indicators	Total (n = 119)	Active CMV1 Infection (n = 41)	Non-Active CMV Infection (n = 78)	P#
CD4⁺T cell count (cells/µL)	1333(0,2138)	3(0,2089)	1523(0,2138)	<0.001
Natural Killer (NK) cell count (cells/µL)	288(0,587)	180(0,568)	331(0,587)	<0.001
B cell count (cells/µL)	288(0,599)	180(0,568)	331(0,599)	<0.001

¹ CMV = Cytomegalovirus. # P < 0.05; Categorical variables were expressed as n (%), Continuous variables were expressed as Mean ± SD or Median (IQRs); Bold font indicates the diference was statistically significant

Among 68 patients with complete CMV antibody data (Table S4), 52 cases (76.5%) were IgG positive + IgM negative, 12 cases (17.6%) were IgG positive + IgM positive, 2 cases (2.9%) were IgG negative + IgM positive, and 2 cases (2.9%) were IgG negative + IgM negative. Combined with CMV DNAemia results, 30 of 52 IgG positive + IgM negative patients (57.7%), 10 of 12 IgG positive + IgM positive patients (83.3%), and both 2 IgG negative + IgM positive patients were CMV DNAemia positive, suggesting latent infection reactivation, latent reactivation or recent reinfection, and primary infection, respectively.

With respect to complication occurrence, the main complications observed in septic patients in this study included acute heart failure (AHF, 7.56%, 9/119), disseminated intravascular coagulation (DIC, 4.20%, 5/119), and septic shock (5.04%, 6/119); no cases of acute respiratory distress syndrome (ARDS) were reported. A comparative analysis of complications between the two groups revealed no statistically significant differences in the incidence of acute respiratory failure (ARF, 2.43% vs. 0.00%, P = 0.999), multiple organ dysfunction syndrome (MODS, 0.00% vs. 1.28%, P = 0.999), septic shock (9.75% vs. 2.56%, P = 0.088), acute kidney injury (AKI, 4.87% vs. 0.00%, P = 0.554), acute exacerbation of chronic obstructive pulmonary disease (AECOPD, 2.43% vs. 0.00%, P = 0.999), AHF (7.31% vs. 7.69%, P = 0.949), or DIC (7.31% vs. 2.56%, P = 0.235) (Table 5).

Table 5.

Treatment measures, complications, and clinical outcomes

Treatment measures, n (%)	Total N = 119	CMV1 DNAemia Yes(n = 41, 34.45%)	CMV DNAemia2 No(n = 78, 65.54%)	P #
Glucocorticoids	30(25.21%)	13(31.70%)	17(21.94%)	0.240
Blood transfusion	20(16.80%)	16(39.02%)	4(5.12%)	<0.001
Gamma globulin infusions	27(22.68%)	13(31.70%)	14(17.94%)	0.088
Immunosuppressive drugs	49(41.17%)	25(60.97%)	24(30.76%)	0.015
Complications, n (%)
ARF3	1(0.84%)	1(2.43%)	0(0.00%)	0.999
MODS4	1(0.84%)	0(0.00%)	1(1.28%)	0.999
Septic shock	6(5.04%)	4(9.75%)	2(2.56%)	0.088
AKI5	2(1.68%)	2(4.87%)	0(0.00%)	0.554
ARDS6	0(0.00%)	0(0.00%)	0(0.00%)	/
AECOPD7	1(0.84%)	1(2.43%)	0(0.00%)	0.999
AHF8	9(7.56%)	3(7.31%)	6(7.69%)	0.949
DIC9	5(4.20%)	3(7.31%)	2(2.56%)	0.235
Duration of hospital stay	15(12,20)	17(12,25)	15(10,20)	0.008
Duration of fever	7(5,10)	10(6,12)	6(4,8)	<0.001
28-day all-cause mortality, n (%)	18(15.12%)	10(24.39%)	8(10.25%)	0.040

¹CMV Cytomegalovirus, ²DNAemia DNA detection by quantitative polymerase chain reaction (qPCR) on peripheral blood samples, ³ARF Acute Respiratory Failure, ⁴MODS Multiple Organ Dysfunction Syndrome, ⁵AKI Acute Kidney Failure, ⁶ARDS Acute Respiratory Distress Syndrome, ⁷AECOPD Acute Exacerbation of Chronic Obstructive Pulmonary Disease, ⁸AHF Acute Heart Failure, ⁹DIC Disseminated Intravascular Coagulation

# P < 0.05; Categorical variables were expressed as n (%), Continuous variables were expressed as Mean ± SD or Median (IQRs); Bold font indicates the diference was statistically signifcant

Clinical outcomes

Analysis of hospitalization-related indicators in 119 septic patients treated in the Department of Hematology revealed significant differences in several key parameters between the CMV DNAemia group (41 cases, 34.45%) and the non-CMV DNAemia group (78 cases, 65.54%) (Table 3). Specifically, the median length of hospital stay in the CMV DNAemia group (17 days, interquartile range: 12–25 days) was significantly longer compared to the non-CMV DNAemia group (15 days, interquartile range: 10–20 days), with a statistically significant difference (P = 0.008). Additionally, the median duration of fever was significantly longer in the CMV DNAemia group (10 days, interquartile range: 6–12 days) than in the non-CMV DNAemia group (6 days, interquartile range: 4–8 days) (P < 0.001).

Further assessment of the time distribution characteristics of relevant outcomes in the two groups using Kaplan-Meier survival analysis (KM analysis) showed that for outcome indicators within 60 days, the Log-rank test result between the CMV DNAemia group and the non-CMV DNAemia group was P = 0.193, indicating no statistically significant difference between the two groups within this time range. For outcome indicators within 25 days, the Log-rank test result between the two groups was P = 0.453, which also showed no statistically significant difference (Fig. 2).

Fig. 2.

Effect of Active CMV Infection on Duration of Department of Hematology admission. A Effect of active CMV infection on duration of 14-day Department of Hematology admission stay. B Effect of active CMV infection on duration of 3-Day fever Department of Hematology admission stay

Regarding treatment interventions and complications, the proportion of patients receiving blood transfusions was significantly higher in the CMV DNAemia group (39.02%, 16/41) compared to the non-CMV DNAemia group (5.12%, 4/78) (P < 0.001). Similarly, a significantly higher proportion of patients in the CMV DNAemia group received immunosuppressive therapy (60.97%, 25/41) compared to the non-CMV DNAemia group (30.76%, 24/78) (P = 0.015). However, no statistically significant differences were observed between the two groups in the use of glucocorticoids (31.70% vs. 21.94%, P = 0.240) or gamma globulin infusions (31.70% vs. 17.94%, P = 0.088). With respect to complication incidence, no significant differences were found between the groups in the occurrence of ARF, MODS, septic shock, or AKI (all P > 0.05). Furthermore, the 28-day all-cause mortality rate was significantly higher in the CMV DNAemia group (24.39%, 10/41) compared to the non-CMV DNAemia group (10.25%, 8/78) (P = 0.040) (Table 3).

Risk factors and predictive value

To identify the risk factors associated with CMV infection (i.e., CMV DNAemia) in septic patients in the Department of Hematology, a multivariate regression analysis was conducted (Table 6). Among the six variables analyzed—arterial partial pressure of PaO₂/FiO₂, white blood cell count, Hb, platelet count, albumin, and T-BIL—only white blood cell count was significantly associated with active CMV infection (β = −0.209, Wald = 5.351, OR = 0.811, 95% CI: 1.000–1.009, P = 0.021), suggesting that lower white blood cell counts may be a risk factor for active CMV infection. Although Hb (β = −0.007, P = 0.574), albumin (β = −0.074, P = 0.055), and PaO₂/FiO₂ (β = 0.04, P = 0.073) did not reach statistical significance (P > 0.05), their regression coefficients suggested a possible trend of association with active CMV infection.

Table 6.

Risk factors for active CMV infection

Variables	β1	Wald	OR2	95% CI3	P#
PaO2/FiO24	0.04	3.212	1.004	1.000–1.009.000.009	0.073
White blood cells (109/L)	0.209	5.351	0.811	0.680–0.969	0.021
Hemoglobin (g/L)	0.007	0.325	0.993	0.969–1.018	0.574
Platelet (109/L)	0.001	0.074	0.999	0.991–1.007	0.786
Albumin (g/L)	0.074	3.697	0.929	0.861–1.001	0.055
T-BIL5 (µmol/L)	0.053	1.825	1.055	0.976–1.140	0.177

^# P< 0.05; ¹β: Regression Coefficient; ²OR: Odds Ratio; ³95% CI: 95% Confidence Interval, ⁴PaO₂/FiO₂ Partial Pressure of Arterial Oxygen/Fraction of Inspired Oxygen, ⁵T-BIL Total Bilirubin.

To further assess the predictive value of each variable for active CMV infection, receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) was calculated (Table 7; Fig. 3A-F). The results indicated that white blood cell count demonstrated the best predictive performance (AUC = 0.746, 95% CI: 0.644–0.849, P < 0.001). At a cut-off value of 5.5 × 10⁹/L, the specificity was 84.6% and sensitivity was 68.2%. Hemoglobin (AUC = 0.725, 95% CI: 0.615–0.836, P < 0.001) and platelet count (AUC = 0.723, 95% CI: 0.612–0.835, P < 0.001) showed comparable predictive accuracy. For Hb, a cut-off of 99 g/L yielded a specificity of 76.9% and sensitivity of 70.7%; for platelet count, a cut-off of 87 × 10⁹/L resulted in a high specificity of 92.3% and a sensitivity of 58.5%. Albumin exhibited moderate predictive value (AUC = 0.654, 95% CI: 0.545–0.764, P = 0.005), with a specificity of 79.5% and sensitivity of 56.1% at a cut-off of 33.6 g/L. In contrast, PaO₂/FiO₂ (AUC = 0.605, P = 0.058) and T-BIL (AUC = 0.607, P = 0.054) showed limited predictive performance and did not achieve statistical significance.

Table 7.

Predictive value of hemoglobin for active CMV infection

	AUC		Cut off	Specificity (%)	Sensitivity (%)	95% CI2	P#
PaO2/FiO22		0.605	368	39.7	80.5	0.502–0.710	0.058
White blood cells (109/L)		0.746	5.5	84.6	68.2	0.644–0.849	<0.001
Hemoglobin (g/L)		0.725	99	76.9	70.7	0.615–0.836	<0.001
Platelet (109/L)		0.723	87	92.3	58.5	0.612–0.835	<0.001
Albumin (g/L)		0.654	33.6	79.5	56.1	0.545–0.764	0.005
T-BIL3 (µmol/L)		0.607	15.25	78.2	46.3	0.496–0.719	0.054

^# P< 0.05; ¹95% CI: 95% Confidence Interval, ²PaO₂/FiO₂ Partial Pressure of Arterial Oxygen/Fraction of Inspired Oxygen,³T-BIL Total Bilirubin

Fig. 3.

Predictive value for active CMV infection

Association between high CMV viral load and poor prognosis, and independent risk factor verification

To clarify whether high CMV viral load is an independent risk factor for poor prognosis, we further analyzed the 41 patients with active CMV infection. All patients were promptly initiated on anti-CMV therapy upon detection of CMV DNA positivity, in accordance with anti-infection guidelines for hematological disease patients, with ganciclovir and foscarnet as the main clinical antiviral agents. Due to the invasive nature of blood sampling, poor peripheral vascular conditions in many hematological patients, and the 2–3-day turnaround time for CMV DNA testing, routine dynamic monitoring of CMV DNA was not performed. Instead, body temperature was used as the primary clinical indicator to evaluate the efficacy of anti-CMV therapy. Each patient underwent only two CMV DNA tests: one when infection control was poor after fever onset (first positive detection) and another before discharge to verify anti-infection efficacy, with all results negative prior to discharge. Therefore, accurate determination of peak viral load and duration of positive CMV DNA detection was not feasible. Based on retrospective analysis of clinical data, we supplemented the duration of fever after initiation of antiviral therapy as a surrogate indicator to roughly reflect the duration of active CMV infection (Table S6). The median viral load at the first positive detection was 7800 copies/mL (IQR: 4200–16200 copies/mL) (Table S5). According to the distribution of the first positive CMV viral load, the 75th percentile (9400 copies/mL) was used as the cutoff value to divide patients into two groups: the high viral load group (first positive CMV viral load ≥ 9400 copies/mL, n = 11, 26.83%) and the low viral load group (first positive CMV viral load < 9400 copies/mL, n = 30, 73.17%). Correlation analysis showed that the first positive CMV viral load was positively correlated with the length of hospital stay (r = 0.318, P = 0.043) and the duration of fever (r = 0.406, P = 0.008) (Table S5). Additionally, the first positive CMV viral load was positively associated with 28-day all-cause mortality (OR = 0.458, 95% CI: 0.251–0.836, P = 0.003) (Table S5).

Univariate logistic regression analysis indicated that compared with the low viral load group, the high viral load group had an increased risk of 28-day all-cause mortality (OR = 2.857, 95% CI: 0.601–13.586, P = 0.187) (Table 8). To exclude confounding factors, multivariate logistic regression analysis was performed with 28-day all-cause mortality as the dependent variable, adjusting for age, SOFA score, white blood cell count, and hemoglobin level. The results showed that after adjustment, high CMV viral load (≥ 9400 copies/mL) was associated with 28-day all-cause mortality, although the statistical significance was marginal (adjusted OR = 2.618, 95% CI: 0.464–14.763, P = 0.097) (Table 8).

Table 8.

Univariate and multivariate logistic regression analyses of high CMV viral load and 28-day

Variables	Univariate Analysis (OR2 [95% CI3], P-value)		Multivariate Analysis (Adjusted OR [95% CI], P-value)
High CMV1 viral load (≥ 9400 copies/mL)		2.857[0.601–13.586], 0.187	2.618[0.464–14.763], 0.097
Age (per 1-year increase)		1.055[0.987–1.127], 0.114	1.066[0.987–1.987,151], 0.105
SOFA score (per 1-point increase)		0.909[0.707–1.169], 0.909	1.003[0.753–1.335], 0.985
White blood cell count (per 1 × 10⁹/L increase)		1.067[0.906–1.257], 0.436	1.136[0.855–1.509], 0.380
Hemoglobin (per 10 g/L increase)		1.008[0.987–1.030], 0.461	0.996[0.960–1.033], 0.826

¹CMV = Cytomegalovirus; ²OR = Odds Ratio; ³95% CI = 95% Confidence Interval

Regarding the surrogate indicator of active infection duration, the duration of fever after antiviral therapy showed that 26.83% (11/41) of patients had fever resolution on day 1, 53.66% (22/41) on day 2, 14.63% (6/41) on day 3, and 4.88% (2/41) on day 4 (Table S6), which indirectly reflected the heterogeneity of CMV infection control efficacy among patients. Collectively, these findings suggest that high CMV viral load may be a potential risk factor for poor prognosis in patients with hematological malignancies complicated by sepsis, and larger sample size studies are needed to further confirm its independent prognostic value.

All 41 patients with active CMV infection (CMV DNAemia-positive) received standardized antiviral therapy, resulting in a 100% treatment rate in the infection group. The choice of antiviral agents was determined based on the patient’s granulocyte count to avoid drug-related adverse reactions: ganciclovir was administered to patients with normal granulocyte counts, while foscarnet was selected for those with neutropenia. This medication selection strategy was guided by the potential risk of ganciclovir-induced neutropenia, which contraindicates its use in patients already presenting with neutropenia. According to clinical practice guidelines for hematological infections, all patients underwent intravenous antiviral therapy for a standard course of 2 weeks to ensure effective clearance of CMV replication.

The therapeutic response was primarily evaluated by monitoring body temperature changes, as fever is a key non-specific clinical symptom of CMV infection. Detailed data on the duration of fever after the initiation of antiviral therapy are presented in Table S6. Due to the ethical consideration of providing necessary treatment for all CMV-infected patients, no untreated control group was established, and thus the independent impact of antiviral therapy on patient outcomes could not be statistically analyzed. The distribution of antiviral agents used in CMV-infected patients is illustrated in Figure S4.

Temporal relationship between CMV infection and complications and analysis of inducing factors

To clarify the temporal correlation between CMV infection and complications and explore whether CMV infection serves as an inducing factor for complications in patients with active CMV infection, we focused on the 41 patients with CMV DNAemia and systematically analyzed the onset timing of CMV DNAemia (defined as the date of the first positive detection) and complications (defined as the date of initial clinical diagnosis or laboratory confirmation). Among these 41 patients, 14 cases of complications were recorded (involving 8 subtypes), with detailed distribution of temporal relationships presented in Table 9. According to the sequence of occurrence, the temporal relationship was categorized into two types: CMV infection preceding complications (10 cases, accounting for 71.43% of all complication cases, including simultaneous onset within ≤ 24 h) and complications preceding CMV infection (4 cases, 28.57%).

Table 9.

Distribution of temporal relationship between cmv infection and various complications (Among 41 CMV-Positive Patients)

Complication Type	Total Cases (n)	CMV Infection Preceding Complications [n(%)]	Complications Preceding CMV Infection [n(%)]
Acute Respiratory Failure (ARF)	1	1(100.00%)	0(0.00%)
Multiple Organ Dysfunction Syndrome (MODS)	0	0(0.00%)	0(0.00%)
Septic Shock	4	3(75.00%)	1(25.00%)
Acute Kidney Injury (AKI)	2	1(50.00%)	1(50.00%)
Acute Respiratory Distress Syndrome (ARDS)	0	0(0.00%)	0(0.00%)
Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD)	1	1(100.00%)	0(0.00%)
Acute Heart Failure (AHF)	3	2(66.67%)	1(33.33%)
Disseminated Intravascular Coagulation (DIC)	3	2(66.67%)	1(33.33%)
Total	14	10(71.43%)	4(28.57%)

Stratified analysis by complication subtype showed that acute respiratory failure (ARF) and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) were entirely preceded by CMV infection (100.00%), with no cases occurring before CMV infection. For septic shock, acute heart failure (AHF), and disseminated intravascular coagulation (DIC), the proportions of CMV infection preceding complications were 75.00%, 66.67%, and 66.67% respectively, indicating that most cases of these complications were associated with prior CMV infection. In contrast, acute kidney injury (AKI) showed an equal distribution of temporal relationships (50.00% each), suggesting a relatively weak association between CMV infection and the induction of this complication. No cases of multiple organ dysfunction syndrome (MODS) or acute respiratory distress syndrome (ARDS) were observed in the 41 CMV-positive patients.

To further verify the inducing effect of CMV infection, we compared the complication incidence among different temporal groups (Table S7). The results showed that the complication incidence in patients with CMV infection preceding complications (Group A) and complications preceding CMV infection (Group B) was both 100.00%, which was significantly higher than that in non-CMV-infected patients (Group C, 14.10%). Specifically, Group A had a statistically significant difference compared with Group C (P < 0.001), and Group B also showed a significant difference (P = 0.02). Combined with the temporal sequence analysis, the high proportion of CMV infection preceding complications (71.43%) and the significant difference in incidence compared with non-CMV-infected patients collectively support that CMV infection may be an inducing factor for complications in patients with hematological malignancies complicated by sepsis. This association may be mediated by CMV-induced immune dysfunction (as evidenced by impaired CD4⁺T cells, NK cells, and B cells in CMV-positive patients) and exacerbated systemic inflammatory response, which warrant further clinical attention.

Data are limited to 41 CMV-positive patients; 2. Some patients had multiple concurrent complications, so the total number of cases across all complication types (14 cases) exceeds the number of CMV-positive patients with complications; 3. “Simultaneous onset (≤ 24 h)” is classified into “CMV Infection Preceding Complications” due to clinical temporal ambiguity; 4. Data are presented as “n(%)”, with percentages calculated based on the total number of cases for each complication type.

Association between sepsis-related inflammatory factors and CMV infection

To clarify the role of inflammatory storms in CMV reactivation, we supplemented the analysis of sepsis-related inflammatory indicators (white blood cell count, C-reactive protein [CRP], procalcitonin [PCT], and interleukin-6 [IL-6]) at admission. Due to incomplete data in some patients, only 99 patients underwent simultaneous testing of complete blood count, CRP, PCT, and IL-6. These 99 patients were divided into the CMV-infected group (n = 33) and non-CMV-infected group (n = 63) according to the original grouping criteria. The differences in inflammatory indicators between the two groups were compared, and the correlation between inflammatory indicators and CMV viral load in the CMV-infected group was further explored (Table 10 and Table S8).

Table 10.

Comparison of inflammatory indicators between CMV-Infected group and non-cmv-infected group

Indicator	CMV1-Infected Group (n = 33) [Median (IQR)]	Non-CMV-Infected Group (n = 63) [Median (IQR)]	Statistic	P#-value
White Blood Cell Count (×10⁹/L)	8.3(0.5,16.7)	2,7(0.3,13.1)	Z = 0.296	0.587
C-Reactive Protein (mg/L)	66.3(0.2,304.6)	64.4(4.3,208.7)	Z = 0.019	0.891
Procalcitonin (ng/mL)	0.321(00.25,62.62)	0.509(0.044,7.230)	Z = 5.217	0.025
Interleukin-6 (pg/mL)	10.3(0.1,99.5)	32.35(0.1,88.9)	Z = 0.378	0.540

The results showed that among the four inflammatory indicators, only PCT levels differed significantly between the two groups (P = 0.025). The median PCT level in the CMV-infected group was 0.321 ng/mL (IQR: 0.025–62.62 ng/mL), which was lower than that in the non-CMV-infected group (median: 0.509 ng/mL, IQR: 0.044–7.230 ng/mL) (Table 10). No statistically significant differences were observed in white blood cell count (Z = 0.296, P = 0.587), CRP (Z = 0.019, P = 0.891), or IL-6 (Z = 0.378, P = 0.540) between the two groups.

Correlation analysis in the CMV-infected group revealed that IL-6 levels were positively correlated with CMV viral load (r = 0.362, P = 0.042), indicating that higher IL-6 levels were associated with increased CMV replication (Table S8). However, white blood cell count (r = 0.004, P = 0.982), CRP (r = 0.277, P = 0.125), and PCT (r = 0.326, P = 0.078) showed no significant correlation with CMV viral load. These findings suggest that IL-6, as a key pro-inflammatory cytokine in sepsis, may be involved in the reactivation and replication of latent CMV, while the role of other inflammatory indicators (CRP, PCT) in CMV reactivation requires further verification with larger sample sizes.

Discussion

Based on the retrospective cohort data of 119 patients with hematological malignancies complicated by sepsis admitted to the Department of Hematology and Oncology, Ningbo Medical Center Lihuili Hospital from June 2022 to June 2025, this study systematically investigated the distribution patterns, influencing factors, and clinical consequences of active cytomegalovirus (CMV) infection. The central findings focus on the three dimensions of “infection characteristics - risk factors - outcome association” within this specific population, addressing a gap in previous studies that predominantly focused on a single condition (either hematological malignancies alone or sepsis alone). According to clinical standards, active CMV infection was defined as plasma CMV DNAemia ≥ 500 copies/mL [12], and the incidence of active CMV infection in this cohort was determined to be 34.45% (41/119). This rate is significantly higher than that observed in patients with sepsis alone [13], and exceeds the lower bound of the infection rate reported in patients with hematological malignancies without sepsis [14]. These findings confirm that the “dual hit” of “underlying immunosuppression due to hematological disease plus acute inflammatory disturbance induced by sepsis” markedly disrupts the latent state of CMV and increases the risk of reactivation.

Analysis of the timing of CMV detection among the 41 infected patients revealed that 75.61% (31/41) tested positive between days 4 and 7 after admission, 17.07% (7/41) tested positive between days 0 and 3, and only 3 cases (7.32%) were positive on day 8 or later. These results indicate that CMV reactivation predominantly occurs early during hospitalization, when patients are in a state of profound immunosuppression and peak inflammation, providing direct evidence to support the implementation of clinical strategies for “early monitoring.” Comparative analysis demonstrated that the median length of hospital stay (17 days vs. 15 days, P = 0.008) and median duration of fever (10 days vs. 6 days, P < 0.001) were significantly longer in the infected group compared to the non-infected group. Additionally, the 28-day all-cause mortality rate was significantly higher in patients with CMV infection (24.39% vs. 10.25%, P = 0.040). These findings suggest that CMV infection may further deteriorate clinical outcomes by exacerbating systemic injury or immune dysfunction, underscoring the clinical urgency of preventing and managing this infection.

The conclusions of this study are highly consistent with the core mechanisms and clinical observations in the field of cytomegalovirus (CMV) infection. On one hand, it confirms that “sepsis-induced inflammatory storm is a catalyst for CMV reactivation.” Previous studies have indicated that sepsis-induced upregulation of pro-inflammatory cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) can disrupt the latent state of CMV [15]. Notably, a recent study by [16] further clarified the critical role of IL-6 in CMV reactivation: they found that IL-6 can directly bind to the CMV immediate-early gene promoter region, activate the JAK-STAT signaling pathway, and promote viral gene transcription and replication—this mechanism is not only verified in CAR-T cell therapy patients but also provides a key theoretical basis for explaining CMV reactivation in sepsis-related inflammatory environments [16]. In this study, the infected group showed significantly higher Sequential Organ Failure Assessment (SOFA) scores (median 10 vs. 6, P < 0.001) and body temperature (median 38.8 °C vs. 37.9 °C, P < 0.001) compared with the non-infected group. Severe community-acquired pneumonia (sCAP), the primary cause of sepsis in this cohort, accounted for 52.94%. The associated pulmonary inflammation may further promote viral replication, which aligns with the aforementioned IL-6-mediated mechanism. On the other hand, the findings support the hypothesis that “CMV infection exacerbates hematological damage.” Existing evidence has demonstrated that CMV infection can worsen bone marrow suppression in patients with hematological malignancies [17]. In this study, the infected group exhibited significantly lower hemoglobin levels (80 g/L vs. 119 g/L, P < 0.001), white blood cell counts (3.10 × 10⁹/L vs. 8.25 × 10⁹/L, P < 0.001), and platelet counts (35 × 10⁹/L vs. 171 × 10⁹/L, P < 0.001) compared with the non-infected group, along with a higher rate of blood transfusion (39.02% vs. 5.12%, P < 0.001), further substantiating the association between CMV infection and impaired hematopoietic function [18].

Meanwhile, the CMV infection rate of 34.45% observed in this study differs from some previously reported values, which can be attributed to three specific characteristics of the study design. First, the overlapping disease profiles of the study population: the enrolled patients had both hematological malignancies (e.g., leukemia, lymphoma) and sepsis (meeting the SEPSIS-3 criteria), which synergistically promote CMV reactivation through “underlying immunosuppression” and “acute inflammatory disturbance,” respectively. Therefore, the infection rate is higher than that observed in patients with sepsis alone, but lower than the higher rates (e.g., 41%) reported in certain studies focusing on patients with hematological malignancies. This is because those studies included individuals who had undergone hematopoietic stem cell transplantation (HCT), while this study excluded patients who had received HCT within three months prior to enrollment. Transplantation-related profound immunosuppression further elevates the risk of CMV infection [19]. Second, differences in CMV detection thresholds: various studies define “active infection” differently. Some use a threshold of CMV DNAemia ≥ 1000 copies/mL [20], whereas this study applied the more commonly used clinical threshold of ≥ 500 copies/mL. When the threshold was recalibrated to ≥ 1000 copies/mL, the infection rate in this cohort decreased to 26.89% (12/41), indicating that the detection threshold is a key factor contributing to observed discrepancies. Third, the impact of detection frequency: in this study, CMV DNAemia was monitored at least once every 7 days according to standard clinical protocols. Although this approach covered the entire hospitalization period, compared with the more frequent monitoring (every 3 days) employed in prospective studies, it may have missed transient, low-level infections, thereby slightly underestimating the true infection rate.

Multivariate regression analysis demonstrated that a decreased white blood cell count is an independent risk factor for active CMV infection (β=−0.209, Wald = 5.351, OR = 0.811, P = 0.021). This association involves a bidirectional pathophysiological mechanism. On one hand, patients with hematological malignancies experience reductions in white blood cell count due to underlying disease processes (e.g., bone marrow infiltration by leukemia) or chemotherapy, which compromises cellular immune defenses against CMV (including T cell- and NK cell-mediated cytotoxicity), thereby facilitating viral reactivation [21]. On the other hand, CMV infection may further decrease white blood cell counts by directly inhibiting the proliferation of bone marrow hematopoietic stem cells and by inducing inflammatory cytokines that damage the bone marrow microenvironment, establishing a vicious cycle of “white blood cell reduction – viral activation – further leukopenia.” The higher rate of immunosuppressant use in the infected group (60.97% vs. 30.76%, P = 0.015) may further exacerbate this cycle by increasing the degree of immunosuppression [22]. Combined with Li et al.’s [16] finding that IL-6 can enhance CMV replication while inhibiting immune cell proliferation, it is plausible that the synergistic effect of IL-6 elevation and white blood cell reduction in sepsis patients further amplifies the risk of CMV reactivation.

This study also found that CMV infection is associated with prolonged hospitalization and increased mortality, with mechanisms encompassing both direct and indirect effects. Regarding direct effects, although no significant differences were observed in the incidence of complications such as acute respiratory failure (ARF) or septic shock between the two groups (all P > 0.05), concurrent detection revealed that CMV was identified in endotracheal aspirate (ETA) specimens from 21.84% (26/119) of patients. A significant positive correlation was observed between plasma CMV load and ETA CMV load (r = 0.558, P < 0.001), suggesting the potential presence of subclinical CMV pneumonia. Such localized infections may prolong the need for respiratory support and indirectly extend the duration of hospital stay. With respect to indirect effects, CMV reactivation can induce immune dysregulation (e.g., excessive inflammation or T cell exhaustion). Patients with hematological malignancies complicated by sepsis are already immunocompromised, and such immune disturbances may further exacerbate organ dysfunction, ultimately resulting in prolonged fever and increased mortality [8, 23]. Li et al. [16] also noted that CMV reactivation-induced IL-6 overexpression can disrupt the balance of pro-inflammatory and anti-inflammatory responses, which may further aggravate organ damage in septic patients and contribute to poor prognosis.

Based on the findings of this study, the early monitoring strategy for cytomegalovirus (CMV) infection can be optimized in two aspects: “monitoring timing” and “monitoring indicators”. Regarding monitoring timing, since 92.68% (38/41) of infections occur within the first 7 days of hospitalization, it is recommended to initiate CMV monitoring immediately after admission and increase the testing frequency to once every 3–4 days during the initial 7-day period, rather than waiting for symptom progression. Concerning monitoring indicators, white blood cell count (AUC = 0.746, P < 0.001), hemoglobin (AUC = 0.725, P < 0.001), and platelet count (AUC = 0.723, P < 0.001) demonstrate moderate to high predictive value for infection. When using 5.5 × 10⁹/L as the cut-off value for white blood cell count, the specificity reaches 84.6%, making it a practical screening parameter. Clinically, priority should be given to CMV DNAemia testing in patients with white blood cell counts below 5.5 × 10⁹/L to reduce the risk of missed diagnoses. Additionally, considering the key role of IL-6 in CMV reactivation [16], monitoring IL-6 levels may provide additional predictive value for high-risk patients, especially those with persistent inflammation, helping to identify potential CMV reactivation earlier. This study provides insights into the adjustment of treatment strategies. First, a balance between blood transfusion and immunosuppressive agent use should be considered, as the transfusion rate was significantly higher in the infected group.

This study is a single-center retrospective cohort study with the following three main limitations. First, limitations in data collection: data were extracted from electronic medical records. Although cross-verified by two researchers, the retrospective design cannot fully address issues such as inconsistent testing timing and missing follow-up data, which may affect the accuracy of infection incidence and outcome analyses. Second, CMV IgM/IgG antibody testing was only performed in 57.1% (68/119) of the study subjects due to constraints of clinical practice and retrospective design, leading to partial missingness of antibody data. Baseline characteristics were not compared between patients with and without antibody testing, which may introduce a certain degree of selection bias. Third, limited by clinical sampling conditions and real-world clinical practice, dynamic monitoring of CMV viral load was not conducted in this study. Instead, the duration of fever was used as a surrogate indicator to evaluate the control of CMV infection, which may fail to accurately reflect the actual process of viral clearance. This limitation may weaken the objective evaluation of antiviral efficacy and affect the accurate assessment of the relationship between viral replication dynamics and prognosis in this study. In addition, the present study also has limitations in sample size, generalizability, detection scope, and mechanistic research. Only 119 patients from a single center were included, which may restrict the generalizability of the conclusions. The scope of CMV detection was relatively narrow, and the in-depth molecular mechanism of CMV reactivation remains to be further explored.

Conclusions

This study explores active cytomegalovirus (CMV) infection in 119 patients with hematological malignancies complicated by sepsis: the incidence of active CMV infection is 34.45%, with 92.68% occurring within 0–7 days of hospitalization, indicating early hospitalization is a key period for CMV monitoring; decreased white blood cell count is an independent risk factor (AUC = 0.746), hemoglobin and platelet count also have good predictive value, and CMV infection rates vary by subtype, being higher in acute leukemia (AL) and non-Hodgkin lymphoma (NHL) but absent in multiple myeloma (MM); active CMV infection correlates with poor outcomes including prolonged hospitalization, fever duration and increased 28-day mortality, with the positive correlation between IL-6 and CMV viral load as well as lower PCT in the infected group providing insights for clinical diagnosis, so routine early CMV monitoring and timely antiviral therapy are recommended for high-risk groups, and our conclusions need verification by multi-center prospective studies, with future research focusing on IL-6-related mechanisms and optimized prediction models.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CMV

Cytomegalovirus

qPCR

Quantitative Polymerase Chain Reaction

DNAemia

DNA detection by quantitative polymerase chain reaction (qPCR) on peripheral blood samples

NAAT

nucleic acid amplification test

BMI

Body Mass Index

SOFA

Sequential Organ Failure Assessment

sCAP

Severe Community-Acquired Pneumonia

COPD

Chronic Obstructive Pulmonary Disease

CVA

Cerebrovascular Accident

CKD

Chronic Kidney Disease

ILD

Interstitial Lung Disease

PaO₂/FiO₂

Partial Pressure of Arterial Oxygen / Fraction of Inspired Oxygen

PT

Prothrombin Time

APTT

Activated Partial Thromboplastin Time

BNP

B-Type Natriuretic Peptide

AST

Aspartate Aminotransferase

T-BIL

Total Bilirubin

Scr

Serum Creatinine

Hb

Hemoglobin

AUC

Area Under the Curve

OR

Odds Ratio

95% CI

95% Confidence Interval

ARF

Acute Respiratory Failure

MODS

Multiple Organ Dysfunction Syndrome

AKI

Acute Kidney Injury

ARDS

Acute Respiratory Distress Syndrome

AECOPD

Acute Exacerbation of Chronic Obstructive Pulmonary Disease

AHF

Acute Heart Failure

DIC

Disseminated Intravascular Coagulation

HCT

Hematopoietic Stem Cell Transplantation

ETA

Endotracheal Aspirate

PCT

Procalcitonin

CRP

C-Reactive Protein

BUN

Serum Urea Nitrogen

Author contributions

Conceptualization, B.C. and S.L.; methodology, W.S. and D.J.; project administration, J.L.; formal analysis, D.J. and X.M.; data collection, X.W., G.W., Q.F. and Q.L.; investigation, X.L. (Xiaoqi Ma) and X.L. (Xiaodong Li); writing—original draft preparation, B.C. and D.J.; writing—review and editing, all authors; supervision, D.J. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ningbo Medical Center Lihuili Hospital Health industry Science and Technology Plan(2025MS012) and Zhejiang Province Health industry Science and Technology Plan (2025HY0868).

Data availability

The data used in this study are original data independently collected by Ningbo Medical Center Lihuili Hospital. Due to restrictions related to the protection of patients’ medical information privacy and relevant ethical standards, the data are not publicly available. Qualified researchers may request access to the data for academic verification purposes upon approval from the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital.

Declarations

Ethics approval and consent to participate

The study was approved by the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital (Approval No. KY2025SL306-01). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. The need for consent to participate was waived by the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.