Infection incidence, timing, and predictors in newly diagnosed multiple myeloma: a real-world retrospective cohort study.

Introduction

Multiple myeloma (MM) is a hematologic malignancy characterized by clonal plasma cell proliferation and profound immune dysfunction. Infections remain a major cause of morbidity and mortality in MM, attributed both to disease-related immune suppression and to treatment-related toxicity ( 1 , 2 ). Previous studies have reported a 7-fold increase in infection risk among MM patients compared to the general population, particularly during the first 6 months of induction therapy ( 3 , 4 ).

The pathophysiology of MM includes several factors that predispose patients to infections. Hypogammaglobulinemia, a hallmark of MM, compromises the immune system’s ability to respond against bacterial pathogens effectively ( 5 ). Additionally, myelosuppression-induced neutropenia significantly increases susceptibility to bacterial and fungal infections ( 6 ). Furthermore, T-cell dysfunction and a reduction in cytotoxic T-cell numbers impair antiviral immunity, leading to an increased incidence of viral infections ( 7 ). Additionally, older age and comorbidities such as chronic kidney disease and diabetes compound this risk ( 8 ).

While prophylactic antibiotic use has been considered as a strategy to reduce infection-related morbidity in MM, its routine implementation remains controversial. Concerns include toxic side effects, the emergence of resistant microorganisms, and the potential for altered therapeutic response to immunomodulatory agents. These limitations highlight the importance of improved risk stratification to guide more targeted infection prevention strategies ( 5 , 9 ).

Induction regimens play a crucial role in modulating the risk of infection. Proteasome inhibitors (e.g., bortezomib), immunomodulatory drugs (e.g., lenalidomide), corticosteroids, and monoclonal antibodies (e.g., daratumumab) all contribute to varying degrees of immunosuppression. These therapeutic regimens not only disrupt immune homeostasis but also determine the severity and type of infections observed during treatment ( 8 , 10 ). In addition to these factors, prolonged hospitalizations and the use of central venous catheters contribute to nosocomial infections, further increasing the infection burden ( 11 ).

Common pathogens isolated in MM patients include Escherichia coli and Klebsiella pneumoniae among gram-negative bacteria, Staphylococcus aureus and Streptococcus pneumoniae among gram-positive bacteria. Viral infections, particularly herpes zoster and cytomegalovirus (CMV) reactivation, are frequently reported, while fungal infections, such as those caused by Candida and Aspergillus species, are more commonly observed in advanced disease stages or patients receiving high-dose corticosteroids ( 3 , 12 ).

The multifactorial nature of infection risk in multiple myeloma, along with the increasing availability of real-world data, makes it crucial to understand the timing and predictors of infectious complications for optimal patient management. In this study, we aimed to evaluate the incidence and temporal distribution of infections during the first 12 months of induction therapy, with a particular focus on the initial 6-months period. By analyzing routinely collected clinical and laboratory parameters, we sought to identify key factors associated with increased infection risk. Our goal was to enable earlier identification of high-risk patients, thereby supporting a more individualized clinical approach. Ultimately, we aimed to generate clinically meaningful insights that could strengthen infection prevention strategies and improve outcomes during this vulnerable phase of treatment.

Materials and methods

Study design and patient selection

This retrospective cohort study was conducted at a single tertiary care center and included patients diagnosed with multiple myeloma between January 2016 and December 2024. Eligible patients were 18 years or older, had a new diagnosis of MM, and received standard induction regimens such as bortezomib-based combinations (e.g., VRD, VCD, VD) or lenalidomide-based doublets (e.g., RD). Information regarding daratumumab-containing regimens was collected; however, due to the limited number of patients receiving such treatments, subgroup analysis was not performed. Inclusion required complete clinical and laboratory data at diagnosis and a minimum of 6 months of follow-up. Patients were excluded if they had previously received systemic therapy for MM, had active infections at baseline, received any form of prophylactic antimicrobial treatment, had a confirmed COVID-19 infection, or had missing key data related to infection status or laboratory parameters. These exclusion criteria aimed to reduce confounding from viral epidemics and antimicrobial interventions, resulting in a COVID-negative, non-prophylaxed study population.

Definition of infections

Infectious episodes were defined as clinically or microbiologically documented events requiring antimicrobial therapy and/or hospitalization. Clinical diagnoses were based on physician assessment of infection-related signs and symptoms, supported by imaging or laboratory findings, in accordance with general diagnostic principles recommended by the Infectious Diseases Society of America (IDSA) ( 13 ). In cases without a clearly identifiable focus, infections were classified based on clinical judgment supported by laboratory and radiological findings, and were included if antimicrobial treatment was initiated.

Data collection

Baseline demographic and clinical data were extracted from the hospital’s electronic medical records. Collected variables included age, sex, and comorbidities such as hypertension, diabetes mellitus, chronic kidney disease, congestive heart failure, coronary artery disease, chronic pulmonary disease [e.g., chronic obstructive pulmonary disease (COPD) or asthma], autoimmune disorders, and a history of prior malignancy.

Peripheral blood neutrophil and lymphocyte counts at the time of infection were also recorded to assess hematologic parameters associated with infectious episodes. These values were obtained from the nearest complete blood count (CBC) performed within 48 h of the documented infection event. This approach reflects the patient’s immune status during infection and allows for the identification of potential hematologic predictors of infection risk.

Disease-related variables, including the International Staging System (ISS) score and the specific induction regimen, were also recorded. ISS was analyzed as an ordinal variable in the statistical analysis. Cytogenetic risk status and lactate dehydrogenase (LDH) levels were excluded from the final analysis due to high rates of missing data; therefore, the R-ISS score could not be reliably calculated, and only the standard ISS system was used. In addition, CMV infections were not systematically assessed, as routine CMV DNA testing was not performed in all patients.

Outcome measures

The primary outcome was defined as the occurrence of any infectious episode during the first 6 months of induction therapy, as this period represents the highest clinical vulnerability. Secondary outcomes included the incidence and temporal distribution of infections over the full 12-months period, allowing for evaluation of infection patterns throughout the first year of treatment.

Infections were considered present if they were clinically or microbiologically documented and required treatment with antibiotics, antivirals, or antifungals, and/or resulted in hospitalization.

To assess infection patterns over time, all infectious episodes were categorized into three intervals: 0–3 months (early phase), 3–6 months (intermediate phase), and 6–12 months (late phase). This temporal stratification allowed for a more detailed evaluation of infection dynamics throughout the first year of therapy.

Statistical analysis

Descriptive statistics were used to summarize baseline characteristics. Continuous variables were expressed as medians with interquartile ranges (IQR), while categorical variables were reported as frequencies and percentages. Associations between baseline clinical characteristics and infection status were analyzed using the chi-square or Fisher’s exact test for categorical variables, and the Mann–Whitney U test for continuous variables.

To identify independent predictors of infection, univariate logistic regression analyses were first performed. Variables with p < 0.10 in univariate analysis, as well as those considered clinically relevant based on prior literature and clinical judgment, were considered for inclusion in the multivariable logistic regression model. Model selection was based on a combination of statistical significance and clinical relevance.

Logistic regression was selected as the primary analytical method due to the binary nature of the outcome variable (occurrence of infection). The extent of missing data was assessed and transparently reported. Missing data were handled by excluding variables with a high proportion of missing values from the final model, and multicollinearity was assessed prior to model construction.

Due to the retrospective design, time-to-event analysis and recurrent event modeling were not performed; this limitation has been acknowledged.

All statistical analyses were performed using Python (version 3.11.0), with the pandas, scipy, and statsmodels libraries.

Ethical approval

Ethical approval for this study was obtained from the Clinical Research Ethics Committee of Marmara University School of Medicine (Protocol No: 09.2023.1454).

Results

Patient characteristics

A total of 161 patients with newly diagnosed multiple myeloma were included in the study. The median age was 64 years (interquartile range [IQR], 59–73), and 59.0% were male. The most common comorbidities were hypertension (26.7%), diabetes mellitus (23.6%), and chronic kidney disease (15.5%). According to the ISS, 40.4% were classified as stage III, 20.5% as stage I, and 18.6% as stage II. The most frequently administered induction regimen was VCD (52.8%), followed by VRD (23.6%), VD/RD (7.5%), and other combinations (16.1%) ( Table 1 ). A total of 33 patients were documented to have undergone autologous stem cell transplantation at our center during the study period.

Infection incidence and timing

During the first 12 months of induction therapy, 82 patients (50.9%) experienced at least one episode of infection. Infections were most frequent in the first 3 months (44 patients, 27.3%), followed by 3–6 months (25 patients, 15.5%) and 6–12 months (23 patients, 14.3%). Infection rates varied significantly across these time periods ( p = 0.004) ( Figure 1A ), indicating increased vulnerability during the early phase of treatment. This temporal pattern further supports the concentration of infections in the early phase of treatment.

Impact of induction regimens

Patients were categorized into four groups based on induction regimens: VCD ( n = 85, 52.8%), VRD ( n = 38, 23.6%), VD/RD ( n = 12, 7.5%), and other protocols ( n = 26, 16.1%). Infection rates were highest in the VD/RD group (83.3%), followed by “other” regimens (61.5%), VCD (60.0%), and VRD (44.7%). A limited number of patients received daratumumab-containing regimens ( n = 5). Due to the small sample size, further subgroup analysis regarding infection risk in this population was not feasible. Although infection rates differed numerically, the overall difference among groups was not statistically significant ( p = 0.10) ( Figure 1B ). The elevated infection rate in the VD/RD group may reflect patient selection for this regimen, which is often used in older or more comorbid individuals.

Infection type and severity

Among the 82 infected patients, pneumonia was the most common infection (28 patients, 34.1%), followed by urinary tract infections (22 patients, 26.8%), infections without a clear focus (11 patients, 13.4%), and catheter-related infections (5 patients, 6.1%). Notably, 58.3% of infected patients (48 individuals) required hospitalization, indicating the clinical severity of these events ( Table 2 ).

Microbiological findings

Among 41 culture-positive infections, Escherichia coli was the most frequently isolated pathogen (12 cases, 29.3%), followed by Klebsiella spp. (7 cases, 17.1%) and Pseudomonas aeruginosa (6 cases, 14.6%) ( Figure 1C and Table 3 ). Gram-negative bacteria were predominant among all identified organisms. Among the E. coli isolates with available susceptibility data, 66.7% were susceptible to gentamicin, while 50% were resistant to TMP-SMX and none showed full susceptibility to levofloxacin.

Hematologic parameters and febrile episodes

Neutrophil and lymphocyte counts at the time of infection were compared between febrile and non-febrile episodes. Neutrophil counts were lower in febrile episodes (median 3085/mmł vs. 3913/mmł), although the difference was not statistically significant ( p = 0.084). In contrast, lymphocyte counts were significantly lower in febrile cases (median 652/mmł vs. 805/mmł; p = 0.040) ( Figure 1D ), suggesting a more profound immunosuppressive state.

Multivariate analysis of infection risk

Multivariate logistic regression analysis identified advanced ISS stage, diabetes mellitus, and chronic kidney disease as independent predictors of infection. The odds of infection increased with each advancing ISS stage (OR: 3.83; 95% CI: 2.16–6.77; p < 0.001). Diabetes mellitus (OR: 3.64; 95% CI: 1.20–11.10; p = 0.023) and chronic kidney disease (OR: 6.01; 95% CI: 1.45–24.90; p = 0.013) were also significantly associated with infection risk. Age, sex, and hypertension were not independent predictors in the final model ( Figure 2A and Table 4 ).

Infection risk by age, ISS stage, and comorbidities

Patients aged ≥ 75 years had a significantly higher infection rate compared to those under 75 (69.2% vs. 46.3%, p = 0.044). Infection rates also increased substantially with advancing ISS stage: 15.2% in stage I, 53.3% in stage II, and 72.3% in stage III ( p < 0.0001). The presence of diabetes mellitus ( p = 0.019) and chronic kidney disease ( p = 0.003) were also significantly associated with increased infection risk ( Figure 2B ), whereas hypertension was not ( p = 0.123).

Mortality analysis

Seventeen patients (10.6%) died within the first year of treatment. Mortality was significantly higher in patients aged ≥ 75 years than in those younger than 75 (31.2% vs. 8.3%, p = 0.016) ( Figure 2C ). There was no significant difference in the number of comorbidities between patients who survived and those who died (median of 1 in both groups, p = 0.91). No individual comorbidity was significantly associated with mortality, though cardiovascular disease showed a trend toward significance ( p = 0.085). Mortality rates by ISS stage were 9.1% (stage I), 3.3% (stage II), and 9.2% (stage III), but the difference was not statistically significant ( p = 0.579), suggesting that factors such as age may have a stronger influence on early mortality.

Hospitalization predictors

A separate analysis explored the relationship between hospitalization and clinical/laboratory variables. Higher ISS stage was significantly associated with hospitalization ( p < 0.001) ( Figure 2D ), as was the presence of chronic kidney disease ( p = 0.042). No significant associations were found for hypertension, diabetes mellitus, age, neutrophil count, or lymphocyte count.

Discussion

Infections continue to represent a major clinical burden in patients with MM, particularly during the initial months of induction therapy. In our cohort, more than half of the patients (50.9%) experienced at least one infectious episode within the first year of treatment, with a significantly higher incidence during the first 3 months. This early vulnerability period is likely driven by a convergence of disease-related immune dysfunction, treatment-induced cytopenias, and underlying comorbid conditions ( 3 ).

Pneumonia was the most common type of infection, consistent with prior data highlighting respiratory tract susceptibility in MM. Notably, gram-negative pathogens, particularly Escherichia coli and Klebsiella species, were predominant in culture-positive cases. These findings support the hypothesis that gastrointestinal and urinary sources of infection are key contributors in this population ( 8 ). Although pathogen distribution was evaluated, we did not perform a stratified analysis based on the timing of infections due to the limited number of culture-positive episodes. Future studies with larger cohorts may help reveal potential temporal shifts in pathogen profiles, such as an early predominance of bacterial infections and later emergence of viral or fungal etiologies. The high rate of hospitalization (58.3%) among patients with infections further emphasizes the clinical severity and healthcare burden associated with infectious complications in MM. The absence of reported CMV infections in our cohort may partly reflect limited routine testing rather than a true absence of viral reactivation.

Although the number of antibiogram-tested isolates was limited, preliminary findings indicated potential resistance to commonly used agents such as TMP-SMX and levofloxacin. Among E. coli isolates, susceptibility to gentamicin was moderate (66.7%), while 50% were resistant to TMP-SMX, and none showed full susceptibility to levofloxacin. These data suggest that the empirical use of TMP-SMX or fluoroquinolones in MM patients should be approached with caution, consistent with recent findings on evolving resistance patterns in hematologic malignancies ( 14 ). Therefore, monitoring local antibiotic resistance patterns is essential in guiding empirical treatment decisions ( 8 , 14 ). Larger-scale studies focusing on pathogen-specific resistance profiles in MM patients are warranted, especially given clinical data from Jena University Hospital, where over 65% of patients developed at least one infectious episode–primarily bacterial–during treatment with novel agents ( 15 ).

Age ≥ 75 years emerged as a significant predictor of both infection and 1-year mortality, underlining the importance of immunosenescence and frailty in clinical outcomes. Additionally, a higher ISS stage was strongly associated with both infection and hospitalization, reinforcing disease burden as a marker of immune compromise. In multivariate analysis, diabetes mellitus and chronic kidney disease were also found to independently increase the risk of infection, suggesting a need to incorporate these factors into individualized risk assessments. These findings are consistent with prior literature linking advanced age, disease stage, and organ dysfunction with heightened infectious risk in multiple myeloma patients ( 8 , 16 – 18 ). In the multivariate model, although several variables were included based on clinical relevance and univariate screening, only ISS stage, diabetes mellitus, and chronic kidney disease remained statistically significant, supporting the robustness of these predictors. In our cohort, 33 patients were documented to have undergone autologous stem cell transplantation; however, the retrospective design and variability in the timing of transplantation in relation to infectious events limited our ability to assess its impact on infection incidence during the first year. Additionally, some transplantation procedures performed at external centers may not have been fully captured in our dataset.

Interestingly, although neutrophil counts did not significantly differ between febrile and non-febrile episodes, lymphopenia was significantly more pronounced in febrile infections. This observation suggests that lymphopenia may reflect a deeper state of immune suppression associated with clinically significant infections. While our retrospective design and limited sample size precluded establishing a precise threshold, the consistent trend underscores its potential as an early biomarker of infection risk. This is consistent with prior studies demonstrating that low absolute lymphocyte count (ALC) is associated with increased infection risk and inferior outcomes in multiple myeloma patients ( 19 , 20 ). Future prospective studies are warranted to validate lymphocyte count as a stratification tool for infection surveillance and preemptive interventions in MM patients.

When comparing induction regimens, no statistically significant differences in infection or mortality rates were observed. However, the highest infection rate was seen in the VD/RD group (83.3%), which may reflect the preferential use of this regimen in older, more frail patients. Similarly, mortality rates were numerically higher in this group, though not statistically significant. These trends underscore the importance of considering patient selection and baseline characteristics when interpreting treatment-related outcomes. This observation is consistent with previous studies showing that frailty is a key determinant of infectious complications and early mortality in transplant-ineligible multiple myeloma patients ( 21 , 22 ).

Although daratumumab-based regimens are increasingly used in multiple myeloma, only a small number of patients in our cohort received these treatments ( n = 5). Therefore, we were unable to draw definitive conclusions regarding their impact on infection risk. Future studies with larger patient populations are needed to further explore this association.

Hospitalization was more frequently required in patients with advanced ISS stage and in those with chronic kidney disease. Other parameters such as age, neutrophil and lymphocyte counts, and additional comorbidities were not independently associated with the need for inpatient care. These findings suggest that disease burden and renal impairment more accurately reflect clinical vulnerability than age or isolated laboratory markers in MM patients. This aligns with previous reports indicating that advanced disease stage and renal dysfunction predict hospitalization and infectious complications, while age and isolated cytopenias are weaker predictors ( 23 , 24 ).

While antibiotic prophylaxis has been proposed as a strategy to mitigate infection risk in MM, its routine use remains controversial. The TEAMM trial demonstrated that levofloxacin prophylaxis significantly reduced febrile episodes and early mortality within the first 12 weeks of treatment in newly diagnosed MM patients ( 5 ). However, concerns regarding antibiotic resistance, C. difficile infection, and microbiome disruption have limited widespread adoption. In our cohort, none of the patients received prophylactic antibiotics; yet, over half experienced infections, predominantly within the first 3 months. These findings underscore the importance of risk-adapted prevention strategies guided by patient-specific factors such as ISS stage, age, and comorbidities, rather than routine blanket prophylaxis ( 25 – 27 ).

Given the predominance of gram-negative pathogens in our cohort, targeted prophylaxis with levofloxacin may be considered as a hypothesis-generating approach for high-risk patients. However, this suggestion should be interpreted with caution, as our data do not directly support it. Such strategies remain speculative and require validation in prospective studies ( 5 , 9 , 28 ).

Conclusion

This study highlights the substantial burden of infections during the first year of multiple myeloma treatment, particularly within the early phases of induction therapy. Readily available clinical parameters–such as advanced ISS stage, diabetes mellitus, and chronic kidney disease–were identified as independent predictors of infection and hospitalization. These factors may serve as practical tools for early risk stratification and may help inform the development of personalized preventive strategies.

Our findings emphasize the importance of individualized, risk-adapted infection prevention strategies during the early treatment phase. By incorporating clinical risk factors into decision-making, healthcare providers may improve surveillance, antimicrobial stewardship, and supportive care.

However, given the single-center design and the limited number of culture-positive infections, our findings should be interpreted with caution and validated through larger, prospective multicenter studies.

Limitations

This study has several limitations that should be acknowledged. First, its single-center retrospective design may limit generalizability and introduce potential selection bias. Second, missing data–particularly regarding cytogenetics and LDH–prevented the calculation of Revised ISS scores, potentially affecting the precision of prognostic stratification. Third, not all infectious episodes were microbiologically confirmed; thus, reliance on clinical judgment may have led to potential misclassification of infection events. Furthermore, the observational design of the study may have introduced potential confounding by indication, as treatment decisions were influenced by baseline patient characteristics, which may have affected the observed associations between clinical variables and infection outcomes. Due to the retrospective structure of the dataset and inconsistencies in the documentation of exact event timing, median time to first infection could not be reliably calculated.

Additionally, treatment protocols and supportive care practices may have evolved over the extended study period, introducing potential heterogeneity. The cause of death was not systematically documented, limiting our ability to determine infection-related versus disease-related mortality. In addition, data on baseline immunoglobulin levels were not consistently available, precluding assessment of hypogammaglobulinemia as a potential risk factor for infection. Information on immunoglobulin replacement therapy (IVIG) was also not systematically recorded and could not be evaluated in the analysis.

Nevertheless, our findings provide meaningful real-world insights into infection patterns and associated risk factors in newly diagnosed MM patients, and may help inform future prospective studies aimed at improving risk stratification and preventive strategies.

Acknowledgments

We thank all colleagues involved in data collection and manuscript preparation.

Funding Statement

The author(s) declared that financial support was not received for this work and/or its publication.

Edited by: Maria Stefania Infante , Hospital Universitario Infanta Leonor, Spain

Reviewed by: Mario Annunziata , Antonio Cardarelli Hospital, Italy

Tommaso Caravita Di Toritto , UOSD Ematologia ASL Roma 1, Italy

Abbreviations: ALC, absolute lymphocyte count; CBC, complete blood count; CI, confidence interval; CKD, chronic kidney disease; CMV, cytomegalovirus; CoNS, coagulase-negative Staphylococcus ; CTCAE, Common Terminology Criteria for Adverse Events; ECOG, Eastern Cooperative Oncology Group; IDSA, Infectious Diseases Society of America; IMiD, immunomodulatory drug; IMWG, International Myeloma Working Group; IQR, interquartile range; ISS, International Staging System; LDH, lactate dehydrogenase; MM, multiple myeloma; OR, odds ratio; PCR, Polymerase Chain Reaction; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RD, lenalidomide and dexamethasone; R-ISS, Revised International Staging System; TMP-SMX, trimethoprim–sulfamethoxazole; UTI, urinary tract infection; URTI, upper respiratory tract infection; VCD, bortezomib, cyclophosphamide, and dexamethasone; VD, bortezomib and dexamethasone; VRD, bortezomib, lenalidomide, and dexamethasone.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval for this study was obtained from the Clinical Research Ethics Committee of Marmara University School of Medicine (Protocol No: 09.2023.1454). 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 because written informed consent was waived by the Clinical Research Ethics Committee of Marmara University Faculty of Medicine because this was a retrospective study using anonymized data obtained from medical records.

Author contributions

OC: Writing – original draft, Writing – review & editing. NN: Writing – review & editing. TT: Writing – review & editing. BP: Writing – review & editing. MT: Writing – review & editing. DD: Writing – review & editing. AY: Writing – review & editing. AB: Writing – review & editing. SS: Writing – review & editing. FT: Writing – review & editing. CG: Writing – review & editing. FA: Writing – review & editing. MM: Writing – review & editing. AFY: Writing – review & editing. IA: Writing – review & editing. ZO: Writing – review & editing. TaT: Writing – review & editing. AT: Writing – original draft, Writing – review & editing.

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 used in the creation of this manuscript. This study used ChatGPT (OpenAI) to improve language and grammar during the preparation of the manuscript. However, all content of the study has been reviewed and edited by the authors following the use of AI/LLM tools.

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Associated Data

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.