Infection Incidence, Timing, and Predictors in Newly Diagnosed Multiple Myeloma: A Real- World Retrospective Cohort Study

Infection Incidence, Timing, and Predictors in Newly Diagnosed Multiple Myeloma: A Real- World Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Infection Incidence, Timing, and Predictors in Newly Diagnosed Multiple Myeloma: A Real- World Retrospective Cohort Study Ozlem Candan¹, Narmin Naghizada, Tekin Tuncel, Beyza Melek Palaz, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8179313/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Infections are a leading cause of morbidity and mortality in multiple myeloma (MM), particularly during the induction phase. Identifying real-world infection patterns and predictors is crucial for guiding preventive strategies. Methods: This retrospective cohort study included 161 newly diagnosed MM patients who received standard induction therapy at a single tertiary center between 2016 and 2024. Clinical, laboratory, and infection-related parameters were analyzed over the first 12 months. Results: Infections occurred in 50.9% of patients, with the highest incidence within the first 3 months (27.3%, p = 0.004). Pneumonia was the most common type (34.1%), and gram-negative bacteria, particularly Escherichia coli , Klebsiella spp. , and Pseudomonas aeruginosa , were predominant. Antibiotic resistance to TMP-SMX and levofloxacin was observed in E. coli isolates. Multivariate analysis identified advanced ISS stage (OR: 3.83), diabetes mellitus (OR: 3.64), and chronic kidney disease (OR: 6.01) as independent predictors of infection ( p < 0.05). Lymphocyte counts were significantly lower in febrile episodes ( p = 0.040), highlighting a possible early immune marker. Hospitalization was more common in patients with advanced ISS and kidney disease. No significant differences were found between induction regimens. Conclusion: Infections pose a significant burden during the first year of MM treatment, particularly in high-risk patients during early induction. Readily available clinical parameters can aid in early risk stratification. These findings support a risk-adapted approach to prophylaxis, including the selective use of levofloxacin in patients with diabetes or kidney disease during the first 3 months of therapy. Figures Figure 1 Figure 2 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 six 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-month 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. 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). Inclusion required complete clinical and laboratory data at diagnosis and a minimum of six 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. These evaluations followed general diagnostic principles recommended by the Infectious Diseases Society of America (IDSA) (13). 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 hours of the documented infection event. This approach aims to reflect the patient’s immune status during infection and identify possible hematologic predictors of infection risk. Disease-related variables, including the International Staging System (ISS) score and the specific induction regimen, were also recorded. 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. Outcome Measures The primary outcome was the occurrence of any infectious episode during the first six months of induction therapy. 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 statistically significant associations were then entered into a multivariate logistic regression model. 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). 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. 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%). 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%) (Table 3, Figure 1C). 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/mm³ vs. 3913/mm³), although the difference was not statistically significant (p = 0.084). In contrast, lymphocyte counts were significantly lower in febrile cases (median 652/mm³ vs. 805/mm³; 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 (Table 4, Figure 2A). 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 three 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 (14). 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. 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 (15). Therefore, monitoring local antibiotic resistance patterns is essential in guiding empirical treatment decisions (14, 15). Larger-scale studies focusing on pathogen-specific resistance profiles in MM patients are warranted, especially considering clinical data from Jena University Hospital, where over 65% of patients developed at least one infectious episode—primarily of bacterial origin—during treatment with novel agents (16). Age ≥75 years emerged as a significant predictor of both infection and one-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 (14, 17-19). 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 (20, 21). 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 (22, 23). 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 (24, 25) 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 three 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 (26-28). Given the predominance of gram-negative pathogens in our cohort, which showed a predominance of E. coli , Klebsiella spp., and Pseudomonas aeruginosa , targeted prophylaxis with levofloxacin may be a feasible interim strategy for high-risk patients. Specifically, patients with diabetes mellitus and/or chronic kidney disease may benefit from levofloxacin prophylaxis during the first three months of induction therapy. For transplant candidates, prophylaxis may be extended until autologous transplantation; for others, it can be maintained throughout the induction period. This selective approach balances the need for infection prevention with the imperative to minimize unnecessary antimicrobial exposure, consistent with recent IMWG consensus recommendations advocating risk-adapted prophylaxis strategies in multiple myeloma patients (5, 9, 29). 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 support the development of personalized preventive strategies. Our findings emphasize the importance of individualized infection prevention, particularly the consideration of levofloxacin prophylaxis in high-risk patients 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 recommendations 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 resulted in under- or overestimation of infection rates. Additionally, treatment protocols and supportive care practices may have evolved over the 9-year study period, introducing potential heterogeneity. The cause of death was not systematically documented, limiting our ability to determine infection-related versus disease-related mortality. Despite these limitations, our findings provide meaningful real-world insights into infection patterns and risk factors among newly diagnosed MM patients. These data may serve as a basis for future prospective studies aimed at improving infection risk stratification and preventive strategies in clinical practice. 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 Declarations Ethical approval and consent to participate In accordance with institutional policy for retrospective studies, the ethics committee granted a waiver of additional informed consent because all data were anonymized and obtained from existing medical records. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Consent for publication Not applicable. Data availability The corresponding author can provide the data supporting the findings of this study upon reasonable request. Due to ethical and privacy restrictions, the data are not publicly available. Competing interests The authors declare no competing interests. Funding Not applicable. Author contributions The study was conceptualized and designed by Ayse Tulin Tuglular, Tayfur Toptas, and Ozlem Candan. Ayse Tulin Tuglular and Tayfur Toptas provided methodological guidance and supervision. Infection-related planning and microbiological data supervision were performed by Tekin Tuncel and Zekaver Odabası. Formal analysis and statistical evaluation were conducted by Ozlem Candan and Tayfur Toptas. Data collection and curation were performed by Ozlem Candan, Derya Demirtas, Ahmet Mert Yanik, Fatma Temiz, Beyza Melek Palaz, Narmin Naghizada, Mustafa Alperen Tunc, Ceren Uzunoglu Guren, Arda Bayar, Secil Salim, Fatma Arıkan, and Meral Ulukoylu Menguc. The first draft of the manuscript was written by Ozlem Candan. Isik Atagunduz, Ayse Tulin Tuglular, Tayfur Toptas, Zekaver Odabası, and Asu Fergun Yilmaz conducted critical review and editing. All authors reviewed and approved the final version of the manuscript. Acknowledgments The authors thank all colleagues involved in data collection and manuscript preparation. Clinical trial registration This study was not registered as a clinical trial, as it did not meet the criteria requiring registration. Disclaimer for use of external material No external materials were reproduced in this study. References Ying L, YinHui T, Yunliang Z, Sun H. 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Satlin MJ, Vardhana S, Soave R, Shore TB, Mark TM, Jacobs SE, et al. Impact of prophylactic levofloxacin on rates of bloodstream infection and fever in neutropenic patients with multiple myeloma undergoing autologous hematopoietic stem cell transplantation. Biol Blood Marrow Transplant. 2015;21(10):1808–14. Tables Table 1. Baseline characteristics of patients (n = 161) Characteristic Median (IQR) / n (%) Age, median (IQR) 64 (59–73) Sex (Male) 95 (59.0%) Hypertension 43 (26.7%) Diabetes Mellitus 38 (23.6%) Chronic kidney disease 25 (15.5%) ISS Stage Stage 1 33 (20.5%) Stage 2 30 (18.6%) Stage 3 65 (40.4%) Not available 33 (20.5%) Induction Regimen VCD 85 (52.8%) VRD 38 (23.6%) VD/RD 12 (7.5%) Other 26 (16.1%) Paraprotein Subtype IgG Kappa 48 (29.8%) IgG Lambda 44 (27.3%) IgA Kappa 22 (13.7%) IgA Lambda 18 (11.2%) Free Kappa 13 (8.1%) Free Lambda 9 (5.6%) Other / Unknown 7 (4.3%) ECOG Performance Status 0–2 134 (83.2%) 3–4 12 (7.5%) Not available 15 (9.3%) IQR: Interquartile range; ISS: International Staging System; VCD: Bortezomib, cyclophosphamide, and dexamethasone; VRD: Bortezomib, lenalidomide, and dexamethasone; VD/RD: Bortezomib + dexamethasone / Lenalidomide + dexamethasone; ECOG: Eastern Cooperative Oncology Group. Table 2. Distribution and Severity (Grade 3-4) of Infections (n = 82) Type of Infection n (%) Grade 3-4 (%) Pneumonia 28 (34.1%) 18 (64.3%) UTI 22 (26.8%) 13 (59.1%) No identifiable focus 11 (13.4%) 6 (54.5%) Catheter-related infection 5 (6.1%) 5 (100%) URTI 4 (4.9%) 1 (25%) Soft tissue / local infection 4 (4.9%) 2 (50%) Gastrointestinal infection 4 (4.9%) 2 (50%) Other 4 (4.9%) 1 (25%) UTI: Urinary tract infection; URTI: Upper respiratory tract infection. Grade 3–4 infections were defined according to Common Terminology Criteria for Adverse Events, version 5.0 (CTCAE v5.0) Table 3. Distribution of Identified Microorganisms (n = 41) Microorganism n (%) Escherichia coli 12 (29.3%) Klebsiella spp. 7 (17.1%) Pseudomonas aeruginosa 6 (14.6%) Streptococcus pneumoniae 5 (12.2%) Staphylococcus aureus 5 (12.2%) Enterococcus spp. 3 (7.3%) CoNS 2 (4.9%) Acinetobacter spp. 1 (2.4%) E. coli: Escherichia coli; CoNS: Coagulase-negative Staphylococcus; S. aureus: Staphylococcus aureus; S. pneumoniae: Streptococcus pneumoniae Table 4. Univariate and Multivariate Logistic Regression Analysis for Infection Risk Variable Univariate OR 95% CI p-value Multivariate OR 95% CI p-value Age 1.02 0.99–1.06 0.248 1.03 0.99–1.09 0.162 Sex 0.65 0.32–1.32 0.236 1.01 0.42–2.41 0.987 Hypertension 1.50 0.74–3.03 0.258 0.65 0.25–1.70 0.376 Diabetes Mellitus 2.13 0.93–4.88 0.073 3.64 1.20–11.09 0.023 CKD 6.33 1.75–22.88 0.005 6.02 1.45–24.88 0.013 ISS 3.51 2.13–5.80 0.000 3.83 2.16–6.77 0.000 OR: Odds Ratio; CI: Confidence Interval; CKD: Chronic Kidney Disease; ISS: International Staging System Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8179313","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554270569,"identity":"b97d6bef-aa46-4158-9cc9-07057206a624","order_by":0,"name":"Ozlem 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Infections were most frequent in the first 3 months (n=44), followed by 3–6 months (n=25), and 6–12 months (n=23). The difference in infection incidence across time intervals was statistically significant (p = 0.004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Infection rates by induction regimen. The highest infection rate was observed in patients receiving VD/RD (83.3%), followed by other regimens (61.5%), VCD (60.0%), and VRD (44.7%). Although numerically different, the variation in infection rates among regimens did not reach statistical significance (p = 0.10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e Distribution of microorganisms isolated from 41 culture-positive infections. Escherichia coli was the most common pathogen (29.3%), followed by Klebsiella spp. (17.1%) and P. aeruginosa (14.6%), indicating a predominance of gram-negative bacteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u003c/strong\u003e Lymphocyte count by febrile status. Median lymphocyte count was significantly lower in febrile episodes compared to non-febrile ones (652/mm³ vs. 805/mm³; p = 0.040), suggesting more profound immunosuppression in febrile patients.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8179313/v1/98f09176e186ec7aaf108b1b.png"},{"id":97396007,"identity":"baae6715-630b-44f9-b30a-a30b36846617","added_by":"auto","created_at":"2025-12-04 00:15:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":499650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfection and Mortality Risk by Clinical Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Multivariate logistic regression analysis showing independent risk factors for infection. Advanced ISS stage (OR: 3.83; 95% CI: 2.16–6.77; p \u0026lt; 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 significantly associated with infection risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Infection status by comorbidity. Patients with diabetes mellitus had a higher infection rate (69.2%) than those without (p = 0.019). Similarly, 84.0% of patients with chronic kidney disease developed infection compared to those without (p = 0.003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e One-year mortality by age group. Patients aged ≥75 years had significantly higher mortality (31.2%) compared to those \u0026lt;75 years (8.3%; p = 0.016), indicating age as a key predictor of early mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u003c/strong\u003e Hospitalization rates by ISS stage. Hospitalization was significantly more common in patients with advanced disease: Stage I (16.7%), Stage II (37.5%), and Stage III (70.0%) (p \u0026lt; 0.001), emphasizing the role of disease burden in infection severity.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8179313/v1/f4af39233f955470bc9bd1a0.png"},{"id":104880858,"identity":"d78d3491-944f-4862-99fe-1ca92c7eb5c7","added_by":"auto","created_at":"2026-03-18 09:14:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1903744,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8179313/v1/816e9378-39c8-44a6-9c18-c112311e0c0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Infection Incidence, Timing, and Predictors in Newly Diagnosed Multiple Myeloma: A Real- World Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple 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 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Previous studies have reported a 7-fold increase in infection risk among MM patients compared to the general population, particularly during the first six months of induction therapy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe 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 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Additionally, myelosuppression-induced neutropenia significantly increases susceptibility to bacterial and fungal infections (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Furthermore, T-cell dysfunction and a reduction in cytotoxic T-cell numbers impair antiviral immunity, leading to an increased incidence of viral infections (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, older age and comorbidities such as chronic kidney disease and diabetes compound this risk (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile 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 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eInduction 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 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In addition to these factors, prolonged hospitalizations and the use of central venous catheters contribute to nosocomial infections, further increasing the infection burden (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCommon pathogens isolated in MM patients include \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e among gram-negative bacteria, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e among gram-positive bacteria. Viral infections, particularly herpes zoster and cytomegalovirus (CMV) reactivation, are frequently reported, while fungal infections, such as those caused by \u003cem\u003eCandida\u003c/em\u003e and \u003cem\u003eAspergillus\u003c/em\u003e species, are more commonly observed in advanced disease stages or patients receiving high-dose corticosteroids (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe 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-month 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.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Patient Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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). Inclusion required complete clinical and laboratory data at diagnosis and a minimum of six 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of Infections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInfectious 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. These evaluations followed general diagnostic principles recommended by the Infectious Diseases Society of America (IDSA) (13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline 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.\u003c/p\u003e\n\u003cp\u003ePeripheral 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 hours of the documented infection event. This approach aims to reflect the patient’s immune status during infection and identify possible hematologic predictors of infection risk.\u003c/p\u003e\n\u003cp\u003eDisease-related variables, including the International Staging System (ISS) score and the specific induction regimen, were also recorded. 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was the occurrence of any infectious episode during the first six months of induction therapy. Infections were considered present if they were clinically or microbiologically documented and required treatment with antibiotics, antivirals, or antifungals, and/or resulted in hospitalization.\u003c/p\u003e\n\u003cp\u003eTo 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive 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.\u003c/p\u003e\n\u003cp\u003eTo identify independent predictors of infection, univariate logistic regression analyses were first performed. Variables with statistically significant associations were then entered into a multivariate logistic regression model.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using Python (version 3.11.0), with the pandas, scipy, and statsmodels libraries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Clinical Research Ethics Committee of Marmara University School of Medicine (Protocol No: 09.2023.1454).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 161 patients with newly diagnosed multiple myeloma were included in the study. The median age was 64 years (interquartile range [IQR], 59\u0026ndash;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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfection Incidence and Timing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring 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\u0026ndash;6 months (25 patients, 15.5%) and 6\u0026ndash;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of Induction Regimens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients 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 \u0026apos;other\u0026apos; regimens (61.5%), VCD (60.0%), and VRD (44.7%). 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfection Type and Severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobiological Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 41 culture-positive infections, \u003cem\u003eEscherichia coli\u003c/em\u003e was the most frequently isolated pathogen (12 cases, 29.3%), followed by \u003cem\u003eKlebsiella\u003c/em\u003e spp. (7 cases, 17.1%) and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (6 cases, 14.6%) (Table 3, Figure 1C). Gram-negative bacteria were predominant among all identified organisms. Among the \u003cem\u003eE. coli\u003c/em\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHematologic Parameters and Febrile Episodes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeutrophil 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/mm\u0026sup3; vs. 3913/mm\u0026sup3;), although the difference was not statistically significant (p = 0.084). In contrast, lymphocyte counts were significantly lower in febrile cases (median 652/mm\u0026sup3; vs. 805/mm\u0026sup3;; p = 0.040) (Figure 1D), suggesting a more profound immunosuppressive state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Analysis of Infection Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate 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\u0026ndash;6.77; p \u0026lt; 0.001). Diabetes mellitus (OR: 3.64; 95% CI: 1.20\u0026ndash;11.10; p = 0.023) and chronic kidney disease (OR: 6.01; 95% CI: 1.45\u0026ndash;24.90; p = 0.013) were also significantly associated with infection risk. Age, sex, and hypertension were not independent predictors in the final model (Table 4, Figure 2A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfection Risk by Age, ISS Stage, and Comorbidities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients aged \u0026ge;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 \u0026lt; 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeventeen patients (10.6%) died within the first year of treatment. Mortality was significantly higher in patients aged \u0026ge;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHospitalization Predictors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA separate analysis explored the relationship between hospitalization and clinical/laboratory variables. Higher ISS stage was significantly associated with hospitalization (p \u0026lt; 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.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eInfections 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 three months. This early vulnerability period is likely driven by a convergence of disease-related immune dysfunction, treatment-induced cytopenias, and underlying comorbid conditions (3).\u003c/p\u003e\n\u003cp\u003ePneumonia was the most common type of infection, consistent with prior data highlighting respiratory tract susceptibility in MM. Notably, gram-negative pathogens, particularly \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e 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 (14). 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.\u003c/p\u003e\n\u003cp\u003eAlthough the number of antibiogram-tested isolates was limited, preliminary findings indicated potential resistance to commonly used agents such as TMP-SMX and levofloxacin. Among \u003cem\u003eE. coli\u003c/em\u003e 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 (15). Therefore, monitoring local antibiotic resistance patterns is essential in guiding empirical treatment decisions (14, 15). Larger-scale studies focusing on pathogen-specific resistance profiles in MM patients are warranted, especially considering clinical data from Jena University Hospital, where over 65% of patients developed at least one infectious episode\u0026mdash;primarily of bacterial origin\u0026mdash;during treatment with novel agents (16).\u003c/p\u003e\n\u003cp\u003eAge \u0026ge;75 years emerged as a significant predictor of both infection and one-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 (14, 17-19).\u003c/p\u003e\n\u003cp\u003eInterestingly, 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 (20, 21). Future prospective studies are warranted to validate lymphocyte count as a stratification tool for infection surveillance and preemptive interventions in MM patients.\u003c/p\u003e\n\u003cp\u003eWhen 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 (22, 23).\u003c/p\u003e\n\u003cp\u003eHospitalization 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 (24, 25)\u003c/p\u003e\n\u003cp\u003eWhile 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 three 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 (26-28).\u003c/p\u003e\n\u003cp\u003eGiven the predominance of gram-negative pathogens in our cohort, which showed a predominance of \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e spp., and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, targeted prophylaxis with levofloxacin may be a feasible interim strategy for high-risk patients. Specifically, patients with diabetes mellitus and/or chronic kidney disease may benefit from levofloxacin prophylaxis during the first three months of induction therapy. For transplant candidates, prophylaxis may be extended until autologous transplantation; for others, it can be maintained throughout the induction period. This selective approach balances the need for infection prevention with the imperative to minimize unnecessary antimicrobial exposure, consistent with recent IMWG consensus recommendations advocating risk-adapted prophylaxis strategies in multiple myeloma patients (5, 9, 29).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis 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\u0026mdash;such as advanced ISS stage, diabetes mellitus, and chronic kidney disease\u0026mdash;were identified as independent predictors of infection and hospitalization. These factors may serve as practical tools for early risk stratification and support the development of personalized preventive strategies.\u003c/p\u003e\n\u003cp\u003eOur findings emphasize the importance of individualized infection prevention, particularly the consideration of levofloxacin prophylaxis in high-risk patients during the early treatment phase. By incorporating clinical risk factors into decision-making, healthcare providers may improve surveillance, antimicrobial stewardship, and supportive care.\u003c/p\u003e\n\u003cp\u003eHowever, given the single-center design and the limited number of culture-positive infections, our recommendations should be interpreted with caution and validated through larger, prospective multicenter studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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\u0026mdash;particularly regarding cytogenetics and LDH\u0026mdash;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 resulted in under- or overestimation of infection rates.\u003c/p\u003e\n\u003cp\u003eAdditionally, treatment protocols and supportive care practices may have evolved over the 9-year study period, introducing potential heterogeneity. The cause of death was not systematically documented, limiting our ability to determine infection-related versus disease-related mortality.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, our findings provide meaningful real-world insights into infection patterns and risk factors among newly diagnosed MM patients. These data may serve as a basis for future prospective studies aimed at improving infection risk stratification and preventive strategies in clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALC: Absolute Lymphocyte Count\u003cbr\u003e\u0026nbsp;CBC: Complete Blood Count\u003cbr\u003e\u0026nbsp;CI: Confidence Interval\u003cbr\u003e\u0026nbsp;CKD: Chronic Kidney Disease\u003cbr\u003e\u0026nbsp;CMV: Cytomegalovirus\u003cbr\u003e\u0026nbsp;CoNS: Coagulase-Negative Staphylococcus\u003cbr\u003e\u0026nbsp;CTCAE: Common Terminology Criteria for Adverse Events\u003cbr\u003e\u0026nbsp;ECOG: Eastern Cooperative Oncology Group\u003cbr\u003e\u0026nbsp;IDSA: Infectious Diseases Society of America\u003cbr\u003e\u0026nbsp;IMiD: Immunomodulatory Drug\u003cbr\u003e\u0026nbsp;IMWG: International Myeloma Working Group\u003cbr\u003e\u0026nbsp;IQR: Interquartile Range\u003cbr\u003e\u0026nbsp;ISS: International Staging System\u003cbr\u003e\u0026nbsp;LDH: Lactate Dehydrogenase\u003cbr\u003e\u0026nbsp;MM: Multiple Myeloma\u003cbr\u003e\u0026nbsp;OR: Odds Ratio\u003cbr\u003e\u0026nbsp;PCR: Polymerase Chain Reaction\u003cbr\u003e\u0026nbsp;PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003cbr\u003e\u0026nbsp;RD: Lenalidomide and Dexamethasone\u003cbr\u003e\u0026nbsp;R-ISS: Revised International Staging System\u003cbr\u003e\u0026nbsp;TMP-SMX: Trimethoprim–Sulfamethoxazole\u003cbr\u003e\u0026nbsp;UTI: Urinary Tract Infection\u003cbr\u003e\u0026nbsp;URTI: Upper Respiratory Tract Infection\u003cbr\u003e\u0026nbsp;VCD: Bortezomib, Cyclophosphamide, and Dexamethasone\u003cbr\u003e\u0026nbsp;VD: Bortezomib and Dexamethasone\u003cbr\u003e\u0026nbsp;VRD: Bortezomib, Lenalidomide, and Dexamethasone\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with institutional policy for retrospective studies, the ethics committee granted a waiver of additional informed consent because all data were anonymized and obtained from existing medical records. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author can provide the data supporting the findings of this study upon reasonable request. Due to ethical and privacy restrictions, the data are not publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conceptualized and designed by Ayse Tulin Tuglular, Tayfur Toptas, and Ozlem Candan. Ayse Tulin Tuglular and Tayfur Toptas provided methodological guidance and supervision. Infection-related planning and microbiological data supervision were performed by Tekin Tuncel and Zekaver Odabası. Formal analysis and statistical evaluation were conducted by Ozlem Candan and Tayfur Toptas. Data collection and curation were performed by Ozlem Candan, Derya Demirtas, Ahmet Mert Yanik, Fatma Temiz, Beyza Melek Palaz, Narmin Naghizada, Mustafa Alperen Tunc, Ceren Uzunoglu Guren, Arda Bayar, Secil Salim, Fatma Arıkan, and Meral Ulukoylu Menguc. The first draft of the manuscript was written by Ozlem Candan. Isik Atagunduz, Ayse Tulin Tuglular, Tayfur Toptas, Zekaver Odabası, and Asu Fergun Yilmaz conducted critical review and editing. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all colleagues involved in data collection and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not registered as a clinical trial, as it did not meet the criteria requiring registration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer for use of external material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external materials were reproduced in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYing L, YinHui T, Yunliang Z, Sun H. Lenalidomide and the risk of serious infection in patients with multiple myeloma: a systematic review and meta-analysis. Oncotarget. 2017;8(28):46593.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlimark C, Holmberg E, Mellqvist U-H, Landgren O, Bj\u0026ouml;rkholm M, Hultcrantz M, et al. Multiple myeloma and infections: a population-based study on 9253 multiple myeloma patients. Haematologica. 2015;100(1):107.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlimark CH, Carlson K, Day C, Einarsdottir S, Juliusson G, Karma M, et al. Risk of infections in multiple myeloma. A population-based study on 8,672 multiple myeloma patients diagnosed 2008\u0026ndash;2021 from the Swedish Myeloma Registry. Haematologica. 2024;110(1):163.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS\u0026oslash;rrig R, Klausen TW, Salomo M, Vangsted A, Gimsing P. Risk factors for infections in newly diagnosed multiple myeloma patients: a Danish retrospective nationwide cohort study. Eur J Haematol. 2019;102(2):182\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDrayson MT, Bowcock S, Planche T, Iqbal G, Pratt G, Yong K, et al. Levofloxacin prophylaxis in patients with newly diagnosed myeloma (TEAMM): a multicentre, double-blind, placebo-controlled, randomised, phase 3 trial. Lancet Oncol. 2019;20(12):1760\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeh BW, Harrison SJ, Worth LJ, Spelman T, Thursky KA, Slavin MA. Risks, severity and timing of infections in patients with multiple myeloma: a longitudinal cohort study in the era of immunomodulatory drug therapy. Br J Haematol. 2015;171(1):100\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRussell BM, Avigan DE. Immune dysregulation in multiple myeloma: the current and future role of cell-based immunotherapy. Int J Hematol. 2023;117(5):652\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalmaceda N, Aziz M, Chandrasekar VT, McClune B, Kambhampati S, Shune L, et al. Infection risks in multiple myeloma: a systematic review and meta-analysis of randomized trials from 2015 to 2019. BMC Cancer. 2021;21:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaje NS, Anaissie E, Kumar SK, Lonial S, Martin T, Gertz MA, et al. Consensus guidelines and recommendations for infection prevention in multiple myeloma: a report from the International Myeloma Working Group. Lancet Haematol. 2022;9(2):e143\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTicona K, Tun A, Guevara E. Risks of upper respiratory tract infection and pneumonia in patients with multiple myeloma receiving Daratumumab: A systematic review and meta-analysis of randomized controlled trials. American Society of Clinical Oncology; 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTobar P, Gonzalez Mosquera LF, Cardenas Maldonado DD, Moscoso B, Podrumar AI, Cuenca JA. Clinical and financial implications of central venous catheters bloodstream infections in patients with multiple myeloma in the United States. Wolters Kluwer Health; 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeh BW, Teng JC, Urbancic K, Grigg A, Harrison SJ, Worth LJ, et al. Invasive fungal infections in patients with multiple myeloma: a multi-center study in the era of novel myeloma therapies. Haematologica. 2015;100(1):e28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaplitz RA, Kennedy EB, Bow EJ, Crews J, Gleason C, Hawley DK, et al. Outpatient management of fever and neutropenia in adults treated for malignancy: American Society of Clinical Oncology and Infectious Diseases Society of America clinical practice guideline update. J Clin Oncol. 2018;36(14):1443\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalmaceda N, Aziz M, Chandrasekar VT, McClune B, Kambhampati S, Shune L, et al. Infection risks in multiple myeloma: a systematic review and meta-analysis of randomized trials from 2015 to 2019. BMC Cancer. 2021;21(1):730.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuevara-Ram\u0026iacute;rez P, Cadena-Ullauri S, Paz-Cruz E, Ruiz-Pozo VA, Tamayo-Trujillo R, Cabrera-Andrade A, et al. Gut microbiota disruption in hematologic cancer therapy: molecular insights and implications for treatment efficacy. Int J Mol Sci. 2024;25(19):10255.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrioli A, N\u0026auml;gler TM, Yomade O, R\u0026uuml;thrich MM, Scholl S, Frietsch JJ, et al. Sex-disaggregated analysis of biology, treatment tolerability, and outcome of multiple myeloma in a German cohort. Oncol Res Treat. 2022;45(9):494\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoore KLF, Turesson I, Genell A, Klausen TW, Knut-Bojanowska D, Redder L, et al. Improved survival in myeloma patients\u0026ndash;a nationwide registry study of 4,647 patients\u0026thinsp;\u0026ge;\u0026thinsp;75 years treated in Denmark and Sweden. Haematologica. 2022;108(6):1640.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim C, Sinha P, Harrison SJ, Quach H, Slavin MA, Teh BW. Epidemiology and risks of infections in patients with multiple myeloma managed with new generation therapies. Clin Lymphoma Myeloma Leuk. 2021;21(7):444\u0026ndash;50. e3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalumbo A, Bringhen S, Ludwig H, Dimopoulos MA, Blad\u0026eacute; J, Mateos MV, et al. Personalized therapy in multiple myeloma according to patient age and vulnerability: a report of the European Myeloma Network (EMN). Blood J Am Soc Hematol. 2011;118(17):4519\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerri GM, Yildirim C, Do NV, Brophy M, Park JS, Munshi NC, et al. Lymphopenia predicts poor outcomes in newly diagnosed multiple myeloma. Blood Adv. 2025;9(1):78\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllegra A, Tonacci A, Musolino C, Pioggia G, Gangemi S. Secondary immunodeficiency in hematological malignancies: focus on multiple myeloma and chronic lymphocytic leukemia. Front Immunol. 2021;12:738915.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpataro F, Armentaro G, Di Gioia G, Meloni P, Rossi I, Williams M et al. Impact of frailty on infection risk in non-transplant eligible multiple myeloma patients: a systematic review and meta-analysis. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOffidani M, Corvatta L, Bringhen S, Gentili S, Gay F, Maracci L, et al. Infection complications in 476 patients with newly diagnosed multiple myeloma treated with lenalidomide or bortezomib combinations. American Society of Hematology Washington, DC; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValković T, Gačić V, Ivandić J, Petrov B, Dobrila-Dintinjana R, Dadić-Hero E, et al. Infections in hospitalised patients with multiple myeloma: main characteristics and risk factors. Turkish J Hematol. 2015;32(3):234.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei W, Shi H, Chen H, Chen X, Peng R, Yu W, et al. Clinicopathologic predictors of renal response and survival in newly diagnosed multiple myeloma with renal injury: a retrospective study. Clin Experimental Med. 2025;25(1):48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSedhom D, Elsaid M, Sedhom R. Clostridium difficile Infection in Patients With Hematologic Malignancy and Neutropenic Fever: A Clinical Overview: 149. Official journal of the American College of Gastroenterology|. ACG. 2018;113:S83\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFreifeld AG, Bow EJ, Sepkowitz KA, Boeckh MJ, Ito JI, Mullen CA, et al. Clinical practice guideline for the use of antimicrobial agents in neutropenic patients with cancer: 2010 update by the Infectious Diseases Society of America. Clin Infect Dis. 2011;52(4):e56\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZiegler M, Han JH, Landsburg D, Pegues D, Reesey E, Gilmar C, et al. editors. Impact of levofloxacin for the prophylaxis of bloodstream infection on the gut microbiome in patients with hematologic malignancy. Open forum infectious diseases. Oxford University Press US; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSatlin MJ, Vardhana S, Soave R, Shore TB, Mark TM, Jacobs SE, et al. Impact of prophylactic levofloxacin on rates of bloodstream infection and fever in neutropenic patients with multiple myeloma undergoing autologous hematopoietic stem cell transplantation. Biol Blood Marrow Transplant. 2015;21(10):1808\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of patients (n = 161)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR) / n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eAge, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e64 (59\u0026ndash;73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eSex (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e95 (59.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e43 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e38 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e25 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eISS Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e33 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e30 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e65 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e33 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eInduction Regimen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eVCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e85 (52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eVRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e38 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eVD/RD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e12 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e26 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eParaprotein Subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eIgG Kappa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e48 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eIgG Lambda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e44 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eIgA Kappa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e22 (13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eIgA Lambda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e18 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eFree Kappa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e13 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eFree Lambda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e9 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eOther / Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e7 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eECOG Performance Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e0\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e134 (83.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e3\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e12 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e15 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eIQR: Interquartile range; ISS: International Staging System; VCD: Bortezomib, cyclophosphamide, and dexamethasone; VRD: Bortezomib, lenalidomide, and dexamethasone; VD/RD: Bortezomib + dexamethasone / Lenalidomide + dexamethasone; ECOG: Eastern Cooperative Oncology Group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Distribution and Severity (Grade 3-4) of Infections (n = 82)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 3-4 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e28 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e18 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eUTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e22 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e13 (59.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eNo identifiable focus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e11 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e6 (54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eCatheter-related infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e5 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eURTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e4 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eSoft tissue / local infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e4 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eGastrointestinal infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e4 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e4 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eUTI: Urinary tract infection; URTI: Upper respiratory tract infection. Grade 3\u0026ndash;4 infections were defined according to Common Terminology Criteria for Adverse Events, version 5.0 (CTCAE v5.0)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Distribution of Identified Microorganisms (n = 41)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroorganism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eEscherichia coli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e12 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eKlebsiella spp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e7 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003ePseudomonas aeruginosa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e6 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStreptococcus pneumoniae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e5 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStaphylococcus aureus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e5 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eEnterococcus spp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e3 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eCoNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e2 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eAcinetobacter spp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eE. coli: Escherichia coli; CoNS: Coagulase-negative Staphylococcus; S. aureus: Staphylococcus aureus; S. pneumoniae: Streptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Univariate and Multivariate Logistic Regression Analysis for Infection Risk\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.99\u0026ndash;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.99\u0026ndash;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.32\u0026ndash;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.42\u0026ndash;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.74\u0026ndash;3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.25\u0026ndash;1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.93\u0026ndash;4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.20\u0026ndash;11.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.75\u0026ndash;22.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.45\u0026ndash;24.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eISS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2.13\u0026ndash;5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e2.16\u0026ndash;6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eOR: Odds Ratio; CI: Confidence Interval; CKD: Chronic Kidney Disease; ISS: International Staging System\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8179313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8179313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eInfections are a leading cause of morbidity and mortality in multiple myeloma (MM), particularly during the induction phase. Identifying real-world infection patterns and predictors is crucial for guiding preventive strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective cohort study included 161 newly diagnosed MM patients who received standard induction therapy at a single tertiary center between 2016 and 2024. Clinical, laboratory, and infection-related parameters were analyzed over the first 12 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eInfections occurred in 50.9% of patients, with the highest incidence within the first 3 months (27.3%, \u003cem\u003ep\u003c/em\u003e = 0.004). Pneumonia was the most common type (34.1%), and gram-negative bacteria, particularly \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella spp.\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, were predominant. Antibiotic resistance to TMP-SMX and levofloxacin was observed in \u003cem\u003eE. coli\u003c/em\u003e isolates.\u003cbr\u003e\nMultivariate analysis identified advanced ISS stage (OR: 3.83), diabetes mellitus (OR: 3.64), and chronic kidney disease (OR: 6.01) as independent predictors of infection (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Lymphocyte counts were significantly lower in febrile episodes (\u003cem\u003ep\u003c/em\u003e = 0.040), highlighting a possible early immune marker.\u003c/p\u003e\n\u003cp\u003eHospitalization was more common in patients with advanced ISS and kidney disease. No significant differences were found between induction regimens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eInfections pose a significant burden during the first year of MM treatment, particularly in high-risk patients during early induction. Readily available clinical parameters can aid in early risk stratification. These findings support a risk-adapted approach to prophylaxis, including the selective use of levofloxacin in patients with diabetes or kidney disease during the first 3 months of therapy.\u003c/p\u003e","manuscriptTitle":"Infection Incidence, Timing, and Predictors in Newly Diagnosed Multiple Myeloma: A Real- World Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 00:15:06","doi":"10.21203/rs.3.rs-8179313/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f49cb7b-c7c1-4fa6-82ce-e73122c82899","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T09:12:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 00:15:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8179313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8179313","identity":"rs-8179313","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}