Impact of frailty on infection risk in non-transplant eligible multiple myeloma patients: a systematic review and meta-analysis

Impact of frailty on infection risk in non-transplant eligible multiple myeloma patients: a systematic review and meta-analysis | 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 Systematic Review Impact of frailty on infection risk in non-transplant eligible multiple myeloma patients: a systematic review and meta-analysis Federico Spataro, Giuseppe Armentaro, Giuseppe Di Gioia, Pierluigi Meloni, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6797423/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 Multiple myeloma (MM), a plasma-cell neoplasm predominantly diagnosed in the elderly, is profoundly influenced by patients’ frailty status. Yet, the precise relationship between this geriatric vulnerability and the risk of life‐threatening (grade 3–4) infections in autologous stem cell transplant (ASCT)‐ineligible MM cohorts remains largely uncharted. Methods We conducted a systematic review and meta‑analysis to uncover how frailty fuels infection risk in newly diagnosed multiple myeloma (NDMM) patients who are not eligible for ASCT. We included studies that classified participants as fit, intermediate, or frail and reported their infection outcomes. By comparing the risk of severe infections across these frailty categories, we exposed the hidden cost of this geriatric vulnerability. Results. Across six studies (n = 1,710), frailty afflicted 46.4% of patients. Non‑frail individuals experienced a 23% lower risk of severe infection than their frail counterparts (RR 0.77; 95% CI 0.66–0.90). In subgroup analyses, fit patients slashed their infection risk by 33% versus frail peers (RR 0.67; 95% CI 0.43–1.04), while those deemed intermediate registered a 14% reduction (RR 0.86; 95% CI 0.72–1.01). Directly comparing fit to intermediate categories yielded an RR of 0.85 (95% CI 0.51–1.40), spotlighting how even modest dips in resilience can tip the scales toward vulnerability. Conclusions. Frailty dramatically raises infection risk in ASCT‑ineligible NDMM patients, with the frailest facing the greatest danger. Even more striking, the intermediate group’s infection rates align more closely with the frail than the fit, suggesting our current mid‑tier label may be hiding serious vulnerability. These results underscore the urgency of embedding comprehensive frailty assessments into routine care and refining stratification tools to accurately flag high‑risk patients and enable truly personalized, preemptive infection management. Hematology multiple myeloma frailty infection meta-analysis Figures Figure 1 Figure 2 Figure 3 1. Background Multiple myeloma (MM) is a hematologic malignancy characterized by clonal plasma cell proliferation in the bone marrow, leading to immune suppression, bone destruction, and end-organ damage. The disease predominantly affects older adults, with a median age at diagnosis of approximately 66–70 years. 1 Given the aging population, an increasing number of MM patients are considered ineligible for high-dose chemotherapy with autologous stem cell transplantation (ASCT), due to comorbidities and frailty. This makes frailty assessment a crucial step in treatment decision-making. Various tools have been developed to screen for frailty in MM, including the International Myeloma Working Group Frailty Index (IMWG-FI), the Simplified Frailty Score, and the Vulnerable Elders Survey-13 (VES-13). The IMWG-FI classifies patients into three categories: fit, intermediate, and frail, based on age, comorbidities, and functional status. This classification aids in tailoring treatment strategies, as frail patients are burdened with a higher risk of experiencing adverse events and reduced tolerance to standard therapy. 2 The Simplified Frailty Score, an alternative model, incorporates age, performance status, and comorbidities to provide a quick and efficient assessment of patient frailty. 3 The VES-13 is a 13-item self-administered questionnaire originally developed for the geriatric population to predict functional decline and mortality. It has been adapted in MM to stratify patients into fit (VES-13 score < 3) and vulnerable/frail (VES-13 score ≥ 3) categories, with treatment adjustments made accordingly to minimize toxicity while maintaining therapeutic efficacy. 4 One of the most severe complications in MM is infection, particularly grade 3–4 infections, which are associated with increased morbidity, prolonged hospitalizations, and higher mortality rates. 5 MM patients are at increased risk of infections due to underlying immune dysfunction, bone marrow suppression, and treatment-related immunosuppression. 5 Several predictive models have been proposed to estimate infection risk, including the FIRST score, GEM-PETHEMA score, and IRMM score, which incorporate laboratory and clinical parameters to stratify patients into different risk categories. 6 – 8 However, these models do not fully integrate frailty classification, underscoring the need for further investigation into the independent role of frailty in infection risk. Given the growing emphasis on frailty assessments in MM clinical trials, standardizing frailty definitions and their impact on infection risk and overall patient outcomes remains a priority. We performed a systematic review and meta-analysis to assess the impact of frailty on the risk of severe infections (grade 3–4) in newly diagnosed multiple myeloma (NDMM) patients ineligible for ASCT, by comparing infection rates among fit, intermediate, and frail individuals. 2. Methods 2.1 Search strategy and selection criteria This systematic review and meta-analysis were performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 9 The study protocol is registered in PROSPERO (registration ID: CRD420250654904). We conducted a comprehensive search of the MEDLINE and LILACS databases from inception (no backwards time limit) to February 1st, 2025, to identify studies evaluating the risk of infections in frailty groups of NDMM patients ineligible for ASCT. The complete list of search terms is detailed in Fig. 1 of the appendix. Prospective and retrospective studies were included according to the following inclusion criteria: (1) enrolled non-transplant eligible NDMM patients, (2) classify patients into frailty categories (fit, intermediate, and frail, or non-frail and frail), and (3) reported the number of patients who developed grade 3–4 infections. Studies lacking the necessary data were excluded. No restrictions were applied concerning language or publication date. Additionally, reference lists of included studies, citations, and recent reviews were meticulously screened to identify any further relevant articles. 2.2 Data collection process Titles and abstracts were screened, full texts reviewed, data extracted, and the risk of bias or study quality assessed independently by two reviewers (FS and AGS) using a standardized, web-based system (Rayyan). 10 Any disagreements were resolved through consensus. For each study included, we extracted data on study characteristics, settings, eligibility criteria, populations studied, interventions, and reported outcomes. 2.3 Outcomes The risk of infection was the main outcome, and it was calculated as the ratio between the number of patients who developed grade 3–4 infections and the total number of patients within the same frailty group, in each study. 11 Non-frail patients included subjects classified as fit or intermediate (“intermediate” in this meta-analysis). The risk of infection was assessed using the Risk Ratio (RR) as the effect measure. The RR for each study was obtained through meta-analytic pooling, by comparing the proportion of patients who developed grade 3–4 infections in the non-frail groups to the proportion in the frail groups (non-frail vs frail). Moreover, when possible, an additional analysis was performed comparing fit vs intermediate patients. If a study did not report overall infection data, grade 3–4 pneumonia data were used instead. Given that pneumonia is a major infectious complication in multiple myeloma, it was considered a reasonable proxy for overall infection risk. 2.4 Data analysis and risk of bias assessment Summary measures were pooled using the DerSimonian and Laird random-effects model, with heterogeneity estimated via the Mantel-Haenszel method. Effect size from individual studies were pooled using RR. 12 The summary of findings tables was created through the GRADEpro GDT software (available at gradepro.org ), and all statistical analyses were performed with ProMeta 3.0 and RevMan software. To assess study quality, we applied the Quality Appraisal of Case Series Studies Checklist developed by the Institute of Health Economics (IHE) (accessible at http://www.ihe.ca/research-programs/rmd/cssqac/cssqac-about ). Responses were categorized as "yes," "unclear/partial," or "no." Studies were deemed of acceptable quality (low to moderate risk of bias) if ≥ 70% of responses were "yes". 13 Publication bias was evaluated through funnel plots visual inspection. 14 – 16 The quality of evidence was assessed using the GRADE approach. 17 Meta-regression analyses were performed to assess how the magnitude of outcome variables varied based on study-level factors, including: 1) mean age, 2) study duration, 3) proportion of females, 4) International Scoring System stage III (ISS III), and 5) incidence of severe hematologic toxicities (neutropenia, lymphopenia, leukopenia, thrombocytopenia and anemia). 18 Each variable was calculated by comparing the proportion of patients in a specific frailty category to the one in a different frailty category within the same study (e.g., for ISS III in a single study, the ratio of ISS III fit patients to ISS III intermediate patients). To ensure robustness, we additionally performed leave-one-out analysis, by systematically excluding each study to explore the influence of individual studies on the pooled estimates. Between-study heterogeneity was tested using the 𝝌² test and reported according to the I² statistic. 19 3. Results 3.1 Study selection The bibliographic searches yielded 157 records. After the initial screening and triage process, 6 articles met the inclusion criteria and were included in the meta-analysis (Fig. 1). 3.2 Quality assessment and risk of bias The overall quality for all outcomes was deemed acceptable (low risk of bias) in most studies. All 6 studies (100%) reported ≥ 70% “yes” responses according to the critical appraisal tool adopted (S-Table 1). Hence, the overall certainty of the evidence for the risk of infection outcome was judged to be high (S-Table 2). 3.3 Studies’ and patients’ characteristics Table 1 summarizes the six included studies. Only one study had retrospective design, while five out of six were multicentric studies. Two studies provided data stratified by subgroups based on pharmacological treatment. Specifically, Mateos et al. 20 classified patients into those receiving daratumumab, bortezomib, melphalan, and prednisone (“Mateos DVMP”) and those treated with bortezomib, melphalan, and prednisone (“Mateos VMP”). Similarly, Facon et al. 21 categorized patients into those receiving daratumumab, lenalidomide, and dexamethasone (“Facon D-Rd”) and those treated with lenalidomide and dexamethasone (“Facon Rd”). The studies by Stege et al. 22 , which included frail patients, and Groen et al. 23 , which included intermediate patients, originate from the HOVON-143 trial, reporting outcomes on different frailty subgroups. Since the aim of the meta-analysis focuses on the comparison between non-frail and frail patients, we considered these two publications as a single study to avoid data duplication and ensure an appropriate population-level comparison (“Stege-Groen” in the Forrest plot). Moreover, as shown in Table 1, the treatment regimens administered to the participants vary across the included studies. To assess frailty, Mateos et al. 20 and Facon et al. 21 studies used the Simplified Frailty Scale, Nakazato et al. 24 applied the VES-13, Stege-Groen utilized the IMWG-FI, and Zhang et al. 25 employed the DynaFiT. Only for the study by Nakazato et al. 24 , we considered grade 3–4 pneumonia as infection data. The baseline patient population included 1,710 individuals (832 females, 50.6%), with a mean age of 73.8 years, of whom 1,161 completed the studies. The sample size of the studies varied, ranging from 47 patients to 369 patients. The duration of treatment varied across the studies, ranging from 12 to 36.4 months, with a mean duration of 24.8 months. At baseline, 375 patients (21.9%) were at ISS stage I, 727 (42.5%) at ISS stage II, and 606 (35.4%) at ISS stage III. A total number of 436 patients (25.5%) developed infections. 3.4 Risk of infection Compared to frail patients, non-frail (fit + intermediate) ones showed a RR of 0.77 (95% CI: 0.66–0.90), indicating a 23% lower risk of infection in non-frail patients. Heterogeneity analysis showed low between-study variability (Heterogeneity: Tau²=0.0; Chi²=3.52, df = 6, p = 0.74; I²=0%; Fig. 2). A sensitivity analysis, including only prospective studies—excluding the single retrospective study—was also performed. This pooled analysis yielded a relative risk (RR) of 0.78 (95% CI: 0.61–1.00) (S-Figure 1). A subgroup analysis was conducted to compare infection risk separately for the two categories of non-frail patients vs frail ones (Fig. 3). For the subgroup fit vs frail, the analysis revealed a RR of 0.67 (95% CI: 0.43–1.04). Heterogeneity analysis indicated moderate variability across studies (Tau²=0.07; Chi²=8.12, df = 5, p = 0.15; I²=38%; S-Figure 2). In the leave-one-out sensitivity analysis, when excluding the “Facon Rd Fi-Fr” from this subgroup meta-analysis, the RR dropped to 0.58 (95% CI: 0.39–0.86) as shown in S-Figure 3; heterogeneity reduced to 0% (Tau²= 0.00; Chi²=3.45, df = 4, p = 0.49; I²=0%; S-Figure 2). For intermediate vs frail, the analysis showed a RR of 0.86 (95% CI: 0.72–1.01); heterogeneity analysis suggested no substantial variability between studies (Tau²= 0.00; Chi²=1.67, df = 4, p = 0.80; I²=0%). Finally, subgroup analysis was conducted to compare infection risk in fit vs intermediate patients. The RR was 0.85 (95% CI: 0.51–1.40), with moderate heterogeneity: Tau²=0.06, Chi²=6.26, df = 4 (p = 0.18); I²=36% (Fig. 3). Given the moderate heterogeneity, we performed the meta-regression to identify potential sources of heterogeneity among studies. No substantial difference in infection risk was observed, according to study duration (p = 0.163), age (p = 0.598), proportion of female patients (p = 0.155), neutropenia (p = 0.294), lymphocytopenia (p = 0.238), leukopenia (p = 0.163), thrombocytopenia (p = 0.848) or anemia (p = 0.785) (S-Table 3). Nevertheless, the meta-regression revealed a significant association between ISS III stage (p = 0.007) and the association of frailty status with infection risk (Fig. 3). The variable was calculated by comparing the proportion of fit ISS III patients to the proportion of intermediate ISS III patients within the same study. The analysis revealed that studies with a lower ISS III ratio (i.e., a greater proportion of ISS stage III patients among intermediate patients) paradoxically showed a higher infection risk in the fit group compared to the intermediate group. This counterintuitive finding suggests that other factors—beyond disease stage—may be influencing infection susceptibility or that group imbalances might be confounding the comparison. To support this interpretation, a separate meta-regression was conducted using ISS stage I as a moderator, which showed no significant association (p = 0.363), reinforcing the inconsistency of the relationship and its limited explanatory value. 4. Discussion Frailty represents a health burden in the epidemiologic transition towards the aging of the global population, and it has emerged as a crucial factor influencing clinical outcomes in multiple myeloma, a disease that predominantly affects older adults with an already compromised physiological reserve. Frailty in MM patients is associated with an increased vulnerability to treatment-related toxicities, reduced survival, and increased susceptibility to severe infections. The immunosuppressive nature of MM, coupled with the impact of aging and comorbidities, predisposes frail patients to a significantly higher infection risk. In clinical practice, frailty assessment is increasingly integrated into treatment decision-making, helping to balance therapeutic efficacy with the risk of adverse effects. Several scoring systems, including the IMWG-FI, Simplified Frailty Score, and VES-13, have been developed to classify patients based on functional and clinical parameters, allowing for a more individualized therapeutic approach. However, despite the widespread use of these models of screening and classification, the precise impact of frailty on infection risk remains incompletely understood. A recent systematic review highlighted the impact of frailty in multiple myeloma clinical trials, where its prevalence widely varied due to differing assessment methods. Nevertheless, frail patients consistently showed worse outcomes, with lower progression-free survival and higher treatment toxicity, particularly infections and neutropenia. Although frailty was increasingly considered in trial analyses, the lack of standardized definitions limited comparability. 26 Moreover, a meta-analysis conducted by Balmaceda et al., 27 quantified the monthly risk of infection, pneumonia, and neutropenia in multiple myeloma patients across different treatment phases in clinical trials. It revealed that these complications remain significant in both frontline and relapsed/refractory settings, though lower in maintenance therapy. Notably, three-drug regimens did not necessarily increase infection risk compared to two-drug regimens, suggesting that patient-related factors played a major role. 28 Therefore, our meta-analysis was conducted to provide additional support to the existing literature by elucidating the risk of severe infections in multiple myeloma patients based on the frailty status. 29 By quantifying the impact of frailty on infection susceptibility, we sought to increase the understanding of how patient vulnerability influences treatment-related complications. The results of this meta-analysis confirm that frailty plays a significant role in determining infection risk in non-transplant-eligible NDMM patients. The overall RR for non-frail (fit plus intermediate-fit) versus frail patients was 0.77 (95% CI: 0.66–0.90), indicating that robust individuals had a 23% lower risk of developing severe infections (grade 3–4) compared to frail ones. The absence of heterogeneity observed (I²=0%) suggests that this association was consistent across the included studies, reinforcing the validity of the findings. These results support the notion that frail MM patients, due to their impaired immune function and poorer overall health status, are at a substantially increased risk of infection, underscoring the importance of infection prevention strategies in this population. A subgroup analysis further explored the differential infection risk among frailty categories. When comparing fit versus frail patients, the RR was 0.67 (95% CI: 0.43–1.04); although non-statistically significant (due to reduced power for comparison), these results suggest that fit patients may show lower infection risk than frail patients. The moderate heterogeneity (I²=38%) observed in this subgroup suggests that variations in frailty assessment tools and treatment regimens may have influenced the results. However, when the single outlier comparison group was removed, RR became 0.58 (95% CI: 0.39–0.86, p = 0.0005), meaning 42% lower infection risk than frail patients. These findings align with clinical observations that frail MM patients experience higher infection rates due to an impaired immune system, disease burden, and treatment-related immunosuppression. However, the results pointing towards potentially increased infection risk in intermediate patients, compared to fit patients, suggest that this group may have a distinct risk profile that requires further investigation. On the other hand, the intermediate vs frail comparison yielded an RR of 0.86 (95% CI: 0.73–1.01), indicating 14% lower infection risk than frail patients, albeit without reaching statistical significance. Similarly, when comparing fit and intermediate patients, the relative risk was 0.85 (95% CI: 0.51–1.40). While the lack of statistical significance due to wide 95%CI prevents definitive interpretation of these results, our findings point towards a potential slightly lower risk of infection in fit patients compared to intermediate ones, with moderate heterogeneity (I²=36%), suggesting some variability across studies. To explore possible sources of heterogeneity, we conducted a meta-regression using study-level moderators, including demographic characteristics, rates of hematologic toxicities, and the ISS staging system. In particular, we assessed the ratio between the compared frailty categories within each study. The analysis revealed that the proportion of ISS stage III patients was significantly associated with the effect size in the fit vs intermediate comparison (p = 0.007). Interestingly, studies with a lower ISS III ratio—indicating a higher proportion of ISS stage III patients among the intermediate group—paradoxically reported higher infection rates in fit patients than in intermediates. To assess the robustness of this unexpected finding, we also performed a separate meta-regression using ISS stage I as a moderator. This yielded no significant association (p = 0.363), contradicting the prior result and reinforcing the inconsistency of the observed relationship. These findings suggest that while ISS stage III may contribute to explaining heterogeneity between studies, it may not reliably account for infection risk itself. Importantly, the ISS system was developed as a prognostic tool for overall survival in multiple myeloma and has not been validated as a predictor of infection risk. Therefore, although the ISS III regression result may have exploratory value, indicating that the intermediate group was more severely ill in some studies, it may not offer a reliable mechanistic explanation for infection susceptibility. Beyond between-study variability, the clinical interpretability of this analysis is limited. From a practical standpoint, the observed paradox (i.e., higher infection risk in fit vs intermediate patients in certain studies) is more likely due to residual confounding, group imbalance, or chance, rather than a true biological signal related to disease stage. This paradox further highlights the complexity of frailty as a multidimensional construct that cannot be fully explained by disease stage alone. Frailty reflects not only biological age and tumor burden, but also encompasses functional capacity, comorbid conditions, cognitive status, and social determinants of health—all of which interplay to influence patient outcomes. Therefore, infection risk in multiple myeloma should be approached through a more holistic lens, recognizing that traditional disease-based stratification systems, such as the ISS, are insufficient to capture the full spectrum of patient vulnerability. These findings have important clinical implications. First, frailty assessment should be considered an essential component of baseline evaluation in all MM patients. Identifying frail individuals at diagnosis allows for the early implementation of supportive measures aimed at minimizing infection risk, such as prophylactic antibiotics, antiviral and antifungal agents when appropriate, immunoglobulin replacement in selected cases, and prompt vaccination against pathogens like influenza, pneumococcus, and varicella-zoster virus. 30 Second, the higher susceptibility to infections observed in frail patients underscores the need for personalized treatment approaches that go beyond standard risk stratification. Tailoring therapy to frailty status does not necessarily imply undertreatment; rather, it supports the need to optimize treatment intensity and supportive care to the patient’s overall resilience. For example, dose adjustments, alternative administration schedules, and the use of less immunosuppressive regimens may help maintain efficacy while reducing toxicity. Moreover, close clinical monitoring, early identification of infectious complications, and integration of geriatric and palliative care principles can significantly improve outcomes in this population. The intermediate frailty group also deserves special attention. Although traditionally considered as lying between fit and frail categories, our findings suggest that their infection risk closely approaches that of frail patients, indicating they may share similar vulnerabilities and should not be underestimated in clinical risk assessments. Future research should aim to characterize the clinical trajectory of intermediate patients better, identify predictors of progression toward frailty, and determine which interventions are most effective in preserving function and preventing complications in this group. Importantly, these observations reinforce the idea that frailty is not a static condition, but rather a dynamic state that can evolve. Periodic reassessment of frailty status during the disease course may allow clinicians to adjust treatment plans and supportive care strategies accordingly. Incorporating frailty monitoring into routine clinical follow-up could help preemptively identify patients at rising risk of infection, before clinical deterioration occurs. In light of these considerations, clinical trials in multiple myeloma should routinely include frailty stratification and report infection-related outcomes by frailty subgroups. This would enable a better understanding of how different therapeutic strategies perform across the frailty spectrum and would support evidence-based guidelines that are more attuned to patient heterogeneity. Moreover, novel interventional studies targeting frailty itself—through exercise, nutrition, or cognitive support—may ultimately reduce infection risk and improve quality of life and survival in older MM patients. 5. Conclusion Frailty plays a pivotal role in shaping the infection risk landscape in non-transplant-eligible multiple myeloma patients. Its assessment should become standard practice to guide not only treatment selection but also comprehensive supportive care planning. By addressing frailty proactively, clinicians can help mitigate preventable complications, optimize therapeutic benefit, and deliver truly patient-centred care. Further research is needed to refine frailty-specific interventions and to establish standardized approaches that can be readily implemented in both clinical trials and routine practice. In parallel, improving and harmonizing frailty stratification tools will be essential, particularly to better characterize intermediate patients, who often fall into a clinical grey zone with potentially underestimated risk. Abbreviations ASCT, autologous stem cell transplantation IMWG-FI, International Myeloma Working Group Frailty Index ISS, International Scoring System MM, multiple myeloma NDMM, newly diagnosed multiple myeloma RR, risk ratio VES-13, Vulnerable Elders Survey-13 Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the University of Bari Medical School (Study No. 1879, Protocol No. 808 approved on 02/10/2024). Consent for publication Not applicable. Availability of data and materials Data will be provided by the author upon reasonable request. Competing interests Authors declare no competing interests. Funding and Acknowledgements This study was funded by the Italian network of excellence for advanced diagnosis -INNOVA-, “Ministero della Salute” (code PNC-E3-2022-23683266 PNC-HLS-DA, to AGS) and by European Union - Next Generation EU - NRRP M6C2 - Investment 2.1 Enhancement and strengthening of biomedical research in the NHS (Italian Ministry of Health grant n. PNRR-POC-2022-12375862 to AGS). This research was also supported by the postgraduate school of Allergy and Clinical Immunology Program, Bari Aldo Moro University. Moreover, this study was funded by “Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale - PRIN” (code 2022ZKKWLW to AGS) and from the “Società Italiana di Medicina Interna—SIMI” 2023 Research Award (CAMEL to AGS). Author contributions FS, LB and AGS conceived the concept of the manuscript. FS and AGS did the article search. FS, AGS wrote the first manuscript draft. FS, PM, IR, MW, LB, RC, RV and MD extracted the data from each manuscript. FS, GA, GDG, RL, RV, RT performed the data analysis. GFR and LB critically reviewed the manuscript. AGS critically reviewed the manuscript, corrected the manuscript and secured financial support. All authors have read and approved the manuscript. References Kazandjian D (2016) Multiple myeloma epidemiology and survival: A unique malignancy. Semin Oncol 43:676–681 Palumbo A, Bringhen S, Mateos MV, Larocca A, Facon T, Kumar SK et al (2015) Geriatric assessment predicts survival and toxicities in elderly myeloma patients: an International Myeloma Working Group report. 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Stat Med 21:1539–1558 Mateos MV, Dimopoulos MA, Cavo M, Suzuki K, Knop S, Doyen C et al (2021) Daratumumab Plus Bortezomib, Melphalan, and Prednisone Versus Bortezomib, Melphalan, and Prednisone in Transplant-Ineligible Newly Diagnosed Multiple Myeloma: Frailty Subgroup Analysis of ALCYONE. Clin Lymphoma Myeloma Leuk 21:785–798 Facon T, Cook G, Usmani SZ, Hulin C, Kumar S, Plesner T et al (2022) Daratumumab plus lenalidomide and dexamethasone in transplant-ineligible newly diagnosed multiple myeloma: frailty subgroup analysis of MAIA. Leukemia 36:1066–1077 Stege CAM, Nasserinejad K, van der Spek E, Bilgin YM, Kentos A, Sohne M, van Kampen RJW et al (2021) Ixazomib, daratumumab, and low-dose dexamethasone in frail patients with newly diagnosed multiple myeloma: the Hovon 143 study. J Clin Oncol 39:2758–2767 Groen K, Stege CAM, Nasserinejad K, de Heer K, van Kampen RJW, Leys RBL et al (2023) Ixazomib, daratumumab and low-dose dexamethasone in intermediate-fit patients with newly diagnosed multiple myeloma: an open-label phase 2 trial. EClinicalMedicine 63:102167 Nakazato T, Hagihara M, Sahara N, Tamai Y, Ishii R, Tamaki S et al (2021) Phase II clinical trial of personalized VCD-VTD sequential therapy using the Vulnerable Elders Survey-13 (VES-13) for transplant-ineligible patients with newly diagnosed multiple myeloma. Ann Hematol 100:2745–2754 Zhang Y, Liang X, Xu W, Yi X, Hu R, Ma X, Yan Y et al (2024) Individualized dynamic frailty-tailored therapy (DynaFiT) in elderly patients with newly diagnosed multiple myeloma: a prospective study. J Hematol Oncol 17:48 Mian H, McCurdy A, Giri S, Grant S, Rochwerg B, Winks E et al (2023) The prevalence and outcomes of frail older adults in clinical trials in multiple myeloma: A systematic review. Blood Cancer J 13:6 Balmaceda N, Aziz M, Chandrasekar VT, McClune B, Kambhampati S, Shune L, Abdallah AO, Anwer F, Majeed A, Qazilbash M, Ganguly S, McGuirk J, Mohyuddin GR (2021) Infection risks in multiple myeloma: a systematic review and meta-analysis of randomized trials from 2015 to 2019. BMC Cancer 21:730 Solimando AG, Malerba E, Leone P, Prete M, Terragna C, Cavo M, Racanelli V (2022) Drug resistance in multiple myeloma: Soldiers and weapons in the bone marrow niche. Front Oncol 12:973836 Cook G, Pawlyn C, Cairns DA, Jackson GH (2022) Defining FiTNEss for treatment for multiple myeloma. Lancet Healthy Longev 3:e729–e730 Desantis V, Borrelli P, Panebianco T, Fusillo A, Bochicchio D et al (2024) Comprehensive analysis of clinical outcomes, infectious complications and microbiological data in newly diagnosed multiple myeloma patients: a retrospective observational study of 92 subjects. Clin Exp Med 24:137 Tables Table 1 is available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table1.docx Table 1 Supplementary.docx Supplementary 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-6797423","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":464865112,"identity":"0860b8c3-570b-4c33-9a02-6976d49ea2b7","order_by":0,"name":"Federico Spataro","email":"","orcid":"","institution":"Department of Precision and Regenerative Medicine and Ionian Area - DiMePRe-J, Guido Baccelli Unit of Internal Medicine, School of Medicine, University of Bari Aldo Moro, 70124 Bari, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Federico","middleName":"","lastName":"Spataro","suffix":""},{"id":464865113,"identity":"818c25b9-943b-47ee-a14f-3eaa07c5b78c","order_by":1,"name":"Giuseppe Armentaro","email":"","orcid":"","institution":"Geriatrics Division, \"Renato Dulbecco\" University Hospital of Catanzaro, 88100, Catanzaro, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Armentaro","suffix":""},{"id":464865114,"identity":"d4dac3c3-5ffe-41cc-ae1c-37b6dd60b1e1","order_by":2,"name":"Giuseppe Di Gioia","email":"","orcid":"","institution":"Liver Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"Di","lastName":"Gioia","suffix":""},{"id":464865115,"identity":"96672e0b-5f4c-4341-899e-f2f215325c03","order_by":3,"name":"Pierluigi Meloni","email":"","orcid":"","institution":"Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Pierluigi","middleName":"","lastName":"Meloni","suffix":""},{"id":464865116,"identity":"1d7dc871-5a89-45c4-a7a6-b8fc029b5262","order_by":4,"name":"Ilaria Rossi","email":"","orcid":"","institution":"Department of Medicine and Aging Science, \"Clinica Medica\" Institute, 'SS Annunziata' Hospital, \"G. d'Annunzio\" University, 66100 Chieti, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Ilaria","middleName":"","lastName":"Rossi","suffix":""},{"id":464865117,"identity":"4bdef3a4-6b4b-4922-99fa-e393b4bca10a","order_by":5,"name":"Michela Williams","email":"","orcid":"","institution":"Center for Basic and Clinical Immunology Research (CISI), WAO Center of Excellence, University of Naples Federico II, Naples, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Michela","middleName":"","lastName":"Williams","suffix":""},{"id":464865118,"identity":"47eff4e7-638c-4802-b2ff-7bdb0e20e65d","order_by":6,"name":"Giulio Francesco Romiti","email":"","orcid":"","institution":"Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Giulio","middleName":"Francesco","lastName":"Romiti","suffix":""},{"id":464865119,"identity":"709d2fa0-5982-4504-b755-fbdd8a9c7778","order_by":7,"name":"Rosanna Villani","email":"","orcid":"","institution":"Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Rosanna","middleName":"","lastName":"Villani","suffix":""},{"id":464865120,"identity":"9dd5cf11-7ae0-4c93-a5da-57f875bae2be","order_by":8,"name":"Leonardo Bencivenga","email":"","orcid":"","institution":"Department of Translational Medical Sciences, University of Naples \"Federico II\", Naples, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"","lastName":"Bencivenga","suffix":""},{"id":464865121,"identity":"aeafdfc3-1be5-4cdd-a212-830276a794b8","order_by":9,"name":"Antonio Giovanni Solimando","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYNACAxDB3HAASDI2sDcwMCQCMRtO5cwwLYxQLTwHoFpw6mGGMRgbIKREApSNQwv/7P6DjwsKbBjk2xsbD92ouSPbL/nG7MHDHXYMfPINWLVI3DnMbDzDII3B4MzBhsM5x54Zz5ydY26QeCYZt8NuJLNJ8xgcZjCQSARqYTucuOF2jplEYhszTi3yMC3yM0Ba/gG13DwD0lKPU4sBTAvDDaCW3Daglhs8IC2HcWoxvJFsbMxjkMYD9ktu32HjmT1pZRKJZ47zsLElYNUidyPx4WOePzZy8u3Nhz/nfDss289+eJvkzx3VcvLNB3D4HwJ4iBAZBaNgFIyCUUA0AAA56V3Ct3dZigAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Precision and Regenerative Medicine and Ionian Area - DiMePRe-J, Guido Baccelli Unit of Internal Medicine, School of Medicine, University of Bari Aldo Moro, 70124 Bari, Italy.","correspondingAuthor":true,"prefix":"","firstName":"Antonio","middleName":"Giovanni","lastName":"Solimando","suffix":""}],"badges":[],"createdAt":"2025-06-01 19:37:44","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6797423/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6797423/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83905418,"identity":"60cd343a-efbe-4983-9955-c80ffe0b25fa","added_by":"auto","created_at":"2025-06-04 10:12:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMedline: (((multiple myeloma[Title]) OR (myeloma[Title])) AND ((frailty[Text Word]) OR (frailty score[Text Word]))) NOT (review[Publication Type])\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e#1 Multiple myeloma [Ti] OR myeloma [Ti] - #2 frailty [TW] OR frailty score [TW] - #3 review [PT] - #1 AND #2 NOT #3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLILACS: ((\"multiple myeloma\" OR myeloma) AND (frailty OR \"frailty score\")) AND NOT review\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6797423/v1/e4a5fe19115e1246c51fda93.png"},{"id":83905916,"identity":"3bbb1fc8-7a05-4fde-a6df-bc699a205219","added_by":"auto","created_at":"2025-06-04 10:20:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":231237,"visible":true,"origin":"","legend":"\u003cp\u003eFi, fit; Fr, frail; In, intermediate; Fi/In, sum of fit and intermediate patients representing the non-frail group; RR, risk ratio.\u003c/p\u003e\n\u003cp\u003eA) meta-analysis on non-frail \u003cem\u003evs\u003c/em\u003e frail multiple myeloma patient assessing the risk of infection, reported as RR. B) funnel plot under random effect model.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6797423/v1/ca562c1e8885c8ba0b6381b0.png"},{"id":83905421,"identity":"895ae8c1-b7f2-4852-84f9-c2811d2d96da","added_by":"auto","created_at":"2025-06-04 10:12:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":393767,"visible":true,"origin":"","legend":"\u003cp\u003eFi, fit; Fr, frail; In, intermediate; RR, risk ratio.\u003c/p\u003e\n\u003cp\u003eA) meta-analysis on fit versus frail subgroup assessing the risk of infection, reported as RR. B) meta-analysis on intermediate versus frail subgroup assessing the risk of infection, reported as RR. C) meta-analysis on fit versus\u003cem\u003e \u003c/em\u003eintermediate assessing the risk of infection reported as RR. D) meta-regression analysis for fit versus intermediate subgroup analysis about infection risk based on the ISS stage III reported in each study. ISS III variable was calculated by comparing the proportion of fit ISS III patients to the proportion of intermediate ISS III within the same study.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6797423/v1/b2d2084dbc521e715a0cf696.png"},{"id":83906656,"identity":"e9949ffb-b479-4182-b715-bb83c94e947b","added_by":"auto","created_at":"2025-06-04 10:28:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1484076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6797423/v1/a27feb36-42ad-4295-a7dc-236d28db2619.pdf"},{"id":83905424,"identity":"19cdff96-f11a-41c8-a787-dc68db963cd4","added_by":"auto","created_at":"2025-06-04 10:12:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4544635,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6797423/v1/3beee7f23b9eb91c18215a6f.docx"},{"id":83905425,"identity":"ef696f5e-1e32-4117-87a8-674061971308","added_by":"auto","created_at":"2025-06-04 10:12:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5485165,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary\u003c/p\u003e","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6797423/v1/7b5c96a5e74728739500d5e2.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImpact of frailty on infection risk in non-transplant eligible multiple myeloma patients: a systematic review and meta-analysis\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eMultiple myeloma (MM) is a hematologic malignancy characterized by clonal plasma cell proliferation in the bone marrow, leading to immune suppression, bone destruction, and end-organ damage. The disease predominantly affects older adults, with a median age at diagnosis of approximately 66\u0026ndash;70 years.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Given the aging population, an increasing number of MM patients are considered ineligible for high-dose chemotherapy with autologous stem cell transplantation (ASCT), due to comorbidities and frailty. This makes frailty assessment a crucial step in treatment decision-making.\u003c/p\u003e \u003cp\u003eVarious tools have been developed to screen for frailty in MM, including the International Myeloma Working Group Frailty Index (IMWG-FI), the Simplified Frailty Score, and the Vulnerable Elders Survey-13 (VES-13). The IMWG-FI classifies patients into three categories: fit, intermediate, and frail, based on age, comorbidities, and functional status. This classification aids in tailoring treatment strategies, as frail patients are burdened with a higher risk of experiencing adverse events and reduced tolerance to standard therapy.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The Simplified Frailty Score, an alternative model, incorporates age, performance status, and comorbidities to provide a quick and efficient assessment of patient frailty.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The VES-13 is a 13-item self-administered questionnaire originally developed for the geriatric population to predict functional decline and mortality. It has been adapted in MM to stratify patients into fit (VES-13 score\u0026thinsp;\u0026lt;\u0026thinsp;3) and vulnerable/frail (VES-13 score\u0026thinsp;\u0026ge;\u0026thinsp;3) categories, with treatment adjustments made accordingly to minimize toxicity while maintaining therapeutic efficacy.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOne of the most severe complications in MM is infection, particularly grade 3\u0026ndash;4 infections, which are associated with increased morbidity, prolonged hospitalizations, and higher mortality rates.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e MM patients are at increased risk of infections due to underlying immune dysfunction, bone marrow suppression, and treatment-related immunosuppression.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Several predictive models have been proposed to estimate infection risk, including the FIRST score, GEM-PETHEMA score, and IRMM score, which incorporate laboratory and clinical parameters to stratify patients into different risk categories.\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e However, these models do not fully integrate frailty classification, underscoring the need for further investigation into the independent role of frailty in infection risk.\u003c/p\u003e \u003cp\u003eGiven the growing emphasis on frailty assessments in MM clinical trials, standardizing frailty definitions and their impact on infection risk and overall patient outcomes remains a priority. We performed a systematic review and meta-analysis to assess the impact of frailty on the risk of severe infections (grade 3\u0026ndash;4) in newly diagnosed multiple myeloma (NDMM) patients ineligible for ASCT, by comparing infection rates among fit, intermediate, and frail individuals.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Search strategy and selection criteria\u003c/h2\u003e \u003cp\u003eThis systematic review and meta-analysis were performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The study protocol is registered in PROSPERO (registration ID: CRD420250654904). We conducted a comprehensive search of the MEDLINE and LILACS databases from inception (no backwards time limit) to February 1st, 2025, to identify studies evaluating the risk of infections in frailty groups of NDMM patients ineligible for ASCT. The complete list of search terms is detailed in Fig.\u0026nbsp;1 of the appendix.\u003c/p\u003e \u003cp\u003eProspective and retrospective studies were included according to the following inclusion criteria: (1) enrolled non-transplant eligible NDMM patients, (2) classify patients into frailty categories (fit, intermediate, and frail, or non-frail and frail), and (3) reported the number of patients who developed grade 3\u0026ndash;4 infections. Studies lacking the necessary data were excluded.\u003c/p\u003e \u003cp\u003eNo restrictions were applied concerning language or publication date. Additionally, reference lists of included studies, citations, and recent reviews were meticulously screened to identify any further relevant articles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection process\u003c/h2\u003e \u003cp\u003eTitles and abstracts were screened, full texts reviewed, data extracted, and the risk of bias or study quality assessed independently by two reviewers (FS and AGS) using a standardized, web-based system (Rayyan).\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Any disagreements were resolved through consensus. For each study included, we extracted data on study characteristics, settings, eligibility criteria, populations studied, interventions, and reported outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcomes\u003c/h2\u003e \u003cp\u003eThe risk of infection was the main outcome, and it was calculated as the ratio between the number of patients who developed grade 3\u0026ndash;4 infections and the total number of patients within the same frailty group, in each study.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Non-frail patients included subjects classified as fit or intermediate (\u0026ldquo;intermediate\u0026rdquo; in this meta-analysis).\u003c/p\u003e \u003cp\u003eThe risk of infection was assessed using the Risk Ratio (RR) as the effect measure. The RR for each study was obtained through meta-analytic pooling, by comparing the proportion of patients who developed grade 3\u0026ndash;4 infections in the non-frail groups to the proportion in the frail groups (non-frail vs frail). Moreover, when possible, an additional analysis was performed comparing fit \u003cem\u003evs\u003c/em\u003e intermediate patients.\u003c/p\u003e \u003cp\u003eIf a study did not report overall infection data, grade 3\u0026ndash;4 pneumonia data were used instead. Given that pneumonia is a major infectious complication in multiple myeloma, it was considered a reasonable proxy for overall infection risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis and risk of bias assessment\u003c/h2\u003e \u003cp\u003eSummary measures were pooled using the DerSimonian and Laird random-effects model, with heterogeneity estimated via the Mantel-Haenszel method. Effect size from individual studies were pooled using RR.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe summary of findings tables was created through the GRADEpro GDT software (available at \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003egradepro.org\u003c/span\u003e), and all statistical analyses were performed with ProMeta 3.0 and RevMan software. To assess study quality, we applied the Quality Appraisal of Case Series Studies Checklist developed by the Institute of Health Economics (IHE) (accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ihe.ca/research-programs/rmd/cssqac/cssqac-about\u003c/span\u003e\u003cspan address=\"http://www.ihe.ca/research-programs/rmd/cssqac/cssqac-about\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Responses were categorized as \"yes,\" \"unclear/partial,\" or \"no.\" Studies were deemed of acceptable quality (low to moderate risk of bias) if\u0026thinsp;\u0026ge;\u0026thinsp;70% of responses were \"yes\".\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePublication bias was evaluated through funnel plots visual inspection.\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The quality of evidence was assessed using the GRADE approach.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMeta-regression analyses were performed to assess how the magnitude of outcome variables varied based on study-level factors, including: 1) mean age, 2) study duration, 3) proportion of females, 4) International Scoring System stage III (ISS III), and 5) incidence of severe hematologic toxicities (neutropenia, lymphopenia, leukopenia, thrombocytopenia and anemia).\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Each variable was calculated by comparing the proportion of patients in a specific frailty category to the one in a different frailty category within the same study (e.g., for ISS III in a single study, the ratio of ISS III fit patients to ISS III intermediate patients).\u003c/p\u003e \u003cp\u003eTo ensure robustness, we additionally performed leave-one-out analysis, by systematically excluding each study to explore the influence of individual studies on the pooled estimates. Between-study heterogeneity was tested using the \u0026#120652;\u0026sup2; test and reported according to the \u003cem\u003eI\u0026sup2;\u003c/em\u003e statistic.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study selection\u003c/h2\u003e \u003cp\u003eThe bibliographic searches yielded 157 records. After the initial screening and triage process, 6 articles met the inclusion criteria and were included in the meta-analysis (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Quality assessment and risk of bias\u003c/h2\u003e \u003cp\u003eThe overall quality for all outcomes was deemed acceptable (low risk of bias) in most studies. All 6 studies (100%) reported\u0026thinsp;\u0026ge;\u0026thinsp;70% \u0026ldquo;yes\u0026rdquo; responses according to the critical appraisal tool adopted (S-Table\u0026nbsp;1). Hence, the overall certainty of the evidence for the risk of infection outcome was judged to be high (S-Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Studies\u0026rsquo; and patients\u0026rsquo; characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;1 summarizes the six included studies. Only one study had retrospective design, while five out of six were multicentric studies.\u003c/p\u003e \u003cp\u003eTwo studies provided data stratified by subgroups based on pharmacological treatment. Specifically, Mateos et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e classified patients into those receiving daratumumab, bortezomib, melphalan, and prednisone (\u0026ldquo;Mateos DVMP\u0026rdquo;) and those treated with bortezomib, melphalan, and prednisone (\u0026ldquo;Mateos VMP\u0026rdquo;). Similarly, Facon et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e categorized patients into those receiving daratumumab, lenalidomide, and dexamethasone (\u0026ldquo;Facon D-Rd\u0026rdquo;) and those treated with lenalidomide and dexamethasone (\u0026ldquo;Facon Rd\u0026rdquo;). The studies by Stege et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003c/sup\u003e which included frail patients, and Groen et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, which included intermediate patients, originate from the HOVON-143 trial, reporting outcomes on different frailty subgroups. Since the aim of the meta-analysis focuses on the comparison between non-frail and frail patients, we considered these two publications as a single study to avoid data duplication and ensure an appropriate population-level comparison (\u0026ldquo;Stege-Groen\u0026rdquo; in the Forrest plot).\u003c/p\u003e \u003cp\u003eMoreover, as shown in Table\u0026nbsp;1, the treatment regimens administered to the participants vary across the included studies.\u003c/p\u003e \u003cp\u003eTo assess frailty, Mateos et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and Facon et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e studies used the Simplified Frailty Scale, Nakazato et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e applied the VES-13, Stege-Groen utilized the IMWG-FI, and Zhang et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e employed the DynaFiT.\u003c/p\u003e \u003cp\u003eOnly for the study by Nakazato et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, we considered grade 3\u0026ndash;4 pneumonia as infection data.\u003c/p\u003e \u003cp\u003eThe baseline patient population included 1,710 individuals (832 females, 50.6%), with a mean age of 73.8 years, of whom 1,161 completed the studies. The sample size of the studies varied, ranging from 47 patients to 369 patients. The duration of treatment varied across the studies, ranging from 12 to 36.4 months, with a mean duration of 24.8 months. At baseline, 375 patients (21.9%) were at ISS stage I, 727 (42.5%) at ISS stage II, and 606 (35.4%) at ISS stage III. A total number of 436 patients (25.5%) developed infections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Risk of infection\u003c/h2\u003e \u003cp\u003eCompared to frail patients, non-frail (fit\u0026thinsp;+\u0026thinsp;intermediate) ones showed a RR of 0.77 (95% CI: 0.66\u0026ndash;0.90), indicating a 23% lower risk of infection in non-frail patients. Heterogeneity analysis showed low between-study variability (Heterogeneity: Tau\u0026sup2;=0.0; Chi\u0026sup2;=3.52, df\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;=\u0026thinsp;0.74; I\u0026sup2;=0%; Fig.\u0026nbsp;2). A sensitivity analysis, including only prospective studies\u0026mdash;excluding the single retrospective study\u0026mdash;was also performed. This pooled analysis yielded a relative risk (RR) of 0.78 (95% CI: 0.61\u0026ndash;1.00) (S-Figure 1).\u003c/p\u003e \u003cp\u003eA subgroup analysis was conducted to compare infection risk separately for the two categories of non-frail patients vs frail ones (Fig.\u0026nbsp;3). For the subgroup fit \u003cem\u003evs\u003c/em\u003e frail, the analysis revealed a RR of 0.67 (95% CI: 0.43\u0026ndash;1.04). Heterogeneity analysis indicated moderate variability across studies (Tau\u0026sup2;=0.07; Chi\u0026sup2;=8.12, df\u0026thinsp;=\u0026thinsp;5, p\u0026thinsp;=\u0026thinsp;0.15; I\u0026sup2;=38%; S-Figure 2). In the leave-one-out sensitivity analysis, when excluding the \u0026ldquo;Facon Rd Fi-Fr\u0026rdquo; from this subgroup meta-analysis, the RR dropped to 0.58 (95% CI: 0.39\u0026ndash;0.86) as shown in S-Figure 3; heterogeneity reduced to 0% (Tau\u0026sup2;= 0.00; Chi\u0026sup2;=3.45, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.49; I\u0026sup2;=0%; S-Figure 2). For intermediate \u003cem\u003evs\u003c/em\u003e frail, the analysis showed a RR of 0.86 (95% CI: 0.72\u0026ndash;1.01); heterogeneity analysis suggested no substantial variability between studies (Tau\u0026sup2;= 0.00; Chi\u0026sup2;=1.67, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.80; I\u0026sup2;=0%).\u003c/p\u003e \u003cp\u003eFinally, subgroup analysis was conducted to compare infection risk in fit \u003cem\u003evs\u003c/em\u003e intermediate patients. The RR was 0.85 (95% CI: 0.51\u0026ndash;1.40), with moderate heterogeneity: Tau\u0026sup2;=0.06, Chi\u0026sup2;=6.26, df\u0026thinsp;=\u0026thinsp;4 (p\u0026thinsp;=\u0026thinsp;0.18); I\u0026sup2;=36% (Fig.\u0026nbsp;3). Given the moderate heterogeneity, we performed the meta-regression to identify potential sources of heterogeneity among studies. No substantial difference in infection risk was observed, according to study duration (p\u0026thinsp;=\u0026thinsp;0.163), age (p\u0026thinsp;=\u0026thinsp;0.598), proportion of female patients (p\u0026thinsp;=\u0026thinsp;0.155), neutropenia (p\u0026thinsp;=\u0026thinsp;0.294), lymphocytopenia (p\u0026thinsp;=\u0026thinsp;0.238), leukopenia (p\u0026thinsp;=\u0026thinsp;0.163), thrombocytopenia (p\u0026thinsp;=\u0026thinsp;0.848) or anemia (p\u0026thinsp;=\u0026thinsp;0.785) (S-Table\u0026nbsp;3). Nevertheless, the meta-regression revealed a significant association between ISS III stage (p\u0026thinsp;=\u0026thinsp;0.007) and the association of frailty status with infection risk (Fig.\u0026nbsp;3). The variable was calculated by comparing the proportion of fit ISS III patients to the proportion of intermediate ISS III patients within the same study. The analysis revealed that studies with a lower ISS III ratio (i.e., a greater proportion of ISS stage III patients among intermediate patients) paradoxically showed a higher infection risk in the fit group compared to the intermediate group. This counterintuitive finding suggests that other factors\u0026mdash;beyond disease stage\u0026mdash;may be influencing infection susceptibility or that group imbalances might be confounding the comparison. To support this interpretation, a separate meta-regression was conducted using ISS stage I as a moderator, which showed no significant association (p\u0026thinsp;=\u0026thinsp;0.363), reinforcing the inconsistency of the relationship and its limited explanatory value.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFrailty represents a health burden in the epidemiologic transition towards the aging of the global population, and it has emerged as a crucial factor influencing clinical outcomes in multiple myeloma, a disease that predominantly affects older adults with an already compromised physiological reserve. Frailty in MM patients is associated with an increased vulnerability to treatment-related toxicities, reduced survival, and increased susceptibility to severe infections. The immunosuppressive nature of MM, coupled with the impact of aging and comorbidities, predisposes frail patients to a significantly higher infection risk. In clinical practice, frailty assessment is increasingly integrated into treatment decision-making, helping to balance therapeutic efficacy with the risk of adverse effects. Several scoring systems, including the IMWG-FI, Simplified Frailty Score, and VES-13, have been developed to classify patients based on functional and clinical parameters, allowing for a more individualized therapeutic approach. However, despite the widespread use of these models of screening and classification, the precise impact of frailty on infection risk remains incompletely understood. A recent systematic review highlighted the impact of frailty in multiple myeloma clinical trials, where its prevalence widely varied due to differing assessment methods. Nevertheless, frail patients consistently showed worse outcomes, with lower progression-free survival and higher treatment toxicity, particularly infections and neutropenia. Although frailty was increasingly considered in trial analyses, the lack of standardized definitions limited comparability.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMoreover, a meta-analysis conducted by Balmaceda et al.,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e quantified the monthly risk of infection, pneumonia, and neutropenia in multiple myeloma patients across different treatment phases in clinical trials. It revealed that these complications remain significant in both frontline and relapsed/refractory settings, though lower in maintenance therapy. Notably, three-drug regimens did not necessarily increase infection risk compared to two-drug regimens, suggesting that patient-related factors played a major role.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTherefore, our meta-analysis was conducted to provide additional support to the existing literature by elucidating the risk of severe infections in multiple myeloma patients based on the frailty status.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e By quantifying the impact of frailty on infection susceptibility, we sought to increase the understanding of how patient vulnerability influences treatment-related complications. The results of this meta-analysis confirm that frailty plays a significant role in determining infection risk in non-transplant-eligible NDMM patients. The overall RR for non-frail (fit plus intermediate-fit) versus frail patients was 0.77 (95% CI: 0.66\u0026ndash;0.90), indicating that robust individuals had a 23% lower risk of developing severe infections (grade 3\u0026ndash;4) compared to frail ones. The absence of heterogeneity observed (I\u0026sup2;=0%) suggests that this association was consistent across the included studies, reinforcing the validity of the findings. These results support the notion that frail MM patients, due to their impaired immune function and poorer overall health status, are at a substantially increased risk of infection, underscoring the importance of infection prevention strategies in this population.\u003c/p\u003e \u003cp\u003eA subgroup analysis further explored the differential infection risk among frailty categories. When comparing fit versus frail patients, the RR was 0.67 (95% CI: 0.43\u0026ndash;1.04); although non-statistically significant (due to reduced power for comparison), these results suggest that fit patients may show lower infection risk than frail patients. The moderate heterogeneity (I\u0026sup2;=38%) observed in this subgroup suggests that variations in frailty assessment tools and treatment regimens may have influenced the results. However, when the single outlier comparison group was removed, RR became 0.58 (95% CI: 0.39\u0026ndash;0.86, p\u0026thinsp;=\u0026thinsp;0.0005), meaning 42% lower infection risk than frail patients. These findings align with clinical observations that frail MM patients experience higher infection rates due to an impaired immune system, disease burden, and treatment-related immunosuppression. However, the results pointing towards potentially increased infection risk in intermediate patients, compared to fit patients, suggest that this group may have a distinct risk profile that requires further investigation.\u003c/p\u003e \u003cp\u003eOn the other hand, the intermediate \u003cem\u003evs\u003c/em\u003e frail comparison yielded an RR of 0.86 (95% CI: 0.73\u0026ndash;1.01), indicating 14% lower infection risk than frail patients, albeit without reaching statistical significance. Similarly, when comparing fit and intermediate patients, the relative risk was 0.85 (95% CI: 0.51\u0026ndash;1.40). While the lack of statistical significance due to wide 95%CI prevents definitive interpretation of these results, our findings point towards a potential slightly lower risk of infection in fit patients compared to intermediate ones, with moderate heterogeneity (I\u0026sup2;=36%), suggesting some variability across studies.\u003c/p\u003e \u003cp\u003eTo explore possible sources of heterogeneity, we conducted a meta-regression using study-level moderators, including demographic characteristics, rates of hematologic toxicities, and the ISS staging system. In particular, we assessed the ratio between the compared frailty categories within each study. The analysis revealed that the proportion of ISS stage III patients was significantly associated with the effect size in the fit \u003cem\u003evs\u003c/em\u003e intermediate comparison (p\u0026thinsp;=\u0026thinsp;0.007). Interestingly, studies with a lower ISS III ratio\u0026mdash;indicating a higher proportion of ISS stage III patients among the intermediate group\u0026mdash;paradoxically reported higher infection rates in fit patients than in intermediates. To assess the robustness of this unexpected finding, we also performed a separate meta-regression using ISS stage I as a moderator. This yielded no significant association (p\u0026thinsp;=\u0026thinsp;0.363), contradicting the prior result and reinforcing the inconsistency of the observed relationship. These findings suggest that while ISS stage III may contribute to explaining heterogeneity between studies, it may not reliably account for infection risk itself. Importantly, the ISS system was developed as a prognostic tool for overall survival in multiple myeloma and has not been validated as a predictor of infection risk. Therefore, although the ISS III regression result may have exploratory value, indicating that the intermediate group was more severely ill in some studies, it may not offer a reliable mechanistic explanation for infection susceptibility. Beyond between-study variability, the clinical interpretability of this analysis is limited. From a practical standpoint, the observed paradox (i.e., higher infection risk in fit \u003cem\u003evs\u003c/em\u003e intermediate patients in certain studies) is more likely due to residual confounding, group imbalance, or chance, rather than a true biological signal related to disease stage.\u003c/p\u003e \u003cp\u003eThis paradox further highlights the complexity of frailty as a multidimensional construct that cannot be fully explained by disease stage alone. Frailty reflects not only biological age and tumor burden, but also encompasses functional capacity, comorbid conditions, cognitive status, and social determinants of health\u0026mdash;all of which interplay to influence patient outcomes. Therefore, infection risk in multiple myeloma should be approached through a more holistic lens, recognizing that traditional disease-based stratification systems, such as the ISS, are insufficient to capture the full spectrum of patient vulnerability.\u003c/p\u003e \u003cp\u003eThese findings have important clinical implications. First, frailty assessment should be considered an essential component of baseline evaluation in all MM patients. Identifying frail individuals at diagnosis allows for the early implementation of supportive measures aimed at minimizing infection risk, such as prophylactic antibiotics, antiviral and antifungal agents when appropriate, immunoglobulin replacement in selected cases, and prompt vaccination against pathogens like influenza, pneumococcus, and varicella-zoster virus.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSecond, the higher susceptibility to infections observed in frail patients underscores the need for personalized treatment approaches that go beyond standard risk stratification. Tailoring therapy to frailty status does not necessarily imply undertreatment; rather, it supports the need to optimize treatment intensity and supportive care to the patient\u0026rsquo;s overall resilience. For example, dose adjustments, alternative administration schedules, and the use of less immunosuppressive regimens may help maintain efficacy while reducing toxicity. Moreover, close clinical monitoring, early identification of infectious complications, and integration of geriatric and palliative care principles can significantly improve outcomes in this population.\u003c/p\u003e \u003cp\u003eThe intermediate frailty group also deserves special attention. Although traditionally considered as lying between fit and frail categories, our findings suggest that their infection risk closely approaches that of frail patients, indicating they may share similar vulnerabilities and should not be underestimated in clinical risk assessments. Future research should aim to characterize the clinical trajectory of intermediate patients better, identify predictors of progression toward frailty, and determine which interventions are most effective in preserving function and preventing complications in this group.\u003c/p\u003e \u003cp\u003eImportantly, these observations reinforce the idea that frailty is not a static condition, but rather a dynamic state that can evolve. Periodic reassessment of frailty status during the disease course may allow clinicians to adjust treatment plans and supportive care strategies accordingly. Incorporating frailty monitoring into routine clinical follow-up could help preemptively identify patients at rising risk of infection, before clinical deterioration occurs.\u003c/p\u003e \u003cp\u003eIn light of these considerations, clinical trials in multiple myeloma should routinely include frailty stratification and report infection-related outcomes by frailty subgroups. This would enable a better understanding of how different therapeutic strategies perform across the frailty spectrum and would support evidence-based guidelines that are more attuned to patient heterogeneity. Moreover, novel interventional studies targeting frailty itself\u0026mdash;through exercise, nutrition, or cognitive support\u0026mdash;may ultimately reduce infection risk and improve quality of life and survival in older MM patients.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eFrailty plays a pivotal role in shaping the infection risk landscape in non-transplant-eligible multiple myeloma patients. Its assessment should become standard practice to guide not only treatment selection but also comprehensive supportive care planning. By addressing frailty proactively, clinicians can help mitigate preventable complications, optimize therapeutic benefit, and deliver truly patient-centred care. Further research is needed to refine frailty-specific interventions and to establish standardized approaches that can be readily implemented in both clinical trials and routine practice. In parallel, improving and harmonizing frailty stratification tools will be essential, particularly to better characterize intermediate patients, who often fall into a clinical grey zone with potentially underestimated risk.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASCT, autologous stem cell transplantation\u003c/p\u003e \u003cp\u003eIMWG-FI, International Myeloma Working Group Frailty Index\u003c/p\u003e \u003cp\u003eISS, International Scoring System\u003c/p\u003e \u003cp\u003eMM, multiple myeloma\u003c/p\u003e \u003cp\u003eNDMM, newly diagnosed multiple myeloma\u003c/p\u003e \u003cp\u003eRR, risk ratio\u003c/p\u003e \u003cp\u003eVES-13, Vulnerable Elders Survey-13\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the University of Bari Medical School (Study No. 1879, Protocol No. 808 approved on 02/10/2024).\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be provided by the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Italian network of excellence for advanced diagnosis -INNOVA-, \u0026ldquo;Ministero della Salute\u0026rdquo; (code PNC-E3-2022-23683266 PNC-HLS-DA, to AGS) and by European Union - Next Generation EU - NRRP M6C2 - Investment 2.1 Enhancement and strengthening of biomedical research in the NHS (Italian Ministry of Health grant n. PNRR-POC-2022-12375862 to AGS). This research was also supported by the postgraduate school of Allergy and Clinical Immunology Program, Bari Aldo Moro University. Moreover, this study was funded by \u0026ldquo;Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale - PRIN\u0026rdquo; (code 2022ZKKWLW to AGS) and from the \u0026ldquo;Società Italiana di Medicina Interna\u0026mdash;SIMI\u0026rdquo; 2023 Research Award (CAMEL to AGS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFS, LB and AGS conceived the concept of the manuscript. FS and AGS did the article search. FS, AGS wrote the first manuscript draft. FS, PM, IR, MW, LB, RC, RV and MD extracted the data from each manuscript. FS, GA, GDG, RL, RV, RT performed the data analysis. GFR and LB critically reviewed the manuscript. AGS critically reviewed the manuscript, corrected the manuscript and secured financial support. All authors have read and approved the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKazandjian D (2016) Multiple myeloma epidemiology and survival: A unique malignancy. Semin Oncol 43:676\u0026ndash;681\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalumbo A, Bringhen S, Mateos MV, Larocca A, Facon T, Kumar SK et al (2015) Geriatric assessment predicts survival and toxicities in elderly myeloma patients: an International Myeloma Working Group report. Blood 125:2068\u0026ndash;2074\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFacon T, Dimopoulos MA, Meuleman N, Belch A, Mohty M, Chen WM et al (2020) A simplified frailty scale predicts outcomes in transplant-ineligible patients with newly diagnosed multiple myeloma treated in the FIRST (MM-020) trial. 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Lancet Healthy Longev 3:e729\u0026ndash;e730\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesantis V, Borrelli P, Panebianco T, Fusillo A, Bochicchio D et al (2024) Comprehensive analysis of clinical outcomes, infectious complications and microbiological data in newly diagnosed multiple myeloma patients: a retrospective observational study of 92 subjects. Clin Exp Med 24:137\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"009aa9a5-8e41-4d69-9853-610e2850e847","identifier":"10.13039/501100003196","name":"Ministero della Salute","awardNumber":"PNC-E3-2022-23683266 PNC-HLS-DA","order_by":0},{"identity":"fcac6c36-8361-4830-8dd9-8c01597fa5c4","identifier":"10.13039/501100003196","name":"Ministero della Salute","awardNumber":"PNRR-POC-2022-12375862","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Bari Aldo Moro","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"multiple myeloma, frailty, infection, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-6797423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6797423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple myeloma (MM), a plasma-cell neoplasm predominantly diagnosed in the elderly, is profoundly influenced by patients’ frailty status. Yet, the precise relationship between this geriatric vulnerability and the risk of life‐threatening (grade 3–4) infections in autologous stem cell transplant (ASCT)‐ineligible MM cohorts remains largely uncharted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a systematic review and meta‑analysis to uncover how frailty fuels infection risk in newly diagnosed multiple myeloma (NDMM) patients who are not eligible for ASCT. We included studies that classified participants as fit, intermediate, or frail and reported their infection outcomes. By comparing the risk of severe infections across these frailty categories, we exposed the hidden cost of this geriatric vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross six studies (n = 1,710), frailty afflicted 46.4% of patients. Non‑frail individuals experienced a 23% lower risk of severe infection than their frail counterparts (RR 0.77; 95% CI 0.66–0.90). In subgroup analyses, fit patients slashed their infection risk by 33% versus frail peers (RR 0.67; 95% CI 0.43–1.04), while those deemed intermediate registered a 14% reduction (RR 0.86; 95% CI 0.72–1.01). Directly comparing fit to intermediate categories yielded an RR of 0.85 (95% CI 0.51–1.40), spotlighting how even modest dips in resilience can tip the scales toward vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty dramatically raises infection risk in ASCT‑ineligible NDMM patients, with the frailest facing the greatest danger. Even more striking, the intermediate group’s infection rates align more closely with the frail than the fit, suggesting our current mid‑tier label may be hiding serious vulnerability. These results underscore the urgency of embedding comprehensive frailty assessments into routine care and refining stratification tools to accurately flag high‑risk patients and enable truly personalized, preemptive infection management.\u003c/p\u003e","manuscriptTitle":"Impact of frailty on infection risk in non-transplant eligible multiple myeloma patients: a systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 10:12:44","doi":"10.21203/rs.3.rs-6797423/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":"d374ed5d-567b-48c7-bac1-4684fc71a23b","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49351881,"name":"Hematology"}],"tags":[],"updatedAt":"2025-06-04T10:12:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 10:12:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6797423","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6797423","identity":"rs-6797423","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}