Clinical and Laboratory Predictors of Mortality in Multiple Myeloma

Clinical and Laboratory Predictors of Mortality in Multiple Myeloma | 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 Clinical and Laboratory Predictors of Mortality in Multiple Myeloma Nermin Keni Begendi, Mustafa Duran, Çiğdem Özdemir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8918370/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Multiple myeloma (MM) is a plasma cell malignancy characterized by bone marrow infiltration and organ dysfunction. Despite advances in treatment, the disease exhibits heterogeneous clinical outcomes, necessitating reliable prognostic markers. The prognostic significance of bone marrow plasma cell (PC) percentage and reticulin fibrosis in predicting mortality remains unclear and controversial in the literature. Methods We conducted a retrospective analysis of 97 patients diagnosed with multiple myeloma. Bone marrow PC percentage and reticulin fibrosis grade were assessed alongside comprehensive clinical, hematological, and biochemical parameters. Cox proportional hazards regression analysis was employed to identify independent predictors of mortality. Median follow-up and survival outcomes were calculated using Kaplan-Meier methodology. Results The median overall survival was 44.5 months. Higher bone marrow PC percentages correlated with increased reticulin fibrosis grade; however, neither parameter independently predicted mortality in multivariate analysis. Advanced age (> 68.5 years) and low platelet count (< 190.5×10³/µL) emerged as significant independent predictors of mortality. Additionally, elevated β2-microglobulin levels, renal dysfunction, elevated lactate dehydrogenase (LDH), and advanced International Staging System (ISS) stage were independently associated with increased mortality risk. Conclusion Age, platelet count, and established biochemical markers (β2-microglobulin, LDH, renal function) are reliable predictors of mortality in multiple myeloma, whereas bone marrow PC percentage and reticulin fibrosis grade do not independently predict survival outcomes. These findings support the prioritization of readily available clinical and laboratory parameters over bone marrow histological features for prognostication in MM. Further prospective multicenter studies are warranted to validate these findings and refine risk stratification strategies. multiple myeloma prognostic factors mortality risk plasma cell percentage bone marrow fibrosis platelet count β2-microglobulin survival analysis Introduction Multiple myeloma (MM) is a malignancy of clonal plasma cells (PCs) characterized by the production of monoclonal immunoglobulins and infiltration of the bone marrow, which leads to end-organ damage such as anemia, renal failure, hypercalcemia, and osteolytic bone disease [ 1 – 3 ]. MM is typically preceded by precursor conditions, including monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM [ 4 , 5 ]. Despite significant advances in therapy, MM remains incurable and displays a heterogeneous clinical course. The considerable variability in survival outcomes highlights the need for reliable prognostic markers that reflect tumor burden, disease biology, and host factors [ 6 ]. MM is a malignancy in which interactions between tumor cells and the bone marrow microenvironment play a critical role, not only in disease progression but also in treatment planning. A PCs percentage of ≥ 10% in the bone marrow is a key diagnostic criterion for MM. Higher PC percentages are associated with greater bone marrow infiltration and more pronounced systemic manifestations [ 7 – 9 ]. However, the prognostic significance of PCs burden for survival remains controversial. Some studies have reported that patients with a PCs percentage > 50% have significantly lower overall survival (OS) and progression-free survival (PFS) compared to those with ≤ 50% [ 9 ]. Bone marrow fibrosis (BMF), resulting from cytokine-mediated activation of fibroblasts, is another pathological feature observed in a subset of MM patients [ 10 ]. BMF is characterized by the accumulation of reticulin or collagen fibers in the bone marrow stroma. However, reticulin is a normal component of the bone marrow and may increase in both benign and malignant diseases. Some studies have found no association between the presence of BMF and OS or PFS [ 10 ], whereas others have reported that BMF is common in newly diagnosed multiple myeloma (NDMM) and is associated with higher ISS stage and poorer survival [ 11 ]. The International Staging System (ISS) and impaired renal function are established prognostic factors in MM [ 12 ]. Additionally, anemia, renal dysfunction, hypercalcemia, and advanced age have all been associated with poor prognosis in MM patients [ 13 – 16 ]. This study aimed to evaluate the relationship between hematological and biochemical parameters, bone marrow PC percentage, reticulin fibrosis grade, and mortality risk in patients diagnosed with MM. Materials and Methods Study Design and Patient Population: Study design : Retrospective cross-sectional study desing This retrospective cross-sectional, observational study included 97 patients diagnosed with MM according to the International Myeloma Working Group (IMWG) 2014 criteria and followed up between January 2017 and December 2025 at Afyon Health Sciences University [ 17 ]. Demographic data, laboratory findings, bone marrow clonal PC percentage, and fibrosis grades from biopsy specimens at diagnosis were obtained from patient records. The percentages of PCs were obtained from bone marrow biopsy reports. The fibrosis stage was examined by a pathologist and defined as reticulin fibrosis, scored according to the European Consensus for bone marrow fibrosis grading and cellularity assessment, where grade 1 indicates mild reticulin fibrosis, and grades 2–3 indicate moderate to severe fibrosis [ 18 ]. Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA). Normality was assessed with the Kolmogorov–Smirnov test. Non-parametric methods were applied when appropriate. Cox proportional hazards regression analysis was used to identify independent predictors of mortality. The Hazard Ratio (HR), 95% confidence intervals (CI), and p -values were calculated for all variables. The level of statistical significance was set at p < 0.05. Ethical Approval The study was approved by the Afyon Health Sciences University Ethics Committee (approval date: 5 December 2025, approval number: 2025/827). The study was conducted in accordance with the Declaration of Helsinki. Results The mean age of patients diagnosed with MM was 67.15 ± 9.52 years, and 60.8% were male. The median PC percentage ranged from 10% to 95%, with a mean of 60.56% ± 26.11. The proportion of patients with reticulin fibrosis grade 2 was 23.4%, grade 3 was 19.1%, and grade 1 was 57.4%. ISS stage 3 was observed in 58.1% of patients. The rate of IgG-positive patients was 57.7%, and IgA-positive patients was 26.8%. The rates of kappa and lambda light chain positivity were 60.8% and 40.2%, respectively. CD138 was detected in all patients, while CD56 positivity was observed in 66% (Table 1). Table 1. Baseline demographic, laboratory, and disease characteristics. Variable Mean ± SD / n (%) Min–Max Demographic and Clinical Age (years) 67.15 ± 9.52 41–80 Gender Female 39 (39.2) — Male 58 (60.8) — Hematological Parameters Hemoglobin (g/dL) 10.28 ± 2.12 4.5–16.04 Leukocyte count (×10³/μL) 6.76 ± 2.77 1.37–15.83 Neutrophil count (×10³/μL) 7.71 ± 15.07 0.71–75.2 Lymphocyte count (×10³/μL) 1.41 ± 0.69 0.09–3.55 Monocyte count (×10³/μL) 1.05 ± 2.4 0.09–15.3 Hematocrit (%) 32.33 ± 5.62 21.9–46.3 Platelet count (×10³/μL) 189.62 ± 88.84 2.49–483 Red blood cell count (×10⁶/μL) 3.42 ± 0.78 2.16–6.13 Platelet-to-RBC ratio 55.68 ± 27.32 1–137.1 Biochemical Parameters β2-microglobulin (mg/L) 7.98 ± 6.95 1.4–31 Total protein (g/dL) 8.31 ± 4.03 3.4–39 Albumin (g/dL) 3.61 ± 1.06 1.74–7.51 Globulin (g/dL) 4.27 ± 2.12 1.1–9.36 Urea (mg/dL) 56.2 ± 34.36 16.5–161.1 Creatinine (mg/dL) 1.66 ± 1.55 0.31–7.18 ESR (mm/h) 80.56 ± 36.47 12–141 LDH (U/L) 268.91 ± 163.53 103–1058 Bone Marrow Characteristics Plasma cell percentage (%) 60.56 ± 26.11 10–95 Reticulin fibrosis grade Grade 1 54 (57.4) — Grade 2 22 (23.4) — Grade 3 18 (19.1) — ISS stage Stage 1 5 (11.6) — Stage 2 13 (30.2) — Stage 3 25 (58.1) — Immunoglobulin Type IgG positive 57 (57.7) — IgM positive 1 (1.0) — IgA positive 26 (26.8) — IgD positive 2 (2.1) — Light Chain Type Kappa 59 (60.8) — Lambda 39 (40.2) — Immunophenotype CD138 expression 97 (100) — CD56 expression 64 (66.0) — On average, patients received 2.26 ± 1.5 different treatment regimens. At the time of evaluation, 40% of patients had stable disease, 16.8% were in remission, 26.3% had refractory disease, and 16.8% had progressive disease. An ECOG performance score of 3 was observed in 42.7% of patients (Table 2). Table 2. Treatment response and ECOG performance status. Variable Mean ± SD / n (%) Min–Max Number of treatments 2.26 ± 1.5 1–8 Response status Stable disease 38 (40.0) — Remission 16 (16.8) — Refractory disease 25 (26.3) — Disease progression 16 (16.8) — ECOG performance score ECOG score 1 18 (18.8) — ECOG score 2 23 (24.0) — ECOG score 3 41 (42.7) — ECOG score 4 14 (14.6) — Correlation Between Plasma Cell Percentage and Other Parameters As the plasma cell percentage increased, the erythrocyte sedimentation rate (ESR) increased significantly, while hemoglobin (HGB), lymphocyte count, platelet count (PLT), red blood cell count (RBC), and albumin levels decreased significantly (Table 3). Table 3. Correlation analysis between Plasma Cell percentage and other parameters. Plazma Hücre Oranı Hemoglobin (HGB) r -0,533** p <0,001 Leukocyte count r -0,196 p 0,115 Neutrophil count r -0,334 p 0,175 Lymphocyte count r -0,279* p 0,045 Monocyte count r 0,011 p 0,937 HTC r -0,21 p 0,387 Platelet (PLT) count r -0,268* p 0,031 Red blood cell (RBC) count r -0,430** p 0,002 PLT/RBC r 0,056 p 0,703 B2mg r 0,109 p 0,411 Total Protein r 0,048 p 0,709 Albumin r -0,374** p 0,002 Globulin r 0,119 p 0,38 ÜRE r -0,033 p 0,81 Creatinine r 0,01 p 0,937 ESR (Sedim, mm/h) r 0,273* p 0,029 CRAB r 0,136 p 0,342 LDH r 0,116 p 0,36 Spearman's Rho Correlation Analysis *Correlation is significant at the 0.05 level. **Correlation is significant at the 0.01 level. The mean plasma cell percentage was significantly higher in patients with moderate to severe bone marrow fibrosis (grades 2–3) compared to those with mild fibrosis (grade 1) ( p < 0.001) (Table 4). Table 4. Plasma cell percentage according to reticulin fibrosis grade. Fibrosis Grade Mean PC % ± SD p -Value Grade 1 (mild) 51.66 ± 24.47 68.5 years) was the strongest predictor (HR = 2.85, 95% CI: 1.52–5.35, p = 0.001). Low platelet count (<190.5×10³/μL) was also significantly associated with increased mortality (HR = 2.12, 95% CI: 1.18–3.81, p = 0.012). Elevated β2-microglobulin, high creatinine, elevated urea, high LDH, and advanced ISS stage (stage 3) were also independent predictors of poor survival. Notably, neither bone marrow plasma cell percentage nor reticulin fibrosis grade independently predicted mortality (Table 5). Table 5. Cox regression analysis of mortality predictors. Variable Hazard Ratio (HR) 95% CI p -Value Age > 68.5 years 2.85 1.52–5.35 0.001 Platelet count < 190.5×10³/μL 2.12 1.18–3.81 0.012 β2-microglobulin (per mg/L increase) 1.08 1.03–1.13 0.002 Creatinine (per mg/dL increase) 1.24 1.08–1.43 0.003 Urea (per mg/dL increase) 1.01 1.00–1.02 0.018 LDH (per U/L increase) 1.002 1.001–1.004 0.008 ISS stage 3 2.45 1.25–4.80 0.009 Hemoglobin (per g/dL increase) 0.88 0.78–0.99 0.035 Plasma cell percentage 1.01 0.99–1.02 0.456 Reticulin fibrosis grade 2–3 1.32 0.74–2.35 0.347 The median overall survival was 44.5 months (95% CI: 38.2–50.8 months). Kaplan–Meier survival analysis demonstrated significantly shorter survival in patients with age >68.5 years, platelet count <190.5×10³/μL, elevated β2-microglobulin, renal dysfunction, and ISS stage 3 disease. However, survival did not differ significantly based on plasma cell percentage or reticulin fibrosis grade. Discussion This retrospective cross-sectional study of MM patients evaluated the prognostic significance of clinical, laboratory, and bone marrow histological parameters. Our findings demonstrate that age, platelet count, β2-microglobulin, renal function markers, LDH, and ISS stage are independent predictors of mortality, while bone marrow PCs and reticulin fibrosis grade do not independently predict survival outcomes. Advanced age has been consistently identified as an adverse prognostic factor in MM. Older patients often have reduced tolerance to intensive chemotherapy, higher comorbidity burden, and poorer functional status, all of which contribute to inferior outcomes [13,14]. Some studies have reported that patients over 65 years have significantly shorter overall survival compared to younger patients [13,14]. In contrast, one report found that OS did not differ by age group, although PFS was shorter in younger patients [10]. In our study, consistent with most of the literature, advanced age (>68.5 years) was associated with worse outcomes, with these patients experiencing shorter survival. The incidence of BMF in MM patients is reported to be approximately 8–57% in the literature. The presence of BMF has been related to the magnitude of PC infiltration and was associated with poorer prognosis [11,19]. Higher frequency and degree of fibrosis and higher PC percentage have been reported to be associated with a poorer response to treatment [20]. In our study, the rate of grade 2–3 fibrosis in patients was 42.5%. Consistent with the literature, PC percentage was significantly higher in patients who had moderate to severe BMF compared to those with mild fibrosis ( p < 0.001). Furthermore, one study found that patients with a bone marrow PCs percentage above 60% had shorter PFS and OS, and these differences remained significant in multivariate analysis [21]. Another study reported that anemia, renal failure, and hypercalcemia, in addition to high bone marrow PCs percentages, were associated with lower survival rates [22]. In our study, the mean PCs percentage was 60.56% ± 26.11. As the PCs percentage increased, ESR increased significantly, while hemoglobin, lymphocyte, and platelet counts decreased significantly. However, in multivariate Cox regression analysis, neither PCs percentage nor reticulin fibrosis grade independently predicted mortality, suggesting that these histological parameters reflect disease burden but do not add independent prognostic value beyond established clinical and laboratory markers. Hypercalcemia, kidney failure, anemia, and bone disease are referred to as CRAB findings and indicate active disease. There are studies examining the relationship between CRAB findings and prognosis; one of them showed that anemia and hypercalcemia did not affect survival [23]. In another analysis, the presence of osteolytic bone disease and hypercalcemia were related to poor prognosis [24]. A study of MM patients showed that hemoglobin levels >8 g/dL, lower bone marrow PC count (<20%), and normal creatinine and calcium levels positively impacted survival [25]. Hemoglobin and platelet counts were also significant predictors in our study, reflecting bone marrow failure and extensive disease involvement. Anemia in MM is multifactorial, resulting from PC infiltration of the bone marrow, reduced erythropoietin production due to renal impairment, and chronic inflammation with cytokine-mediated suppression of erythropoiesis. One study found that moderate to severe thrombocytopenia (<100×10³/μL) was associated with shorter survival, suggesting that platelet count can serve as an accessible prognostic biomarker and indicator of the tumor microenvironment [26]. In our study, both anemia and thrombocytopenia were associated with higher mortality rates, consistent with the majority of the literature. ROC analysis showed that a platelet count above 190.5×10³/μL was associated with a higher mean survival time, highlighting its clinical utility as a simple prognostic marker. Severe renal impairment is associated with high mortality and morbidity rates and poor outcomes [27,28]. Renal dysfunction, characterized by elevated urea and creatinine levels, is a frequent complication of MM, occurring in approximately 50% of patients at diagnosis and is associated with increased mortality [29]. Additionally, elevated β2-microglobulin indicates high tumor burden and poor prognosis [29]. In our study, elevated urea and creatinine levels confirmed their importance as significant predictors of mortality and their continued role in guiding treatment plans, as reflected in the CRAB criteria and ISS staging system. Our findings have important clinical implications. While bone marrow biopsy remains essential for diagnosis, our results suggest that readily available clinical and laboratory parameters; particularly age, platelet count, renal function markers, and β2-microglobulin provide sufficient prognostic information for risk stratification and treatment planning. This is particularly relevant in resource-limited settings where repeated bone marrow biopsies may not be feasible. The identification of platelet count as an independent predictor is particularly noteworthy, as it is a simple, inexpensive, and routinely available parameter that can be easily monitored during follow-up. Limitations This study has several limitations. First, its retrospective design may introduce selection bias. Second, the relatively small sample size of patients may limit the generalizability of our findings. Third, we did not include cytogenetics-based risk classification (Revised ISS scoring), which is an important prognostic factor in MM. Fourth, treatment regimens varied among patients, which may have influenced survival outcomes. Despite these limitations, our study provides comprehensive evaluation of clinical and laboratory data, PC percentages, and the degree of reticulin fibrosis obtained from biopsy samples, and identifies platelet count as a simple, accessible prognostic marker. Conclusion Clinical and laboratory markers such as age, hematological parameters (particularly platelet count), renal function markers (urea and creatinine), β2-microglobulin, and ISS stage remain the most reliable predictors of mortality in MM, with age being the most important independent predictor. While bone marrow PC percentage and reticulin fibrosis grade reflect disease burden and bone marrow dysfunction, their prognostic roles do not add independent value beyond established clinical markers. These findings support the use of readily available clinical and laboratory parameters over bone marrow histology for prognostication. Prospective studies involving larger patient cohorts and incorporating genetic risk factors (such as Revised ISS scoring with cytogenetics) are needed to better define the integrated role of these parameters in predicting treatment responses and outcomes in MM. Declarations Ethical Approval and Consent to Participate This retrospective cross-sectional study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Afyon Health Sciences University. Due to the retrospective nature of the study design and the fact that a portion of the patients had deceased during the study period, individual informed consent was not obtained. The patient consent form is not among the documents required by our hospital's ethics committee for retrospective studies. Publication Permission All authors have reviewed and approved the final manuscript for publication. The manuscript has not been published previously elsewhere. All authors consent to the publication of this work in its current form. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to patient privacy restrictions. Acknowledgments: The authors would like to thank the staff of the Department of Pathology and Hematology at Afyon Health Sciences University for their assistance with data collection. We also acknowledge the patients who contributed to this research. Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ Contributions: Nermin Keni Begendi: Conceptualization, methodology, data collection, formal analysis, writing – original draft, writing – review and editing, supervision, project administration. Mustafa Duran: Data collection, formal analysis, writing – review and editing, validation. Çiğdem Özdemir: Histopathological assessment, bone marrow evaluation, reticulin fibrosis grading, writing – review and editing, validation. All authors have read and approved the final manuscript. References van de Donk N, Pawlyn C, Yong KL. Multiple myeloma. Lancet. 2021;397:410–27. Cowan AJ, Green DJ, Kwok M, Lee S, Coffey DG, Holmberg LA, Tuazon S, Gopal AK, Libby EN. Diagnosis and management of multiple myeloma: A review. JAMA. 2022;327:464–77. Went M, Sud A, Försti A, Halvarsson BM, Weinhold N, Kimber S, van Duin M, Thorleifsson G, Holroyd A, Johnson DC, et al. Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma. 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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-8918370","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618111368,"identity":"b7a77a2f-93d4-447c-afe5-5b3a2ee309ce","order_by":0,"name":"Nermin Keni Begendi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYLCCBAYGAwYGxsYHQDYPHylamg1AWtiItQikmk0CxCKoxZy99+mGhzsYjPmlm9sqv+bYybAxMD98dAOPFsue42Y3Es8wmEnOOdh2W3ZbMtBhbMbGOfgcdCON7UZiG4ONAZC8LbmNGaiFh00ar5b7zyBa7IFkseS2eiK03GADazEzkEhsY/y47TARWs6AHSZhLHEjsVmacdtxHjZmQn45fozt5s82G8P+GekPP/7cVm3Pz9788DE+LVAAjhEGZh4wSVg5AjD+IEX1KBgFo2AUjBgAAG2nRE+9PSPxAAAAAElFTkSuQmCC","orcid":"","institution":"Afyonkarahisar Sağlık Bilimleri Üniversitesi","correspondingAuthor":true,"prefix":"","firstName":"Nermin","middleName":"Keni","lastName":"Begendi","suffix":""},{"id":618111370,"identity":"0fc956bf-d191-427b-a1ba-7844440249d9","order_by":1,"name":"Mustafa Duran","email":"","orcid":"","institution":"Afyonkarahisar Sağlık Bilimleri Üniversitesi","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Duran","suffix":""},{"id":618111371,"identity":"8de733dc-63c4-4a33-960d-da3827eb8bb2","order_by":2,"name":"Çiğdem Özdemir","email":"","orcid":"","institution":"Afyonkarahisar Sağlık Bilimleri Üniversitesi","correspondingAuthor":false,"prefix":"","firstName":"Çiğdem","middleName":"","lastName":"Özdemir","suffix":""}],"badges":[],"createdAt":"2026-02-19 15:25:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8918370/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8918370/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106355118,"identity":"b5a6f6a0-fc5e-4bcc-aea1-e8ac35aa7a57","added_by":"auto","created_at":"2026-04-07 18:26:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":813230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8918370/v1/850a16b9-020e-49ba-84eb-e1b6ecbf98ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eClinical and Laboratory Predictors of Mortality in Multiple Myeloma\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is a malignancy of clonal plasma cells (PCs) characterized by the production of monoclonal immunoglobulins and infiltration of the bone marrow, which leads to end-organ damage such as anemia, renal failure, hypercalcemia, and osteolytic bone disease [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MM is typically preceded by precursor conditions, including monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite significant advances in therapy, MM remains incurable and displays a heterogeneous clinical course. The considerable variability in survival outcomes highlights the need for reliable prognostic markers that reflect tumor burden, disease biology, and host factors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMM is a malignancy in which interactions between tumor cells and the bone marrow microenvironment play a critical role, not only in disease progression but also in treatment planning. A PCs percentage of \u0026ge;\u0026thinsp;10% in the bone marrow is a key diagnostic criterion for MM. Higher PC percentages are associated with greater bone marrow infiltration and more pronounced systemic manifestations [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the prognostic significance of PCs burden for survival remains controversial. Some studies have reported that patients with a PCs percentage\u0026thinsp;\u0026gt;\u0026thinsp;50% have significantly lower overall survival (OS) and progression-free survival (PFS) compared to those with \u0026le;\u0026thinsp;50% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBone marrow fibrosis (BMF), resulting from cytokine-mediated activation of fibroblasts, is another pathological feature observed in a subset of MM patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. BMF is characterized by the accumulation of reticulin or collagen fibers in the bone marrow stroma. However, reticulin is a normal component of the bone marrow and may increase in both benign and malignant diseases. Some studies have found no association between the presence of BMF and OS or PFS [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], whereas others have reported that BMF is common in newly diagnosed multiple myeloma (NDMM) and is associated with higher ISS stage and poorer survival [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe International Staging System (ISS) and impaired renal function are established prognostic factors in MM [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, anemia, renal dysfunction, hypercalcemia, and advanced age have all been associated with poor prognosis in MM patients [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the relationship between hematological and biochemical parameters, bone marrow PC percentage, reticulin fibrosis grade, and mortality risk in patients diagnosed with MM.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patient Population:\u003c/h2\u003e \u003cp\u003eStudy design : Retrospective cross-sectional study desing\u003c/p\u003e \u003cp\u003eThis retrospective cross-sectional, observational study included 97 patients diagnosed with MM according to the International Myeloma Working Group (IMWG) 2014 criteria and followed up between January 2017 and December 2025 at Afyon Health Sciences University [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Demographic data, laboratory findings, bone marrow clonal PC percentage, and fibrosis grades from biopsy specimens at diagnosis were obtained from patient records. The percentages of PCs were obtained from bone marrow biopsy reports. The fibrosis stage was examined by a pathologist and defined as reticulin fibrosis, scored according to the European Consensus for bone marrow fibrosis grading and cellularity assessment, where grade 1 indicates mild reticulin fibrosis, and grades 2\u0026ndash;3 indicate moderate to severe fibrosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA). Normality was assessed with the Kolmogorov\u0026ndash;Smirnov test. Non-parametric methods were applied when appropriate. Cox proportional hazards regression analysis was used to identify independent predictors of mortality. The Hazard Ratio (HR), 95% confidence intervals (CI), and \u003cem\u003ep\u003c/em\u003e-values were calculated for all variables. The level of statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\u003ch3\u003eEthical Approval\u003c/h3\u003e\n\u003cp\u003eThe study was approved by the Afyon Health Sciences University Ethics Committee (approval date: 5 December 2025, approval number: 2025/827). The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age of patients diagnosed with MM was 67.15 \u0026plusmn; 9.52 years, and 60.8% were male. The median PC percentage ranged from 10% to 95%, with a mean of 60.56% \u0026plusmn; 26.11. The proportion of patients with reticulin fibrosis grade 2 was 23.4%, grade 3 was 19.1%, and grade 1 was 57.4%. ISS stage 3 was observed in 58.1% of patients. The rate of IgG-positive patients was 57.7%, and IgA-positive patients was 26.8%. The rates of kappa and lambda light chain positivity were 60.8% and 40.2%, respectively. CD138 was detected in all patients, while CD56 positivity was observed in 66% (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline demographic, laboratory, and disease characteristics.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"53%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD / \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMin\u0026ndash;Max\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic and Clinical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.15 \u0026plusmn; 9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41\u0026ndash;80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematological Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.28 \u0026plusmn; 2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u0026ndash;16.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLeukocyte count (\u0026times;10\u0026sup3;/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.76 \u0026plusmn; 2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37\u0026ndash;15.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeutrophil count (\u0026times;10\u0026sup3;/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.71 \u0026plusmn; 15.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.71\u0026ndash;75.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLymphocyte count (\u0026times;10\u0026sup3;/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u0026ndash;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMonocyte count (\u0026times;10\u0026sup3;/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.05 \u0026plusmn; 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u0026ndash;15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHematocrit (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.33 \u0026plusmn; 5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.9\u0026ndash;46.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatelet count (\u0026times;10\u0026sup3;/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e189.62 \u0026plusmn; 88.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.49\u0026ndash;483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRed blood cell count (\u0026times;10⁶/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.42 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.16\u0026ndash;6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatelet-to-RBC ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.68 \u0026plusmn; 27.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u0026ndash;137.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiochemical Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;2-microglobulin (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.98 \u0026plusmn; 6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4\u0026ndash;31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal protein (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.31 \u0026plusmn; 4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.61 \u0026plusmn; 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.74\u0026ndash;7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobulin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.27 \u0026plusmn; 2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u0026ndash;9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrea (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.2 \u0026plusmn; 34.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.5\u0026ndash;161.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66 \u0026plusmn; 1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u0026ndash;7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eESR (mm/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.56 \u0026plusmn; 36.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u0026ndash;141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e268.91 \u0026plusmn; 163.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e103\u0026ndash;1058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBone Marrow Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlasma cell percentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.56 \u0026plusmn; 26.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u0026ndash;95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReticulin fibrosis grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrade 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54 (57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eISS stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunoglobulin Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgG positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgM positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgA positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIgD positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLight Chain Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKappa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59 (60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLambda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunophenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD138 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD56 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOn average, patients received 2.26 \u0026plusmn; 1.5 different treatment regimens. At the time of evaluation, 40% of patients had stable disease, 16.8% were in remission, 26.3% had refractory disease, and 16.8% had progressive disease. An ECOG performance score of 3 was observed in 42.7% of patients (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Treatment response and ECOG performance status.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"0%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD / \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMin\u0026ndash;Max\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of treatments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.26 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStable disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRemission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRefractory disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisease progression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG performance score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECOG score 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECOG score 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECOG score 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECOG score 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eCorrelation Between Plasma Cell Percentage and Other Parameters\u003c/h3\u003e\n\u003cp\u003eAs the plasma cell percentage increased, the erythrocyte sedimentation rate (ESR) increased significantly, while hemoglobin (HGB), lymphocyte count, platelet count (PLT), red blood cell count (RBC), and albumin levels decreased significantly (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Correlation analysis between Plasma Cell percentage and other parameters.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"96%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlazma H\u0026uuml;cre Oranı\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eHemoglobin\u0026nbsp;(HGB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0,533**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eLeukocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0,196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNeutrophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0,334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eLymphocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0,279*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eMonocyte count\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eHTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0,21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ePlatelet (PLT) count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0,268*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eRed blood cell (RBC) count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0,430**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ePLT/RBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eB2mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eTotal Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0,374**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eGlobulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026Uuml;RE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0,033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eESR (Sedim, mm/h)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,273*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eCRAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0,36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpearman\u0026apos;s Rho Correlation Analysis *Correlation is significant at the 0.05 level. **Correlation is significant at the 0.01 level.\u003c/p\u003e\n\u003cp\u003eThe mean plasma cell percentage was significantly higher in patients with moderate to severe bone marrow fibrosis (grades 2\u0026ndash;3) compared to those with mild fibrosis (grade 1) (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Plasma cell percentage according to reticulin fibrosis grade.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"0%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFibrosis Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMean PC % \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrade 1 (mild)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.66 \u0026plusmn; 24.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrades 2\u0026ndash;3 (moderate\u0026ndash;severe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.58 \u0026plusmn; 22.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCox proportional hazards regression analysis identified several independent predictors of mortality. Advanced age (\u0026gt;68.5 years) was the strongest predictor (HR = 2.85, 95% CI: 1.52\u0026ndash;5.35, \u003cem\u003ep\u003c/em\u003e = 0.001). Low platelet count (\u0026lt;190.5\u0026times;10\u0026sup3;/\u0026mu;L) was also significantly associated with increased mortality (HR = 2.12, 95% CI: 1.18\u0026ndash;3.81, \u003cem\u003ep\u003c/em\u003e = 0.012). Elevated \u0026beta;2-microglobulin, high creatinine, elevated urea, high LDH, and advanced ISS stage (stage 3) were also independent predictors of poor survival. Notably, neither bone marrow plasma cell percentage nor reticulin fibrosis grade independently predicted mortality (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Cox regression analysis of mortality predictors.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"0%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHazard Ratio (HR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge \u0026gt; 68.5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52\u0026ndash;5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatelet count \u0026lt; 190.5\u0026times;10\u0026sup3;/\u0026mu;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u0026ndash;3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;2-microglobulin (per mg/L increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u0026ndash;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCreatinine (per mg/dL increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08\u0026ndash;1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrea (per mg/dL increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026ndash;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDH (per U/L increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eISS stage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.25\u0026ndash;4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemoglobin (per g/dL increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.78\u0026ndash;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlasma cell percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u0026ndash;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReticulin fibrosis grade 2\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u0026ndash;2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe median overall survival was 44.5 months (95% CI: 38.2\u0026ndash;50.8 months). Kaplan\u0026ndash;Meier survival analysis demonstrated significantly shorter survival in patients with age \u0026gt;68.5 years, platelet count \u0026lt;190.5\u0026times;10\u0026sup3;/\u0026mu;L, elevated \u0026beta;2-microglobulin, renal dysfunction, and ISS stage 3 disease. However, survival did not differ significantly based on plasma cell percentage or reticulin fibrosis grade.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cross-sectional study of MM patients evaluated the prognostic significance of clinical, laboratory, and bone marrow histological parameters. Our findings demonstrate that age, platelet count, β2-microglobulin, renal function markers, LDH, and ISS stage are independent predictors of mortality, while bone marrow PCs and reticulin fibrosis grade do not independently predict survival outcomes.\u003c/p\u003e\n\u003cp\u003eAdvanced age has been consistently identified as an adverse prognostic factor in MM. Older patients often have reduced tolerance to intensive chemotherapy, higher comorbidity burden, and poorer functional status, all of which contribute to inferior outcomes [13,14]. Some studies have reported that patients over 65 years have significantly shorter overall survival compared to younger patients [13,14]. In contrast, one report found that OS did not differ by age group, although PFS was shorter in younger patients [10]. In our study, consistent with most of the literature, advanced age (\u0026gt;68.5 years) was associated with worse outcomes, with these patients experiencing shorter survival.\u003c/p\u003e\n\u003cp\u003eThe incidence of BMF in MM patients is reported to be approximately 8–57% in the literature. The presence of BMF has been related to the magnitude of PC infiltration and was associated with poorer prognosis [11,19]. Higher frequency and degree of fibrosis and higher PC percentage have been reported to be associated with a poorer response to treatment [20]. In our study, the rate of grade 2–3 fibrosis in patients was 42.5%. Consistent with the literature, PC percentage was significantly higher in patients who had moderate to severe BMF compared to those with mild fibrosis (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eFurthermore, one study found that patients with a bone marrow PCs percentage above 60% had shorter PFS and OS, and these differences remained significant in multivariate analysis [21]. Another study reported that anemia, renal failure, and hypercalcemia, in addition to high bone marrow PCs percentages, were associated with lower survival rates [22]. In our study, the mean PCs percentage was 60.56% ± 26.11. As the PCs percentage increased, ESR increased significantly, while hemoglobin, lymphocyte, and platelet counts decreased significantly. However, in multivariate Cox regression analysis, neither PCs percentage nor reticulin fibrosis grade independently predicted mortality, suggesting that these histological parameters reflect disease burden but do not add independent prognostic value beyond established clinical and laboratory markers.\u003c/p\u003e\n\u003cp\u003eHypercalcemia, kidney failure, anemia, and bone disease are referred to as CRAB findings and indicate active disease. There are studies examining the relationship between CRAB findings and prognosis; one of them showed that anemia and hypercalcemia did not affect survival [23]. In another analysis, the presence of osteolytic bone disease and hypercalcemia were related to poor prognosis [24]. A study of MM patients showed that hemoglobin levels \u0026gt;8 g/dL, lower bone marrow PC count (\u0026lt;20%), and normal creatinine and calcium levels positively impacted survival [25].\u003c/p\u003e\n\u003cp\u003eHemoglobin and platelet counts were also significant predictors in our study, reflecting bone marrow failure and extensive disease involvement. Anemia in MM is multifactorial, resulting from PC infiltration of the bone marrow, reduced erythropoietin production due to renal impairment, and chronic inflammation with cytokine-mediated suppression of erythropoiesis. One study found that moderate to severe thrombocytopenia (\u0026lt;100×10³/μL) was associated with shorter survival, suggesting that platelet count can serve as an accessible prognostic biomarker and indicator of the tumor microenvironment [26]. In our study, both anemia and thrombocytopenia were associated with higher mortality rates, consistent with the majority of the literature. ROC analysis showed that a platelet count above 190.5×10³/μL was associated with a higher mean survival time, highlighting its clinical utility as a simple prognostic marker.\u003c/p\u003e\n\u003cp\u003eSevere renal impairment is associated with high mortality and morbidity rates and poor outcomes [27,28]. Renal dysfunction, characterized by elevated urea and creatinine levels, is a frequent complication of MM, occurring in approximately 50% of patients at diagnosis and is associated with increased mortality [29]. Additionally, elevated β2-microglobulin indicates high tumor burden and poor prognosis [29]. In our study, elevated urea and creatinine levels confirmed their importance as significant predictors of mortality and their continued role in guiding treatment plans, as reflected in the CRAB criteria and ISS staging system.\u003c/p\u003e\n\u003cp\u003eOur findings have important clinical implications. While bone marrow biopsy remains essential for diagnosis, our results suggest that readily available clinical and laboratory parameters; particularly age, platelet count, renal function markers, and β2-microglobulin provide sufficient prognostic information for risk stratification and treatment planning. This is particularly relevant in resource-limited settings where repeated bone marrow biopsies may not be feasible. The identification of platelet count as an independent predictor is particularly noteworthy, as it is a simple, inexpensive, and routinely available parameter that can be easily monitored during follow-up.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations. First, its retrospective design may introduce selection bias. Second, the relatively small sample size of patients may limit the generalizability of our findings. Third, we did not include cytogenetics-based risk classification (Revised ISS scoring), which is an important prognostic factor in MM. Fourth, treatment regimens varied among patients, which may have influenced survival outcomes. Despite these limitations, our study provides comprehensive evaluation of clinical and laboratory data, PC percentages, and the degree of reticulin fibrosis obtained from biopsy samples, and identifies platelet count as a simple, accessible prognostic marker.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eClinical and laboratory markers such as age, hematological parameters (particularly platelet count), renal function markers (urea and creatinine), \u0026beta;2-microglobulin, and ISS stage remain the most reliable predictors of mortality in MM, with age being the most important independent predictor. While bone marrow PC percentage and reticulin fibrosis grade reflect disease burden and bone marrow dysfunction, their prognostic roles do not add independent value beyond established clinical markers. These findings support the use of readily available clinical and laboratory parameters over bone marrow histology for prognostication. Prospective studies involving larger patient cohorts and incorporating genetic risk factors (such as Revised ISS scoring with cytogenetics) are needed to better define the integrated role of these parameters in predicting treatment responses and outcomes in MM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cross-sectional study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Afyon Health Sciences University. Due to the retrospective nature of the study design and the fact that a portion of the patients had deceased during the study period, individual informed consent was not obtained. The patient consent form is not among the documents required by our hospital\u0026apos;s ethics committee for retrospective studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication Permission\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed and approved the final manuscript for publication. The manuscript has not been published previously elsewhere. All authors consent to the publication of this work in its current form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author. The data are not publicly available due to patient privacy restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the staff of the Department of Pathology and Hematology at Afyon Health Sciences University for their assistance with data collection. We also acknowledge the patients who contributed to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNermin Keni Begendi:\u003c/strong\u003e Conceptualization, methodology, data collection, formal analysis, writing \u0026ndash; original draft, writing \u0026ndash; review and editing, supervision, project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMustafa Duran:\u003c/strong\u003e Data collection, formal analysis, writing \u0026ndash; review and editing, validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Ccedil;iğdem \u0026Ouml;zdemir:\u003c/strong\u003e Histopathological assessment, bone marrow evaluation, reticulin fibrosis grading, writing \u0026ndash; review and editing, validation.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan de Donk N, Pawlyn C, Yong KL. 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The impact of bone marrow fibrosis and JAK2 expression on clinical outcomes in patients with newly diagnosed multiple myeloma treated with immunomodulatory agents and/or proteasome inhibitors. Cancer Med. 2020;9:5869\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L, Richardson P, Caltagirone S, Lahuerta JJ, Facon T, et al. Revised international staging system for multiple myeloma: A report from International Myeloma Working Group. J Clin Oncol. 2015;33:2863\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Saleh AS, Parmar HV, Visram A, Muchtar E, Buadi FK, Go RS, Lacy MQ, Dispenzieri A, Hayman SR, Hobbs M, et al. Increased bone marrow plasma-cell percentage predicts outcomes in newly diagnosed multiple myeloma patients. 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International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2023;24:e406\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThiele J, Kvasnicka HM, Facchetti F, Franco V, van der Walt J, Orazi A. European consensus on grading bone marrow fibrosis and assessment of cellularity. Haematologica. 2005;90:1128\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul B, Lekovic D, Patel C, Gottlieb J, Hoffman R, Mascarenhas J. The impact of bone marrow fibrosis and JAK2 expression on clinical outcomes in patients with newly diagnosed multiple myeloma treated with immunomodulatory agents and/or proteasome inhibitors. Cancer Med. 2020;9:5869\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabarovic E, Valkovic T, Stifter S, Seili-Bekafigo I, Fuckar D, Jonjic N. Assessment of bone marrow fibrosis and angiogenesis in monitoring patients with multiple myeloma. 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S1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavriatopoulou M, Terpos E, Kastritis E, Dimopoulos MA. Current treatments for renal failure due to multiple myeloma. Expert Opin Pharmacother. 2016;17:2165\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimopoulos MA, Sonneveld P, Leung N, Merlini G, Ludwig H, Kastritis E, Goldschmidt H, Joshua D, Orlowski RZ, Powles R, et al. International Myeloma Working Group recommendations for the diagnosis and management of myeloma-related renal impairment. Leukemia. 2023;37:1575\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajkumar SV. Multiple myeloma: 2024 update on diagnosis, risk-stratification, and management. Am J Hematol. 2024;99:1802\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"multiple myeloma, prognostic factors, mortality risk, plasma cell percentage, bone marrow fibrosis, platelet count, β2-microglobulin, survival analysis","lastPublishedDoi":"10.21203/rs.3.rs-8918370/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8918370/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMultiple myeloma (MM) is a plasma cell malignancy characterized by bone marrow infiltration and organ dysfunction. Despite advances in treatment, the disease exhibits heterogeneous clinical outcomes, necessitating reliable prognostic markers. The prognostic significance of bone marrow plasma cell (PC) percentage and reticulin fibrosis in predicting mortality remains unclear and controversial in the literature.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective analysis of 97 patients diagnosed with multiple myeloma. Bone marrow PC percentage and reticulin fibrosis grade were assessed alongside comprehensive clinical, hematological, and biochemical parameters. Cox proportional hazards regression analysis was employed to identify independent predictors of mortality. Median follow-up and survival outcomes were calculated using Kaplan-Meier methodology.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median overall survival was 44.5 months. Higher bone marrow PC percentages correlated with increased reticulin fibrosis grade; however, neither parameter independently predicted mortality in multivariate analysis. Advanced age (\u0026gt;\u0026thinsp;68.5 years) and low platelet count (\u0026lt;\u0026thinsp;190.5\u0026times;10\u0026sup3;/\u0026micro;L) emerged as significant independent predictors of mortality. Additionally, elevated β2-microglobulin levels, renal dysfunction, elevated lactate dehydrogenase (LDH), and advanced International Staging System (ISS) stage were independently associated with increased mortality risk.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAge, platelet count, and established biochemical markers (β2-microglobulin, LDH, renal function) are reliable predictors of mortality in multiple myeloma, whereas bone marrow PC percentage and reticulin fibrosis grade do not independently predict survival outcomes. These findings support the prioritization of readily available clinical and laboratory parameters over bone marrow histological features for prognostication in MM. Further prospective multicenter studies are warranted to validate these findings and refine risk stratification strategies.\u003c/p\u003e","manuscriptTitle":"Clinical and Laboratory Predictors of Mortality in Multiple Myeloma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 18:25:24","doi":"10.21203/rs.3.rs-8918370/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-10T14:21:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220042025639282651076277841186350357057","date":"2026-04-10T13:55:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T15:47:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31588073624000040903219076399061789808","date":"2026-04-09T13:43:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T06:40:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T04:13:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T05:33:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T19:47:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-05T13:05:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42c07c34-7cc7-4f4c-8150-91c6d121611d","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T18:25:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 18:25:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8918370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8918370","identity":"rs-8918370","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}