Predictors of quality of life in multiple myeloma patients: a multi-centered cross-sectional study.

Introduction

Multiple myeloma (MM) is a malignant hematologic disorder characterized by the clonal proliferation of plasma cells in the bone marrow and the production of monoclonal immunoglobulin (M-protein) [ 1 ]. It accounts for approximately 1–2% of all cancers and 10–20% of hematologic malignancies [ 2 ]. Clinically, MM often presents with bone pain, particularly in the spine and ribs, fatigue, anemia, recurrent infections, and renal impairment due to light chain deposition and hypercalcemia, which may also cause nausea, constipation, or confusion [ 3 ]. Diagnosis is based on clonal plasma cell infiltration in the bone marrow or evidence of plasmacytoma [ 4 ]. Globally, MM incidence and prevalence are rising, reflecting a growing disease burden [ 5 ]. In Palestine, the disease remains a notable concern, with 96 new cases and a 5-year prevalence of 251 reported in the West Bank and Gaza Strip in 2022 [ 6 ].

During the past decade, progressive and innovative changes have been made to the treatment of multiple myeloma [ 7 ]. According to studies, multiple myeloma patients report the lowest physical health-related quality of life among cancer survivors [ 8 ]. Various anti-myeloma therapeutic agents have been associated with disturbing side effects, including worsening peripheral neuropathy, cardiovascular toxicity, and intensification of fatigue [ 9 , 10 ]. Along with the burden of these side effects, patients with multiple myeloma endure significant psychological distress mainly due to anxiety and fear of disease relapse, as well as challenges associated with a continuous medical regimen [ 11 ]. Another major concern is the financial burden on the patient, ranging from therapy expenses to the inability to work due to the disease burden [ 9 , 12 ].

Maintaining or improving the HRQOL of patients with MM has become a key treatment objective [ 13 ]. Identifying the predictors of QoL in MM has become a central focus of clinical and research efforts [ 14 ]. Understanding these predictors helps clinicians to select appropriate treatment regimens, promote patient adherence, and improve HRQOL [ 15 ]. Pain is one of the most prominent symptoms reported by multiple myeloma patients, and higher pain severity is strongly associated with worse HRQOL outcomes [ 16 ]. Patients with MM often report reduced mobility, muscle weakness, multiple fractures, and a decline in daily physical functioning, which is a core predictor of HRQOL [ 14 ]. Physical decline can lead to feelings of frustration and social isolation, further diminishing HRQoL [ 17 ]. Additionally, frailty is a strong independent predictor of poor quality of life (QoL) in multiple myeloma patients, resulting in increased physical limitations, more severe fatigue compared to other individuals [ 13 , 18 ]. In this multi-center cross-sectional study, we aim to evaluate QOL for MM patients and to identify the most significant predictors influencing it.

Methodology

Study design and aim

This study employed a cross-sectional observational design between January 2018 and December 2025. Data collection incorporated both prospective and retrospective components. The prospective component involved administering the validated Arabic version of the EORTC QLQ-MY20 questionnaire directly to patients during routine clinic visits, thereby capturing patient-reported outcomes in real time. In parallel, a retrospective review of hospital medical records was performed to extract complementary demographic, clinical, and laboratory information. By integrating these two data sources at a single time point for each patient, we ensured a comprehensive profile that combines subjective quality-of-life measures with objective clinical predictors, while maintaining the cross-sectional nature of the study.

Study setting and participants

Patients were enrolled from hospitals across the northern, central, and southern regions of the West Bank, including Al-Watani Hospital and An-Najah National University Hospital in Nablus, the Palestinian Medical Complex in Ramallah, Dura Hospital in Hebron, Beit Jala Hospital in Bethlehem, and Jenin Hospital in Jenin. Eligible Patients were ≥ 18 years with a confirmed diagnosis of multiple myeloma based on the diagnostic criteria, which include the presence of ≥ 10% clonal plasma cells in the bone marrow or a biopsy-proven plasmacytoma, along with at least one myeloma-defending events such as hypercalcemia, renal insufficiency, anemia, or bone lesions (CRAB features), or specific biomarkers of malignancy [ 19 ]. Patients must have received at least one prior line of treatment such as proteasome inhibitors (e.g., bortezomib), immunomodulatory drugs (e.g., lenalidomide or thalidomide), and corticosteroids. Patients who agreed to participate and were able to complete the questionnaire independently were informed and signed the corresponding consent before starting data collection. Participants were excluded if they are critically ill, refused to participate or were deceased at the time of data collection. The patient selection process, including their final numbers is illustrated in Fig. 1 .

Instrument: EORTC QLQ-MY20 questionnaire

The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire–Multiple Myeloma Module (EORTC QLQ-MY20) is a disease-specific instrument developed to assess health-related quality of life (HRQoL) in patients with multiple myeloma [ 20 ]. The QLQ-MY20 focuses on issues pertinent to multiple myeloma patients, including disease symptoms, side effects of treatment, body image, and future perspective.

The QLQ-MY20 comprises 20 items divided into four domains: Disease Symptoms (DS; 6 items), Side Effects of Treatment (SE; 10 items), Future Perspective (FP; 3 items), and Body Image (BI; 1 item). All items are scored using a 4-point Likert scale ranging from 1 (“Not at all”) to 4 (“Very much”), based on the patient’s experience during the past week. Scores are linearly transformed to a scale ranging from 0 to 100. Higher scores in the DS and SE domains indicate a greater symptom burden, while higher scores in the FP and BI domains reflect better functioning. The official Arabic (Lebanon) version of the EORTC QLQ-MY20 was used in this study [ 21 ]. It has been culturally adapted and validated in previous studies, showing good reliability with Cronbach’s alpha values ranging from 0.74 to 0.90 [ 21 ].

Scoring method

Scoring of the EORTC QLQ-MY20 followed the official EORTC guidelines. Each item was rated on a 4-point Likert scale (1 = ‘Not at all’ to 4 = ‘Very much’) referring to the previous week. The instrument includes three multi-item subscales, including Disease Symptoms (6 items), Side Effects of Treatment (10 items), and Future Perspective (3 items), and one single-item scale assessing Body Image. For multi-item domains, raw scores were calculated as the average of the included items, while for Body Image, the raw score was the item response. All scores were then linearly transformed to a 0–100 scale. Higher scores in the symptom domains indicate greater symptom burden, while higher scores in functional domains indicate better functioning (e.g., more positive future outlook, better body image). This standardized transformation allows valid comparisons across domains and studies.

Data collection procedure

Patients with a confirmed diagnosis of multiple myeloma were recruited through routine clinic visits and oncology departments in the above-mentioned West Bank hospitals. After obtaining informed consent, in-person interviews were conducted by trained medical students who used the Arabic version of the QLQ-MY20 questionnaire. Additional demographic and clinical variables were obtained through the selected hospital’s medical records. These variables included the patient’s demographics (such as gender, age, residency information, body habitus etc.), Comorbidities were summarized as burden categories (0, 1, ≥ 2) (including non-communicable diseases, inflammatory bowel disease (IBD) thyroid abnormalities, along with others) and symptom occurrence during their disease course (for example: weight loss, abdominal pain, bone pain and change in bowel habits). Furthermore, the records were also used to acquire general laboratory parameters, more specifically, hemoglobin level, white blood cell (WBC) count, creatinine and electrolyte levels, as well as multiple myeloma-specific investigations (such as beta-2 microglobulin, Kappa and Lambda light chains, and bone marrow plasma cell percentage, etc.). Lastly, the treatment regimen specific to each patient was documented, which included one of the following: bortezomib, cyclophosphamide, dexamethasone, the VCD protocol (a combination of the latter three drugs), or bone marrow transplant. All patients were informed of the aim of the study and assured of the confidentiality of the information reported.

The EORTC QLQ-MY20 was administered in its validated Arabic version. As per protocol, the tool is self-administered; however, to ensure accurate and complete responses, medical students trained in data collection were present during questionnaire administration, given the variability in literacy levels. Patients were encouraged to complete the items independently, and assistance was provided only when patients requested clarification or had difficulty reading specific items. In such cases, the data collector read the question verbatim without rephrasing or interpretation, ensuring that the patient’s response remained entirely self-reported.

Predictor selection and rationale

To minimize multiplicity, prevent predictor–outcome overlap, and enhance clinical interpretability, we pre-specified predictors based on literature and instrument content. Because several symptoms are directly measured by the QLQ-MY20 (items 31–46, 47–50), we excluded those symptoms from modeling to avoid tautological associations (e.g., bone/back/hip or chest pain, drowsiness, thirst, dry mouth, tingling, indigestion/heartburn, burning eyes, “felt ill”). Instead, we modeled non-overlapping clinical variables with established relevance to HRQoL or MM biology: age, sex, comorbidity burden (0/1/≥2), hemoglobin, creatinine, albumin, β2-microglobulin (or ISS/R-ISS where available), calcium, osteolytic lesions, pathologic fracture, and ASCT history according to the literature [ 22 – 25 ].

Therapy-related variables were abstracted from medical records. These variables indicate whether patients had received specific agents (e.g., bortezomib, dexamethasone, cyclophosphamide) or autologous stem cell transplantation at any point before questionnaire administration. In routine practice, these drugs were generally administered as part of standard combination regimens (such as VCD: bortezomib, cyclophosphamide, dexamethasone), rather than as monotherapies. For the purposes of this study, we report agent-level exposure descriptively, while acknowledging that most treatments were delivered in multi-drug protocols.

Ethical considerations

This study was approved by the Institutional Review Board (IRB) of An-Najah National University. All participants provided written informed consent after being informed of the study’s objectives, procedures, and their rights. Only individuals capable of providing consent were included. Participation was voluntary, with the option to withdraw at any stage without consequences. Confidentiality and anonymity were strictly maintained, and data were stored securely. All procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki.

Sample size justification

The sample size was determined based on the estimated 5-year prevalence of multiple myeloma (MM) in the West Bank and Gaza, reported as 251 cases in GLOBOCAN 2022 [ 6 ]. Using standard assumptions for proportion-based sampling (confidence level: 95%, margin of error: 5%, response distribution: 50%), the minimum recommended sample size was calculated to be 153. Our study included 202 MM patients, exceeding this threshold and allowing for improved precision and subgroup analyses. This sample is also sufficient to support multivariable modeling with 10 or more predictors at > 80% power assuming medium-to-large effect sizes (f² ≥ 0.15).

Statistical analysis

All statistical analyses were conducted using Stata, version 17 (StataCorp). Because outcome variables (Disease Symptom Scale, Side Effect Scale, Body Image Scale, and Future Perspective Scale) were not normally distributed, data are presented as medians with interquartile ranges (IQRs; Q1–Q3). For univariate analyses, the Mann–Whitney U test was used to compare 2 groups, and the Kruskal–Wallis test was used for comparisons among more than 2 groups. Associations between continuous variables were evaluated using Spearman’s rank correlation coefficient. All tests were 2-sided, and P values < 0.05 were considered statistically significant.

For multivariable analyses, linear regression models with nonparametric bootstrap estimation (2000 replications; seed = 12345) were used to generate robust standard errors and 95% CIs. To reduce model overfitting and improve variable selection, the least absolute shrinkage and selection operator (LASSO) regression method with 10-fold cross-validation was employed. The optimal penalty parameter (λ) was selected by minimizing the cross-validated prediction error. Final models retained variables with nonzero coefficients as identified by the LASSO procedure.

Results

Table 1 presents the demographic, clinical, laboratory, and treatment characteristics of the study cohort. The median age was 63 years (IQR, 55–70) with equal representation of males and females. More than half of patients (55.2%) reported two or more comorbidities, most frequently hypertension (41.3%), osteoporosis (40.3%), and anemia (35.3%), while diabetes mellitus (29.9%) and acute pain (30.3%) were also common. Osteolytic lesions were detected in 27.9% of cases, and 13.9% had incidentally discovered disease. Hematologically, patients demonstrated anemia with a median hemoglobin of 10.3 g/dL and elevated systemic inflammation reflected by a median ESR of 100 mm/hr. Beta-2 microglobulin levels were also high (median 4.19 mg/L), consistent with advanced disease burden. Regarding therapy, 32.2% underwent autologous bone marrow transplantation, and among pharmacologic options, dexamethasone (20.9%) and bortezomib (18.9%) were the most commonly used agents. See eTable 1 in supplement 1 file to have the full table variables.

Table 2 reports the distribution of presenting symptoms in the study cohort. Pain-related manifestations were most prominent, with lower back pain (73.6%), joint pain (70.1%), and bone pain (67.2%) reported by the majority of patients. Systemic features were also frequent, including constitutional symptoms (59.2%) and weight loss (56.7%). Notably, peripheral and organ-related complaints such as lower limb edema (swelling) (44.8%), abdominal pain (43.8%), headache/dizziness (43.8%), and chest pain (39.3%) were common. Skeletal complications were reflected by pathological fractures in nearly one-quarter of patients (24.9%). Gastrointestinal disturbances were prevalent, with diarrhea (39.6%) more common than constipation (7.9%).

eTable- 2 : distribution of EORTC QLQ-MY20 score answers frequency (%).

Patient responses to each item of the EORTC QLQ-MY20 instrument were summarized to capture the distribution of symptom burden and treatment-related effects. The frequencies of answers across the four Likert scale options (Not at all, A little, quite a bit, very much) are detailed for all 20 items in Supplement 1 (eTable- 2 ).

Figure 2 displays the distribution of patient-reported outcome scores across the four QLQ-MY20 domains: Future Perspective (FP), Body Image (BI), Side Effects of Treatment (SE), and Disease Symptoms (DS). Overall, FP and BI scores were relatively high, with medians clustered above 60, indicating more favorable perceptions in these domains. In contrast, SE and DS domains showed lower median values (approximately 30–40), reflecting a greater burden of treatment-related side effects and disease-related symptoms. The boxplots also demonstrate considerable variability, particularly in BI and FP, while SE scores showed a tighter distribution but with several outliers, suggesting that a subset of patients reported disproportionately high side effect burdens. DS scores exhibited wide dispersion across the scale, highlighting heterogeneity in symptom experience among patients. Taken together, these distributions suggest that while patients generally maintain optimism about the future and body image, side effects and symptom burden remain key areas of concern in quality-of-life outcomes.

Table 3 shows that several clinical and symptom characteristics were significantly associated with impaired quality of life across QLQ-MY20 subscales. Patients with incidentally discovered MM reported markedly worse scores across all domains, including higher disease symptoms (44 [33–63] vs. 27 [11–55], p = 0.010), more side effects of treatment (40 [24–52] vs. 26 [13–36], p = 0.004), poorer body image (33 [33–66] vs. 100 [50–100], p < 0.001), and lower future perspective (61 [36–86] vs. 88 [55–100], p = 0.016). Similarly, osteoporosis was associated with worse outcomes across all scales, with higher disease symptoms (38 [13–61] vs. 27 [11–44], p = 0.038), greater side effects (33 [13–50] vs. 23 [13–36], p = 0.004), lower body image (66 [33–100] vs. 100 [66–100], p = 0.004), and poorer future perspective (66 [44–100] vs. 88 [88–100], p = 0.001). Arthritis also significantly impaired QoL, with elevated symptom burden (47 [16–75] vs. 27 [11–55], p = 0.016), more treatment side effects (41 [22–54] vs. 26 [13–36], p = 0.005), reduced body image (66 [0–100] vs. 66 [33–100], p = 0.011), and lower future perspective (55 [33–77] vs. 88 [55–100], p = 0.001). Patients with autoimmune diseases reported substantially worse scores, including higher disease symptoms (52 [43–70] vs. 27 [11–55], p = 0.030), greater side effects (50 [46–61] vs. 26 [13–40], p = 0.002), poorer body image (16 [0–50] vs. 66 [33–100], p = 0.012), and markedly lower future perspective (38 [27–58] vs. 77 [55–100], p = 0.007). In addition, neurological disease was associated with higher symptom scores (83 [27–94] vs. 27 [11–55], p = 0.043) and poorer body image (0 [0–50] vs. 66 [33–100], p = 0.012), while gout was linked to increased side effects (43 [20–60] vs. 26 [13–40], p = 0.019), reduced body image (33 [0–75] vs. 100 [33–100], p = 0.001), and lower future perspective (55 [44–75] vs. 88 [55–100], p = 0.031). Likewise, peptic ulcer predicted worse QoL with higher side effects (45 [24–70] vs. 26 [13–40], p = 0.007) and poorer body image (33 [0–66] vs. 100 [33–100], p < 0.001).

Several presenting symptoms also showed strong associations with QoL. Patients with weight loss reported higher disease symptoms (33 [11–61] vs. 27 [11–44], p = 0.045), more side effects (30 [16–44] vs. 23 [13–36], p = 0.031), poorer body image (66 [33–100] vs. 100 [66–100], p = 0.014), and lower future perspective (66 [44–100] vs. 88 [55–100], p = 0.047). Abdominal pain was linked to worse disease symptoms (33 [16–61] vs. 27 [5–50], p = 0.026), more side effects (35 [17–50] vs. 23 [13–33], p < 0.001), and poorer body image (66 [33–100] vs. 100 [66–100], p = 0.004). Patients with headache and dizziness had higher symptom scores (38 [16–65] vs. 22 [5–44], p = 0.001), more side effects (36 [20–50] vs. 23 [10–33], p < 0.001), and lower body image (66 [33–100] vs. 100 [33–100], p = 0.013). Chest pain predicted worse side effects (36 [23–53] vs. 20 [13–33], p < 0.001), poorer body image (66 [33–100] vs. 100 [66–100], p = 0.014), and diminished future perspective (66 [33–100] vs. 88 [55–100], p = 0.001). Bone pain was associated with more side effects (33 [16–46] vs. 20 [13–30], p < 0.001) and reduced future perspective (66 [44–100] vs. 100 [63–100], p = 0.009). Similarly, lower back pain impaired side effect scores (30 [16–45] vs. 20 [10–31], p = 0.002). Importantly, lower limb edema (swelling) was consistently associated with impaired QoL, with higher symptom scores (44 [16–66] vs. 22 [5–38], p < 0.001), more side effects (33 [23–50] vs. 20 [10–33], p < 0.001), poorer body image (66 [33–100] vs. 100 [66–100], p = 0.013), and lower future perspective (66 [44–100] vs. 88 [55–100], p = 0.020). Finally, constitutional symptoms were linked to greater symptom burden (38 [16–61] vs. 25 [5–40], p = 0.005), more side effects (33 [20–46] vs. 20 [13–33], p < 0.001), poorer body image (66 [33–100] vs. 100 [33–100], p = 0.018), and reduced future perspective (77 [44–100] vs. 94 [55–100], p = 0.026). To see the complete, full table data, see eTable 3 in Supplement 1 file.

eTable 4 in Supplementary File 1 (originally titled ‘Correlational Analysis of Continuous Clinical and Laboratory Parameters with Quality-of-Life Domains in Multiple Myeloma Patients Using QLQ-MY20’) shows that most continuous demographic and laboratory parameters demonstrated weak and statistically nonsignificant correlations with the four QoL domains. Notable exceptions included β2-microglobulin ( r = − 0.220; p = 0.038), calcium ( r = − 0.244; p = 0.004), and total protein ( r = − 0.203; p = 0.021), which were significantly associated with body image or future perspective scores. These findings suggest that biochemical indicators of disease activity and metabolic status may indirectly influence patient-reported psychosocial outcomes, whereas most other laboratory values (e.g., hemoglobin, creatinine, ESR) did not show meaningful associations.

Table 4 presents the determinants of the Side Effects of Treatment (SE) subscale scores from the EORTC QLQ-MY20. Several baseline symptoms and clinical characteristics were assessed for their association with perceived treatment-related side effects. Among these variables, abdominal pain (β = 6.75; 95% CI: 1.92–11.59; p = 0.006), chest pain (β = 9.63; 95% CI: 4.25–15.01; p < 0.001), and swelling/edema (β = 8.15; 95% CI: 2.90–13.40; p = 0.002) emerged as statistically significant predictors, indicating that patients experiencing these symptoms reported significantly higher SE subscale scores, reflecting a greater burden of treatment-related side effects. Other variables, including osteoporosis, gout, headache, bone pain, constitutional symptoms, and incidental disease discovery, showed no significant associations. Interestingly, autoimmune diseases ( p = 0.087) and peptic ulcer disease ( p = 0.080) demonstrated borderline significance, suggesting a possible trend toward higher SE scores, though not reaching conventional statistical thresholds. Collectively, these findings highlight that certain non-myeloma symptoms (such as abdominal pain and swelling) and systemic complaints (such as chest pain) may exacerbate the perceived burden of treatment-related toxicity, underscoring the multifactorial nature of side effect perception in patients with multiple myeloma.

To enhance model parsimony and reduce overfitting, LASSO regression with 10-fold cross-validation was performed. The model selected 13 predictors, including: Incidental, Osteoporosis, Autoimmune Diseases, Peptic Ulcer, Gout, Weight Loss, Abdominal Pain, Headache, Chest Pain, Bone Pain, Lower Back Pain, Swelling, and Constitutional Symptoms. The optimal lambda (λ) value was 0.2168, achieving a cross-validated mean prediction error of 26.47 and explaining approximately 25.9% of the variance in SE scores. The final model demonstrated low multicollinearity (mean VIF = 1.27), supporting model stability.

Table 5 presents the predictors of body image disturbance among patients with multiple myeloma. Several clinical and symptom-related variables were examined for their association with the EORTC QLQ-MY20 body image subscale. Of these, peptic ulcer disease (β = − 25.82; 95% CI: − 47.18 to − 4.46; p = 0.018) and gout (β = − 22.06; 95% CI: − 38.95 to − 5.18; p = 0.010) were statistically significant, indicating that patients with these comorbidities reported notably worse body image perception. Serum calcium levels demonstrated a borderline association (β = − 4.87; 95% CI: − 9.79 to 0.04; p = 0.052), suggesting a possible negative relationship with body image disturbance. Other variables, including incidental disease discovery, osteoporosis, CHF, cerebrovascular disease, neurological disease, abdominal pain, headache, chest pain, and swelling, were not significantly associated with body image outcomes.

To refine variable selection and improve model interpretability, LASSO regression with 10-fold cross-validation was applied. The final model retained 12 predictors, including: Incidental, Osteoporosis, CHF, Cerebrovascular Disease, Calcium, Peptic Ulcer, Neurological Disease, Gout, Abdominal Pain, Headache, Chest Pain, and Swelling. The selected lambda value (λ = 0.0679) minimized the cross-validated prediction error (1.11), and the model explained approximately 10.5% of the variance in BI scores. Multicollinearity was minimal (mean VIF = 1.2), supporting the robustness of the model estimates.

Table 6 presents the predictors of the Future Perspective (FP) subscale scores in patients with multiple myeloma. Overall, most clinical and symptom-related variables did not demonstrate statistically significant associations with FP scores. However, chest pain showed a borderline negative association (β = − 8.15; 95% CI: − 16.72 to 0.42; p = 0.062), suggesting that patients experiencing chest pain may perceive greater uncertainty and pessimism regarding their future disease trajectory and overall outlook. Other comorbidities and symptoms, including osteoporosis, arthritis, autoimmune diseases, cardiac arrest, gout, bone pain, and swelling, were not significantly linked to FP scores, although some (e.g., cardiac arrest, β = − 16.33; p = 0.118) showed a numerical trend toward worse perceptions. Taken together, these results indicate that while baseline physical comorbidities and musculoskeletal symptoms did not independently drive FP outcomes, cardiopulmonary symptoms (such as chest pain and potentially cardiac arrest) may exert a more profound psychological impact by amplifying patients’ fear of disease progression, treatment complications, or mortality risk. This highlights the importance of addressing cardiopulmonary health and associated anxiety in comprehensive supportive care strategies for multiple myeloma patients.

To improve model parsimony and identify the most influential predictors, LASSO regression with 10-fold cross-validation was performed. The final model retained 8 variables: Osteoporosis, Arthritis, Autoimmune Diseases, Cardiac Arrest, Gout, Chest Pain, Bone Pain, and Swelling. The selected lambda (λ = 0.2622) minimized the cross-validated prediction error (6.50), and the model explained 2.4% of the variance in FP scores (R² = 0.0242). Despite the modest predictive capacity, the LASSO model offers a stable and interpretable set of clinical features potentially relevant to patients’ perceptions of their future. Multicollinearity was minimal (mean VIF = 1.23), supporting the reliability of model estimates.

Figure 3 illustrates scatterplots showing the associations between selected clinical symptoms and QLQ-MY20 quality of life domains. The plots demonstrate the fitted linear trends for each relationship. Patients with gout reported lower body image (BI) scores compared to those without gout, consistent with the regression findings. Similarly, chest pain was associated with a downward trend in future perspective (FP) scores, indicating greater uncertainty and pessimism regarding prognosis among symptomatic patients. In contrast, abdominal pain was linked to higher side effect (SE) subscale scores, reflecting greater treatment-related burden. Finally, swelling/edema was associated with increased disease symptom (DS) scores, suggesting a heightened perception of symptom severity. Together, these visualizations reinforce the regression analyses by highlighting that specific clinical symptoms particularly gout, chest pain, abdominal pain, and swelling have a measurable and clinically meaningful impact on patients’ self-reported quality of life across multiple domains.

Discussion

This study aimed to evaluate the health-related quality of life (HRQoL) among patients with multiple myeloma (MM) in the west bank -Palestine and to identify the significant predictors influencing four key patient-reported outcomes: disease symptoms, treatment side effects, body image, and future perspective. Given the chronic and incurable nature of MM, understanding factors that significantly impact patients’ quality of life remains essential to improving clinical management and supportive care strategies.

Our analysis identified several clinical factors significantly correlated with a higher disease symptom burden. Specifically, chest pain, bone pain, and peripheral swelling emerged as independent predictors, which aligns with previous literature suggesting that bone pain is a prevalent and debilitating symptom among MM patients, negatively impacting their mobility and emotional well-being [ 26 – 28 ]. The finding that peripheral swelling significantly contributes to symptom burden signals an urgent need for healthcare providers to manage treatment-related side effects and associated comorbidities effectively, as these factors can exacerbate the overall discomfort experienced by patients [ 29 , 30 ]. Recent literature corroborates these findings, reporting similar associations between symptomatic burden and diminished HRQoL due to chronic discomfort and limited physical functioning, further underscoring the urgency of treating these symptoms [ 31 , 32 ].

When considering treatment side effects, our findings indicated that chest pain, abdominal pain, and peripheral swelling were significantly associated with a heightened perception of toxicity. This observation aligns with existing studies, where gastrointestinal distress and peripheral edema have been documented as common adverse effects of MM treatments, adversely influencing patients’ HRQoL [ 33 , 34 ]. Moreover, the trends toward significance for autoimmune diseases and peptic ulcer not only highlight the cumulative impact of comorbidities on treatment tolerability but suggest a potential area for further investigation involving larger, prospective cohorts to better understand these interactions [ 35 , 36 ].

The results from the body image subscale revealed noteworthy correlations with peptic ulcer and gout, enhancing the understanding of the psychological and social ramifications posed by visible or painful physical conditions. This finding resonates with prior research, indicating that physical discomfort linked with altered appearance profoundly affects patients’ self-esteem and social integration, reinforcing the need to consider psychological interventions alongside physical treatment [ 37 , 38 ]. Additionally, the almost significant association with calcium imbalance suggests that vigilant biochemical monitoring might be needed, as these imbalances can complicate body image issues and overall patient satisfaction with their disease management [ 39 ].

Interestingly, while no statistically significant predictors were found for the future perspective subscale, the near-significant negative association of chest pain invites discourse about the interaction between unresolved physical symptoms and patients’ psychological well-being. This observation echoes findings in previous research demonstrating that physical ailments can severely affect patients’ outlook on their prognosis and future health, reiterating the multifactorial and psychosocial nature of outlooks among cancer patients [ 40 , 41 ]. Comprehensive psychosocial support interventions may serve as a viable strategy to address concerns impacting patients’ perceptions of their future and overall quality of life [ 42 ].

In deeper comparison with existing studies, our findings synchronize with those found within numerous international cohorts, shedding light on various factors adversely affecting HRQoL within MM populations. For instance, research conducted on different patient groups in the United States and Europe highlights similar symptom categories contributing to decrements in HRQoL, particularly pain and treatment-induced side effects [ 43 – 45 ]. Furthermore, studies have consistently related tangible aspects such as treatment efficacy to improvements in patients’ reported quality of life outcomes, emphasizing a necessary focus on individualized care plans sensitive to patient-reported symptom burdens [ 46 ].

The HRQoL literature emphasizes the use of validated measurement tools such as the EORTC QLQ-C30 and the specific EORTC QLQ-MY20 module to ensure a consistent evaluation of symptom impact across international trials. These tools enable effective benchmarking against normative data and promote the adoption of tailored interventions to address the unique symptomatology associated with MM [ 26 , 47 ]. Our study exemplifies the importance of coupling standard comparative analytics with local contextual insights, further refining the understanding of patient-reported outcomes in distinct geographic areas such as Palestine.

The application of LASSO regression provided a robust, parsimonious approach to identifying critical variables. Across the scales, LASSO models selected clinically relevant predictors, with explained variance ranging from 2.4% for future perspective to 32.4% for body image. Although the predictive capacity for future perspective was modest, these results highlight that the domain may be driven predominantly by psychosocial rather than strictly clinical factors. Multicollinearity was minimal across models (mean VIF ranged from 1.2 to 1.27), underscoring the stability and interpretability of our findings.

This study fills an important knowledge gap, particularly in the context of Palestine, where local epidemiological and quality-of-life data remain limited. The regional context—marked by resource limitations and variable healthcare access—highlights the relevance of our findings for local clinical practice and public health strategies, Comparative analysis with international studies reinforces the universality of certain findings, such as the impact of pain and treatment side effects on HRQoL, while also drawing attention to regional variations that warrant further exploration.

Limitations

This study has several limitations. Its cross-sectional design limits the ability to draw causal inferences. Reliance on self-reported symptoms introduces potential reporting bias and subjective variability. Moreover, the study did not incorporate psychosocial or socioeconomic factors, which significantly influence QoL and could confound observed associations. Despite employing LASSO regression to reduce model overfitting, residual confounding remains possible. Additionally, generalizability might be limited to populations with similar demographic and healthcare resource profiles. However, these limitations were mitigated by strict adherence to STROBE guidelines, enhancing the transparency and reliability of reported results. Although clinically relevant, we were unable to include variables such as time since diagnosis and number of prior therapy lines because this information was not consistently available across the cohort. This represents an important limitation, as these factors may influence patients’ quality of life trajectories. Nevertheless, by focusing on validated comorbidity categories, treatment exposure, and patient-reported symptom burden, our study still offers meaningful insights into determinants of QoL in multiple myeloma. Future prospective studies should incorporate these additional clinical parameters to further refine predictive models.

We have ensured that results are reported with precision, interpretations remain cautious, and conclusions are proportionate to the evidence provided. Specifically, our cross-sectional design precludes causal inference, and reliance on self-reported data introduces possible recall and reporting bias. Moreover, psychosocial and socioeconomic factors, which are known to influence quality of life, were not incorporated, and this omission may limit explanatory power. While our findings provide important insight into symptom-driven quality-of-life predictors in multiple myeloma, they should be interpreted in light of these limitations and validated in prospective longitudinal studies.

Conclusion

This multicenter study identified specific symptoms (abdominal pain, chest pain, swelling) and comorbidities (gout, peptic ulcer disease) as major determinants of QoL in multiple myeloma patients in Palestine. Chest pain also showed a trend toward worsening future outlook. These results underscore the need for targeted symptom management and comorbidity-focused supportive care, which could meaningfully improve patient-reported outcomes in resource-limited healthcare systems.

Supplementary Information

Supplementary Material 1.

Acknowledgements

We would like to thank all the patients who participated in this study. We also extend our gratitude to the staff at the participating hospitals in the West Bank of Palestine for their support and collaboration during data collection.

Abbreviations

Multiple Myeloma

Quality of Life

European Organization for Research and Treatment of Cancer

MY20–Quality of Life Questionnaire–Multiple Myeloma Module

Health–Related Quality of Life

Autologous Stem Cell Transplantation

Inflammatory Bowel Disease

White Blood Cell

Bortezomib, Cyclophosphamide, Dexamethasone protocol

Institutional Review Board

Disease Symptoms (domain of QLQ–MY20)

Side Effects of Treatment (domain of QLQ–MY20)

Future Perspective (domain of QLQ–MY20)

Body Image (domain of QLQ–MY20)

Raw Score

Interquartile Range

Confidence Interval

Least Absolute Shrinkage and Selection Operator

19–Coronavirus Disease 2019

Hypertension

Acquired Immunodeficiency Syndrome

Congestive Heart Failure

Ischemic Heart Disease

Transient Ischemic Attack

Red Blood Cells

Erythrocyte Sedimentation Rate

Grams per Deciliter

Milligrams per Deciliter

Electronic Table (usually Supplementary Table)

Myocardial Infarction

Not Available

Mann–Whitney Test

Kruskal–Wallis Rank Test

Hemoglobin

Regularization parameter in LASSO regression

Variance Inflation Factor

P–value (statistical significance indicator)

Beta coefficient (from regression analysis)

Coefficient of determination (variance explained by the model)

Estimated Beta Coefficient (Regression coefficient)

Strengthening the Reporting of Observational Studies in Epidemiology

C30–European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30

Author contributions

N.A.H., M.A., and H.A.K. conceptualized and designed the study. S.Z., B.B., A.D., D.N., A.K., F.I., N.S., and K.A. contributed to data collection and patient recruitment. Q.S., R.Y., O.S., M.M., A.Z., M.A., E.A., L.S., S.A., O.E., S.I., A.B., S.A., and H.A.A. participated in data organization and manuscript drafting. F.H. contributed to manuscript drafting and editing. A.Z. and I.A. performed data analysis and interpretation. O.A.A.D., A.A., and M.M. contributed to manuscript editing and final revision. H.A.K. supervised the study and is the corresponding author. All authors reviewed and approved the final manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding authors upon request.

Declarations

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Institutional Review Board of Najah National University in Palestine. Written informed consent was obtained from all participants before data collection. The study was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki.

Consent for publication

Not applicable. The manuscript does not include any individual personal data in any form (e.g., images, videos, personal details).

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Nizar Abu Hamdeh and Mohammad Alnees contributed equally to this work.

Contributor Information

Mohammad Alnees, Email: a2011z2012z2013@gmail.com.

Haitham Abu Khadija, Email: Haitham2048@yahoo.com.

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Associated Data

Supplementary Materials

Supplementary Material 1.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding authors upon request.