Клинические характеристики, лабораторные меры и качество жизни как предвестники соблюдения лекарств в множестве миеломы.

Introduction

Multiple myeloma (MM) is a clonal plasma cell malignancy marked by the overproduction of monoclonal immunoglobulins (M-protein) due to abnormal plasma cell proliferation in the bone marrow 1 . MM accounts for about 1% of all cancers and 10% of hematologic malignancies globally. Contributing factors include chronic immunological dysregulation, bone marrow microenvironment changes, and genetic abnormalities. Symptoms commonly include CRAB features: bone pain, anemia, renal impairment, hypercalcemia, and infections 1 , 2 . The criteria developed by the International Myeloma Working Group (IMWG) are used to diagnose MM 3 . Epidemiologically, MM represents approximately 160,000 new cases annually worldwide, with higher incidence rates in developed nations 4 . According to GLOBOCAN 2022, Palestine reported 251 prevalent cases of multiple myeloma over five years, providing important local context for disease burden in the West Bank and Gaza 5 , MM accounts for 1.1% of all cancer cases, with Gaza seeing 2% of hematologic malignancies 6 .

In high-resource settings, bortezomib–lenalidomide–dexamethasone (VRd) and, more recently, daratumumab–VRd (Dara-VRd) are preferred frontline regimens for transplant-eligible patients according to contemporary EHA–ESMO guidelines and randomized trials such as GRIFFIN 7 , 8 . However, across many low- and middle-income countries, including parts of the Middle East, access to lenalidomide and daratumumab is limited, requiring treatment protocols to be adapted to local resource constraints 9 , 10 . In the participating Palestinian public oncology centers, bortezomib–cyclophosphamide–dexamethasone (VCD/CyBorD) has therefore represented the pragmatic standard first-line induction regimen during the study period.

Among treatment regimens, bortezomib-based triplets remain a key component of multiple myeloma therapy. Contemporary international guidelines from NCCN, EHA–ESMO, and IMWG-aligned expert reviews recommend lenalidomide–bortezomib–dexamethasone (VRd) and daratumumab-based quadruplets as preferred first-line options in newly diagnosed MM 7 , 11 , 12 . Nevertheless, these same sources acknowledge cyclophosphamide–bortezomib–dexamethasone (VCD/CyBorD) as an acceptable induction alternative when lenalidomide or anti-CD38 antibodies are unavailable, contraindicated, or unaffordable 7 , 11 , 12 . Furthermore, real-world data from resource-limited settings show that CyBorD/VCD is frequently used as first-line therapy and yields better outcomes than older regimens such as VAD, supporting its description as a well-established and moderately effective regimen 13 . Adherence to drug therapy in MM remains a significant challenge that can undermine treatment outcomes. The Morisky Medication Adherence Scale-4 items (MMAS-4) is a tool for assessing medication adherence, often used in conditions like hypertension, where scores distinguish between high and low-moderate adherence. It evaluates patient behaviors related to taking medication, with specific questions revealing common adherence challenges and contributing to the identification of adherence predictors 14 . In this study, medication adherence was assessed using the Arabic version of the (MMAS-4), which has been formally translated and validated in Arabic-speaking populations 15 . Multiple factors contribute to poor adherence, including the complexity of oral treatment regimens, the necessity of managing several medications with different dosing schedules, and the burden of persistent side effects like nausea, fatigue, and neuropathy. These adverse effects can lead patients to skip doses in an attempt to minimize discomfort deliberately 16 . Additionally, unintentional non-adherence is often reported due to forgetfulness, particularly among patients with busy lifestyles or polypharmacy. Socioeconomic constraints, such as high medication costs and inadequate support systems, further exacerbate the issue and compromise treatment consistency 16 . These adherence barriers, when coupled with the physical and psychological burden of MM, highlight the need for integrated assessments of both treatment adherence and patient quality of life.

Health-related quality of life (HRQoL) refers to a person’s overall well-being concerning their physical, emotional, and social functioning, especially when affected by illness or medical treatment 17 , 18 . In patients with multiple myeloma (MM), HRQoL is often significantly reduced. This reduction is attributed to disease-related symptoms such as bone pain, fatigue, and weakness, as well as treatment-related side effects from chemotherapy, corticosteroids, and long-term maintenance drugs 18 , 19 . As a result, many MM patients report diminished physical function, emotional distress, and impaired social roles, contributing to further HRQoL decline. To assess this, researchers and clinicians use standardized tools such as the scale of the European Organization for Research and Treatment of Cancer (EORTC) and its disease-specific module, the QLQ-MY20, which is validated and widely used in myeloma research 19 . By enabling patient self-reporting, these tools have shown that MM patients, particularly those receiving intensive chemotherapy, often report lower HRQoL scores compared to those receiving supportive or maintenance therapy 18 .

Quality of life and medication adherence are two interconnected aspects of managing chronic illnesses, particularly in oncology. In the case of multiple myeloma, non-adherence can significantly compromise therapeutic outcomes and negatively affect patients’ quality of life 20 . Previous studies have shown that poor adherence to therapy is associated with increased symptom burden, treatment failure, and deterioration in both physical and psychological health 21 , 22 . Conversely, patients reporting higher health-related quality of life may demonstrate greater adherence, possibly due to enhanced motivation and stronger perceived benefit from therapy 23 . This bidirectional relationship suggests a potential predictive role of quality of life in medication adherence. In this multi-centric cross-sectional study, we aim to evaluate whether quality of life scores, measured at the time of study enrollment during ongoing treatment, can predict medication adherence during short-term follow-up among patients with multiple myeloma, as the first study to examine this relationship in this patient population.

Methodology

Study design

This was a multi-center cross-sectional study using retrospectively collected clinical data from January 2018 to December 2025, with a prospective short-term adherence assessment window. The primary objective was to evaluate whether quality of life (QOL), measured at enrollment during ongoing treatment, predicts short-term medication adherence in patients with multiple myeloma. At baseline, patients completed the EORTC QLQ-MY20 questionnaire, and adherence was assessed one month later using the Morisky Green Levine (MGL) scale.

Study setting, diagnostic criteria, and participants

Because patterns of diagnostic presentation were relevant to several clinical variables analyzed, incidental disease discovery was defined as the diagnosis of multiple myeloma made during routine laboratory evaluation (e.g., detection of unexplained hypergammaglobulinemia or mild anemia) or imaging performed for unrelated clinical reasons, in patients who were asymptomatic and lacked CRAB-related end-organ damage at the time of detection. This definition ensured consistent classification across participating centers. The study recruited patients from five West Bank hospitals: Jenin Governmental Hospital (Jenin), Al-Watani Hospital (Nablus), The Palestinian Complex (Ramallah), Beit Jala Hospital (Bethlehem), and Dura Hospital (Hebron). Eligible participants were adults (≥ 18 years) with confirmed multiple myeloma defined by IMWG criteria, including ≥ 10% clonal plasma cells in the bone marrow or biopsy-proven plasmacytoma together with a myeloma-defining event 24 . The study intentionally recruited a heterogeneous, real-world cohort encompassing patients at different phases of therapy, including those newly diagnosed and receiving induction, individuals on maintenance treatment, and patients with relapsed or refractory disease receiving second-line therapy or beyond. To ensure that adherence and quality-of-life assessments reflected meaningful exposure to treatment, participants were required to have completed at least one full cycle of their current regimen at the time of enrollment.

All treatment regimens in this cohort were administered through oncology day-care clinics. No patients received a completely home-based regimen; therefore, comparisons between at-home therapy and day-care–based treatment were not possible within this dataset. All antimyeloma treatment regimens were delivered through hospital-based oncology day-care clinics. The VCD regimen was the standard induction protocol in our centers. To ensure accurate verification of full triplet administration, we performed cycle-level tracking of each component of the regimen. For every treatment visit, bortezomib, cyclophosphamide, and dexamethasone were individually coded as ‘received’ or ‘not received’ based on medication administration records. A patient was coded as having received ‘VCD’ for a given cycle only if all three medications were administered during that cycle. If any component was missed, delayed, or unavailable, the VCD cycle indicator was coded as ‘0‘. Exclusion criteria included critical illness, refusal to participate, death during data collection, or low baseline adherence. In total, 221 patients were excluded, leaving a final sample of 250 patients ( Fig. 1 ).

Study variables and data collection

Laboratory variables obtained from hospital records included hemoglobin, hematocrit, WBC, RBC, platelet count, plasma cell percentage in bone marrow, kappa free light chains (KFLC), lambda free light chains (LFLC), creatinine, beta-2 microglobulin, ESR, uric acid, and calcium. Drug history covered use of cyclophosphamide, dexamethasone, bortezomib, and VCD protocol cycles. Demographic variables collected via interviews included age, gender, residency (PTC-Local: resides in the same governorate as treatment center; PTC-non-local: resides outside the governorate), inpatient/outpatient status, smoking with pack-years, and incidental diagnosis. Cardiac arrest, autoimmune diseases, neurological deficits, and deformities were all moved from the manuscript to the Supplementary File 1 as shown in eTable 1 and eTable 2. ABO blood group was extracted as part of the routinely documented demographic information in the medical records; however, because it has no biological or theoretical relevance to medication adherence or quality-of-life outcomes in multiple myeloma, it was excluded from the primary tables and multivariable models and is presented only in Supplementary eTables 1 and 2 for descriptive completeness.

Clinical and QoL variables

Cytogenetic risk categories (e.g., del(17p), t(4;14), t(14;16)), molecular mutation profiles, and ECOG performance status were not uniformly documented across participating centers and were therefore not collected. These markers are key predictors of prognosis and treatment selection in multiple myeloma, but they primarily inform survival and disease progression rather than day-to-day medication-taking behavior 25 , 26 . The present study instead focused on routinely available, patient-level factors with a direct plausible link to adherence, including comorbidities, standard laboratory parameters (hemoglobin, creatinine, calcium), symptom burden, treatment-related side effects, and EORTC QLQ-MY20 domains as patient-reported measures of functional status, psychological well-being, and disease impact 27 . Evidence from oral anticancer therapies further indicates that adherence is most strongly influenced by side effects, symptom burden, regimen complexity, and patient beliefs, rather than underlying tumour biology 28 , 29 .

Comorbidities and clinical manifestations were extracted directly from medical records as documented diagnoses by the treating physicians. The variables included weight loss, pain (bone, joint, chest, or lower back), constitutional symptoms, fractures, gastrointestinal complaints (e.g., gastritis, nausea, abdominal discomfort), prior malignancies, COVID-19, diabetes, hypertension, inflammatory bowel disease, osteoporosis, anemia, thyroid disease, arthritis, neurological deficits, deformities, osteolytic lesions, skin rash, autoimmune conditions, and major cardiac, pulmonary, renal, hepatic, and vascular comorbidities. Symptoms and comorbidities were coded as present or absent. Importantly, the specific etiology of each manifestation was not routinely documented (e.g., H. pylori status for gastritis, chemotherapy-induced versus disease-related symptoms), and therefore, symptoms were captured in their clinical form rather than categorized by underlying cause.

For quality of life, patients completed the validated Arabic version of the EORTC QLQ-MY20 at enrollment. Adherence was assessed one month later using the Arabic MMAS-4 (MGL scale). Both instruments were administered face-to-face or by phone by trained medical students under hematology supervision. Retrospective clinical data were retrieved from electronic records. All patients gave informed consent, and confidentiality was strictly maintained.

Instruments and tools

Medication adherence

To assess medication adherence, the 4-item Morisky Medication Adherence Scale (MMAS-4) was used. This is a self-report tool designed to measure patient adherence to medication 30 . MMAS-4 comprises four questions answered with either “yes” (0) or “no” 1 , targeting common barriers to adherence 31 , such as forgetfulness, carelessness, discontinuing medication upon feeling better, and stopping medication when feeling worse 31 . The total score ranges from 0 to 4, with higher scores indicating greater adherence 30 . The validated Arabic version of the MMAS-4 was utilized in this study, as supported by 15 . The internal consistency of the MMAS-4, as measured by Cronbach’s alpha, has been reported in multiple myeloma literatures to range outside the appropriate range of 0.70 and 0.90; it has ranged between 0.0342 and 0.41, depending on population and context 16 , 32 .

Scoring method

MMAS-4 is a four-item questionnaire with a score scheme of “Yes” = 0 and “No” =1. The sum of the score for each of the four items yields the overall adherence score, which ranges from 0 to 4 31 , 33 . These items are intended to assess common behaviors that might result in non-adherence with a total score interpretation as follows: a score of 4 reflects a high adherence, a score of 2–3 reflects medium adherence, and a score of 0–1 reflects low adherence 24 .

Disease-specific quality of life

To assess disease-specific quality of life (QOL), we used in our study the European Organization for Research and Treatment of Cancer quality of life questionnaire – Multiple Myeloma (EORTC QLQ-MY20). EORTC QLQ-MY20 is a 20-item questionnaire that was specifically designed to assess QOL in Multiple Myeloma (MM) patients 34 . It is a validated extension to the EORTC QLQ-C30 core questionnaire and is mainly comprised of 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) 2 , 26 , 32 .

All items are scored using a 4-point Likert scale ranging from 1 (“not at all”) to 4 (“very much”) and are linearly transformed into a 0–100 scale 34 . Higher scores on SE and DS indicate worse symptoms or side effects, while higher scores on FP and BI reflect better functioning or outlook 34 . The internal consistency of the EORTC QLQ-MY20 has been confirmed in various studies, with reported Cronbach’s alpha values ranging from 0.72 to 0.90 across different subscales and populations 35 . The validated Arabic version of the EORTC QLQ-MY20 was used in this study, as previously translated and validated, as reported by 36 .

Scoring method

EORTC QLQ-MY20 is a 20-item disease-specific quality of life questionnaire. Each one of those items is 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 last week. It includes 4 scales, three of which are multi-item scales and one is a single-item scale; those scales are: Disease symptoms (DS; items 31–36), side effects of treatment (SE; items 37–46), future perspective (FP; items 48–50), and body image (BI; item 47). Raw scores for each multi-item scale in the QLQ-20MY (DS, SE, FP) are calculated by taking the average of the scores of the corresponding items within each scale. The raw score for a given domain is then calculated using the following formula: Raw Score = (RS) = I1 + I2+…+In /n, where (I1 …… In) represent the score of the individual items and (n) represents the number of items. For a single-item scale (BI), the raw score is simply the item’s response value. These scores are then linearly transformed to a 0–100 scale to standardize them in accordance with EORTC QLQ-MY20 guidelines.

Statistical analysis

Data analysis was performed using IBM SPSS Statistics (version 25) and Stata (version 17). Descriptive statistics were utilized to summarize demographic, clinical, laboratory, and treatment characteristics. Continuous variables are presented as means with standard deviations (SD), and categorical variables as frequencies and percentages.

Associations between baseline demographic, clinical, and laboratory characteristics with medication adherence categories (low, medium, high), as measured by the MGL questionnaire, were assessed using chi-square tests for categorical variables and ANOVA for continuous variables.

Due to the large sample size ( n = 250), the assumption of normality for the EORTC QLQ-MY20 scores was considered reasonable based on the Central Limit Theorem 37 , 38 . Therefore, parametric analyses (one-way ANOVA) were employed to evaluate differences in quality-of-life domains (Future Perspective, Body Image, Side Effects, Disease Symptoms) across medication adherence categories. Variable selection for multivariable modeling was based on a “thumb rule” statistical approach, wherein variables with a univariate p-value < 0.25 were retained for further analysis. This selection was supplemented by clinical judgment and existing evidence from prior studies 16 , 28 , 39 – 41 .

Because the univariate analyses (Table 2 ) involved multiple exploratory comparisons across a large number of baseline clinical and demographic variables, no formal adjustment for multiple testing was applied. These univariate p-values are therefore reported descriptively and interpreted with caution. Formal statistical inference relied on multivariable ordinal logistic regression models, which were strengthened using several methodological steps to ensure robustness, precision, and control of Type I error.

First, all continuous predictors, including QLQ-MY20 subscale scores, age, and total number of medications, were standardized (z-scored) to facilitate interpretation and comparability across domains. Second, multicollinearity was assessed using variance inflation factors (VIF), which demonstrated no concerning collinearity (mean VIF 2.1; all non-treatment predictors VIF < 3), indicating stable relationships among covariates. Third, variables with very low event counts (< 5%), such as cardiac arrest, autoimmune diseases, neurological deficit, and physical deformity, were excluded from multivariable analyses to avoid sparse-data bias and unstable estimates. Future studies with substantially larger sample sizes are encouraged to re-evaluate these clinically relevant variables within adequately powered models. Fourth, clinically relevant variables, including proximity to the treatment center, smoking, family history of MM, prior cancer history, COVID-19 infection, diabetes, hypertension, gastritis, incidental diagnosis, osteolytic lesions on X-ray, kidney stones, total medication burden, and treatment modalities (bortezomib, cyclophosphamide, dexamethasone, and VCD protocol), were retained to adjust for potential confounding .Together, these steps ensured that the multivariable models provided reliable, adjusted estimates and mitigated the increased risk of false-positive findings inherent to unadjusted exploratory comparisons.

A backward elimination strategy was then applied to refine the model, ensuring both clinical relevance and parsimony. Model development adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines 42 .

To determine the predictive role of quality-of-life domains on medication adherence, ordinal logistic regression models were utilized. Models were adjusted for potential confounding variables, including age, gender, residency, smoking status, family history of MM, prior cancer history, COVID-19 status, diabetes mellitus, hypertension, gastritis, incidental diagnosis, neurological deficit, deformity, osteolytic lesions on X-ray, autoimmune diseases, cardiac arrest, kidney stones, and treatment modalities (bortezomib, cyclophosphamide, dexamethasone, VCD protocol). The results are presented as regression coefficients with 95% confidence intervals (CI) and corresponding p-values. A significance level was set at p < 0.05 for all analyses.

Sample size calculation

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 5 . 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 250 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).

Ethical considerations

This study was reviewed and approved by the Institutional Review Board (IRB) of Palestine Polytechnic University, Hebron, Palestine. All procedures adhered to the ethical standards of the Helsinki Declaration and local regulations for research involving human participants. Written informed consent was obtained from all participants before enrollment after they were provided with clear information regarding the study objectives, data collection process, and confidentiality safeguards. Participation was entirely voluntary, with the right to withdraw at any point without consequences for ongoing medical care. Data were anonymized and stored securely, ensuring that patient identities remained confidential throughout the study.

Fig. 1 Study Flowchart: Patient Inclusion, Assessment Tools, and Outcome Stratification.

Results

Table 1 Demographic, Clinical, and Laboratory Characteristics of the Study Cohort ( n = 250). Background variables Variable Frequency (%) Age (mean ± SD) 62.4 ± 11.7 Gender Male 140 (56.0%) Female 110 (44.0%) Type of residency PTC-Local 160 (64.0%) PTC-non-local 90 (36.0%) Smoking Yes 84 (33.6%) Smoking pack years Less than 10 8 (3.2%) More than 10 76 (30.4%) Incidental disease discovery 41 (16.4%) Background chronic medical condition Family history of MM Yes 32 (12.8%) Prior history of cancers Yes 28 (11.2%) COVID − 19 Yes 111 (44.4%) Diabetes mellitus Yes 78 (31.2%) HTN Yes 91 (36.4%) Osteoporosis Yes 100 (40.0%) Hepatomegaly Yes 8 (3.2%) Gastritis Yes 34 (13.6%) Anemia Yes 100 (40.0%) Hypothyroidism Yes 6 (2.4%) Hyperthyroidism Yes 3 (1.2%) Arthritis Yes 46 (18.4%) Acute pain Yes 107 (42.8%) Unspecific pain Yes 87 (34.8%) Osteolytic lesion on X-ray yes 69 (27.6%) Skin rash Yes 14 (5.6%) Arrhythmia Yes 9 (3.6%) IHD Yes 46 (18.4%) CHF Yes 16 (6.4%) TIA Yes 6 (2.4%) Cerebrovascular disease Yes 9 (3.6%) Chronic lung disease Yes 27 (10.8%) Acute renal failure Yes 12 (4.8%) Peptic ulcer Yes 23 (9.2%) Neurological disease Yes 6 (2.4%) Peripheral vascular disease Yes 20 (8.0%) Gout Yes 26 (10.4%) Calculus of Kidney Yes 16 (6.4%) Chronic kidney disease Yes 22 (8.8%) Dementia Yes 22 (8.8%) Symptoms frequency Weight loss Yes 139 (55.6%) Abdominal pain Yes 118 (47.2%) Headache and dizziness Yes 112 (44.8%) Joint pain Yes 173 (69.2%) Chest pain Yes 105 (42.0%) Bone pain Yes 159 (63.6%) Lower back pain Yes 191 (76.4%) Lower limb edema Yes 119 (47.6%) Constitutional symptoms Yes 150 (60.0%) Pathological fracture Yes 82 (32.8%) Change in bowel habits Diarrhea 95 (38.0%) Constipation 22 (8.8%) lab values Lab value mean ± SD Hg 10.8 ± 2 Hematocrit 32.6 ± 6 WBC 7.3 ± 3.5 RBC 3.76 ± 0.9 Platelet count 237 ± 90.9 Plasma cells percentage in the bone marrow 48.2 ± 25.9 Kappa Free Light Chains (KFLC) 509.8 ± 438.7 Lambda Free Light Chains (LFLC) 150.6 ± 199.9 Creatinine 1.36 ± 1.38 Beta2-Microglobluin 6 ± 5.1 ESR 95.1 ± 41.2 Albumin level 3.66 ± 0.67 Total protein 8.14 ± 2.1 Calcium 9.3 ± 1.3 Uric acid 6.3 ± 2.2 Therapeutic Option Bortezomib Yes 37 (14.8%) Cyclophosphamide Yes 8 (3.2%) Dexamethasone Yes 41 (16.4%) VCD protocol Yes 18 (7.2%) Undergone autologous bone marrow transplant Yes 84 (33.6%) Total number of medications 2.4 ± 1.64 MM, multiple myeloma; SD, standard deviation; PTC, Proximity to Treatment Center; PTC-Local, resides in the same governorate as treatment center; PTC-non-local, resides outside the governorate; HTN, hypertension; COVID-19, coronavirus disease 2019; IHD, ischemic heart disease; CHF, congestive heart failure; TIA, transient ischemic attack; ESR, erythrocyte sedimentation rate; Hg, hemoglobin; WBC, white blood cell count; RBC, red blood cell count; KFLC, kappa free light chains; LFLC, lambda free light chains; VCD, bortezomib-cyclophosphamide-dexamethasone protocol.

Table 1 summarizes the demographic, clinical, laboratory, and treatment characteristics of the 250 multiple myeloma (MM) patients included in this study. The mean age was 62.4 years (SD ± 11.7), with a slight male predominance (56.0%). The majority of patients resided in the same governorate as treatment center (PTC-Local) (64.0%), and 33.6% reported a history of smoking, with most having smoked for more than 10 pack-years. A range of background comorbidities was observed, including hypertension (36.4%), diabetes mellitus (31.2%), anemia (40.0%), arthritis (18.4%), ischemic heart disease (18.4%), and a family history of MM in 12.8% of the cohort. COVID-19 infection was documented in 44.4% of patients. Regarding symptomatology, the most frequently reported symptoms included lower back pain (76.4%), joint pain (69.2%), bone pain (63.6%), and constitutional symptoms such as fatigue and weight loss (60.0% and 55.6%, respectively). Pathological fractures were present in nearly one-third of patients (32.8%), while 38.0% reported diarrhea, and 8.8% reported constipation. Laboratory findings showed a mean hemoglobin level of 10.8 g/dL (SD ± 2) and a mean plasma cell percentage in bone marrow of 48.2% (SD ± 25.9). Elevated inflammatory markers were noted, with a mean ESR of 95.1 mm/hr (SD ± 41.2). The mean beta-2 microglobulin was 6 mg/L (SD ± 5.1), and the mean albumin level was 3.66 g/dL (SD ± 0.67), reflecting the disease burden and nutritional status of the cohort. In terms of treatment modalities, 14.8% of patients received bortezomib, 3.2% cyclophosphamide, 16.4% dexamethasone, and 7.2% were managed with the VCD protocol. Notably, 33.6% of the cohort underwent autologous bone marrow transplantation. Overall, this table provides a comprehensive overview of the baseline characteristics and disease burden in this cohort, setting the context for analyzing quality of life (QOL) and medication adherence in subsequent sections. For the full data set , see eTable 1 in Supplementary File 1.

Fig. 2 Distribution of EORTC QLQ-MY20 Domain Scores Among Multiple Myeloma Patients.

Figure 2 displays the distribution of EORTC QLQ-MY20 domain scores among the study participants across the four domains: (A) Future Perspective , (B) Body Image , (C) Side Effects , and (D) Disease Symptoms. The functional domains demonstrated higher mean scores, with the Future Perspective scale averaging 62 ± 32.2 and Body Image averaging 58 ± 37.3 , indicating moderately high perceived future outlook and body image among patients. Conversely, the symptom domains reflected moderate symptom burden, with the Side Effect scale averaging 39.8 ± 23.3 and the Disease Symptoms scale averaging 42.6 ± 27.6. The box plots illustrate variability within each domain, with wider interquartile ranges particularly in the functional domains, reflecting heterogeneity in patient-reported quality of life within this multiple myeloma cohort. These distributions provide a visual summary of the baseline QOL landscape , contextualizing subsequent analyses linking these domains to medication adherence.

Fig. 3 Bar chart showing the Distribution of Medication Adherence Categories Among Multiple Myeloma Patients.

Figure 3 illustrates the distribution of patients across low, medium, and high medication adherence categories as assessed using the MGL questionnaire in this cross-sectional study of patients with multiple myeloma. Among the 250 patients, 20.0% were classified as having low adherence, 38.4% as medium adherence, and 41.6% as high adherence. The majority of patients (80%) demonstrated medium to high adherence to their medication regimens, indicating a generally favorable adherence pattern within this cohort. This distribution provides important context for interpreting the association between quality-of-life domains and medication adherence, supporting your analysis of factors influencing adherence in patients with multiple myeloma.

Table 2 Association Between Baseline Variables and Medication Adherence Categories as Measured by the MGL Questionnaire. Variable Subgroup Medication adherence category P value Low ( n = 50) Medium ( n = 96) High ( n = 104) Age (mean ± SD) 61.6 ± 11.5 61.8 ± 12.4 63.3 ± 11.3 0.608 Gender Male 28(56%) 53 (55.2%) 59(56.7%) 0.96 Female 22(44%) 43 (44.8%) 45(43.3%) Type of residency PTC-Local 15(30%) 69 (71.9%) 76(73.1%) < 0.001 PTC-non-local 35(70%) 27 (28.1%) 28(26.9%) Smoking Yes 12(24.0%) 31 (32.3%) 41(39.4%) 0.159 No 38(76.0%) 65 (67.7%) 63(60.6%) Incidental disease discovering Yes 8(16.0%) 26 (27.1%) 7(6.7%) < 0.001 No 42(84.0%) 70 (72.9%) 97(93.3%) Family history of MM Yes 6(12.0%) 14 (14.6%) 12(11.5%) 0.781 No 44(88.0%) 82 (85.4%) 92(88.5%) Prior history of cancers Yes 3(6.0%) 15 (15.6%) 10(9.6%) 0.164 No 47(94.0%) 81 (84.4%) 94(90.4%) COVID − 19 Yes 27(54.0%) 44 (45.8%) 40(38.5%) 0.175 No 23(46.0%) 52 (54.2%) 64(61.5%) Diabetes mellitus Yes 18(36.0%) 26 (27.1%) 34(32.7%) 0.525 No 32(64.0%) 70 (72.9%) 70(67.3%) HTN Yes 20(40.0%) 33 (34.4%) 38(36.5%) 0.822 No 30(60.0%) 63 (65.6%) 66(63.5%) Inflammatory bowel disease Yes 0(0.0%) 2 (2.1%) 5(4.8%) 0.208 No 50(100.0%) 94 (97.9%) 99(95.2%) Osteoporosis Yes 16(32.0%) 42 (43.8%) 42(40.4%) 0.361 No 34(68.0%) 54 (56.2%) 62(59.6%) Hepatomegaly Yes 1(2.0%) 5 (5.2%) 2(1.9%) 0.354 No 49(98.0%) 91 (94.8%) 102(98.1%) Gastritis Yes 1(2.0%) 19 (19.8%) 14(13.5%) 0.011 No 49(98.0%) 77 (80.2%) 90(86.5%) Anemia Yes 15(30.0%) 42 (43.8%) 43(41.3%) 0.240 No 35(70.0%) 54 (56.2%) 61(58.7%) Hypothyroidism Yes 1(2.0%) 3 (3.1%) 2(1.9%) 0.833 No 49(98.0%) 93 (96.9%) 102(98.1%) Hyperthyroidism Yes 0(0.0%) 2 (2.1%) 1(1.0%) 0.520 No 50(100.0%) 94 (97.9%) 103(99.0%) Arthritis Yes 7(14.0%) 19 (19.8%) 20(19.2%) 0.653 No 43(86.0%) 77 (80.2%) 84(80.8%) Acute pain Yes 18(36.0%) 43 (44.8%) 46(44.2%) 0.532 No 32(64.0%) 53 (55.2%) 58(55.8%) Unspecific pain Yes 14(28.0%) 38 (39.6%) 35(33.7%) 0.332 No 36(72.0%) 58 (60.4%) 69(66.3%) Osteolytic lesion on X-ray Yes 8(16.0%) 30 (31.3%) 31(29.8%) 0.113 No 42(84.0%) 66 (68.8%) 73(70.2%) Skin rash Yes 3(6.0%) 8 (8.3%) 3(2.9%) 0.236 No 47(94.0%) 88 (91.7%) 101(97.1%) Arrhythmia Yes 2(4.0%) 5 (5.2%) 2(1.9%) 0.446 No 48(96.0%) 91 (94.8%) 102(98.1%) IHD Yes 7(14.0%) 22 (22.9%) 17(16.3%) 0.307 No 43(86.0%) 74 (77.1%) 87(83.7%) CHF Yes 3(6.0%) 6 (6.2%) 7(6.7%) 0.984 No 47(94.0%) 90 (93.8%) 97(93.3%) TIA Yes 1(2.0%) 3 (3.1%) 2(1.9%) 0.833 No 49(98.0%) 93 (96.9%) 102(98.1%) Cerebrovascular disease Yes 2(4.0%) 2 (2.1%) 5(4.8%) 0.586 No 48(96.0%) 94 (97.9%) 99(95.2%) Chronic lung disease Yes 5(10.0%) 13 (13.5%) 9(8.7%) 0.510 No 45(90.0%) 83 (86.5%) 95(91.3%) Acute renal failure Yes 2(4.0%) 6 (6.2%) 4(3.8%) 0.687 No 48(96.0%) 90 (93.8%) 100(96.2%) Peptic ulcer Yes 2(4.0%) 10 (10.4%) 11(10.6%) 0.359 No 48(96.0%) 86 (89.6%) 93(89.4%) Neurological disease Yes 1(2.0%) 1 (1.0%) 4(3.8%) 0.429 No 49(98.0%) 95 (99.0%) 100(96.2%) Peripheral vascular disease Yes 3(6.0%) 11 (11.5%) 6(5.8%) 0.270 No 47(94.0%) 85 (88.5%) 98(94.2%) Gout Yes 5(10.0%) 12 (12.5%) 9(8.7%) 0.653 No 45(90.0%) 84 (87.5%) 95(91.3%) Calculus of Kidney Yes 2(4.0%) 10 (10.4%) 4(3.8%) 0.117 No 48(96.0%) 86 (89.6%) 100(96.2%) Chronic kidney disease Yes 2(4.0%) 8 (8.3%) 12(11.5%) 0.299 No 48(96.0%) 88 (91.7%) 92(88.5%) Dementia Yes 4(8.0%) 10 (10.4%) 8(7.7%) 0.760 No 46(92.0%) 86 (89.6%) 96(92.3%) Weight loss Yes 25(50.0%) 53 (55.2%) 61(58.7%) 0.599 No 25(50.0%) 43 (44.8%) 43(41.3%) Abdominal pain Yes 25(50.0%) 45 (46.9%) 48(46.2%) 0.905 No 25(50.0%) 51 (53.1%) 56(53.8%) Headache and dizziness Yes 21(42.0%) 48 (50.0%) 43(41.3%) 0.384 No 29(58.0%) 48 (50.0%) 61(58.7%) Joint pain Yes 35(70.0%) 69 (71.9%) 69(66.3%) 0.627 No 15(30.0%) 27 (28.1%) 35(33.7%) Chest pain Yes 18(36.0%) 41 (42.7%) 46(44.2%) 0.607 No 32(64.0%) 55 (57.3%) 58(55.8%) Bone pain Yes 30(60.0%) 64 (66.7%) 65(62.5%) 0.634 No 20(40.0%) 32 (33.3%) 39(37.5%) Lower back pain Yes 39(78.0%) 71 (74.0%) 81(77.9%) 0.846 No 11(22.0%) 25 (26.0%) 23(22.1%) Lower limb edema Yes 26(52.0%) 41 (42.7%) 52(50.0%) 0.503 No 24(48.0%) 55 (57.3%) 52(50.0%) Constitutional symptoms Yes 27(54.0%) 62 (64.6%) 61(58.7%) 0.382 No 23(46.0%) 34 (35.4%) 43(41.3%) Pathological fracture Yes 15(30.0%) 33 (34.4%) 34(32.7%) 0.845 No 35(70.0%) 63 (65.6%) 70(67.3%) Change in bowel habits Diarrhea 23(46.0%) 30 (31.3%) 42(40.4%) 0.223 Constipation 5(10.0%) 6 (6.3%) 11(10.6%) No 22(44.0%) 60 (62.5%) 51(49.0%) Hg 10.6 ± 1.6 10.8 ± 2.1 10.9 ± 2.0 0.800 Hematocrit 31.6 ± 4.7 32.8 ± 6.3 32.9 ± 6.3 0.375 WBC 6.94 ± 2.66 7.3 ± 4.3 7.4 ± 2.98 0.718 RBC 3.61 ± 0.63 3.86 ± 1.16 3.75 ± 0.77 0.295 Platelet count 226 ± 85 233 ± 95 245 ± 90 0.442 Plasma cells percentage in the bone marrow 43.5 ± 27.2 46 ± 26.1 52 ± 24.6 0.094 Kappa Free Light Chains (KFLC) 549 ± 456 498 ± 388 500 ± 475 0.775 Lambda Free Light Chains (LFLC) 127 ± + 90.1 177 ± 237 137 ± 200 0.246 Creatinine 1.4 ± 1.1 1.32 ± 1.82 1.75 ± 0.99 0.942 Beta2-Microglobluin 6.2 ± 3.7 5.4 ± 2.9 6.6 ± 6.9 0.264 ESR 95.3 ± 42.8 95.6 ± 41.5 94.5 ± 40.7 0.98 Albumin level 3.65 ± 0.59 3.63 ± 0.71 3.7 ± 0.68 0.838 Total protein 8.1 ± 2.2 7.9 ± 1.91 8.4 ± 2.1 0.178 Calcium 9.1 ± 0.97 9.2 ± 1.2 9.4 ± 1.48 0.480 Uric acid 6.3 ± 1.9 6.17 ± 2.3 6.4 ± 2.3 0.776 Bortezomib Yes 10(20.0%) 14 (14.6%) 13(12.5%) 0.472 No 40(80.0%) 82 (85.4%) 91(87.5%) Cyclophosphamide Yes 0(0.0%) 5 (5.2%) 3(2.9%) 0.225 No 50(100.0%) 91 (94.8%) 101(97.1%) Dexamethasone Yes 11(22.0%) 17 (17.7%) 13(12.5%) 0.295 No 39(78.0%) 79 (82.3%) 91(87.5%) VCD protocol Yes 2(4.0%) 8 (8.3%) 8(7.7%) 0.603 No 48(96.0%) 88 (91.7%) 96(92.3%) Undergone autologous bone marrow transplant Yes 17(34.0%) 33 (34.4%) 34(32.7%) 0.954 No 33(66.0%) 63 (65.6%) 70(67.3%) Number of medications 3.3 ± 1.85 2.5 ± 1.32 1.8 ± 1.56 < 0.001 MGL, Morisky Green Levine; MM, multiple myeloma; SD, standard deviation; PTC, Proximity to Treatment Center; PTC-Local, resides in the same governorate as treatment center; PTC-non-local, resides outside the governorate; HTN, hypertension; COVID-19, coronavirus disease 2019; IHD, ischemic heart disease; CHF, congestive heart failure; TIA, transient ischemic attack; ESR, erythrocyte sedimentation rate; Hg, hemoglobin; WBC, white blood cell count; RBC, red blood cell count; KFLC, kappa free light chains; LFLC, lambda free light chains; VCD, bortezomib-cyclophosphamide-dexamethasone protocol.

Table 2 presents the associations between demographic, clinical, laboratory, and treatment variables and medication adherence categories (low, medium, high) as assessed by the MGL questionnaire. Significant associations were observed for incidental disease discovery, type of residency , and gastritis . Patients with incidental disease discovery were more likely to have medium adherence ( n = 26, 27.1%) compared to low ( n = 8, 16.0%) or high adherence ( n = 7, 6.7%), ( p < 0.001). Type of residency demonstrated strong association ( p < 0.001), with non-local PTC residency ( resides outside the governorate) markedly overrepresented in the low-adherence group (70%) compared with medium adherence (28.1%) and high adherence (26.9%). Gastritis was more prevalent in medium adherence patients ( n = 19, 19.8%) than in low ( n = 1, 2.0%) or high adherence ( n = 14, 13.5%), ( p = 0.011). Finally, the number of medications demonstrated a strong inverse relationship with adherence ( p < 0.001), with low-adherence patients using the highest number of medications (3.3 ± 1.85) compared with medium (2.5 ± 1.32) and high adherence groups (1.8 ± 1.56). All other demographic, comorbid, laboratory, and treatment-related variables did not show statistically significant differences across adherence categories ( p > 0.05). For the full data set , see eTable 2 in Supplementary File 1.

Table 3 Association Between EORTC QLQ-MY20 Quality of Life Domains and Medication Adherence Categories as Measured by the MGL Questionnaire. EORTC QLQ-MY20 outcome Medication adherence category P value Low Medium High Future perspective scale 26 ± 17.2 66.4 ± 27.3 74.8 ± 29.5 < 0.001 Body image scale 24.6 ± 26.8 63.2 ± 33.9 69.2 ± 35.6 < 0.001 Side effect scale 68.7 ± 11.8 33.3 ± 17.9 31.85 ± 21.1 < 0.001 Disease symptoms scale 65.9 ± 16.5 43 ± 26.9 31.1 ± 25.6 < 0.001 EORTC QLQ-MY20, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Multiple Myeloma module; MGL, Morisky Green Levine; QOL, quality of life; MM, multiple myeloma; FP, future perspective; BI, body image; SE, side effects; DS, disease symptoms; SD, standard deviation.

Table 3 demonstrates the association between EORTC QLQ-MY20 quality of life (QOL) domain scores and medication adherence categories in patients with multiple myeloma, using the MGL questionnaire for adherence assessment. Functional scales (Future Perspective [FP] and Body Image [BI]) showed a strong positive association with medication adherence (both p < 0.001). Patients with high adherence exhibited markedly higher FP scores (mean ± SD: 74.8 ± 29.5) and BI scores (69.2 ± 35.6) compared to those with low adherence (FP: 26 ± 17.2; BI: 24.6 ± 26.8). This indicates that better perceived future outlook and body image are linked to improved medication-taking behaviors in this cohort. Conversely, symptom scales (Side Effect [SE] and Disease Symptoms [DS]) demonstrated a strong inverse association with adherence (both p < 0.001). Patients in the low adherence group reported significantly higher SE (68.7 ± 11.8) and DS (65.9 ± 16.5) scores, indicating greater symptom burden , while those in the high adherence group had lower SE (31.85 ± 21.1) and DS (31.1 ± 25.6) scores, reflecting better symptom control. These findings align with the conceptual framework that higher functional QOL domains are associated with better adherence , while a higher symptom burden correlates with lower adherence. This underlines the critical role of QOL in influencing medication-taking behavior in MM patients, suggesting that interventions aiming to improve patient-reported QOL may enhance medication adherence and subsequent treatment outcomes.

Regarding clarity and conciseness, eTable 3 (titled ‘Responses to the 4-Item Morisky Green Levine (MGL) Medication Adherence Questionnaire and Adherence Categories’) has been moved to the Supplementary Materials and is now presented as eTable 3 in Supplementary File 1.

eTable 3 presents patient-reported adherence behaviors and overall adherence levels. Forgetfulness was the most frequent barrier, with 34.0% of patients admitting they sometimes forgot to take medications. Intentional non-adherence was also notable: 30.0% reported skipping doses within the past two weeks, 25.6% stopped medication when feeling better, and 32.0% discontinued when experiencing side effects or feeling worse. Overall adherence classification showed that 41.6% of patients achieved high adherence, while 38.4% had medium adherence and 20.0% demonstrated low adherence.

Fig. 4 Violin Plots of EORTC QLQ-MY20 Domain Scores by Medication Adherence Categories in Multiple Myeloma Patients.

Figure 4 illustrates the distribution of four QLQ-MY20 domains across low, medium, and high adherence groups, as assessed by the Morisky Green Levine questionnaire. Functional domains (Future Perspective and Body Image) showed progressively higher scores with increasing adherence (Future Perspective: 26 ± 17.2 to 74.8 ± 29.5; Body Image: 24.6 ± 26.8 to 69.2 ± 35.6). In contrast, symptom domains (Side Effects and Disease Symptoms) showed inverse associations, with scores decreasing from 68.7 ± 11.8 to 31.9 ± 21.1 and from 65.9 ± 16.5 to 31.1 ± 25.6, respectively. White dots represent medians, boxes the interquartile range, and outer shapes the full distribution. These plots visually reinforce the study’s key finding that better functional QOL and lower symptom burden are strongly associated with higher medication adherence in multiple myeloma.

Table 4 Ordinal Logistic Regression Analysis of EORTC QLQ-MY20 Quality of Life Domains as Predictors of Medication Adherence Categories (low, medium, high). Predictor Crude Coeff (95% CI) Crude p -value Adjusted Coeff (95% CI) a Adjusted p -value a Future perspective scale 1.130 (0.847, 1.414) < 0.001 1.044 (0.730, 1.358) < 0.001 Body image scale 0.808 (0.548, 1.067) < 0.001 0.757 (0.469, 1.045) < 0.001 Side effect scale –1.159 (–1.452, − 0.865) < 0.001 –1.134 (–1.475, − 0.793) < 0.001 Disease symptoms scale –0.926 (–1.193, − 0.660) < 0.001 –1.020 (–1.333, − 0.706) < 0.001 EORTC QLQ-MY20, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Multiple Myeloma module; QOL, quality of life; MM, multiple myeloma; FP, future perspective; BI, body image; SE, side effects; DS, disease symptoms; CI, confidence interval, a: Adjusted for age, gender, residency status, smoking status, family history of MM, prior personal cancer history, COVID-19 infection, diabetes mellitus, hypertension, gastritis, incidental diagnosis, osteolytic lesions on X-ray, kidney stones, total number of medications, and treatment modalities including bortezomib, cyclophosphamide, dexamethasone, and VCD protocol.

Table 4 presents the results of ordinal logistic regression assessing the associations between standardized EORTC QLQ-MY20 quality of life (QOL) domain scores and medication adherence categories (low, medium, high) among patients with multiple myeloma. Higher scores on functional QOL domains, Future Perspective (FP) and Body Image (BI), were significantly associated with higher odds of being in a better adherence category. The adjusted coefficient for the FP domain was 1.044 (95% CI: 0.730, 1.358; p < 0.001), while the BI domain demonstrated an adjusted coefficient of 0.757 (95% CI: 0.469, 1.045; p < 0.001). These findings indicate that improved future outlook and better body image perceptions are strongly associated with enhanced medication adherence.

In contrast, higher symptom burden was significantly associated with lower adherence. The Side Effects (SE) domain showed an adjusted coefficient of − 1.134 (95% CI: − 1.475, − 0.793; p < 0.001), and the Disease Symptoms (DS) domain showed an adjusted coefficient of − 1.020 (95% CI: − 1.333, − 0.706; p < 0.001). These negative coefficients indicate that greater perceived symptom severity substantially reduces the likelihood of optimal adherence.

Overall, these results confirm the hypothesized directional relationship: better functional QOL improves adherence, whereas worse symptom-related QOL diminishes adherence. This underscores the importance of incorporating patient-reported outcomes into clinical decision-making and highlights how interventions targeting QOL improvement may enhance adherence and optimize treatment outcomes in this patient population. Complete multivariable models for each QLQ-MY20 subdomain are presented in eTable 4, eTable 5, eTable 6, and eTable 7 in Supplementary File 1.

Discussion

Study aim and key findings

The primary aim of this multicenter cross-sectional observational study was to evaluate whether baseline quality of life (QoL) scores, as measured by the EORTC QLQ-MY20, can predict medication adherence among patients with multiple myeloma (MM), assessed using the Morisky Medication Adherence Scale-4 (MMAS-4). To the best of our knowledge, this is the first study to investigate this specific relationship in MM patients. The main findings revealed that most patients exhibited medium to high adherence levels. While several demographic and clinical variables showed no significant association with adherence, certain factors, including incidental diagnosis and comorbidities such as gastritis, were significantly associated with adherence patterns. Notably, a strong and consistent association was observed between QoL domains and medication adherence: higher scores in functional domains (e.g., Future Perspective and Body Image) were positively associated with adherence, whereas higher symptom burden (e.g., Side Effects and Disease Symptoms) was inversely related.

Our study aligns with existing research indicating that side effects and disease symptoms negatively influence medication adherence. For example, Peipert et al. 41 reported that an increase in patient-reported side effect bother was significantly associated with a higher hazard of early treatment discontinuation (HR: 3.08; 95% CI: 1.18–8.02). This strongly supports our findings that a greater symptom burden contributes to poorer medication-taking behavior. On the other hand, Jacobs et al. 28 found that the symptom burden experienced by cancer patients, including manifestations like fatigue, drowsiness, and memory problems, negatively impacts their adherence to prescribed medication regimens. Moreover, as Tesfaye et al. 40 note that patients with chronic illnesses face a complex array of symptoms, including nausea, vomiting, loss of appetite, fatigue, muscle cramps, swelling (edema), dry and itchy skin, shortness of breath, and sleep problems. The sheer number and severity of these symptoms create a substantial burden. This burden reduces patients’ motivation and energy, making it difficult to adhere to complex, multi-pill regimens.

However, other studies offer a more nuanced perspective on the link between symptoms and discontinuation. Cransac et al. 16 and Feiten et al. 39 both observed high adherence rates in multiple myeloma patients receiving immunomodulatory drugs (IMIDs) despite the presence of symptoms. Cransac et al. 16 reported a mean medication possession ratio (MPR) of 0.97 ± 0.06 in 63 patients, with 94% showing high adherence (MPR ≥ 0.90) even with toxicities. Similarly, Feiten et al. 39 found a 97% adherence rate, noting that while toxicities occurred, only 16% of therapies were discontinued, and 27% had dose modifications due to side effects. This suggests that for many patients, the perceived benefits of the medication may outweigh the discomfort from side effects or disease symptoms.

Table 4 revealed that the adjusted coefficient for the association between future perspective score and drug adherence is significant in MM patients, suggesting that being optimistic about the future is associated with better adherence. This result aligns with other studies. Brittany Sansbury et al. 43 , for example, reported that higher time perspective correlates with better drug adherence; their analysis showed a positive coefficient 0.12 (90% CI: 0.10 to 0.28) between time perspective and MMAS-measured drug adherence. Moreover, Lishuo Shi et al. 44 also reported findings consistent with our results. They found that higher emotional distress scores on QLQ-C30 (which can conceptually be viewed as the inverse of future perspectives) were associated with higher drug non-adherence, with a coefficient value of 0.25 (95% CI: 0.11 to 0.28) indicating a positive association. On the other hand, some studies did not particularly demonstrate a similar result. For example, Kaur et al. 45 did not demonstrate a significant predictive effect of future perspective on therapy adherence. However, they did not report a coefficient value. Taken together, our study findings show that the future perspective domain can be considered a significant predictor of medication adherence in MM patients, and we hypothesize that FP is associated with high medication adherence as they feel more optimistic and thus take their medication regularly.

Our study has revealed a significant association between Body Image and drug adherence among patients with MM, as shown in Table 4 . This result is supported by Ung et al. 46 , which showed improved medication adherence in patients who reported better scores in health-related quality of life, including Body Image. This was supported by a significant p-value < 0.001. Some studies have underscored the importance of Body Image in cancer patients. For instance, Bahrami et al. 47 did not find a significant association between Body Image and the type of treatment among cancer patients ( p = 0.6), highlighting the relevance of assessing Body Image in relation to treatment perception. In all, our study has shown that Body Image can be used as a predictive factor for medication adherence and can be assessed in situations where concerns about adherence exist, this conclusion is supported by the observed significant positive association in our results and the alignment with previous literature and reinforced by our hypothesis that states that as well as the patients feel well about their body image, their adherence to their drugs gets improved as they get emotionally and socially optimistic about the efficacy of the treatment which they get. Moreover, interpretation of adherence patterns in this study also depends on the clinical context in which treatment was delivered. Because all treatment regimens were administered in oncology day-care clinics, this study could not evaluate differences between home-based therapy and day-care–delivered treatment. Future research conducted in settings where oral or fully home-dispensed regimens are used would be well-suited to explore this clinically relevant comparison.

In exploratory analyses (eTable 8), patients with osteolytic lesions on X-ray had lower Future Perspective and Body Image scores and higher Disease Symptoms scores compared with those without osteolytic lesions, indicating poorer perceived outlook, more disturbed self-image, and greater symptom burden in the context of radiographic skeletal disease. These findings are consistent with recent work from Palestinian MM cohorts showing that specific symptoms and comorbidities including abdominal pain, chest pain, swelling, gout, and peptic ulcer disease are important determinants of QLQ-MY20 side-effects, body image, and future perspective domains 48 . Together, this growing body of evidence supports the concept that both structural skeletal damage and broader symptom and exposure profiles meaningfully shape disease-specific quality of life in multiple myeloma, particularly in resource-limited settings like Palestine.

Implications in the Era of CD38-based quadruplet regimens

In recent years, frontline management of multiple myeloma has increasingly adopted CD38 antibody–based quadruplet regimens that combine a proteasome inhibitor, an immunomodulatory agent, dexamethasone, and an anti-CD38 monoclonal antibody. These regimens are now recommended in contemporary international guidelines as standard options for newly diagnosed patients 49 . Large randomized trials of daratumumab-based quadruplets, such as D-VRd in transplant-ineligible or transplant-deferred patients, have shown higher minimal residual disease (MRD) negativity rates, deeper responses, and improved progression-free survival compared with earlier triplet regimens, setting a new benchmark for disease control 50 . At the same time, these multi-agent combinations, often followed by prolonged lenalidomide maintenance, substantially increase treatment complexity, pill burden, and cumulative toxicity, reinforcing the clinical importance of sustained medication adherence and routine monitoring of patient-reported tolerability 51 . Although most patients in our cohort were treated with VCD-based protocols in a resource-constrained setting, the mechanisms we identified, where better Future Perspective and Body Image scores and lower Side Effects and Disease Symptoms scores predicted higher adherence, are likely to be at least as relevant, and potentially even more pronounced, in the context of contemporary quadruplet regimens. CD38-based combinations impose greater cumulative treatment burden, making patients’ perceptions of future outlook, treatment impact, and symptom experience critically important determinants of medication-taking behavior. Our findings therefore support integrating structured QLQ-MY20 assessment and targeted adherence counseling into routine clinical practice for patients receiving modern quadruplet therapy, where the balance between improved survival outcomes and increasing treatment burden is particularly delicate.

The treatment context in which our cohort was managed is important for interpreting both the adherence patterns and the generalizability of our findings. In high-income health systems, VRd and, increasingly, Dara-VRd are recommended as preferred frontline regimens for transplant-eligible newly diagnosed MM, reflecting evidence from trials such as GRIFFIN that the addition of daratumumab to VRd deepens response and improves progression-free outcomes 7 , 8 . By contrast, resource-constrained settings such as the Middle East, and specifically the Palestinian public healthcare system, face documented challenges related to limited availability and reimbursement of novel agents, necessitating pragmatic adaptation of international guidelines and greater reliance on affordable, bortezomib-based triplets 9 , 10 . In our participating centers, bortezomib–cyclophosphamide–dexamethasone (VCD/CyBorD) has therefore been used as the standard first-line induction regimen; this is consistent with real-world data from the Balkan Myeloma Study Group, which describes VCD/CyBorD as a ‘very popular regimen’ with significant activity and favorable tolerability, particularly for patients with renal dysfunction and in resource-poor environments 10 . Consequently, our results primarily reflect quality-of-life and adherence dynamics under VCD-based induction in a resource-limited public setting; while the core mechanisms we observed, namely, that better functional and future-perspective scores are associated with higher adherence, whereas greater symptom and side-effect burden predict lower adherence, are likely to be relevant across other modern triplets or quadruplets, extrapolation of absolute adherence rates to settings where VRd or Dara-VRd is routinely used should be made with caution.

Adherence considerations in the relapsed/refractory setting

Although our cohort included patients across various treatment phases, most were receiving upfront therapy at the time of assessment, and the adherence patterns we observed therefore reflect predominantly early-line treatment behavior. Adherence in the relapsed/refractory phase may differ meaningfully due to cumulative toxicities, longer treatment histories, increasing regimen complexity, and greater reliance on oral agents such as immunomodulatory drugs and targeted therapies. Patients with relapsed disease may also experience treatment fatigue and fluctuating motivation, factors that can further challenge adherence. Exploring adherence determinants specifically in the relapsed/refractory setting, where therapeutic burden is often higher and long-term disease control depends heavily on sustained, high-fidelity medication intake, represents an important direction for future research.

Strengths

This study has several methodological strengths. First, we adjusted for a wide range of potential confounders, including demographic, clinical, and treatment-related factors, reducing bias in estimating the link between quality of life (QOL) and adherence. Second, the use of ordinal logistic regression allowed a nuanced analysis of adherence across low, medium, and high categories. Third, we employed two internationally validated instruments: the EORTC QLQ-MY20 for disease-specific QOL and the MMAS-4 for adherence, ensuring robust measurement. Finally, the multi-center design across five West Bank hospitals enhances the generalizability of findings to real-world multiple myeloma (MM) care.

Limitations

Several limitations should be acknowledged. First, baseline QOL was measured during ongoing therapy rather than at diagnosis, raising the possibility of reverse causality where prior adherence may have already influenced QOL. Second, adherence was assessed over a short one-month window, limiting inference about long-term patterns in this chronic disease. Third, although we adjusted for many covariates, residual confounding is possible, particularly from unmeasured socioeconomic or psychosocial factors. We did not collect cytogenetic risk categories, molecular mutation profiles, or ECOG performance status because these variables were inconsistently documented across centers; although they are established prognostic markers for survival in multiple myeloma 25 , 26 they are not expected to directly drive medication adherence, which is more closely determined by symptom burden, treatment tolerability, and health-related quality of life as captured by our clinical variables and QLQ-MY20 measures 27 – 29 .

In the present study, the association between patient-reported quality of life (QOL) and medication adherence was evaluated using both exploratory univariate analyses and robust multivariable modeling. Although the univariate analyses involved multiple comparisons across a wide range of clinical and demographic variables, these results were reported descriptively and not used for inferential conclusions. This approach is consistent with best practices in exploratory epidemiologic research, particularly when the primary statistical inference is based on a rigorously adjusted multivariable framework.

To ensure the validity and stability of the inferential models, we implemented several methodological enhancements. Continuous predictors, including the four QLQ-MY20 subscales, age, and total medication burden, were standardized (z-scored) to improve interpretability and enable direct comparison of effect sizes across domains. Multicollinearity diagnostics demonstrated no concerning correlations among predictors (mean VIF = 2.1), confirming that the included variables contributed unique information to the model. Moreover, variables with extremely low event frequencies, such as cardiac arrest, autoimmune diseases, neurological deficits, and physical deformities, were excluded to avoid sparse-data bias and unstable estimates. Clinically relevant covariates, including proximity to the treatment center, smoking status, metabolic comorbidities, prior cancer history, incidental diagnosis, radiographic findings, kidney stones, total medication burden, and treatment regimens, were intentionally retained to provide a comprehensive adjustment strategy.

After accounting for these factors, the functional QOL domains of Future Perspective and Body Image remained strong independent predictors of higher medication adherence, suggesting that psychological outlook and self-perception play meaningful roles in treatment engagement among patients with multiple myeloma. In contrast, the symptom-related domains (Side Effects and Disease Symptoms) were associated with markedly lower adherence, underscoring the impact of symptom burden on patients’ ability to maintain complex treatment regimens. These findings highlight the integral role of patient-reported outcomes in guiding clinical decision-making and suggest that targeted interventions to enhance functional well-being and reduce symptom burden may meaningfully improve adherence and, ultimately, treatment outcomes in this population.

A further limitation is that comorbidities and symptom profiles were captured as documented clinical diagnoses without etiologic subclassification. For instance, gastritis was recorded as a clinical entity without confirmation of H. pylori infection, endoscopic findings, or attribution to treatment-related toxicity. Similarly, systemic symptoms such as fatigue, pain, and gastrointestinal discomfort may arise from multiple sources (disease activity, treatment side effects, or unrelated conditions), but their specific causes were not consistently recorded in the medical files. As a result, symptoms were analyzed in their clinical form rather than by underlying etiology, which may introduce heterogeneity in symptom interpretation.

An additional limitation of this study is the absence of formal myeloma treatment-response data, such as IMWG-defined complete response, very good partial response, partial response, or stable disease. Depth of response and MRD negativity are established predictors of progression-free and overall survival and are closely linked to symptom burden and health-related quality of life in multiple myeloma 52 – 54 . Patients with suboptimal response may experience more persistent disease-related symptoms, greater treatment burden, and higher psychological distress, all of which can independently reduce adherence to oral anticancer therapy 28 , 54 , 55 . In our cohort, myeloma-specific QoL subdomains from the EORTC QLQ-MY20 (higher Future Perspective and Body Image scores, and lower Disease Symptoms and Side Effects scores) were independently associated with higher adherence categories, which is clinically consistent with the pattern expected in patients with better disease control and fewer treatment-related problems. Nevertheless, because objective IMWG response categories and MRD assessments were not consistently available at the time QoL and adherence were measured, we were unable to adjust for biological response status directly. This limitation introduces the possibility of residual confounding, and our results should therefore be interpreted as reflecting the patient-reported interface between disease control, treatment toxicity, and medication-taking behavior rather than effects fully independent of treatment response. Although our cohort included patients at different treatment phases, most were receiving upfront therapy at the time of assessment; therefore, adherence patterns in the relapsed/refractory setting, where treatment fatigue, cumulative toxicity, and regimen complexity may further influence adherence, were not extensively captured and warrant dedicated evaluation in future studies.

Finally, the cross-sectional nature of QOL assessment, combined with the short follow-up, restricts causal inference. Thus, while our findings are predictive, they should not be interpreted as definitive causal relationships.

Clinical take-home message

In multiple myeloma, patient-reported quality of life is a powerful signal of whether medicines will be taken as prescribed. Higher Future Perspective and Body Image scores track with better adherence, while greater side-effect and symptom scores identify patients at risk. Embedding brief QLQ-MY20 screening into routine visits can trigger focused symptom control and support that protect treatment continuity.

Conclusion

In this multi-center study, several patient-reported quality-of-life domains demonstrated strong and independent associations with medication adherence in multiple myeloma. Patients who reported a more positive future perspective and better body image tended to show higher adherence, whereas greater symptom burden and treatment-related side effects were linked to lower adherence. These findings suggest that routine QOL assessment may help identify patients at risk for suboptimal adherence and guide supportive interventions aimed at improving treatment continuity. While the associations observed are not causal, they highlight meaningful behavioral signals that merit further investigation in prospective and longitudinal studies.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Abbreviations

Acquired immunodeficiency syndrome

Analysis of variance

Autologous stem cell transplantation

Beta-2 microglobulin

Body image (domain of EORTC QLQ-MY20)

Congestive heart failure

Confidence interval

Chronic kidney disease

Coronavirus disease 2019

Hypercalcemia, renal insufficiency, anemia, bone lesions

Disease symptoms (domain of EORTC QLQ-MY20)

European Organization for Research and Treatment of Cancer

Erythrocyte sedimentation rate

Future perspective (domain of EORTC QLQ-MY20)

Glucocorticoid

Hemoglobin

Hematocrit

Hypertension

Inflammatory bowel disease

Ischemic heart disease

Immunomodulatory drugs

International Myeloma Working Group

Kappa free light chains

Lambda free light chains

Morisky, Green and Levine adherence scale

Myocardial infarction

Multiple myeloma

Morisky Medication Adherence Scale, 4-item

Medication possession ratio

Peripheral vascular disease

Quality of life

EORTC Quality of Life Questionnaire, Multiple Myeloma module

EORTC Quality of Life Questionnaire, Core 30

Red blood cell count

Standard deviation

Side effects (domain of EORTC QLQ-MY20)

Statistical Package for the Social Sciences

Transient ischemic attack

Bortezomib, cyclophosphamide, dexamethasone regimen

Very much (response category in questionnaire)

World Health Organization Quality of Life instrument

White blood cell count IRB Institutional Review Board

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.

Acknowledgements

AcknowledgementsThe authors sincerely thank all patients who generously contributed to this study. We also extend our heartfelt appreciation to the healthcare staff and hospital teams across the West Bank, Palestine, for their invaluable support and collaboration throughout the research. Special recognition is given to the Palestinian Clinical Research Center for their committed assistance and logistical facilitation.

Author contributions

Study conception and design: NAH, MA, HAK. Data acquisition: DN, YZF, AbD, MM, AD, SH, SB, HKh, KI, NH, SA, GG, NG, LEL. Data analysis and interpretation: NAH, MA, IAR, AAH, AZ. Manuscript drafting: NAH, MA, OE, SH, SB, HKh, KI, NH. Critical revision of the manuscript for important intellectual content: NAH, MA, AK, MZ, LS, MB, HAK, DN, YZF, AbD, MM, AD. Statistical analysis: NAH, MA, IAR. Supervision and project administration: NAH, MA, IAR, DN, OE, HAK. All authors read and approved the final manuscript. †These authors contributed equally to this work. Corresponding authors: MA, HAK.

Funding

No financial support was received from governmental, commercial, or non-profit funding bodies for this study.

Data availability

The data supporting the conclusions of this work can be accessed from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Approval for this study was obtained from the Institutional Review Board (IRB) at Palestine Polytechnic University, Hebron, Palestine. Prior to participation, all individuals were clearly informed about the purpose, methodology, possible risks, and anticipated benefits of the research, and each signed a written consent form. Participant confidentiality was rigorously preserved, and the study was conducted in accordance with the ethical standards of the Declaration of Helsinki.

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