Оценка риска и прогнозирование самоубийств у нескольких пациентов с миэломой.

Introduction

Advancements in multiple myeloma (MM) treatment over the past two decades have significantly improved survival rates. In 2024, an estimated 35,780 new MM cases were diagnosed, accounting for 1.8% of all new cancer diagnoses, with approximately 12,540 deaths projected [ 1 ]. Mortality rates have declined from 3.8 per 100,000 in 2001 to 2.8 per 100,000 in 2022, primarily due to the effectiveness of proteasome inhibitors, immunomodulatory drugs, and monoclonal antibodies in both newly diagnosed and relapsed/refractory MM cases [ 2 ]. However, despite these therapeutic advances, MM remains a chronic, incurable condition, with patients enduring prolonged treatments that contribute to significant physical and psychological burdens.

The psychological toll of MM is profound, as treatment-related stress, alongside the ongoing challenges of disease management, often exacerbates distress and psychiatric symptoms. Studies have consistently shown that cancer patients, including those with MM, face a higher risk of suicide, with distress playing a key role [ 3 ]. These findings highlight the need for proactive mental health screening and timely interventions as part of comprehensive MM care [ 4 ]. Despite this, the relationship between specific patient characteristics and suicide risk remains underexplored.

Existing research on suicide risk in hematologic malignancies, particularly MM, has not thoroughly examined key demographic and clinical factors such as age, gender, race, socioeconomic status, and disease-related variables [ 5 ]. Identifying these predictors is essential for refining cancer care strategies and developing targeted interventions to support MM patients at increased risk of suicide. Addressing this gap could lead to more effective mental health screenings and suicide prevention strategies [ 6 ].

To bridge this gap, our population-based study used data from the Surveillance, Epidemiology, and End Results (SEER) program to compare suicide risk trends among MM patients to those in the general US population. While the SEER database provides US-specific insights, the findings have broader implications for global populations facing similar challenges. By focusing on completed suicides, our study aims to identify patients at elevated risk, offering valuable insights to inform clinical practice, and policy development and ultimately improve outcomes for MM patients worldwide.

Materials and methods

Study population

This population-based cohort study utilized the Surveillance, Epidemiology, and End Results (SEER) program accessed via SEER*Stat software, version 8.4.3 (available at https://seer.cancer.gov/seerstat/ ). SEER comprises a network of population-based cancer registries across the USA that collect and provide clinicopathologic, demographic, and survival outcome data [ 7 ]. The study specifically accessed the SEER 17 database, which consolidates information from cancer registries in 13 states, covering about 27% of the US population. Ethical review or informed consent was not pursued as SEER data are publicly available. Comparison with the general US population was conducted using data from the 2001–2020 Census and incidence rates per million population, a widely accepted and statistically robust approach.

Patient selection and variables

Patients diagnosed with multiple myeloma (MM) were identified using specific codes: 9731/3 (solitary plasmacytoma of bone), 9732/3 (plasma cell myeloma), and 9734/3 (extraosseous plasmacytoma), as defined by the guidelines outlined in the “Site Recode the International Classification of Diseases for Oncology, third edition (ICD-O-3) /WHO 2008” (available at https://seer.cancer.gov/seertools/hemelymph/ ). MM was designated as the first malignant primary indicator to ensure clarity in associating cancer sites with suicide, particularly in cases where patients had multiple malignancies.

For each patient, comprehensive demographic, clinicopathologic, treatment, and follow-up data were collected. Demographic variables included sex, age at diagnosis, marital status, race (categorized as White, Black, or others including Asian/Pacific Islanders and American Indian/Native Alaskans), median household income, and year of diagnosis. Age at diagnosis was categorized into four groups: ≤ 39 years, 40–59 years, 60–79 years, and ≥ 80 years. Marital status was classified as with partner (including married or unmarried with a domestic partner), divorced/separated, never married, and widowed. Follow-up by the SEER program included vital status and survival duration since MM diagnosis, along with cause of death (COD) derived from death certificates. Suicide-related deaths were specifically identified by the “COD to site record” classification in the SEER database as “suicide and self-inflicted injury.” Patients with incomplete data were excluded, resulting in a final cohort of 83,333 patients included in this study. Supplementary Fig. 1 illustrates the selection process for identifying MM patients for inclusion.

Statistical analyses

Patients were categorized into three groups based on outcomes—survived, died by suicide, and died from other causes—to enable comparison of baseline characteristics. Chi-square analysis assessed variations in suicide rates among these groups. To compare observed versus expected suicides relative to the general US population, data were sourced from the Centers for Disease Control and Prevention’s Injury Statistics Query and Reporting System ( https://www.cdc.gov/injury/wisqars/fatal.html ). Standardized mortality ratios (SMRs) were calculated by dividing actual suicides among MM patients by expected numbers. Incidence rate ratios (IRRs) for suicide were computed based on observed counts over person-years at risk, with 95% confidence intervals (CIs) estimated using Byar’s approximation.

The cohort was divided into training and validation sets at a 7:3 ratio, with the entire cohort serving as internal validation. These datasets presented categorical data as numbers and percentages, and intergroup comparisons were conducted using Chi-square tests. Baseline characteristics between groups were assessed using Student’s t -tests for continuous variables (mean ± SD) and Chi-square tests for categorical variables (frequencies and proportions).

For predictive modeling, univariate and multivariate Cox proportional hazard models identified potential predictors, with a stepwise approach used to develop the final model. Significant predictors ( P < 0.05) from multivariate analysis were retained. Risk scores were derived and visualized using nomograms. The nomogram’s predictive performance was evaluated using the C-index, ROC curve, and calibration plot. An AUC > 0.65 indicated acceptable predictive accuracy. Calibration plots were used to assess predictive accuracy against actual survival rates in the training set.

Statistical analyses were performed using R software ( www.r-project.org ), with P < 0.05 indicating statistical significance, except for univariate analysis.

Results

Baseline characteristics and causes of deaths (CODs) in the MM cohort

This study analyzed data from the SEER database covering 2001 to 2020, identifying a cohort of 83,333 patients (45.17% female) diagnosed with MM. Most patients were over 60 years old (59,967, 71.96%), married or in a partnership (50,204, 60.25%), and identified as White (61,416, 73.7%). To examine characteristics related to suicide, we categorized the cohort into those who died by suicide (89, 0.11%), those who died from other causes (50,763, 60.92%), and those still alive (32,481, 38.98%). The median age at diagnosis was 67 years for the entire cohort, 65 years for the suicide group, and 71 years for those who died from other causes. Additional baseline characteristics, including income levels, year of diagnosis, presence of extramedullary conditions, and chemotherapy status, are summarized in Supplementary Table 1 .

We also analyzed the rankings of various CODs among the MM patient cohort, excluding those who died specifically from MM (Supplementary Fig. 2 A). Cardiovascular diseases emerged as the leading COD, followed by pulmonary and infectious diseases. Notably, suicide was the ninth most prevalent COD, accounting for 0.66% of all non-MM-related deaths.

Suicide risk variation by demographic and clinical factors

We applied epidemiological concepts to analyze the suicide rate among the enrolled patients. Out of 83,333 MM patients observed over 322,709 person-years, 89 suicides were recorded, resulting in a suicide rate of 27.58 per 100,000 person-years. Detailed suicide rates and SMR data are presented in Table 1 . In comparison, the suicide rate in the broader US population [ 8 ] was 12.61 per 100,000 person-years, leading to an SMR of 2.186 (95% CI = 1.756–2.690) for the entire MM cohort. Excluding those diagnosed at age ≤ 39 years, MM patients showed an elevated suicide risk compared to the general population across various risk factors. The highest factors for suicide among MM patients were being American Indian/Alaska Native, Asian/HI naive/Pac. Islander (SMR = 3.788, 95% CI = 1.390–8.246), having a household income of ≤ $50,000 (SMR = 3.733, 95% CI = 2.041–6.263), and being diagnosed between 2000 and 2006 (SMR = 2.922, 95% CI = 2.035–4.063).

Specifically, suicide cases peaked at the time of initial MM diagnosis ( n = 36) and subsequently declined over time (Supplementary Fig. 2 B). Further analysis revealed that the first 3 months post-diagnosis exhibited the highest suicide rates, with an SMR of 124.549 (95% CI 74.986–194.5) and an incidence rate ratio (IRR) of 56.968 (95% CI 32.769–94.254) (Supplementary Table 2 ). Additionally, the third year post-diagnosis showed an increase in suicide events, with an SMR of 4.404 (95% CI 2.345–7.531) and an IRR of 2.014 (95% CI 1.032–3.624). The cumulative suicide-specific mortality increased steadily with longer follow-up periods, despite a notable overall decline in suicides among MM patients over time following diagnosis (Supplementary Fig. 3 ).

Suicides among males ( n = 80) were almost nine times higher than among females ( n = 9). Detailed analysis grouped by sexes (Supplementary Table 3 ) revealed that male MM patients had a higher suicide risk across various ages at diagnosis, races, and diagnosis years, when compared to the general male population. For female MM patients, an elevated suicide rate was observed only among those aged ≤ 39 (SMR = 4.733, 95% CI 0.120–26.369) or ≥ 80 (SMR = 3.742, 95% CI 0.453–13.523), Black (SMR = 1.477, 95% CI 0.037–8.231) or other racial groups (SMR = 7388, 95% CI 1.523–21.599), and those diagnosed during 2007 to 2013 (SMR = 1.918, 95% CI 0.704–4.175).

For all patients combined, the suicide rate increased with age, peaking in those diagnosed at over 80 years old (SMR = 2.041, 95% CI 0.979–3.754). The median time to suicide for patients diagnosed with MM between ages 40 and 59 was 47.5 months, while it was only 21.9 months for those diagnosed at over 80 years old (Supplementary Fig. 2 C). Patients not in a relationship, including divorced or separated (2.687, 95% CI 1.341–4.807), and never married (2.671, 95% CI 1.495–4.405), followed by widowed (2.357, 95% CI 1.130–4.334), also showed higher SMRs. Decreasing trends of suicide events were identified among patients from families with higher income levels and those diagnosed more recently.

Factors associated with suicide in MM patients

To explore determinants of suicide among MM patients, we conducted a comprehensive analysis comparing those who committed suicide with those still alive. A total of 32,570 MM patients were divided into a training group of 22,802 patients and a validation group of 9768 patients, adhering to a 7:3 ratio. The baseline demographic and clinical characteristics of both groups are delineated in Supplementary Table 4 . The median survival time was observed to be 48 months. No statistically significant disparities were discerned between the training and validation groups (all P > 0.05).

In the training group, univariate analysis identified several significant factors associated with suicide risk, including ages over 80 years, male sex, Black ethnicity, income ≥ $75,000, and diagnosis years between 2007 and 2020 ( P < 0.0001, Fig. 1 A). Multivariate analysis highlighted significant predictors: older age at diagnosis (≥ 80 years vs. ≤ 39 years: HR = 30.77; 95% CI 3.84–246.6), male gender (vs. female HR = 7.86; 95% CI 3.94–15.7), Black ethnicity (vs. White HR = 0.46; 95% CI 0.22–0.96), income ≥ $75,000 (vs. ≤ $50,000 HR = 0.36; 95% CI 0.18–0.68), and diagnosis years falling within 2007–2013 (HR = 0.28; 95% CI 0.17–0.45) or 2014–2020 (HR = 0.05; 95% CI 0.02–0.1) compared to 2000–2006. These factors were used to develop a nomogram (Fig. 1 B), which allows for the calculation of each patient’s total suicide risk score by summing the scores associated with the selected predictors.

Development and validation of suicide risk nomograms

To evaluate the precision of the predicted risk model, we used the Concordance index (C-index) and receiver operating characteristic (ROC) curves. The C-index values for the nomogram-derived risk prediction within the training, validation, and overall cohorts were 0.81, 0.85, and 0.83, respectively. ROC curve analyses demonstrated commendable accuracy, as indicated by the area under the curve (AUC) values at various time intervals. Specifically, the 1-year AUCs were 0.826, 0.853, and 0.841; the 3-year AUCs were 0.776, 0.852, and 0.801; and the 5-year AUCs were 0.750, 0.836, and 0.774, respectively (Fig. 2 ). The calibration plots in Fig. 3 depict the survival probabilities at 1, 3, and 5 years across the training, validation, and overall cohorts, demonstrating the model’s high degree of predictive accuracy.

Discussion

Understanding the complex interplay between cancer diagnosis and mental health is crucial for improving patient outcomes. It is well established that cancer patients have a higher incidence of suicide compared to the general population [ 9 , 10 ]. Multiple myeloma (MM), given its chronic and debilitating nature, presents a heightened risk of suicide. Using the SEER dataset, which represents 27% of the US population and includes diverse demographics, we analyzed factors such as gender, economic status, racial background, and diagnostic details in relation to suicide rates among MM patients.

Suicide constitutes a significant proportion of deaths in our study. From 2001 to 2020, there was an increase in both diagnosed MM cases and age-related non-MM mortality rates, including deaths from cardiovascular diseases [ 11 ], infection [ 12 ], and secondary malignancies [ 13 ]. This rise in malignant diseases is linked to higher rates of mental health issues such as depression, cancer recurrence fears, and suicidal ideation [ 9 ]. Previous research in Sweden found that MM patients had the highest risk of completed suicide (IRR = 3.52) and attempted suicide (IRR = 2.13) compared to leukemia and Hodgkin lymphoma [ 14 ]. Our study advances the field by developing a predictive model for MM-related suicide risk using clinical and demographic data, aiming to enable timely healthcare interventions.

Our findings reveal that suicide rates peak within the first 3 months after diagnosis, underscoring this period as critical for intervention. Early screening should target patients at higher baseline suicide risk, aligning with Fang et al.’s findings of increased suicide risk shortly after a cancer diagnosis [ 15 ]. Both clinical factors, such as high-risk chromosomal abnormalities [ 16 ], and sociodemographic factors, including psychiatric comorbidities and marital status, contribute to this elevated risk. Hence, suicide risk screening should be prioritized during the initial diagnostic phase.

Advanced age is also a significant factor associated with higher suicide rates in MM, reflecting broader trends in cancer populations [ 17 ]. MM symptoms, including bone destruction, renal dysfunction, and chemotherapy-induced neuropathy, lead to functional impairments that heighten psychological distress and suicide risk, particularly in older patients [ 18 ]. These factors negatively impact the quality of life and treatment outcomes, making elderly patients particularly vulnerable [ 19 , 20 ].

Gender differences are evident, with males having higher suicide rates than females, potentially due to females’ generally better coping mechanisms and social support networks [ 21 ]. Our analysis also highlighted racial disparities, with higher suicide rates in non-Black and non-White racial groups, especially among females. These differences may result from unequal access to advanced MM treatments and supportive care, as well as potential biological factors affecting treatment response [ 22 , 23 ].

Notably, suicide rates declined during the periods 2007–2013 and 2014–2020, corresponding with significant advancements in myeloma care [ 24 ]. The inclusion of income status in our model revealed that financial difficulties contribute to higher suicide risk. Patients with incomes of ≤ $50,000 showed increased suicide risk, reflecting broader issues of financial toxicity and reduced access to cancer care [ 25 , 26 ]. Improved access to treatments such as autologous stem cell transplants (ASCT) has been linked to better outcomes, but disparities based on socioeconomic status and race persist [ 27 ]. Our findings emphasize the need for enhanced social and economic support for vulnerable populations.

This study has limitations, including the exclusion of known suicide risk factors such as previous attempts, family history, and major depressive disorders, which may confound the results. Depression is prevalent among cancer patients but often underdiagnosed in oncology settings [ 28 , 29 ]. Addressing depression through early detection and treatment is crucial for suicide prevention [ 30 ]. Additionally, our study did not include non-fatal suicide attempts, although Japanese research indicates that being unmarried, having a less advanced cancer stage, and experiencing depression are associated with suicidal ideation in MM patients [ 31 ]. We also did not assess marital status, despite its established link to suicide risk among cancer patients [ 32 ]. Marriage offers emotional and tangible support, which are crucial for reducing suicide risk, suggesting that future studies should explore the specific benefits of social support within marriage [ 33 ].

Predicting suicide in MM patients is challenging due to the lower ratio of self-harm events to completed suicides [ 34 , 35 ]. Existing validated tools for suicide risk screening, along with interventions like cognitive-behavioral therapy, problem-solving therapy, and crisis response planning, have shown efficacy in reducing suicide risk [ 36 ]. Providers should invest additional time in high-risk patients to foster strong patient-provider relationships, which can mitigate suicidal ideation [ 37 ]. High-risk individuals should be referred to mental health professionals, and modifiable risk factors such as pain, substance abuse, and psychiatric disorders should be addressed to further reduce suicide risk.

Conclusion

Patients with multiple myeloma (MM) experience a markedly higher suicide rate compared to the general population, influenced by factors such as gender, age, race, marital status, household income, and the timing of diagnosis and treatment. Despite these risks, many hematologists currently lack the necessary expertise to effectively evaluate and address suicide risk in MM patients, which may lead to missed opportunities for intervention. By utilizing accessible predictive variables, we developed a reliable model to help MM physicians quickly identify individuals at risk of suicide, enabling timely preventive measures. For high-risk patients, intervention by psychologists or psychiatrists is recommended. Future research should explore the relationship between MM suicide risk and factors such as transplantation status, treatment modalities, treatment-related adverse effects, patient physical symptoms, and levels of family or environmental support to refine prevention strategies and improve patient outcomes.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 476 KB)

Acknowledgements

Jiaxin Shen and Xiaofen Wen are grateful to Shantou University Medical College (Shantou, China) and the University of Sassari (Sassari, Italy), for the joint PhD program.

Author contribution

Jiaxin Shen: Writing – review & editing, Writing – original draft, Methodology, Validation, Formal analysis, Funding Acquisition, Conceptualization. Shaoze Lin: Writing – review & editing, Software. Hongfang Tao: Writing – review & editing, Validation, Resources. Leonardo A. Sechi: Writing – review & editing, Validation, Investigation. Claudio Fozza: Writing – review & editing, Supervision, Resources. Xiaofen Wen: Writing – review & editing, Software, Funding acquisition, Conceptualization.

Funding

This work was supported by the Science and Technology Special Project of Guangdong Province (grant numbers 2019ST022 and STKJ2021060), the Medical Research Foundation of Guangdong Province (grant number A2021244), and the “Dengfeng Project” for the High-level Hospital Construction Project of the, First Affiliated Hospital of Shantou University Medical College Supporting Funding (grant number 202003–8).

Data availability

All data can be obtained through SEER, available at https://seer.cancer.gov/seerstat/ .

Declarations

Ethics approval

Ethical review or informed consent was not pursued as SEER data are publicly available.

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.

Contributor Information

Claudio Fozza, Email: cfozza@uniss.it.

Xiaofen Wen, Email: xiaofenwen53@gmail.com.

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Associated Data

Supplementary Materials

Supplementary file1 (DOCX 476 KB)

Data Availability Statement

All data can be obtained through SEER, available at https://seer.cancer.gov/seerstat/ .