Abstract
Background
Cell and gene therapy (CGT) offers breakthrough solutions for hematological diseases. Patient acceptance is a key determinant of its successful clinical translation. This study investigates CGT acceptance among hematological patients and explores its primary determinants.
Methods
A cross-sectional study was conducted (June–July 2025) with 385 hematological patients at a single specialized hematology center (Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences). Each participant completed a sociodemographic and clinical questionnaire, the Risk Perception Assessment Scale for Patients with Hematological Diseases, and the EuroQol 5-Dimensional 5-Level (EQ-5D-5L) questionnaire. Correlation and multivariable ordinal logistic regression analyses were performed using SPSS 26.0.
Results
Most patients (76.6%) were cautious toward CGT. Risk perception (OR = 0.944; 95% CI: 0.921–0.967), gender (male vs female: OR = 1.754; 95% CI: 1.061–2.901), medical insurance status (insured vs uninsured: OR = 5.485; 95% CI: 2.020–14.895), disease types (acute leukemia vs other hematological diseases: OR = 2.936; 95% CI: 1.169–7.374), and prior experience with CGT-related clinical trials (with experience vs without: OR = 6.278; 95% CI: 2.798–14.083) were five key predictors. Physical risk and economic risk constituted the two primary risk barriers hindering CGT acceptance. Subgroup analysis further showed that household income and treatment modality were significant factors for patients with acute leukemia and those with myelodysplastic syndrome, respectively. All P < 0.05.
Conclusion
Enhancing CGT acceptance among patients with hematological diseases may be facilitated through tailored educational interventions and expanded financial support. However, further multicenter and prospective studies are required to validate these cross-sectional associations and evaluate the actual effectiveness of specific interventions in clinical practice.
Keywords: hematological diseases, patient preference, risk perception, cell and gene therapy
Introduction
Major hematological diseases represent a formidable global public health challenge. The intricate pathogenesis and high mortality rates associated with these conditions impose a significant disease burden on society.1 While conventional therapies, such as chemotherapy and hematopoietic stem cell transplantation, have markedly improved patient prognosis, their clinical application remains hindered by various constraints.2 Driven by rapid advancements in synthetic biology and precision medicine, emerging biotechnologies—most notably cell and gene therapy (CGT), primarily represented by modalities such as chimeric antigen receptor T-cell (CAR-T) therapy and gene editing—are currently reshaping the therapeutic landscape for hematological disorders.3–5 As an innovative modality, CGT leverages cutting-edge techniques such as gene editing and cellular modification to treat diseases by repairing, replacing, or altering human cells or genetic material.6,7 The fundamental value of CGT lies in its ability to transcend the limitations of traditional “symptomatic” interventions, thereby facilitating a transformative paradigm shift in medicine from “disease management” toward “definitive cure”.8
While CGT has demonstrated unprecedented clinical potential, its path toward clinical translation remains encumbered by numerous uncertainties inherent to such complex biotherapeutic modalities.9,10 Technical bottlenecks in precise gene delivery, potential off-target risks, and sporadic serious adverse events reported in clinical trials11,12 have subjected the safety and ethical legitimacy of these therapies to rigorous scrutiny from both the scientific community and the public.13–16 Notably, these concerns may manifest differently depending on the specific modality: cell therapies like CAR-T are often scrutinized by patients for acute clinical toxicities,17 whereas in vivo gene editing tends to evoke deeper ethical debates regarding long-term genetic alterations.14 These technical uncertainties and ethical debates collectively constitute significant psychological barriers for patients considering CGT. Because hematological patients are both potential beneficiaries and direct risk-bearers, their acceptance of this cutting-edge technology not only dictates individual treatment decisions but also fundamentally influences the clinical uptake and standardized implementation of these therapies in the future healthcare market.
The exploration of patient and public acceptance of CGT has emerged as a prominent scholarly focus globally in recent years.18 Inquiries into the acceptance of CGT among Belgian patients with hemophilia indicate that the majority maintain a positive attitude;19,20 similarly, a study on the perspectives of UK hemophilia patients and clinical experts revealed a high willingness to adopt CGT among the patient cohort.21 Existing research in China primarily focuses on the general public’s willingness to accept CGT and its influencing factors,13,22 while there remains a paucity of in-depth empirical research targeting patient populations in clinical settings. Therefore, this study aims to investigate the current status of CGT acceptance among patients with hematological diseases and to identify the core influencing factors. In this context, “acceptance” is defined as patients’ general willingness to embrace CGT as a future therapeutic concept, rather than an evaluation of a current specific treatment. Given the high uncertainty associated with these innovations, this acceptance operates as a nuanced continuum of decision-making readiness rather than a simple binary choice. Accordingly, our study operationalizes it into four categorical responses (immediate acceptance, decision after detailed risk information, uncertainty, and refusal) to appropriately reflect the real-world risk-benefit deliberation required before committing to novel biotherapeutics. The findings of this research will provide empirical evidence for stakeholders to optimize clinical decision-making and formulate access policies; furthermore, this work holds pivotal value for promoting the standardized implementation of cutting-edge biotechnologies and maximizing clinical benefits for patients.
Methods
Study Design
This study utilized a descriptive and analytical cross-sectional research design. We conducted a questionnaire-based survey via convenience sampling at the Transplantation Center of the Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences (IHCAMS). As a leading national research-oriented specialist hospital in China’s hematology field, its Transplantation Center specifically functions as a primary national referral hub for highly complex and refractory cases. These patients, predominantly characterized by relapsed or refractory status and limited response to conventional therapies, represent the primary target population for the clinical application of CGT. Because such cases are typically widely dispersed across general hospitals—making it challenging to establish large-scale research cohorts—this study leverages the center’s concentrated clinical resources. This strategy provides a sample with high population specificity, ensuring that the findings reflect the distinct perspectives of the primary CGT target population and provide valuable, unique empirical evidence for the clinical translation of cutting-edge biomedical technologies in China. This study adhered to the STROBE guidelines23 (Supplementary Material).
Participants
Demographic data of the target population were collected by healthcare professionals who assisted with the survey in the selected hospital units. The detailed inclusion and exclusion criteria are presented in Table 1. The sample size for this cross-sectional study was determined using a standard formula 
, where Z = 1.96 (corresponding to a 5% significance level, α = 0.05), and d represents the margin of error, set at 0.05. Due to a lack of prior research on the expected acceptance rate (p) of CGT among patients with hematological diseases, p was set at 50%. The minimum sample size was 384. Considering a 20% nonresponse rate, a total of 480 questionnaires were distributed.
Table 1.
Inclusion and Exclusion Criteria
| Inclusion Criteria | Exclusion Criteria |
|---|---|
| 1. A definitive diagnosis of hematological disorders. | 1. Presence of psychiatric disorders. |
| 2. Voluntary participation and provision of signed informed consent. | 2. Presence of severe cognitive impairment. |
| 3. Ability to comprehend the meaning of all items in the questionnaire. | 3. Critical illness or clinical instability precluding cooperation with the study. |
| 4. Ability to complete the online questionnaire either independently or with the assistance of an investigator. | 4. Unwillingness to participate in the study. |
Measures
To ensure the relevance and scientific rigor of the research instrument, a task force was established in collaboration with experts specializing in clinical hematology, CGT research and development, clinical research methodology, medical statistics, and biomedical ethics from IHCAMS. The survey protocol was finalized after multiple rounds of deliberations that accounted for the clinical features of hematological diseases and the technical characteristics of CGT. The panel also conducted a content validity assessment during the scale revision, ensuring the terminological precision and contextual appropriateness of the items. Specifically, the primary outcome—CGT acceptance—was evaluated using a dedicated item with a four-level response option (immediate acceptance, decision after detailed risk information, uncertainty, and refusal). The phrasing of this item was developed based on a review of relevant patient preference literature, and it was subsequently pilot-tested among 20 eligible hematological patients prior to formal administration to ensure readability, clarity, and cultural appropriateness.
Sociodemographic and Clinical Characteristics
The sociodemographic and clinical section comprised 11 items: participants’ gender, age, marital status, religious affiliation, educational level, occupational category, annual per capita household income, medical insurance status, disease types, treatment types, and prior experience with CGT-related clinical trials. Specifically, treatment types were categorized into conventional treatment (chemotherapy and radiotherapy), established cell therapy (hematopoietic stem cell transplantation), emerging biotherapy (CAR-T cell therapy), and untreated status.
Risk Perception Assessment Scale for Patients with Hematological Diseases
Fang et al24 developed a 12-item survey instrument in 2014 to assess medical risk perception levels among patients with chronic diseases, which originally spanned three dimensions: economic, physical/clinical, and psychosocial risks. Based on the original instrument, this study integrated an “Ethics and privacy risk” dimension to encompass genetic privacy, gene editing ethics, and the perceived adequacy of informed consent, resulting in a final 15-item tool with four dimensions. Each item was scored on a five-point Likert scale, ranging from 1 (“not at all concerned”) to 5 (“extremely concerned”). The Cronbach’s α for the scale was 0.902 in this study. Furthermore, the construct validity of this modified scale was evaluated using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test of sphericity, yielding a KMO value of 0.899 (p < 0.001).
EuroQol 5-Dimensional 5-Level (EQ-5D-5L) Questionnaire
Herdman et al25 developed the EuroQol 5-Dimensional 5-Level questionnaire (EQ-5D-5L) in 2011 to assess health-related quality of life. The instrument was subsequently translated and validated for the Chinese context by Luo et al26 in 2013. The scale evaluates five health dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension was rated on five levels of severity, ranging from “no problems” to “extreme problems”.
Data Collection
Data collection was conducted from June to July 2025. The electronic questionnaire was distributed to eligible participants online through the Wenjuanxing platform (https://www.wjx.cn). At the inception of the survey, an electronic informed consent form was presented; participants could only proceed to the questionnaire after providing digital consent, whereas the sessions for those who declined were automatically terminated by the platform. Designated healthcare staff briefed participants on the study’s objectives and instructions. During this briefing, CGT was explicitly defined in lay language to encompass emerging biotherapies, primarily represented by CAR-T cell therapy and gene editing, to differentiate it from conventional treatments. Furthermore, staff explicitly clarified that participants were evaluating their general willingness to accept such therapies as a hypothetical, future therapeutic concept, regardless of their specific clinical indications or eventual treatment paths. The survey was completed independently and anonymously, taking approximately 15–20 minutes. In cases where participants’ physical conditions precluded self-administration, family members or caregivers were permitted to assist, with strict instructions to faithfully record the participants’ actual perspectives. During questionnaire administration, healthcare staff refrained from providing any leading explanations; if participants encountered difficulty in understanding specific items, staff offered only essential and objective clarifications. To prevent duplicate submissions and ensure data integrity, questionnaire access was restricted to one entry per verified user account, IP address, and device. Additionally, to ensure data quality, questionnaires with a completion time of less than 180 seconds were deemed invalid and excluded from the analysis.
Ethical Considerations
This study was approved by the Tianjin University Health Research Ethics Committee (TJUE-2025-357). The study was conducted in accordance with the principles of the Declaration of Helsinki. Participation in this study was voluntary, and participants had the right to refuse or withdraw at any time without penalty.
Data Analysis
This study used IBM SPSS Statistics 26.0 software to analyze the collected data. The participants’ sociodemographic and clinical characteristics were analyzed descriptively using frequencies and percentages. Due to limited individual sample sizes, multiple myeloma (n = 2), lymphoma (n = 10), and aplastic anemia (n = 15) were merged into an “other hematological diseases” category to enhance statistical power and satisfy the requirements for inferential testing. Given the non-normal distribution of CGT acceptance, Mann–Whitney and Kruskal–Wallis tests were employed to assess differences in acceptance across sociodemographic and clinical groups, while Spearman correlation was used to evaluate associations between acceptance, risk perception, and quality of life. Additionally, since the individual risk perception dimensions followed a normal distribution, paired-samples t-tests were performed to compare their standardized scores, thereby providing a detailed profile of the risk barriers hindering CGT acceptance. Furthermore, factors influencing CGT acceptance were identified via ordinal logistic regression, followed by subgroup analyses based on disease type. Statistical significance was defined as p < 0.05.
Results
Participants’ Sociodemographic and Clinical Characteristics
A total of 480 questionnaires were distributed, and 393 were returned. After excluding invalid questionnaires with incomplete responses or excessively short completion times (< 180 seconds), 385 valid questionnaires were finally included. Among these respondents, 212 (55.1%) were male and 173 (44.9%) were female. Participants aged 46–60 years were the most prevalent (33.5%), followed by those aged 31–45 years (29.1%) and 18–30 years (28.1%). Table 2 summarizes the participants’ sociodemographic and clinical characteristics.
Table 2.
Sociodemographic and Clinical Characteristics of Patients (N=385)
| Variable | n (%) | 1 (n=36) |
2 (n=21) |
3 (n=295) |
4 (n=33) |
Z/H | p |
|---|---|---|---|---|---|---|---|
| Gender | |||||||
| Male | 212 (55.1%) | 21 | 7 | 158 | 26 | −2.220 | 0.026 |
| Female | 173 (44.9%) | 15 | 14 | 137 | 7 | ||
| Age (years) | |||||||
| <18 | 12 (3.1%) | 0 | 1 | 10 | 1 | 2.708 | 0.608 |
| 18–30 | 108 (28.1%) | 18 | 2 | 80 | 8 | ||
| 31–45 | 112 (29.1%) | 5 | 10 | 85 | 12 | ||
| 46–60 | 129 (33.5%) | 11 | 7 | 101 | 10 | ||
| >60 | 24 (6.2%) | 2 | 1 | 19 | 2 | ||
| Marital status | |||||||
| Unmarried | 99 (25.7%) | 8 | 3 | 79 | 9 | 4.737 | 0.192 |
| Married | 277 (71.9%) | 28 | 18 | 209 | 22 | ||
| Divorced | 8 (2.1%) | 0 | 0 | 6 | 2 | ||
| Widowed | 1 (0.3%) | 0 | 0 | 1 | 0 | ||
| Religious affiliation | |||||||
| Yes | 30 (7.8%) | 5 | 0 | 22 | 3 | −0.186 | 0.853 |
| No | 355 (92.2%) | 31 | 21 | 273 | 30 | ||
| Education level | |||||||
| Primary school and below | 23 (6.0%) | 2 | 1 | 19 | 1 | 5.064 | 0.281 |
| Junior high school | 74 (19.2%) | 3 | 8 | 53 | 10 | ||
| Senior high/Vocational school | 75 (19.5%) | 4 | 4 | 59 | 8 | ||
| Junior college/Bachelor’s degree | 187 (48.6%) | 24 | 6 | 148 | 9 | ||
| Master’s degree and above | 26 (6.8%) | 3 | 2 | 16 | 5 | ||
| Occupational category | |||||||
| Government sector staff | 45 (11.7%) | 2 | 3 | 33 | 7 | 12.208 | 0.142 |
| Enterprise employees | 68 (17.7%) | 5 | 2 | 54 | 7 | ||
| Self-employed | 28 (7.3%) | 2 | 1 | 23 | 2 | ||
| Freelancers | 41 (10.6%) | 4 | 1 | 33 | 3 | ||
| Students | 55 (14.3%) | 5 | 2 | 45 | 3 | ||
| Farmers | 46 (11.9%) | 4 | 4 | 32 | 6 | ||
| Retired | 35 (9.1%) | 3 | 3 | 28 | 1 | ||
| Unemployed | 64 (16.6%) | 11 | 5 | 45 | 3 | ||
| Others | 3 (0.8%) | 0 | 0 | 2 | 1 | ||
| PCI (RMB) | |||||||
| <10,000 | 85 (22.1%) | 3 | 7 | 66 | 9 | 7.893 | 0.096 |
| 10,000–20,000 | 63 (16.4%) | 8 | 4 | 44 | 7 | ||
| 20,000–30,000 | 53 (13.8%) | 1 | 5 | 43 | 4 | ||
| 30,000–50,000 | 101 (26.2%) | 19 | 2 | 75 | 5 | ||
| >50,000 | 83 (21.6%) | 5 | 3 | 67 | 8 | ||
| Medical insurance | |||||||
| Yes | 362 (94.0%) | 25 | 21 | 285 | 31 | −3.777 | <0.001 |
| No | 23 (6.0%) | 11 | 0 | 10 | 2 | ||
| Disease types | |||||||
| Acute leukemia | 251 (65.2%) | 16 | 12 | 197 | 26 | 15.484 | 0.001 |
| Chronic leukemia | 25 (6.5%) | 10 | 0 | 13 | 2 | ||
| Myelodysplastic syndrome | 82 (21.3%) | 7 | 6 | 64 | 5 | ||
| Other hematological diseases | 27 (7.0%) | 3 | 3 | 21 | 0 | ||
| Treatment types | |||||||
| Conventional treatment | 121 (31.4%) | 11 | 5 | 95 | 10 | 5.795 | 0.122 |
| Established cell therapy | 208 (54.0%) | 21 | 12 | 161 | 14 | ||
| Emerging biotherapy | 25 (6.5%) | 2 | 0 | 17 | 6 | ||
| Untreated | 31 (8.1%) | 2 | 4 | 22 | 3 | ||
| Prior experience with CGT-related clinical trials | |||||||
| Yes | 40 (10.4%) | 0 | 0 | 29 | 11 | −4.629 | <0.001 |
| No | 345 (89.6%) | 36 | 21 | 266 | 22 |
Notes: 1 = Refusal; 2 = Uncertain; 3 = Decide after detailed risk information; 4 = Immediate acceptance; PCI = Annual per capita household income (RMB); other hematological diseases include multiple myeloma (n = 2), lymphoma (n = 10), and aplastic anemia (n = 15); Bold text indicates statistical significance (p < 0.05).
Participants’ Acceptance of CGT
The results of this study indicate that the surveyed patients with hematological diseases generally exhibited a cautious attitude toward CGT acceptance. As shown in Table 3, the majority of patients (76.6%) chose to “decide after detailed risk information”, while the combined proportion of patients who would immediately accept or refuse the therapy was less than 20%.
Table 3.
Acceptance of CGT Among Patients with Hematological Diseases
| Acceptance Levels of CGT | n (%) |
|---|---|
| Refusal | 36 (9.4%) |
| Uncertain | 21 (5.5%) |
| Decide after detailed risk information | 295 (76.6%) |
| Immediate acceptance | 33 (8.6%) |
Associations of Sociodemographic and Clinical Characteristics with Patients’ Acceptance of CGT
In the present study, gender, medical insurance status, disease types, and prior experience with CGT-related clinical trials were significantly associated with the acceptance of CGT among patients with hematological diseases (all P < 0.05).
Associations of Risk Perception Level and Quality of Life with Patients’ Acceptance of CGT
Spearman correlation analysis revealed that risk perception level among patients with hematological diseases was negatively correlated with their acceptance of CGT (r = −0.239, p < 0.01). Furthermore, a negative correlation was observed between patients’ quality of life and their acceptance of CGT (r = −0.156, p < 0.01).
Comparison of Standardized Scores Across Risk Perception Dimensions
Paired-samples t-tests showed that the standardized score for ethics and privacy risk (M = 2.76, SD = 1.07) was significantly lower than those for physical risk (M = 3.87, SD = 0.84; t (384) = 22.99, p < 0.001), economic risk (M = 3.78, SD = 0.80; t (384) = 19.64, p < 0.001), and psychosocial risk (M = 2.89, SD = 0.92; t (384) = 3.13, p = 0.002).
Multivariable Ordinal Logistic Regression Analysis of Factors Influencing CGT Acceptance Among Patients with Hematological Diseases
With the acceptance of CGT as the dependent variable (1 = refusal, 2 = uncertain, 3 = decide after detailed risk information, 4 = immediate acceptance), an ordinal logistic regression analysis was performed. Variables that demonstrated statistical significance in the univariable and correlation analyses were included as independent variables (Table 4), as the inclusion of additional non-significant clinical confounders violated the proportional odds assumption. The likelihood ratio test yielded χ2 = 76.773 (p < 0.001), indicating that the regression model was statistically significant. The goodness-of-fit test resulted in χ2 = 1039.539 (p = 0.361), confirming a good model fit. Furthermore, the test of parallel lines showed χ2 = 18.577 (p = 0.291), demonstrating that the proportional odds assumption was satisfied. Multicollinearity diagnostics revealed that the tolerance values for all independent variables were greater than 0.1, and the variance inflation factor (VIF) values ranged between 1 and 2, indicating the absence of multi-collinearity among the independent variables.
Table 4.
Variable Assignments and Definitions
| Variable | Assignment |
|---|---|
| Risk perception level | Continuous scale score (higher scores indicate higher perceived risk) |
| Quality of life | Continuous scale score (higher scores indicate better quality of life) |
| Gender | Male = 1, Female = 2 |
| Medical insurance status | Yes = 0, No = 1 |
| Disease types | Acute leukemia = 1, Chronic leukemia = 2, Myelodysplastic syndrome = 3, Other hematological diseases = 4 |
| Prior experience with CGT-related clinical trials | Yes = 1, No = 2 |
| Annual per capita household income | <10000 = 1, 10,000–20,000 = 2, 20,000–30,000 = 3, 30,000–50,000 = 4, >50,000 = 5 (Unit: RMB) |
Notes: The variable “Annual per capita household income” is included only in subgroup analyses.
Ordinal logistic regression analysis revealed that risk perception level, gender, medical insurance status, disease types, and prior experience with CGT-related clinical trials were significantly associated with patients’ acceptance of CGT (Table 5). Specifically, a higher level of risk perception was associated with lower acceptance (OR = 0.944; 95% CI: 0.921–0.967). Compared to female patients, males exhibited a higher likelihood to accept CGT (OR = 1.754; 95% CI: 1.061–2.901). Patients with medical insurance showed greater acceptance than those without coverage (OR = 5.485; 95% CI: 2.020–14.895). Furthermore, patients with acute leukemia demonstrated higher acceptance than those with other hematological diseases, including multiple myeloma, lymphoma, and aplastic anemia (OR = 2.936; 95% CI: 1.169–7.374). Finally, individuals who had participated in CGT-related clinical trials reported higher acceptance than those without such experience (OR = 6.278; 95% CI: 2.798–14.083).
Table 5.
Predictors of CGT Acceptance in the Overall Sample
| Variable | Category | β | SE | Wald | p | OR (95% CI) |
|---|---|---|---|---|---|---|
| Risk perception level | / | −0.058 | 0.012 | 22.464 | <0.001 | 0.944 (0.921–0.967) |
| Quality of life | / | 0.005 | 0.032 | 0.022 | 0.882 | 1.005 (0.943–1.070) |
| Gender (Ref: Female) | Male | 0.562 | 0.257 | 4.792 | 0.029 | 1.754 (1.061–2.901) |
| Medical insurance (Ref: No) | Yes | 1.702 | 0.510 | 11.148 | 0.001 | 5.485 (2.020–14.895) |
| Disease types (Ref: Other hematological diseases) | AL | 1.077 | 0.470 | 5.249 | 0.022 | 2.936 (1.169–7.374) |
| CL | 0.040 | 0.649 | 0.004 | 0.951 | 1.041 (0.292–3.714) | |
| MDS | 0.612 | 0.515 | 1.411 | 0.235 | 1.844 (0.672–5.063) | |
| Prior experience with CGT-related clinical trials (Ref: No) | Yes | 1.837 | 0.412 | 19.860 | <0.001 | 6.278 (2.798–14.083) |
Abbreviations: AL, acute leukemia; CL, chronic leukemia; MDS, myelodysplastic syndrome.
Subgroup Analysis of Disease Types and Influencing Factors of Patient Acceptance of CGT
A subgroup analysis was conducted following stratification by disease types to identify factors associated with CGT acceptance. The likelihood ratio test yielded χ2 = 45.645 (p < 0.001), indicating that the regression model was statistically significant. The goodness-of-fit test showed χ2 = 708.675 (p = 0.475), confirming a good model fit. The test of parallel lines resulted in χ2 = 22.426 (p = 0.070), demonstrating that the proportional odds assumption was satisfied. Multicollinearity diagnostics showed that the tolerance values for all independent variables were greater than 0.1, and the variance inflation factor (VIF) values ranged between 1 and 2, indicating that there was no multicollinearity between variables.
In the acute leukemia subgroup, risk perception level, annual per capita household income, and prior experience with CGT-related clinical trials were significant factors associated with CGT acceptance (Table 6). Specifically, a higher level of risk perception was associated with lower acceptance of CGT (OR = 0.930, 95% CI: 0.901–0.959). Compared to patients with an annual per capita household income exceeding 50,000 RMB, those with an income of 30,000–50,000 RMB demonstrated lower acceptance significantly (OR = 0.365, 95% CI: 0.138–0.962). Furthermore, patients who had participated in CGT-related clinical trials exhibited higher acceptance levels than those without such experience (OR = 4.504, 95% CI: 1.735–11.681).
Table 6.
Predictors of CGT Acceptance in Acute Leukemia Patients
| Variable | Category | β | SE | Wald | p | OR (95% CI) |
|---|---|---|---|---|---|---|
| Risk perception level | / | −0.073 | 0.016 | 21.663 | <0.001 | 0.930 (0.901–0.959) |
| Quality of life | / | 0.010 | 0.041 | 0.061 | 0.804 | 1.010 (0.932–1.094) |
| Annual per capita household income (Ref: >50000 RMB) | <10,000 | −0.182 | 0.476 | 0.146 | 0.703 | 0.834 (0.328–2.121) |
| 10,000–20,000 | −0.981 | 0.538 | 3.322 | 0.068 | 0.375 (0.131–1.07) | |
| 20,000–30,000 | −0.217 | 0.552 | 0.155 | 0.694 | 0.805 (0.273–2.37) | |
| 30,000–50,000 | −1.008 | 0.494 | 4.160 | 0.041 | 0.365 (0.138–0.962) | |
| Prior experience with CGT-related clinical trials (Ref: No) | Yes | 1.505 | 0.487 | 9.558 | 0.002 | 4.504 (1.735–11.681) |
Due to sample size limitations in certain subgroups (chronic leukemia and other hematological diseases) and the severe category sparseness of response distributions (myelodysplastic syndrome [MDS] group), multivariable ordinal logistic regression models were unable to yield robust parameter estimates. To ensure the reliability of the results, only univariable exploratory analyses were conducted for these subgroups. In the chronic leukemia group, medical insurance status was significantly associated with patients’ acceptance of CGT, with insured patients exhibiting significantly higher acceptance than those without insurance (Z = −2.239, p = 0.025). In the MDS group, prior experience with CGT-related clinical trials and the specific treatment modality were significantly associated with patients’ acceptance of CGT. Specifically, patients with clinical trial experience exhibited significantly higher acceptance than those without such experience (Z = −2.289, p = 0.022). Furthermore, patients receiving emerging biotherapies such as CAR-T demonstrated higher acceptance of CGT, whereas those treated with established cell therapy reported relatively lower acceptance levels (H = 12.426, p = 0.006). In the “other hematological diseases” group, gender was a significant factor, with male patients showing higher acceptance than female patients (Z = −2.016, p = 0.044).
Discussion
Overall Status of CGT Acceptance Among Patients with Hematological Diseases
In this study, a substantial majority (76.6%) of surveyed patients held a cautious attitude toward CGT, opting to “decide after detailed risk information”, while only 8.6% explicitly expressed “immediate acceptance”. Previous research has noted that patients’ treatment willingness is influenced by the interplay between “perceived benefit” and “perceived risk”.27 The high proportion of patients with a cautious stance in the present study suggests that perceived risk has become the predominant factor constraining the decision-making process for CGT in this group. This may be attributed to the low level of awareness regarding CGT among the respondents, leading to a tendency toward conservative decision-making to avoid potential risks.17,22 Furthermore, the low refusal rate of only 9.4% indirectly reflects that this patient population still holds high favorable expectations for CGT, although this inclination is currently limited by concerns over safety and the uncertainty of potential risks. Therefore, in the future clinical translation of CGT, healthcare institutions should prioritize the establishment of standardized, visualized, and accessible risk disclosure systems and shared decision-making models to alleviate patients’ decisional anxiety by enhancing information symmetry.
Key Factors Associated with CGT Acceptance Among Patients with Hematological Diseases
Previous research on cancer patients has indicated that individual characteristics are a primary source of heterogeneity in treatment preferences.28 In the present study, the acceptance of CGT among the surveyed patients with hematological diseases (96.1% of whom had malignancies) likewise exhibited significant variations based on individual characteristics, including gender, medical insurance status, and disease types. Consistent with the majority of previous findings,29,30 male patients demonstrated a higher willingness to accept CGT. While not directly examined in our study, one plausible explanation is that male patients may exhibit greater risk tolerance when confronted with emerging therapies characterized by a “high-risk, high-benefit” profile, whereas female patients might maintain a more cautious stance regarding long-term uncertainties and potential toxicities.31,32 Future qualitative research would be valuable to explore these underlying psychosocial dynamics. Patients with medical insurance coverage showed higher levels of CGT acceptance. This correlation likely stems from the substantial costs associated with CGT;33,34 insurance directly reduces the proportion of out-of-pocket expenses, effectively alleviating long-term anxiety regarding poverty caused by illness,35,36 thereby fostering a greater predisposition toward CGT. Regarding disease categories, patients with acute leukemia demonstrated higher acceptance compared to those with other hematological conditions (multiple myeloma, lymphoma, and aplastic anemia). This discrepancy could hypothetically stem from the high clinical aggressiveness and prognostic urgency of acute leukemia,37,38 where the imperative for survival might outweigh concerns over CGT-related risks. However, it should be noted that patients with other disease types accounted for only 7% of the study population, and this category encompasses three distinct diseases, potentially exerting a confounding effect that may introduce bias to the findings. Furthermore, patients with prior experience in CGT-related clinical trials showed higher acceptance. This may be due to the fact that previous trial participation enhanced patients’ understanding of CGT,39 providing more realistic expectations of safety and potential benefits, thus mitigating fear and resistance stemming from information asymmetry. Notably, this study found no statistically significant correlation between educational level and CGT acceptance, which diverges from previous research. This inconsistency may be related to the skewed sample distribution within this study.
In this study, both risk perception level and quality of life (QoL) were negatively correlated with patients’ acceptance of CGT. Higher levels of risk perception reflect patients’ deep-seated concerns regarding the safety and potential side effects of CGT, while a lower QoL suggests that the fear of risks may outweigh the desire for life quality improvement. Ultimately, this risk-aversive mindset may attenuate patients’ willingness to accept CGT. Therefore, the clinical translation of CGT should implement differentiated and stratified guidance based on patients’ risk perception level and QoL. However, upon adjusting for multiple covariates in the multivariable model, the variable of QoL lost its statistical significance. This attenuation suggests a substantial overlap in shared variance between QoL and more proximal determinants, such as disease types and risk perception level. In clinical reality, a patient’s quality of life is heavily dictated by the objective severity of their specific hematological condition; once these dominant clinical and psychological factors are controlled for, the residual variance of QoL no longer yields incremental predictive value for CGT acceptance.
Further analysis using paired-samples t-tests on the individual dimensions of risk perception revealed that scores for ethics and privacy risk were significantly lower than those for all other dimensions, particularly physical risk and economic risk. For patients with critical hematological conditions, the acute threat to survival and the high costs of advanced therapies compel them to prioritize clinical safety and economic feasibility over abstract bioethical concerns.
Subgroup analyses based on disease types reveal significant heterogeneity in CGT acceptance across patients with different hematological conditions. Specifically, within the acute leukemia subgroup, middle-income patients exhibited higher sensitivity to financial risks. This may be attributed to the fact that this population lacks the financial resilience of high-income earners while also facing barriers in accessing social medical assistance targeted at the lowest-income group.40 In the MDS subgroup, patients who had previously undergone emerging biotherapies, such as CAR-T cell therapy, demonstrated higher acceptance of CGT, potentially because their prior treatment experiences enhanced their awareness and trust in such therapies. It should be noted, however, that due to the small cohorts in certain subgroups (chronic leukemia, MDS, and other hematological diseases) and the reliance on multiple univariable tests, these findings are strictly exploratory and must be interpreted with caution. Their robustness warrants further validation in future studies.
Pathways for Enhancing the Acceptance of CGT Among Patients with Hematological Diseases
In this study, risk perception level, gender, medical insurance status, disease types, and prior experience with CGT-related clinical trials were identified as significant predictors of patients’ acceptance of CGT. Physical risk and economic risk constituted the two primary risk barriers hindering CGT acceptance. These findings suggest that the combination of enhanced science-based communication interventions and robust financial support holds pivotal implications for promoting patient acceptance. Consequently, healthcare institutions and relevant authorities should pivot from traditional hazard disclosure to systematic risk communication strategies, alongside developing dedicated patient decision aids (PtDAs). By providing transparent and accurate therapeutic information, such instruments have the potential to mitigate the pronounced information asymmetry.41–43
Crucially, for vulnerable populations—specifically female and uninsured patients who are often characterized by heightened risk perceptions—rudimentary information disclosure is insufficient to shift conservative attitudes. Therefore, it may be highly beneficial to integrate the “framing effect” into the granular design of educational and promotional materials. For instance, strategically combining a loss frame that emphasizes disease progression risks and long-term survival declines associated with foregoing treatment with a gain frame that highlights the potential clinical benefits of intervention could dynamically reshape patients’ risk-related cognitive architecture. Concurrently, diversified reimbursement models should be explored,44,45 such as accelerating medical insurance inclusion or expanding commercial insurance indemnification, to alleviate financial burdens. Furthermore, future intervention paradigms should be tailored for female patients, those without clinical trial experience, and individuals diagnosed with specific hematologic conditions (multiple myeloma, lymphoma, and aplastic anemia). Implementing tailored health communication and psychological support for these specific cohorts could serve as actionable benchmarks for multi-dimensionally fostering patients’ willingness for CGT clinical translation. (Figure 1).
Figure 1.
Integrated framework of cell and gene therapy (CGT) acceptance status, influencing factors, and intervention pathways among patients with hematological diseases.
Notes: The top chevron arrows illustrate the progressive workflow from status assessment to influencing factors and intervention pathways. In the first section, the connecting arrow links the hematological patient population with their current acceptance distribution. In the remaining sections, solid arrows indicate the associations of determinants with acceptance, and the application of informational and financial interventions toward the target population, while dashed lines represent subgroup analysis pathways. Specific hematologic conditions include multiple myeloma, lymphoma, and aplastic anemia.
Limitations
This study is subject to several limitations. First, the cross-sectional design allows only for the description of correlations between variables, precluding the establishment of definitive causal inferences. Second, the data in this study were collected through self-reported questionnaires, which may introduce a degree of social desirability bias. Third, our sampling frame was restricted to a single national transplant-referral center. Because this cohort predominantly consisted of patients with relapsed or refractory conditions, the generalizability of our findings to broader, unselected hematological patient populations in general hospital settings may be limited, despite this demographic currently being the primary target for CGT. Finally, due to the limited total sample size, fully stratified analyses across all specific disease subtypes were not feasible; instead, certain disease categories were consolidated during statistical modeling. This aggregation may have masked granular variations in CGT acceptance preferences among specific patient cohorts and potentially introduced residual confounding. Subsequent studies with larger sample sizes are needed to further dissect the profound impact of disease heterogeneity on patients’ acceptance of CGT.
Conclusions
This study found that, within a transplant-referral cohort, the majority of patients with hematological diseases maintain a cautious stance toward CGT, preferring to make informed decisions after a comprehensive understanding of the associated risks. Risk perception, gender, medical insurance status, disease types, and prior experience with CGT-related clinical trials were identified as key factors associated with patient acceptance of CGT. Physical risk and economic risk constituted the two primary risk barriers hindering CGT acceptance. Furthermore, subgroup analyses revealed that annual per capita household income and treatment types were significant predictors of CGT acceptance for patients with acute leukemia and MDS, respectively. Consequently, healthcare institutions and relevant authorities should collaborate to refine risk communication systems and explore diversified payment models. However, given the cross-sectional design, further multicenter and prospective studies are needed to validate these findings and evaluate the actual effectiveness of specific educational or financial interventions during the clinical translation of CGT.
Acknowledgments
We would like to acknowledge the support of the General Project of National Natural Science Foundation of China (Grant No. 72474152) for the project “Study on the Elastic Control Mechanism of Biological Triage Process”, and the Tianjin Health Research Project (Grant No. TJWJ2025MS052). Additionally, Yanwen Qiao and Hui Yang contributed equally to this work as co-second authors.
Funding Statement
The General Project of National Natural Science Foundation of China: Study on the Elastic Control Mechanism of Biological Triage Process (72474152); The Tianjin Health Research Project (TJWJ2025MS052).
Abbreviations
CGT, Cell and gene therapy; IHCAMS, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Science; STROBE, Strengthening the reporting of observational studies in epidemiology; EQ-5D-5L, EuroQol 5-Dimensional 5-Level; SPSS, Statistical Product and Service Solutions; AL, acute leukemia; CL, chronic leukemia; MDS, Myelodysplastic syndrome; QoL, Quality of life; CAR-T, Chimeric Antigen Receptor T-cell.
Data Sharing Statement
The datasets used and/or analyzed during the current study are available from the corresponding author, Yongzhong Zhang, on reasonable request.
Ethics Approval and Consent to Participate
The study was conducted in accordance with the principles of the Declaration of Helsinki. This study was approved by the Tianjin University Health Research Ethics Committee (TJUE-2025-357). Written informed consent was taken from the study participants after assuring the anonymity and confidentiality of their data.
Disclosure
Dr Yongzhong Zhang reports Support for the manuscript from National Natural Science Foundation of China, during the conduct of the study.
Dr Chuan Wang reports Support for the manuscript from Tianjin Health Research Project, during the conduct of the study. The authors declare no competing interests.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author, Yongzhong Zhang, on reasonable request.