Decisional Conflict Regarding Disease-Modifying Treatment Choices Among Patients with Mid-Stage Relapsing-Remitting Multiple Sclerosis

Introduction

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that causes disability, poor quality of life, and productivity loss even at early stages.^1–3 The MS therapeutic landscape has changed considerably in recent years with different treatment options capable of controlling the relapse rate and reducing the risk of disability progression.^4,5 However, many patients still suffer from a variety of disabling symptoms such as fatigue, sphincter disturbances, cognitive problems or pain.^2,6 Furthermore, despite our increasing understanding of numerous prognostic factors, the disease’s natural course varies, often making it challenging for neurologists to reliably predict their patients’ long-term prognosis.⁷

Selecting an appropriate disease-modifying treatment (DMTs) for MS presents numerous challenges.^1,5 The expanding repertoire of available therapies, each characterized by its unique efficacy, safety considerations, and method of administration, complicates the decision-making process.^1,8 In addition, patient preferences play a crucial role in decision-making, as MS patients often have unique experiences and priorities that can significantly influence their treatment choices.⁹ Factors such as lifestyle, personal goals, the impact of treatment side effects, and desired level of involvement in managing the disease vary from patient to patient over time and are influenced by emotional, social, and experiential factors.^9–11

Decisional conflict is defined as personal uncertainty about what to do when the choice between different options involves risk, regret or a challenge to personal life values.^12,13 In a complex MS treatment landscape context, patients can doubt whether their treatment decision was right, leading to decisional conflict.^14–16 In a recent study with 254 MS patients in the United Kingdom, 53% of participants reported decisional conflict, especially in those who were unsatisfied with their treatment.¹⁴ The aim of this study was to assess the decisional conflict related to the choice of a disease-modifying therapy and its associated factors in patients with mid-stage relapsing-remitting multiple sclerosis (RRMS).

Materials and Methods

This non-interventional, cross-sectional study was conducted at 19 hospital-based neuro-immunology care units in Spain (FACE-MS study). Key inclusion criteria included a diagnosis of RRMS according to 2017 revised McDonald criteria, a disease duration of 3 to 8 years, and being on disease-modifying therapy.¹⁷ Focusing on patients in a mid-term stage of MS provides a more nuanced perspective on the disease’s impact and treatment outcomes while minimizing the risk of bias associated with early disease stages.^18–20 The decision to study patients with a disease duration of 3 to 8 years was intended to capture individuals with a more comprehensive understanding of the disease trajectory. These patients have already passed the initial phase of processing the diagnosis and have experienced some degree of disease progression and response to treatment.²⁰

Patients were invited to participate in the study by their treating neurologists in the context of their regular follow-up visits. The study was approved by the research ethics board of the Hospital de Galdakao-Usansolo (Galdakao, Spain) and performed in accordance with the 1964 Helsinki Declaration and its later amendments. All participants provided a written informed consent and were recruited from January to November 2022.

The level of comfort with a treatment decision was evaluated using the 4-item Decisional Conflict Scale (DCS SURE).²¹ Patients were asked to recall when they initiated their disease-modifying treatment or changed their most recent treatment. The scores of the four items were summed, with a score of less than 4 indicating the presence of decisional conflict.²¹ The DCS SURE has shown adequate internal consistency, moderate correlation to the full 16-item DCS, and adequate sensitivity and specificity of its cutoff score for identifying clinically significant decisional conflict.^13,22 The validated Spanish version of the instrument was used in this study.²³

In addition, other outcome measures were included based on previously identified associations in the literature with the decision-making process.^24–27 These measures included the SymptoMScreen (SyMS), Brief-Illness Perception Questionnaire (B-IPQ), 5-item Modified Fatigue Impact Scale (MFIS-5), 54-item MS Quality of Life (MSQOL-54), Hospital Anxiety and Depression Scale (HADS), Mishel Uncertainty of Illness Scale (MUIS), Decision Regret Scale (DRS), MS Self-Management Scale-Revised (MSSM-R), MS Knowledge Assessment Scale (MSKAS), General Risk Propensity Scale (GRiPS), Control Preference Scale (CPS), and Symbol Digital Modalities Test (SDMT).^28–39 All questionnaires were administered through an electronic tablet and completed online at the hospital.

The SyMS assesses MS symptom severity across 12 neurologic domains: mobility, hand function, spasticity and stiffness, pain, sensory symptoms, bladder control, fatigue, vision, dizziness, cognition, depression, and anxiety.²⁸ Each item is assessed on a 7-point Likert scale from 0 (not at all affected) to 6 (total limitation). Higher scores indicate more severe symptom endorsement. The B-IPQ assesses cognitive and emotional illness representations.²⁹ It consists of eight items rated on a scale from 0 (minimum) to 10 (maximum). Higher scores indicate a threatening illness perception. The MFIS-5 assesses physical, cognitive, and psychosocial components of fatigue.³⁰ Each item scores on a 5-point Likert scale from 0 (never) to 4 (almost always). Higher scores indicate more severe fatigue. This 54-item MSQOL-54 measures quality of life combining both generic and MS-specific items.³¹ It generates a mental health and a physical health composite scores, and two additional single-item measures: satisfaction with sexual function and change in health. Higher scores indicate better quality of life. The HADS is a 14-item, self-assessment scale to measure symptoms of anxiety and depression.³² Each item is scored on a 4-point Likert scale from 0 to 3. A total subscale score >8 indicates a probable case of anxiety or depression, respectively. The MUIS is a 17-item scale to measure uncertainty about the disease trajectory and the treatments.³³ Each item is assessed on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate greater uncertainty. The DRS is a 5-item scale to measure distress or remorse after a healthcare decision.³⁴ Each item is assessed on a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Higher scores indicate a greater regret. The MSSM-R is a 23-item scale to measure patient´s skills to continue an active life and a satisfying psychological status in the face of MS.³⁵ Each item is assessed on a 5-point Likert scale from 1 (completely disagree) to 5 (completely agree). Higher scores indicate a higher level of self-management. The MSKAS is a 22-item scale to measure MS knowledge.³⁶ Respondents were provided with three answer options for each statement: “true”, “false”, and “I don’t know”. These responses were subsequently coded as either correct (1) or incorrect (0). “I don’t know” responses were also coded as incorrect. Higher scores indicate greater knowledge. The GRiPS is an 8-item scale to measure risk-taken behavior.³⁷ Each item is assessed on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate a higher propensity to take risks. The CPS consists of five cards with a cartoon describing different roles in the decision-making process.³⁸ Patients who prefer to make decisions on their own, or jointly with the physician, or who prefer the physician to make the decision are classified as active, collaborative, or passive, respectively. The SDMT measures patient attention and information processing speed.³⁹ A cut-off of ≤49 correct substitutions is used to identify patients with cognitive impairment.

Statistical Analysis

Descriptive statistics were presented for all variables. Continuous variables were analyzed with measures of central tendency (mean and median), variability/dispersion (standard deviation [SD] and interquartile ranges [IQR]), and categorical variables with absolute and relative frequency distributions (percentages of groups). We chose to employ a binary approach of the DCS SURE based on the recommendations by its creators, which underscores the utility and validity of this binary transformation for identifying clinically significant decisional conflict.⁴⁰ A multivariate logistic regression analysis was conducted to determine the association between patients’ characteristics and lack of decisional conflict (decisional certainty). This analysis was performed using three different models. The first model was a multivariate logistic regression model that included all the covariates that were statistically significant or near significant (p<0.2) in the bivariate analysis plus other clinically significant covariates. The second model was also a multivariate logistic regression model but with a stepwise selection procedure. Finally, the third model was a least absolute shrinkage and selection operator (LASSO) multivariate logistic regression model.⁴¹ LASSO is a penalized regression approach in which the estimates of the regression coefficients are sparse, which means that many components are exactly zero and thus this method automatically deletes unnecessary covariates. This approach relies on the selection of a parameter λ that modulates the accuracy and complexity of the model. The logistic LASSO regression was implemented using the R package glmnet and λ was selected as the value that gives the most accurate and simplest model.

Results

Of the 209 patients who agreed to participate, eight were ineligible for analysis due to not meeting the inclusion criteria. A total of 201 patients were studied, and there were no withdrawals from the study. Mean age (SD) was 38.7 (8.4) years and 74.1% were female. Median (IQR) disease duration and EDSS score were 6.0 (4.0–7.0) years and 1.0 (0–2.0), respectively. Almost half of the patients changed DMTs at least once since diagnosis (n=94, 46.5%). The main cause of the change was self-reported lack of efficacy (63.8%), followed by tolerability or safety problems (38.3%). The main characteristics of the study population are shown in Table 1.

Table 1 Sociodemographic, Clinical, and Trait Characteristics of Participants

Sixty-seven (33.3%) patients reported a decisional conflict. Those patients had a poorer quality of life, a threatening illness perception, lower MS knowledge, and more illness uncertainty, anxiety, depressive symptoms, fatigue, and subjective symptom severity than their counterparts (Table 1).

Lack of decisional conflict was associated with MS knowledge (Odds ratio [OR]=1.195, 95% CI 1.045, 1.383, p=0.013), self-management (OR=1.049, 95% CI 1.013, 1.093, p=0.018), and regret after a healthcare decision (OR=0.860, 95% CI 0.756, 0.973, p=0.018) in the multivariate analysis (Table 2).

Table 2 Lack of Decisional Conflict: Multivariate Logistic Regression Analysis

Discussion

The uncertainty experienced by a patient when faced with making a difficult decision is a phenomenon described in different diseases.⁴² RRMS is a paradigmatic example for investigating the decisional conflict due to the unpredictable trajectory of the disease and the numerous treatment options available, each differing in efficacy, safety, and administration characteristics.^4,7,8 Decisional conflict arises when patients feel unsure about which treatment option to choose, have conflicting values or preferences, or lack adequate information to make a well-informed decision, fear of making the wrong choice, feelings of regret, and the tendency to blame physicians for unfavorable outcomes.^9,10,12

In our study, decisional conflict related to the choice of a DMTs was a common phenomenon (33,3%) in a sample of mid-stage RRMS patients. Two recent studies in MS utilizing standardized scales such as the DCS revealed a significant proportion of patients experiencing conflict regarding their treatment decisions.^14,43 In a study conducted in the United Kingdom, 53% of MS patients reported experiencing decisional conflict.¹⁴ This figure rose to 75% among patients who were initiating treatment and was 44% among those already undergoing treatment. Regret after a decision was higher in those patients who were unsatisfied with their DMTs and those who were receiving low-efficacy agents.¹⁴ In a separate study in Canada, researchers found that 36% of a sample predominantly consisting of treatment-naive patients with RRMS experienced some level of decision-making conflict.⁴³

Patients’ beliefs and expectations about a disease influence their emotional reactions and coping resources.¹¹ In our study, the lack of decisional conflict was significantly associated with illness-related knowledge, self-management, and low regret. An adequate knowledge of the disease has been shown to facilitate a positive behavior change and improve decision-making, treatment adherence and satisfaction with care among patients with MS.^27,44 Self-management in MS embodies the proactive adoption of strategies and skills by individuals to manage their condition, symptoms, and overall well-being.⁴⁵ This approach integrates a comprehensive understanding of pathophysiology and disease progression, recognition and management of symptoms, awareness of treatment options, and the impact of lifestyle factors. By actively monitoring symptoms and changes in their condition, individuals can optimize health outcomes, improve quality of life, and minimize the daily impact of MS. Effective self-management empowers individuals with MS to make informed decisions about their care, enhancing their ability to navigate the complexities of the disease and take charge of their health.⁴⁵ In the current MS therapeutic landscape, characterized by numerous efficacy alternatives, decision regret is influenced by several crucial factors.¹⁴ Regret can stem not only from obvious errors in decision-making, but also from the presence of readily available counterfactual alternatives.^46,47 Decisional conflict can increase the likelihood of decisional regret by fostering uncertainty, emotional distress, and a sense of missed opportunity if the outcome falls short of expectations.

Educational strategies play a crucial role in empowering MS patients to make well-informed decisions regarding DMTs.⁴⁸ These strategies encompass a spectrum of approaches, ranging from simple, practical decision aids that help patients prepare for discussions with healthcare professionals, to more comprehensive educational interventions aimed at reducing decisional conflict during clinical consultations.^43,49,50

Our study has some limitations. First, a selection bias may have influenced the prevalence of decisional conflict, as the study participants may have been skewed towards more motivated or cooperative individuals who chose to participate. This selection bias might not accurately represent the broader population of patients with RRMS, potentially impacting the generalizability of the study’s findings. Second, the reliance on patients’ ability to accurately recall details about their treatment decisions, such as the initiation or change of DMTs. Patients may not recall the specifics of their decision-making process accurately, especially if some time has passed since the decision was made. This could introduce inaccuracies or inconsistencies in the reported levels of decisional conflict, potentially affecting the study’s findings. Third, outcome bias could influence the study results. Patients’ perceptions of their treatment outcomes might influence their retrospective assessment of decisional conflict. For instance, patients who perceive their treatment as effective or ineffective may retrospectively justify their decision-making process, impacting the reported levels of decisional conflict. Third, the perspectives of patients not currently receiving DMTs were not represented in our sample. Finally, the cross-sectional study design limits the ability to establish causal relationships between the factors assessed and decisional conflict, emphasizing the need for future longitudinal studies.

Conclusions

Decisional conflict among mid-stage RRMS patients when choosing DMTs was a common phenomenon. Decisional conflict gives insight into the patient’s perspective on the quality of the decision-making process and decisions being made. Empowering patients with comprehensive disease education and enhancing self-management skills could potentially reduce decisional conflict and improve treatment satisfaction and outcomes in RRMS care. Future studies should consider mitigating these biases through more objective measures or prospective assessments to enhance the validity of their results.