Comparative Adherence Trajectories of Oral Fingolimod and Injectable Disease Modifying Agents in Multiple Sclerosis

Barriers and strategies to medication adherence amongst people with multiple sclerosis and cognitive problems

BACKGROUND

Adherence to disease-modifying treatment (DMT) amongst people with multiple sclerosis (MS) varies greatly. Although research often identifies 'forgetting' as a reason for poor adherence, few studies have considered how cognitive problems impact adherence.

OBJECTIVES

To investigate prevalence of and barriers to adherence, including for people with MS-related cognitive problems, and to identify adherence-related strategies.

METHODS

Recruited via the UK MS Register and MS Society groups, participants completed a Medication Adherence Questionnaire and the Perceived Deficits Questionnaire. A subset were interviewed.

RESULTS

Of 257 participants, 94 % reported being adherent, although 59 % missed ≥1 dose, and 25 % reported cognitive problems. Adherence was lower amongst those with cognitive problems, who experienced more barriers: memory problems; negative feelings about taking medication; and not wanting medication to interfere with activities. Such barriers, along with mood, cognition, and method of DMT administration, explained 17 % of variance in adherence, with intravenous treatment a significant predictor. Cognitive problems explained a unique proportion of variance; however, was non-significant when anxiety was factored in. Interviews highlighted how anxiety about side-effects and injections, and difficulties accepting the diagnosis, hindered use of reminders.

CONCLUSION

Interventions for cognition and mood problems, minimising treatment burden and supporting adjustment to diagnosis, may improve adherence.

Association of oral disease-modifying agents and their adherence trajectories with annual relapses in multiple sclerosis

BACKGROUND

Real-world effectiveness can vary across oral disease-modifying agents (DMAs) and their adherence trajectories in patients with multiple sclerosis (MS). However, previous studies have not considered longitudinal adherence patterns while evaluating oral DMAs.

OBJECTIVES

This study aimed to evaluate the association of oral DMAs and their adherence trajectories with annualized relapse rate (ARR) in patients with MS.

METHODS

This retrospective observational cohort study based on the 2015-2019 MarketScan Commercial Claims and Encounters Database involved continuous enrolled adults (18-64 years) with ≥1 MS diagnosis (ICD-9/10-CM:340/G35) and ≥ 1 oral DMA prescription. Patients were grouped into incident fingolimod (FIN), teriflunomide (TER), and dimethyl fumarate (DMF) users based on the index DMA with a one-year washout period. Annual DMA adherence trajectories based on the monthly Proportion of Days Covered (PDC) one year after treatment initiation were identified using Group-Based Trajectory Modeling (GBTM). The validated claims-based ARR was evaluated during the one-year follow-up period using generalized boosted model-based inverse probability treatment weights with negative binomial regression model.

RESULTS

The study cohort consisted of 994 MS patients who initiated with FIN (23.0%), TER (22.3%), and DMF (54.7%) during the study period. GBTM grouped eligible patients into three adherence trajectories: complete adherers (59.2%), slow decliners (23.8%), and rapid decliners (17.0%). The proportion of complete adherers varied across the oral DMAs (FIN: 67.1%, TER: 55.4%, and DMF: 57.4%). The negative binomial regression modeling revealed that, while there was no difference in ARR across the three DMAs, rapid decliners (adjusted incidence rate ratio[aIRR]: 1.6, 95% CI: 1.1-2.4) had a higher rate of relapses compared to completely adherent patients. The type of oral DMAs did not moderate the relationship between ARR and the adherence trajectory groups.

CONCLUSIONS

Adherence trajectories classified as rapid decliners were associated with a higher ARR than complete adherers after adjusting for their type of oral DMAs. Longitudinal medication adherence patterns are critical in reducing relapse rates in MS.

Medication adherence trajectory of patients with chronic diseases and its influencing factors: A systematic review

AIMS

To synthesize the published studies on medication adherence trajectories among patients with chronic diseases and identify the influencing factors.

DESIGN

Systematic review.

DATA SOURCES

Medline (Ovid), Embase (Ovid) and Web of Science core collection were searched from database inception to 1 July 2022.

REVIEW METHODS

Potentially eligible articles were independently screened by three reviewers using set inclusion and exclusion criteria. The Joanna Briggs Institute critical appraisal checklist for cohort studies was used to appraise the quality of the included articles. Three reviewers independently evaluated the quality, extracted data and resolved differences by consensus. Results were presented using descriptive synthesis, and the prevalence of recategorised medication adherence trajectories was calculated from the published data.

RESULTS

Fifty studies were included. Medication adherence trajectories among patients with chronic diseases were synthesized into six categories: adherence, non-adherence, decreasing adherence, increasing adherence, fluctuating adherence and moderate adherence. Low and moderate evidence showed that (1) patient-related factors, including age, sex, race, marital status and mental status; (2) healthcare team and system-related factors, including healthcare utilization, insurance and primary prescriber specialty; (3) socioeconomic factors including education, income and employment status; (4) condition-related factors including complications and comorbidities and (5) therapy-related factors including the number of medications, use of other medications, and prior medication adherence behaviours were factors influencing the medication adherence trajectory. Marital status and prior medication adherence behaviour were the only influencing factors with moderate evidence of an effect.

CONCLUSION

The medication adherence trajectory among patients with chronic diseases varied widely. Further studies are warranted to determine contributory factors.

IMPLICATIONS FOR THE PROFESSION

Healthcare providers should be aware that patients' medication adherence has different trajectories and should take appropriate measures to improve patients' medication adherence patterns.

PATIENT OR PUBLIC CONTRIBUTION

None. As a systematic review, patients and the public were not involved.

Assessing treatment switch among patients with multiple sclerosis: A machine learning approach

BACKGROUND

Patients with multiple sclerosis (MS) frequently switch their Disease-Modifying Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and compare the random forest (RF) machine learning (ML) model with the logistic regression (LR) model for predicting DMA switching among MS patients.

METHODS

This retrospective longitudinal study used the TriNetX data from a federated electronic medical records (EMR) network. Between September 2010 and May 2017, adults (aged ≥18) MS patients with ≥1 DMA prescription were identified, and the earliest DMA date was assigned as the index date. Patients prescribed any DMAs different from their index DMAs were considered as treatment switch. . The RF and LR models were built with 72 baseline characteristics and trained with 70% of the randomly split data after up-sampling. Area Under the Curves (AUC), accuracy, recall, G-measure, and F-1 score were used to evaluate the model performance.

RESULTS

In this study, 7258 MS patients with ≥1 DMA were identified. Within two years, 16% of MS patients switched to a different DMA. The RF model obtained significantly better discrimination than the LR model (AUC = 0.65 vs. 0.63, p < 0.0001); however, the RF model had a similar predictive performance to the LR model with respect to F- and G-measures (RF: 72% and 73% vs. LR: 72% and 73%, respectively). The most influential features identified from the RF model were age, type of index medication, and year of index.

CONCLUSIONS

Compared to the LR model, RF performed better in predicting DMA switch in MS patients based on AUC measures; however, judged by F- and G-measures, the RF model performed similarly to LR. Further research is needed to understand the role of ML techniques in predicting treatment outcomes for the decision-making process to achieve optimal treatment goals.

Use of follow-on fingolimod for multiple sclerosis: Analysis of effectiveness and patient reported outcomes in a real-world clinical setting

BACKGROUND

Follow-on disease modifying therapies (FO-DMTs) do not always require Phase III studies. There are concerns that cheaper FO-DMTs are only used to reduce healthcare costs. However, the well-being of people with MS (pwMS) should be a priority. We aimed to evaluate the efficacy, safety and treatment satisfaction of one of the FO- Fingolimod (FTY) used in Turkey with the approval of Turkish Ministry of Health.

METHODS

PwMS under FTY were recruited from 13 centers and real-world data and answers of satisfaction and adherence statements of pwMS on FTY treatment were analyzed.

RESULTS

Data of 239 pwMS were obtained. The duration of FTY treatment was 2.5 ± 0.8 (1-4) years in pwMS who were included in the study and whose treatment continued for at least one year. Significant decreases in annual relapse rate (p < 0.001), Expanded Disability Status Scale (p < 0.001) and neuroimaging findings (p < 0.001) were observed. While 64% of the patients were satisfied and 71.5% were found to adherent with this FO-FTY.

CONCLUSION

This multicenter retrospective study found that the efficacy, safety and treatment adherence of a prescribed FO-FTY were consistent with the results of real-world studies. Studies including real-world data may provide guidance to address issues related to FO-FTY use.

Comparative adherence trajectories of oral disease-modifying agents in multiple sclerosis

STUDY OBJECTIVE

This study compared the adherence trajectories of fingolimod (FIN), teriflunomide (TER), and dimethyl fumarate (DMF) users with multiple sclerosis (MS) as there is limited evidence regarding the comparative adherence patterns of different oral disease-modifying agents (DMAs).

DESIGN

A retrospective cohort study DATA SOURCE: 2015-2019 IBM MarketScan Commercial Claims Database.

PATIENTS

Adults (≥18 years) with MS (International Classification of Diseases [ICD]-9/10-Clinical Modification [CM]:340/G35) diagnosis and ≥1 DMA prescription.

INTERVENTION

Incident FIN-, TER-, or DMF use based on the index DMA with 1 year of washout period.

MEASUREMENTS

The DMA adherence trajectories based on the proportion of days covered (PDC) were examined using the Group-Based Trajectory Modeling (GBTM) one year after the treatment initiation. Generalized boosting models (GBM)-based inverse probability treatment weights (IPTW) were incorporated in multinomial logistic regression to assess the comparative adherence trajectories across oral DMAs with FIN group as a reference category.

MEASUREMENTS AND MAIN RESULTS

The study cohort consisted of 1913 patients with MS who were initiated with FIN (24.2%, n = 462), TER (24.0%, n = 458), and DMF (51.9%, n = 993) during 2016-2018. The adherence rate (PDC ≥ 0.8) among FIN, TER, and DMF users was found to be 70.8% (n = 327), 59.6% (n = 273), and 61.0% (n = 606), respectively. The GBTM grouped patients into three adherence trajectories: Complete Adherers-59.1%, Slow Decliners-22.6%, and Rapid Discontinuers-18.3%. The multinomial logistic regression model involving GBM-based IPTW revealed that DMF (adjusted odds ratio [aOR]: 2.32, 95% confidence interval [CI]:1.57-3.42) and TER (aOR: 2.50, 95% CI: 1.62-3.88) users had higher odds to be rapid discontinuers relative to FIN users. In addition, TER users were more likely (aOR: 1.50, 95% CI: 1.06-2.13) to be slow decliners compared with FIN users.

CONCLUSION

Teriflunomide and DMF were associated with poorer adherence trajectories than FIN. More research is needed to evaluate the clinical implications of these adherence trajectories of oral DMAs to optimize the management of MS.

The Quality of Life and Medication Adherence in Patients with Multiple Sclerosis-Cross-Sectional Study

Multiple sclerosis (MS) is a chronic, degenerative autoimmune inflammatory disease of the central nervous system. MS is characterized by a wide range of symptoms and unpredictable prognosis, which can severely affect patient quality of life (QOL). The treatment strategy includes acute relapse treatment, disease-modifying treatment (DMT), and symptomatic therapy. Adherence to long-term DMTs is essential in order to maximize the therapeutic effects for MS and is crucial to health-related quality of life (HRQOL). This study aimed to evaluate the relationship between QOL and adherence to DMTs in MS patients. A group of 344 patients (73% females) aged 39.1 years with relapsing-reemitting MS were included. The Multiple Sclerosis International Quality of Life (MusiQOL) and the Multiple Sclerosis Treatment Adherence Questionnaire (MS-TAQ) were used. An injection of interferon (IFN)-β1b was used in 107 patients, IFN-β1a in 94 patients, and glatiramer acetate in 34 patients. The oral treatment includes teriflunomide in 14 patients, dimethyl fumarate in 86 patients, and fingolimod in nine patients. No statistically significant differences (p > 0.05) were observed in adherent (ADH) vs. non-adherent patients (non-ADH) in MusiQOL. The total adherence rate was 72% (MS-TAQ). An analysis of the univariate logistic regression model showed an effect of only the activities of daily living (ADL) and relationship with the healthcare system (RHCS) domains on the level of adherence to treatment recommendations. The other variables studied do not affect the level of adherence. Higher QOL levels in the ADL and RHCS domains affect medication adherence in MS patients. Our findings could help manage MS patients, promoting interventions on ADLs and good relationships with healthcare providers to improve their adherence to therapy and result in better QOL.

Physical and Mental Health-Related Quality of Life Trajectories Among People With Multiple Sclerosis

BACKGROUND AND OBJECTIVES

Most studies of health-related quality of life (HRQoL) in multiple sclerosis (MS) have been cross-sectional. The few longitudinal studies have not accounted for potential heterogeneity in HRQOL trajectories. There may be groups of individuals with common physical or mental HRQoL trajectories over time. Identification of early risk factors for membership in trajectories with poor HRQoL would inform on those at risk. We aimed to identify physical and mental HRQoL trajectories among people with MS and early risk factors for membership in the trajectory groups with the worst HRQoL.

METHODS

Between 2004 and 2020, we queried NARCOMS participants regarding HRQoL using the RAND-12, demographics, fatigue, and physical impairments (using the Patient-Determined Disease Steps scale). We included participants who were enrolled in the NARCOMS registry within 3 years of MS diagnosis, lived in the United States, reported physician-confirmed MS, and had ≥3 HRQoL observations. We used group-based trajectory modeling to determine whether there were distinct clusters of individuals who followed similar HRQoL trajectories over time. We evaluated whether baseline participant characteristics associated with the probability of trajectory group membership using a multinomial logit model.

RESULTS

We included 4,888 participants who completed 57,564 HRQoL questionnaires between 1 and 27 years after MS diagnosis. Participants had a mean (SD) age of 41.7 (9.5) years at diagnosis, and 3,978 participants (81%) were women. We identified 5 distinct physical HRQoL trajectories and 4 distinct mental HRQoL trajectories. Older age at diagnosis, worse physical impairments, and worse fatigue were associated with an increased odds of being in the group with the worst physical HRQoL when compared with being in the other 4 groups. Income ≤$50,000 and no postsecondary education were associated with an increased odds of membership in the group with the lowest mental HRQoL when compared with that in the other 3 groups.

DISCUSSION

We identified groups of people with MS who reported similar physical and mental HRQoL trajectories over time. There are early risk factors for membership in the groups with the worst HRQoL that are easily identifiable by clinicians, providing an opportunity for early interventions.

Static and group-based trajectory analyses of factors associated with non-adherence in patients with multiple sclerosis newly-initiating once- or twice-daily oral disease-modifying therapy

Background

Increased understanding of adherence may facilitate optimal targeting of interventions.

Objective

To utilize group-based trajectory modeling (GBTM) to understand longitudinal patterns of adherence and factors associated with non-adherence in patients with multiple sclerosis (MS) newly-initiating once-/twice-daily oral disease-modifying therapy (DMT) (fingolimod, dimethyl fumarate, or teriflunomide).

Methods

Commercial plan data were analyzed using proportion of days covered (PDC) to evaluate factors associated with non-adherence. GBTM clustered patient subgroups with similar longitudinal patterns of adherence measured by monthly PDC (≥80%) and multinomial logistic regression identified factors associated with adherence trajectory subgroups.

Results

Among 7689 patients, 39.5% were non-adherent to once-/twice-daily oral DMTs. Characteristics associated with non-adherence (PDC<80%) included younger age, female, depression or migraine, switching during follow-up, more frequent dosing, relapse, and absence of magnetic resonance imaging. GBTM elucidated three adherence subgroups: Immediately Non-Adherent (14.9%); Gradually Non-Adherent (19.5%), and Adherent (65.6%). Additional factors associated with adherence (i.e. region, chronic lung disease) were identified and factors differed among trajectory subgroups.

Conclusion

These analyses confirmed that a significant proportion of patients with MS are non-adherent to once-/twice-daily oral DMTs. Unique patterns of non-adherence and factors associated with patterns of adherence emerged. The approach demonstrated how quantitative trajectories can help clinicians develop tailored interventions.

Prescribing of disease modifying agents in older adults with multiple sclerosis

BACKGROUND

The use of disease-modifying agents (DMAs) to treat Multiple Sclerosis (MS) in older adults is debated as the disease activity decreases with aging. However, limited data exist regarding prescribing patterns of DMAs among older adults with MS.

OBJECTIVE

To examine prescribing patterns of DMAs and the factors associated with DMA prescribing practices among older adults with MS using electronic medical records (EMR) data.

METHODS

A retrospective longitudinal cohort study was conducted using the TriNetX, a federated EMR network from the US, data from 2016 to 2019. The study included older adults (≥60 years) with MS diagnosis and at least one prescription record during the study period. Patients with DMA prescriptions were identified and further classified into injectable, oral, or infusion users based on their last DMA prescription. A multivariable logistic regression model was used to evaluate the factors associated with prescribing of DMAs. A multinomial logistic regression model was also used to determine the factors associated with prescribing a particular dosage form of DMA.

RESULTS

The study cohort consisted of 12,922 older adults with MS, with 2,455 (18.99%) receiving DMA prescriptions. The commonly prescribed DMAs were injectables (10.46%), followed by orals (6.06%) and infusions (2.40%). Multivariable logistic regression revealed that older adults between 60- to 64 years (Adjusted Odds Ratio [aOR]= 2.38) and 65-69 years (aOR=1.60) had higher odds of receiving DMA compared to older adults of 70 years and above. African Americans (aOR=1.71) had higher odds of receiving DMA prescriptions compared to Caucasians. The presence of symptoms (pain, fatigue, speech, walking difficulty) and use of symptomatic medication (anti-fatigue medication, bladder dysfunction medication, antispasmodics, antidepressants, and relapse medication) increased the odds of being prescribed DMAs. Multinomial logistic regression found that patients 60-64 years of age had higher odds of being prescribed infusion (aOR, 95% Confidence Interval [CI] =2.06, 1.35-3.15) and oral (65-69 years: aOR=1.60, 1.24-2.07) over injectable DMAs compared to the older adults aged 70 years and above.Older males (aOR=1.68, 95% CI: 1.23-2.30) were associated with increased odds of being prescribed infusion DMA over injectable DMA compared to females. The presence of comorbidities such as coagulopathy and peripheral vascular disorders decreased the odds of being prescribed oral DMA over injectable DMA. Patients with cerebellar symptoms had an increased likelihood of being prescribed with an infusion DMA over injectable DMA. Patients using drugs for treating relapses had higher odds of being prescribed an infusion DMA over an injectable DMA. In terms of healthcare utilization, older adults with outpatient visits had higher odds of being prescribed an infusion DMA over an injectable DMA, while older adults with inpatient visits had lower odds of being prescribed an infusion DMA over an injectable DMA.

CONCLUSION

Nearly one in five older adults with MS are prescribed DMAs, with a majority receiving injectable DMAs. Several demographic and clinical factors were associated with DMA prescribing . This study fills the data gap regarding the utilization of DMAs in older adults with MS.

Prevalence and duration of prescribed opioid use during pregnancy: a cohort study from the Quebec Pregnancy Cohort

Background

Recent studies show a rapid growth among pregnant women using high potency opioids for common pain management during their pregnancy. No study has examined the duration of treatment among strong opioid users and weak opioid users during pregnancy. We aimed to investigate the prevalence of prescribed opioid use during pregnancy, in Quebec; and to compare the duration of opioid treatment between strong opioid users and weak opioid users.

Methods

Using the Quebec Pregnancy Cohort (1998–2015), we included all pregnancies covered by the Quebec Public Prescription Drug Insurance Program. Opioid exposure was defined as filled at least one prescription for any opioid during pregnancy or before pregnancy but with a duration that overlapped the beginning of pregnancy. Prevalence of opioids use was calculated for all pregnancies, according to pregnancy outcome, trimester of exposure, and individual opioids. The duration of opioid use during pregnancy was analyzed according to 8 categories based on cumulative duration (< 90 days vs. ≥90 days), duration of action (short-acting vs. long-acting) and strength of the opioid (weak vs. strong).

Results

Of 442,079 eligible pregnancies, 20,921 (4.7%) were exposed to opioids. Among pregnancies ending with deliveries (n = 249,234), 5.4% were exposed to opioids; the prevalence increased by 40.3% from 3.9% in 1998 to 5.5% in 2015, more specifically a significant increase in the second and third trimesters of pregnancy. Weak opioid, codeine was the most commonly dispensed opioid (70% of all dispensed opioids), followed by strong opioid, hydromorphone (11%), morphine (10%), and oxycodone (5%). The prevalence of codeine use decreased by 47% from 4.3% in 2005 to 2.3% in 2015, accompanied by an increased use of strong opioid, morphine (0.029 to 1.41%), hydromorphone (0.115 to 1.08%) and oxycodone (0.022 to 0.44%), from 1998 to 2015. The average durations of opioid exposure were significantly longer among pregnancies exposed to strong opioid as compared to weak opioid regardless of the cumulative duration or duration of action (P < 0.05).

Conclusions

Given the differences in the safety profile between strong opioids and the major weak opioid codeine, the increased use of strong opioids during pregnancy with longer treatment duration raises public health concerns.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-04270-x.

Association between multiple sclerosis disease severity and adherence to disease-modifying therapies

BACKGROUND: For multiple sclerosis (MS) patients taking disease-modifying therapies (DMTs), adherence to treatment is a key component of achieving beneficial outcomes, such as delayed disease progression and the reduction and prevention of symptoms and relapses. OBJECTIVES: The aim of this study was to assess the impact of a claims-based measure of MS disease severity on DMT adherence in a one-year study period. METHODS: Patients were identified from Humana Medicare Advantage claims data from January 1, 2013 to December 31, 2015. Patients over the age of 18 with at least 12 months of continuous enrollment and > 1 outpatient MS visit with DMT use prior to the index date were included. Patients who switched DMT type (oral, platform, IV) during the study period were excluded. Medication possession ratios (MPR) for DMTs were calculated from pharmacy and medical claims over 12 months of claims data, and a previously developed claims algorithm was used to determine MS disease severity. Patients with MPRs of 0.8 or higher were considered adherent to DMT treatment. Multivariable logistic regression was used to evaluate the association of MS disease severity, gender, DMT type, and age category with DMT adherence. RESULTS: The study population of 3,347 patients had an average MPR of 84.7 (75% were classified as adherent). Multivariable logistic analysis demonstrated that compared to the 18-45 age group, the 46-64 and 65+ age groups were 1.33 (OR: 1.33 [95% CI 1.08-1.64]) and 1.55 (OR: 1.55, [95% CI 1.18-2.05]) times more likely to be adherent. Patients with a high level of MS disease severity were 53% (OR: 0.47, [95% CI 0.36-0.62]) less likely to be adherent compared to those with low MS disease severity. No significant difference was identified for gender or DMT type (oral, platform, or IV). CONCLUSIONS: Increased age and lower MS disease severity were associated with better DMT adherence. MS disease severity should be considered when assessing risk for low DMT adherence. DISCLOSURES: No funding supported this project. The authors have nothing to disclose. Preliminary results were previously presented virtually at AMCP Annual 2020 in April 2020.