Antiseizure medication adherence trajectories in Medicare beneficiaries with newly treated epilepsy

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.

Lamotrigine and exogenous estrogen among females with epilepsy: A retrospective analysis of administrative claims data

OBJECTIVE

Exogenous estrogen reduces lamotrigine serum concentrations. Little is known about whether providers adjust lamotrigine doses for addition of exogenous estrogen among people with epilepsy, despite expert recommendations. We determined the incidence of dose increases in lamotrigine following incident prescription of estrogen among females with epilepsy (FWE) in claims data.

METHODS

We used Optum's de-identified Clinformatics® Data Mart Database to create a cohort of U.S. FWE prescribed lamotrigine at a stable dose, with a subsequent prescription for estrogen from 2011 to 2021. We calculated cumulative incidence functions of dose increases in lamotrigine following prescription of exogenous estrogen. We performed a Cox proportional hazards model for multiple candidate predictors of a lamotrigine dose increase.

RESULTS

The cohort included 643 FWE, with median age of 31 (interquartile ratio [IQR] 20-42). The cumulative incidence of any lamotrigine increase was 28% (95% confidence interval [CI] 25%-32%). The median number of days after the first estrogen fill until the first lamotrigine adjustment was 118 (IQR 48-188). In unadjusted Cox models, older age, use of estrogen in hormone replacement therapy as opposed to contraception, and annual household income of $50,000-$99,999 (compared with <$50,000) were significant negative predictors of a dose adjustment in lamotrigine with hazard ratios (HRs) of 0.82 (95% CI 0.72-0.92), 0.63 (95% CI 0.42-0.95), and 0.62 (95% CI 0.40-0.95). In the adjusted Cox model, age and income remained significant predictors with HRs of 0.79 (95% CI 0.66-0.94) and 0.59 (95% CI 0.36-0.95).

CONCLUSION

Dose increase of lamotrigine following addition of exogenous estrogen is rare among U.S. FWE, with potential disparities based on age and income level. More guidance may be needed for providers on this topic.

Medication compliance of children with epilepsy: a cross-sectional survey

BACKGROUND

Good medication compliance is very important for the prognosis of children with epilepsy. We aimed to evaluate the status and influencing factors of medication compliance in children with epilepsy and to provide insights to the clinical nursing care of children with epilepsy.

METHODS

We selected epileptic children admitted to Children's Hospital of Nanjing Medical University from February 1, 2022 to August 31, 2022. Self-designed questionnaire and medication compliance scale were used to evaluate the characteristics and medication compliance of children with epilepsy. Pearson correlation analysis and multivariate logistic regression were used to analyze the influencing factors of medication compliance.

RESULTS

A total of 156 children with epilepsy were included, the incidence of poor compliance in children with epilepsy was 37.18%. Pearson correlation analysis indicated that age(r = 0.622), courses of epilepsy(r = 0.553), parental education level(r = 0.506), monthly household income(r = 0.652) and number of drugs taken(r = 0.577) were correlated with the compliance(all P<0.05). Logistic regression analyses indicated that age ≤ 6 y(OR = 2.104, 95%CI: 1.712 ~ 2.527), courses of epilepsy ≤ 3 years(OR = 2.661, 95%CI: 2.089 ~ 2.941), low parental education level(OR = 1.977, 95%CI: 1.314 ~ 2.351), monthly household income ≤ 5000 RMB(OR = 2.812, 95%CI: 2.194 ~ 3.181), number of drugs taken ≥ 3(OR = 3.025, 95%CI: 2.336 ~ 3.475) were the influencing factors of medication compliance in children with epilepsy(all P<0.05).

CONCLUSIONS

The medication compliance of children with epilepsy needs to be improved, and the medication compliance of children is affected by age, courses of epilepsy, parental education level, monthly household income and number of drugs taken. Clinical medical personnel take targeted nursing measures against these factors to improve the medication compliance of children with epilepsy.

Disparities in adherence and emergency department utilization among people with epilepsy: A machine learning approach

PURPOSE

We used a machine learning approach to identify the combinations of factors that contribute to lower adherence and high emergency department (ED) utilization.

METHODS

Using Medicaid claims, we identified adherence to anti-seizure medications and the number of ED visits for people with epilepsy in a 2-year follow up period. We used three years of baseline data to identify demographics, disease severity and management, comorbidities, and county-level social factors. Using Classification and Regression Tree (CART) and random forest analyses we identified combinations of baseline factors that predicted lower adherence and ED visits. We further stratified these models by race and ethnicity.

RESULTS

From 52,175 people with epilepsy, the CART model identified developmental disabilities, age, race and ethnicity, and utilization as top predictors of adherence. When stratified by race and ethnicity, there was variation in the combinations of comorbidities including developmental disabilities, hypertension, and psychiatric comorbidities. Our CART model for ED utilization included a primary split among those with previous injuries, followed by anxiety and mood disorders, headache, back problems, and urinary tract infections. When stratified by race and ethnicity we saw that for Black individuals headache was a top predictor of future ED utilization although this did not appear in other racial and ethnic groups.

CONCLUSIONS

ASM adherence differed by race and ethnicity, with different combinations of comorbidities predicting lower adherence across racial and ethnic groups. While there were not differences in ED use across races and ethnicity, we observed different combinations of comorbidities that predicted high ED utilization.

Making Up Missed Anti-Seizure Medication Doses: Double or Nothing?

Handling Delayed or Missed Dose of Antiseizure Medications: A Model-Informed Individual Remedial Dosing Li ZR, Wang CY, Lin WW, Chen YT, Liu XQ, Jiao Z. Neurology . 2023;100(9):e921-e931. doi:10.1212/WNL.0000000000201604 Background and Objectives: Antiseizure medications are the major treatment modality for patients with epilepsy. Delayed or missed doses are common during long-term or lifelong anti-epilepsy treatment. This study aims to explore optimal individualized remedial dosing regimens for delayed or missed doses of 11 commonly used antiseizure medications. Methods: In order to explore remedial dosing regimens, Monte Carlo simulation was employed based on previously identified and published population pharmacokinetic models. Six remedial strategies for delayed or missed doses were investigated. The deviation time outside the individual therapeutic range was used to evaluate each remedial regimen. The influences of patients’ demographics, concomitant medication, and scheduled dosing intervals on remedial regimens were assessed. RxODE and Shiny in R were employed to perform Monte Carlo simulation and recommend individual remedial regimens. Results: The recommended remedial regimens were highly correlated to delayed time, scheduled dosing interval, and half-life of the antiseizure medication. Moreover, the optimal remedial regimens for pediatric and adult patients were different. The renal function, along with concomitant medication that affect the clearance of the antiseizure medication, may also influence the remedial regimens. A web-based dashboard was developed to provide individualized remedial regimens for the delayed or missed dose, and a user-defined module with all parameters that could be defined flexibly by the user was also built. Discussion: Monte Carlo simulation based on population pharmacokinetic models may provide a rational approach to propose remedial regimens for delayed or missed doses of antiseizure medications in pediatric and adult patients with epilepsy.

Adherence patterns in antiseizure medications influencing risk of sudden unexplained death in epilepsy: A data linkage study using dispensed prescriptions

OBJECTIVE

Medication adherence is considered an important risk factor for sudden unexpected death in epilepsy (SUDEP), although measurement accuracy is difficult. Using prescription dispensations, this study aims to estimate antiseizure medication (ASM) adherence and identify adherence patterns that influence epilepsy mortality.

METHODS

This is a retrospective cohort study of tertiary epilepsy outpatients seen at St Vincent's Hospital Melbourne, Victoria, Australia, from January 1, 2012 until December 31, 2017. Privacy-preserving data linkage with the Australian national prescription, death, and coroner's databases was performed. We fitted a four-cluster longitudinal group-based trajectory model for ASM adherence from recurring 90-day windows of prescription dispensations during a 3-year "landmark period" from January 1, 2012 to December 31, 2014, using the AdhereR package. We estimated the risk of SUDEP and all-cause death for each adherence pattern during an "observation period" from January 1, 2015 to December 31, 2017. The Cox proportional hazards and logistic regression models were adjusted for age, sex, socioeconomic status, epilepsy duration, comorbidity, drug resistance, and inadequate seizure control.

RESULTS

One thousand one hundred eighty-seven participants were observed for a median of 3.2 years (interquartile range = 2.4-4.0 years). We observed 66 deaths with 10 SUDEP cases during the observation period. We identified four patterns of ASM adherence: good, 51%; declining, 24%; poor, 16%; and very poor, 9%. Declining adherence was associated with an increased risk for SUDEP, with hazard ratio (HR) = 8.43 (95% confidence interval [CI] = 1.10-64.45) at 1 year and HR = 9.17 (95% CI = 1.16-72.21) at 3 years. Compared to no ASM therapeutic change, the addition of a second to fourth ASM offered increased protection against SUDEP in patients with continuing drug-resistant epilepsy.

SIGNIFICANCE

ASM nonadherence was observed in half of outpatients with epilepsy. A declining pattern of adherence, observed in a quarter of patients, was associated with more than eight times increased risk of SUDEP. Any ongoing medication interventions must include strategies to maintain and improve ASM adherence if we are to reduce the risk of SUDEP.

Racial and Ethnic Differences in Antiseizure Medications Among People With Epilepsy on Medicaid: A Case of Potential Inequities

Background and Objectives

Being on a newer, second-, and third-generation antiseizure medication (ASM) may represent an important marker of quality of care for people with epilepsy. We sought to examine whether there were racial/ethnic differences in their use.

Methods

Using Medicaid claims data, we identified the type and number of ASMs, as well as the adherence, for people with epilepsy over a 5-year period (2010-2014). We used multilevel logistic regression models to examine the association between newer-generation ASMs and adherence. We then examined whether there were racial/ethnic differences in ASM use in models adjusted for demographics, utilization, year, and comorbidities.

Results

Among 78,534 adults with epilepsy, 17,729 were Black, and 9,376 were Hispanic. Overall, 25.6% were on older ASMs, and being solely on second-generation ASMs during the study period was associated with better adherence (adjusted odds ratio: 1.17, 95% confidence interval [CI]: 1.11-1.23). Those who saw a neurologist (3.26, 95% CI: 3.13-3.41) or who were newly diagnosed (1.29, 95% CI: 1.16-1.42) had higher odds of being on newer ASMs. Importantly, Black (0.71, 95% CI: 0.68-0.75), Hispanic (0.93, 95% CI: 0.88-0.99), and Native Hawaiian and Other Pacific Island individuals (0.77, 95% CI: 0.67-0.88) had lower odds of being on newer ASMs when compared with White individuals.

Discussion

Generally, racial and ethnic minoritized people with epilepsy have lower odds of being on newer-generation ASMs. Greater adherence by people who were only on newer ASMs, their greater use among people seeing a neurologist, and the opportunity of a new diagnosis point to actionable leverage points for reducing inequities in epilepsy care.

Optimization of Therapy in Patients with Epilepsy and Psychiatric Comorbidities: Key Points

Psychiatric disorder comorbidity in patients with epilepsy (PWE) is very frequent with a mean percentage prevalence of up to 50% and even higher. Such a high frequency suggests that epilepsy and psychiatric disorders might share common pathological pathways. Various aspects contribute in making the matter very complex from a therapeutic point of view. Some antiseizure medications (ASMs), namely valproic acid, carbamazepine, and lamotrigine, have mood-stabilising effects and are routinely used for the treatment of bipolar disorder in patients who do not have epilepsy. Pregabalin and, to a lesser extent, gabapentin, exerts anxiolytic effects. However, several ASMs, in particular levetiracetam, topiramate, and perampanel, may contribute to psychiatric disorders, including depression, aggressive behaviour, and even psychosis. If these ASMs are prescribed, the patient should be monitored closely. A careful selection should be made also with psychotropic drugs. Although most of these can be safely used at therapeutic doses, bupropion, some tricyclic antidepressants, maprotiline, and clozapine may alter seizure threshold and facilitate epileptic seizures. Interactions between ASMs and psychotropic medication may make it difficult to predict individual response. Pharmacokinetic interactions can be assessed with drug monitoring and are consequently much better documented than pharmacodynamic interactions. Another aspect that needs a careful evaluation is patient adherence to treatment. Prevalence of non-adherence in PWE and psychiatric comorbidities is reported to reach values even higher than 70%. A careful evaluation of all these aspects contributes in optimizing therapy with a positive impact on seizure control, psychiatric wellbeing, and quality of life.

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

BACKGROUND

For the two-thirds of patients with epilepsy who achieve seizure remission on antiseizure medications (ASMs), patients and clinicians must weigh the pros and cons of long-term ASM treatment. However, little work has evaluated how often ASM discontinuation occurs in practice. We describe the incidence of and predictors for sustained ASM fill gaps to measure discontinuation in individuals potentially eligible for ASM withdrawal.

METHODS

This was a retrospective cohort of Medicare beneficiaries. We included patients with epilepsy by requiring International Classification of Diseases codes for epilepsy/convulsions plus at least one ASM prescription each year 2014-2016, and no acute visit for epilepsy 2014-2015 (i.e., potentially eligible for ASM discontinuation). The main outcome was the first day of a gap in ASM supply (30, 90, 180, or 360 days with no pills) in 2016-2018. We displayed cumulative incidence functions and identified predictors using Cox regressions.

RESULTS

Among 21,819 beneficiaries, 5191 (24%) had a 30-day gap, 1753 (8%) had a 90-day gap, 803 (4%) had a 180-day gap, and 381 (2%) had a 360-day gap. Predictors increasing the chance of a 180-day gap included number of unique medications in 2015 (hazard ratio [HR] 1.03 per medication, 95% confidence interval [CI] 1.01-1.05) and epileptologist prescribing physician (≥25% of that physician's visits for epilepsy; HR 2.37, 95% CI 1.39-4.03). Predictors decreasing the chance of a 180-day gap included Medicaid dual eligibility (HR 0.75, 95% CI 0.60-0.95), number of unique ASMs in 2015 (e.g., 2 versus 1: HR 0.37, 95% CI 0.30-0.45), and greater baseline adherence (> 80% versus ≤80% of days in 2015 with ASM pill supply: HR 0.38, 95% CI 0.32-0.44).

CONCLUSIONS

Sustained ASM gaps were rarer than current guidelines may suggest. Future work should further explore barriers and enablers of ASM discontinuation to understand the optimal discontinuation rate.

Changes in the Use of Brand Name and Generic Medications and Total Prescription Cost Among Medicare Beneficiaries With Epilepsy

BACKGROUND AND OBJECTIVE

To characterize trends in antiseizure medication (ASM) fills and total prescription costs in people with epilepsy.

METHODS

This was a retrospective cohort study of beneficiaries with epilepsy (ASM, plus ICD codes) in a 20% random Medicare sample, with continuous Fee-For-Service coverage (Parts A, B, and D) in 2008-2018. We summed the number of pill days and costs (adjusted to 2018 dollars) per person-year for each ASM. ASMs were categorized into brand vs generic, first vs newer generation, and enzyme inducers vs noninducers.

RESULTS

There were 77,000-133,000 beneficiaries with epilepsy per year. The most common ASM was phenytoin in 2008, which shifted to levetiracetam in 2018 (2008: phenytoin 25%, levetiracetam 14%; 2018: phenytoin 9%, levetiracetam 27%). Brand name (2008: 56%; 2018: 14%), first-generation (2008: 55%; 2018: 32%), and enzyme-inducing ASMs (2008: 44%; 2018: 24%) each decreased over time as a proportion of pill days. The number of brand pill days per person-year initially decreased (e.g., 2008: 250; 2009: 121; 2010: 96) but then plateaued (2013-2018: between 66 and 69) given a notable increase in lacosamide pill days per person (2008: 0; 2018: 20). Total brand name costs per year initially decreased 2008-2010 (2008: $150 million; 2010: $72 million) but then increased after 2010 (2018: $256 million). In 2018, brand name ASMs represented 79% of costs despite representing only 14% of pill days, a 1-year pill supply became 277% more expensive for brand name medications but 42% less expensive for generic medications over time (2008: brand ∼$2,800 vs generic ∼$800; 2018: brand ∼$10,700 vs generic ∼$460), and many common brand name ASMs cost approximately 10-fold more per pill day than their generic equivalents.

DISCUSSION

First-generation and enzyme-inducing ASMs waned from 2008 to 2018. Although brand name ASMs initially waned translating into lower costs and potentially higher value care, after 2010, brand name costs markedly increased because of increasing use of lacosamide plus a 277% increase in per-pill cost of brand name ASMs. Brand name ASMs represented a minority of prescriptions, but the majority of costs.

Reappraisal of the Medical Research Council Antiepileptic Drug Withdrawal Study: contamination‐adjusted and dose‐response re‐analysis

Objective

The 1991 Medical Research Council (MRC) Study compared seizure relapse for seizure‐free patients randomized to withdraw vs continue of antiseizure medications (ASMs). We re‐analyzed this trial to account for crossover between arms using contamination‐adjusted intention to treat (CA ITT) methods, to explore dose‐response curves, and to validate predictions against external data. ITT assesses the effect of being randomized to withdraw, as‐treated analysis assesses the confounded effect of withdrawing, but CA ITT assesses the unconfounded effect of actually withdrawing.

Methods

CA ITT involves two stages. First, we used randomized arm to predict whether patients withdrew their ASM (logistic) or total daily ASM dose (linear). Second, we used those values to predict seizure occurrence (logistic).

Results

The trial randomized 503 patients to withdraw and 501 patients to continue ASMs. We found that 316 of 376 patients (88%) who were randomized to withdraw decreased their dose at every pre‐seizure visit, compared with 35 of 424 (8%) who were randomized to continue (p < .01). Adjusted odds ratios of a 2‐year seizure for those who withdrew vs those who did not was 1.3 (95% confidence interval [CI] 0.9–1.9) in the as‐treated analysis, 2.5 (95% CI 1.9–3.4) comparing those randomized to withdraw vs continue for ITT, and 3.1 (95% CI 2.1–4.5) for CA ITT. Probabilities (withdrawal vs continue) were 28% vs 24% (as‐treated), 40% vs 22% (ITT), and 43% vs 21% (CA ITT). Differences between ITT and CA ITT were greater when varying the predictor (reaching zero ASMs) or outcome (1‐year seizures). As‐treated dose‐response curves demonstrated little to no effects, but larger effects in CA ITT analysis. MRC data overpredicted risk in Lossius data, with moderate discrimination (areas under the curve ~0.70).

Significance

CA ITT results (the effect of actually withdrawing ASMs on seizures) were slightly greater than ITT effects (the effect of recommend withdrawing ASMs on seizures). How these findings affect clinical practice must be individualized.

Antiseizure medication treatment pathways in US Medicare beneficiaries with newly treated epilepsy

Objective

This study was undertaken to characterize antiseizure medication (ASM) treatment pathways in Medicare beneficiaries with newly treated epilepsy.

Methods

This was a retrospective cohort study using Medicare claims. Medicare is the United States' federal health insurance program for people aged 65 years and older plus younger people with disabilities or end‐stage renal disease. We included beneficiaries with newly treated epilepsy (International Classification of Diseases codes for epilepsy/convulsions 2014–2017, no ASM in the previous 2 years). We displayed the sequence of ASM fills using sunburst plots overall, then stratified by mood disorder, age, and neurologist prescriber. We tabulated drug costs for each pathway.

Results

We included 21 458 beneficiaries. Levetiracetam comprised the greatest number of pill days (56%), followed by gabapentin (11%) and valproate (8%). There were 22 288 unique treatment pathways. The most common pathways were levetiracetam monotherapy (43%), gabapentin monotherapy (10%), and valproate monotherapy (5%). Gabapentin was the most common second‐ and third‐line ASM. Whereas only 2% of pathways involved first‐line lacosamide, those pathways accounted for 19% of cost. Gabapentin and valproate use was increased and levetiracetam use was decreased in beneficiaries with mood disorders compared to beneficiaries without mood disorders. Levetiracetam use was increased and gabapentin, valproate, lamotrigine, and topiramate use was decreased in beneficiaries aged >65 years compared with those aged 65 years or less. Lamotrigine, levetiracetam, and lacosamide use was increased and gabapentin use was decreased in beneficiaries whose initial prescriber was a neurologist compared to those whose prescriber was not a neurologist.

Significance

Levetiracetam monotherapy was the most common pathway, although substantial heterogeneity existed. Lacosamide accounted for a small percentage of ASMs but a disproportionately large share of cost. Neurologists were more likely to prescribe lamotrigine compared with nonneurologists, and lamotrigine was prescribed far less frequently than may be endorsed by guidelines. Future work may explore patient‐ and physician‐driven factors underlying ASM choices.

Adherence to Antiseizure vs Other Medications Among US Medicare Beneficiaries With and Without Epilepsy

BACKGROUND AND OBJECTIVE

The objectives of this study were to compare adherence to antiseizure medications (ASMs) vs non-ASMs among individuals with epilepsy, to assess the degree to which variation in adherence is due to differences between individuals vs between medication classes among individuals with epilepsy, and to compare adherence in individuals with vs without epilepsy.

METHODS

This was a retrospective cohort study using Medicare. We included beneficiaries with epilepsy (≥1 ASM, plus ICD-9-CM diagnostic codes) and a 20% random sample without epilepsy. Adherence for each medication class was measured by the proportion of days covered (PDC) in 2013 to 2015. We used Spearman correlation coefficients, Cohen κ statistics, and multilevel logistic regressions.

RESULTS

There were 83,819 beneficiaries with epilepsy. Spearman correlation coefficients between ASM PDCs and each of the 5 non-ASM PDCs ranged from 0.44 to 0.50; Cohen κ ranged from 0.33 to 0.38; and within-person differences between the PDC of each ASM minus the PDC of each non-ASM were all statistically significant (p < 0.01), although median differences were all very close to 0. Fifty-four percent of variation in adherence across medications was due to differences between individuals. Adjusted predicted probabilities of adherence were as follows: ASMs 74% (95% confidence interval [CI] 73%-74%), proton pump inhibitors 74% (95% CI 74%-74%), antihypertensives 77% (95% CI 77%-78%), selective serotonin reuptake inhibitors 77% (95% CI 77%-78%), statins 78% (95% CI 78%-79%), and levothyroxine 82% (95% CI 81%-82%). Adjusted predicted probabilities of adherence to non-ASMs were 80% (95% CI 80%-81%) for beneficiaries with epilepsy vs 77% (95% CI 77%-77%) for beneficiaries without epilepsy.

DISCUSSION

Among individuals with epilepsy, ASM adherence and non-ASM adherence were moderately correlated, half of the variation in adherence was due to between-person rather than between-medication differences, adjusted adherence was slightly lower for ASMs than several non-ASMs, and epilepsy was associated with a quite small increase in adherence to non-ASMs. Nonadherence to ASMs may provide an important cue to the clinician to inquire about adherence to other potentially life-prolonging medications as well. Although efforts should focus on improving ASM adherence, patient-level rather than purely medication-specific behaviors are also critical to consider when developing interventions to optimize adherence.