Exit Strategy: Balancing the Risks and Rewards of Antiseizure Medication Withdrawal

The majority of people with epilepsy achieves long-term seizure-freedom and may consider withdrawal of their anti-seizure medications (ASMs). Withdrawal of ASMs can yield substantial benefits but may be associated with potential risks. This review critically examines the existing literature on ASM withdrawal, emphasizing evidence-based recommendations, where available. Our focus encompasses deprescribing strategies for individuals who have attained seizure freedom through medical treatment, those who have undergone successful epilepsy surgery, and individuals initiated on ASMs following acute symptomatic seizures. We explore state-of-the-art prognostic models in these scenarios that could guide the decision-making process. The review underscores the importance of a collaborative shared-decision approach between patients, caregivers, and physicians. We describe the subjective and objective factors influencing these decisions and illustrate how trade-offs may be effectively managed in practice

HOSPITAL Score and LACE Index to Predict Mortality in Multimorbid Older Patients.

BACKGROUND Estimating life expectancy of older adults informs whether to pursue future investigation and therapy. Several models to predict mortality have been developed but often require data not immediately available during routine clinical care. The HOSPITAL score and the LACE index were previously validated to predict 30-day readmissions but may also help to assess mortality risk. We assessed their performance to predict 1-year and 30-day mortality in hospitalized older multimorbid patients with polypharmacy. METHODS We calculated the HOSPITAL score and LACE index in patients from the OPERAM (OPtimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly) trial (patients aged ≥ 70 years with multimorbidity and polypharmacy, admitted to hospital across four European countries in 2016-2018). Our primary and secondary outcomes were 1-year and 30-day mortality. We assessed the overall accuracy (scaled Brier score, the lower the better), calibration (predicted/observed proportions), and discrimination (C-statistic) of the models. RESULTS Within 1 year, 375/1879 (20.0%) patients had died, including 94 deaths within 30 days. The overall accuracy was good and similar for both models (scaled Brier score 0.01-0.08). The C-statistics were identical for both models (0.69 for 1-year mortality, p = 0.81; 0.66 for 30-day mortality, p = 0.94). Calibration showed well-matching predicted/observed proportions. CONCLUSION The HOSPITAL score and LACE index showed similar performance to predict 1-year and 30-day mortality in older multimorbid patients with polypharmacy. Their overall accuracy was good, their discrimination low to moderate, and the calibration good. These simple tools may help predict older multimorbid patients' mortality after hospitalization, which may inform post-hospitalization intensity of care

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

Antiseizure medication withdrawal risk estimation and recommendations: A survey of American Academy of Neurology and EpiCARE members

Objective Choosing candidates for antiseizure medication (ASM) withdrawal in well‐controlled epilepsy is challenging. We evaluated (a) the correlation between neurologists' seizure risk estimation (“clinician predictions”) vs calculated predictions, (b) how viewing calculated predictions influenced recommendations, and (c) barriers to using risk calculation.MethodsWe asked US and European neurologists to predict 2‐year seizure risk after ASM withdrawal for hypothetical vignettes. We compared ASM withdrawal recommendations before vs after viewing calculated predictions, using generalized linear models. Results Three‐hundred and forty‐six neurologists responded. There was moderate correlation between clinician and calculated predictions (Spearman coefficient 0.42). Clinician predictions varied widely, for example, predictions ranged 5%‐100% for a 2‐year seizure‐free adult without epileptiform abnormalities. Mean clinician predictions exceeded calculated predictions for vignettes with epileptiform abnormalities (eg, childhood absence epilepsy: clinician 65%, 95% confidence interval [CI] 57%‐74%; calculated 46%) and surgical vignettes (eg, focal cortical dysplasia 6‐month seizure‐free mean clinician 56%, 95% CI 52%‐60%; calculated 28%). Clinicians overestimated the influence of epileptiform EEG findings on withdrawal risk (26%, 95% CI 24%‐28%) compared with calculators (14%, 95% 13%‐14%). Viewing calculated predictions slightly reduced willingness to withdraw (−0.8/10 change, 95% CI −1.0 to −0.7), particularly for vignettes without epileptiform abnormalities. The greatest barrier to calculator use was doubting its accuracy (44%). Significance Clinicians overestimated the influence of abnormal EEGs particularly for low‐risk patients and overestimated risk and the influence of seizure‐free duration for surgical patients, compared with calculators. These data may question widespread ordering of EEGs or time‐based seizure‐free thresholds for surgical patients. Viewing calculated predictions reduced willingness to withdraw particularly without epileptiform abnormalities

Frequency of and factors associated with antiseizure medication discontinuation discussions and decisions in patients with epilepsy: a multicenter retrospective chart review

Objective: Guidelines suggest considering antiseizure medication (ASM) discontinuation in patients with epilepsy who become seizure-free. Little is known about how discontinuation decisions are being made in practice. We measured the frequency of, and factors associated with, discussions and decisions surrounding ASM discontinuation. Methods: We performed a multicenter retrospective cohort study at the University of Michigan (UM) and two Dutch centers: Wilhelmina Children's Hospital (WCH) and Stichting Epilepsie Instellingen Nederland (SEIN). We screened all children and adults with outpatient epilepsy visits in January 2015 and included those with at least one visit during the subsequent 2 years where they were seizure-free for at least one year. We recorded whether charts documented (1) a discussion with the patient about possible ASM discontinuation and (2) any planned attempt to discontinue at least one ASM. We conducted multilevel logistic regressions to determine factors associated with each outcome. Results: We included 1058 visits from 463 patients. Of all patients who were seizure-free at least one year, 248/463 (53%) had documentation of any discussion and 98/463 (21%) planned to discontinue at least one ASM. Corresponding frequencies for patients who were seizure-free at least 2 years were 184/285 (65%) and 74/285 (26%). The probability of discussing or discontinuing increased with longer duration of seizure freedom. Still, even for patients who were 10 years seizure-free, our models predicated that in only 49% of visits was a discontinuation discussion documented, and in only 16% of visits was it decided to discontinue all ASMs. Provider-to-provider variation explained 18% of variation in whether patients discontinued any ASM. Significance: Only approximately half of patients with prolonged seizure freedom had a documented discussion about ASM discontinuation. Discontinuation was fairly rare even among low-risk patients. Future work should further explore barriers to and facilitators of counseling and discontinuation attempts

Variation in seizure risk increases from antiseizure medication withdrawal among patients with well-controlled epilepsy: a pooled analysis

Objective: Guidelines suggest considering antiseizure medication (ASM) discontinuation in seizure-free patients with epilepsy. Past work has poorly explored how discontinuation effects vary between patients. We evaluated (1) what factors modify the influence of discontinuation on seizure risk; and (2) the range of seizure risk increase due to discontinuation across low- versus high-risk patients. Methods: We pooled three datasets including seizure-free patients who did and did not discontinue ASMs. We conducted time-to-first-seizure analyses. First, we evaluated what individual patient factors modified the relative effect of ASM discontinuation on seizure risk via interaction terms. Then, we assessed the distribution of 2-year risk increase as predicted by our adjusted logistic regressions. Results: We included 1626 patients, of whom 678 (42%) planned to discontinue all ASMs. The mean predicted 2-year seizure risk was 43% [95% confidence interval (CI) 39%–46%] for discontinuation versus 21% (95% CI 19%–24%) for continuation. The mean 2-year absolute seizure risk increase was 21% (95% CI 18%–26%). No individual interaction term was significant after correcting for multiple comparisons. The median [interquartile range (IQR)] risk increase across patients was 19% (IQR 14%–24%; range 7%–37%). Results were unchanged when restricting analyses to only the two RCTs. Significance: No single patient factor significantly modified the influence of discontinuation on seizure risk, although we captured how absolute risk increases change for patients that are at low versus high risk. Patients should likely continue ASMs if even a 7% 2-year increase in the chance of any more seizures would be too much and should likely discontinue ASMs if even a 37% risk increase would be too little. In between these extremes, individualized risk calculation and a careful understanding of patient preferences are critical. Future work will further develop a two-armed individualized seizure risk calculator and contextualize seizure risk thresholds below which to consider discontinuation. Plain Language Summary: Understanding how much antiseizure medications (ASMs) decrease seizure risk is an important part of determining which patients with epilepsy should be treated, especially for patients who have not had a seizure in a while. We found that there was a wide range in the amount that ASM discontinuation increases seizure risk—between 7% and 37%. We found that no single patient factor modified that amount. Understanding what a patient's seizure risk might be if they discontinued versus continued ASM treatment is critical to making informed decisions about whether the benefit of treatment outweighs the downsides

Antiseizure medication withdrawal risk estimation and recommendations: a survey of American Academy of Neurology and EpiCARE members

Objective: Choosing candidates for antiseizure medication (ASM) withdrawal in well-controlled epilepsy is challenging. We evaluated (a) the correlation between neurologists' seizure risk estimation (“clinician predictions”) vs calculated predictions, (b) how viewing calculated predictions influenced recommendations, and (c) barriers to using risk calculation. Methods: We asked US and European neurologists to predict 2-year seizure risk after ASM withdrawal for hypothetical vignettes. We compared ASM withdrawal recommendations before vs after viewing calculated predictions, using generalized linear models. Results: Three-hundred and forty-six neurologists responded. There was moderate correlation between clinician and calculated predictions (Spearman coefficient 0.42). Clinician predictions varied widely, for example, predictions ranged 5%-100% for a 2-year seizure-free adult without epileptiform abnormalities. Mean clinician predictions exceeded calculated predictions for vignettes with epileptiform abnormalities (eg, childhood absence epilepsy: clinician 65%, 95% confidence interval [CI] 57%-74%; calculated 46%) and surgical vignettes (eg, focal cortical dysplasia 6-month seizure-free mean clinician 56%, 95% CI 52%-60%; calculated 28%). Clinicians overestimated the influence of epileptiform EEG findings on withdrawal risk (26%, 95% CI 24%-28%) compared with calculators (14%, 95% 13%-14%). Viewing calculated predictions slightly reduced willingness to withdraw (−0.8/10 change, 95% CI −1.0 to −0.7), particularly for vignettes without epileptiform abnormalities. The greatest barrier to calculator use was doubting its accuracy (44%). Significance: Clinicians overestimated the influence of abnormal EEGs particularly for low-risk patients and overestimated risk and the influence of seizure-free duration for surgical patients, compared with calculators. These data may question widespread ordering of EEGs or time-based seizure-free thresholds for surgical patients. Viewing calculated predictions reduced willingness to withdraw particularly without epileptiform abnormalities

The ACTTION-APS-AAPM Pain Taxonomy (AAAPT) Multidimensional Approach to Classifying Acute Pain Conditions.

Objective: With the increasing societal awareness of the prevalence and impact of acute pain, there is a need to develop an acute pain classification system that both reflects contemporary mechanistic insights and helps guide future research and treatment. Existing classifications of acute pain conditions are limiting, with a predominant focus on the sensory experience (e.g., pain intensity) and pharmacologic consumption. Consequently, there is a need to more broadly characterize and classify the multidimensional experience of acute pain. Setting: Consensus report following expert panel involving the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION), American Pain Society (APS), and American Academy of Pain Medicine (AAPM). Methods: As a complement to a taxonomy recently developed for chronic pain, the ACTTION public-private partnership with the US Food and Drug Administration, the APS, and the AAPM convened a consensus meeting of experts to develop an acute pain taxonomy using prevailing evidence. Key issues pertaining to the distinct nature of acute pain are presented followed by the agreed-upon taxonomy. The ACTTION-APS-AAPM Acute Pain Taxonomy will include the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Future efforts will consist of working groups utilizing this taxonomy to develop diagnostic criteria for a comprehensive set of acute pain conditions. Perspective: The ACTTION-APS-AAPM Acute Pain Taxonomy (AAAPT) is a multidimensional acute pain classification system designed to classify acute pain along the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Conclusions: Significant numbers of patients still suffer from significant acute pain, despite the advent of modern multimodal analgesic strategies. Mismanaged acute pain has a broad societal impact as significant numbers of patients may progress to suffer from chronic pain. An acute pain taxonomy provides a much-needed standardization of clinical diagnostic criteria, which benefits clinical care, research, education, and public policy. For the purposes of the present taxonomy, acute pain is considered to last up to seven days, with prolongation to 30 days being common. The current understanding of acute pain mechanisms poorly differentiates between acute and chronic pain and is often insufficient to distinguish among many types of acute pain conditions. Given the usefulness of the AAPT multidimensional framework, the AAAPT undertook a similar approach to organizing various acute pain conditions