Quality Indicator for Epilepsy Treatment 15 (QUIET-15): Intervening after recurrent seizures in the elderly

Adherence to epilepsy quality indicators in a tertiary referral center

BACKGROUND

Quality indicators play an important role in healthcare quality and patient safety. The aim of this study is to identify specific clinical pharmacy interventions to improve adherence to quality indicators and minimize risks among patients with epilepsy.

MATERIAL AND METHODS

A prospective, two-phase, observational study was conducted in a neurology outpatient clinic of a tertiary university hospital. In the first phase of the study, the rate of adherence to the quality indicators was evaluated with a checklist containing the quality indicators. In the second phase of the study, an expert panel meeting was convened to identify clinical pharmacist interventions to reduce the risks associated with non-adherence. The Fine-Kinney method was used to prioritize risks, and adherence rates with each quality improvement indicator (QI) were calculated.

RESULTS

The study found that adherence rates were highest for QIs involving estimating the number and type of seizures, providing medical treatment or referring patients with evidence of mood disorders to mental healthcare, and co-managing prenatal care for women with epilepsy. The most non-adherence rates were found in QIs involving quality-of-life assessment, daily folate supplementation, and addressing the decreased effectiveness of oral contraception. The annual review of information about educational issues was also poorly provided. An expert panel decided to integrate a clinical pharmacist into the outpatient clinic to improve medication adherence, side-effect assessment, drug interaction assessment, patient education, lifestyle-modification education, depression/suicide-related behavior screening, quality-of-life assessment, and effectiveness evaluation of oral contraceptives for female patients using enzyme-inducing ASM.

CONCLUSION

The study shows that medication adherence, assessment of side effects, drug interactions, and patient education are inadequately provided by neurologists in patients with epilepsy. Clinical pharmacists have a crucial role in reducing potential risks of non-adherence with quality indicators. By integrating clinical pharmacy services into routine epilepsy care processes, the quality of care can be improved. Future studies should focus on implementing these interventions and evaluating their impact on patient outcomes.

Hospital readmissions in older adults with epilepsy in the US - A population-based study

OBJECTIVE

Our objective was to determine proportions, causes, and predictors of 30-day readmissions among older adults with epilepsy. Understanding predictors of readmissions may inform future interventions aimed at reducing avoidable hospitalizations in this vulnerable population.

METHODS

Individuals 65 years or older with epilepsy were identified using previously validated ICD-9-CM codes in any diagnostic position in the 2014 Nationwide Readmissions Database. Proportions of 30-day readmissions and causes of readmissions in older adults with epilepsy were compared to both older adults without and younger adults (18-64 years old) with epilepsy. We identified predictors of readmission in older adults with epilepsy using logistic regression.

RESULTS

There were 92,030 older adults with, 3,166,852 older adults without, and 168,622 younger adults with epilepsy. Proportions of readmissions were higher in older adults with (16.2%) than older adults without (12.5%) and younger adults with epilepsy (15.1%). The main cause of readmission for older adults with and without epilepsy was septicemia, and epilepsy/seizure in younger adults with epilepsy. Predictors of 30-day readmissions in older adults with epilepsy were: non-elective admissions (OR 1.37, 95%CI 1.27-1.48), public insurance (Medicaid vs. private insurance OR 1.19, 95%CI 1.02-1.39; Medicare vs. private insurance OR 1.11, 95%CI 1.00-1.22), lower median household income for patient's zip code ($1-$39,999 vs. $66,000 + OR 1.15, 95% CI 1.08-1.22), hospital location in large metropolitan areas (OR 1.22, 95%CI 1.05-1.42), higher Charlson-Deyo comorbidity index (OR 1.11, 95%CI 1.10-1.02), and male sex (OR 1.04, 95%CI 1.00-1.09).

SIGNIFICANCE

Our findings suggest that targeted interventions to reduce the risk of infection may potentially reduce readmission in older people with epilepsy, similarly to those without. Provision of coordinated care and appropriate discharge planning may reduce readmissions particularly in those who are males, are of lower socioeconomic status and with more comorbidities.

Predictors of early seizure recurrence among elderly inpatients admitted to a tertiary center: A prospective cohort study

PURPOSE

Early seizure recurrence is common among elderly inpatients and is related to increased morbidity and a longer length of hospital stay. There are few studies on the short-term outcomes of seizures in the elderly population. We aimed to identify the predictors of early seizure recurrence among elderly inpatients.

METHODS

We prospectively enrolled patients aged 60 years and older from a tertiary center who had seizures that either led to their hospital admission or occurred during hospitalization. We analyzed the demographic and hospitalization data, characteristics and etiology of seizures, and neurological and clinical comorbidities. Kaplan-Meier analysis was performed to determine the 30-day cumulative recurrence rates. The logrank test was used to analyze the risk of seizure recurrence within 30 days after the index seizure. Multivariable logistic regression analysis was used to identify risk factors for the recurrence of seizures within 30 days.

RESULTS

Overall, 109 patients (mean age: 75.9 ± 9.6 years) were enrolled. The mean age at the first-ever seizure was 74 ± 11.7 years. Unprovoked seizures occurred in 59.6% of the patients. Cerebrovascular disorders were the most prevalent etiology (52.3%). Early seizure recurrence, defined as within 30 days, occurred in 27.5% of patients. Multimorbidity was found in 95.4% of our inpatients (6.3 ± 2.3 [95% confidence interval, 5.4-7.2]), and the number of comorbidities was higher among those who had seizure recurrence than that among those who did not have seizure recurrence (p = 0.02). The probability of seizure recurrence was similar among the subgroups of patients who experienced acute seizures versus unprovoked seizures (both p = 0.03), and seizure recurrence was associated with a longer length of hospital stay (p = 0.005) compared to that of patients who did not experience seizure recurrence. After multivariate analysis, sepsis (p = 0.011), psychiatric disorders (p = 0.032), and cardiac arrhythmias (p = 0.037) were identified as risk factors for early seizure recurrence.

CONCLUSIONS

Higher multimorbidity and a longer length of stay were associated with early seizure recurrence; and sepsis, psychiatric disorders, and cardiac arrhythmias were independent risk factors for early seizure recurrence among elderly inpatients.

High health care costs in minority groups of older US Medicare beneficiaries with epilepsy

OBJECTIVE

To examine health care costs in diverse older Medicare beneficiaries with epilepsy.

METHODS

Using 2008-2010 claims data, we conducted a longitudinal cohort study of a random sample of Medicare beneficiaries augmented for minority representation. Epilepsy cases (n = 36 912) had ≥1 International Classification of Diseases, Ninth Edition (ICD-9) 345.x or ≥2 ICD-9 780.3x claims, and ≥1 antiepileptic drug (AED) in 2009; new cases (n = 3706) had no seizure/epilepsy claims nor AEDs in the previous 365 days. Costs were measured by reimbursements for all care received. High cost was defined as follow-up 1-year cost ≥ 75th percentile. Logistic regressions examined association of high cost with race/ethnicity, adjusting for demographic, clinical, economic, and treatment quality factors. In cases with continuous 2-year data, we obtained costs in two 6-month periods before and two after the index event.

RESULTS

Cohort was ~62% African Americans (AAs), 11% Hispanics, 5% Asians, and 2% American Indian/Alaska Natives. Mean costs in the follow-up were ~$30 000 (median = $11 547; new cases, mean = $44 642; median = $25 008). About 19% white compared to 27% AA cases had high cost. AA had higher odds of high cost in adjusted analyses (odds ratio [OR] = 1.20, 95% confidence interval [CI] = 1.11-1.29), although this was only marginally significant when adjusting for AED adherence (OR = 1.09, 95% CI = 1.01-1.18, P = 0.03). Factors associated with high cost included ≥1 comorbidity, neurological care, and low AED adherence. Costs were highest at ~$17 000 in the 6 months immediately before and after the index event (>$29 000 for new cases).

SIGNIFICANCE

The financial sequelae of epilepsy among older Americans disproportionally affect minorities. Studies should examine contributors to high costs.

Identification of patients who will not achieve seizure remission within 5 years on AEDs

OBJECTIVE

To identify people with epilepsy who will not achieve a 12-month seizure remission within 5 years of starting treatment.

METHODS

The Standard and New Antiepileptic Drug (SANAD) study is the largest prospective study in patients with epilepsy to date. We applied a recently developed multivariable approach to the SANAD dataset that takes into account not only baseline covariates describing a patient's history before diagnosis but also follow-up data as predictor variables.

RESULTS

Changes in number of seizures and treatment history were the most informative time-dependent predictors and were associated with history of neurologic insult, epilepsy type, age at start of treatment, sex, and having a first-degree relative with epilepsy. Our model classified 95% of patients. Of those classified, 95% of patients observed not to achieve remission at 5 years were correctly classified (95% confidence interval [CI] 89.5%-100%), with 51% identified by 3 years and 90% within 4 years of follow-up. Ninety-seven percent (95% CI 93.3%-98.8%) of patients observed to achieve a remission within 5 years were correctly classified. Of those predicted not to achieve remission, 76% (95% CI 58.5%-88.2%) truly did not achieve remission (positive predictive value). The predictive model achieved similar accuracy levels via external validation in 2 independent United Kingdom-based datasets.

CONCLUSION

Our approach generates up-to-date predictions of the patient's risk of not achieving seizure remission whenever new clinical information becomes available that could influence patient counseling and management decisions.