Automated Identification of Fall-related Injuries in Unstructured Clinical Notes.

Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses challenges for large-scale research. In this study, we developed and evaluated Natural Language Processing (NLP) models to address this issue. We utilized all available clinical notes from the Mass General Brigham for 2,100 older adults, identifying 154,949 paragraphs of interest through automatic scanning for FRI-related keywords. Two clinical experts directly labeled 5,000 paragraphs to generate benchmark-standard labels, while 3,689 validated patterns were annotated, indirectly labeling 93,157 paragraphs as validated-standard labels. Five NLP models, including vanilla BERT, RoBERTa, Clinical-BERT, Distil-BERT, and SVM, were trained using 2,000 benchmark paragraphs and all validated paragraphs. BERT-based models were trained in three stages: Masked Language Modeling, General Boolean Question Answering (QA), and QA for FRI. For validation, 500 benchmark paragraphs were used, and the remaining 2,500 for testing. Performance metrics (precision, recall, F1 scores, Area Under ROC [AUROC] or Precision-Recall [AUPR] curves) were employed by comparison, with RoBERTa showing the best performance. Precision was 0.90 [0.88-0.91], recall [0.90-0.93], F1 score 0.90 [0.89-0.92], AUROC and AUPR curves of 0.96 [0.95-0.97]. These NLP models accurately identify FRIs from unstructured clinical notes, potentially enhancing clinical notes-based research efficiency.

Centromedian region thalamic responsive neurostimulation mitigates idiopathic generalized and multifocal epilepsy with focal to bilateral tonic-clonic seizures.

OBJECTIVE: Although >30% of epilepsy patients have drug-resistant epilepsy (DRE), typically those with generalized or multifocal disease have not traditionally been considered surgical candidates. Responsive neurostimulation (RNS) of the centromedian (CM) region of the thalamus now appears to be a promising therapeutic option for this patient population. We present outcomes following CM RNS for 13 patients with idiopathic generalized epilepsy (IGE) and eight with multifocal onsets that rapidly generalize to bilateral tonic-clonic (focal to bilateral tonic-clonic [FBTC]) seizures. METHODS: A retrospective review of all patients undergoing bilateral CM RNS by the senior author through July 2022 were reviewed. Electrodes were localized and volumes of tissue activation were modeled in Lead-DBS. Changes in patient seizure frequency were extracted from electronic medical records. RESULTS: Twenty-one patients with DRE underwent bilateral CM RNS implantation. For 17 patients with at least 1 year of postimplantation follow-up, average seizure reduction from preoperative baseline was 82.6% (SD = 19.0%, median = 91.7%), with 18% of patients Engel class 1, 29% Engel class 2, 53% Engel class 3, and 0% Engel class 4. There was a trend for average seizure reduction to be greater for patients with nonlesional FBTC seizures than for other patients. For patients achieving at least Engel class 3 outcome, median time to worthwhile seizure reduction was 203.5 days (interquartile range = 110.5-343.75 days). Patients with IGE with myoclonic seizures had a significantly shorter time to worthwhile seizure reduction than other patients. The surgical targeting strategy evolved after the first four subjects to achieve greater anatomic accuracy. SIGNIFICANCE: Patients with both primary and rapidly generalized epilepsy who underwent CM RNS experienced substantial seizure relief. Subsets of these patient populations may particularly benefit from CM RNS. The refinement of lead targeting, tuning of RNS system parameters, and patient selection are ongoing areas of investigation.

Benzodiazepine Initiation and the Risk of Falls or Fall-Related Injuries in Older Adults Following Acute Ischemic Stroke.

Background: Benzodiazepine use in older adults following acute ischemic stroke (AIS) is common, yet short-term safety concerning falls or fall-related injuries remains unexplored. Methods: We emulated a hypothetical randomized trial of benzodiazepine use during the acute post stroke recovery period to assess incidence of falls or fall related injuries in older adults. Using linked data from the Get With the Guidelines Registry and Mass General Brigham's electronic health records, we selected patients aged 65 and older admitted for Acute Ischemic Stroke (AIS) between 2014 and 2021 with no documented prior stroke and no benzodiazepine prescriptions in the previous 3 months. Potential for immortal-time and confounding biases was addressed via separate inverse-probability weighting strategies. Results: The study included 495 patients who initiated inpatient benzodiazepines within three days of admission and 2,564 who did not. After standardization, the estimated 10-day risk of falls or fall-related injuries was 694 events per 1000 (95% confidence interval CI: 676-709) for the benzodiazepine initiation strategy and 584 events per 1000 (95% CI: 575-595) for the non-initiation strategy. Subgroup analyses showed risk differences of 142 events per 1000 (95% CI: 111-165) and 85 events per 1000 (95% CI: 64-107) for patients aged 65 to 74 years and for those aged 75 years or older, respectively. Risk differences were 187 events per 1000 (95% CI: 159-206) for patients with minor (NIHSS≤ 4) AIS and 32 events per 1000 (95% CI: 10-58) for those with moderate-to-severe AIS. Conclusions: Initiating inpatient benzodiazepines within three days of AIS is associated with an elevated 10-day risk of falls or fall-related injuries, particularly for patients aged 65 to 74 years and for those with minor strokes. This underscores the need for caution with benzodiazepines, especially among individuals likely to be ambulatory during the acute and sub-acute post-stroke period.

Improving Seizure Frequency Documentation and Classification.

Background and Objectives: Accurate and reliable seizure data are essential for evaluating treatment strategies and tracking the quality of care in epilepsy clinics. This quality improvement project aimed to increase seizure documentation (i.e., documentation of seizure frequency from 80% to 100%, date of last seizure from 35% to 50%, and International League Against Epilepsy (ILAE) seizure classification from 35% to at least 50%) over 6 months. Methods: We surveyed 7 epileptologists to determine their perceived seizure frequency, ILAE classification, and date of last seizure documentation habits. Baseline data were collected weekly from September to December 2021. Subsequently, we implemented a newly created flowsheet in our Electronic Health Record (EHR) based on the Epilepsy Learning Healthcare System (ELHS) Case Report Forms to increase seizure documentation in a standardized way. Two epileptologists tested this flowsheet tool in their epilepsy clinics between February 2022 and July 2022. Data were collected weekly and compared with documentation from other epileptologists within the same group. Results: Epileptologists at our center believed they documented seizure frequency for 84%-87% of clinic visits, which aligned with baseline data collection, showing they recorded seizure frequency for 83% of clinic visits. Epileptologists believed they documented ILAE classification for 47%-52% of clinic visits, and baseline data showed this was documented in 33% of clinic visits. They also reported documenting the date of the last seizure for 52%-63% of clinic visits, but this occurred in only 35% of clinic visits. After implementing the new flowsheet, documentation increased to nearly 100% for all fields being completed by the providers who tested the flowsheet. Discussion: We demonstrated that by implementing an easy-to-use standardized EHR documentation tool, our documentation of critical metrics, as defined by the ELHS, improved dramatically. This shows that simple and practical interventions can substantially improve clinically meaningful documentation.

Association of Early Seizure Prophylaxis With Posttraumatic Seizures and Mortality: A Meta-analysis With Evidence Quality Assessment.

Purpose of the Review: To evaluate the quality of evidence about the association of primary seizure prophylaxis with antiseizure medication (ASM) within 7 days postinjury and the 18- or 24-month epilepsy/late seizure risk or all-cause mortality in adults with new-onset traumatic brain injury (TBI), in addition to early seizure risk. Results: Twenty-three studies met the inclusion criteria (7 randomized and 16 nonrandomized studies). We analyzed 9,202 patients, including 4,390 in the exposed group and 4,812 in the unexposed group (894 in placebo and 3,918 in no ASM groups). There was a moderate to serious bias risk based on our assessment. Within the limitations of existing studies, our data revealed a lower risk for early seizures in the ASM prophylaxis group compared with placebo or no ASM prophylaxis (risk ratio [RR] 0.43, 95% confidence interval [CI] 0.33-0.57, p < 0.00001, I 2 = 3%). We identified high-quality evidence in favor of acute, short-term primary ASM use to prevent early seizures. Early ASM prophylaxis was not associated with a substantial difference in the 18- or 24-month risk of epilepsy/late seizures (RR 1.01, 95% CI 0.61-1.68, p = 0.96, I 2 = 63%) or mortality (RR 1.16, 95% CI 0.89-1.51, p = 0.26, I 2 = 0%). There was no evidence of strong publication bias for each main outcome. The overall quality of evidence was low and moderate for post-TBI epilepsy risk and all-cause mortality, respectively. Summary: Our data suggest that the evidence showing no association between early ASM use and 18- or 24-month epilepsy risk in adults with new-onset TBI was of low quality. The analysis indicated a moderate quality for the evidence showing no effect on all-cause mortality. Therefore, higher-quality evidence is needed as a supplement for stronger recommendations.

Identification of patients with epilepsy using automated electronic health records phenotyping.

OBJECTIVE: Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy. METHODS: The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping. RESULTS: Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set. SIGNIFICANCE: A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.

Comparative Effectiveness and Safety of Seizure Prophylaxis Among Adults After Acute Ischemic Stroke.

BACKGROUND: Older adults occasionally receive seizure prophylaxis in an acute ischemic stroke (AIS) setting, despite safety concerns. There are no trial data available about the net impact of early seizure prophylaxis on post-AIS survival. METHODS: Using a stroke registry (American Heart Association's Get With The Guidelines) individually linked to electronic health records, we examined the effect of initiating seizure prophylaxis (ie, epilepsy-specific antiseizure drugs) within 7 days of an AIS admission versus not initiating in patients ≥65 years admitted for a new, nonsevere AIS (National Institutes of Health Stroke Severity score ≤20) between 2014 and 2021 with no recorded use of epilepsy-specific antiseizure drugs in the previous 3 months. We addressed confounding by using inverse-probability weights. We performed standardization accounting for pertinent clinical and health care factors (eg, National Institutes of Health Stroke Severity scale, prescription counts, seizure-like events). RESULTS: The study sample included 151 patients who received antiseizure drugs and 3020 who did not. The crude 30-day mortality risks were 219 deaths per 1000 patients among epilepsy-specific antiseizure drugs initiators and 120 deaths per 1000 among noninitiators. After standardization, the estimated mortality was 251 (95% CI, 190-307) deaths per 1000 among initiators and 120 (95% CI, 86-144) deaths per 1000 among noninitiators, corresponding to a risk difference of 131 (95% CI, 65-200) excess deaths per 1000 patients. In the prespecified subgroup analyses, the risk difference was 52 (95% CI, 11-72) among patients with minor AIS and 138 (95% CI, 52-222) among moderate-to-severe AIS patients. Similarly, the risk differences were 86 (95% CI, 18-118) and 157 (95% CI, 57-219) among patients aged 65 to 74 years and ≥75 years, respectively. CONCLUSIONS: There was a higher risk of 30-day mortality associated with initiating versus not initiating seizure prophylaxis within 7 days post-AIS. This study does not support the role of seizure prophylaxis in reducing 30-day poststroke mortality.

No short-term mortality from benzodiazepine use post-acute ischemic stroke after accounting for bias.

BACKGROUND AND OBJECTIVES: Older adults receive benzodiazepines for agitation, anxiety, and insomnia after acute ischemic stroke (AIS). No trials have been conducted to determine if benzodiazepine use affects poststroke mortality in the elderly. METHODS: We examined the association between initiating benzodiazepines within 1 week after AIS and 30-day mortality. We included patients ≥65 years, admitted for new nonsevere AIS (NIH-Stroke-Severity[NIHSS]≤ 20), 2014-2020, with no recorded benzodiazepine use in the previous 3 months and no contraindication for use. We linked a stroke registry to electronic health records, used inverse-probability weighting to address confounding, and estimated the risk difference (RD). A process of cloning, weighting, and censoring was used to avoid immortal time bias. RESULTS: Among 2,584 patients, 389 received benzodiazepines. The crude 30-day mortality risk from treatment initiation was 212/1,000 among patients who received benzodiazepines, while the 30-day mortality was 34/1,000 among those who did not. When follow-up was aligned on day of AIS admission and immortal time was assigned to the two groups, the estimated risks were 27/1,000 and 22/1,000, respectively. Upon further adjustment for confounders, the RD was 5 (-12 to 19) deaths/1,000 patients. CONCLUSION: The observed higher 30-day mortality associated with benzodiazepine initiation within 7 days was largely due to bias.

Evaluating The Accuracy Of Medicare Risk Adjustment For Alzheimer's Disease And Related Dementias.

In 2020 Medicare reintroduced Alzheimer's disease and related dementias (ADRD) Hierarchical Condition Categories (HCCs) to risk-adjust Medicare Advantage and accountable care organization (ACO) payments. The potential for Medicare spending increases from this policy change are not well understood because the baseline accuracy of ADRD HCCs is uncertain. Using linked 2016-18 claims and electronic health record data from a large ACO, we evaluated the accuracy of claims-based ADRD HCCs against a reference standard of clinician-adjudicated disease. An estimated 7.5 percent of beneficiaries had clinician-adjudicated ADRD. Among those with ADRD HCCs, 34 percent did not have clinician-adjudicated disease. The false-negative and false-positive rates were 22.7 percent and 3.2 percent, respectively. Medicare spending for those with false-negative ADRD HCCs exceeded that of true positives by $14,619 per beneficiary. If, after the reintroduction of risk adjustment for ADRD, all false negatives were coded as having ADRD, expenditure benchmarks for beneficiaries with ADRD would increase by 9 percent. Monitoring ADRD coding could become challenging in the setting of concurrent incentives to decrease false-negative rates and increase false-positive rates.

Identifying Medicare Beneficiaries With Delirium.

BACKGROUND: Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes. METHODS: Using a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV). RESULTS: Beneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)]. CONCLUSIONS: A delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.

A replicable, open-source, data integration method to support national practice-based research & quality improvement systems.

OBJECTIVES: The Epilepsy Learning Healthcare System (ELHS) was created in 2018 to address measurable improvements in outcomes for people with epilepsy. However, fragmentation of data systems has been a major barrier for reporting and participation. In this study, we aimed to test the feasibility of an open-source Data Integration (DI) method that connects real-life clinical data to national research and quality improvement (QI) systems. METHODS: The ELHS case report forms were programmed as EPIC SmartPhrases at Mass General Brigham (MGB) in December 2018 and subsequently as EPIC SmartForms in June 2021 to collect actionable, standardized, structured epilepsy data in the electronic health record (EHR) for subsequent pull into the external national registry of the ELHS. Following the QI methodology in the Chronic Care Model, 39 providers, epileptologists and neurologists, incorporated the ELHS SmartPhrase into their clinical workflow, focusing on collecting diagnosis of epilepsy, seizure type according to the International League Against Epilepsy, seizure frequency, date of last seizure, medication adherence and side effects. The collected data was stored in the Enterprise Data Warehouse (EDW) without integration with external systems. We developed and validated a DI method that extracted the data from EDW using structured query language and later preprocessed using text mining. We used the ELHS data dictionary to match fields in the preprocessed notes to obtain the final structured dataset with seizure control information. For illustration, we described the data curated from the care period of 12/2018-12/2021. RESULTS: The cohort comprised a total of 1806 patients with a mean age of 43 years old (SD: 17.0), where 57% were female, 80% were white, and 84% were non-Hispanic/Latino. Using our DI method, we automated the data mining, preprocessing, and exporting of the structured dataset into a local database, to be weekly accessible to clinicians and quality improvers. During the period of SmartPhrase implementation, there were 5168 clinic visits logged by providers documenting each patient's seizure type and frequency. During this period, providers documented 59% patients having focal seizures, 35% having generalized seizures and 6% patients having another type. Of the cohort, 45% patients had private insurance. The resulting structured dataset was bulk uploaded via web interface into the external national registry of the ELHS. CONCLUSIONS: Structured data can be feasibly extracted from text notes of epilepsy patients for weekly reporting to a national learning healthcare system.

Evaluation of Claims-Based Ascertainment of Alzheimer Disease and Related Dementias Across Health Care Settings.

This cohort study evaluates the ascertainment of Alzheimer disease and related dementia using diagnostic codes in various health care settings.

Anti-seizure medication treatment and outcomes in acute ischemic stroke patients undergoing continuous EEG monitoring.

OBJECTIVES: To determine the association of anti-seizure medication (ASM) treatment with outcomes in acute ischemic stroke (AIS) patients undergoing continuous electroencephalography (cEEG). METHODS: Retrospective analysis of AIS patients admitted between 2012 and 2019. The following are the inclusion criteria: age ≥ 18 years and ≥ 16 h of cEEG within the first 7 days of admission. ASM treatment exposure was defined as > 48 h of treatment after the first 24 h of cEEG. The primary outcome measure was 90-day mortality, and the secondary outcome was 90-day functional recovery (Modified Ranking Scale 0-3). Propensity scores were used to adjust for baseline covariates and presence of epileptiform abnormalities (seizures, periodic and rhythmic patterns). RESULTS: One hundred thirteen patients met the inclusion criteria; 39 (34.5%) were exposed to ASM. ASM treatment was not associated with 90-day mortality (propensity adjusted HR 1.0 [0.31-3.27], p = 0.999) or functional outcomes (adjusted HR 0.99 [0.32-3.02], p = 0.989), compared to no treatment. CONCLUSIONS: In our study, ASM treatment in AIS patients with cEEG abnormalities was not significantly associated with a change in 90-day mortality and functional recovery. Larger comparative effectiveness studies are indicated to identify which acute ischemic stroke patients with cEEG abnormalities benefit most from ASM treatment.

Drivers of US health care spending for persons with seizures and/or epilepsies, 2010-2018.

OBJECTIVE: This study was undertaken to characterize spending for persons classified with seizure or epilepsy and to determine whether spending has increased over time. METHODS: In this cross-sectional study, we pooled data from the Medical Expenditure Panel Survey (MEPS) household component files for 2010-2018. We matched cases to controls on age and sex of a population-based sample of MEPS respondents (community-dwelling persons of all ages) with records associated with a medical event (e.g., outpatient visit, hospital inpatient) for seizure, epilepsy, or both. Outcomes were weighted to be representative of the civilian, noninstitutionalized population. We estimated the treated prevalence of epilepsy and seizure, health care spending overall and by site of care, and trends in spending growth. RESULTS: We identified 1078 epilepsy cases and 2344 seizure cases. Treated prevalence was .38% (95% confidence interval [CI] = .34-.41) for epilepsy, .76% (95% CI = .71-.81) for seizure, and 1.14% (95% CI = 1.08-1.20) for epilepsy or seizure. The difference in annual spending for cases compared to controls was $4580 (95% CI = $3362-$5798) for epilepsy, $7935 (95% CI, $6237-$9634) for seizure, and $6853 (95% CI = $5623-$8084) for epilepsy or seizure, translating into aggregate costs of $5.4 billion, $19.0 billion, and $24.5 billion. From 2010 to 2018, the annual growth rate in total spending incurred for seizures and/or epilepsies was 7.6% compared to 3.6% among controls. SIGNIFICANCE: US economic burden of seizures and/or epilepsies is substantial and warrants interventions focused on their unique and overlapping causes.

Linking the Paul Coverdell National Acute Stroke Program to commercial claims to establish a framework for real-world longitudinal stroke research.

BACKGROUND: Non-interventional large-scale research on real-world patients who had a stroke requires the use of multiple data sources ensuring access to longitudinal data from large populations with clinically-detailed information. We sought to establish a framework for longitudinal research on patients hospitalised with stroke by linking information-rich, deidentified inpatient data from the Paul Coverdell National Acute Stroke Program (PCNASP) to commercial and Medicare Advantage longitudinal claims data. METHODS: All stroke admissions in PCNASP between 2008 and 2015 were evaluated for linkage to longitudinal claims from a commercial insurer using an algorithm based on six available common data fields (patient age, gender, admission date, discharge date, discharge diagnosis and state) and a hospital match. We evaluated the linkage quality (via the percentage of unique records in the linked dataset) and the representativeness of the linked population. We also described medical history, stroke severity and patterns of medication use among the PCNASP-claims linked cohort. RESULTS: The linkage produced uniqueness equal to 99.1%. We identified 5644 linked and 98 896 unlinked patients who had an ischaemic stroke hospitalisation in claims data. Linked patients were younger than unlinked (69.7 vs 72.5 years), but otherwise similar by medical history, prestroke medication use or lab values. Stroke severity was mild and most patients were discharged home. Prestroke and discharge use of antihypertensive and statins in the PCNASP were greater than their use as measured by filled prescriptions in claims. CONCLUSIONS: High-quality linkage between the PCNASP and commercial claims data is feasible. This linkage identified differences between reported or recommended versus actual out-of-hospital medication utilisation, highlighting the importance of longitudinal data availability for research aimed to improve the care of patients who had a stroke.

Seizure Prophylaxis After Spontaneous Intracerebral Hemorrhage.

Importance: Limited evidence is available concerning optimal seizure prophylaxis after spontaneous intracerebral hemorrhage (sICH). Objective: To evaluate which of 4 seizure prophylaxis strategies provides the greatest net benefit for patients with sICH. Design, Setting, and Participants: This decision analysis used models to simulate the following 4 common scenarios: (1) a 60-year-old man with low risk of early (≤7 days after stroke) (10%) and late (3.6% or 9.8%) seizures and average risk of short- (9%) and long-term (30%) adverse drug reaction (ADR); (2) an 80-year-old woman with low risk of early (10%) and late (3.6% or 9.8%) seizures and high short- (24%) and long-term (80%) ADR risks; (3) a 55-year-old man with high risk of early (19%) and late (34.8% or 46.2%) seizures and low short- (9%) and long-term (30%) ADR risks; and (4) a 45-year-old woman with high risk of early (19%) and late (34.8% or 46.2%) seizures and high short- (18%) and long-term (60%) ADR risks. Interventions: The following 4 antiseizure drug strategies were included: (1) conservative, consisting of short-term (7-day) secondary early-seizure prophylaxis with long-term therapy after late seizure; (2) moderate, consisting of long-term secondary early-seizure prophylaxis or late-seizure therapy; (3) aggressive, consisting of long-term primary prophylaxis; and (4) risk guided, consisting of short-term secondary early-seizure prophylaxis among low-risk patients (2HELPS2B score, 0), short-term primary prophylaxis among patients at higher risk (2HELPS2B score, ≥1), and long-term secondary therapy for late seizure. Main Outcomes and Measures: Quality-adjusted life-years (QALYs). Results: For scenario 1, the risk-guided strategy (8.13 QALYs) was preferred over the conservative (8.08 QALYs), moderate (8.07 QALYs), and aggressive (7.88 QALYs) strategies. For scenario 2, the conservative strategy (2.18 QALYs) was preferred over the risk-guided (2.17 QALYs), moderate (2.09 QALYs), and aggressive (1.15 QALYs) strategies. For scenario 3, the aggressive strategy (9.21 QALYs) was preferred over the risk-guided (8.98 QALYs), moderate (8.93 QALYs), and conservative (8.77 QALYs) strategies. For scenario 4, the risk-guided strategy (11.53 QALYs) was preferred over the conservative (11.23 QALYs), moderate (10.93 QALYs), and aggressive (8.08 QALYs) strategies. Sensitivity analyses suggested that short-term strategies (conservative and risk guided) are preferred under most scenarios, and the risk-guided strategy performs comparably to or better than alternative strategies in most settings. Conclusions and Relevance: This decision analytical model suggests that short-term (7-day) prophylaxis dominates longer-term therapy after sICH. Use of the 2HELPS2B score to guide clinical decisions for initiation of short-term primary vs secondary early-seizure prophylaxis should be considered for all patients after sICH.

Anticonvulsant Primary and Secondary Prophylaxis for Acute Ischemic Stroke Patients: A Decision Analysis.

Background and Purpose: We examined the impact of 3 anticonvulsant prophylaxis strategies on quality-adjusted life-years (QALYs) among patients with an incident acute ischemic stroke. Methods: We created a decision tree to evaluate 3 strategies: (1) long-term primary prophylaxis; (2) short-term secondary prophylaxis after an early seizure with lifetime prophylaxis if persistent or late seizures (LSs) developed; and (3) long-term secondary prophylaxis if either early, late, or persistent seizures developed. The outcome was quality-adjusted life expectancy (QALY). We created 4 base cases to simulate common clinical scenarios: (1) female patient aged 40 years with a 2% or 11% lifetime risk of an LS and a 33% lifetime risk of an adverse drug reaction (ADR); (2) male patient aged 65 years with a 6% or 29% LS risk and 60% ADR risk; (3) male patient aged 50 years with an 18% or 65% LS risk and 33% ADR risk; and (4) female patient aged 80 years with a 29% or 83% LS risk and 80% ADR risk. In sensitivity analyses, we altered the parameters and assumptions. Results: Across all 4 base cases, primary prophylaxis yielded the fewest QALYs when compared with secondary prophylaxis. For example, under scenario 1, strategies 2 and 3 resulted in 7.17 QALYs each, but strategy 1 yielded only 6.91 QALYs. Under scenario 4, strategies 2 and 3 yielded 2.85 QALYs compared with 1.40 QALYs for strategy 1. Under scenarios in which patients had higher ADR risks, strategy 2 led to the most QALYs. Conclusions: Short-term therapy with continued anticonvulsant prophylaxis only after postischemic stroke seizures arise dominates lifetime primary prophylaxis in all scenarios examined. Our findings reinforce the necessity of close follow-up and discontinuation of anticonvulsant seizure prophylaxis started during acute ischemic stroke hospitalization.

Identifying Medicare beneficiaries with dementia.

BACKGROUND/OBJECTIVES: No data exist regarding the validity of International Classification of Disease (ICD)-10 dementia diagnoses against a clinician-adjudicated reference standard within Medicare claims data. We examined the accuracy of claims-based diagnoses with respect to expert clinician adjudication using a novel database with individual-level linkages between electronic health record (EHR) and claims. DESIGN: In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims-based definitions of dementia against the reference standard. SETTING: Mass-General-Brigham Healthcare (MGB), Massachusetts, USA. PARTICIPANTS: From an eligible population of 40,690 fee-for-service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates. INTERVENTION: None. MEASUREMENTS: We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration-in-the-large [CITL] and calibration slope) of three ICD-10 claims-based definitions of dementia against clinician-adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10-fold cross-validation (CV). RESULTS: Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non-Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best-performing definition demonstrated excellent accuracy (CV-AUC 0.94; 95% CI 0.92-0.96) and was well-calibrated to the reference standard of clinician-adjudicated dementia (CV-CITL <0.001, CV-slope 0.97). CONCLUSION: This study is the first to validate ICD-10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real-world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD-9 definition of dementia.