P2Y12 inhibitor use predicts hematoma expansion in patients with intracerebral hemorrhage.

OBJECTIVE: Hematoma expansion (HE) predicts disability and death after acute intracerebral hemorrhage (ICH). Aspirin and anticoagulants have been associated with HE. We tested the hypothesis that P2Y12 inhibitors predict subsequent HE in patients. We explored laboratory measures of P2Y12 inhibition and dual antiplatelet therapy with aspirin (DAPT). METHODS: We prospectively identified patients with ICH. Platelet activity was measured with the VerifyNow-P2Y12 assay. Hematoma volumes for initial and follow-up CTs were calculated using a validated semi-automated technique. HE was defined as the difference between hematoma volumes on the initial and follow-up CT scans. Nonparametric statistics were performed with Kruskal-Wallis H, and correction for multiple comparisons performed with Dunn's test. RESULTS: In 194 patients, 15 (7.7%) were known to take a P2Y12 inhibitor (clopidogrel in all but one). Patients taking a P2Y12 inhibitor had more HE compared to patients not taking a P2Y12 inhibitor (3.5 [1.2-11.9] vs. 0.1 [-0.8-1.4] mL, p = 0.004). Patients taking DAPT experienced the most HE (7.2 [2.6-13.8] vs. 0.0 [-1.0-1.1] mL, p = 0.04). The use of P2Y12 inhibitors was associated with less P2Y12 activity (178 [149-203] vs. 288 [246-319] P2Y12 reaction units, p = 0.005). INTERPRETATION: Patients taking a P2Y12 inhibitor had more HE and less P2Y12 activity. The effect was most pronounced in patients on DAPT, suggesting a synergistic effect of P2Y12 inhibitors and aspirin with respect to HE. Acute reversal of P2Y12 inhibitors in acute ICH requires further study.

Fixed- Versus Variable-Dose Prothrombin Complex Concentrate for the Emergent Reversal of Vitamin K Antagonists: A Systematic Review and Meta-Analysis.

OBJECTIVES: Four-factor prothrombin complex concentrate (4-PCC) is recommended for rapid reversal of vitamin K antagonists (VKAs) such as warfarin, yet optimal dosing remains uncertain. DATA SOURCES: A systematic review was conducted of PubMed, Embase, and Ovid MEDLINE (Wolters Kluwer) databases from January 2000 to August 2023 for clinical studies comparing fixed- vs. variable-dose 4-PCC for emergent VKA reversal with at least one reported clinical outcome. STUDY SELECTION: Abstracts and full texts were assessed independently and in duplicate by two reviewers. DATA EXTRACTION: Data were extracted independently and in duplicate by two reviewers using predefined extraction forms. DATA SYNTHESIS: The analysis comprised three randomized trials and 16 cohort studies comprising a total of 323 participants in randomized trials (161 in fixed dosage and 162 in variable dosage) and 1912 patients in cohort studies (858 in fixed-dose and 1054 in variable dose). Extracranial bleeding was the predominant indication, while intracranial hemorrhage varied. Overall, a fixed-dose regimen may be associated with a lower dose of 4-PCC and results in a reduction in 4-PCC administration time compared with a variable-dose regimen. A fixed-dose regimen also likely results in increased clinical hemostasis. While there is no clear difference between the two regimens in terms of achieving a goal international normalized ratio (INR) less than 2, a fixed-dose regimen is less likely to achieve a goal INR less than 1.5. High certainty evidence indicates that the fixed-dose regimen reduces both mortality and the occurrence of thromboembolic events. Additional subgroup analyses provides exploratory data to guide future studies. CONCLUSIONS: A fixed-dose regimen for 4-PCC administration provides benefits over a variable-dose regimen in terms of dose reduction, faster administration time, improved clinical hemostasis, and reduced mortality and thromboembolic events. Further studies are warranted to better refine the optimal fixed-dose regimen.

Blood pressure medication and acute kidney injury after intracerebral haemorrhage: an analysis of the ATACH-II trial.

Background: Acute blood pressure (BP) reduction is standard of care after acute intracerebral haemorrhage (ICH). More acute BP reduction is associated with acute kidney injury (AKI). It is not known if the choice of antihypertensive medications affects the risk of AKI. Methods: We analysed data from the ATACH-II clinical trial. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. We analysed antihypertensive medication from two sources. The first was a case report form that specified the use of labetalol, diltiazem, urapidil or other. We tested the hypothesis that the secondary medication was associated with AKI with χ2 test. Second, we tested the hypotheses the dosage of diltiazem was associated with AKI using Mann-Whitney U test. Results: AKI occurred in 109 of 1000 patients (10.9%). A higher proportion of patients with AKI received diltiazem after nicardipine (12 (29%) vs 21 (12%), p=0.03). The 95%ile (90%-99% ile) of administered diltiazem was 18 (0-130) mg in patients with AKI vs 0 (0-30) mg in patients without AKI (p=0.002). There was no apparent confounding by indication for diltiazem use. Conclusions: The use of diltiazem, and more diltiazem, was associated with AKI in patients with acute ICH.

Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.

The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and corresponding improvements in management. Interventions such as thrombolytics and mechanical thrombectomy improve patient outcomes after stroke in selected patients; however, significant gaps remain in our ability to select patients, predict complications, and understand outcomes. Big data and the computational methods needed to analyze it can address these gaps. For example, automated analysis of neuroimaging to estimate the volume of brain tissue that is ischemic and salvageable can help triage patients for acute interventions. Data-intensive computational techniques can perform complex risk calculations that are too cumbersome to be completed by humans, resulting in more accurate and timely prediction of which patients require increased vigilance for adverse events such as treatment complications. To handle the accumulation of complex medical data, a variety of advanced computational techniques referred to as machine learning and artificial intelligence now routinely complement traditional statistical inference. In this narrative review, we explore data-intensive techniques in stroke research, how it has informed the management of stroke patients, and how current work could shape clinical practice in the future.

Fall Risk Prediction Models During the Initial COVID-19 Surge: Could Predictive Analytics Be Used in a Resource-Constrained Environment?

During the first COVID surge, multiple changes in nurse staffing and workflows were made to support care delivery in a resource-constrained environment. We hypothesized that there was a higher rate of inpatient falls during the COVID surge. Furthermore, we predicted that an automated predictive analytic algorithm would perform as well as the Johns Hopkins Fall Risk Assessment. A retrospective review of falls for 3 months before and the first 3 months of the first COVID surge was conducted. We determined the total number of falls and the overall fall rate and examined the distribution of scores and accuracy of fall predictive models for both groups. There was a statistically significant increase in fall rate during the first 3 months of the COVID surge compared with the 3 prior months (2.48/1000 patient-days vs 1.89/1000 patient-days respectively; P = .041). The Johns Hopkins instrument had a greater sensitivity of 78.9% compared with 57.0% for the predictive analytic model. Specificity and accuracy of the predictive analytic model were higher than the Johns Hopkins instrument (71.3% vs 54.1% and 71.2% vs 54.3%, respectively). These findings suggest that the automated predictive analytic model could be used in a resource-constrained environment to accurately classify patients' risk of fall.

Recruitment in Acute Stroke Trials: Challenges and Potential Solutions.

Randomized clinical trials of acute stroke have led to major advances in acute stroke therapy over the past decade. Despite these successes, recruitment in acute trials is often difficult. We outline challenges in recruitment for acute stroke trials and present potential solutions, which can increase the speed and decrease the cost of identifying new treatments for acute stroke. One of the largest opportunities to increase the speed of enrollment and make trials more generalizable is expansion of inclusion criteria whose impact on expected recruitment can be assessed by epidemiologic and registry databases. Another barrier to recruitment besides the number of eligible patients is availability of study investigators limited to business hours, which may be helped by financial support for after-hours call. The wider use of telemedicine has accelerated quicker stroke treatment at many hospitals and has the potential to accelerate research enrollment but requires training of clinical investigators who are often inexperienced with this approach. Other potential solutions to enhance recruitment include rapid prehospital notification of clinical investigators of potential patients, use of mobile stroke units, advances in the process of emergency informed consent, storage of study medication in the emergency department, simplification of study treatments and data collection, education of physicians to improve equipoise and enthusiasm for randomization of patients within a trial, and clear recruitment plans, and even potentially coenrollment, when there are competing trials at sites. Without successful recruitment, scientific advances and clinical benefit for acute stroke patients will lag.

Magnesium Sulfate and Hematoma Expansion: An Ancillary Analysis of the FAST-MAG Randomized Trial.

BACKGROUND: Intracerebral hemorrhage (ICH) is the deadliest form of stroke. In observational studies, lower serum magnesium has been linked to more hematoma expansion (HE) and intracranial hemorrhage, implying that supplemental magnesium sulfate is a potential acute treatment for patients with ICH and could reduce HE. FAST-MAG (Field Administration of Stroke Therapy - Magnesium) was a clinical trial of magnesium sulfate started prehospital in patients with acute stroke within 2 hours of last known well enrolled. CT was not required prior to enrollment, and several hundred patients with acute ICH were enrolled. In this ancillary analysis, we assessed the effect of magnesium sulfate treatment upon HE in patients with acute ICH. METHODS: We retrospectively analyzed data that were prospectively collected in the FAST-MAG study. Patients received intravenous magnesium sulfate or matched placebo within 2 hours of onset. We compared HE among patients allocated to intravenous magnesium sulfate or placebo with a Mann-Whitney U. We used the same method to compare neurological deficit severity (National Institutes of Health Stroke Scale) and global disability (modified Rankin Scale) at 3 months. RESULTS: Among 268 patients with ICH meeting study entry criteria, mean 65.4±13/4 years, 33% were female, and 211 (79%) had a history of hypertension. Initial deficit severities were median (interquartile range) of 4 (3-5) on the Los Angeles Motor Scale in the field and National Institutes of Health Stroke Scale score of 16 (9.5-25.5) early after hospital arrival. Follow-up brain imaging was performed a median of 17.1 (11.3-22.7) hours after first scan. The magnesium and placebo groups did not statistically differ in hematoma volume on arrival, 10.1 (5.6-28.7) versus 12.4 (5.6-28.7) mL (P=0.6), or HE, 2.0 (0.1-7.4) versus 1.5 (-0.2 to 8) mL (P=0.5). There was no difference in functional outcomes (modified Rankin Scale score of 3-6), 59% versus 50% (P=0.5). CONCLUSIONS: Magnesium sulfate did not reduce HE or improve functional outcomes at 90 days. A benefit for patients with initial hypomagnesemia was not addressed. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT00059332.

Predicting Early Seizures After Intracerebral Hemorrhage with Machine Learning.

BACKGROUND: Seizures are a harmful complication of acute intracerebral hemorrhage (ICH). "Early" seizures in the first week after ICH are a risk factor for deterioration, later seizures, and herniation. Ideally, seizure medications after ICH would only be administered to patients with a high likelihood to have seizures. We developed and validated machine learning (ML) models to predict early seizures after ICH. METHODS: We used two large datasets to train and then validate our models in an entirely independent test set. The first model ("CAV") predicted early seizures from a subset of variables of the CAVE score (a prediction rule for later seizures)-cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL-whereas early seizure was the dependent variable. We attempted to improve on the "CAV" model by adding anticoagulant use, antiplatelet use, Glasgow Coma Scale, international normalized ratio, and systolic blood pressure ("CAV + "). For each model we used logistic regression, lasso regression, support vector machines, boosted trees (Xgboost), and random forest models. Final model performance was reported as the area under the receiver operating characteristic curve (AUC) using receiver operating characteristic models for the test data. The setting of the study was two large academic institutions: institution 1, 634 patients; institution 2, 230 patients. There were no interventions. RESULTS: Early seizures were predicted across the ML models by the CAV score in test data, (AUC 0.72, 95% confidence interval 0.62-0.82). The ML model that predicted early seizure better in the test data was Xgboost (AUC 0.79, 95% confidence interval 0.71-0.87, p = 0.04) compared with the CAV model AUC. CONCLUSIONS: Early seizures after ICH are predictable. Models using cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL had a good accuracy rate, and performance improved with more independent variables. Additional methods to predict seizures could improve patient selection for monitoring and prophylactic seizure medications.

Recombinant factor VIIa for hemorrhagic stroke treatment at earliest possible time (FASTEST): Protocol for a phase III, double-blind, randomized, placebo-controlled trial.

INTRODUCTION: Intracerebral hemorrhage is the deadliest form of stroke. Hematoma expansion, growth of the hematoma between the baseline computed tomography scan and a follow-up computed tomography scan at 24 ± 6 h, predicts long-term disability or death. Recombinant factor VIIa (rFVIIa) has reduced hematoma expansion in previous clinical trials with a variable effect on clinical outcomes, with the greatest impact on hematoma expansion and potential benefit when administered within 2 h of symptom onset. METHODS: Factor VIIa for Hemorrhagic Stroke Treatment at Earliest Possible Time (FASTEST, NCT03496883) is a randomized controlled trial that will enroll 860 patients at ∼100 emergency departments and mobile stroke units in five countries. Patients are eligible for enrollment if they have acute intracerebral hemorrhage within 2 h of symptom onset confirmed by computed tomography, a hematoma volume of 2 to 60 mL, no or small volumes of intraventricular hemorrhage, do not take anticoagulant medications or concurrent heparin/heparinoids (antiplatelet medications are permissible), and are not deeply comatose. Enrolled patients will receive rFVIIa 80 µg/kg or placebo intravenously over 2 min. The primary outcome measure is the distribution of the ordinal modified Rankin Scale at 180 days. FASTEST is monitored by a Data Safety Monitoring Board. Safety endpoints include thrombotic events (e.g. myocardial infarction). Human subjects research is monitored by an external Institutional Review Board in participating countries. DISCUSSION: In the US, FASTEST will be first NIH StrokeNet Trial with an Exception from Informed Consent which allows enrollment of non-communicative patients without an immediately identifiable proxy.

Comparison of Two Different Models to Predict Fall Risk in Hospitalized Patients.

BACKGROUND: Fall prevention is a patient safety and economic priority for health care organizations. An automated model within the electronic medical record (EMR) that accurately predicts risk for falling would be valuable for mitigation of inpatient falls. The aim of this study was to validate the reliability of an EMR-based computerized predictive model (ROF Model) for inpatient falls. The hypothesis was that the ROF Model would be similar to the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) in predicting fall events in the inpatient setting at a large academic medical center. METHODS: This observational study compared the falls predicted by each model against actual falls over an eight-month period in a single institution. Descriptive statistics were used to compare the distribution of scores and accuracy of fall risk categorization for each model immediately preceding a fall. RESULTS: For 35,709 inpatient encounters, the total fall rate was 0.92%. Of the 329 patients who fell, 60.8% were high risk by ROF Model (fall rate 1.82%), and 75.4% were high risk by JHFRAT (fall rate 1.39%). The ROF Model had a better specificity than the JHFRAT (69.7% vs. 49.2%) but a similar C-statistic (0.717 vs. 0.702) and a lower sensitivity (60.8% vs. 79.3%). CONCLUSION: The performance of the ROF Model was similar to that of the JHFRAT in predicting inpatient falls. This comparison provides evidence to support a transition to a more automated process. Future studies will determine prospectively if implementation of the ROF Model will reduce falls in the inpatient setting.

Antiplatelet Medications and Biomarkers of Hemostasis May Explain the Association of Hematoma Appearance and Subsequent Hematoma Expansion After Intracerebral Hemorrhage.

BACKGROUND: To test the hypothesis that appearances of intracranial hematomas on diagnostic computed tomography (CT) are not idiosyncratic and reflect a biologically plausible mechanism, we evaluated the association between hematoma appearance on CT, biomarkers of platelet activity, and antiplatelet or anticoagulant medication use prior to admission. METHODS: We studied 330 consecutively identified patients from 2006 to 2019. Biomarkers of platelet activity (platelet aspirin assay) and medication history (aspirin, clopidogrel) were prospectively recorded on admission. A blinded interpreter recorded the presence of hematoma appearances from the diagnostic scan. Associations were tested with parametric or nonparametric statistics, as appropriate. RESULTS: The black hole sign (101, 30%) was most prevalent, followed by the island sign (57, 17%) and blend sign (32, 10%). There was reduced platelet activity in patients with a black hole sign (511 [430-610] vs. 562 [472-628] aspirin reaction units, P = 0.01) or island sign (505 [434-574] vs. 559 [462-629] aspirin reaction units, P = 0.004). Clopidogrel use prior to admission was associated with the black hole sign (odds ratio 2.25, 95% confidence interval 1.02-4.98, P = 0.04). CONCLUSIONS: In patients with acute intracerebral hemorrhage, hematoma appearances on CT are associated with biomarkers of platelet activity and clopidogrel use prior to admission. Appearances of intracranial hematomas on CT may reflect reduced hemostasis from antiplatelet medication use.

Clusters Across Multiple Domains of Health-Related Quality of Life Reveal Complex Patient Outcomes After Subarachnoid Hemorrhage.

Patients with aneurysmal subarachnoid hemorrhage (ruptured brain aneurysm) often have reduced health-related quality of life at follow-up in multiple domains (e.g., cognitive function and social function). We tested the hypothesis that there are distinct patterns of patient outcomes across domains of health-related quality of life, "complex patient outcomes," in survivors of subarachnoid hemorrhage. DESIGN: Patients with subarachnoid hemorrhage were prospectively identified. Clinical data were prospectively recorded. Health-related quality of life was prospectively assessed at 3-month follow-up using the National Institutes of Health Patient Reported Outcomes Measurement Information System and neuro-quality of life in the domains of mobility, cognitive function, satisfaction with social roles, and depression. We used k-means clustering to analyze prospectively recorded health-related quality of life data, identifying clusters of complex patient outcomes. Decision tree analysis identified index hospital stay factors predictive of a patient having a particular complex patient outcome at follow-up. SETTING: Academic medical center. PATIENTS: One hundred three survivors of subarachnoid hemorrhage. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 103 patients, of whom 75 (72.8%) were female, and mean age was 53.6 ± 13.4 years. There were three complex patient outcomes: health-related quality of life greater than 1 sd better than the U.S. mean across all domains (n = 23, 22.3%), health-related quality of life greater than 1 sd worse than U.S. mean across all domains (n = 26, 25.2%), and satisfaction with social roles greater than 0.5 sd worse than U.S. mean with cognitive function, depression, and mobility scores near the U.S. mean (n = 54, 52.4%). In decision tree analysis, hospital disposition and Hunt and Hess Grade were associated with complex patient outcome. CONCLUSIONS: Complex patient outcomes across multiple domains of health-related quality of life at follow-up after subarachnoid hemorrhage are distinct and may be predictable.

Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease.

Damage to specific brain circuits can cause specific neuropsychiatric symptoms. Therapeutic stimulation to these same circuits may modulate these symptoms. To determine whether these circuits converge, we studied depression severity after brain lesions (n = 461, five datasets), transcranial magnetic stimulation (n = 151, four datasets) and deep brain stimulation (n = 101, five datasets). Lesions and stimulation sites most associated with depression severity were connected to a similar brain circuit across all 14 datasets (P < 0.001). Circuits derived from lesions, deep brain stimulation and transcranial magnetic stimulation were similar (P < 0.0005), as were circuits derived from patients with major depression versus other diagnoses (P < 0.001). Connectivity to this circuit predicted out-of-sample antidepressant efficacy of transcranial magnetic stimulation and deep brain stimulation sites (P < 0.0001). In an independent analysis, 29 lesions and 95 stimulation sites converged on a distinct circuit for motor symptoms of Parkinson's disease (P < 0.05). We conclude that lesions, transcranial magnetic stimulation and DBS converge on common brain circuitry that may represent improved neurostimulation targets for depression and other disorders.

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing.

Background and Purpose: This study aims to determine whether machine learning (ML) and natural language processing (NLP) from electronic health records (EHR) improve the prediction of 30-day readmission after stroke. Methods: Among index stroke admissions between 2011 and 2016 at an academic medical center, we abstracted discrete data from the EHR on demographics, risk factors, medications, hospital complications, and discharge destination and unstructured textual data from clinician notes. Readmission was defined as any unplanned hospital admission within 30 days of discharge. We developed models to predict two separate outcomes, as follows: (1) 30-day all-cause readmission and (2) 30-day stroke readmission. We compared the performance of logistic regression with advanced ML algorithms. We used several NLP methods to generate additional features from unstructured textual reports. We evaluated the performance of prediction models using a five-fold validation and tested the best model in a held-out test dataset. Areas under the curve (AUCs) were used to compare discrimination of each model. Results: In a held-out test dataset, advanced ML methods along with NLP features out performed logistic regression for all-cause readmission (AUC, 0.64 vs. 0.58; p < 0.001) and stroke readmission prediction (AUC, 0.62 vs. 0.52; p < 0.001). Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.

The Story of Intracerebral Hemorrhage: From Recalcitrant to Treatable Disease.

This invited special report is based on an award presentation at the World Stroke Organization/European Stroke Organization Conference in November of 2020 outlining progress in the acute management of intracerebral hemorrhage (ICH) over the past 35 years. ICH is the second most common and the deadliest type of stroke for which there is no scientifically proven medical or surgical treatment. Prospective studies from the 1990s onward have demonstrated that most growth of spontaneous ICH occurs within the first 2 to 3 hours and that growth of ICH and resulting volumes of ICH and intraventricular hemorrhage are modifiable factors that can improve outcome. Trials focusing on early treatment of elevated blood pressure have suggested a target systolic blood pressure of 140 mm Hg, but none of the trials were positive by their primary end point. Hemostatic agents to decrease bleeding in spontaneous ICH have included desmopressin, tranexamic acid, and rFVIIa (recombinant factor VIIa) without clear benefit, and platelet infusions which were associated with harm. Hemostatic agents delivered within the first several hours have the greatest impact on growth of ICH and potentially on outcome. No large Phase III surgical ICH trial has been positive by primary end point, but pooled analyses suggest that earlier ICH removal is more likely to be beneficial. Recent trials emphasize maximization of clot removal and minimizing brain injury from the surgical approach. The future of ICH therapy must focus on delivery of medical and surgical therapies as soon as possible if we are to improve outcomes.

Prothrombin Complex Concentrate for Emergent Reversal of Intracranial Hemorrhage in Patients with Ventricular Assist Devices.

BACKGROUND: Intracranial hemorrhage (ICH) is a devastating complication for patients with ventricular assist devices (VADs). The safety of emergent anticoagulation reversal with four-factor prothrombin complex concentrate (PCC) and optimal timing of anticoagulation resumption are not clear. In addition, lactate dehydrogenase (LDH) is used as a biomarker for thromboembolic risk, but its utility in guiding anticoagulation management after reversal with PCC has not be described. METHODS: We retrospectively reviewed a consecutive series of patients with VADs presenting with ICH between 2014 and 2020 who received four-factor PCC for rapid anticoagulation reversal. We collected the timing of PCC administration, timing of resumption of anticoagulation, survival, occurrence of thromboembolic events, and LDH levels throughout hospitalization. RESULTS: We identified 16 ICH events in 14 patients with VADs treated with rapid anticoagulation reversal using four-factor PCC (11 intraparenchymal, 4 subdural, 1 subarachnoid hemorrhage). PCC was administered at a mean of 3.3 ± 0.3 h after imaging diagnosis of ICH. Overall mortality was 63%. Survivors had higher presenting Glasgow Coma Scale (median 15, interquartile range [IQR] 15-15 versus 14, IQR 8-14.7, P = 0.041). In all six instances where the patient survived, anticoagulation was resumed on average 9.16 ± 1.62 days after reversal. There were no thromboembolic events prior to resumption of anticoagulation. Three events occurred after anticoagulation resumption and within 3 months of reversal: VAD thrombosis in a patient with thrombosis at the time of reversal, ischemic stroke, and readmission for elevated LDH in the setting of subtherapeutic international normalized ratio. CONCLUSIONS: Our limited series found no thromboembolic complications immediately following anticoagulation reversal with PCC prior to resumption of anticoagulation. LDH trends may be useful to monitor thromboembolic risk after reversal.

Early Seizures Are Predictive of Worse Health-Related Quality of Life at Follow-Up After Intracerebral Hemorrhage.

OBJECTIVES: Early seizures are a common complication of intracerebral hemorrhage, occurring in ~10% of patients. However, the independent effect of early seizures on patient outcomes, particularly health-related quality of life, is unclear. Without a potential benefit to patient outcomes, the widespread use (~40%) of prophylactic seizure medications has no reasonable chance of improving patient outcomes. We tested the hypothesis that health-related quality of life at follow-up is different between patients with and without early seizures (and secondarily, with nonconvulsive status epilepticus) after intracerebral hemorrhage. DESIGN: Patients with intracerebral hemorrhage were enrolled in an observational cohort study that prospectively collected clinical data and health-related quality of life at follow-up. SETTING: Academic medical center. PATIENTS: One-hundred thirty-three patients whose health-related quality of life was assessed 3 months after intracerebral hemorrhage onset. MEASUREMENTS AND MAIN RESULTS: Health-related quality of life was obtained at 3 months after intracerebral hemorrhage onset. T Scores of health-related quality of life were modeled with multivariable linear models accounting for severity with the intracerebral hemorrhage Score and hematoma location. Health-related quality of life was measured with National Institutes of Health Patient Reported Outcomes Measurement Information System/Neuroquality of life, expressed in T Scores (U.S. normal 50 ± 10). The modified Rankin Scale (a global measure) was a secondary outcome. There were 12 patients (9%) with early seizures. T Scores of health-related quality of life at follow-up were lower (worse) in patients with early seizure compared with patients without an early seizure (44 [32.75-51.85] vs 30.25 [18.9-39.15]; p = 0.04); results for other domains of health-related quality of life were similar. The association persisted in multivariable models. There was no association between early seizures and prophylactic seizure medications (p = 0.4). Results for patients with nonconvulsive status epilepticus were similar. There was no association between early seizures and the modified Rankin Scale at 3 months. CONCLUSIONS: Early seizures and nonconvulsive status epilepticus were associated with lower health-related quality of life at follow-up in survivors of intracerebral hemorrhage.