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BACKGROUND: The objective of this study was to define clinically meaningful phenotypes of intracerebral hemorrhage (ICH) using machine learning. METHODS: We used patient data from two US medical centers and the Antihypertensive Treatment of Acute Cerebral Hemorrhage-II clinical trial. We used k-prototypes to partition patient admission data. We then used silhouette method calculations and elbow method heuristics to optimize the clusters. Associations between phenotypes, complications (e.g., seizures), and functional outcomes were assessed using the Kruskal-Wallis H-test or χ2 test. RESULTS: There were 916 patients; the mean age was 63.8 ± 14.1 years, and 426 patients were female (46.5%). Three distinct clinical phenotypes emerged: patients with small hematomas, elevated blood pressure, and Glasgow Coma Scale scores > 12 (n = 141, 26.6%); patients with hematoma expansion and elevated international normalized ratio (n = 204, 38.4%); and patients with median hematoma volumes of 24 (interquartile range 8.2-59.5) mL, who were more frequently Black or African American, and who were likely to have intraventricular hemorrhage (n = 186, 35.0%). There were associations between clinical phenotype and seizure (P = 0.024), length of stay (P = 0.001), discharge disposition (P < 0.001), and death or disability (modified Rankin Scale scores 4-6) at 3-months' follow-up (P < 0.001). We reproduced these three clinical phenotypes of ICH in an independent cohort (n = 385) for external validation. CONCLUSIONS: Machine learning identified three phenotypes of ICH that are clinically significant, associated with patient complications, and associated with functional outcomes. Cerebellar hematomas are an additional phenotype underrepresented in our data sources.

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INTRODUCTION: The ongoing OPTIMISTmain study, an international, multicenter, stepped-wedge cluster randomized trial, aims to determine effectiveness and safety of low-intensity versus standard monitoring in thrombolysis-treated patients with mild-to-moderate acute ischemic stroke (AIS). An embedded process evaluation explored integration and impact of the intervention on care processes at participating US sites. METHODS: A mixed-methods approach with quantitative and qualitative data were collected between September 2021 and November 2022. Implementer surveys were undertaken at pre- and post-intervention phases to understand the perceptions of low-intensity monitoring strategy. A sample of stroke care nurses were invited to participate in semi-structured interviews at an early stage of post-intervention. Qualitative data were analyzed deductively using the normalization process theory; quantitative data were tabulated. RESULTS: Interviews with 21 nurses at 8 hospitals have shown low-intensity monitoring was well accepted, as there were less time constraints and reduced workload for each patient. There were initial safety concerns over missing deteriorating patients and difficulties in changing established routines. Proper training, education, and communication, and changing the habits and culture of care, were key elements to successfully adopting the new monitoring care into routine practice. Similar results were found in the post-intervention survey (42 nurses from 13 hospitals). Nurses reported time being freed up to provide patient education (56%), daily living care (50%), early mobilization (26%), mood/cognition assessment (44%), and other aspects (i.e. communication, family support). CONCLUSIONS: Low-intensity monitoring for patients with mild-to-moderate acute ischemic stroke, facilitated by appropriate education and organizational support, appears feasible and acceptable at US hospitals.

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OBJECTIVE: Advance directives (ADs) are integral to health care, allowing patients to specify surrogate decision-makers and treatment preferences in case of loss of capacity. The present study sought to identify determinants of ADs among stroke survivors. METHODS: In this cross-sectional study (Care Attitudes and Preferences in Stroke Survivors [CAPriSS]), community-dwelling stroke survivors were surveyed on ADs; validated scales were used to query palliative care knowledge and attitudes towards life-sustaining treatments. Logistic regression was used to determine variables associated with ADs. RESULTS: Among 562 community-dwelling stroke survivors who entered the survey after screening questions confirmed eligibility, 421 (74.9%) completed survey components with relevant variables of interest. The median age was 69 years (IQR 58-75 years); 53.7% were male; and 15.0% were Black. Two hundred and fifty-one (59.6%) respondents had ADs. Compared to stroke survivors without ADs, those with ADs were more likely to be older (median age 72 vs. 61 years; p<0.001), White (91.2% vs. 75.9%, p<0.001), and male (58.6% vs. 46.5%, p = 0.015), and reported higher education (p<0.001) and income (p = 0.011). Ninety-eight (23.3%) participants had "never heard of palliative care". Compared to participants without ADs, participants with ADs had higher Palliative Care Knowledge Scale (PaCKS) scores (median 10 [IQR 5-12] vs. 7 [IQR 0-11], p<0.001), and lower scores on the Attitudes Towards Life-Sustaining Treatments Scale (indicating a more negative attitude towards life-sustaining treatments; median 23 [IQR 18-28] vs. 29 [IQR 24-35], p<0.001). Multivariable logistic regression identified age (OR 1.62 per 10 year increase, 95% CI 1.30-2.02; p<0.001), prior advance care planning discussion with a physician (OR 1.73, 95% CI 1.04-2.86; p = 0.034), PaCKS scores (OR 1.06 per 1 point increase, 95% CI 1.01-1.12; p = 0.018), and Attitudes Towards Life-Sustaining Treatments Scale scores (OR 0.91 per 1 point increase, 95% CI 0.88-0.95; p<0.001) as variables independently associated with ADs. CONCLUSIONS: Age, prior advance care planning discussion with a physician, palliative care knowledge, and attitudes towards life-sustaining treatments were independently associated with ADs.

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Introduction: Stroke mortality has declined, with differential changes by race; stroke is now the 5th leading cause of death overall, but 2nd leading cause of death in Black individuals. Little is known about recent race/ethnic and sex trends in in-hospital mortality after acute ischemic stroke (AIS) and whether system-level factors contribute to possible differences. Methods: Using the National Inpatient Sample, adults (≥18 years) with a primary diagnosis of AIS from 2006 to 2017 (n=643,912) were identified. We assessed in-hospital mortality by race/ethnicity (White, Black, Hispanic, Asian/Pacific Islander [API], other), sex, and age. Hospitals were categorized by proportion of non-White patients served: <25% ("predominantly White patients"), 25-50% ("mixed race/ethnicity profile"), and ≥50% ("predominantly non-White patients"). Using survey adjusted logistic regression, the association between race/ethnicity and odds of mortality was assessed, adjusting for key sociodemographic, clinical, and hospital characteristics (e.g., age, comorbidities, stroke severity, do not resuscitate orders, and palliative care). Results: Overall, mortality decreased from 5.0% in 2006 to 2.9% in 2017 (p<0.001). Comparing 2012-2017 to 2006-2011, there was a 68% reduction in mortality odds overall after adjusting for covariates, most prominent in White individuals (69%) and smallest in Black individuals (57%). Compared to White patients, Black and Hispanic patients had lower odds of mortality (adjusted odds ratio (aOR) 0.82, 95% CI 0.78-0.87 and aOR 0.93, 95% CI 0.87-1.00), primarily driven by those >65 years (age x ethnicity interaction p < 0.0001). Compared to White men, Black, Hispanic, and API men, and Black women had lower aOR of mortality. The differences in mortality between White and non-White patients were most pronounced in hospitals predominantly serving White patients (aOR 0.80, 0.74-0.87) compared to mixed hospitals (aOR 0.85, 0.79-0.91) and predominantly non-White hospitals (aOR 0.88, 0.81-0.95; interaction effect: p=0.005). Discussion: AIS mortality decreased dramatically in recent years in all race/ethnic subgroups. Overall, non-White AIS patients had lower mortality than their White counterparts, a difference that was most striking in hospitals predominantly serving White patients. Further study is needed to understand these differences and to what extent biological, sociocultural, and system-level factors play a role.

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Introduction Careful monitoring of patients who receive intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) is resource-intensive, and potentially less relevant in those with mild degrees of neurological impairment who are at low-risk of symptomatic intracerebral hemorrhage (sICH) and other complications. \ Methods OPTIMISTmain is an international, multicenter, prospective, stepped wedge, cluster randomized, blinded outcome assessed trial aims to determine whether a less-intensity monitoring protocol is at least as effective, safe and efficient as standard post-IVT monitoring in patients with mild deficits post-AIS. Clinically-stable adult patients with mild AIS (defined by a NIHSS <10) who do not require intensive care within 2 hours post-IVT are recruited at hospitals in Australia, Chile, China, Malaysia, Mexico, UK, US and Vietnam. An average of 15 patients recruited per period (overall 60 patient participants) at 120 sites for a total of 7200 IVT-treated AIS patients will provide 90% power (one-sided α 0.025). The initiation of eligible hospitals is based on a rolling process whenever ready, stratified by country. Hospitals are randomly allocated using permuted blocks into 3 sequences of implementation, stratified by country and the projected number of patients to be recruited over 12 months. These sequences have four periods that dictate the order in which they are to switch from control (usual care) to intervention (implementation of low intensity monitoring protocol) to different clusters of patients in a stepped manner. Compared to standard monitoring, the low-intensity monitoring protocol includes assessments of neurological and vital signs every 15 minutes for 2 hours, 2 hourly (versus every 30 minutes) for 8 hours, and 4 hourly (versus every 1 hour) until 24 hours, post-IVT. The primary outcome measure is functional recovery, defined by the modified Rankin scale (mRS) at 90 days, a seven-point ordinal scale (0 [no residual symptom] to 6 [death]). Secondary outcomes include death or dependency, length of hospital stay, and health-related quality of life, sICH and serious adverse events. Conclusion OPTIMISTmain will provide Level I evidence for the safety and effectiveness of a low-intensity post-IVT monitoring protocol in patients with mild severity of AIS.

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Racial and ethnic inequities in stroke care are ubiquitous. Acute reperfusion therapies, i.e., IV thrombolysis (IVT) and mechanical thrombectomy (MT), are central to acute stroke care and are highly efficacious at preventing death and disability after stroke. Disparities in the use of IVT and MT in the USA are pervasive and contribute to worse outcomes among racial and ethnic minority individuals with ischemic stroke. A meticulous understanding of disparities and underlying root causes is necessary in order to develop targeted mitigation strategies with lasting effects. This review details racial and ethnic disparities in the use of IVT and MT after stroke and highlights inequities in the underlying process measures as well as the contributing root causes. Furthermore, this review spotlights the systemic and structural inequities that contribute to race-based differences in the use of IVT and MT, including geographic and regional differences and differences based on neighborhood, zip code, and hospital type. In addition, recent promising trends suggesting improvements in racial and ethnic IVT and MT disparities and potential approaches for future solutions to achieve equity in stroke care are briefly discussed.

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Mechanical thrombectomy (MT) is the standard of care for the treatment of acute ischemic stroke due to large vessel occlusion, but the capacity to deliver this treatment can be limited in less populous areas and island territories. Here, we describe the case of a man who developed right MCA syndrome while in Bermuda who was successfully diagnosed, transported over 800 miles to the East Coast of the USA, and treated with MT within 24 h. This case underscores the benefits of having organized systems of care and demonstrates the feasibility of urgent transoceanic patient transportation for stroke requiring MT.

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Dysphagia management is a core component of quality stroke care. Speech-Language Pathologists (SLPs) play a key role in the management of post-stroke dysphagia. We sought to elicit perceptions, attitudes, and practice patterns regarding post-stroke dysphagia management among SLPs in the United States. We conducted a survey among SLPs registered with the American Speech-Language-Hearing Association who indicated that they care for acute stroke patients. A total of 336 participants completed the survey. Over half of the participants (58.6%) indicated that they obtain objective swallow testing in ≥ 60% of their post-stroke dysphagia patients. Almost 1 in 5 SLPs indicated that they are often unable to perform objective dysphagia testing due to limited resources (18.8% indicated resource limitations; 78.9% indicated no resources limitations; 2.4% were unsure). SLPs in hospitals without stroke center certification had higher odds of indicating limited resources compared to SLPs in certified stroke centers (OR 2.08, 95% CI 1.11-3.87). Over 75% indicated that percutaneous endoscopic gastrostomy (PEG) tubes after stroke are placed too early. SLPs who obtain objective swallow testing in ≥ 60% of patients had higher odds of indicating that PEG tubes are placed too early (OR 1.70, 95% CI 1.13-2.56). While 19.4% indicated that the optimal timing for PEG after stroke is < 7 days after admission, 25.0% indicated that the optimal timing is > 12 days. Almost 35% indicated that health care system pressures influence their recommendations, and 47.6% indicated that ≥ 25% of PEGs could be avoided if patients were given up to 7 more days for swallowing recovery.

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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.

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BACKGROUND AND PURPOSE: Intravenous thrombolysis (IVT) after ischemic stroke is underutilized in racially/ethnically minoritized groups. We aimed to determine the regional and geographic variability in racial/ethnic IVT disparities in the United States. METHODS: Acute ischemic stroke admissions between 2012 and 2018 were identified in the National Inpatient Sample. Multivariable logistic regression was used to test the association between IVT and race/ethnicity, stratified by geographic region and controlling for demographic, clinical, and hospital characteristics. RESULTS: Of the 545 509 included cases, 47 031 (8.6%) received IVT. Racially/ethnically minoritized groups had significantly lower adjusted odds of IVT compared with White people in the South Atlantic region (odds ratio [OR], 0.86 [95% CI, 0.82-0.91]), the East North Central region (OR, 0.91 [95% CI, 0.85-0.97]) and the Pacific region (OR, 0.90 [95% CI, 0.85-0.96]). In the South Atlantic region, IVT use in racial/ethnic minority groups was below the national average of all racial/ethnic minority patients (P=0.002). Compared with White patients, Black patients had lower odds of IVT in the Middle Atlantic region (OR, 0.84 [95% CI, 0.78-0.91]), the South Atlantic region (OR, 0.78 [95% CI, 0.74-0.82]), and the East North Central region (OR, 0.86 [95% CI, 0.79-0.93]). In the South Atlantic region, this difference was below the national average for Black people (P<0.001). Hispanic patients had significantly lower use of IVT only in the Pacific region (OR, 0.92 [95% CI, 0.85-0.99]), while Asian/Pacific Islander patients had lower odds of IVT in the Mountain (OR, 0.76 [95% CI, 0.59-0.98]) and Pacific region (OR, 0.89 [95% CI, 0.82-0.97]). CONCLUSIONS: Racial/ethnic disparities in IVT use in the United States vary by region. Geographic hotspots of lower IVT use in racially/ethnically minoritized groups are the South Atlantic region, driven predominantly by lower use of IVT in Black patients, and the East North Central and Pacific regions.

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OBJECTIVE: To develop a risk prediction score identifying patients with intracerebral hemorrhage (ICH) at low risk for critical care. METHODS: We retrospectively analyzed data of 451 patients with ICH between 2010 and 2018. The sample was randomly divided into a development and a validation cohort. Logistic regression was used to develop a risk score by weighting independent predictors of intensive care unit (ICU) needs according to strength of association. The risk score was tested in the validation cohort and externally validated in a dataset from another institution. RESULTS: The rate of ICU interventions was 80.3%. Systolic blood pressure (SBP), Glasgow Coma Scale (GCS) score, intraventricular hemorrhage (IVH), and ICH volume were independent predictors of critical care, resulting in the following point assignments for the Intensive Care Triaging in Spontaneous Intracerebral Hemorrhage (INTRINSIC) score: SBP 160 to 190 mm Hg (1 point), SBP >190 mm Hg (3 points); GCS 8 to 13 (1 point), GCS <8 (3 points); ICH volume 16 to 40 cm3 (1 point), ICH volume >40 cm3 (2 points); and presence of IVH (1 point), with values ranging between 0 and 9. Among patients with a score of 0 and no ICU needs during their emergency department stay, 93.6% remained without critical care needs. In an external validation cohort of patients with ICH, the INTRINSIC score achieved an area under the receiver operating characteristic curve of 0.823 (95% confidence interval 0.782-0.863). A score <2 predicted the absence of critical care needs with 48.5% sensitivity and 88.5% specificity, and a score <3 predicted the absence of critical care needs with 61.7% sensitivity and 83.0% specificity. CONCLUSION: The INTRINSIC score identifies patients with ICH who are at low risk for critical care interventions. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that the INTRINSIC score identifies patients with ICH at low risk for critical care interventions.

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[Figure: see text].

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BACKGROUND/OBJECTIVE: Demonstrating a benefit of acute treatment to patients with intracerebral hemorrhage (ICH) requires identifying which patients have a potentially modifiable outcome, where treatment could favorably shift a patient's expected outcome. A decision rule for which patients have a modifiable outcome could improve the targeting of treatments. We sought to determine which patients with ICH have a modifiable outcome. METHODS: Patients with ICH were prospectively identified at two institutions. Data on hematoma volumes, medication histories, and other variables of interest were collected. ICH outcomes were evaluated using the modified Rankin Scale (mRS), assessed at 14 days and 3 months after ICH, with "good outcome" defined as 0-3 (independence or better) and "poor outcome" defined as 4-6 (dependence or worse). Supervised machine learning models identified the best predictors of good versus poor outcomes at Institution 1. Models were validated using repeated fivefold cross-validation as well as testing on the entirely independent sample at Institution 2. Model fit was assessed with area under the ROC curve (AUC). RESULTS: Model performance at Institution 1 was strong for both 14-day (AUC of 0.79 [0.77, 0.81] for decision tree, 0.85 [0.84, 0.87] for random forest) and 3 month (AUC of 0.75 [0.73, 0.77] for decision tree, 0.82 [0.80, 0.84] for random forest) outcomes. Independent predictors of functional outcome selected by the algorithms as important included hematoma volume at hospital admission, hematoma expansion, intraventricular hemorrhage, overall ICH Score, and Glasgow Coma Scale. Hematoma expansion was the only potentially modifiable independent predictor of outcome and was compatible with "good" or "poor" outcome in a subset of patients with low hematoma volumes, good Glasgow Coma scale and premorbid modified Rankin Scale scores. Models trained on harmonized data also predicted patient outcomes well at Institution 2 using decision tree (AUC 0.69 [0.63, 0.75]) and random forests (AUC 0.78 [0.72, 0.84]). CONCLUSIONS: Patient outcomes are predictable to a high level in patients with ICH, and hematoma expansion is the sole-modifiable predictor of these outcomes across two outcome types and modeling approaches. According to decision tree analyses predicting outcome at 3 months, patients with a high Glasgow Coma Scale score, less than 44.5 mL hematoma volume at admission, and relatively low premorbid modified Rankin Score in particular have a modifiable outcome and appear to be candidates for future interventions to improve outcomes after ICH.

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