Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.

Purpose To develop a highly generalizable weakly supervised model to automatically detect and localize image-level intracranial hemorrhage (ICH) by using study-level labels. Materials and Methods In this retrospective study, the proposed model was pretrained on the image-level Radiological Society of North America dataset and fine-tuned on a local dataset by using attention-based bidirectional long short-term memory networks. This local training dataset included 10 699 noncontrast head CT scans in 7469 patients, with ICH study-level labels extracted from radiology reports.

Phenotypes of Patients with Intracerebral Hemorrhage, Complications, and Outcomes.

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.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.

Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.

Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare.

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.

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.

External Validation of a Tool to Identify Low-Risk Patients With Isolated Subdural Hematoma and Preserved Consciousness.

Study Objective: The SafeSDH Tool was derived to identify patients with isolated (no other type of intracranial hemorrhage) subdural hematoma who are at very low risk of neurologic deterioration, neurosurgical intervention, or death. Patients are low risk by the tool if they have none of the following: use of anticoagulant or nonaspirin antiplatelet agent, Glasgow Coma Score (GCS) <14, more than 1 discrete hematoma, hematoma thickness >5 mm, or midline shift. We attempted to externally validate the SafeSDH Tool.

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.

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.

Patterns of diverse and changing sentiments towards COVID-19 vaccines: a sentiment analysis study integrating 11 million tweets and surveillance data across over 180 countries.

Objectives: Vaccines are crucial components of pandemic responses. Over 12 billion coronavirus disease 2019 (COVID-19) vaccines were administered at the time of writing. However, public perceptions of vaccines have been complex.

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.

Effect of Magnesium on Deterioration and Symptomatic Hemorrhagic Transformation in Cerebral Ischemia: An Ancillary Analysis of the FAST-MAG Trial.

Background: Magnesium (Mg) is a neuroprotectant in preclinical models. Lower serum Mg levels have been associated with symptomatic hemorrhagic transformation (HT) in patients with ischemic stroke. Early treatment of acute ischemic stroke with Mg may reduce rates of symptomatic HT.

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.

Comparison between machine learning methods for mortality prediction for sepsis patients with different social determinants.

Background: Sepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis patients can interfere with the risk prediction performances using machine learning.

Methods: We analyzed a cohort of critical care patients from the Medical Information Mart for Intensive Care (MIMIC)-III database.

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.

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.

The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform.

Objective: Learning healthcare systems use routinely collected data to generate new evidence that informs future practice. While implementing an electronic health record (EHR) system can facilitate this goal for individual institutions, meaningfully aggregating data from multiple institutions can be more empowering. Cosmos is a cross-institution, single EHR vendor-facilitated data aggregation tool.

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.

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.

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

Unlabelled: 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).

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.

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

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. 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.

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).