Deep learning models using intracranial and scalp EEG for predicting sedation level during emergence from anaesthesia.

Background: Maintaining an appropriate depth of anaesthesia is important for avoiding adverse effects from undermedication or overmedication during surgery. Electroencephalography (EEG) has become increasingly used to achieve this balance. Investigating the predictive power of intracranial EEG (iEEG) and scalp EEG for different levels of sedation could increase the utility of EEG monitoring.

PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications.

While medication intake is common among pregnant women, medication safety remains underexplored, leading to unclear guidance for patients and healthcare professionals. PregMedNet addresses this gap by providing a multifaceted maternal medication safety framework based on systematic analysis of 1.19 million mother-baby dyads from U.

Patterns of clinical and imaging presentations in patients with spontaneous intracranial hypotension due to spinal cerebrospinal fluid venous fistula: A single-center retrospective cross-sectional study.

Objective: To identify distinct clinical or imaging subtypes of spontaneous intracranial hypotension (SIH) due to spinal cerebrospinal fluid (CSF) venous fistula (CVF).

Background: Spontaneous intracranial hypotension is classically understood to present clinically with an orthostatic headache and stereotyped brain magnetic resonance imaging (MRI) findings; however, most prior literature examining clinical and brain MRI features of SIH has focused on all types of spinal CSF leaks concurrently. This study aimed to evaluate whether data support the possibility of internally consistent subtypes based on brain imaging features and clinical symptoms analogous to those seen in primary headache syndromes.

Artificial Intelligence in Perioperative Care: Opportunities and Challenges.

Artificial intelligence (AI) applications have great potential to enhance perioperative care. This paper explores promising areas for AI in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.

Long-Term Epidural Patching Outcomes and Predictors of Benefit in Patients With Suspected CSF Leak Nonconforming to ICHD-3 Criteria.

Background And Objectives: Spinal CSF leaks lead to spontaneous intracranial hypotension (SIH). While International Classification of Headache Disorders, Third Edition (ICHD-3) criteria necessitate imaging confirmation or low opening pressure (OP) for SIH diagnosis, their sensitivity may be limited. We offered epidural blood patches (EBPs) to patients with symptoms suggestive of SIH, with and without a documented low OP or confirmed leak on imaging.

EEG response of dexmedetomidine during drug induced sleep endoscopy.

Introduction: Dexmedetomidine is one of the anesthetics of choice for drug induced sleep endoscopy (DISE), with advantages including limited respiratory depression, analgesia, and decreased incidence of emergence delirium. However, challenges with determining sedation levels and prolonged recovery have limited its usage. An improved understanding of the effect of dexmedetomidine on the level of sedation and the corresponding electroencephalographic (EEG) changes could help overcome these barriers.

Evaluation of patient state index, bispectral index, and entropy during drug induced sleep endoscopy with dexmedetomidine.

Multiple electroencephalographic (EEG) monitors and their associated EEG markers have been developed to aid in assessing the level of sedation in the operating room. While many studies have assessed the response of these markers to propofol sedation and anesthetic gases, few studies have compared these markers when using dexmedetomidine, an alpha-2 agonist. Fifty-one patients underwent drug induced sleep endoscopy with dexmedetomidine sedation.

Distinct clinical phenotypes for Crohn's disease derived from patient surveys.

Background: Defining clinical phenotypes provides opportunities for new diagnostics and may provide insights into early intervention and disease prevention. There is increasing evidence that patient-derived health data may contain information that complements traditional methods of clinical phenotyping. The utility of these data for defining meaningful phenotypic groups is of great interest because social media and online resources make it possible to query large cohorts of patients with health conditions.

Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease.

For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices.

Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Background: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitoring data from cardiac implantable electronic devices, we sought to evaluate if machine learned signatures of AF burden could provide prognostic information on near-term risk of stroke when compared to conventional risk scores.

Methods And Results: We retrospectively identified Veterans Health Administration serviced patients with cardiac implantable electronic device remote monitoring data and at least one day of device-registered AF.

Mendelian Disease Associations Reveal Novel Insights into Inflammatory Bowel Disease.

Background: Monogenic diseases have been shown to contribute to complex disease risk and may hold new insights into the underlying biological mechanism of Inflammatory Bowel Disease (IBD).

Methods: We analyzed Mendelian disease associations with IBD using over 55 million patients from the Optum's deidentified electronic health records dataset database. Using the significant Mendelian diseases, we performed pathway enrichment analysis and constructed a model using gene expression datasets to differentiate Crohn's disease (CD), ulcerative colitis (UC), and healthy patient samples.

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.

Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the O6-methylguanine methyltransferase (MGMT) gene may impact the efficacy and sensitivity of temozolomide, and hence may affect overall patient survival.

A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease.

Summary: Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype.

Identification of UT-A1- and AQP2-interacting proteins in rat inner medullary collecting duct.

The urea channel UT-A1 and the water channel aquaporin-2 (AQP2) mediate vasopressin-regulated transport in the renal inner medullary collecting duct (IMCD). To identify the proteins that interact with UT-A1 and AQP2 in native rat IMCD cells, we carried out chemical cross-linking followed by detergent solubilization, immunoprecipitation, and LC-MS/MS analysis of the immunoprecipitated material. The analyses revealed 133 UT-A1-interacting proteins and 139 AQP2-interacting proteins, each identified in multiple replicates.

Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system.

Objective: As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports.

Methods: FDA safety evaluators assessed 326 reports for medication-related causality.

Imputing gene expression to maximize platform compatibility.

Unlabelled: Microarray measurements of gene expression constitute a large fraction of publicly shared biological data, and are available in the Gene Expression Omnibus (GEO). Many studies use GEO data to shape hypotheses and improve statistical power. Within GEO, the Affymetrix HG-U133A and HG-U133 Plus 2.

Predicting hospital visits from geo-tagged Internet search logs.

The steady rise in healthcare costs has deprived over 45 million Americans of healthcare services (1, 2) and has encouraged healthcare providers to look for opportunities to improve their operational efficiency. Prior studies have shown that evidence of healthcare seeking intent in Internet searches correlates well with healthcare resource utilization. Given the ubiquitous nature of mobile Internet search, we hypothesized that analyzing geo-tagged mobile search logs could enable us to machine-learn predictors of future patient visits.

Beat-to-beat spatiotemporal variability in the T vector is associated with sudden cardiac death in participants without left ventricular hypertrophy: the Atherosclerosis Risk in Communities (ARIC) Study.

Background: Despite advances in prevention and treatment of cardiovascular disease, sudden cardiac death (SCD) remains a clinical challenge. Risk stratification in the general population is needed.

Methods And Results: Beat-to-beat spatiotemporal variability in the T vector was measured as the mean angle between consecutive T-wave vectors (mean TT' angle) on standard 12-lead ECGs in 14 024 participants in the Atherosclerosis Risk in Communities (ARIC) study.

Repolarization lability measured on 10-second ECG by spatial TT' angle: reproducibility and agreement with QT variability.

Background: Reproducibility of spatial TT' angle on the 10-second ECG and its agreement with QT variability has not been previously studied.

Methods: We analyzed 2 randomly selected 10-second segments within 3-minute resting orthogonal ECG in 172 healthy IDEAL study participants (age 38.1±15.

Mechanical alternans is associated with mortality in acute hospitalized heart failure: prospective mechanical alternans study (MAS).

Background: Acute hospitalized heart failure (AHHF) is associated with 40% to 50% risk of death or rehospitalization within 6 months after discharge. Timely (before hospital discharge) risk stratification of patients with AHHF is crucial. We hypothesized that mechanical alternans (MA) and T-wave alternans (TWA) are associated with postdischarge outcomes in patients with AHHF.

Comparison of sum absolute QRST integral, and temporal variability in depolarization and repolarization, measured by dynamic vectorcardiography approach, in healthy men and women.

Background: Recently we showed the predictive value of sum absolute QRST integral (SAI QRST) and repolarization lability for risk stratification of sudden cardiac death (SCD) in heart failure patients. The goal of this study was to compare SAI QRST and metrics of depolarization and repolarization variability in healthy men and women.

Methods: Orthogonal ECGs were recorded at rest for 10 minutes in 160 healthy men and women (mean age 39.

ECG marker of adverse electrical remodeling post-myocardial infarction predicts outcomes in MADIT II study.

Background: Post-myocardial infarction (MI) structural remodeling is characterized by left ventricular dilatation, fibrosis, and hypertrophy of the non-infarcted myocardium.

Objective: The goal of our study was to quantify post-MI electrical remodeling by measuring the sum absolute QRST integral (SAI QRST). We hypothesized that adverse electrical remodeling predicts outcomes in MADIT II study participants.

QT variability paradox after premature ventricular contraction in patients with structural heart disease and ventricular arrhythmias.

Background: Increased repolarization lability is known to be associated with the risk of ventricular tachycardia (VT)/ventricular fibrillation (VF). Premature ventricular contractions (PVCs) are excluded from the analysis of QT variability. However, QT dynamics after PVCs is poorly understood.

Intracardiac J-point elevation before the onset of polymorphic ventricular tachycardia and ventricular fibrillation in patients with an implantable cardioverter-defibrillator.

Background: The clinical importance of the J-point elevation on electrocardiogram is controversial.

Objective: To study intracardiac J-point amplitude before ventricular arrhythmia.

Methods: Baseline 12-lead electrocardiogram and far-field right ventricular intracardiac implantable cardioverter-defibrillator electrograms were recorded at rest in 494 patients (mean age 60.