Hospital-Based Medicine

Exploring key risk factors for loss to follow-up after hospitalization for acute stroke.

BACKGROUND: A significant proportion of stroke patients are lost to follow-up (LTFU) after discharge, which may increase risks of morbidity, mortality, and unnecessary hospitalization. We aimed to identify predictors of post-discharge LTFU in acute stroke patients from a large academic hospital system. METHODS: Using the American Heart Association's Get With the Guidelines registry, we conducted a...

Early hemodynamic phenotyping in sepsis using transthoracic echocardiography: A proof-of-concept study in a north African ICU.

BACKGROUND: Cardiovascular dysfunction in sepsis is heterogeneous and contributes to poor outcomes. Hemodynamic phenotyping may delineate pathophysiologically distinct subgroups with implications for prognosis and individualized management. No studies have evaluated such phenotypes in African intensive care units (ICUs). We aimed to explore cardiovascular phenotypes in patients with sepsis using c...

Impact of GPT-4-Generated Discharge Letters on Patients' Medical Comprehension: Prospective Crossover Study.

BACKGROUND: Patients often struggle to understand standard hospital discharge letters, increasing the risk of medication errors and misunderstandings....

Patient perceptions of artificial intelligence in ophthalmology: a cross-sectional survey study.

BACKGROUND/AIMS: Patients have largely been excluded from discussions on the use of their health data in developing medical artificial intelligence (A...

An integrated random forest- and LASSO-derived nomogram for predicting postoperative nosocomial infections in colorectal cancer patients.

Ryanodine receptor 2 mutations in catecholaminergic polymorphic ventricular tachycardia: From molecular mechanisms to precision medicine.

Catecholaminergic polymorphic ventricular tachycardia is a classic example of the successful transfer of genetic cardiology from gene discovery to imp...

Interpretable machine learning for chronic kidney disease prediction: Insights from SHAP and LIME analyses.

Chronic kidney disease (CKD) is a progressive condition requiring early detection for optimal patient outcomes. This study developed an interpretable ...

Comprehensive assessment of postoperative metachronous liver metastasis risk in colon cancer based on inflammatory indicators: a multicenter prospective study.

OBJECTIVE: Insidiousness is a hallmark of metachronous liver metastasis. Owing to the absence of a comprehensive machine-learning model integrating sy...

Stratified Causal Inference for Intensive Care Unit Risk Prediction: Informatics-Based Modeling of Anesthetic Drug Combinations.

BACKGROUND: Postoperative intensive care unit (ICU) admission affects 15% to 20% of surgical patients and represents a major source of morbidity and h...

Assessment of the Modified Rankin Scale in Electronic Health Records With a Fine-Tuned Large Language Model: Development and Internal Validation.

BACKGROUND: The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observat...

Multi-scale kernel and electrode attention network for EEG-based epileptic seizure detection.

Epilepsy is a common chronic neurological disorder, and automated detection of epileptic seizures using multi-channel electroencephalography (EEG) is ...

[Utilizing machine learning and SHAP analysis to develop a prognostic survival prediction model for gastric cancer patients following radical gastrectomy].

Objective: To construct a prediction model and website for the overall survival (OS) of gastric cancer patients after radical gastrectomy based on SHa...

Explainable multimodal deep learning models for variable-length sequences in critically ill patients.

OBJECTIVE: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record...

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BACKGROUND AND AIM: Over recent decades, formal requirements for medical records have been strengthened, for example through patients' rights of acces...

Machine Learning Models for Mortality Prediction in Intensive Care Unit Patients With Ischemic Stroke Associated With Intracranial Artery Stenosis: Retrospective Cohort Study.

BACKGROUND: Mortality prediction in intensive care unit (ICU) patients with ischemic stroke complicated by intracranial artery stenosis or occlusion r...

Non-pharmacological treatment of AECOPD.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) are acute events characterized by rapid worsening of dyspnea, cough, and sputum...

The prognostic value of the PALBI score for mortality prediction in stroke: an internal and external validation study.

Recent studies show that there's a link between liver problems and how well someone does after having a stroke. The platelet-albumin-bilirubin (palbi)...

Protocol for the development and validation of a questionnaire to evaluate user-robot interaction in the compounding of hazardous drugs in a hospital setting.

INTRODUCTION: The automation of hazardous drug preparation in hospitals using robotic systems is an effective strategy to enhance safety, quality, and...

Improved outcomes and donor utilization in heart transplantation with 10 °C static cold storage.

OBJECTIVE: To compare 10 °C static cold storage (SCS) with traditional ice for cardiac allograft preservation, its impact on post-transplant outcomes,...

Effective connectivity of implicit emotion processing in adolescent psychiatric inpatients: Dynamic causal modeling across bipolar disorder and other psychopathologies.

BACKGROUND: Pediatric bipolar disorder(BD) is difficult to distinguish from other psychiatric disorders, a challenge which can result in delayed or in...