Adverse Pregnancy Outcomes in Patients with Polycystic Ovary Syndrome with Pre-Conceptional Hyperandrogenism: A Multi-Institutional Registry-Based Retrospective Cohort Study.

: Women with polycystic ovarian syndrome (PCOS) are at higher risk for pregnancy complications. The PCOS population is heterogeneous, with different phenotypes linked to varying risks of adverse outcomes. However, literature on pre-conceptional hyperandrogenism is limited and based on small sample sizes.

The added value of anosmic subtype on motor subtype in Parkinson's disease: a pilot study.

This study investigates whether incorporating olfactory dysfunction into motor subtypes of Parkinson's disease (PD) improves associations with clinical outcomes. PD is commonly divided into motor subtypes, such as postural instability and gait disturbance (PIGD) and tremor-dominant PD (TDPD), but non-motor symptoms like olfactory dysfunction remain underexplored. We assessed 157 participants with PD using the University of Pennsylvania Smell Identification Test (UPSIT), Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (M-UPDRS), Montreal Cognitive Assessment (MoCA), 39-item Parkinson's Disease Questionnaire Summary Index (PDQ-39 SI), and 99mTc-TRODAT-1 imaging.

Rhythm Control Better Prevents Stroke than Rate Control in Patients with Concomitant Hypertrophic Cardiomyopathy and Atrial Fibrillation: A Nationwide Population Based Cohort Study with Long-Term Follow-Up.

Background: Atrial fibrillation (AF) increases the risks of stroke and mortality. It remains unclear whether rhythm control reduces the risk of stroke in patients with AF concomitant with hypertrophic cardiomyopathy (HCM).

Methods: We identified AF patients with HCM who were ≥ 18 years old in the Taiwan National Health Insurance Database.

A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction.

Deep learning analysis of electrocardiography (ECG) may predict cardiovascular outcomes. We present a novel multi-task deep learning model, the ECG-MACE, which predicts the one-year first-ever major adverse cardiovascular events (MACE) using 2,821,889 standard 12-lead ECGs, including training (n = 984,895), validation (n = 422,061), and test (n = 1,414,933) sets, from Chang Gung Memorial Hospital database in Taiwan. Data from another independent medical center (n = 113,224) was retrieved for external validation.

Universal representations in cardiovascular ECG assessment: A self-supervised learning approach.

Background: The 12-lead electrocardiogram (ECG) is an established modality for cardiovascular assessment. While deep learning algorithms have shown promising results for analyzing ECG data, the limited availability of labeled datasets hinders broader applications. Self-supervised learning can learn meaningful representations from the unlabeled data and transfer the knowledge to downstream tasks.

A new birth weight chart and optimal birth weight percentiles for predicting infant mortality.

Prescriptive BW charts can facilitate discrimination between normal and abnormal birthweight. This study aimed to develop a prescriptive BW chart specific to Asian populations and assess its utility in predicting infant mortality. A retrospective cohort study was conducted using data from Taiwan National Health Insurance Research Database and National Birth Reporting Database.

Enhancing systematic review efficiency in hand surgery using artificial intelligence (natural language processing) for abstract screening.

The aim of the present study was to train a natural language processing model to recognize key text elements from research abstracts related to hand surgery, enhancing the efficiency of systematic review screening. A sample of 1600 abstracts from a systematic review of distal radial fracture treatment outcomes was annotated to train the natural language processing model. To assess time-saving potential, 200 abstracts were processed by the trained models in two experiments, where reviewers accessed natural language processing predictions to include or exclude articles.

Association between Semaglutide and Nonarteritic Anterior Ischemic Optic Neuropathy: A Multinational Population-Based Study.

Purpose: To investigate whether semaglutide increases the risk of nonarteritic anterior ischemic optic neuropathy (NAION) in the general population.

Design: This retrospective cohort study used a deidentified global electronic medical records database. The enrollment period was extended from January 2017 to August 2023, with observations concluding in August 2024.

Tinnitus and dementia risk: a nationwide population-based case-control study.

Objective: This study aimed to determine if a history of tinnitus is associated with the risk of developing dementia.

Method: A nationwide population-based case-control study including all eligible adults in Taiwan.

Results: A total of 15 686 patients were included in the study, with 7843 individuals making up each of the case and control groups.

Glycemic variability's impact on painful diabetic peripheral neuropathy in type 2 diabetes patients.

Hyperglycemia in type 2 diabetes leads to diabetic peripheral neuropathy (DPN) and neuropathic pain, yet the association between glycemic variability and painful DPN remains insufficiently evidenced. To address this, we conducted a prospective longitudinal cohort study involving adult type 2 diabetes patients at a medical center. DPN was identified using the Michigan Neuropathy Screening Instrument (MNSI), and neuropathic pain was assessed with the Taiwan version of the Douleur Neuropathique 4 (DN4-T) questionnaire.

Association of sodium-glucose cotransporter 2 inhibitors with risk of incident dementia and all-cause mortality in older patients with type 2 diabetes: A retrospective cohort study using the TriNetX US collaborative networks.

Background: Limited evidence exists to support any specific medication over others to prevent dementia in older patients with type 2 diabetes (T2D). We investigated whether treatment with sodium-glucose cotransporter 2 (SGLT-2) inhibitors is associated with a lower risk of incident dementia and all-cause mortality, relative to dipeptidyl peptidase-4 (DPP-4) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1 RA).

Methods: In this retrospective, active-comparator cohort study, we used data from the TriNetX electronic health records network.

Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers Is Associated With a Reduced Risk of Poststroke Epilepsy in Patients With Ischemic Stroke.

Background: Poststroke epilepsy (PSE) is a common complication after ischemic stroke. This study investigates the association between the use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) and the risk of PSE in patients with ischemic stroke.

Methods And Results: A population-based retrospective cohort study was conducted using Taiwan's National Health Insurance Research Database between 2000 and 2015.

Assessment of the impact of resting heart rate on the risk of major adverse cardiovascular events after ischemic stroke: a retrospective observational study.

Background: Although elevated heart rate is a risk factor for cardiovascular morbidity and mortality in healthy people, the association between resting heart rate and major cardiovascular risk in patients after acute ischemic stroke remains debated. This study evaluated the association between heart rate and major adverse cardiovascular events after ischemic stroke.

Methods: We conducted a retrospective cohort study analyzing data from the Chang Gung Research Database for 21,655 patients with recent ischemic stroke enrolled between January 1, 2010, and September 30, 2018.

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans.

Background: Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans.

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram.

Background: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling.

Methods: This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis.

Diabetic peripheral neuropathy: age-stratified glycemic control.

Background: We explore the effect of suboptimal glycemic control on the incidence of diabetic peripheral neuropathy (DPN) in both non-elderly and elderly patients with type 2 diabetes mellitus (T2DM).

Methods: A 6-year follow-up study (2013-2019) enrolled T2DM patients aged >20 without DPN. Participants were classified into two groups: those below 65 years (non-elderly) and those 65 years or older (elderly).

Polygenic Risk Score Predicts Earlier-Onset Adult Systemic Lupus Erythematosus and First-Year Renal Diseases in a Taiwanese Cohort.

Objectives: This study aimed to develop a predictive model using polygenic risk score (PRS) to forecast renal outcomes for adult systemic lupus erythematosus (SLE) in a Taiwanese population.

Methods: Patients with SLE (n=2782) and matched non-SLE controls (n=11 128) were genotyped using Genome-Wide TWB 2.0 single-nucleotide polymorphism (SNP) array.

Predicting ischemic stroke patients' prognosis changes using machine learning in a nationwide stroke registry.

Accurately predicting the prognosis of ischemic stroke patients after discharge is crucial for physicians to plan for long-term health care. Although previous studies have demonstrated that machine learning (ML) shows reasonably accurate stroke outcome predictions with limited datasets, to identify specific clinical features associated with prognosis changes after stroke that could aid physicians and patients in devising improved recovery care plans have been challenging. This study aimed to overcome these gaps by utilizing a large national stroke registry database to assess various prediction models that estimate how patients' prognosis changes over time with associated clinical factors.

The use of individual-based FDG-PET volume of interest in predicting conversion from mild cognitive impairment to dementia.

Background: Based on a longitudinal cohort design, the aim of this study was to investigate whether individual-based F fluorodeoxyglucose positron emission tomography (F-FDG-PET) regional signals can predict dementia conversion in patients with mild cognitive impairment (MCI).

Methods: We included 44 MCI converters (MCI-C), 38 non-converters (MCI-NC), 42 patients with Alzheimer's disease with dementia, and 40 cognitively normal controls. Data from annual cognitive measurements, 3D T1 magnetic resonance imaging (MRI) scans, and F-FDG-PET scans were used for outcome analysis.