This study was conducted to investigate the prevalence and associated factors of depression in medical students. This cross-sectional study investigated the prevalence and associated factors of depression in medical students from May 2018 to April 2019. Depression was diagnosed using the nine-item Patient Health Questionnaire. We evaluated the following potential predictors: demographic data, stressors, psychiatric comorbidities, emotional intelligence (EI), and perceived social support. The association between potential factors and depression was analyzed using multiple logistic regression analysis. The prevalence of depression was 149 of 706 students with 12.5% suicidality. Second- and fourth-year medical students were high-risk groups. Risk factors identified were insufficient income, physical illness, and previous psychiatric illness. Depression in medical students likely coincides with anxiety, internet addiction, sleep problems, and loneliness. Highly associated stressors were personal relationships, physical health, mental health, difficulties in social relationships, satisfaction with grades, and boredom with medical education. Protective EI factors included emotional self-control, problem-solving abilities, inner peace, and life satisfaction. Up to 21.1% of medical students had depression. In this study, among multiple known risk factors of depression, we found that EI is the novel protective factor against depression among medical students. EI training might be protective intervention for medical students in the future.

Background: Respiratory syncytial virus (RSV) infection is a significant burden for children under 5 years of age. While RSV is a common cause of acute lower respiratory tract infection (ALRTI), routine confirmatory testing is not widely practiced. Identifying risk factors for RSV infection can help inform clinical decision-making and guide appropriate management in emergency departments. The objective of this study is to determine the risk factors associated with RSV infection in children hospitalized with ALRTI. Methods: A retrospective case-control study was conducted among pediatric patients under 5 years of age diagnosed with ALRTI at Naresuan University Hospital between January 2018 and December 2020. Data were extracted from electronic medical records. RSV diagnoses were confirmed using a rapid RSV antigen test. Potential risk factors—including prematurity, family history of atopic disease, past respiratory illness, history of ALRTI contact, and underlying disease—were analyzed using a multivariable logistic model in Stata version 17.0. Results: Among 3,579 children hospitalized with ALRTI, 459 (13%) were diagnosed with RSV infection. Peaks in RSV-ALRTI were observed in September, October, and November across 2018–2020. Significant risk factors identified through multivariable logistic regression analysis included the rainy season [odds ratio (OR) =15.51; 95% confidence interval (CI): 1.74–138.42; P=0.01], daycare attendance (OR =3.00; 95% CI: 1.27–7.10; P=0.01), lung crepitations (OR =2.60; 95% CI: 1.23–5.50; P=0.01), and oxygen supplementation (OR =2.38; 95% CI: 1.07–5.30; P=0.03). Conclusions: This study highlights the significant clinical burden of RSV in ALRTI, with RSV-ALRTI accounting for 13% of all ALRTI cases. Notably, RSV-ALRTI cases peaked during the rainy season, specifically in September, October, and November. The identified risk factors for RSV-ALRTI include the rainy season, daycare attendance, lung crepitations, and the need for oxygen supplementation. These findings underscore the importance of early identification and management of high-risk patients, particularly during peak seasonal periods. Clinically, these risk factors can help guide targeted prevention and treatment strategies, improving patient outcomes and optimizing resource allocation in healthcare settings.

The aim of this study was to develop clinical predictor tools for guiding the use of computed tomography (CT) head scans in non-traumatic Thai patients presented with seizure. A prediction model using a retrospective cross-sectional design was conducted. We recruited adult patients (aged ≥ 18 years) who had been diagnosed with seizures by their physicians and had undergone CT head scans for further investigation. Positive CT head defined as the presence of any new lesion that related to the patient’s presented seizure officially reported by radiologist. A total of 9 candidate predictors were preselected. The prediction model was developed using a full multivariable logistic regression with backward stepwise elimination. We evaluated the model’s predictive performance in terms of its discriminative ability and calibration via AuROC and calibration plot. The application was then constructed based on final model. A total of 362 patients were included into the analysis which comprising of 71 patients with positive CT head findings and 291 patients with normal results. Six final predictors were identified including: Glasgow coma scale, the presence of focal neurological deficit, history of malignancy, history of CVA, Epilepsy, and the presence of alcohol withdrawal symptom. In terms of discriminative ability, the final model demonstrated excellent performance (AuROC of 0.82 (95% CI: 0.76–0.87)). The calibration plot illustrated a good agreement between observed and predicted risks. This prediction model offers a reliable tool for effectively reduce unnecessary use and instill confidence in supporting physicians in determining the need for CT head scans in non-traumatic patients with seizures.

Background: Febrile patients with tachycardia present diverse profiles that complicate triage. Although the Emergency Severity Index (ESI) is widely used in Thailand, inter-rater variability limits consistency. Machine learning (ML) may enhance reliability using routinely collected triage data. The objectives were to develop and evaluate ML models predicting ESI levels 1-3 in febrile tachycardic adults and identify the best model for clinical use. Methods: This diagnostic prediction study analyzed adults with fever (≥ 37.6 °C) and pulse rate > 100 beats per minute in the triage area of Lampang Hospital, Thailand, during June - August 2024. Patients with complete data were included, whereas referrals and expert-disagreement cases were excluded. Expert-assigned ESI levels were the outcome. Thirteen routinely collected triage variables were evaluated as candidate predictors. The dataset (n = 500) was randomly split 80:20 into development and testing sets. Random forest, extreme gradient boosting (XGBoost), and gradient boosting machine models were developed using five-fold cross-validation with class-weighting for imbalance correction. Performance was assessed using area under the receiver operating characteristic curve (AuROC), calibration, and confusion matrices, with attention to clinically relevant misclassification. Results: XGBoost demonstrated the best discrimination with AuROC values of 1.00 (confidence interval (CI): 0.99 - 1.00), 0.94 (CI: 0.89 - 0.98), and 0.97 (CI: 0.93 - 1.00) for ESI levels 1-3 in the test set. Calibration showed the lowest Brier scores, and misclassification was minimal, supporting strong predictive consistency across categories. Conclusions: XGBoost was selected for integration into the Smart ER system as the Thailand Triage Prediction System (TTPS), providing real-time prediction to enhance triage accuracy, support decision-making, and improve workflow.

Background: A previous study developed a predictive model for identifying positive findings on computed tomography (CT) brain imaging in patients with non-traumatic seizures. One key predictor was the Glasgow Coma Scale (GCS), categorized into three groups (≤ 8, 9 - 13, >13). However, in real-world practice, some bedridden patients have low baseline GCS, which may lead to overprediction and unnecessary CT imaging. The original model yielded a false positive of 16.6%, exceeding the predefined threshold of less than 15%. This study aimed to improve the model by replacing categorized GCS with GCS change from baseline to reduce false positives below 15%, while maintaining false negatives below 5%. Methods: This diagnostic predictive study included patients with non-traumatic seizures who underwent CT brain imaging at the emergency department between November 2019 and November 2023. The original predictors were retained, with GCS change from baseline replacing the categorized GCS. Multivariable logistic regression analysis was used to estimate multivariable odds ratios. Discriminative performance was assessed using the area under the receiver operating characteristic (AuROC) curve. A trade-off between sensitivity and specificity was applied to identify a probability cut-off that met the target for false positives and false negatives. The revised model was named the “SeizCT optimized model”. Results: Of the 625 patients included, 18.9% had positive CT findings. The majority were male (74.9%) with a mean age of 55 years. The SeizCT optimized model incorporated six predictors: prior stroke (> 3 months), current cancer, GCS change from baseline, alcohol withdrawal symptoms, epilepsy, and focal neurological deficit. The model demonstrated an AuROC of 0.8221 (95% confidence interval (CI): 0.7813, 0.8629). Using a threshold probability of 22.57%, it outperformed the original model (AuROC of 0.8156; 95% CI: 0.7598, 0.8746) with narrower confidence intervals. It achieved a false negative of 4.8% and a false positive of 14.4%. Conclusions: The SeizCT optimized model showed improved performance over the original tool, reducing both false positives and false negatives within the predefined thresholds. External validation is recommended.