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/Objectives: Preventing recurrent anaphylaxis is crucial for patient safety. This study aimed to identify predictive factors and develop a prediction model to estimate recurrence risk, thereby enhancing targeted preventive strategies. Methods: This prognostic prediction study used a retrospective observational cohort design, analyzing medical records from an anaphylaxis registry at Naresuan University Hospital, Phitsanulok, Thailand, between March 2011 and February 2021. We developed a prediction model using multivariable Cox proportional hazards regression analysis. Statistically significant and clinically relevant predictors were weighted into a risk score derived from hazard ratio regression coefficients. Model performance was evaluated using the area under the receiver operating characteristic curve (AuROC), calibration metrics, and decision curve analysis. Results: Over the 10-year period, 381 patients experienced 439 anaphylaxis episodes, including 58 recurrences (13.2%). The final model comprised six predictors: history of food, insect, and drug allergies; asthma; chest discomfort; and severe anaphylaxis. Corresponding risk scores were 4, 5, 5.5, 1, 2.5, and 1.5 points, respectively. Total scores ranged from 0 to 19.5 and were categorized into low (9.0) risk groups. The high-risk group had a likelihood ratio positive (LHR+) of 4.65. The model demonstrated acceptable discrimination (AuROC 0.773 (95% CI: 0.714–0.832)) and good calibration. Bootstrap validation showed consistent performance (AuROC 0.773 (95% CI: 0.714–0.831)). Decision curve analysis indicated clinical utility across relevant threshold probabilities. Conclusions: This prediction model provides a simple, clinically applicable tool for estimating the risk of recurrent anaphylaxis and may support improved prevention and management strategies.

Background: Fungal sensitization is strongly associated with asthma severity and increased outdoor spore exposure correlates with higher asthmarelated hospitalization. However, data on outdoor airborne fungal spores in lower northern Thailand are limited. Objective: To assess changes in outdoor airborne mold spore patterns and counts over two distinct periods, ten years apart, in Phitsanulok, lower northern Thailand, to optimize allergic disease management. Materials and Methods: Mold spores were collected from August 2013 to July 2014 and from August 2022 to July 2023, using a Burkard seven-day volumetric spore trap positioned 15 meters above ground. Exposed slides were stained for microscopic analysis, and meteorological data were recorded. Results: Monthly outdoor airborne mold spore levels ranged from 336.5 to 939.6 spores/m³ (2013-2014) and 257.7 to 821.8 spores/m³ (2022-2023), with peaks in June (2013-2014) and May to June (2022-2023). Cladosporium spp. was the most abundant spore type in both periods. Rankings for the sixth to eleventh spore types were consistent, while the top five varied. Cladosporium spp. peaked in July 2014 and May to June 2023, approaching 500 spores/m³, a threshold for allergic risk. Overall, total spore concentrations were highest in the early rainy season, as rainfall increased from late April through June. In both periods, many spore types exhibited lower concentrations during months with the lowest relative humidity (March to April) and during peak rainfall (September). Conclusion: Cladosporium spp. accounted for the majority of outdoor mold spores. Many spore types increased at the onset of the rainy season and decreased during months with the lowest humidity.

Patients with end-stage kidney disease (ESKD) often exhibit suboptimal responses to COVID-19 vaccination. Tixagevimab-Cilgavimab, a neutralizing long-acting antibody (LAAB), has demonstrated effectiveness in preventing severe COVID-19 and hospitalization among immunocompromised populations. This study aimed to evaluate the efficacy and safety of Tixagevimab-Cilgavimab in ESKD patients receiving hemodialysis, peritoneal dialysis, or kidney transplantation. This single-center, retrospective cohort study was conducted at Naresuan University Hospital, Phitsanulok, Thailand, and included patients with end-stage kidney disease (ESKD) receiving maintenance hemodialysis, peritoneal dialysis, or kidney transplantation between June 2022 and June 2023, during the peak of the Omicron variant. Patients who received a single 150/150 mg dose of Tixagevimab-Cilgavimab were compared to those who did not, in terms of time to first COVID-19 infection and hospitalization within 6 months. Cox proportional hazards models were used to evaluate associations, adjusted for age, sex, type 2 diabetes, dyslipidemia, systolic and diastolic blood pressure, serum creatinine, number of COVID-19 vaccine doses, and prior COVID-19 infection. Safety was assessed by comparing creatine kinase (CK) levels before and after treatment using generalized estimating equations (GEE). Of 117 patients, 58 received Tixagevimab-Cilgavimab (mean age 59 ± 15 years); 92% were on dialysis and 8% had undergone kidney transplantation. COVID-19 infection occurred in 10.3% of the LAAB group versus 11.9% in the control group. In the adjusted Cox model, LAAB use was significantly associated with a reduced risk of COVID-19 infection (adjusted HR: 0.20; 95% CI: 0.04–0.95; p = 0.043). No variables were significantly associated with hospitalization, although LAAB use showed a non-significant trend toward reduced hospitalization risk (adjusted HR: 0.08; 95% CI: 0.01–1.56; p = 0.096). No local or systemic adverse effects were reported. CK levels remained unchanged after administration. Tixagevimab-Cilgavimab was effective in reducing the risk of COVID-19 infection among ESKD patients, without evidence of adverse effects, supporting its use as a prophylactic agent in this high-risk population.