Headache disorder is not only a common complaint but also a global burden. Pharmacotherapeutic and non-pharmacotherapeutic approaches have been developed for its treatment and prophylaxis. The present study included a systematic review of psychological treatments for primary headache disorder accessible in Korea. We included English and Korean articles from EMBASE, MEDLINE, Cochrane library database, SCOPUS, ScienceDirect, Web of Science, CINAHL, PsycArticles and Korean database, KoreaMed and KMBASE which studied primary headache and medication-overuse headache. The primary efficacy measure was the number of headache days per month, while secondary efficacy measures were the number of headache attacks per week, headache index, treatment response rate, and migraine disability assessment. The meta-analysis was performed using R 3.5.1. to obtain pooled mean difference and pooled relative risk with 95% confidence interval (CI) for continuous data and dichotomous data, respectively. From 12,773 identified articles, 27 randomized clinical trials were identified. Primary outcome showed significant superiority of psychological treatments (pooled mean difference = − 0.70, 95% CI [− 1.22, − 0.18]). For the secondary outcomes, the number of headache attacks (pooled mean difference = − 1.15, 95% CI [− 1.63, − 0.67]), the headache index (pooled mean difference = − 0.92, 95% CI [− 1.40 to − 0.44]) and the treatment response rate (pooled relative risk = 3.13, 95% CI [2.24, 4.37]) demonstrated significant improvements in the psychological treatment group over the control group. Psychological treatments for primary headache disorder reduced headache frequency and the headache index. Future research using standardized outcome measures and strategies for reducing bias is needed.

Background/Objectives: Data availability can affect the performance of AI-based early warning scores (EWSs). This study evaluated how the extent of missing data and imputation strategies influence the predictive performance of the VitalCare–Major Adverse Event Score (VC-MAES), an AI-based EWS that uses last observation carried forward and normal-value imputation for missing values, to forecast clinical deterioration events, including unplanned ICU transfers, cardiac arrests, or death, up to 6 h in advance. Methods: We analyzed real-world data from 6039 patient encounters at Keimyung University Dongsan Hospital, Republic of Korea. Performance was evaluated under three scenarios: (1) using only vital signs and age, treating all other variables as missing; (2) reintroducing a full set of real-world clinical variables; and (3) imputing missing values drawn from a distribution within one standard deviation of the observed mean or using Multiple Imputation by Chained Equations (MICE). Results: VC-MAES achieved the area under the receiver operating characteristic curve (AUROC) of 0.896 using only vital signs and age, outperforming traditional EWSs, including the National Early Warning Score (0.797) and the Modified Early Warning Score (0.722). Reintroducing full clinical variables improved the AUROC to 0.918, whereas mean-based imputation or MICE decreased the performance to 0.885 and 0.827, respectively. Conclusions: VC-MAES demonstrates robust predictive performance with limited inputs, outperforming traditional EWSs. Incorporating actual clinical data significantly improved accuracy. In contrast, mean-based or MICE imputation yielded poorer results than the default normal-value imputation, potentially due to disregarding the “informative presence” embedded in missing data patterns. These findings underscore the importance of understanding missingness patterns and employing imputation strategies that consider the decision-making context behind data availability to enhance model reliability.

Background: Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea. Methods: Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold. Results: Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001). Conclusions: The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

Background: In hospitals, Code Blue is an emergency that refers to a patient requiring immediate resuscitation. Over 85% of patients with cardiopulmonary arrest exhibit abnormal vital sign trends prior to the event. Continuous monitoring and accurate interpretation of clinical data through artificial intelligence (AI) models can contribute to preventing critical events. Objective: This study aims to evaluate changes in clinical outcomes following the use of VitalCare (Major Adverse Event Score and Mortality Score), which is an AI-based early warning system, and to validate the performance of the algorithm. Methods: A retrospective analysis was conducted by extracting electronic health record data, using a total of 30,785 inpatient cases from general wards and intensive care units. A comparative analysis was performed by setting a 3-month period before and after the system implementation. For clinical evaluation, we measured the incidence rates of Code Blue and adverse events, the proportion of prolonged hospitalization, and the frequency of early interventions. The area under the receiver operating characteristic curve (AUROC) was calculated to assess the performance of the algorithm. Results: This study demonstrated that, following the implementation of VitalCare, there was a 24.97% reduction in major events such as Code Blue (P=.004) and the proportion of prolonged hospitalization in general wards (P<.05), along with a significant increase in the rate of early interventions. The model performance exhibited superior outcomes compared with traditional scoring systems, with a Major Adverse Event Score AUROC of 0.865 (95% CI 0.857‐0.873) and Mortality Score AUROC of 0.937 (95% CI 0.931‐0.944). Conclusions: A well-developed AI-based model that provides high predictive power can contribute to the prevention of major in-hospital events by providing early predictive information to clinicians. Additionally, it plays a crucial role in effectively addressing unmet needs and challenges in terms of human resources and practical procedures.

Background: This retrospective study introduces an AI-driven VitalCare-Major Adverse Event Score (VC-MAES) developed to predict major in-hospital adverse events and determine optimal cutoff thresholds. VC-MAES was originally developed to predict a composite outcome including unplanned intensive care unit (ICU) transfer, in-hospital cardiac arrest, and mortality; the present study evaluates its performance for the composite of unplanned ICU transfer and in-hospital cardiac arrest. Methods: Patients aged ≥19 years in Yongin Severance Hospital between 1 March 2020, and 31 December 2022, were included. The primary outcome was clinica l deterioration, defined as unplanned ICU transfer or in-hospital cardiac arrest. Secondary outcomes included model performance metrics (area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, F1 score) and optimal alarm frequency (number of alarms per 100 patient-days) across cutoff determination methods (Youden’s index, F1 score, Euclidean distance, and alarm-based approach); they were used to determine optimal cutoff values. 20 July 2026. Results: VC-MAES achieved an area under the ROC curve of 0.895(full evaluable sample) at 6-h intervals, outperforming traditional early warning systems. It exhibited improved sensitivity (0.5853) compared to NEWS (0.3775) and MEWS (0.3049) while maintaining a specificity exceeding 0.95, balancing alarm frequency and predictive accuracy (all figures based on each model’s own full evaluable sample: VC-MAES, n = 7425 events; NEWS, n = 3494; MEWS, n = 3198). On the common prediction time-point sample (n = 1678 events), the alarm-based cutoff yielded a sensitivity of 0.5244 for VC-MAES versus 0.2271 for NEWS and 0.1740 for MEWS, with alarm frequencies of 9.67, 7.07, and 5.58 per 100 patient-days, respectively. When compared on a common set of prediction time-points, VC-MAES (AUROC 0.832 on the common sample, 95% CI: 0.822–0.842) significantly outperformed NEWS (AUROC 0.743, 95% CI: 0.732–0.756; DeLong Z = −14.19, p < 0.0001) and MEWS (AUROC 0.706, 95% CI: 0.693–0.720; DeLong Z = −18.22, p < 0.0001). ROC-based methods, such as Youden’s index and Euclidean distance, were deemed unsuitable for practical clinical use. Conclusions: Conventional ROC-based cutoff selection methods may not be suitable for real-world clinical implementation due to excessive alarm burden. An alarm-based cutoff approach provides a more clinically applicable strategy by balancing predictive performance with manageable alarm frequency. These findings highlight the importance of incorporating clinical usability into cutoff determination for AI-based early warning systems.

Background: AI-based models for predicting mortality have shown potential for intensive care unit (ICU) patients, but evidence regarding their cost-effectiveness remains limited. Objective: This study aimed to evaluate the population-level cost-utility of an AI-based mortality prediction strategy activated during ICU admission in Korea. Methods: A lifetime Markov model followed a hypothetical cohort of Korean adults from age 19 years in the general-population state to capture ICU admissions, including recurrent ICU admissions, occurring over the lifetime horizon. AI-based mortality risk monitoring and its implementation cost were applied only when an individual entered the ICU state. Model inputs were derived from national claims data, published utility estimates, and cost sources. Outcomes were expressed as incremental cost-effectiveness ratios (ICERs) in Korean won (KRW) per quality-adjusted life year (QALY). Deterministic and probabilistic sensitivity analyses were conducted. Results: The AI-assisted strategy yielded an ICER of 8.37 million KRW (approximately US $6200) per QALY, below the societal willingness-to-pay (WTP) threshold of 40 million KRW (approximately US $29,600) per QALY in Korea. The incremental costs and QALYs were lifetime expected values per member of the general-population starting cohort rather than outcomes per directly monitored ICU admission. Probabilistic sensitivity analysis showed an 83.94% probability of cost-effectiveness at the threshold. One-way sensitivity analysis identified the true-positive rate, AI implementation cost, and postdischarge well-state utility value as the most influential parameters. Additional claims-based analyses indicated that ICU transition pathways and patient age were strongly associated with survival outcomes. Conclusions: Under modeled assumptions, AI-assisted ICU mortality prediction showed potential cost-effectiveness compared with usual care in the Korean critical care setting. These findings should be interpreted as decision-analytic estimates rather than direct evidence that the AI system reduces ICU mortality in real patients. Prospective real-world evaluations are needed to confirm clinical effectiveness and implementation value.