Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Delirium is a common but serious complication in critically ill patients. Family visitation has been shown to reduce delirium; however, during the coronavirus disease 2019 (COVID-19) pandemic, intensive care units (ICUs) restricted regular visitation to prevent the spread of infection. This study aimed to evaluate the association between visitation policies and incidence of delirium in the ICUs. Methods: This was a retrospective before-and-after study conducted in medical and surgical ICUs at a tertiary hospital. Adult patients admitted to an ICU during one of two periods were included: before the COVID-19 pandemic (June 2017 to May 2019) with regular visitation and during the pandemic (June 2020 to May 2022) with prohibited visitation. Delirium was assessed using the Confusion Assessment Method for the ICU. The primary outcome was association between delirium incidence and visitation policy. Results: Totals of 1,566 patients from the pre-COVID-19 period and 1,404 patients from the COVID-19 period were analyzed. The incidence of delirium was higher during the COVID-19 period (48.1% vs. 38.4%, P<0.001). After adjusting for relevant variables, the restricted visitation policy during COVID-19 remained a risk factor for delirium (odds ratio, 1.37; 95% CI, 1.13–1.65; P=0.001). Conclusions: Complete restriction of ICU visitations during the COVID-19 pandemic was associated with a significant increase in delirium incidence. These findings suggest the importance of visitation policies on patient outcomes and suggest the need for alternative strategies, such as video visitation, to mitigate the adverse effects of visitation restrictions during pandemics.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: Sarcopenia is a poor prognostic factor in non–small cell lung cancer (NSCLC). However, its prognostic significance in patients with NSCLC receiving immune checkpoint inhibitors (ICIs) and its relationship with lymphopenia remain unclear. We aimed to investigate the prognostic role of sarcopenia and its effect on lymphocyte recovery in patients with stage III NSCLC treated with concurrent chemoradiotherapy (CCRT) followed by ICI. Materials and Methods: We retrospectively evaluated 151 patients with stage III NSCLC who received definitive CCRT followed by maintenance ICI between January 2016 and June 2022. Sarcopenia was evaluated by measuring the skeletal muscle area at the L3 vertebra level using computed tomography scans. Lymphocyte level changes were assessed based on measurements taken before and during CCRT and at 1, 2, 3, 6, and 12 months post-CCRT completion. Results: Even after adjusting for baseline absolute lymphocyte count through propensity score-matching, patients with pre-radiotherapy (RT) sarcopenia (n=86) exhibited poor lymphocyte recovery and a significantly high incidence of grade ≥ 3 lymphopenia during CCRT. Pre-RT sarcopenia and grade ≥ 3 lymphopenia during CCRT emerged as prognostic factors for overall survival and progression-free survival, respectively. Concurrent chemotherapy dose adjustments, objective response after CCRT, and discontinuation of maintenance ICI were also analyzed as independent prognostic factors. Conclusion Our results demonstrated an association between pre-RT sarcopenia and poor survival, concurrent chemotherapy dose adjustments, and impaired lymphocyte recovery after definitive CCRT. Moreover, CCRT-induced lymphopenia not only contributed to poor prognosis but may have also impaired the therapeutic efficacy of subsequent maintenance ICI, ultimately worsening treatment outcomes.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Despite widespread coronavirus disease 2019 (COVID-19) vaccine use, research on the association between vaccines and cerebrovascular venous sinus thrombosis (CVST) in diverse populations is limited. This study aimed to address this gap. Data from the World Health Organization pharmacovigilance database (1968–2024; total reports = 8,909,484) were used.Reporting odds ratios (RORs) and information components (ICs) were calculated to assess the association between each drug and CVST. In total, 851 cases were identified as vaccineassociated CVST, of which 527 (61.93%) occurred in female patients. Only Ad5-vectored COVID-19 vaccines had the highest ROR and IC value with CVST (ROR, 4.78; 95% confidence interval, 4.34–5.28; IC, 2.15). The risk of CVST increased with age, with the 45–64-years age group having an IC of 1.35, while the 65 years and older group had a higher IC of 2.08.The findings highlight the need for clinicians to recognize the potential risks of CVST and prioritize rigorous monitoring and research to ensure patient safety.

Despite widespread coronavirus disease 2019 (COVID-19) vaccine use, research on the association between vaccines and cerebrovascular venous sinus thrombosis (CVST) in diverse populations is limited. This study aimed to address this gap. Data from the World Health Organization pharmacovigilance database (1968–2024; total reports = 8,909,484) were used.Reporting odds ratios (RORs) and information components (ICs) were calculated to assess the association between each drug and CVST. In total, 851 cases were identified as vaccineassociated CVST, of which 527 (61.93%) occurred in female patients. Only Ad5-vectored COVID-19 vaccines had the highest ROR and IC value with CVST (ROR, 4.78; 95% confidence interval, 4.34–5.28; IC, 2.15). The risk of CVST increased with age, with the 45–64-years age group having an IC of 1.35, while the 65 years and older group had a higher IC of 2.08.The findings highlight the need for clinicians to recognize the potential risks of CVST and prioritize rigorous monitoring and research to ensure patient safety.

Purpose: Sarcopenia is a poor prognostic factor in non–small cell lung cancer (NSCLC). However, its prognostic significance in patients with NSCLC receiving immune checkpoint inhibitors (ICIs) and its relationship with lymphopenia remain unclear. We aimed to investigate the prognostic role of sarcopenia and its effect on lymphocyte recovery in patients with stage III NSCLC treated with concurrent chemoradiotherapy (CCRT) followed by ICI. Materials and Methods: We retrospectively evaluated 151 patients with stage III NSCLC who received definitive CCRT followed by maintenance ICI between January 2016 and June 2022. Sarcopenia was evaluated by measuring the skeletal muscle area at the L3 vertebra level using computed tomography scans. Lymphocyte level changes were assessed based on measurements taken before and during CCRT and at 1, 2, 3, 6, and 12 months post-CCRT completion. Results: Even after adjusting for baseline absolute lymphocyte count through propensity score-matching, patients with pre-radiotherapy (RT) sarcopenia (n=86) exhibited poor lymphocyte recovery and a significantly high incidence of grade ≥ 3 lymphopenia during CCRT. Pre-RT sarcopenia and grade ≥ 3 lymphopenia during CCRT emerged as prognostic factors for overall survival and progression-free survival, respectively. Concurrent chemotherapy dose adjustments, objective response after CCRT, and discontinuation of maintenance ICI were also analyzed as independent prognostic factors. Conclusion Our results demonstrated an association between pre-RT sarcopenia and poor survival, concurrent chemotherapy dose adjustments, and impaired lymphocyte recovery after definitive CCRT. Moreover, CCRT-induced lymphopenia not only contributed to poor prognosis but may have also impaired the therapeutic efficacy of subsequent maintenance ICI, ultimately worsening treatment outcomes.