Background: Delivering a poor prognosis to patients and their families is critically challenging in pediatric populations. The application of palliative care (PC) provides a bridge between accepting the occurrence of mortality and offering lifelong support.However, little is known about the specifics of PC. This study aims to explore the unmet need for PC in pediatric populations. Methods: We retrospectively reviewed the medical records of mortality cases in the Department of Pediatric Hematology and Oncology at Chang Gung Memorial Hospital. Statistical tests, including Chi-square and Student’s t-tests, were applied to determine the differences between early and late intervention groups in terms of the timing of PC introduction. Results: During the study period, 41 patients were included. Their median age was 11.8 years (IQR, 7.6-15.9). The majority of the disease statuses were refractory or relapsing (R/R). The incidence of memento application was significantly higher in the early intervention group (47.6% vs. 10%, P=0.0081). Vital signs variations tended to be end-of-life (EoL) indicators in this study. Conclusion The early introduction of PC encourages families to accompany their beloved child. EoL signs in the pediatric population include vital sign variations. With the presence of relevant EoL signs, clinical physicians can apply PC earlier to meet the needs.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: This study aimed to report the results of Korean Antimicrobial Resistance Monitoring System (KARMS) for uncomplicated cystitis (UC) in 2023. Materials and Methods: KARMS was established for the surveillance of antimicrobial resistance in urinary tract infections with the cooperation of Korean nationwide medical centers. Data from patients with UC have been collected in the web-based KARMS database. Demographic data, uropathogen distribution, and antimicrobial susceptibility of representative pathogens were analyzed. Results: A total of 885 patients’ data were collected in KARMS database. The mean patient age was 56.39±18.26 years. The number of postmenopausal and recurrent cystitis were 530 (61.1%) and 102 (11.5%), respectively. Escherichia coli was the most frequently identified uropathogen (654/871, 75.1%). Regarding antimicrobial susceptibility, 94.9% were susceptible to fosfomycin, 90.5% to nitrofurantoin, 58.4% to ciprofloxacin, 83.6% to cefotaxime, and 100.0% to ertapenem. ESBL positivity was 13.7% (96/702), and significantly higher in tertiary hospital (23.1%, p<0.001), postmenopausal (15.9%, p=0.044), and recurrent cystitis (24.7%, p=0.001).Fluoroquinolone resistance was significantly higher in tertiary hospital (47.4%, p=0.001), postmenopausal (44.9%, p<0.001), and recurrent cystitis (59.8%, p<0.001). In addition, postmenopausal (odds ratio [OR] 1.96, 95% confidence interval [CI] 1.38–2.77, p<0.001) and recurrent cystitis (OR 2.37, 95% CI 1.44–3.92, p=0.001) were associated with increased fluoroquinolone resistance. Conclusions These data provide information on the distribution of uropathogen and the status of antimicrobial resistance in UC of South Korea. In addition, KARMS will be a useful reference in the future through the continuous surveillance system construction over the years.

Background: Delivering a poor prognosis to patients and their families is critically challenging in pediatric populations. The application of palliative care (PC) provides a bridge between accepting the occurrence of mortality and offering lifelong support.However, little is known about the specifics of PC. This study aims to explore the unmet need for PC in pediatric populations. Methods: We retrospectively reviewed the medical records of mortality cases in the Department of Pediatric Hematology and Oncology at Chang Gung Memorial Hospital. Statistical tests, including Chi-square and Student’s t-tests, were applied to determine the differences between early and late intervention groups in terms of the timing of PC introduction. Results: During the study period, 41 patients were included. Their median age was 11.8 years (IQR, 7.6-15.9). The majority of the disease statuses were refractory or relapsing (R/R). The incidence of memento application was significantly higher in the early intervention group (47.6% vs. 10%, P=0.0081). Vital signs variations tended to be end-of-life (EoL) indicators in this study. Conclusion The early introduction of PC encourages families to accompany their beloved child. EoL signs in the pediatric population include vital sign variations. With the presence of relevant EoL signs, clinical physicians can apply PC earlier to meet the needs.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: Delivering a poor prognosis to patients and their families is critically challenging in pediatric populations. The application of palliative care (PC) provides a bridge between accepting the occurrence of mortality and offering lifelong support.However, little is known about the specifics of PC. This study aims to explore the unmet need for PC in pediatric populations. Methods: We retrospectively reviewed the medical records of mortality cases in the Department of Pediatric Hematology and Oncology at Chang Gung Memorial Hospital. Statistical tests, including Chi-square and Student’s t-tests, were applied to determine the differences between early and late intervention groups in terms of the timing of PC introduction. Results: During the study period, 41 patients were included. Their median age was 11.8 years (IQR, 7.6-15.9). The majority of the disease statuses were refractory or relapsing (R/R). The incidence of memento application was significantly higher in the early intervention group (47.6% vs. 10%, P=0.0081). Vital signs variations tended to be end-of-life (EoL) indicators in this study. Conclusion The early introduction of PC encourages families to accompany their beloved child. EoL signs in the pediatric population include vital sign variations. With the presence of relevant EoL signs, clinical physicians can apply PC earlier to meet the needs.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.