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/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/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/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.

INTRODUCTION: Adolescent depression is prevalent, and teen suicide rates are on the rise locally. A systemic review to understand associated risk and protective factors is important to strengthen measures for the prevention and early detection of adolescent depression and suicide in Singapore. This systematic review aims to identify the factors associated with adolescent depression in Singapore. METHODS: A systematic search on the following databases was performed on 21 May 2020: PubMed, EMBASE and PsycINFO. Full texts were reviewed for eligibility, and the included studies were appraised for quality using the Newcastle Ottawa Scale. Narrative synthesis of the finalised articles was performed through thematic analysis. RESULTS: In total, eight studies were included in this review. The four factors associated with adolescent depression identified were: (1) sociodemographic factors (gender, ethnicity); (2) psychological factors, including childhood maltreatment exposure and psychological constructs (hope, optimism); (3) coexisting chronic medical conditions (asthma); and (4) lifestyle factors (sleep inadequacy, excessive internet use and pathological gaming). CONCLUSION The identified factors were largely similar to those reported in the global literature, except for sleep inadequacy along with conspicuously absent factors such as academic stress and strict parenting, which should prompt further research in these areas. Further research should focus on current and prospective interventions to improve mental health literacy, targeting sleep duration, internet use and gaming, and mitigating the risk of depression in patients with chronic disease in the primary care and community setting.
Humans ; Singapore/epidemiology* ; Adolescent ; Risk Factors ; Depression/etiology* ; Protective Factors ; Male ; Female ; Life Style ; Suicide

Purpose: This study aims to prospectively analyze the effects of anticholinergic therapy using imidafenacin on detrusor overactivity occurring after robot-assisted radical prostatectomy (RARP). Materials and Methods: Patients were followed-up at outpatient visits 2–4 weeks post-surgery (visit 2) to confirm the presence of urinary incontinence. Those confirmed with urinary incontinence were randomly assigned in a 1:1 ratio to the anticholinergic medication group (imidafenacin 0.1 mg twice daily) or the control group. Patients were followed-up at 1, 3, and 6 months post-surgery for observational assessments, including the International Prostate Symptom Score (IPSS) and Overactive Bladder Symptom Score (OABSS). Results: A total of 49 patients (25 in the treatment group and 24 in the control group) were randomized for the study. There were no differences observed between the groups in terms of age, comorbidities, prostate size, or pathological staging. According to the IPSS questionnaire results, there was no statistically significant difference between the medication and control groups (p=0.161).However, when comparing storage and voiding symptoms separately, there was a statistically significant improvement in storage symptom scores (p=0.012). OABSS also revealed statistically significant improvement in symptoms from 3 months post-surgery (p=0.005), which persisted until 6 months post-surgery (IPSS storage: p=0.023, OABSS: p=0.013). Conclusions In the case of urinary incontinence that occurs after RARP, even if the function of the intrinsic sphincter is sufficiently preserved, if urinary incontinence persists due to changes in the bladder, pharmacological therapy using imidafenacin can be beneficial in managing urinary incontinence.

Background/Aims: The pathophysiology of lean nonalcoholic fatty liver disease (NAFLD) is unclear but has been shown to be associated with more diverse pathogenic mechanisms than that of obese NAFLD. We investigated the characteristics of genetic or metabolic lean NAFLD in a health checkup cohort. Methods: This retrospective cross-sectional study analyzed single nucleotide polymorphism data for 6,939 health examinees. Lean individuals were categorized according to a body mass index cutoff of 23 kg/m 2 . Single nucleotide polymorphisms were analyzed using genotyping arrays. Results: The prevalence of lean NAFLD was 21.6% among all participants with NAFLD, and the proportion of lean NAFLD was 18.5% among lean participants. The prevalence of metabolic syndrome and diabetes among lean patients with NAFLD was 12.4% and 10.4%, respectively.Lean NAFLD appeared to be metabolic-associated in approximately 20.1% of patients. The homozygous minor allele (GG) of PNPLA3 (rs738409) and heterozygous minor alleles (CT, TT) of TM6SF2 (rs58542926) were associated with lean NAFLD. However, the prevalence of fatty liver was not associated with the genetic variants MBOAT7 (rs641738), HSD17B13 (rs72613567), MARC1 (rs2642438), or AGXT2 (rs2291702) in lean individuals. Lean NAFLD appeared to be associated with PNPLA3 or TM6SF2 genetic variation in approximately 32.1% of cases. Multivariate risk factor analysis showed that metabolic risk factors, genetic risk variants, and waist circumference were independent risk factors for lean NAFLD. Conclusions In a considerable number of patients, lean NAFLD did not appear to be associated with known genetic or metabolic risk factors. Further studies are required to investigate additional risk factors and gain a more comprehensive understanding of lean NAFLD.

Background/Aims: Chronic hepatitis B (CHB) and fatty liver (FL) often co-exist, but natural history data of this dual condition (CHB-FL) are sparse. Via a systematic review, conventional meta-analysis (MA) and individual patient-level data MA (IPDMA), we compared liver-related outcomes and mortality between CHB-FL and CHB-no FL patients. Methods: We searched 4 databases from inception to December 2021 and pooled study-level estimates using a random- effects model for conventional MA. For IPDMA, we evaluated outcomes after balancing the two study groups with inverse probability treatment weighting (IPTW) on age, sex, cirrhosis, diabetes, ALT, HBeAg, HBV DNA, and antiviral treatment. Results: We screened 2,157 articles and included 19 eligible studies (17,955 patients: 11,908 CHB-no FL; 6,047 CHB-FL) in conventional MA, which found severe heterogeneity (I2=88–95%) and no significant differences in HCC, cirrhosis, mortality, or HBsAg seroclearance incidence (P=0.27–0.93). IPDMA included 13,262 patients: 8,625 CHB-no FL and 4,637 CHB-FL patients who differed in several characteristics. The IPTW cohort included 6,955 CHB-no FL and 3,346 CHB-FL well-matched patients. CHB-FL patients (vs. CHB-no FL) had significantly lower HCC, cirrhosis, mortality and higher HBsAg seroclearance incidence (all p≤0.002), with consistent results in subgroups. CHB-FL diagnosed by liver biopsy had a higher 10-year cumulative HCC incidence than CHB-FL diagnosed with non-invasive methods (63.6% vs. 4.3%, p<0.0001). Conclusions IPDMA data with well-matched CHB patient groups showed that FL (vs. no FL) was associated with significantly lower HCC, cirrhosis, and mortality risk and higher HBsAg seroclearance probability.