Objective: The Penn Alcohol Craving Scale (PACS) is a five-item, single-dimension questionnaire that is used to measure a patient’s alcohol craving. We sought to develop the Chinese version of the PACS (PACS-C) and assess its reliability and validity. Methods: A total of 160 Taiwanese patients with alcohol use disorder were enrolled in this study. The internal consistency and concurrent validity of the PASC-C with the visual analogue scale (VAS) for craving, the Yale–Brown Obsessive Compulsive Scale for heavy drinking (YBOCS-hd), and the Severity of Alcohol Dependence Questionnaire (SADQ) were assessed. The test–retest reliability of the PASC-C was evaluated 1 day after the baseline measurements. Confirmatory factor analysis (CFA) was performed to examine the psychometric properties of the PACS-C. Results: The PACS-C exhibited good internal consistency (Cronbach’s α=0.95) and test–retest reliability (r=0.97). This scale showed high correlations with the VAS (r=0.81) and YBOCS-hd (r=0.81 and 0.79 for the obsession and compulsion subscales, respectively), and moderate correlation with the SADQ-C (r=0.47). Furthermore, CFA results revealed that the PACS-C had good fit indices under various models. Conclusion The PACS-C appears to be a reliable and valid tool for assessing alcohol craving in patients with alcohol use disorder in Taiwan.

Objective: Previous studies have shown that certain severe mental illnesses (SMIs) increase the risk of dementia, but those that increase the risk to a greater degree in comparison with other SMIs are unknown. Furthermore, physical illnesses may alter the risk of developing dementia, but these cannot be well-controlled. Methods: Using the Taiwan National Health Insurance Research Database, patients with schizophrenia, bipolar disorder and major depressive disorder (MDD) were recruited. We also recruited normal healthy subjects as the control group.All subjects were aged over 60 years, and the duration of follow-up was from 2008 to 2015. Multiple confounders were adjusted, including physical illnesses and other variables. Use of medications, especially benzodiazepines, was analyzed in a sensitivity analysis. Results: 36,029 subjects (MDD: 23,371, bipolar disorder: 4,883, schizophrenia: 7,775) and 108,084 control subjects were recruited after matching according to age and sex. The results showed that bipolar disorder had the highest hazard ratio (HR) (HR: 2.14, 95% confidence interval [CI]: 1.99−2.30), followed by schizophrenia (HR: 2.06, 95% CI: 1.93−2.19) and MDD (HR: 1.60, 95% CI: 1.51−1.69). The results remained robust after adjusting for covariates, and sensitivity analysis showed similar results. Anxiolytics use did not increase the risk of dementia in any of the three groups of SMI patients. Conclusion SMIs increase the risk of dementia, and among them, bipolar disorder confers the greatest risk of developing dementia. Anxiolytics may not increase the risk of developing dementia in patients with an SMI, but still need to be used with caution in clinical practices.

Background/Aims: A high-quality sample allows for next-generation sequencing and the administration of more tailored precision medicine treatments. We aimed to evaluate whether heparinized wet suction can obtain higher quality samples than the standard dry-suction method during endoscopic ultrasound (EUS)-guided biopsy of pancreatic masses. Methods: A prospective randomized crossover study was conducted. Patients with a solid pancreatic mass were randomly allocated to receive either heparinized wet suction first or dry suction first. For each method, two needle passes were made, followed by a switch to the other method for a total of four needle punctures. The primary outcome was the aggregated white tissue length. Histological blood contamination, diagnostic performance and adverse events were analyzed as secondary outcomes. In addition, the correlation between white tissue length and the extracted DNA amount was analyzed. Results: A total of 50 patients were enrolled, and 200 specimens were acquired (100 with heparinized wet suction and 100 with dry suction), with one minor bleeding event. The heparinized wet suction approach yielded specimens with longer aggregated white tissue length (11.07 mm vs 7.96 mm, p=0.001) and less blood contamination (p=0.008). A trend towards decreasing tissue quality was observed for the 2nd pass of the dry-suction method, leading to decreased diagnostic sensitivity and accuracy, although the accumulated diagnostic performance was comparable between the two suction methods. The amount of extracted DNA correlated positively to the white tissue length (p=0.001, Spearman ̕s ρ=0.568). Conclusions Heparinized wet suction for EUS tissue acquisition of solid pancreatic masses can yield longer, bloodless, DNA-rich tissue without increasing the incidence of adverse events (ClinicalTrials.gov. identifier NCT04707560).

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

OBJECTIVE: Hospitalization of patients with delirium after visiting the emergency department (ED) is often required. However, the readmission risk after discharge from the ED should also be considered. This study aimed to explore whether (i) immediate hospitalization influences the readmission risk of patients with delirium; (ii) the readmission risk is affected by various risk factors; and (iii) the healthcare cost differs between groups within 28 days of the first ED visit. METHODS: Using the National Health Insurance Research Database, the data of 2,780 subjects presenting with delirium at an ED visit from 2000 to 2008 were examined. The readmission risks of the groups of patients (i.e., patients who were and were not admitted within 24 hours of an ED visit) within 28 days were compared, and the effects of the severities of different comorbidities (using Charlson’s comorbidity index, CCI), age, gender, diagnosis and differences in medical healthcare cost were analyzed. RESULTS: Patients without immediate hospitalization had a higher risk of readmission within 3, 7, 14, or 28 days of discharge from the ED, especially subjects with more severe comorbidities (CCI≥3) or older patients (≥65 years). Subjects with more severe comorbidities or older subjects who were not admitted immediately also incurred a greater healthcare cost for re-hospitalization within the 28-day follow-up period. CONCLUSION: Patients with delirium with a higher CCI or of a greater age should be carefully considered for immediate hospitalization from ED for further examination in order to reduce the risk of re-hospitalization and cost of healthcare.
Comorbidity ; Delirium* ; Delivery of Health Care* ; Diagnosis ; Emergencies* ; Emergency Service, Hospital* ; Follow-Up Studies ; Health Care Costs* ; Hospitalization* ; Humans ; National Health Programs ; Risk Factors

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.