There is no information on the effects of variable amounts of water given less than 2 hour before induction of general anesthesia, although most patients receive oral premedication at that time. We have studied the effect of the volume of water which should accompany diazepam 0.2 mg/kg oral premedication given 90 minutes before induction of anesthesia in 75 patients undergoing elective laparoscopic cholecystectomy. The patients, ASA physical status I, randomly assigned to one of five groups. Fifteen patients were received no premedication (group A), 15 patients 50 ml (group B), 15 patients 100 ml (group C), 15 patients 150 ml (group D), and 15 patients 200 ml of water (group E). Immediately following induction of anesthesia the gastric fluid was obtained by suction on a nasogastric tube and its volume and pH were measured. Gastric fluid volume showed no statistically significant differences among the groups. Values for pH among the groups were also similar. All premedication groups reported a reduction in thirst after water intake, while only group D reported significant anxiolysis. We conclude that 150 ml is the ideal amount of water given with oral prnedication 90 minutes before general anesthesia.
Anesthesia ; Anesthesia, General ; Anxiety* ; Cholecystectomy, Laparoscopic ; Diazepam* ; Drinking ; Humans ; Hydrogen-Ion Concentration* ; Premedication* ; Suction ; Thirst ; Water*

BACKGROUND: Spontaneous recovery of severe alopecia areata is rare and the condition is difficult to treat. OBJECTIVE: The aim of this study is to investigate and compare the effects and safety of steroid pulse therapy between oral and intravenous administrations between 1999 and 2010 at the Department of Dermatology, National Cheng Kung University Hospital. METHODS: Data were retrospectively retrieved. A satisfactory response was defined as more than 75% hair regrowth in the balding area. RESULTS: A total of 85 patients with more than 50% hair loss were identified and treated, with an overall satisfactory response rate of 51.8%. The mean follow-up time was 37.6 months, with a relapse rate of 22.7%. Patients with alopecia areata (hereafter, AA) of recent onset within one year showed higher response rates (p<0.001) and lower relapse rates compared to patients with AA persisting for more than 1 year. Further, even in patients with alopecia totalis, alopecia universalis or ophiasis type, early treatment resulted in a satisfactory response rate of 47% among the treated patients. In general, oral therapy was as effective and well-tolerated as intravenous therapy. CONCLUSION: The response rate is determined by disease severity and time of intervention, not by the administration form of steroid pulse therapy. Oral steroid pulse therapy can be considered as the first-line treatment for patients with severe AA of recent onset within one year.
Administration, Intravenous ; Adrenal Cortex Hormones ; Alopecia Areata* ; Alopecia* ; Dermatology ; Early Intervention (Education)* ; Follow-Up Studies ; Hair ; Humans ; Pulse Therapy, Drug ; Recurrence ; Retrospective Studies*

INTRODUCTIONThere is no single method of measuring insulin resistance that is both accurate and can be easily performed by general researchers. We validate the accuracy of oral glucose insulin sensitivity (OGIS) in the Chinese by comparing the OGIS120 and OGIS180, homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (OUICKI) with steady-state plasma glucose (SSPG) in different glucose tolerance subjects.

MATERIALS AND METHODSWe enrolled 515 subjects, aged between 20 and 75 years old, during routine health evaluations. All subjects were divided into normal, obese, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and type 2 diabetes (T2D) groups. Participants had a 3-hour oral glucose tolerance test (OGTT) and SSPG with an insulin suppression test. The relationships between SSPG and OGIS120, OGIS180, HOMA-IR, and QUICKI were evaluated.

RESULTSThe normal group had the highest OGIS120, OGIS180 and lowest SSPG as compared with the other 4 groups. OGIS180, HOMA-IR and QUICKI in all 5 groups were significantly related to SSPG (r = 0.397-0.621, all P <0.05). OGIS120 in all 5 groups was not significantly related to SSPG (r = 0.003-0.226). Additionally, the r value of OGIS180 against SSPG was not higher than the other 2 insulin sensitivity surrogates from OGTT.

CONCLUSIONSAlthough OGIS180 was more accurate in estimating insulin sensitivity than OGIS120 in the Chinese, it was not superior to the traditional surrogates such as HOMA-IR or QUICKI.

Adult ; Aged ; Case-Control Studies ; China ; Female ; Glucose Tolerance Test ; methods ; Humans ; Insulin Resistance ; Male ; Middle Aged ; Prediabetic State ; diagnosis ; Young Adult

Background/Aims: Cirrhosis is the most important risk factor of hepatocellular carcinoma (HCC), and patients with cirrhosis are recommended to receive semiannual surveillance for early HCC detection. However, early cirrhosis is often asymptomatic and can go undiagnosed for years, leading to underuse of HCC surveillance in clinical practice. We characterized the frequency and associated factors of unrecognized cirrhosis in a national sample of patients with HCC from the United States. Methods: HCC patients aged 68 years and older, diagnosed during 2011 to 2015 were included from the SEERMedicare Linked Database. If cirrhosis was diagnosed within 6 months immediately preceding HCC diagnosis or after HCC diagnosis, cases were categorized as unrecognized cirrhosis. Factors associated with unrecognized cirrhosis were identified using logistic regression analyses. Factors associated with overall survival were evaluated using Cox regression analyses. Results: Among 5,098 HCC patients, 74.8% patients had cirrhosis. Among those with cirrhosis, 57.4% had unrecognized cirrhosis, with the highest proportion (76.3%) among those with NAFLD-related HCC. Male sex (aOR: 2.12, 95% CI: 1.83–2.46), non-Hispanic Black race (aOR: 1.93, 95% CI: 1.45–2.57), and NAFLD etiology (aOR: 4.46, 95% CI: 3.68–5.41) were associated with having unrecognized cirrhosis. Among NAFLD-related HCC patients, male sex (aOR: 2.32, 95% CI: 1.71–3.14) was associated with unrecognized cirrhosis. Unrecognized cirrhosis was independently associated with worse overall survival (aHR: 1.17, 95% CI: 1.08–1.27) compared to recognized cirrhosis. Conclusions Unrecognized cirrhosis is common in NAFLD-related HCC, particularly among male and Black patients, highlighting these groups as important intervention targets to improve HCC surveillance uptake and outcomes.

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.