Hypoglycemia is a common finding in both daily clinical practice and acute care settings. The causes of severe hypoglycemia (SH) are multi-factorial and the major etiologies are iatrogenic, infectious diseases with sepsis and tumor or autoimmune diseases. With the advent of aggressive lowering of HbA1c values to achieve optimal glycemic control, patients are at increased risk of hypoglycemic episodes. Iatrogenic hypoglycemia can cause recurrent morbidity, sometime irreversible neurologic complications and even death, and further preclude maintenance of euglycemia over a lifetime of diabetes. Recent studies have shown that hypoglycemia is associated with adverse outcomes in many acute illnesses. In addition, hypoglycemia is associated with increased mortality among elderly and non-diabetic hospitalized patients. Clinicians should have high clinical suspicion of subtle symptoms of hypoglycemia and provide prompt treatment. Clinicians should know that hypoglycemia is associated with considerable adverse outcomes in many acute critical illnesses. In order to reduce hypoglycemia-associated morbidity and mortality, timely health education programs and close monitoring should be applied to those diabetic patients presenting to the Emergency Department with SH. ED disposition strategies should be further validated and justified to achieve balance between the benefits of euglycemia and the risks of SH. We discuss relevant issues regarding hypoglycemia in emergency and critical care settings.
Diabetes Mellitus/drug therapy ; Humans ; Hypoglycemia/blood/*chemically induced/*complications/epidemiology ; Hypoglycemic Agents/adverse effects/therapeutic use ; Insulin/adverse effects/therapeutic use

OBJECTIVE: Postoperative delirium (POD) is a highly prevalent complex neuropsychiatric syndrome in elderly patients. However, its pathophysiology is currently unknown. Early detection and prevention of POD is important; therefore, the aim of this study was to investigate the link between preoperative insulin growth factor 1 (IGF-1) levels in the serum and POD in the Chinese elderly patients. METHODS: One hundred and three patients who were undergoing an orthopedic operation took part in the study. Preoperative serum IGF-1 levels were measured. POD was determined daily using the Confusion Assessment Method (CAM) and DSM-IV TR. Baseline serum IGF-1 levels were compared between patients who did and did not develop POD. Correlation coefficients were calculated to evaluate relationship between baseline characteristics and serum IGF-1 levels. The relationship between baseline biomarkers and delirium status was investigated using logistic regression analysis, adjusting for potential confounding variables. RESULTS: Twenty-three patients developed POD. The POD group had lower MMSE scores and higher CCI scores and proportions of acute admission. Preoperative serum IGF-1 levels were correlated with MMSE scores and age (MMSE: r=0.230, p<0.05; age: r=-0.419, p<0.001). Baseline serum IGF-1 levels did not differ between patients who did and did not develop POD, even after adjusting for potential confounding factors, MMSE score, and age. CONCLUSION: No association was found between preoperative IGF-1 levels and POD, suggesting that they are not direct biomarkers of the incidence of POD among the Chinese elderly population. Further research with larger sample sizes is warranted to clarify the relationship.
Aged* ; Asian Continental Ancestry Group* ; Biomarkers ; Confounding Factors (Epidemiology) ; Delirium* ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Incidence ; Insulin ; Insulin-Like Growth Factor I ; Logistic Models ; Orthopedics ; Sample Size

Bipolar disorder (BD) is a major psychiatric disorder that is easily misdiagnosed. Patient adherence to a treatment regimen is of utmost importance for successful outcomes in BD. Several trials of antipsychotics suggested that depot antipsychotics, including long-acting first- and second-generation agents, are effective in preventing non-adherence, partial adherence, and in reducing relapse in BD. Various long-acting injectable (LAI) antipsychotics are available, including fluphenazine decanoate, haloperidol decanoate, olanzapine pamoate, risperidone microspheres, paliperidone palmitate, and aripiprazole monohydrate. Due to the increasing number of BD patients receiving LAI antipsychotics, treatment guidelines have been developed. However, the clinical applicability of LAI antipsychotics remains a global cause for concern, particularly in Asian countries. Expert physicians from Taiwan participated in a consensus meeting, which was held to review key areas based on both current literature and clinical practice. The purpose of this meeting was to generate a practical and implementable set of recommendations for LAI antipsychotic use to treat BD; target patient groups, dosage, administration, and adverse effects were considered. Experts recommended using LAI antipsychotics in patients with schizophrenia, rapid cycling BD, BD I, and bipolar-type schizoaffective disorder. LAI antipsychotic use was recommended in BD patients with the following characteristics: multiple episodes and low adherence; seldom yet serious episodes; low adherence potential per a physician's clinical judgment; preference for injectable agents over oral agents; and multiple oral agent users still experiencing residual symptoms.
Antipsychotic Agents* ; Asian Continental Ancestry Group ; Bipolar Disorder* ; Consensus* ; Fluphenazine ; Haloperidol ; Humans ; Judgment ; Microspheres ; Patient Compliance ; Psychotic Disorders ; Recurrence ; Risperidone ; Schizophrenia ; Taiwan ; Aripiprazole ; Paliperidone Palmitate

OBJECTIVE: To investigate the effect of M3P (containing Deer antler, Cordyceps sinensis, Rhodiola rosea, and Panax ginseng); an herbal remedy with the function of tonifying Kidney (Shen) and invigorating Spleen (Pi), replenishing qi and nourishing blood; on fatigue alleviation, endurance capacity and toxicity. METHODS: Swimming with weight-loading of 24 male ICR mice was used to evaluate the endurance capacity, and fatigue-related plasma biomarkers were determined. Mice were randomly assigned to control or M3P treatment groups with 6 mice for each group and were orally administered with M3P everyday for 8 weeks at doses 0, 10, 33 or 100 mg/kg. Swimming time to exhaustion was measured in a specialized water tank. Lliver and kidney functions, body weight, and hematological profile were determined to evaluate the safety and toxicity after long-term M3P administration. RESULTS: M3P supplementation 100 mg/kg significantly increased swimming endurance time up to approximate 2.4 folds of controls (P<0.05). The plasma concentrations of cortisol and hepatic glycogen content were significantly increased in mice received M3P (P<0.05, P<0.01 respectively). The lactic acid level and blood glucose were not changed after M3P treatment (P>0.05). The liver and kidney functions muscle damage biomarker creatine, body weight, and hemograms were not altered in M3P supplementation (P>0.05). CONCLUSION M3P supplementation may improve swimming endurance accompanied by increasing hepatic glycogen content and serum cortisol level without major toxicity.
Animals ; Body Weight ; Deer ; Dietary Supplements ; Fatigue/drug therapy* ; Hydrocortisone ; Liver Glycogen ; Male ; Mice ; Mice, Inbred ICR ; Muscle, Skeletal ; Swimming/physiology*

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.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

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.