PURPOSE: Chronic kidney disease (CKD) is a condition characterized by the gradual loss of kidney function over time. Self-management programs have been widely applied to chronic disease education programs, which are designed to delay deteriorating kidney functions, preclude depression, and improve quality of life. This study aims to analyze effectiveness of self-management programs in bettering CKD patients' eGFR, mitigating depression symptoms and improving quality of life in randomized control or clinical trials. METHODS: Using key terms, a search was conducted in English-language, peer-reviewed journals on CKD that were published between 2002 and 2014 on databases including CINAHL, Cochrane Library, MEDLINE. The measurable variables included CKD patients' eGFR, depression, and quality of life. Random and fixed effects meta analysis were applied with standard error and correlation based measure of effect size. RESULTS: Eight studies met the inclusion criteria. A self-management program significantly impacted CKD patients' depression and mental quality-of-life dimensions, with an effect size of .29 [95% confidence interval (CI) (0.07, 0.53)] and −.42 [95% CI (−0.75, −0.10)]. However, the intervention of a self-management program had no significant effect on patients' eGFR as well as physical quality-of-life dimensions, with effect sizes of .06 [95% CI (−0.69, 0.81)] and −.16 [95% CI (−0.81, 0.50)]. CONCLUSIONS: Self-management programs of patients with chronic kidney disease can improve the depression and mental quality of life. Aside from providing more objective evidence-based results, this study provides a reference for clinical health care personnel who tend to patients with CKD.
Cognitive Therapy/methods ; Depressive Disorder/*etiology/therapy ; Glomerular Filtration Rate/*physiology ; Humans ; *Quality of Life ; Randomized Controlled Trials as Topic ; Renal Insufficiency, Chronic/physiopathology/psychology/*therapy ; Self Care/*methods

OBJECTIVE: Several cell line studies have demonstrated thioridazine’s anticancer, multidrug resistance-reversing and apoptosis-inducing properties in various tumors. We conducted this nationwide population-based study to investigate the association between thioridazine use and cancer risk among adult patients with schizophrenia. METHODS: Based on the Psychiatric Inpatient Medical Claim of the National Health Insurance Research Database of Taiwan, a total of 185,689 insured psychiatric patients during 2000 to 2005 were identified. After excluding patients with prior history of schizophrenia, only 42,273 newly diagnosed patients were included. Among them, 1,631 patients ever receiving thioridazine for more than 30 days within 6 months were selected and paired with 6,256 randomly selected non-thioridazine controls. These patients were traced till 2012/12/31 to see if they have any malignancy. RESULTS: The incidence rates of hypertension and cerebrovascular disease were higher among cases than among matched controls. The incidence of hyperlipidemia, coronary artery disease and chronic pulmonary disease did not differ between the two groups. By using Cox proportional hazard model for cancer incidence, the crude hazard ratio was significantly higher in age, hypertension, hyperlipidemia, cerebrovascular disease, coronary artery disease and chronic pulmornary disease. However, after adjusting for other covariates, only age and hypertension remained significant. Thioridazine use in adult patients with schizophrenia had no significant association with cancer. CONCLUSION: Despite our finding that thioridazine use had no prevention in cancer in adult patients with schizophrenia. Based on the biological activity, thioridazine is a potential anticancer drug and further investigation in human with cancer is warranted.
Adult* ; Cell Line ; Cerebrovascular Disorders ; Coronary Artery Disease ; Humans ; Hyperlipidemias ; Hypertension ; Incidence ; Inpatients ; Lung Diseases ; National Health Programs ; Proportional Hazards Models ; Schizophrenia ; Taiwan ; Thioridazine*

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.