1. Clinical significance of skin rash in dengue fever: A focus on discomfort, complications, and disease outcome
Hsin-Wei HUANG ; Han-Chi TSENG ; Chih-Hung LEE ; Shang-Hung LIN ; Hung-Yi CHUANG ; Hung-Yi CHUANG
Asian Pacific Journal of Tropical Medicine 2016;9(7):713-718
Objectives To assess whether the cutaneous features in patients with dengue fever are associated with abnormal blood biochemistry, complications, and poor disease outcome. Methods Forty five patients with dengue fever were identified at a medical center in Kaohsiung, Taiwan, from September to November 2014. All cases were exclusively caused by type 1 dengue virus. Patients were classified into two groups, based on the presence or absence of skin rash, and their rash was subclassified into maculopapular, morbilliform, and petechial types. Clinical symptoms, laboratory data, disease outcome, and complications were compared between the two groups. Results Thirty two patients with dengue fever developed skin rash (SP group, n = 32) while the rest of 13 did not (SN group, n = 13). The patient numbers in the maculopapular, morbilliform, and petechial group were 4, 21, and 7, respectively. The SP group was younger (P = 0.001), experienced more pruritus (P = 0.008) and more swollen palms/soles (P = 0.015) than the SN group. However, the SN group had greater genital mucosa involvement (P = 0.008), higher platelet transfusion rate (P = 0.003), and lower hemoglobin and hematocrit levels (P = 0.030) than the SP group. Patients with morbilliform lesions had a higher incidence of palm/sole swelling, less genital mucosal involvement, and a lower platelet transfusion rate than did patients with maculopapular or petechial lesions. Conclusions Cutaneous manifestations provide an important clue to dengue fever. In patients with dengue fever, those with skin rash tend to have itching and swelling of the palms/soles, however, those without skin rash tend to have more complications and poor disease outcomes.
2.Metformin and statins reduce hepatocellular carcinoma risk in chronic hepatitis C patients with failed antiviral therapy
Pei-Chien TSAI ; Chung-Feng HUANG ; Ming-Lun YEH ; Meng-Hsuan HSIEH ; Hsing-Tao KUO ; Chao-Hung HUNG ; Kuo-Chih TSENG ; Hsueh-Chou LAI ; Cheng-Yuan PENG ; Jing-Houng WANG ; Jyh-Jou CHEN ; Pei-Lun LEE ; Rong-Nan CHIEN ; Chi-Chieh YANG ; Gin-Ho LO ; Jia-Horng KAO ; Chun-Jen LIU ; Chen-Hua LIU ; Sheng-Lei YAN ; Chun-Yen LIN ; Wei-Wen SU ; Cheng-Hsin CHU ; Chih-Jen CHEN ; Shui-Yi TUNG ; Chi‐Ming TAI ; Chih-Wen LIN ; Ching-Chu LO ; Pin-Nan CHENG ; Yen-Cheng CHIU ; Chia-Chi WANG ; Jin-Shiung CHENG ; Wei-Lun TSAI ; Han-Chieh LIN ; Yi-Hsiang HUANG ; Chi-Yi CHEN ; Jee-Fu HUANG ; Chia-Yen DAI ; Wan-Long CHUNG ; Ming-Jong BAIR ; Ming-Lung YU ;
Clinical and Molecular Hepatology 2024;30(3):468-486
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.
3.Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program
Ming-Ying LU ; Chung-Feng HUANG ; Chao-Hung HUNG ; Chi‐Ming TAI ; Lein-Ray MO ; Hsing-Tao KUO ; Kuo-Chih TSENG ; Ching-Chu LO ; Ming-Jong BAIR ; Szu-Jen WANG ; Jee-Fu HUANG ; Ming-Lun YEH ; Chun-Ting CHEN ; Ming-Chang TSAI ; Chien-Wei HUANG ; Pei-Lun LEE ; Tzeng-Hue YANG ; Yi-Hsiang HUANG ; Lee-Won CHONG ; Chien-Lin CHEN ; Chi-Chieh YANG ; Sheng‐Shun YANG ; Pin-Nan CHENG ; Tsai-Yuan HSIEH ; Jui-Ting HU ; Wen-Chih WU ; Chien-Yu CHENG ; Guei-Ying CHEN ; Guo-Xiong ZHOU ; Wei-Lun TSAI ; Chien-Neng KAO ; Chih-Lang LIN ; Chia-Chi WANG ; Ta-Ya LIN ; Chih‐Lin LIN ; Wei-Wen SU ; Tzong-Hsi LEE ; Te-Sheng CHANG ; Chun-Jen LIU ; Chia-Yen DAI ; Jia-Horng KAO ; Han-Chieh LIN ; Wan-Long CHUANG ; Cheng-Yuan PENG ; Chun-Wei- TSAI ; Chi-Yi CHEN ; Ming-Lung YU ;
Clinical and Molecular Hepatology 2024;30(1):64-79
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.