1.Major Determinants and Long-Term Outcomes of Successful Balloon Dilatation for the Pediatric Patients with Isolated Native Valvular Pulmonary Stenosis: A 10-Year Institutional Experience.
Meng Luen LEE ; Jui Wen PENG ; Guo Jhueng TU ; San Yi CHEN ; Jyong You LEE ; Shu Lin CHANG
Yonsei Medical Journal 2008;49(3):416-421
PURPOSE: We report herein major determinants and long- term outcomes of balloon dilatation (BD) for 27 pediatric patients with isolated native valvular pulmonary stenosis (VPS). MATERIALS AND METHODS: From May 1997 to May 2003, 27 pediatric patients with VPS (pressure gradients> or =40mmHg) were enrolled in this retrospective study. Single-balloon maneuver was applied in 26 patients, and double- balloon maneuver in 1. After BD, the pressure gradients were documented simultaneously by pullback maneuver by cardiac catheterization and echocardiography within 24 hours, at 1- month, 3-month, 1-year, and 4-to-10-year follow-ups. RESULTS: Before BD, the echocardiographic gradients ranged from 40 to 101mmHg (61+/-19, 55), and from 40 to 144mmHg (69+/-32, 60) by pressure recordings. After BD, the gradients ranged from 12 to 70mmHg (29+/-13, 27) by pressure recording (p<0.001), and from 11 to 64mmHg (27+/-12, 26) by echocardiography within 24 hrs (p<0.001). The ratios of the systolic pressure of the right ventricle to those of the left ventricle were 55 to 157% (89+/-28, 79%) before BD, and 30 to 79% (47+/-13, 42%) after BD p<0.001). Follow-up (7.7+/-5.7, 4.5 years) echocardiographic gradients ranged from 11 to 61mmHg (25+/-11, 24). Two patients did not have immediate success owing to infundibular spasm. Improved right ventricular compliance could be accounted for the ultimate success in these 2 patients. The ultimate successful rate was 100%. CONCLUSION: BD can achieve excellent long-term outcomes in the pediatric patients with isolated native VPS.
Adolescent
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Balloon Dilatation/adverse effects/*methods
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Child
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Child, Preschool
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Echocardiography
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Female
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Follow-Up Studies
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Humans
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Infant
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Infant, Newborn
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Male
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Pulmonary Valve Stenosis/pathology/physiopathology/*therapy
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Retrospective Studies
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Time Factors
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Treatment Outcome
2.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.