1.Liquid Chromatography-Tandem Mass Spectrometry Outperforms Radioimmunoassay in Guiding Surgical Decisions Based on Adrenal Venous Sampling in Primary Aldosteronism
Bo-Ching LEE ; Chien-Wei HUANG ; Chin-Chen CHANG ; Guan-Yuan CHEN ; Jia-Zheng HUANG ; Pin-Chen CHEN ; Te-I WENG ; Kao-Lang LIU ; Vin-Cent WU ; Yen-Hung LIN ;
Endocrinology and Metabolism 2025;40(6):1002-1011
Background:
Adrenal venous sampling (AVS) is essential for diagnosing unilateral aldosterone oversecretion in primary aldosteronism (PA). Traditionally, AVS relies on radioimmunoassay (RIA) to measure plasma aldosterone concentration (PAC), although RIA has limited specificity and considerable variability. This study evaluated the role of liquid chromatography-tandem mass spectrometry (LC-MS/MS) in AVS and its impact on clinical outcomes.
Methods:
Among 230 patients with PA (May 2020 to April 2023) who underwent AVS, successful sampling was achieved in 182 patients (79.1%) under unstimulated conditions and 206 patients (89.6%) under stimulated conditions. PAC levels from peripheral and adrenal veins measured by LC-MS/MS were compared with RIA results. Patient outcomes were categorized according to the Primary Aldosteronism Surgical Outcomes criteria.
Results:
LC-MS/MS showed significant correlations with PAC levels measured by RIA in AVS (r=0.40 [unstimulated] and r=0.56 [stimulated]; both P<0.001). However, lateralization concordance between RIA and LC-MS/MS was moderate, at only 57.7% (unstimulated) and 64.6% (stimulated). LC-MS/MS identified more unilateral disease than RIA under both unstimulated (61.5% vs. 37.4%, P<0.001) and stimulated conditions (36.4% vs. 9.7%, P<0.001). Patients achieving complete clinical success after adrenalectomy were more accurately identified by LC-MS/MS than RIA under stimulated (55.6% vs. 22.2%, P=0.035), but not in unstimulated conditions.
Conclusion
LC-MS/MS outperformed RIA in identifying unilateral disease, resulting in higher rates of complete clinical success in adrenalectomy patients when surgical decisions were based on LC-MS/MS lateralization results.
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.
3.Multislice CT Scans in Patients on Extracorporeal Membrane Oxygenation: Emphasis on Hemodynamic Changes and Imaging Pitfalls.
Kao Lang LIU ; Yu Feng WANG ; Yeun Chung CHANG ; Shu Chien HUANG ; Shyh Jye CHEN ; Yuk Ming TSANG ; Chin Chen CHANG
Korean Journal of Radiology 2014;15(3):322-329
This pictorial review provides the principles of extracorporeal membrane oxygenation (ECMO) support and associated CT imaging features with emphasis on the hemodynamic changes and possible imaging pitfalls encountered. It is important that radiologists in ECMO centers apply well-designed imaging protocols and familiarize themselves with post-contrast CT imaging findings in patients on ECMO.
Adult
;
Aorta, Thoracic/physiopathology/radiography
;
Contrast Media/administration & dosage/pharmacokinetics
;
Extracorporeal Membrane Oxygenation/classification/*methods
;
Female
;
Heart-Assist Devices
;
Hemodynamics/*physiology
;
Humans
;
Intra-Aortic Balloon Pumping/instrumentation
;
Male
;
Middle Aged
;
*Multidetector Computed Tomography
;
Regional Blood Flow/physiology
;
Retrospective Studies
;
Ventricular Dysfunction, Left/physiopathology/radiography

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