Purpose: Preoperative localization plays an important role in secondary hyperparathyroidism (SHPT) surgery. The advantages of neck ultrasound (US) include high availability and low cost. However, the reported sensitivity of US is 54%– 76%, and the reason for missed parathyroid glands (PGs) on US has been rarely addressed. Methods: Fifty-four patients who were diagnosed with renal SHPT from September 2020 to March 2022 were included in this retrospective study. Preoperative localization included surgeon-oriented US and technetium 99m-sestamibi singlephoton emission CT (SPECT)/CT. Results: A total of 212 PGs were pathologically confirmed, resulting in a success rate of 96.2% (52 of 54). Using echo, 193 PGs (91.0%) were accurately localized, while 19 glands (9.0%) were not identified, including those in ectopic positions (n = 12, at thymus or intrathyroid or others), of small size (<1 cm, n = 6), or overlapping with an ipsilateral PG (n = 1). US accurately detected 4 PGs in 36 (66.7%) patients, while SPECT/CT localized 4 glands in 19 patients (35.2%). Although the number of US-detectable PGs was not associated with success rate, it showed a significant negative correlation with surgical time (rs = –0.459, P = 0.002). Conclusion US detected 4 glands in 66% of SHPT patients with a sensitivity of 90% for localization. Ectopic position and small size were the most common reasons for the failure to detect PG on US. Complete preoperative echo localization might shorten operating time.

Purpose: Preoperative localization plays an important role in secondary hyperparathyroidism (SHPT) surgery. The advantages of neck ultrasound (US) include high availability and low cost. However, the reported sensitivity of US is 54%– 76%, and the reason for missed parathyroid glands (PGs) on US has been rarely addressed. Methods: Fifty-four patients who were diagnosed with renal SHPT from September 2020 to March 2022 were included in this retrospective study. Preoperative localization included surgeon-oriented US and technetium 99m-sestamibi singlephoton emission CT (SPECT)/CT. Results: A total of 212 PGs were pathologically confirmed, resulting in a success rate of 96.2% (52 of 54). Using echo, 193 PGs (91.0%) were accurately localized, while 19 glands (9.0%) were not identified, including those in ectopic positions (n = 12, at thymus or intrathyroid or others), of small size (<1 cm, n = 6), or overlapping with an ipsilateral PG (n = 1). US accurately detected 4 PGs in 36 (66.7%) patients, while SPECT/CT localized 4 glands in 19 patients (35.2%). Although the number of US-detectable PGs was not associated with success rate, it showed a significant negative correlation with surgical time (rs = –0.459, P = 0.002). Conclusion US detected 4 glands in 66% of SHPT patients with a sensitivity of 90% for localization. Ectopic position and small size were the most common reasons for the failure to detect PG on US. Complete preoperative echo localization might shorten operating time.

Purpose: Preoperative localization plays an important role in secondary hyperparathyroidism (SHPT) surgery. The advantages of neck ultrasound (US) include high availability and low cost. However, the reported sensitivity of US is 54%– 76%, and the reason for missed parathyroid glands (PGs) on US has been rarely addressed. Methods: Fifty-four patients who were diagnosed with renal SHPT from September 2020 to March 2022 were included in this retrospective study. Preoperative localization included surgeon-oriented US and technetium 99m-sestamibi singlephoton emission CT (SPECT)/CT. Results: A total of 212 PGs were pathologically confirmed, resulting in a success rate of 96.2% (52 of 54). Using echo, 193 PGs (91.0%) were accurately localized, while 19 glands (9.0%) were not identified, including those in ectopic positions (n = 12, at thymus or intrathyroid or others), of small size (<1 cm, n = 6), or overlapping with an ipsilateral PG (n = 1). US accurately detected 4 PGs in 36 (66.7%) patients, while SPECT/CT localized 4 glands in 19 patients (35.2%). Although the number of US-detectable PGs was not associated with success rate, it showed a significant negative correlation with surgical time (rs = –0.459, P = 0.002). Conclusion US detected 4 glands in 66% of SHPT patients with a sensitivity of 90% for localization. Ectopic position and small size were the most common reasons for the failure to detect PG on US. Complete preoperative echo localization might shorten operating time.

Purpose: Preoperative localization plays an important role in secondary hyperparathyroidism (SHPT) surgery. The advantages of neck ultrasound (US) include high availability and low cost. However, the reported sensitivity of US is 54%– 76%, and the reason for missed parathyroid glands (PGs) on US has been rarely addressed. Methods: Fifty-four patients who were diagnosed with renal SHPT from September 2020 to March 2022 were included in this retrospective study. Preoperative localization included surgeon-oriented US and technetium 99m-sestamibi singlephoton emission CT (SPECT)/CT. Results: A total of 212 PGs were pathologically confirmed, resulting in a success rate of 96.2% (52 of 54). Using echo, 193 PGs (91.0%) were accurately localized, while 19 glands (9.0%) were not identified, including those in ectopic positions (n = 12, at thymus or intrathyroid or others), of small size (<1 cm, n = 6), or overlapping with an ipsilateral PG (n = 1). US accurately detected 4 PGs in 36 (66.7%) patients, while SPECT/CT localized 4 glands in 19 patients (35.2%). Although the number of US-detectable PGs was not associated with success rate, it showed a significant negative correlation with surgical time (rs = –0.459, P = 0.002). Conclusion US detected 4 glands in 66% of SHPT patients with a sensitivity of 90% for localization. Ectopic position and small size were the most common reasons for the failure to detect PG on US. Complete preoperative echo localization might shorten operating time.

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.