ABSTRACT
Background/Aims@#Standard triple therapy (STT; proton pump inhibitor [PPI]+clarithromycin+amoxicillin) used for Helicobacter pylori (H. pylori) eradication has shown low treatment success rates in recent years, which is most likely attributable to increased clarithromycin resistance. In this study, we compared treatment success rates of tailored therapy (TT) using real-time polymerase chain reaction (RT-PCR) and empirical STT. @*Methods@#This retrospective study included 650 patients with H. pylori infection, who visited Eunpyeong St. Mary’s Hospital in Korea; 343 patients received TT based on RT-PCR assays, and 307 patients received STT. Eradication success was defined as a negative 13C-urea breath test result 4~8 weeks after treatment completion. Patients who failed first-line therapy and those with clarithromycin resistance received bismuth-containing quadruple therapy (BQT; PPI+bismuth+metronidazole+tetracycline). @*Results@#Intention-to-treat analysis showed that H. pylori eradication rates were higher in patients who received RT-PCR–based TT than in those who were treated using empirical STT (80.5% [190/236] vs. 70.4% [216/307], P=0.069). Per-protocol (PP) analysis showed similar results (84.4% [190/225] vs. 74.7% [216/289], P=0.007). PP analysis showed that 7-day TT treatment was associated with a higher eradication rate than that observed with 10- to 14-day STT (85.2% [178/209] vs. 73.8% [59/80], P=0.029). The clarithromycin resistance rate was 27.9% (87/312). The eradication success rate was 89.2% (74/83) in patients with clarithromycin resistance, who received BQT as first-line therapy. @*Conclusions@#The treatment success rate was higher in patients who received 7-day RT-PCR–based TT than in those who were administered 10- to 14-day empirical treatment.
ABSTRACT
Objective@#This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. @*Materials and Methods@#We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1–10, 11–100, 101–400, > 400) was evaluated. @*Results@#In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and falsepositive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). @*Conclusion@#The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.