Objectives: The standard first-line treatment for Helicobacter pylori infection typically involves proton pump inhibitors, amoxicillin, and clarithromycin (PAC), yet the eradication success rates are not entirely satisfactory. Recognizing bismuth’s antibacterial properties and its potential to enhance antibiotic efficacy, this study compared the eradication success rates of a 7-day course of PAC with bismuth (PACB) versus PAC alone in patients with clarithromycin-susceptible H. pylori infections. Methods: We conducted a retrospective review at Eunpyeong St. Mary’s Hospital involving 499 patients with confirmed clarithromycin-susceptible H. pylori infection. These patients were treated either with PACB or PAC for 7 days. Clarithromycin resistance-associated point mutations were evaluated using reverse transcriptase polymerase chain reaction. Successful eradication was confirmed by a negative 13C-urea breath test. Results: Of the patients, 261 received PACB therapy, and 238 received PAC therapy. The intention-to-treat analysis showed eradication success rates of 82.8% (216/261) for PACB and 89.1% (212/238) for PAC (p=0.093). The per-protocol analysis revealed eradication rates of 85.3% (215/252) for PACB and 90.5% (210/232) for PAC (p=0.081). The incidence of adverse effects was similar between the two groups, with 41.3% (104/252) in the PACB group and 34.1% (79/232) in the PAC group (p=0.102). Conclusions Adding bismuth to the standard 7-day PAC regimen did not significantly increase eradication rates in patients with clarithromycin-susceptible H. pylori infections compared to PAC alone.

Objectives: The standard first-line treatment for Helicobacter pylori infection typically involves proton pump inhibitors, amoxicillin, and clarithromycin (PAC), yet the eradication success rates are not entirely satisfactory. Recognizing bismuth’s antibacterial properties and its potential to enhance antibiotic efficacy, this study compared the eradication success rates of a 7-day course of PAC with bismuth (PACB) versus PAC alone in patients with clarithromycin-susceptible H. pylori infections. Methods: We conducted a retrospective review at Eunpyeong St. Mary’s Hospital involving 499 patients with confirmed clarithromycin-susceptible H. pylori infection. These patients were treated either with PACB or PAC for 7 days. Clarithromycin resistance-associated point mutations were evaluated using reverse transcriptase polymerase chain reaction. Successful eradication was confirmed by a negative 13C-urea breath test. Results: Of the patients, 261 received PACB therapy, and 238 received PAC therapy. The intention-to-treat analysis showed eradication success rates of 82.8% (216/261) for PACB and 89.1% (212/238) for PAC (p=0.093). The per-protocol analysis revealed eradication rates of 85.3% (215/252) for PACB and 90.5% (210/232) for PAC (p=0.081). The incidence of adverse effects was similar between the two groups, with 41.3% (104/252) in the PACB group and 34.1% (79/232) in the PAC group (p=0.102). Conclusions Adding bismuth to the standard 7-day PAC regimen did not significantly increase eradication rates in patients with clarithromycin-susceptible H. pylori infections compared to PAC alone.

Objectives: The standard first-line treatment for Helicobacter pylori infection typically involves proton pump inhibitors, amoxicillin, and clarithromycin (PAC), yet the eradication success rates are not entirely satisfactory. Recognizing bismuth’s antibacterial properties and its potential to enhance antibiotic efficacy, this study compared the eradication success rates of a 7-day course of PAC with bismuth (PACB) versus PAC alone in patients with clarithromycin-susceptible H. pylori infections. Methods: We conducted a retrospective review at Eunpyeong St. Mary’s Hospital involving 499 patients with confirmed clarithromycin-susceptible H. pylori infection. These patients were treated either with PACB or PAC for 7 days. Clarithromycin resistance-associated point mutations were evaluated using reverse transcriptase polymerase chain reaction. Successful eradication was confirmed by a negative 13C-urea breath test. Results: Of the patients, 261 received PACB therapy, and 238 received PAC therapy. The intention-to-treat analysis showed eradication success rates of 82.8% (216/261) for PACB and 89.1% (212/238) for PAC (p=0.093). The per-protocol analysis revealed eradication rates of 85.3% (215/252) for PACB and 90.5% (210/232) for PAC (p=0.081). The incidence of adverse effects was similar between the two groups, with 41.3% (104/252) in the PACB group and 34.1% (79/232) in the PAC group (p=0.102). Conclusions Adding bismuth to the standard 7-day PAC regimen did not significantly increase eradication rates in patients with clarithromycin-susceptible H. pylori infections compared to PAC alone.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Objectives: The standard first-line treatment for Helicobacter pylori infection typically involves proton pump inhibitors, amoxicillin, and clarithromycin (PAC), yet the eradication success rates are not entirely satisfactory. Recognizing bismuth’s antibacterial properties and its potential to enhance antibiotic efficacy, this study compared the eradication success rates of a 7-day course of PAC with bismuth (PACB) versus PAC alone in patients with clarithromycin-susceptible H. pylori infections. Methods: We conducted a retrospective review at Eunpyeong St. Mary’s Hospital involving 499 patients with confirmed clarithromycin-susceptible H. pylori infection. These patients were treated either with PACB or PAC for 7 days. Clarithromycin resistance-associated point mutations were evaluated using reverse transcriptase polymerase chain reaction. Successful eradication was confirmed by a negative 13C-urea breath test. Results: Of the patients, 261 received PACB therapy, and 238 received PAC therapy. The intention-to-treat analysis showed eradication success rates of 82.8% (216/261) for PACB and 89.1% (212/238) for PAC (p=0.093). The per-protocol analysis revealed eradication rates of 85.3% (215/252) for PACB and 90.5% (210/232) for PAC (p=0.081). The incidence of adverse effects was similar between the two groups, with 41.3% (104/252) in the PACB group and 34.1% (79/232) in the PAC group (p=0.102). Conclusions Adding bismuth to the standard 7-day PAC regimen did not significantly increase eradication rates in patients with clarithromycin-susceptible H. pylori infections compared to PAC alone.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Objectives: The standard first-line treatment for Helicobacter pylori infection typically involves proton pump inhibitors, amoxicillin, and clarithromycin (PAC), yet the eradication success rates are not entirely satisfactory. Recognizing bismuth’s antibacterial properties and its potential to enhance antibiotic efficacy, this study compared the eradication success rates of a 7-day course of PAC with bismuth (PACB) versus PAC alone in patients with clarithromycin-susceptible H. pylori infections. Methods: We conducted a retrospective review at Eunpyeong St. Mary’s Hospital involving 499 patients with confirmed clarithromycin-susceptible H. pylori infection. These patients were treated either with PACB or PAC for 7 days. Clarithromycin resistance-associated point mutations were evaluated using reverse transcriptase polymerase chain reaction. Successful eradication was confirmed by a negative 13C-urea breath test. Results: Of the patients, 261 received PACB therapy, and 238 received PAC therapy. The intention-to-treat analysis showed eradication success rates of 82.8% (216/261) for PACB and 89.1% (212/238) for PAC (p=0.093). The per-protocol analysis revealed eradication rates of 85.3% (215/252) for PACB and 90.5% (210/232) for PAC (p=0.081). The incidence of adverse effects was similar between the two groups, with 41.3% (104/252) in the PACB group and 34.1% (79/232) in the PAC group (p=0.102). Conclusions Adding bismuth to the standard 7-day PAC regimen did not significantly increase eradication rates in patients with clarithromycin-susceptible H. pylori infections compared to PAC alone.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.