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.

Purpose: Patients with stage I colorectal cancer (CRC) rarely experience recurrence after curative resection. Therefore, the risk factors for stage I CRC recurrence are yet to be established. We aimed to identify risk factors for stage I CRC recurrence. Methods: MEDLINE, Embase, and Cochrane Library were searched for articles published between 1990 and 2022. The pooled proportions and hazard ratios (HRs) were calculated. Fixed- or random-effect models were considered based on heterogeneity, using Cochran’s Q-statistic and the I2 -test. Results: Nine studies involving 19,440 patients were included. Nine analyzed risk factors were identified. T2 stage (pooled HR, 2.070; 95% confidence interval [CI], 1.758–2.438; P < 0.001; I2 =0.0%), lymphovascular invasion (HR, 1.685; 95% CI, 1.420–1.999; P < 0.001; I2 = 0.0%), venous invasion (HR, 1.794; 95% CI, 1.515–2.125; P < 0.001; I2 = 0.0%), CEA level (HR, 1.472; 95% CI, 1.093–1.983; P = 0.011; I2 = 1.8%) and rectal cancer (HR, 2.981; 95% CI, 2.378–3.735; P < 0.001; I2 = 0.0%) were risk factors for the recurrence. However, the risk of recurrence in right-sided colon cancer was lower than in leftsided colon cancer. (HR, 0.712; 95% CI, 0.537–0.944; P = 0.018; I2 = 0.0%). No statistically significant differences were observed in the number of harvested lymph nodes, age, and sex. Conclusion T2 stage, lymphovascular invasion, venous invasion, CEA level, rectal cancer, and left-sided colon cancer were risk factors for recurrence in stage I CRC. Intensive monitoring and surveillance are warranted for patients with high-risk features of recurrence.

Purpose: Patients with stage I colorectal cancer (CRC) rarely experience recurrence after curative resection. Therefore, the risk factors for stage I CRC recurrence are yet to be established. We aimed to identify risk factors for stage I CRC recurrence. Methods: MEDLINE, Embase, and Cochrane Library were searched for articles published between 1990 and 2022. The pooled proportions and hazard ratios (HRs) were calculated. Fixed- or random-effect models were considered based on heterogeneity, using Cochran’s Q-statistic and the I2 -test. Results: Nine studies involving 19,440 patients were included. Nine analyzed risk factors were identified. T2 stage (pooled HR, 2.070; 95% confidence interval [CI], 1.758–2.438; P < 0.001; I2 =0.0%), lymphovascular invasion (HR, 1.685; 95% CI, 1.420–1.999; P < 0.001; I2 = 0.0%), venous invasion (HR, 1.794; 95% CI, 1.515–2.125; P < 0.001; I2 = 0.0%), CEA level (HR, 1.472; 95% CI, 1.093–1.983; P = 0.011; I2 = 1.8%) and rectal cancer (HR, 2.981; 95% CI, 2.378–3.735; P < 0.001; I2 = 0.0%) were risk factors for the recurrence. However, the risk of recurrence in right-sided colon cancer was lower than in leftsided colon cancer. (HR, 0.712; 95% CI, 0.537–0.944; P = 0.018; I2 = 0.0%). No statistically significant differences were observed in the number of harvested lymph nodes, age, and sex. Conclusion T2 stage, lymphovascular invasion, venous invasion, CEA level, rectal cancer, and left-sided colon cancer were risk factors for recurrence in stage I CRC. Intensive monitoring and surveillance are warranted for patients with high-risk features of recurrence.

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.

Purpose: Patients with stage I colorectal cancer (CRC) rarely experience recurrence after curative resection. Therefore, the risk factors for stage I CRC recurrence are yet to be established. We aimed to identify risk factors for stage I CRC recurrence. Methods: MEDLINE, Embase, and Cochrane Library were searched for articles published between 1990 and 2022. The pooled proportions and hazard ratios (HRs) were calculated. Fixed- or random-effect models were considered based on heterogeneity, using Cochran’s Q-statistic and the I2 -test. Results: Nine studies involving 19,440 patients were included. Nine analyzed risk factors were identified. T2 stage (pooled HR, 2.070; 95% confidence interval [CI], 1.758–2.438; P < 0.001; I2 =0.0%), lymphovascular invasion (HR, 1.685; 95% CI, 1.420–1.999; P < 0.001; I2 = 0.0%), venous invasion (HR, 1.794; 95% CI, 1.515–2.125; P < 0.001; I2 = 0.0%), CEA level (HR, 1.472; 95% CI, 1.093–1.983; P = 0.011; I2 = 1.8%) and rectal cancer (HR, 2.981; 95% CI, 2.378–3.735; P < 0.001; I2 = 0.0%) were risk factors for the recurrence. However, the risk of recurrence in right-sided colon cancer was lower than in leftsided colon cancer. (HR, 0.712; 95% CI, 0.537–0.944; P = 0.018; I2 = 0.0%). No statistically significant differences were observed in the number of harvested lymph nodes, age, and sex. Conclusion T2 stage, lymphovascular invasion, venous invasion, CEA level, rectal cancer, and left-sided colon cancer were risk factors for recurrence in stage I CRC. Intensive monitoring and surveillance are warranted for patients with high-risk features of recurrence.

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.