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: The association between the fecal microbiota and colorectal cancer (CRC) risk has been suggested in epidemiologic studies. However, data from large-scale population-based studies are lacking. Materials and Methods: In this case-control study, we recruited 283 CRC patients from the Center for Colorectal Cancer, National Cancer Center Hospital, Korea to perform 16S rRNA gene sequencing of fecal samples. A total of 283 age- and sex-matched healthy participants were selected from 890 cohort of healthy Koreans that are publicly available (PRJEB33905). The microbial dysbiosis index (MDI) was calculated based on the differentially abundant species. The association between MDI and CRC risk was observed using conditional logistic regression. Sparse Canonical Correlation Analysis was performed to integrate species data with microbial pathways obtained by PICRUSt2. Results: There is a significant divergence of the microbial composition between CRC patients and controls (permutational multivariate analysis of variance p=0.001). Those who were in third tertile of the MDI showed a significantly increased risk of CRC in the total population (odds ratio [OR], 6.93; 95% confidence interval [CI], 3.98 to 12.06; p-trend < 0.001) compared to those in the lowest tertile. Similar results were found for men (OR, 6.28; 95% CI, 3.04 to 12.98; p-trend < 0.001) and women (OR, 7.39; 95% CI, 3.10 to 17.63; p-trend < 0.001). Bacteroides coprocola and Bacteroides plebeius species and 12 metabolic pathways were interrelated in healthy controls that explain 91% covariation across samples. Conclusion Dysbiosis in the fecal microbiota may be associated with an increased risk of CRC. Due to the potentially modifiable nature of the gut microbiota, our findings may have implications for CRC prevention among Koreans.

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.

Purpose: The association between the fecal microbiota and colorectal cancer (CRC) risk has been suggested in epidemiologic studies. However, data from large-scale population-based studies are lacking. Materials and Methods: In this case-control study, we recruited 283 CRC patients from the Center for Colorectal Cancer, National Cancer Center Hospital, Korea to perform 16S rRNA gene sequencing of fecal samples. A total of 283 age- and sex-matched healthy participants were selected from 890 cohort of healthy Koreans that are publicly available (PRJEB33905). The microbial dysbiosis index (MDI) was calculated based on the differentially abundant species. The association between MDI and CRC risk was observed using conditional logistic regression. Sparse Canonical Correlation Analysis was performed to integrate species data with microbial pathways obtained by PICRUSt2. Results: There is a significant divergence of the microbial composition between CRC patients and controls (permutational multivariate analysis of variance p=0.001). Those who were in third tertile of the MDI showed a significantly increased risk of CRC in the total population (odds ratio [OR], 6.93; 95% confidence interval [CI], 3.98 to 12.06; p-trend < 0.001) compared to those in the lowest tertile. Similar results were found for men (OR, 6.28; 95% CI, 3.04 to 12.98; p-trend < 0.001) and women (OR, 7.39; 95% CI, 3.10 to 17.63; p-trend < 0.001). Bacteroides coprocola and Bacteroides plebeius species and 12 metabolic pathways were interrelated in healthy controls that explain 91% covariation across samples. Conclusion Dysbiosis in the fecal microbiota may be associated with an increased risk of CRC. Due to the potentially modifiable nature of the gut microbiota, our findings may have implications for CRC prevention among Koreans.

Purpose: The association between the fecal microbiota and colorectal cancer (CRC) risk has been suggested in epidemiologic studies. However, data from large-scale population-based studies are lacking. Materials and Methods: In this case-control study, we recruited 283 CRC patients from the Center for Colorectal Cancer, National Cancer Center Hospital, Korea to perform 16S rRNA gene sequencing of fecal samples. A total of 283 age- and sex-matched healthy participants were selected from 890 cohort of healthy Koreans that are publicly available (PRJEB33905). The microbial dysbiosis index (MDI) was calculated based on the differentially abundant species. The association between MDI and CRC risk was observed using conditional logistic regression. Sparse Canonical Correlation Analysis was performed to integrate species data with microbial pathways obtained by PICRUSt2. Results: There is a significant divergence of the microbial composition between CRC patients and controls (permutational multivariate analysis of variance p=0.001). Those who were in third tertile of the MDI showed a significantly increased risk of CRC in the total population (odds ratio [OR], 6.93; 95% confidence interval [CI], 3.98 to 12.06; p-trend < 0.001) compared to those in the lowest tertile. Similar results were found for men (OR, 6.28; 95% CI, 3.04 to 12.98; p-trend < 0.001) and women (OR, 7.39; 95% CI, 3.10 to 17.63; p-trend < 0.001). Bacteroides coprocola and Bacteroides plebeius species and 12 metabolic pathways were interrelated in healthy controls that explain 91% covariation across samples. Conclusion Dysbiosis in the fecal microbiota may be associated with an increased risk of CRC. Due to the potentially modifiable nature of the gut microbiota, our findings may have implications for CRC prevention among Koreans.