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.

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: To assess the reliability and accuracy of an automated Cobb angle measurement (ACAM) using a convolutional neural network (CNN) for scoliosis evaluation and to compare measurement times. Materials and Methods: ACAM was applied to spine radiographs in 411 patients suspected of scoliosis. Observer 1 (consensus of two musculoskeletal radiologists) and observer 2 (a radiology resident) measured Cobb angle (CA). CA measurements were categorized using observer 1’s measurements as the reference standard. Inter-observer reliability and correlation were assessed using intraclass correlation coefficient (ICC) and Spearman’s rank correlation coefficient, respectively. Accuracy and measurement time of ACAM and observers were evaluated. Results: ACAM demonstrated excellent reliability and very high correlation with observer 1 (ICC = 0.976, Spearman’s rank correlation = 0.948), with a mean CA difference of 1.1. Overall accuracy was high (88.2%), particularly in mild (92.2%) and moderate (96%) scoliosis. Accuracy was lower in spinal asymmetry (77.1%) and higher in severe scoliosis (95%), although the CA was lower compared to the observers. ACAM significantly reduced measurement time by nearly half compared to the observers (p < 0.001). Conclusion ACAM using CNN enhances CA measurement for assessing mild or moderate scoliosis, despite limitations in spinal asymmetry or severe scoliosis. Nonetheless, it substantially decreases measurement time.

Purpose: To assess the reliability and accuracy of an automated Cobb angle measurement (ACAM) using a convolutional neural network (CNN) for scoliosis evaluation and to compare measurement times. Materials and Methods: ACAM was applied to spine radiographs in 411 patients suspected of scoliosis. Observer 1 (consensus of two musculoskeletal radiologists) and observer 2 (a radiology resident) measured Cobb angle (CA). CA measurements were categorized using observer 1’s measurements as the reference standard. Inter-observer reliability and correlation were assessed using intraclass correlation coefficient (ICC) and Spearman’s rank correlation coefficient, respectively. Accuracy and measurement time of ACAM and observers were evaluated. Results: ACAM demonstrated excellent reliability and very high correlation with observer 1 (ICC = 0.976, Spearman’s rank correlation = 0.948), with a mean CA difference of 1.1. Overall accuracy was high (88.2%), particularly in mild (92.2%) and moderate (96%) scoliosis. Accuracy was lower in spinal asymmetry (77.1%) and higher in severe scoliosis (95%), although the CA was lower compared to the observers. ACAM significantly reduced measurement time by nearly half compared to the observers (p < 0.001). Conclusion ACAM using CNN enhances CA measurement for assessing mild or moderate scoliosis, despite limitations in spinal asymmetry or severe scoliosis. Nonetheless, it substantially decreases measurement time.

Purpose: To assess the reliability and accuracy of an automated Cobb angle measurement (ACAM) using a convolutional neural network (CNN) for scoliosis evaluation and to compare measurement times. Materials and Methods: ACAM was applied to spine radiographs in 411 patients suspected of scoliosis. Observer 1 (consensus of two musculoskeletal radiologists) and observer 2 (a radiology resident) measured Cobb angle (CA). CA measurements were categorized using observer 1’s measurements as the reference standard. Inter-observer reliability and correlation were assessed using intraclass correlation coefficient (ICC) and Spearman’s rank correlation coefficient, respectively. Accuracy and measurement time of ACAM and observers were evaluated. Results: ACAM demonstrated excellent reliability and very high correlation with observer 1 (ICC = 0.976, Spearman’s rank correlation = 0.948), with a mean CA difference of 1.1. Overall accuracy was high (88.2%), particularly in mild (92.2%) and moderate (96%) scoliosis. Accuracy was lower in spinal asymmetry (77.1%) and higher in severe scoliosis (95%), although the CA was lower compared to the observers. ACAM significantly reduced measurement time by nearly half compared to the observers (p < 0.001). Conclusion ACAM using CNN enhances CA measurement for assessing mild or moderate scoliosis, despite limitations in spinal asymmetry or severe scoliosis. Nonetheless, it substantially decreases measurement time.

Background: This study aimed to investigate the recent prevalence, management, and comorbidities of diabetes among Korean adults aged ≥30 years by analyzing nationally representative data. Methods: This study used data from the Korea National Health and Nutrition Examination Survey from 2016 to 2018, and the percentage and total number of people ≥30 years of age with diabetes and impaired fasting glucose (IFG) were estimated. Results: In 2018, 13.8% of Korean adults aged ≥30 years had diabetes, and adults aged ≥65 years showed a prevalence rate of 28%. The prevalence of IFG was 26.9% in adults aged ≥30 years. From 2016 to 2018, 35% of the subjects with diabetes were not aware of their condition. Regarding comorbidities, 53.2% and 61.3% were obese and hypertensive, respectively, and 72% had hypercholesterolemia as defined by low-density lipoprotein cholesterol (LDL-C) ≥100 mg/dL in people with diabetes. Of the subjects with diabetes, 43.7% had both hypertension and hypercholesterolemia. With regard to glycemic control, only 28.3% reached the target level of <6.5%. Moreover, only 11.5% of subjects with diabetes met all three targets of glycosylated hemoglobin, blood pressure, and LDL-C. The percentage of energy intake from carbohydrates was higher in diabetes patients than in those without diabetes, while that from protein and fat was lower in subjects with diabetes. Conclusion The high prevalence and low control rate of diabetes and its comorbidities in Korean adults were confirmed. More stringent efforts are needed to improve the comprehensive management of diabetes to reduce diabetes-related morbidity and mortality.

Background: Infantile hemangioma (IH) is one of the most common tumors in infants. IH occurs more commonly in premature and low birth weight infants, but only a few studies have analyzed the risk factors for IH in neonatal intensive care units (NICUs) in Korea. Objective: We investigated the risk factors for IH in patients admitted to the NICU at a single institution. Methods: A single-center retrospective case-control study was conducted in 37 patients with hemangioma and 185 matched-control babies who were admitted to the NICU between 2013 and 2020. Odds ratios (ORs) and multivariate conditional logistic regression models were used to determine the associations between IH and the potential risk factors. Results: Of the 1,206 neonates admitted to the NICU, 37 had IH, and the prevalence was 3.1%. IH was most commonly found on the trunk (33.3%), followed by the head and neck (29.4%). After adjustment, the risk factors found to be significantly associated with IH were female sex (OR=3.0, 95% confidence interval [CI]=1.4∼6.8), extreme preterm (OR=6.9, 95% CI=1.3∼38.0), very low birth weight (OR=11.9, 95% CI=2.9∼49.3), low 1-minute Apgar scores of ＜7 (OR=2.3, 95% CI=1.1∼4.9), and multiple gestation (OR=5.0, 95% CI=1.7∼14.9). Conclusion This matched case–control study revealed that risk factors such as female sex, extremely preterm birth, very low birth weight, low 1-minute Apgar score, and multiple gestations may affect the occurrence of IH. Therefore, if these risk factors are present, they need to be actively managed at an early stage through close physical examination to prevent complications.

Background: Infantile hemangioma (IH) is one of the most common tumors in infants. IH occurs more commonly in premature and low birth weight infants, but only a few studies have analyzed the risk factors for IH in neonatal intensive care units (NICUs) in Korea. Objective: We investigated the risk factors for IH in patients admitted to the NICU at a single institution. Methods: A single-center retrospective case-control study was conducted in 37 patients with hemangioma and 185 matched-control babies who were admitted to the NICU between 2013 and 2020. Odds ratios (ORs) and multivariate conditional logistic regression models were used to determine the associations between IH and the potential risk factors. Results: Of the 1,206 neonates admitted to the NICU, 37 had IH, and the prevalence was 3.1%. IH was most commonly found on the trunk (33.3%), followed by the head and neck (29.4%). After adjustment, the risk factors found to be significantly associated with IH were female sex (OR=3.0, 95% confidence interval [CI]=1.4∼6.8), extreme preterm (OR=6.9, 95% CI=1.3∼38.0), very low birth weight (OR=11.9, 95% CI=2.9∼49.3), low 1-minute Apgar scores of ＜7 (OR=2.3, 95% CI=1.1∼4.9), and multiple gestation (OR=5.0, 95% CI=1.7∼14.9). Conclusion This matched case–control study revealed that risk factors such as female sex, extremely preterm birth, very low birth weight, low 1-minute Apgar score, and multiple gestations may affect the occurrence of IH. Therefore, if these risk factors are present, they need to be actively managed at an early stage through close physical examination to prevent complications.