Background: Despite the considerable disease burden due to trauma injury, sufficient effort has not been made for the assessment of nationwide trauma care status in Korea. We explored the feasibility of a diagnosis code-based injury severity measuring method in light of its realworld usage. Methods: We used datasets from the National Emergency Department Information System to calculate the survival risk ratios (SRRs) and the Korean Trauma Data Bank to predict models, respectively. The target cohort was split into training and validation datasets using stratified random sampling in an 8:2 ratio. We established six major mortality prediction models depending on the included parameters: 1) the Trauma and Injury Severity Score (TRISS) (age, sex, original Revised Trauma Score [RTS], Injury Severity Score [ISS]), 2) extended International Classification of Diseases-based Injury Severity Score (ICISS) 1 (age, sex, original RTS, ICISS using international SRRs), 3) extended ICISS 2 (age, sex, original RTS, ICISS using Korean SRRs based on 4-digit diagnosis codes), 4) extended ICISS 3 (age, sex, original RTS, ICISS using Korean SRRs based on full-digit diagnosis codes), 5) extended ICISS 4 (age, sex, modified RTS, and ICISS using Korean SRRs based on 4-digit diagnosis codes), 6) extended ICISS 5 (age, sex, modified RTS, and ICISS using Korean SRRs based on full-digit diagnosis codes). We estimated the model using training datasets and fitted it to the validation datasets. We measured the area under the receiver operating characteristic curve (AUC) for discriminative ability. Overall performance was also evaluated using the Brier score. Results: We observed the feasibility of the extended ICISS models, though their performance was slightly lower than the TRISS model (training cohort, AUC 0.936–0.938 vs. 0.949).Regarding SRR calculation methods, we did not find statistically significant differences.The alternative use of the Alert, Voice, Pain, Unresponsive Scale instead of the Glasgow Coma Scale in the RTS calculation did not degrade model performance. Conclusion The availability of the practical ICISS model was observed based on the model performance. We expect our ICISS model to contribute to strengthening the Korean Trauma Care System by utilizing mortality prediction and severity classification.

Background: Despite the considerable disease burden due to trauma injury, sufficient effort has not been made for the assessment of nationwide trauma care status in Korea. We explored the feasibility of a diagnosis code-based injury severity measuring method in light of its realworld usage. Methods: We used datasets from the National Emergency Department Information System to calculate the survival risk ratios (SRRs) and the Korean Trauma Data Bank to predict models, respectively. The target cohort was split into training and validation datasets using stratified random sampling in an 8:2 ratio. We established six major mortality prediction models depending on the included parameters: 1) the Trauma and Injury Severity Score (TRISS) (age, sex, original Revised Trauma Score [RTS], Injury Severity Score [ISS]), 2) extended International Classification of Diseases-based Injury Severity Score (ICISS) 1 (age, sex, original RTS, ICISS using international SRRs), 3) extended ICISS 2 (age, sex, original RTS, ICISS using Korean SRRs based on 4-digit diagnosis codes), 4) extended ICISS 3 (age, sex, original RTS, ICISS using Korean SRRs based on full-digit diagnosis codes), 5) extended ICISS 4 (age, sex, modified RTS, and ICISS using Korean SRRs based on 4-digit diagnosis codes), 6) extended ICISS 5 (age, sex, modified RTS, and ICISS using Korean SRRs based on full-digit diagnosis codes). We estimated the model using training datasets and fitted it to the validation datasets. We measured the area under the receiver operating characteristic curve (AUC) for discriminative ability. Overall performance was also evaluated using the Brier score. Results: We observed the feasibility of the extended ICISS models, though their performance was slightly lower than the TRISS model (training cohort, AUC 0.936–0.938 vs. 0.949).Regarding SRR calculation methods, we did not find statistically significant differences.The alternative use of the Alert, Voice, Pain, Unresponsive Scale instead of the Glasgow Coma Scale in the RTS calculation did not degrade model performance. Conclusion The availability of the practical ICISS model was observed based on the model performance. We expect our ICISS model to contribute to strengthening the Korean Trauma Care System by utilizing mortality prediction and severity classification.

Background: Despite the considerable disease burden due to trauma injury, sufficient effort has not been made for the assessment of nationwide trauma care status in Korea. We explored the feasibility of a diagnosis code-based injury severity measuring method in light of its realworld usage. Methods: We used datasets from the National Emergency Department Information System to calculate the survival risk ratios (SRRs) and the Korean Trauma Data Bank to predict models, respectively. The target cohort was split into training and validation datasets using stratified random sampling in an 8:2 ratio. We established six major mortality prediction models depending on the included parameters: 1) the Trauma and Injury Severity Score (TRISS) (age, sex, original Revised Trauma Score [RTS], Injury Severity Score [ISS]), 2) extended International Classification of Diseases-based Injury Severity Score (ICISS) 1 (age, sex, original RTS, ICISS using international SRRs), 3) extended ICISS 2 (age, sex, original RTS, ICISS using Korean SRRs based on 4-digit diagnosis codes), 4) extended ICISS 3 (age, sex, original RTS, ICISS using Korean SRRs based on full-digit diagnosis codes), 5) extended ICISS 4 (age, sex, modified RTS, and ICISS using Korean SRRs based on 4-digit diagnosis codes), 6) extended ICISS 5 (age, sex, modified RTS, and ICISS using Korean SRRs based on full-digit diagnosis codes). We estimated the model using training datasets and fitted it to the validation datasets. We measured the area under the receiver operating characteristic curve (AUC) for discriminative ability. Overall performance was also evaluated using the Brier score. Results: We observed the feasibility of the extended ICISS models, though their performance was slightly lower than the TRISS model (training cohort, AUC 0.936–0.938 vs. 0.949).Regarding SRR calculation methods, we did not find statistically significant differences.The alternative use of the Alert, Voice, Pain, Unresponsive Scale instead of the Glasgow Coma Scale in the RTS calculation did not degrade model performance. Conclusion The availability of the practical ICISS model was observed based on the model performance. We expect our ICISS model to contribute to strengthening the Korean Trauma Care System by utilizing mortality prediction and severity classification.

Background: Despite the considerable disease burden due to trauma injury, sufficient effort has not been made for the assessment of nationwide trauma care status in Korea. We explored the feasibility of a diagnosis code-based injury severity measuring method in light of its realworld usage. Methods: We used datasets from the National Emergency Department Information System to calculate the survival risk ratios (SRRs) and the Korean Trauma Data Bank to predict models, respectively. The target cohort was split into training and validation datasets using stratified random sampling in an 8:2 ratio. We established six major mortality prediction models depending on the included parameters: 1) the Trauma and Injury Severity Score (TRISS) (age, sex, original Revised Trauma Score [RTS], Injury Severity Score [ISS]), 2) extended International Classification of Diseases-based Injury Severity Score (ICISS) 1 (age, sex, original RTS, ICISS using international SRRs), 3) extended ICISS 2 (age, sex, original RTS, ICISS using Korean SRRs based on 4-digit diagnosis codes), 4) extended ICISS 3 (age, sex, original RTS, ICISS using Korean SRRs based on full-digit diagnosis codes), 5) extended ICISS 4 (age, sex, modified RTS, and ICISS using Korean SRRs based on 4-digit diagnosis codes), 6) extended ICISS 5 (age, sex, modified RTS, and ICISS using Korean SRRs based on full-digit diagnosis codes). We estimated the model using training datasets and fitted it to the validation datasets. We measured the area under the receiver operating characteristic curve (AUC) for discriminative ability. Overall performance was also evaluated using the Brier score. Results: We observed the feasibility of the extended ICISS models, though their performance was slightly lower than the TRISS model (training cohort, AUC 0.936–0.938 vs. 0.949).Regarding SRR calculation methods, we did not find statistically significant differences.The alternative use of the Alert, Voice, Pain, Unresponsive Scale instead of the Glasgow Coma Scale in the RTS calculation did not degrade model performance. Conclusion The availability of the practical ICISS model was observed based on the model performance. We expect our ICISS model to contribute to strengthening the Korean Trauma Care System by utilizing mortality prediction and severity classification.

PURPOSE: The aim of this study was to evaluate the efficacy of pamidronate therapy in children and adolescents with secondary osteoporosis. METHODS: Nine patients (7 males, 2 females, 13.2 +/- 2.5 years, 10.1-17.4 years) with secondary osteoporosis who had a history of severe bone pain and/or fracture were enrolled. Intravenous pamidronate 1.5 mg/kg (0.5 mg/kg for 3 consecutive days) was given every 6 to 8 weeks for 0.86 +/- 0.15 years (6 or 8 cycles). Bone mineral density (BMD) in lumbar spine and femoral neck and their Z-scores were measured before treatment, after the fourth and last cycle (sixth or eighth cycle). RESULTS: Underlying diseases were as follows; neurofibromatosis type 1 (n = 2), epilepsy with/without cerebral palsy (N=2), autoimmune disease treated with steroid (n = 2), hematologic malignancy (n = 3). Bone pain was relieved in most of the patients after the first cycle of treatment, and no more fracture occurred thereafter. There was a significant increase in BMD Z-score of the lumbar spine and femoral neck after the last cycle of therapy, compared to baseline values (from -3.91 +/- 1.79 to 1.86 +/- 1.18, in L1-4 and -3.71 +/- 1.83 to -2.53 +/- 1.77 for femoral neck; P = 0.008 and 0.011, respectively). However, there was no significant change in BMD Z-scores between the fourth cycle and the last cycle. Fever developed in 7 out of 9 patients (77.8%), which was relieved by antipyretics. Total serum levels of calcium and phosphorus were significantly decreased (calcium, P = 0.008; phosphorus, P = 0.015) after pamidronate therapy, and three of them experienced symptomatic hypocalcemia during the first cycle. The growth velocity was normal during follow-up periods (mean, 4.47 +/- 1.69 years; range, 1.05 to 6.77 years). CONCLUSION: In conclusion, pamidronate can be administered to the patients with secondary osteoporosis, relieving the symptoms and signs effectively and safely. However, its side effects should be monitored during treatment.
Adolescent ; Antipyretics ; Autoimmune Diseases ; Bone Density ; Calcium ; Cerebral Palsy ; Child ; Diphosphonates ; Epilepsy ; Female ; Femur Neck ; Fever ; Follow-Up Studies ; Hematologic Neoplasms ; Humans ; Hypocalcemia ; Male ; Neurofibromatosis 1 ; Osteoporosis ; Phosphorus ; Spine

PURPOSE: Short sleep duration is associated with obesity. Urinary 6-sulfatoxymelatonin (6-OHMS), the principal metabolite of melatonin, is closely related with sleep. We evaluated the difference in urinary 6-OHMS levels between obese girls and normal weight girls, and the relationship of urinary 6-OHMS with other hormones regulating body weight and metabolism. METHODS: A total of 79 girls (6.3 to 12.4 years) were included in this study, of whom 34 were obese; 15, overweight; and 30, normal-weight. We examined their pubertal status and bone age. Fasting serum levels of total ghrelin, leptin, insulin, and first morning urinary 6-OHMS were measured. Homeostatic model assessment-insulin resistance (HOMA-IR) was calculated from the fasting insulin and glucose levels. RESULTS: There was no significant difference in the creatinine adjusted 6-OHMS levels between the obese girls and the control group. Urinary 6-OHMS did not show any correlations with body mass index (BMI), BMI percentile, total ghrelin, leptin, and HOMA-IR. Negative correlations were found between urinary 6-OHMS levels and chronological and bone ages. CONCLUSION: Our results suggest that melatonin production is not reduced consistently in obese girls.
Body Mass Index ; Body Weight ; Child ; Creatinine ; Fasting ; Ghrelin ; Glucose ; Humans ; Insulin ; Leptin ; Melatonin ; Obesity

Purpose: Despite enzyme replacement therapy (ERT) and/or allogeneic hematopoietic stem cell transplantation, individuals with mucopolysaccharidosis (MPS) I or II often experience significant growth deficiencies. This study aimed to assess the safety and efficacy of recombinant human growth hormone (hGH) treatment in children diagnosed with MPS I or II. Materials and Methods: A total of nine pediatric patients—four with MPS I and five with MPS II—underwent treatment with ERT and hGH at Samsung Medical Center. Results: The mean hGH dose administered was 0.26±0.03 mg/kg/week. In the MPS I group, three patients showed an increase in height Z-score from –4.09±0.83 to –3.68±0.43 after 1 year of hGH treatment, and to –3.10±0.72 by the end of the hGH regimen. In the MPS II group, while the height Z-score of four patients decreased according to standard growth charts, it improved from 1.61±1.79 to 2.71±1.68 based on the disease-specific growth chart through hGH treatment. Two patients discontinued hGH treatment due to lack of efficacy after 22 and 6 months each of treatment, respectively. No new-onset neurological symptoms or necessity for prosthetic or orthopedic surgery were reported during hGH treatment. Conclusion This study provides insights into the impact of hGH on MPS patients, demonstrating its potential to reverse growth deceleration in some cases. Further research is needed to explore the long-term effects of hGH on changes in body composition, muscle strength, and bone health in this population.

Background: There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters. Methods: Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method. Results: The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included. Conclusion We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.

BACKGROUND: We evaluated the efficacy and safety of acarbose add-on therapy in Korean patients with type 2 diabetes mellitus (T2DM) who are inadequately controlled with metformin and sitagliptin. METHODS: A total of 165 subjects were randomized to metformin and sitagliptin (Met+Sita, n=65), metformin, sitagliptin, and acarbose (Met+Sita+Acarb, n=66) and sitagliptin and acarbose (Sita+Acarb, exploratory assessment, n=34) therapy in five institutions in Korea. After 16 weeks of acarbose add-on or metformin-switch therapy, a triple combination therapy was maintained from week 16 to 24. RESULTS: The add-on of acarbose (Met+Sita+Acarb group) demonstrated a 0.44%±0.08% (P<0.001 vs. baseline) decrease in glycosylated hemoglobin (HbA1c) at week 16, while changes in HbA1c were insignificant in the Met+Sita group (−0.09%±0.10%, P=0.113). After 8 weeks of triple combination therapy, HbA1c levels were comparable between Met+Sita and Met+Sita+Acarb group (7.66%±0.13% vs. 7.47%±0.12%, P=0.321). Acarbose add-on therapy demonstrated suppressed glucagon secretion (area under the curve of glucagon, 4,726.17±415.80 ng·min/L vs. 3,314.38±191.63 ng·min/L, P=0.004) in the absence of excess insulin secretion during the meal tolerance tests at week 16 versus baseline. The incidence of adverse or serious adverse events was similar between two groups. CONCLUSION: In conclusion, a 16-week acarbose add-on therapy to metformin and sitagliptin, effectively lowered HbA1c without significant adverse events. Acarbose might be a good choice as a third-line therapy in addition to metformin and sitagliptin in Korean subjects with T2DM who have predominant postprandial hyperglycemia and a high carbohydrate intake.
Acarbose ; Diabetes Mellitus, Type 2 ; Drug Therapy, Combination ; Glucagon ; Hemoglobin A, Glycosylated ; Humans ; Hyperglycemia ; Incidence ; Insulin ; Korea ; Meals ; Metformin ; Sitagliptin Phosphate

Obesity and its related factors are known to suppress the secretion of growth hormone (GH). We aimed to evaluate the influence of body mass index (BMI) on the peak GH response to provocative testing in short children without GH deficiency. We conducted a retrospective review of medical records of 88 children (2-15 yr old) whose height was less than 3 percentile for one's age and sex, with normal results (peak GH level > 10 ng/mL) of GH provocative testing with clonidine and dopamine. Peak stimulated GH level, height, weight, pubertal status and serum IGF-1 level were measured. Univariate analysis showed that the BMI standard deviation score (SDS) correlated negatively with the natural log (ln) of the peak stimulated GH level (ln peak GH). BMI SDS did not correlate significantly with sex, age, pubertal status, or ln IGF-1 level. BMI SDS correlated negatively with ln peak GH level induced by clonidine but not by dopamine. In stepwise multivariate regression analysis, BMI SDS was the only significant predictor of ln peak GH level in the combination of tests and the clonidine test, but not in the dopamine test. In children without GH deficiency, BMI SDS correlates negatively with the peak GH level. BMI SDS should be included in the analysis of the results of GH provocation tests, especially tests with clonidine.
Adolescent ; Body Height ; *Body Mass Index ; Body Weight ; Child ; Child, Preschool ; Clonidine/therapeutic use ; Dopamine/therapeutic use ; Dwarfism/drug therapy ; Female ; Human Growth Hormone/*analysis ; Humans ; Insulin-Like Growth Factor I/analysis ; Male ; Regression Analysis ; Retrospective Studies