Purpose: It is important to understand changes in energy requirements in critically ill patients with sepsis. This study investigates alterations in energy requirements based on the clinical course of sepsis in patients admitted to the surgical intensive care unit (SICU) using indirect calorimetry. Methods: In this prospective study, 36 patients admitted to the surgical intensive care unit with sepsis were analyzed. Using indirect calorimetry, the resting energy expenditure (REE) and respiratory quotient (RQ) were assessed on the 1st, 3rd, and 7th day of Intensive Care Unit admission. Measured REE through indirect calorimetry was compared with the predictive equations (Weight-based, Harris-Benedict, IretonJones, and Penn state 2003) using intraclass correlation coefficient (ICC) and Bland-Altman analysis. Results: Measured REE was highest on Day 1 and remained unchanged on Day 3 and 7 (Day 1 vs. Day 3 vs. Day 7: 24.29 ± 3.72 kcal/kg vs. 22.42 ± 3.72 kcal/kg vs. 23.26 ± 5.78 kcal/kg). RQ decreased on Day 3 but increased on Day 7 after caloric intake (Day 1 vs. Day 3 vs. Day 7: 0.69 ± 0.06 vs. 0.67 ± 0.05 vs. 0.71 ± 0.06). Comparing the correlation between the 4 predictive equations and the measured REE, the Penn state 2003 equation demonstrated the highest correlation at each time point, although it showed a decreasing trend over time (Penn state equation ICC: Day 1-0.71, Day 3-0.65, Day 7-0.53). Conclusion In sepsis patients, it is necessary to understand metabolic changes according to the clinical course and provide appropriate calories as determined by using indirect calorimetry when the patients enter the stable phase.

Purpose: It is important to understand changes in energy requirements in critically ill patients with sepsis. This study investigates alterations in energy requirements based on the clinical course of sepsis in patients admitted to the surgical intensive care unit (SICU) using indirect calorimetry. Methods: In this prospective study, 36 patients admitted to the surgical intensive care unit with sepsis were analyzed. Using indirect calorimetry, the resting energy expenditure (REE) and respiratory quotient (RQ) were assessed on the 1st, 3rd, and 7th day of Intensive Care Unit admission. Measured REE through indirect calorimetry was compared with the predictive equations (Weight-based, Harris-Benedict, IretonJones, and Penn state 2003) using intraclass correlation coefficient (ICC) and Bland-Altman analysis. Results: Measured REE was highest on Day 1 and remained unchanged on Day 3 and 7 (Day 1 vs. Day 3 vs. Day 7: 24.29 ± 3.72 kcal/kg vs. 22.42 ± 3.72 kcal/kg vs. 23.26 ± 5.78 kcal/kg). RQ decreased on Day 3 but increased on Day 7 after caloric intake (Day 1 vs. Day 3 vs. Day 7: 0.69 ± 0.06 vs. 0.67 ± 0.05 vs. 0.71 ± 0.06). Comparing the correlation between the 4 predictive equations and the measured REE, the Penn state 2003 equation demonstrated the highest correlation at each time point, although it showed a decreasing trend over time (Penn state equation ICC: Day 1-0.71, Day 3-0.65, Day 7-0.53). Conclusion In sepsis patients, it is necessary to understand metabolic changes according to the clinical course and provide appropriate calories as determined by using indirect calorimetry when the patients enter the stable phase.

Purpose: It is important to understand changes in energy requirements in critically ill patients with sepsis. This study investigates alterations in energy requirements based on the clinical course of sepsis in patients admitted to the surgical intensive care unit (SICU) using indirect calorimetry. Methods: In this prospective study, 36 patients admitted to the surgical intensive care unit with sepsis were analyzed. Using indirect calorimetry, the resting energy expenditure (REE) and respiratory quotient (RQ) were assessed on the 1st, 3rd, and 7th day of Intensive Care Unit admission. Measured REE through indirect calorimetry was compared with the predictive equations (Weight-based, Harris-Benedict, IretonJones, and Penn state 2003) using intraclass correlation coefficient (ICC) and Bland-Altman analysis. Results: Measured REE was highest on Day 1 and remained unchanged on Day 3 and 7 (Day 1 vs. Day 3 vs. Day 7: 24.29 ± 3.72 kcal/kg vs. 22.42 ± 3.72 kcal/kg vs. 23.26 ± 5.78 kcal/kg). RQ decreased on Day 3 but increased on Day 7 after caloric intake (Day 1 vs. Day 3 vs. Day 7: 0.69 ± 0.06 vs. 0.67 ± 0.05 vs. 0.71 ± 0.06). Comparing the correlation between the 4 predictive equations and the measured REE, the Penn state 2003 equation demonstrated the highest correlation at each time point, although it showed a decreasing trend over time (Penn state equation ICC: Day 1-0.71, Day 3-0.65, Day 7-0.53). Conclusion In sepsis patients, it is necessary to understand metabolic changes according to the clinical course and provide appropriate calories as determined by using indirect calorimetry when the patients enter the stable phase.

Background/Aims: This study evaluated the long-term cardiovascular complications among Korean patients with hypertension and compared them with that of controls without hypertension. Methods: The Korean Hypertension Cohort (KHC) enrolled 11,043 patients with hypertension and followed them for more than 10 years. Age- and sex-matched controls without hypertension were enrolled at a 1:10 ratio. We compared the incidence of cardiovascular events and death among patients and controls without hypertension. Results: The mean age was 59 years, and 34.8% and 16.5% of the patients belonged to the high and moderate cardiovascular risk groups, respectively. During the 10-year follow-up, 1,591 cardiovascular events (14.4%) with 588 deaths (5.3%) occurred among patients with hypertension and 7,635 cardiovascular events (6.9%) with 4,826 deaths (4.4%) occurred among controls. Even the low-risk population with hypertension showed a higher cardiovascular event rate than the population without hypertension. Although blood pressure measurements in the clinic showed remarkable inaccuracy compared with those measured in the national health examinations, systolic blood pressure (SBP) ≥ 150 mmHg was significantly associated with a higher risk of cardiovascular events. Conclusions This long-term follow-up study confirmed the cardiovascular event rates among Korean hypertensive patients were substantial, reaching 15% in 10 years. SBP levels ≥ 150 mmHg were highly associated with occurrence of cardiovascular event rates.

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7–12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343;median age [IQR], 10 [4–15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5–14] years; male:female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

Since its introduction, laparoscopic surgery has been often preferred over open surgery in obstetrics and gynecology due to its advantages, such as less bleeding, lower incidence of adhesions, reduced postoperative pain, short hospital stay, and quick return to daily life. However, in the case of complex surgeries, laparoscopy presented some limitations. Nonetheless, since the 1980s, medical robots have been introduced to overcome the technical limitations of laparoscopy and start a new age for minimally invasive surgery. In this review, we explore the indications and advantages and disadvantages of robotic surgery in the field of gynecology, and try to assess the recent trend of robotic surgery.

Purpose: To evaluate the applicability of Greulich-Pyle (GP) standards to bone age (BA) assessment in healthy Korean children using manual and deep learning-based methods. Materials and Methods: We collected 485 hand radiographs of healthy children aged 2–17 years (262 boys) between 2008 and 2017. Based on GP method, BA was assessed manually by two radiologists and automatically by two deep learning-based BA assessment (DLBAA), which estimated GP-assigned (original model) and optimal (modified model) BAs. Estimated BA was compared to chronological age (CA) using intraclass correlation (ICC), Bland-Altman analysis, linear regression, mean absolute error, and root mean square error. The proportion of children showing a difference >12 months between the estimated BA and CA was calculated. Results: CA and all estimated BA showed excellent agreement (ICC ≥0.978, p<0.001) and significant positive linear correlations (R2 ≥0.935, p<0.001). The estimated BA of all methods showed systematic bias and tended to be lower than CA in younger patients, and higher than CA in older patients (regression slopes ≤-0.11, p<0.001). The mean absolute error of radiologist 1, radiologist 2, original, and modified DLBAA models were 13.09, 13.12, 11.52, and 11.31 months, respectively. The difference between estimated BA and CA was >12 months in 44.3%, 44.5%, 39.2%, and 36.1% for radiologist 1, radiologist 2, original, and modified DLBAA models, respectively. Conclusion Contemporary healthy Korean children showed different rates of skeletal development than GP standard-BA, and systemic bias should be considered when determining children’s skeletal maturation.

Background/Aims: Hypertension (HT) has a significant impact on public health and medical expenses. However, HT is a chronic disease that requires the long-term follow-up of a large number of patients. Methods: The Korean Hypertension Cohort (KHC) study aimed to develop a model for calculating cardiovascular risk in HT patients by linking and utilizing the detailed clinical and longitudinal data from hospitals and the national health insurance claim database, respectively. This cohort had a planned sample size of over 11,000 HT patients and 100,000 non-HT controls. Eligible patients were hypertensive patients, who were presenting for the first time and were diagnosed with HT as a main disease from 2006 to 2011. Long-term survival data over a period of approximately 9 years were obtained from the national health insurance claim and national health examination data. Results: This cohort enrolled 11,083 patients with HT. The mean age was 58.87 ± 11.5 years, 50.5% were male, and 31.4% were never-treated HT. Of the enrolled patients, 32.9% and 37.7% belonged to the high and moderate cardiovascular risk groups, respectively. Initial blood pressures were 149.4 ± 18.5/88.5 ± 12.5 mmHg. During the 2 years hospital data follow-up period, blood pressures lowered to 130.8 ± 14.1/78.0 ± 9.7 mmHg with 1.9 ± 1.0 tablet doses of antihypertensive medication. Cardiovascular events occurred in 7.5% of the overall patients; 8.5%, 8.8%, and 4.7% in the high, moderate, and low risk patients, respectively. Conclusions The KHC study has provided important information on the long-term outcomes of HT patients according to the blood pressure, comorbid diseases, medication, and adherence, as well as health behaviors and health resource use.

Background/Aims: Hypertension (HT) has a significant impact on public health and medical expenses. However, HT is a chronic disease that requires the long-term follow-up of a large number of patients. Methods: The Korean Hypertension Cohort (KHC) study aimed to develop a model for calculating cardiovascular risk in HT patients by linking and utilizing the detailed clinical and longitudinal data from hospitals and the national health insurance claim database, respectively. This cohort had a planned sample size of over 11,000 HT patients and 100,000 non-HT controls. Eligible patients were hypertensive patients, who were presenting for the first time and were diagnosed with HT as a main disease from 2006 to 2011. Long-term survival data over a period of approximately 9 years were obtained from the national health insurance claim and national health examination data. Results: This cohort enrolled 11,083 patients with HT. The mean age was 58.87 ± 11.5 years, 50.5% were male, and 31.4% were never-treated HT. Of the enrolled patients, 32.9% and 37.7% belonged to the high and moderate cardiovascular risk groups, respectively. Initial blood pressures were 149.4 ± 18.5/88.5 ± 12.5 mmHg. During the 2 years hospital data follow-up period, blood pressures lowered to 130.8 ± 14.1/78.0 ± 9.7 mmHg with 1.9 ± 1.0 tablet doses of antihypertensive medication. Cardiovascular events occurred in 7.5% of the overall patients; 8.5%, 8.8%, and 4.7% in the high, moderate, and low risk patients, respectively. Conclusions The KHC study has provided important information on the long-term outcomes of HT patients according to the blood pressure, comorbid diseases, medication, and adherence, as well as health behaviors and health resource use.