• Korean Journal of Community Nutrition
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• v.26 no.4
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• pp.280-295
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• 2021

Objectives: This study aimed to examine the characteristics of patients according to their nutritional status as assessed by five nutritional screening tools: Patient-Generated Subjective Global Assessment (PG-SGA), NUTRISCORE, Nutritional Risk Index (NRI), Prognostic Nutritional Index (PNI), and Controlling Nutritional Status (CONUT) and to compare the agreement, sensitivity, and specificity of these tools. Methods: A total of 952 gastric cancer patients who underwent gastrectomy and chemotherapy from January 2009 to December 2012 at the Samsung Medical Center were included. We categorized patients into malnourished and normal according to the five nutritional screening tools 1 month after surgery and compared their characteristics. We also calculated the Spearman partial correlation, Cohen's Kappa coefficient, the area under the curve (AUC), sensitivity, and specificity of each pair of screening tools. Results: We observed 86.24% malnutrition based on the PG-SGA and 85.82% based on the NUTRISCORE among gastric cancer patients in our study. When we applied NRI or CONUT, however, the malnutrition levels were less than 30%. Patients with malnutrition as assessed by the PG-SGA, NUTRISCORE, or NRI had lower intakes of energy and protein compared to normal patients. When NRI, PNI, or CONUT were used to identify malnutrition, lower levels of albumin, hemoglobin, total lymphocyte count, total cholesterol, and longer postoperative hospital stays were observed among patients with malnutrition compared to those without malnutrition. We found relatively high agreement between PG-SGA and NUTRISCORE; sensitivity was 90.86% and AUC was 0.78. When we compared NRI and PNI, sensitivity was 99.64% and AUC was 0.97. AUC ranged from 0.50 to 0.67 for comparisons between CONUT and each of the other nutritional screening tools. Conclusions: Our study suggests that PG-SGA and NRI have a relatively high agreement with the NUTRISCORE and PNI, respectively. Further cohort studies are needed to examine whether the nutritional status assessed by PG-SGA, NUTRISCORE, NRI, PNI, and CONUT predicts the gastric cancer prognosis.

• Korean Journal of Community Nutrition
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• v.27 no.3
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• pp.205-222
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• 2022

Objectives: This study examined the characteristics of patients according to nutritional status assessed by five nutritional screening tools: Patient-Generated Subjective Global Assessment (PG-SGA), NUTRISCORE, Nutritional Risk Index (NRI), Prognostic Nutritional Index (PNI), and Controlling Nutritional Status (CONUT) and to compare the agreement, sensitivity, and specificity of these tools. Methods: A total of 952 gastric cancer patients who underwent gastrectomy and chemotherapy from January 2009 to December 2012 were included. The patients were categorized into malnutrition and normal status according to five nutritional screening tools one month after surgery. The Spearman partial correlation, Cohen's Kappa coefficient, the area under the curve (AUC), sensitivity, and specificity of each two screening tools were calculated. Results: Malnutrition was observed in 86.24% of patients based on the PG-SGA and 85.82% based on the NUTRISCORE. When NRI or CONUT were applied, the proportions of malnutrition were < 30%. Patients with malnutrition had lower intakes of energy and protein than normal patients when assessed using the PG-SGA, NUTRISCORE, or NRI. Lower levels of albumin, hemoglobin, total lymphocyte count, and total cholesterol and longer postoperative hospital stays were observed among patients with malnutrition compared to normal patients when NRI, PNI, or CONUT were applied. Relatively high agreement for NUTRISCORE relative to PG-SGA was found; the sensitivity was 90.86%, and the AUC was 0.78. When NRI, PNI, and CONUT were compared, the sensitivities were 23.72% for PNI relative to NRI, 44.53% for CONUT relative to NRI, and 90.91% for CONUT relative to PNI. The AUCs were 0.95 for NRI relative to PNI and 0.91 for CONUT relative to PNI. Conclusions: NUTRISCORE had a high sensitivity compared to PG-SGA, and CONUT had a high sensitivity compared to PNI. NRI had a high specificity compared to PNI. This relatively high sensitivity and specificity resulted in 77.00% agreement between PNI and CONUT and 77.94% agreement between NRI and PNI. Further cohort studies will be needed to determine if the nutritional status assessed by PG-SGA, NUTRISCORE, NRI, PNI, and CONUT predicts the gastric cancer prognosis.

• Journal of Preventive Medicine and Public Health
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• v.48 no.1
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• pp.48-52
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• 2015

Objectives: This retrospective cohort study aimed at calculating some parameters of changes in the findings of the subsequent screening mammography (SSM) in female Korean volunteers. Methods: The study included screenees aged 30 to 79 years who underwent SSM voluntarily after testing negative in the baseline screenings performed between January 2007 and December 2011. A change to a positive result was defined as category 4 or 5 by using the American College of Radiology Breast Imaging Reporting and Data System. The proportion of results that had changed to positive (CP, %) was calculated by dividing the number of cases with results that were positive in the SSM by the total number of study participants. The rate of results that had changed to positive (CR, cases per 100 000 screenee-months) was calculated by dividing the number of cases with results that were positive in the SSM by the total number of months of the follow-up period. Results: The overall CP and CR in all age groups (n=77 908) were 2.26% and 93.94 cases per 100 000 screenee-months, respectively. The median CP interval in the subjects who had positive SSM results was 30 to 36 months, while that in the age group of 30 to 39 years was shorter. Conclusions: Different screening intervals should be considered among women aged between 30 and 59 years. In addition, a strategy for a screening program should be developed for the age group of 30 to 39 years, in particular.

• Journal of Preventive Medicine and Public Health
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• v.52 no.6
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• pp.393-404
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• 2019

Objectives: The purpose of this study was to determine the associations between blood hemoglobin (Hgb) levels and the risk of death by specific causes. Methods: Using the National Health Insurance Services-National Health Screening Cohort (n=487 643), we classified serum Hgb levels into 6 sex-specific groups. Cox regression analysis was used to analyze the associations between Hgb levels and the risk of cause-specific death. Results: Hgb levels in male population showed a U-shaped, J-shaped, or inverse J-shaped association with the risk of death from ischemic heart disease, acute myocardial infarction, liver cancer, cirrhosis and chronic obstructive pulmonary disease (COPD) (all non-linear p<0.05; hazard ratio [HR]; 95% confidence interval [CI]) for the lowest and the highest Hgb levels for the risk of each cause of death in male population: HR, 1.14; 95% CI, 0.98 to 1.34; HR, 2.87; 95% CI, 1.48 to 5.57; HR, 1.16; 95% CI, 0.96 to 1.40; HR, 3.05; 95% CI, 1.44 to 6.48; HR, 1.36; 95% CI, 1.18 to 1.56; HR, 2.11; 95% CI, 1.05 to 4.26; HR, 3.64; 95% CI, 2.49 to 5.33; HR, 5.97; 95% CI, 1.44 to 24.82; HR, 1.62; 95% CI, 1.14 to 2.30; HR, 3.84; 95% CI, 1.22 to 12.13, respectively), while in female population, high Hgb levels were associated with a lower risk of death from hypertension and a higher risk of death from COPD (overall p<0.05; HR, 1.86; 95% CI, 1.29 to 2.67 for the lowest Hgb levels for hypertension; overall p<0.01, HR, 6.60; 95% CI, 2.37 to 18.14 for the highest Hgb levels for COPD). For the risk of lung cancer death by Hgb levels, a linear negative association was found in male population (overall p<0.01; the lowest Hgb levels, HR, 1.17; 95% CI, 1.05 to 1.33) but an inverse J-shaped association was found in female population (non-linear p=0.01; HR, 1.25; 95% CI, 0.96 to 1.63; HR, 2.58; 95% CI, 1.21 to 5.50). Conclusions: Both low and high Hgb levels were associated with an increased risk of death from various causes, and some diseases showed different patterns according to sex.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.1009-1018
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• 2022

Objective: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. Materials and Methods: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). Results: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. Conclusion: This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

• Journal of Hospice and Palliative Care
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• v.19 no.2
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• pp.119-126
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• 2016

Purpose: This study analyzed the difference in survival time of patients with delirium according to sedative medication. Methods: From January 2012 through December 2013, a retrospective cohort study was performed using the electronic medical records (EMR) of Pusan National University Hospital. Among 900 patients who died from cancer, we selected 240 who suffered delirium based on the EMR. The Nu-DESC delirium screening test was used to diagnose delirium. Results: The median length of delirium period was five days. Delirium characteristics were dominated by inappropriate behaviors (35.0%). Sedatives were administered in 72.1% of the cases. The most frequently used sedative was haloperidol which was used in 59.6% of cases. The delirium period significantly differed by patients' age (F=3.96, P=0.021), cancer type (F=3.31, P=0.010), chemotherapy (t=-3.44 P=0.001). The average survival time was 16.85 days for the sedative medication group and 9.37 days for the non-medication group, which, however, was not significant (t=1.766, P=0.079). Conclusion: In this study, the use of sedatives did not affect patients' survival time. Thus, appropriate sedative medication can be positively recommended to comfort terminal cancer patients and their families.