BACKGROUND: As Korea advances into the ageing society, the number of elderly person receiving dialysis has increased. Two-year survival rate of the patients who received hemodialysis was 84.2% in 1996. But there is no estimate on the survival rate of the patients over age 65. Elderly persons are more prone to have dialysis complications and have more problems in cardiovascular system. The following is a 5-year-study on the elderly ESRD patients who underwent hemodialysis. METHODS: In this retrospective study, 825 patients had received hemodialysis at Seoul Paik Hospital from Jan. 1997 to Dec. 2002. The elderly group was consisted of 35 patients over age 65 and the non-elderly group was consisted of 43 patients below age 65 who received hemodialysis. And they had been traced for more than six months. The patient`s age, sex, occupation and whether the patient was married or not, had been compiled. Also taken into consideration was etiology, complications, initial laboratory data, electrocardiography, abdominal sonography, echocardiography, ftmndus examination, cause of death. RESULTS: Average age of the elderly and the non-elderly group was 70.1 and 47.4 years(p<0.00). nd parathyroid hormone were different between the two groups(p<0.05), other laboratory data were not. Prevalence of diabetes mellitus and hypertensive nephrosclerosis were not either. The overall 1, 2, 5 year survival rate was 97.3%, 93.4%, 73.7%. And the 5-year survival rate was 88.6% in the non-elderly group and it was 54.1% in the elderly group(Kaplan-Meier method). Causes of death were sepsis(n=3), cerebrovas cular accident(n=2), myocardial infarction, pneumonia and gastrointestinal bleeding, malignancy, withdrawal of treatment(1 patient respectively) in the elderly group and were myocardial infarction, withdrawal of treatment in the non-elderly group(n=2). CONCLUSION: The 5-year survival rate of the elderly patients was lower than the non-elderly(p<0.001). The contributing factor of death was not etiology but cormobid condition according to ageing process and socioeconomic circumstance. In other words, it was cardiovascular disease, infection due to impaired immune system and withdrawal of treatment due to economic problems. So it would be necessary to monitor carefully these factors for the elderly hemodialysis patients to improve survival..
Aged* ; Cardiovascular Diseases ; Cardiovascular System ; Cause of Death ; Diabetes Mellitus ; Dialysis ; Echocardiography ; Electrocardiography ; Hemorrhage ; Humans ; Immune System ; Kidney Failure, Chronic ; Korea ; Myocardial Infarction ; Nephrosclerosis ; Occupations ; Parathyroid Hormone ; Pneumonia ; Prevalence ; Renal Dialysis* ; Retrospective Studies ; Seoul ; Survival Rate

OBJECTIVE: This study examined the mediating role of internet addiction in the association between psychological resilience and depressive symptoms. METHODS: 837 Korean university students completed a survey with items of demographic information, Connor-Davidson Resilience Scale (CD-RISC), Internet Addiction Test (IAT), and Patient Health Questionnaire (PHQ-9) in 2015. The complex associations among psychological resilience, internet addiction, and depressive symptoms were delineated using structural equation models. RESULTS: In the most parsimonious model, the total effect and indirect effect of resilience on depressive symptoms via internet addiction, were statistically significant. The goodness of fit of the measurement model was satisfactory with fit indices, normed fit index (NFI) of 0.990, non-normed fit index (NNFI) of 0.997, comparative fit index (CFI) of 0.998, root mean square error (RMSEA) of 0.018 (90%CI=0.001–0.034); and Akaike Information Criterion (AIC) of -21.049. CONCLUSION: The association between psychological resilience and depressive symptoms was mediated by internet addiction in Korean university students. Enhancement of resilience programs could help prevent internet addiction and reduce the related depression risks.
Depression* ; Humans ; Internet* ; Negotiating* ; Resilience, Psychological

No abstract available.
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Background: With the ultimate goal of developing chatbot content to address consumer inquiries about pressure injuries (PIs), this study analyzed consumer perceptions of PI using big data. Methods: This study collected text data, with PI as the central word, from three search engines (Naver, Daum, Google) from January 2019 through December 2022, using Textom version 4.5. The words were refined through text mining, keyword analysis, and TF-IDF (term frequency-inverse document frequency) analysis. N-gram analysis and centrality visualization were conducted using Ucinet 6.0. The keywords and frequencies were clustered based on the frequency of words used in CONCOR (convergence of iteration correlation) analysis. Results: Consumers for PI showed a high perception of common sites for PI, concept of PI, healthcare facility for PI, PI products, PI care, PI-related life, and PI-related disease. Conclusion Development of chatbot content customized to consumers’ needs, based on seven clusters associated with consumers’ perception of PI obtained through extensive data analysis with PI as the central word, is expected to make a significant contribution to improving consumers’ understanding of PI and enhancing the quality of PI management.

Background: With the ultimate goal of developing chatbot content to address consumer inquiries about pressure injuries (PIs), this study analyzed consumer perceptions of PI using big data. Methods: This study collected text data, with PI as the central word, from three search engines (Naver, Daum, Google) from January 2019 through December 2022, using Textom version 4.5. The words were refined through text mining, keyword analysis, and TF-IDF (term frequency-inverse document frequency) analysis. N-gram analysis and centrality visualization were conducted using Ucinet 6.0. The keywords and frequencies were clustered based on the frequency of words used in CONCOR (convergence of iteration correlation) analysis. Results: Consumers for PI showed a high perception of common sites for PI, concept of PI, healthcare facility for PI, PI products, PI care, PI-related life, and PI-related disease. Conclusion Development of chatbot content customized to consumers’ needs, based on seven clusters associated with consumers’ perception of PI obtained through extensive data analysis with PI as the central word, is expected to make a significant contribution to improving consumers’ understanding of PI and enhancing the quality of PI management.

Background: With the ultimate goal of developing chatbot content to address consumer inquiries about pressure injuries (PIs), this study analyzed consumer perceptions of PI using big data. Methods: This study collected text data, with PI as the central word, from three search engines (Naver, Daum, Google) from January 2019 through December 2022, using Textom version 4.5. The words were refined through text mining, keyword analysis, and TF-IDF (term frequency-inverse document frequency) analysis. N-gram analysis and centrality visualization were conducted using Ucinet 6.0. The keywords and frequencies were clustered based on the frequency of words used in CONCOR (convergence of iteration correlation) analysis. Results: Consumers for PI showed a high perception of common sites for PI, concept of PI, healthcare facility for PI, PI products, PI care, PI-related life, and PI-related disease. Conclusion Development of chatbot content customized to consumers’ needs, based on seven clusters associated with consumers’ perception of PI obtained through extensive data analysis with PI as the central word, is expected to make a significant contribution to improving consumers’ understanding of PI and enhancing the quality of PI management.

Background: With the ultimate goal of developing chatbot content to address consumer inquiries about pressure injuries (PIs), this study analyzed consumer perceptions of PI using big data. Methods: This study collected text data, with PI as the central word, from three search engines (Naver, Daum, Google) from January 2019 through December 2022, using Textom version 4.5. The words were refined through text mining, keyword analysis, and TF-IDF (term frequency-inverse document frequency) analysis. N-gram analysis and centrality visualization were conducted using Ucinet 6.0. The keywords and frequencies were clustered based on the frequency of words used in CONCOR (convergence of iteration correlation) analysis. Results: Consumers for PI showed a high perception of common sites for PI, concept of PI, healthcare facility for PI, PI products, PI care, PI-related life, and PI-related disease. Conclusion Development of chatbot content customized to consumers’ needs, based on seven clusters associated with consumers’ perception of PI obtained through extensive data analysis with PI as the central word, is expected to make a significant contribution to improving consumers’ understanding of PI and enhancing the quality of PI management.