ABSTRACT
Purpose@#The aim of this study was to examine the nutritional intake status of the lung cancer patients who underwent pulmonary resection and to analyze the relationship between the status of the nutritional intake and the occurrence of postoperative pulmonary complications. @*Methods@#This study was a secondary analysis to determine whether the changes in the nutritional intake after surgery were related to pulmonary complications. Data of a total of 89 patients were included in the analysis and the nutritional intake status was confirmed using a 24-hour dietary recall method. The data were analyzed by descriptive statistics, chi-square or Fisher’s exact test, and ANOVA using the SPSS WIN 26.0 program and word clouds were generated using the R software program. @*Results@#Overall, a decrease in the postoperative nutritional intake was observed in the patients who underwent pulmonary resection, except for the intake of fat. The pulmonary complications were identified to be associated with BMI and the presence of comorbidity. Twenty-three out of 74 patients with vitamin E levels below the Estimated Average Requirements developed pulmonary complications after surgery. @*Conclusion@#Lung cancer patients who underwent pulmonary resection generally have difficulty in acquiring appropriate nutritional intake and need balanced nutritional management. Future investigations on the impact of increased vitamin E intake on postoperative pulmonary complications may provide better insight into the relationship between vitamin E intake and pulmonary complication among patients who underwent pulmonary resection.
ABSTRACT
Purpose@#This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). @*Methods@#This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients’ electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow–Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. @*Results@#The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ2 = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. @*Conclusion@#The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.
ABSTRACT
Purpose@#This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). @*Methods@#This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients’ electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow–Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. @*Results@#The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ2 = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. @*Conclusion@#The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.
ABSTRACT
Nursing language plays an important role in describing and defining nursing phenomena and nursing actions. There are numerous vocabularies describing nursing diagnoses, interventions, and outcomes in nursing domain. However, lack of a standardized, unified nursing language is considered as a problem for further development of the discipline of nursing. With this background this study was conducted to construct a semantic network based on cross mapping of existing nursing terminologies. specifically NANDA taxonomy I, Omaha system, HHCC. and NIC. which arc recognized as standard terminology by the American Nurses Association. The nursing semantic network was proposed with the cross mapping results based on the UMLS semantic network.
Subject(s)
American Nurses' Association , Classification , Nursing Diagnosis , Nursing , Semantics , Unified Medical Language System , VocabularyABSTRACT
In this study, we developed MobileNurseTM; a prototype of mobile nursing information system using PDA. MobileNurseTM is designed to communicate with hospital information system(HIS) via mobile support station(MSS) which interchanges and stores clinical data. MobileNurseTM consists of 3 components. The first component is medical order checking module. It enables nurses to retrieve patients' information such as physicians' orders and test results at anywhere or anytime. The second component is nursing recording module which helps nurses to record the results of their practices at bedside. On the autosynchronization of MSS and PDA, the data in PDA and MSS can be interchanged and updated respectively. The last component is nursing unit care planing module. It is helpful for retrieving the nursing care plans of patients that are expected to be done, such as patients' discharge, consultation, or transfer. With use of PDA in clinical environment, nurses can spend more time on caring for patients by reducing time-consuming, redundant paperwork. It is promising that this 'point-of-care system enables nurses to improve the quality of nursing care.