居家醫療管灌個案營養不良風險預測

Prediction of the Home Medical Feeding Patient Elderly at Risk for Malnutrition

近年來，臺灣地區居家醫療個案逐年增加，因無法即時掌握其營養情形，居家醫療個案的營養狀況和問題值得被關注。本研究採橫斷式研究設計，探討居家醫療個案的營養不良風險預測。以立意取樣，選取中部地區居家醫療整合計畫採鼻胃管及造廔口管灌居家醫療個案為研究對象，共210位個案。研究工具採結構式問卷—居家醫療管灌個案營養狀況問卷，進行資料蒐集，以簡易營養評估臺灣修訂二版量表（MNA-T2）作為居家醫療個案營養狀況之評估工具。研究結果發現，接受居家醫療個案有131位（62.4%）呈現營養不良的狀態。針對居家醫療個案MNA-T2量表，經由二元羅吉斯迴歸分析個案營養不良的重要預測因子為配方混和種類（OR, 0.28, 95% CI, 0.13-0.57, p < .001）、需求熱量卡路里高（OR, 1.00, 95% CI, 0.99-1.00, p < .001）及護理營養指導需求高（OR, 1.64,95% CI, 1.01-2.67, p < .044），此模式能成功預測居家醫療個案營養不良與否的正確機率為73.2%。研究結果建議對居家醫療個案應早期瞭解其營養狀況，改善個案營養不良等問題，以減少醫療成本支出。

In recent years, the number of cases of home medical care in Taiwan has been yearly. Since it is impossible to monitor their nutritional status in real-time, it is paying attention to the nutritional status and issues faced by home medical treatment patients. This study adopted a cross-sectional research design to explore the risk prediction of malnutrition among home health care cases. Through purposive sampling, 210 patients were selected for the study, focusing on individuals receiving home medical treatment with nasogastric tubes and ostomy tubes as part of the home medical integration plan in the central region. The research utilized a structured questionnaire, namely the nutritional status questionnaire for home medical management cases, to collect data. The Mini Nutritional Assessment Taiwan Revised Second Edition Scale (MNAT2) was used to assess the nutritional status of home medical cases. The study’s findings revealed that out of the patients who received home medical treatment, 131 (62.4%) were malnourished. Binary logistic regression analysis of the MNA-T2 scale for home medical care cases identified several significant predictors of malnutrition. These predictors included the type of formula mixture (OR, 0.28, 95% CI, 0.13-0.57, p < .001), higher calorie intake requirements (OR, 1.00, 95% CI, 0.99-1.00, p < .001), and a greater need for nursing nutrition instruction (OR, 1.64, 95% CI, 1.01-2.67, p < .044). The model achieved a 73.2% accuracy in correctly predicting the probability of malnutrition in home medical cases. Based on the study’s results, it is recommended to proactively identify the nutritional status of home medical patients to mitigate malnutrition and related issues, thereby reducing medical costs.