研究背景 慢性腎臟病(CKD)第3~5期稱之末期腎臟病前期(pre-ESRD)，若沒有良好控制，短期內可能需要進行透析治療，導致家庭與社會沉重負擔。若能了解社會人口學、就醫、共病等非臨床檢驗數值之變項對短期進入透析治療的影響，甚至建構預測模型，將有助於提前準備或積極介入。
研究目的 建構20-79歲慢性腎臟病第3-5期(末期腎臟病前期，pre-ESRD)病人一年內發生透析事件的預測模型。並探討社會人口學、就醫、共病等非臨床檢驗數值之變項對一年內進入透析治療的影響效應。
研究方法 本研究採回溯型世代研究法(Retrospective cohort study)，以全民健保資料庫、戶籍檔作為資料來源，挑選2016年20-79歲慢性腎臟病第3-5期且尚未進入透析之盛行個案，以進入研究日期(指標日，Index date)之社會人口學、一年內的共病、前一年就醫狀況與頻率等變項，建構指標日後一年內發生透析事件之預測模型，檢視其模型預測能力，並探討影響因子之效應。
研究結果 本研究納入2016年20-79歲第3-5期CKD盛行個案共93,579人。納入社會人口學、一年內的共病、前一年就醫狀況與頻率等變項，利用邏輯斯迴歸進行建模。結果發現皆以逐步選取法(Stepwise)的效果較佳。第3-5期預測模型的表現，ROC曲線下面積(AUC)為0.84、模型的敏感度為0.67、特異度為0.84、陽性預測值為0.33、陰性預測值為0.96及F1-Score為44.2。以年齡層偏低、疾病嚴重程度較高、教育程度為國中學歷、未婚者較多、戶籍地位於北部地區以及低度都市化。CKD之就醫狀況與頻率部分，A時段(指標日前12個月至前7個月)就醫狀況包含有「CKD急診」、「其他疾病急診」、「CKD住院」及「其他疾病住院」的比例較高，就醫頻率則是「CKD急診次數」。B時段(指標日前6個月至指標日)就醫狀況包含有「CKD急診」、「其他疾病急診」及「其他疾病住院」，就醫頻率則是「CKD門診次數」及「CKD住院次數」。共病部分，罹患心臟衰竭、糖尿病、糖尿病伴隨慢性併發症的個案，以上因素導致3-5期個案一年內發生透析的風險較高。第5期預測模型的表現，ROC曲線下面積(AUC)為0.64、模型的敏感度為0.78、特異度為0.38、陽性預測值為0.33、陰性預測值為0.80及F1-Score為49.2。以年齡層40-49歲、教育程度為國小以下、未婚者較多、戶籍地位於北部地區以及低度都市化。CKD之就醫狀況與頻率部分，A時段(指標日前12個月至前7個月)就醫狀況包含有「CKD急診」、「其他疾病急診」。B時段(指標日前6個月至指標日)就醫狀況包含有「CKD急診」、「其他疾病急診」，就醫頻率則是「CKD門診次數」、「CKD住院次數」以及參與pre-ESRD P4P計畫的申報次數。共病部分，罹患心臟衰竭、糖尿病、糖尿病伴隨慢性併發症的個案，以上因素導致5期個案一年內發生透析的風險較高。
結論 社會人口學、就醫頻率與狀況、共病等資料，雖非臨床檢驗數值，但對短期發生透析事件具有預測能力，預測第3~5期個案發生透析的效果尚可，預測第5期個案的效果較差。本研究結果可作為建構次級資料監測pre-ESRD個案不良健康結果預測模型之參考。
關鍵字：慢性腎臟病、末期腎臟病前期、透析、預測模型

Background: Chronic Kidney Disease (CKD) and End Stage Renal Disease (ESRD) are collectively referred to as important chronic disease that cause abnormal renal function, which are the main targets of global public health prevention and treatment. In 2017, the number of chronic cases worldwide was 69.75 million, attributable to approximately 1.2 million deaths from CKD, with a disease prevalence rate of 9.1%. CKD often progresses to the end-stage at the time of diagnosis, and CKD patients need to undergo renal replacement therapy as soon as possible. In Taiwan, due to insufficient kidney sources, patients wait too long for kidney transplantation. Currently, the proportion of patients receiving kidney transplantation therapy is very small. Therefore, the preferred treatment for CKD patients in my country is dialysis, which requires a long course of treatment, and the timing of starting treatment is more important, which will affect the survival rate and quality of life of patients. Several papers around the world have discussed the development of predictive models for the progression of CKD and the timing of individual cases entering treatment. However, for CKD, few literatures use non-clinical data to build relevant predictive outcome models, such as sociodemographic data.
Objectives: Our study collect Prevalent cases of chronic kidney disease stage 3-5 in 20-79 years old and not yet on dialysis in 2016.Using the sociodemographic variables of the index date and the comorbidities, medical treatment status and frequency of the previous year as possible determinants, a predictive model for the occurrence of dialysis events within one year of the index date was constructed, and its impact was discussed. factors and effects.
Methods: The chi-square test was used to analyze whether there were significant differences in the characteristics of CKD patients with or without dialysis within one year, such as sociodemographics, comorbidities in the previous year, and the status and frequency of medical visits in the previous year in our study.Then, using the logistic regression method, variables such as sociodemographic variables, medical treatment status and frequency, and comorbidities were added to establish the occurrence of dialysis events in the 3rd to 5th and 5th phases of CKD cases within one year from the index date. Predictive model of whether or not. In addition to presenting the fit and efficacy of the model to the data, the effects of each variable on the occurrence of dialysis events within one year of the index date were discussed.
Results: This study included a total of 93,579 20-79-year-old CKD cases in 2016. Logistic regression was used to model variables including sociodemographics, comorbidities within one year, and the status and frequency of medical visits in the previous year. The results show that the effect of stepwise selection is better. Phases 3-5 predicted model performance with an area under the ROC curve (AUC) of 0.84, model sensitivity of 0.67, specificity of 0.84, positive predictive value of 0.33, negative predictive value of 0.96 and F1-Score of 44.2. The age group is lower, the severity of the disease is higher, the education level is middle school education, the number of unmarried people is more, the household registration is located in the northern region, and the urbanization is low. In the part of medical treatment status and frequency of CKD, the proportion of medical treatment status in period A (12 months to the first 7 months before the index date) includes "CKD emergency", "other diseases emergency", "CKD hospitalization" and "other diseases hospitalization" High, the frequency of medical treatment is "the number of CKD emergency department". During the B period (6 months before the index day to the index day), the medical conditions include "CKD emergency", "other diseases emergency" and "other diseases hospitalization", and the frequency of medical treatment is "CKD outpatient number" and "CKD hospitalization number". In the comorbidity part, patients with heart failure, diabetes, and chronic complications of diabetes have a higher risk of developing dialysis within one year of stage 3-5 due to the above factors. Phase 5 predictive model performance, the area under the ROC curve (AUC) was 0.64, the model sensitivity was 0.78, the specificity was 0.38, the positive predictive value was 0.33, the negative predictive value was 0.80, and the F1-Score was 49.2. The age group is 40-49 years old, the education level is below the national primary school, there are many unmarried people, the household registration is located in the northern region, and the urbanization is low. The medical treatment status and frequency of CKD, the medical treatment status in period A (12 months to the first 7 months before the index date) includes "CKD emergency" and "other diseases emergency". During the B period (6 months before the index day to the index day), the medical conditions include "CKD emergency", "other diseases emergency", and the frequency of medical visits is "CKD outpatient number", "CKD hospitalization number" and participation in the pre-ESRD P4P plan number of reports. In the comorbidity part, patients with heart failure, diabetes, and diabetes accompanied by chronic complications have a higher risk of developing dialysis within one year of stage 5 cases due to the above factors.
Conclusions: The sociodemographic variables include age, disease severity, education level, marital status and urbanization, and among the medical conditions and frequency, the medical conditions include CKD emergency department and emergency medical treatment for other diseases. The frequency of CKD outpatient visits and CKD hospitalizations, and co-morbidities such as heart failure, chronic lung disease, dementia, diabetes, chronic complications of diabetes, and malignant tumors are all significant factors that affect the occurrence of dialysis events.
Keywords: Chronic kidney disease, pre-ESRD, dialysis, prediction model.

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