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研究生:邱獻章
研究生(外文):Hsien-Jane Chiu
論文名稱:台灣精神分裂症患者服用抗精神病藥物之體重增加預測模式
論文名稱(外文):Predictive models on weight gain among schizophrenic patients with an exposure to anti-psychotics in Taiwan
指導教授:周碧瑟周碧瑟引用關係
指導教授(外文):Pesus Chou
學位類別:博士
校院名稱:國立陽明大學
系所名稱:公共衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:125
中文關鍵詞:精神分裂症患者抗精神病藥物體重增加預測模式邏輯式迴歸分析神經模糊概念模型
外文關鍵詞:Schizophrenic patientsAnti-psychoticsWeight gainPredictive modelsLogistic regression modelNeuro-fuzzy techniques
相關次數:
  • 被引用被引用:6
  • 點閱點閱:201
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  • 收藏至我的研究室書目清單書目收藏:0
在台灣,精神分裂症在一般人群約有0.3-0.5%的終生盛行率。在其慢性化的病程中,病人的認知功能會因疾病而逐漸退化。精神分裂症患者不僅較一般人更容易因各類身體上的疾病或意外事件而生病或死亡,更因其在社會上弱勢地位而較難獲得必須的醫療注意或照顧。
大多的精神分裂症患在就醫後會長期服用傳統或新一代的抗精神藥物,而有多達50%的病人會有體重增加的問題。過重不但形成健康上的隱憂,而且會因不便而影響生活品質,甚至會惡化某些病人的服藥順從性,以致於病情復發。因此雖然疾病本身及抗精神藥造成的健康的問題值得重視,但是其所引發的體重增加現象也值得作進一步的病因分析研究。
這個研究的主要目的在於建立一個明確的體重變化預測模型,以方便臨床醫師加以應用。而體重變化的現象分別以二項式及連續變化的資料型態來分析預測。本研究由玉里榮院及署立桃園療養院共收集了293位患者,在其同意之下進行分析,其中63個來自玉榮的病患以邏輯式迴歸分析的方法來檢測其二項式的體重變化預測,得到了預測靈敏性90%及專一性83%的優良指標。另外的230位來自署桃的參與者則提供了他們的體重連續變化結果進行多變項線性迴歸分析。而模型預測的準確性也達到了92%的水準。為了更進一步方便大部份沒有統計經驗的臨床醫師,可以應用這種預測公式,特別導入了神經模糊概念模型來預測體重的連續變化。而這個方法在進一步的訓練及學習條件之下可以達到80%至98%的準確度。
而經由上述三種不同的統計分析方式,本論文証實臨床的某些預後或結果評估,確實可以藉由公式化的方法加以有效預測,這個結果在未來可以再進一步應用在臨床決策,為實証醫學的發展奠定良好之基礎。
Schizophrenia, with 0.3-0.5% life prevalence in Taiwan, usually deteriorates cognitive function of patients in its chronic natural history of the disease. Schizophrenic patients not only have an increased risk of morbidity and mortality from different physical illness and accidents, but also are minor to acquire general medical cares.
Furthermore, most patients received long term treatment of conventional and atypical antipsychotics, and up to 50% of them had significant weight gain problem. Weight gain will increase the health risk, impair of quality of life, and lead to noncompliance, even relapse. The prevention and management of health risk factors resulting from schizophrenia itself or from antipsychotics treatment are essential in caring for schizophrenic patients. However, the degree of weight gain may depend on individual vulnerability, personal behaviors, and environmental factors.
The aim of this study is to establish a predictive model of body weight gain in antipsychotics-treated schizophrenic patients. This dissertation try to elucidate prediction of clinical outcomes can be predicted based on algorithms with an acceptable coverage of variance we are interested in. The weight gain due to antipsychotics exposure was chosen as the main clinical outcome in this dissertation by two different forms: dichotomous and continuous data type. Two hundred chronic schizophrenic patients were enrolled with at least 6 months hospitalization while approaching from Yu-Li Veterans Hospital (YLVH) and Tao-Yuan Psychiatric Center (TYPC). The dichotomous outcome for weight gain was predicted by the logistic regression model which was established from 67 schizophrenic patients recruited from YLVH. The reliability of this prediction algorithm is warranted by good sensitivity (90%) and specificity (83%). Two hundred thirty schizophrenic patients participating in TYPC were utilized to establish the linear regression model and to test its accuracy of weight gain prediction, which reached 92% compared to the observed values (within 5% confidence interval). For the convenience of users, Neuro-fuzzy techniques were applied to simplify the whole procedure of prediction on the clinical outcome for most clinicians with no thorough knowledge background of biostatistics. The prediction rate will improve from 80% to 98% after appropriate equation learning and training. Throughout these three different approaches, the clinical outcome prediction by algorithms for decision-making is proven effective and it really affords an evidence-based way in medical practice.
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