依序利用Oximetry及PSG診斷阻塞性睡眠缺氧症以降低醫療成本; Diagnosis of Patients with Obstructive Sleep Apnea Using Pulse Oximetry Followed by Polysomnography for Cost Reduction

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Title:	依序利用Oximetry及PSG診斷阻塞性睡眠缺氧症以降低醫療成本;Diagnosis of Patients with Obstructive Sleep Apnea Using Pulse Oximetry Followed by Polysomnography for Cost Reduction
Authors:	陳正和;Chen-Ho Chen
Contributors:	醫務管理學研究所
Keywords:	Obstructive sleep apnea;Oximetry;Support Vector Machine(SVM);Polysomnography (PSG)
Date:	2008-05-31
Issue Date:	2009-08-12 16:22:05 (UTC+8)
Abstract:	阻塞性睡眠呼吸中止症（OSA）是造成車禍的重要原因，患者之肇事率比正常人提高約2-7倍，它同時也是罹患許多慢性病的重要原因。睡眠多項生理檢查（PSG）是一項診斷檢查，它可以測量及記載睡眠時的多個生理變項，它被視為診斷阻塞性睡眠呼吸中止症（OSA）的最佳標準，但其缺點為非常費時且價格昂貴。本研究之主要目的為利用問卷和簡單便宜的家庭式診斷工具來預測高危險群的OSA患者，除了對於降低醫療費用有很大的潛力之外，也可以避免某些病人因睡眠環境的改變所造成之影響。本實驗對於2004年1月至2005年12月期間699個疑似OSA的病人，利用PSG在醫院睡眠中心進行測試。其中，對於48個年齡在20歲以下或85歲以上之病人資料予以排除，因此僅剩下651個病人資料進行後續分析。進行統計分析及特徵篩選之後，利用支援向量機(SVM)進行分類以區分正常及不同嚴重程度之病患。研究的初步結果顯示，血氧不飽和指數(Oxyhemoglobin Desaturation Index, ODI)是最好的預測參數。對於嚴重的OSA病患而言，其靈敏度（Sensitivity）高達87.20％，但用於區別正常與不正常的病人，其靈敏度卻只有70.39％，其他的人口學特性、身體特徵之量測變項及問卷對預測率並沒有提升的效用。經成本效益分析後發現，我們建議可以第一階段以家庭式的血氧濃度偵測器有效診斷重度OSA的病患，然後再利用PSG偵測正常、輕度及中度之病患。利用本論文所建議的方式，以目前台灣的健保收費規範下，可以降低診斷成本達35.72%。 Obstructive sleep apnea (OSA) is a significant cause of motor vehicle crashes and chronic diseases. Polysomnography (PSG) has been widely applied in the diagnosis of OSA that a number of physiologic variables are measured and recorded during sleep. Although PSG is treated as the gold standard for diagnosing OSA, it is time-consuming and expensive. Therefore, clinical prediction of high-risk OSA patients using questionnaires and cheap home diagnostic devices has a great potential in reducing healthcare cost and in eliminating environmental variation for some patients tested in the sleeping center. In this study, a total of 699 patients with possible OSA had been recruited and tested using PSG for overnight attending at the Sleep Center of China Medical University Hospital from Jan. 2004 to Dec. 2005. Subjects with age less than 20 or more than 85 years old were excluded. Therefore only the data of 651 patients were used for further analysis. After statistical analysis and feature selection, support vector machine (SVM) was then used to discriminate normal subjects and patients with different stages of severity. The results show that oxyhemoglobin desaturation index (ODI) alone has the best prediction outcome for the patients with severe OSA with a sensitivity as high as 87.20%. The sensitivity is only 70.39% in discriminating the normal from abnormal subjects. Based on the cost-benefit analysis, we suggest that home-styled oximeter can be used for sifting severe patients from all suspected patients at the first stage, which is then followed by PSG for discriminate normal subjects and mild and moderate patients. It was found that a cost reduction of NT$1629 (35.72%) per case in average can be achieved under the current Taiwanese insurance setting.
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