以效用為基礎之敏感度與特異性決定生物標記之適當切點值 以高密度膽固醇及缺血性腦中風為例

Abstract

前言 選擇一個適當的篩檢工具及篩檢標準，有助於早期偵測疾病的發生。目前，對於慢性疾病相關生物標記切點值的建立，多是建立在大樣本理論的架構下，對於個人主觀的選擇較少被討論。因此，本文主要利用一研究獲得錯誤分組之效用；接著建立臨床切點模式，與前述之效用結果結合，以印證在效用介入下切點值之改變並比較之。 材料及方法 本研究主要分成二部份，第一部份是利用標準博奕法及直接目測法測得比例尺度生物標記與預測腦中風發生之真陽性、真陰性、偽陽性、偽陰性四種情境之效用；第二部份是利用第一部份所得到四種情境之效用值配合貝氏最低成本決策法則及作業接受曲線方法，以總膽固醇及高密度膽固醇和腦中風發生為例，進行考慮個人喜好之效用下之切點值建立。 研究結果 在效用結果方面，69位受測者中，其中男性有30人，女性有39人，平均年齡為37.16±9.99歲。對於真陽性，真陰性，偽陽性和偽陰性的情境效用上，由高到低的排序為真陰性、偽陽性、真陽性、偽陰性。效用值在標準博奕法I為87.53, 81.17, 75.08, 63.06;標準博奕法II為 86.74, 83.64, 80.02, 64.68;在直接目測法為83.17, 74.32, 63.87, 44.16。男性，收入高者，也是有抽煙喝酒習慣者，是較為類似的一群。呈現出效用較高的結果。在效用矩陣R0/R1中，R0<R1，代表大家在希望確認自己是真正有病的效用是高於希望確認自己是真正沒病的效用。換句話說，大家比較在意他真正有病的狀態，至於沒病，是不是被誤判，在意的程度就比較低了。 以貝氏最低成本決策法則之切點決定結果，不做效用調整時，HDL最適切點為40.3mg/dL，膽固醇最適切點為252.4 mg/dL 以標準博奕法I所得之效用比調整，HDL最適切點為42.5 mg/dL，膽固醇最適切點為248.6 mg/dL 以標準博奕法II所得之效用比調整，HDL最適切點為46.0 mg/dL，膽固醇最適切點為242.8 mg/dL 以直接目視法所得之效用比調整，HDL最適切點為43.1 mg/dL，膽固醇最適切點為247.6 mg/dL 三個測量方法中，HDL皆較未調整效用比時為高，膽固醇皆較未調整效用比時為低。 結論 對於預測慢性疾病的生物標記切點值的建立，考量到偽陽性及偽陰性的效用值是有意義的。而貝氏最低成本效益法則可解決在效用值影響下的比例尺度生物標記對於預測慢性疾病發生的切點值之決定。

Objectives Population based screening for a chronic disease using an interval scale biomarker is often involved in selecting an optimal cutoff point. Selecting the optimal cut off point is faced with the misclassification between correct decision and alternative decision. The value of screening and selection of an optimal cutoff point depends on personal preference. High density lipoprotein (HDL) is one of protective factors for cerebral infarct. The cut off point of HDL related the outcome of cerebral infarct may vary from individual to individual. In this paper, we aimed to investigate the utility of misclassification by an illustration of the relationship of HDL to cerebral infarct. We also use the clinical model combined with above utility to prove the change of the cut off point of the interval scale biomarkers. Methods The study divided to two parts: the first part is that we obtain the utility scores of four scenarios of TP, TN, FP and FN with the relationship of HDL to cerebral infarct.by the standard gamble (SG) and visual analogue scale (VAS) approaches. The second part is that we use Bayes’ minimized cost decision rule and ROC curve method combined with utility scores of above four scenarios to determine the optimal cut-off point of HDL for cerebral infarct.

Results Of the 69 people who completed the study, 30(43%) were men and 39(57%) were women, the mean age was 37.16±9.99 years old. The utility score of TN among four scenarios were ordered the highest followed by, FP, TP and FN. The utility scores in standard gamble I was 87.53, 81.17, 75.08, 63.06; in standard gamble II was 86.74, 83.64, 80.02, 64.68; in visual analogue scale was 83.17, 74.32, 63.87, 44.16. For personal characteristics, males who have higher income and have habits of smoking and drinking had higher utility of scenarios. The regret between TN and FP was smaller than that between TP and FN. The results of cut-off value for HDL and Cholesterol performed by Baye’s minimum cost decision rule were that in general population, the cut-off value for HDL and Cholesterol was defined as 40.3 and 252.4 without utility adjustment. The cut-off value for HDL and Cholesterol was defined as 42.5 and 248.6, given utility adjustment from standard gamble I at slope of 31.3. The cut-off value for HDL and Cholesterol was defined as 46.0 and 242.8, given utility adjustment from standard gamble II at slope of 11.9.The cut-off value for HDL and Cholesterol was defined as 43.1 and 247.6, given utility adjustment from visual analogue scale at slope of 26.5. That means utility ratio increases with the level of HDL at decreasing rate and decreases with the level of Cholesterol at decreasing rate. Conclusion The utility of TP, TN, FP and FN involved in population-based screening has been measured by using an example of HDL related to cerebral infarct. The considering the utility of FP and FN is meaningful for the selection of a cut off point of a biomarker related to a disease outcome. Besides, Bayes’ minimum cost decision rule was proposed to solve the problem of selecting optimal cutoff point for chronic diseases with interval scale variable.