周 伽,楊俊杰,周 迎,楊曉波,張華巍,陳韻岱
1解放軍總醫(yī)院 心內科,北京 100853;2南開大學醫(yī)學院,天津 300071
驗前概率聯(lián)合冠脈CT造影對于穩(wěn)定型冠心病的診斷價值
周 伽1,2,楊俊杰1,周 迎1,楊曉波1,2,張華巍1,陳韻岱1
1解放軍總醫(yī)院 心內科,北京 100853;2南開大學醫(yī)學院,天津 300071
目的比較升級的Diamond-Forrester法(updated Diamond-Forrester method,UDFM)和Duke臨床評分(Duke clinical score,DCS)對于冠心病的評估準確性,并進一步分析驗前概率與冠脈CT造影(computed tomographic coronary angiography,CTCA)聯(lián)合應用的診斷準確性。方法納入2012年1月- 2013年12月因穩(wěn)定型心絞痛在解放軍總醫(yī)院心內科先后行CTCA和傳統(tǒng)冠狀動脈造影(conventional coronary angiography,CCA)的患者523例,分別用UDFM和DCS估算每例患者患冠心病的驗前概率。以CCA結果為金標準,分析驗前概率、CTCA及兩者聯(lián)合應用對冠心病的診斷準確性。理論驗后概率根據貝葉斯公式進行計算。結果523例患者中有385例(74%)CCA結果為陽性。與UDFM相比,DCS將更多的CCA結果陽性患者分入高驗前概率組(46% vs 23%,P<0.000 1)。DCS的ROC曲線下面積明顯大于UDFM[0.77(0.73,0.82) vs 0.71(0.66,0.77),P=0.000 9]。根據DCS估算結果劃分的低、中和高3個驗前概率亞組中,CTCA的敏感性、特異性、陽性預測值及陰性預測值分別是94%、98%和97%,94%、87%和55%,91%、94%和93%及96%、96%和77%。中驗前概率亞組的理論驗后概率十分接近實際驗后概率(陽性:94.7% vs 93.6%,陰性:3.7% vs 4.0%)。結論對于穩(wěn)定型心絞痛患者,DCS比UDFM更適用于冠心病驗前概率的估算。將按DCS估算的驗前概率與CTCA聯(lián)合應用,能夠有效提高CTCA的診斷準確性,并避免過度檢查。
冠心?。还诿}CT造影;驗前概率;貝葉斯定理
傳統(tǒng)冠狀動脈造影(conventional coronary angiography,CCA)是診斷冠心病的金標準,但是作為一種有創(chuàng)檢查,其昂貴的費用以及術中、術后并發(fā)癥不能被忽視。已有多項大型、前瞻性研究證明,以CCA為金標準,冠脈CT造影(computed tomographic coronary angiography,CTCA)具有良好的診斷準確性,特別是近乎100%的陰性預測值,提示CTCA可以作為CCA的無創(chuàng)“看門人”[1-4]。然而,CTCA也有其局限性,如較大的輻射劑量及造影劑的使用可能會對患者造成損害[5],其診斷準確性也受多種因素影響,如心率、體質量及血管的鈣化情況[6-7]。具有怎樣臨床特征的病人最適合進行CTCA檢查?目前,在低中患病率人群中,驗前概率與CTCA聯(lián)合應用的臨床價值已得到證實[3,8-10]。本實驗旨在研究在高患病率人群(如因穩(wěn)定型心絞痛而進行CCA檢查的患者)中驗前概率與CTCA診斷準確性是否存在密切聯(lián)系。我們選擇兩種應用最廣泛的驗前概率評估模型,分別是升級的Diamond-Forrester法(updated Diamond-Forrester method,UDFM)[11]和Duke臨床評分(Duke clinical score, DCS)[12]。已有多項研究證明,二者在中低患病率人群中具有較高的評估準確性[10-11,13-14],并且在最近發(fā)布的多個指南中[15-17],二者也作為評估驗前概率的首選模型。我們將探索這兩種模型在高患病率人群中聯(lián)合應用CTCA的臨床診斷價值。
1研究對象 本研究共納入2012年1月- 2013年12月因穩(wěn)定型心絞痛入我科就診并于2周內先后行CTCA和CCA的患者523例。排除標準:不穩(wěn)定型心絞痛和心肌梗死,冠脈的血運重建史(包括冠脈介入治療和冠脈旁路移植手術),腎功能受損(血清肌酐>120μmol/L),心功能Ⅲ或Ⅳ級(NYHA分級),非竇性心律(房顫和頻發(fā)性室性早搏等),嚴重的主動脈疾病以年齡超過90歲。
2患者數據分析 典型的穩(wěn)定型心絞痛主要有以下3個特征:1)具有特定性質和位置;2)由勞累、體力運動或情緒激動誘發(fā);3)經休息或使用硝酸酯類藥物可于數分鐘至十數分鐘內緩解。如果符合以上3個特征中的2個則定義為不典型心絞痛,符合1個或均不符合則定義為非心絞痛[18]。通過電子病歷系統(tǒng)收集患者的相關臨床資料,分別根據UFDM和DCS計算每例患者的驗前概率并分為低(<30%)、中(30% ~ 70%)和高(>70%) 3組[11-12]。當利用DCS估算驗前概率時,>70歲的患者按照70歲(DCS的上限年齡)計算。
3冠脈圖像采集分析 所有患者均接受西門子第2代雙源CT(Somatom Definition flash)掃描。圖像分析由兩位有經驗的閱片醫(yī)師(一位放射科醫(yī)師和一位心內科醫(yī)師)獨立進行,結論不一致時由二者協(xié)商決定。所有CCA圖像均利用德國西門子數字血管造影機采集,由1名有經驗的且對CTCA結果不知情的心內科醫(yī)師分析。所有患者的冠脈根據最新的分段標準[19]進行分析,直徑>1.5 mm的節(jié)段按照以下標準進行分類:無狹窄,1% ~ 49%狹窄,>50%狹窄。陽性患者定義為至少有一節(jié)段血管狹窄>50%。以CCA為金標準,利用四格表計算CTCA的敏感性(sensitivity,Se)、特異性(specificity,Sp)、陽性預測值(positive predictive value,PPV)、陰性預測值(negative predictive value,NPV)及95%置信區(qū)間(confidence interval,CI)。根據貝葉斯公式,驗后比=驗前比×似然比。
4統(tǒng)計方法 計量資料用或者中位數(25%百分位數-75%百分位數)表示,兩組間差異比較用獨立樣本t檢驗或Kruskal-Wallis檢驗。計數資料用頻率(百分比)表示,兩組之間差異比較用χ2檢驗或費舍爾精確檢驗。用Kappa分析評價UDFM和DCS之間的分組一致性。受試者工作特征曲線用來比較兩種評估方法的準確性[20]。Mantel-Haenszel檢驗用來比較不同亞組之間Se、Sp、PPV和NPV的變化趨勢。所有統(tǒng)計均由SAS9.2軟件完成。P<0.05為差異有統(tǒng)計學意義。
1基線臨床資料 共納入523例患者,平均年齡61±9歲,58%為男性,52%臨床癥狀表現(xiàn)為典型心絞痛。其中385例CCA結果為陽性,性別、心率、吸煙史、心電圖改變和心絞痛類型在兩組患者(CCA結果陽性和陰性)中的差異有統(tǒng)計學意義。見表1。
2模型比較 根據UDFM,22%、53%和25%的患者分別被分入低、中和高驗前概率亞組,而DCS將更少的病人分入低驗前概率組(15%)和中驗前概率組(31%)(圖1A)。與UDFM相比,DCS將更多的陽性患者分入高驗前概率組(46% vs 23%,P<0.000 1)(圖1B)。以CCA為金標準,利用ROC曲線比較UDFM和DCS的準確性,得到的DCS的ROC曲線下面積(AUC)明顯大于UDFM[0.77(0.73,0.82) vs 0.71(0.66,0.77),P=0.000 9]。
3CTCA診斷準確性分析 以CCA為金標準,CTCA能夠準確地診斷出大部分陽性患者(真陽性值=368),這使得CTCA表現(xiàn)出較高的Se(97%,95% ~ 99%)和PPV(93%,90% ~ 95%)。隨著驗前概率的增加,Sp和NPV都呈現(xiàn)降低的趨勢(P<0.05)。見表2。不管是在全部患者中還是在3個不同驗前概率亞組中,理論驗后概率都很接近實際驗后概率,在中驗前概率亞組中尤為突出(陽性:94.7% vs 93.6%,陰性:3.7% vs 4.0%)。見表3。
表1 兩組穩(wěn)定型心絞痛患者基線資料比較Tab. 1 Comparison of baseline characteristics of symptomatic patients according to CCA
表2 以CCA為對照分析低、中和高驗前概率亞組中CTCA的診斷準確性Tab. 2 Diagnostic accuracy of CTCA in low, intermediate, and high subgroups according to pre-test probability compared with CCA
表3 不同亞組內陰性和陽性CTCA結果對驗后概率的影響Tab. 3 Impact of CTCA on post-test probability
圖 1 在穩(wěn)定型心絞痛患者中UDFM和DCS的比較Fig. 1 Comparison of UDFM and DCS in symptomatic patientsA: More than half (53%) of the patients were classified as intermediate pre-test probability group using UDFM, compared with 54% as high using DCS. The bars indicated the 95% CI.aP<0.05, vs UDFM. B: The CCA results revealed that most (74%) of the patients had≥50% stenosis.aP<0.05, vs UDFM
作為一種無創(chuàng)影像學檢查手段,CTCA的在冠心病診斷中的應用越來越廣泛。但如何在實際臨床實踐中正確使用CTCA仍存在爭議。本研究結果證實了在冠心病高發(fā)人群中,DCS是一種更準確的驗前概率評估方法,并且在驗前概率的指導下應用CTCA能夠有效提高其診斷準確性,從而避免過度檢查。
本研究中,以CCA結果為對照,DCS對驗前概率的估算具有較高的準確性。但在另一些研究中,DCS被認為會過高估計驗前概率[21-22]。正如Diamond和Kaul[23]所說的“不同的漁網捕到不同的魚”,驗前概率評估模型在不同的研究中表現(xiàn)不同,其原因可能是研究人群之間存在差異。DCS是在一個具有較高患病率(168例患者中有106例CCA結果為陽性)的人群中建立的[12],在患病率較低(23%[21]和31%[22])的研究人群中對DCS進行外部驗證,DCS將會過高估計冠心病的患病可能。因此,對于驗前概率評估模型的使用,應當注意根據研究人群的臨床特點進行謹慎選擇。
已有多項研究證明,驗前概率可以影響CTCA的診斷準確性[4,17,23]。在我們的研究中,各亞組中不同的Sp和NPV也支持這一觀點。我們認為,各亞組假陽性值的不同是引起上述差異的主要原因,而假陽性值主要受冠脈鈣化的程度影響。冠脈鈣化本身就是一個獨立的冠心病危險因素,它與冠心病的驗前概率之間存在較強的相關性[24-26],因此高驗前概率亞組的平均鈣化積分較大;而冠脈鈣化引起的高密度偽影會使CTCA過高估計狹窄的嚴重程度而出現(xiàn)假陽性結果[5-7]。因此在本研究中,高驗前概率亞組中出現(xiàn)了更多的假陽性結果。
驗前概率能夠對CTCA的診斷準確性產生影響,根據貝葉斯定理,它應該是理論驗后概率的重要決定因素。在本研究中,各亞組中陽性的CTCA結果都能夠將理論驗后概率提高到與實際驗后概率接近的程度,即陽性的CTCA結果基本可以明確冠心病的存在。但是陰性的CTCA結果在不同的亞組中卻有不同的理論驗后概率。在中驗前概率亞組中,陰性的CTCA結果將理論驗后概率降至3.7%,十分接近實際驗后概率。因此,對于該類患者,陰性的CTCA結果能夠可靠地排除掉患冠心病的可能,不必進行進一步的檢查。但是在低驗前概率亞組中,陰性的CTCA結果僅將理論驗后概率降至7.9%,遠大于實際驗后概率,這表明即使CTCA得到了一個陰性的結果,仍不能完全排除冠心病的存在,需要更為精準的進一步檢查。在高驗前概率亞組中,陰性CTCA結果對應的理論驗后概率為21%,臨床診斷價值更小。根據貝葉斯定理,除了各亞組之間驗前概率的差異,較高的陰性似然比(negative likelihood ratio,-LR)是得到較高理論驗后概率的重要原因。-LR是反映一項檢查診斷準確性的重要指標,-LR越低說明診斷準確性越高。因此,在高驗前概率亞組中,診斷準確性受冠脈鈣化的影響,CTCA表現(xiàn)出一個較高的-LR是不難理解的。但是低驗前概率亞組中CTCA的-LR仍然較高,這可能與本研究入選人群和樣本量限制有一定關系,需要進一步的研究進行驗證。
本研究的局限性:1)本研究是一個單中心回顧性研究,不可避免地存在選擇偏倚。因此,下一步應當進行多中心、前瞻性和大樣本量的研究以獲得更有說服力的結論。2)冠脈狹窄程度僅通過肉眼觀察重建圖像來判定,診斷結果存在觀察者間的差異[27]。但是通過運用多種圖像重建技術以及安排2名以上醫(yī)師進行互盲的分析診斷,這種變異對研究結果的影響已被降至最低。3)受到各種因素(尤其是鈣化斑塊)的影響,一部分冠脈節(jié)段存在嚴重的偽影,致使管腔狹窄程度評估受限。我們采取的策略是將所有無法評估狹窄程度的節(jié)段都按狹窄>50%記錄,因為在實際臨床實踐中對于陽性或者無法評估的冠脈節(jié)段,往往都需要進行進一步的檢查。這種策略使得本研究中有關CTCA診斷準確性的各項指標能更真實地反映臨床實際情況。
總而言之,我們的研究表明,在因穩(wěn)定型心絞痛而住院的患者中,按DCS估算具有中驗前概率(30% ~ 70%)的患者進行CTCA檢查獲益更大。對于此類患者,陰性的CTCA結果能可靠地排除患病可能,從而可以有效避免過度檢查。
1 Arbab-Zadeh A, Hoe J. Quantification of coronary arterial stenoses by multidetector CT angiography in comparison with conventional angiography methods, caveats, and implicationsp[J]. JACC Cardiovasc Imaging, 2011, 4(2):191-202.
2 Alani A, Nakanishi R, Budoff MJ. Recent improvement in coronary computed tomography angiography diagnostic accuracy[J]. Clin Cardiol, 2014, 37(7): 428-433.
3 Van Werkhoven JM, Heijenbrok MW, Schuijf JD, et al. Diagnostic accuracy of 64-slice multislice computed tomographic coronary angiography in patients with an intermediate pretest likelihood for coronary artery disease[J]. Am J Cardiol, 2010, 105(3):302-305.
4 Gueret P, Deux JF, Bonello L, et al. Diagnostic performance of computed tomography coronary angiography (from the Prospective National Multicenter Multivendor EVASCAN Study)[J]. Am J Cardiol, 2013, 111(4):471-478.
5 Prat-Gonzalez S, Sanz J, Garcia MJ. Cardiac CT: indications and limitations[J]. J Nucl Med Technol, 2008, 36(1): 18-24.
6 Kruk M, Noll D, Achenbach S, et al. Impact of coronary artery Calcium characteristics on accuracy of CT angiography[J]. JACC Cardiovasc Imaging, 2014, 7(1): 49-58.
7 Voros S. What are the potential advantages and disadvantages of volumetric CT scanning?[J]. J Cardiovasc Comput Tomogr, 2009,3(2):67-70.
8 Cheneau E, Vandat B, Bernard L, et al. Routine use of coronary computed tomography as initial, diagnostic test for angina pectoris[J]. Arch Cardiovasc Dis, 2011, 104(1): 29-34.
9 Meijboom WB, Van Mieghem CA, Mollet NR, et al. 64-slice computed tomography coronary angiography in patients with high,intermediate, or low pretest probability of significant coronary artery disease[J]. J Am Coll Cardiol, 2007, 50(15):1469-1475.
10 Wasfy MM, Brady TJ, Abbara S, et al. Comparison of the Diamond-Forrester method and Duke Clinical Score to predict obstructive coronary artery disease by computed tomographic angiography[J]. Am J Cardiol, 2012, 109(7):998-1004.
11 Genders TS, Steyerberg EW, Alkadhi H, et al. A clinical prediction rule for the diagnosis of coronary artery disease: validation,updating, and extension[J]. Eur Heart J, 2011, 32(11): 1316-1330.
12 Pryor DB, Shaw L, Mccants CB, et al. Value of the history and physical in identifying patients at increased risk for coronary artery disease[J]. Ann Intern Med, 1993, 118(2): 81-90.
13 Jensen JM, Voss M, Hansen VB, et al. Risk stratification of patients suspected of coronary artery disease: Comparison of five different models[J]. Atherosclerosis, 2012, 220(2): 557-562.
14 Jensen JM, Ovrehus KA, Nielsen LH, et al. Paradigm of pretest risk stratification before coronary computed tomography[J]. J Cardiovasc Comput Tomogr, 2009, 3(6):386-391.
15 Wolk MJ, Bailey SR, Doherty JU, et al. ACCF/AHA/ASE/ASNC/ HFSA/HRS/SCAI/SCCT/SCMR/STS 2013 multimodality appropriate use criteria for the detection and risk assessment of stable ischemic heart disease: a report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, American Heart Association, American Society of Echocardiography, American Society of Nuclear Cardiology, Heart Failure Society of America,Heart Rhythm Society, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography,Society for Cardiovascular Magnetic Resonance, and Society of Thoracic Surgeons[J]. J Am Coll Cardiol, 2014, 63(4):380-406.
16 Fihn SD, Gardin JM, Abrams J, et al. 2012 ACCF/AHA/ACP/AATS/ PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease a report of the American college of cardiology foundation/American heart association task force on practice guidelines, and the American college of physicians,American association for thoracic surgery, preventive cardiovascular nurses association, society for cardiovascular angiography and interventions, and society of thoracic surgeons[J]. Circulation,2012, 126(25): E354-U191.
17 Task Force Members, Montalescot G, Sechtem U, et al. 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology[J]. Eur Heart J, 2013, 34(38):2949-3003.
18 Somerville W. Tetralogy versus tetrad and a wish for the new journal[J]. J Am Coll Cardiol, 1983, 1(2): 574.
19 Raff GL, Abidov A, Achenbach S, et al. SCCT guidelines for the interpretation and reporting of coronary computed tomographic angiography[J]. J Cardiovasc Comput Tomogr, 2009, 3(2):122-136.
20 Hanley JA, Mcneil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve[J]. Radiology,1982, 143(1): 29-36.
21 Kumamaru KK, Arai T, Morita H, et al. Overestimation of pretest probability of coronary artery disease by Duke clinical score in patients undergoing coronary CT angiography in a Japanese population[J]. J Cardiovasc Comput Tomogr, 2014, 8(3):198-204.
22 Genders TS, Steyerberg EW, Hunink MG, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts[J]. BMJ, 2012, 344:e3485.
23 Diamond GA, Kaul S. Gone fishing!: on the “real-world” accuracy of computed tomographic coronary angiography: Comment on the“Ontario multidetector computed tomographic coronary angiography study”[J]. Arch Intern Med, 2011, 171(11):1029-1031.
24 Rozanski A, Gransar H, Shaw LJ, et al. Impact of coronary artery Calcium scanning on coronary risk factors and downstream testing the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) prospective randomized trial[J]. J Am Coll Cardiol, 2011, 57(15): 1622-1632.
25 Elias-Smale SE, Proen?a RV, Koller MT, et al. Coronary Calcium score improves classification of coronary heart disease risk in the elderly: the Rotterdam study[J]. J Am Coll Cardiol, 2010, 56(17):1407-1414.
26 Madhavan MV, Tarigopula M, Mintz GS, et al. Coronary artery calcification: pathogenesis and prognostic implications[J]. J Am Coll Cardiol, 2014, 63(17):1703-1714.
27 Choudhary G, Atalay MK, Ritter N, et al. Interobserver reliability in the assessment of coronary stenoses by multidetector computed tomography[J]. J Comput Assist Tomogr, 2011, 35(1): 126-134.
Diagnostic accuracy of pre-test probability combined with computed tomographic coronary angiography in patients suspected for stable coronary artery disease
ZHOU Jia1,2, YANG Junjie1, ZHOU Ying1, YANG Xiaobo1,2, ZHANG Huawei1, CHEN Yundai1
1Department of Cardiology, Chinese PLA General Hospital, Beijing 100853, China;2School of Medicine, Nankai University, Tianjin 300071, China
CHEN Yundai. Email: cyundai@medmail.com.cn
ObjectiveTo compare the performance of updated Diamond-Forrester method (UDFM) and Duke clinical score (DCS) in patients with stable angina pectoris and assess the combined application of pre-test probability and computed tomographic coronary angiography (CTCA) in these patients.MethodsFive hundred and twenty-three symptomatic patients who underwent both CTCA and conventional coronary angiography (CCA) in 2 weeks in Chinese PLA General Hospital from January 2012 to December 2013 were enrolled in this study. The pre-test probability was determined using UDFM and DCS for each patient. Receiver operating characteristics (ROC) curves were used to compare two models. The diagnostic accuracy of CTCA for detecting coronary artery disease (CAD) was compared with CCA. The estimated post-test probability was calculated by Bayesian statistics.ResultsOf the 523 patients, 385 (74%) were positive tested by CCA. Compared with UDFM, DCS reclassified more positive patients into high group (46% for DCS vs. 23% for UDFM, P<0.000 1). The areas under ROC curves (AUC) for DCS was significantly greater than that for UDFM [0.77 (0.73, 0.82) vs 0.71 (0.66, 0.77), P=0.000 9]. In patient-based evaluation by CTCA, three pre-test probability groups according to DCS revealed a sensitivity of 94%, 98% and 97%, a specificity of 94%, 87% and 55%, a positive predictive value (PPV) of 91%, 94% and 93%, and a negative predictive value (NPV) of 96%, 96% and 77%, respectively. The estimated post-test probabilities corresponded well with the observed one, especially for the intermediate estimated pre-test probability group (positive: 94.7% vs 93.6%, negative: 3.7% vs 4.0%).ConclusionCompared with UDFM, DCS has a better performance in calculating pretest probabilities in patients with stable angina pectoris. In addition, the combined application of DCS and CTCA can avoid unnecessary tests.
coronary disease; coronary computed tomographic angiography; pre-test probability; Bayesian theorem
R 814.42
A
2095-5227(2015)04-0313-05
10.3969/j.issn.2095-5227.2015.04.004
時間:2015-01-04 11:33
http://www.cnki.net/kcms/detail/11.3275.R.20150104.1332.006.html
2014-09-23
北京市科委首都臨床特色應用研究(Z141107002514103)
Supported by the Study on the Application of Capital, Clinical Characteristics(Z141107002514103)
周伽,男,在讀碩士。研究方向:冠脈CT的臨床應用。Email: zhoujiasirius@126.com
陳韻岱,女,主任醫(yī)師,主任,博士生導師。Email: cy undai@medmail.com.cn