Graft flow predictive equation in distal bypass grafting for critical limb ischemia.
Graft flow predictive equation in distal bypass grafting for critical limb ischemia.
复制标题
严重肢体缺血远端旁路移植术中的移植血流预测方程。
DOI：
10.1016/j.jvs.2018.12.057
复制
发表时间：
2019
影响因子：
4.3
通讯作者：
N. Azuma
中科院分区：
文献类型：
--
作者：
Keisuke Miyake;Shinsuke Kikuchi;H. Okuda;A. Koya;Y. Sawa;N. Azuma
目的移植物流量（graftflow，GF）是预测严重肢体缺血远端旁路移植术后移植物预后的重要指标，但以往的研究未能阐明GF与移植物预后的关系。GF在移植物之间差异显着，并且每个移植物似乎具有取决于各种因素的最佳GF。我们假设，测量的GF（mGF）和最佳估计的GF（eGF）之间的比较将是重要的预测移植物预后。在此，我们的目的是通过评估GF决定因素来开发GF预测方程，并针对临床数据集验证该方程。方法回顾性分析2011年至2016年198例重症肢体缺血患者行静脉移植远端旁路术的临床资料。在这些移植物中，135例术后早期超声检查无任何异常的正常移植物用于开发和验证方程。采用逐步筛选法分析各种解剖学因素和患者相关因素，建立GF预测方程。在建立方程后，根据基于来自135个正常移植物的数据建立的方程，将所有198个移植物分为两组，如下：最佳流量移植物（OFG），其中mGF> eGF-14.6，和次佳流量移植物（SFG），其中mGF< eGF-14.6。使用受试者工作特征曲线确定截止值14.6，以检测移植物异常。通过比较OFG和SFG，评估该方程预测旁路异常和移植物预后的有效性。结果影响移植肾生长因子的因素依次为流出量、血液透析（HD）、糖尿病（DM）和移植肾质量（GQ）。预测方程估计如下：GF（ml/min）=（32.9×径流）+（9.9× G Q）-（13.0× D M）-（35.1× H D）+ 12.1（R2 = 0.71，系数：径流和GQ，3 [良好]，2 [一般]，1 [差]; DM和HD，1 [是]，0 [否]）。在方程的有效性评估中，SFG显示出明显更高的旁路异常率（64.0%比12.2%; P<0.05）。0001），移植物中度狭窄（10.7% vs 1.6%; P=.移植物临界狭窄（28.0% vs 3.2%; P<0.01）。0001），和早期移植物闭塞（17.3%比4.3%; P=. 0037），并且与术后2年内更高的翻修率相关（50.7% vs 34.2%; P=. 026）。SFG组的一期通畅率显著低于对照组（P<0. 05）。0001）和二期通畅率（P=. 0005）。结论GF与径流、GQ以及DM和HD的存在一起被很好地估计。用公式计算的mGF和eGF之间的比较将有助于检测旁路异常并确定额外术中手术的必要性，从而实现最佳结局。
Objective Graft flow (GF) seems to be an important prognostic predictor in distal bypass for critical limb ischemia, but previous studies have failed to clarify the association between GF and the graft prognosis. GF differs significantly among grafts, and each graft seems to have an optimal GF depending on various factors. We hypothesized that comparison between the measured GF (mGF) and optimal estimated GF (eGF) would be important in predicting graft prognosis. Herein, we aimed to develop a GF predictive equation by assessing GF determinants and to validate the equation against a clinical dataset. Methods A total of 198 distal bypasses with vein grafts for critical limb ischemia from 2011 to 2016 were enrolled. Of these grafts, 135 normal grafts without any abnormalities on early postoperative ultrasound examination were used to develop and validate the equation. Various anatomic and patient-related factors were analyzed to detect GF determinants with stepwise selection, and the GF predictive equation was developed with multiple linear regression analysis. After developing the equation, all 198 grafts were categorized into two groups according to the equation developed based on data from the 135 normal grafts as follows: optimal flow grafts (OFGs), in which mGF> eGF–14.6, and suboptimal flow grafts (SFGs), in which mGF< eGF–14.6. The cutoff value of 14.6 was determined using receiver operating characteristic curves to detect graft abnormalities. By comparing OFGs and SFGs, the efficacy of the equation in predicting bypass abnormalities and graft prognosis was assessed. Results The GF determinants were runoff, hemodialysis (HD), diabetes mellitus (DM), and graft quality (GQ). The predictive equation was estimated as follows: GF (ml/min)=(32.9× run-off)+(9.9× G Q)−(13.0× D M)−(35.1× H D)+ 12.1 (R 2= 0.71, coefficient: runoff and GQ, 3 [good], 2 [fair], 1 [poor]; DM and HD, 1 [yes], 0 [no]). In the efficacy assessment of the equation, SFGs showed a significantly higher rate of bypass abnormalities (64.0% vs 12.2%; P<. 0001), graft intermediate stenosis (10.7% vs 1.6%; P=. 0071), graft critical stenosis (28.0% vs 3.2%; P<. 0001), and early graft occlusion (17.3% vs 4.3%; P=. 0037) than OFGs and were associated with a higher rate of revision surgery within 2 years after surgery (50.7% vs 34.2%; P=. 026). SFGs also showed significantly lower primary patency rates (P<. 0001) and secondary patency rates (P=. 0005). Conclusions GF was well-estimated with runoff, GQ, and the presence of DM and HD. A comparison between mGF and eGF, calculated with the equation, will help to detect bypass abnormalities and determine the necessity of additional intraoperative procedures and, thus, achieve optimal outcomes.