Department of Vascular Surgery, Asahikawa Medical University, Asahikawa, Japan; Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.
Department of Vascular Surgery, Asahikawa Medical University, Asahikawa, Japan.
J Vasc Surg. 2019 Oct;70(4):1192-1203.e2. doi: 10.1016/j.jvs.2018.12.057. Epub 2019 May 8.
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.
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.
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×GQ)-(13.0×DM)-(35.1×HD)+12.1 (R = 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).
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.
在治疗严重肢体缺血的远端旁路中，移植物流量（GF）似乎是一个重要的预后预测指标，但以前的研究未能阐明 GF 与移植物预后之间的关系。GF 在移植物之间存在显著差异，每根移植物似乎都有一个取决于各种因素的最佳 GF。我们假设比较测量 GF（mGF）和最佳估计 GF（eGF）将有助于预测移植物的预后。在此，我们旨在通过评估 GF 决定因素来开发一个 GF 预测方程，并通过临床数据集对该方程进行验证。
纳入了 2011 年至 2016 年间因严重肢体缺血而行远端旁路术的 198 例静脉移植物。在这些移植物中，135 例在术后早期超声检查中没有任何异常的正常移植物被用于开发和验证该方程。通过逐步选择分析各种解剖学和患者相关因素来检测 GF 决定因素，并通过多元线性回归分析来开发 GF 预测方程。在开发该方程后，根据 135 例正常移植物的数据，将所有 198 例移植物分为两组：最佳流量移植物（OFGs），即 mGF > eGF - 14.6；和次佳流量移植物（SFGs），即 mGF < eGF - 14.6。使用接收者操作特征曲线确定 14.6 的截断值，以检测移植物异常。通过比较 OFGs 和 SFGs，评估该方程预测旁路异常和移植物预后的效果。
GF 的决定因素是流出量、血液透析（HD）、糖尿病（DM）和移植物质量（GQ）。预测方程估计如下：GF（ml/min）=（32.9×流出量）+（9.9×GQ）-（13.0×DM）-（35.1×HD）+12.1（R = 0.71，系数：流出量和 GQ，3 [良好]，2 [一般]，1 [差]；DM 和 HD，1 [是]，0 [否]）。在对该方程的疗效评估中，SFGs 显示旁路异常（64.0% vs 12.2%；P <.0001）、移植物中间狭窄（10.7% vs 1.6%；P =.0071）、移植物临界狭窄（28.0% vs 3.2%；P <.0001）和早期移植物闭塞（17.3% vs 4.3%；P =.0037）的发生率明显高于 OFGs，并且与术后 2 年内再次手术的发生率（50.7% vs 34.2%；P =.026）较高相关。SFGs 也显示出明显较低的原发性通畅率（P <.0001）和继发性通畅率（P =.0005）。
通过流出量、GQ 和 DM 和 HD 的存在，可以很好地估计 GF。比较测量 GF（mGF）和根据方程计算的最佳估计 GF（eGF）将有助于检测旁路异常，并确定是否需要额外的术中程序，从而获得最佳结果。
