Darman Lily, Khan Nabeeha, Liu Amy, Yuan Karen, Babrowski Trissa, Blecha Matthew
Division of Vascular Surgery, Loyola University Chicago, Stritch School of Medicine, Loyola University Health System, Maywood, IL.
Department of Surgery, Section of Vascular Surgery, University of Chicago Medical Center, Chicago, IL.
J Vasc Surg. 2025 Jul 28. doi: 10.1016/j.jvs.2025.07.029.
Numerous cardiac risk evaluation models exist for patients undergoing major vascular surgical interventions. These models, however, do not consider patients undergoing major amputation for limb ischemia. The purpose of this study was to create risk scores for myocardial infarction (MI) and composite adverse cardiac events after major amputation performed for limb ischemia.
The first step was univariable analysis for the outcomes of perioperative MI and major adverse cardiac events (MACEs) for patients undergoing nonemergent major amputation for limb ischemia (primary risk score study group n = 10,260, validation group n = 5130). Univariable analysis was conducted with χ testing for categorical variables and the Student t test for comparison of means of ordinal variables. Next, binary logistic regression analysis was performed for the outcomes of perioperative MI and MACEs using variables that had achieved a univariable P value of .1 or less. Using this regression analysis, it was determined which variables have a multivariable association for the outcomes as defined by a multivariable regression P value of .05 or less. Weighted cumulative event scores were then created for the outcomes of perioperative MI and MACEs. Variables with a multivariable P value of .05 or less on the regression analysis were included in the scores and weighted based on their respective regression beta coefficient in a point scale. Area under the curve (AUC) analysis and Hosmer-Lemeshow goodness of fit was also conducted for the validation cohort using the same risk score variables and scoring system as the primary study group. A supplementary machine learning analysis was performed to reinforce variable importance.
Variables with a significant (P < .05) multivariable association and meeting inclusion or the MACE risk score included advancing age (adjusted odds ratio [aOR], 1.02/unit time; P < .001), female sex (aOR, 1.28; P = .01), asymptomatic coronary artery disease (CAD) (aOR, 1.36; P = .009), symptomatic CAD (aOR, 1.28; P = .049), coronary artery bypass graft (CABG) more than 5 years ago (aOR, 1.37; P = .014), class II congestive heart failure (CHF) (aOR, 1.30; P = .05), class III CHF (aOR, 1.75; P = .002), class IV CHF (aOR, 5.26, P < .001), COPD (aOR, 1.29; P = .012), end-stage renal disease (ESRD) (aOR, 2.20, P < .001), and renal insufficiency (aOR, 1.76, P < .001). Regarding the risk score for MACE following major amputation for limb ischemia, patients with risk scores of 1 or greater experienced MACEs in just 2.6% of cases. The MACE rate increased in an exponential fashion with rising risk score with rates of 26.9% for patients with scores 16 and higher indicating a 10-fold increased risk. In MI analysis, the following variables achieved a multivariable P value of .05 or less and were thus ultimately included in the risk score for MI: advancing age (aOR, 1.02/unit time; P = .007), asymptomatic CAD (aOR, 1.52; P = .022), symptomatic CAD (aOR, 1.56; P = .038), history of CABG more than 5 years ago (aOR, 1.51; P = .029), class III CHF (aOR, 1.84; P = .019), class IV CHF (aOR, 3.07; P = .002), ESRD on dialysis (aOR, 1.95, P < .001), renal insufficiency (aOR, 1.95, P < .001), and not being on an antiplatelet preoperative (protective aOR 0.65; P = .007). Regarding the risk score for MI, patients with risk scores of 0 or lower had MI rates of just 0.5% with steep escalation noted with advancing risk score as patients with risk scores of 10 and higher had MI rates of 6.0%, indicating a 12-fold higher risk of MI. Risk score AUC values were 0.70 and 0.71, respectively. Patients with MACEs and MI perioperatively had survival rates as low as 42% by the 2-year mark vs 67% to 69% for those without (P < .001). Machine learning confirmed the importance of the key variables and achieved AUC values ranging from 0.77 to 0.94.
Risk scores for perioperative MI and MACEs during hospitalization for major amputation owing to limb ischemia have been created with accurate internal validation. These data have the potential to impact preoperative and perioperative patient management to reduce adverse event rates. The most impactful variables increasing risk of MI and MACEs are advancing age, history of class III or IV CHF, history of CAD, CABG more than 5 years ago, renal insufficiency, and ESRD.
对于接受重大血管外科手术的患者,存在多种心脏风险评估模型。然而,这些模型未考虑因肢体缺血而接受重大截肢手术的患者。本研究的目的是为因肢体缺血进行重大截肢术后的心肌梗死(MI)和复合不良心脏事件创建风险评分。
第一步是对因肢体缺血接受非急诊重大截肢手术的患者的围手术期MI和主要不良心脏事件(MACE)结局进行单变量分析(主要风险评分研究组n = 10260,验证组n = 5130)。对分类变量进行χ检验,对有序变量的均值比较进行Student t检验以进行单变量分析。接下来,使用单变量P值达到0.1或更低的变量对围手术期MI和MACE结局进行二元逻辑回归分析。通过该回归分析,确定哪些变量对于多变量回归P值为0.05或更低所定义的结局具有多变量关联。然后为围手术期MI和MACE结局创建加权累积事件评分。回归分析中多变量P值为0.05或更低的变量被纳入评分,并根据其各自在点量表中的回归β系数进行加权。使用与主要研究组相同的风险评分变量和评分系统,对验证队列进行曲线下面积(AUC)分析和Hosmer-Lemeshow拟合优度检验。进行了补充机器学习分析以强化变量重要性。
具有显著(P < 0.05)多变量关联且符合纳入标准的MACE风险评分变量包括年龄增长(调整优势比[aOR],1.02/单位时间;P < 0.001)、女性(aOR,1.28;P = 0.01)、无症状冠状动脉疾病(CAD)(aOR,1.36;P = 0.009)、有症状CAD(aOR,1.28;P = 0.049)、5年以上冠状动脉旁路移植术(CABG)(aOR,1.37;P = 0.014)、II级充血性心力衰竭(CHF)(aOR,1.30;P = 0.05)、III级CHF(aOR,1.75;P = 0.002)、IV级CHF(aOR,5.26,P < 0.001)、慢性阻塞性肺疾病(COPD)(aOR,1.29;P = 0.012)、终末期肾病(ESRD)(aOR,2.20,P < 0.001)和肾功能不全(aOR,1.76,P < 0.001)。关于因肢体缺血进行重大截肢术后MACE的风险评分,风险评分为1或更高的患者仅2.6%发生MACE。随着风险评分升高,MACE发生率呈指数增长,评分为16及更高的患者发生率为26.9%,表明风险增加10倍。在MI分析中,以下变量的多变量P值达到0.05或更低,因此最终被纳入MI风险评分:年龄增长(aOR,1.02/单位时间;P = 0.007)、无症状CAD(aOR,1.52;P = 0.022)、有症状CAD(aOR,1.56;P = 0.038)、5年以上CABG病史(aOR,1.51;P = 0.029)、III级CHF(aOR,1.84;P = 0.019)、IV级CHF(aOR,3.07;P = 0.002)、透析中的ESRD(aOR,1.95,P < 0.001)、肾功能不全(aOR,1.95,P < 0.001)以及术前未使用抗血小板药物(保护性aOR 0.65;P = 0.007)。关于MI风险评分,风险评分为0或更低的患者MI发生率仅为0.5%,随着风险评分升高急剧上升,评分为10及更高的患者MI发生率为6.0%,表明MI风险高12倍。风险评分AUC值分别为0.70和0.71。围手术期发生MACE和MI的患者到2年时生存率低至42%,而未发生者为67%至69%(P < 0.001)。机器学习证实了关键变量的重要性,并获得了范围从0.77至0.94的AUC值。
已创建因肢体缺血进行重大截肢住院期间围手术期MI和MACE的风险评分,并进行了准确的内部验证。这些数据有可能影响术前和围手术期患者管理,以降低不良事件发生率。增加MI和MACE风险最具影响的变量是年龄增长、III级或IV级CHF病史、CAD病史、5年以上CABG、肾功能不全和ESRD。