Performance of the AUB-HAS2 Cardiovascular Risk Index in vascular surgery patients

Abstract

The American University of Beirut (AUB)-HAS2 risk index is a recently published tool for preoperative cardiovascular evaluation. It is based on six data elements: history of Heart disease, symptoms of Heart disease (angina or dyspnea), Age ⩾ 75 years, Anemia (hemoglobin < 12 mg/dL), emergency Surgery, and vascular Surgery. This study analyzes the performance of a modified AUB-HAS2 index (excluding the vascular surgery element) in a broad spectrum of vascular surgery procedures. The study population consisted of 90,476 vascular surgeries registered in the American College of Surgeons National Surgical Quality Improvement Program database. The performance of the AUB-HAS2 index was studied in seven groups: carotid endarterectomy (CEA), open abdominal aortic aneurysm surgical repair (OAAA), endovascular aortic aneurysm repair, supra-inguinal bypass, infra-inguinal bypass, lower extremity thrombo-endarterectomy, and lower extremity angioplasty. The outcome measure was death, myocardial infarction, or stroke at 30 days after surgery. Each patient was given an AUB-HAS2 score of 0, 1, 2, or > 2 depending on the number of data elements s/he has. The AUB-HAS2 index was able to stratify risk in the majority of patients into low (< 3%, score 0), intermediate (3–10%, score 1–2), and high (> 10%, score > 2) ( p < 0.0001). The receiver operating curve had an area of 0.71 in the overall group and it ranged from 0.60 in CEA patients to 0.75 in OAAA patients. In conclusion, the AUB-HAS2 index is a simple tool that can quickly and effectively stratify the risk of patients undergoing a broad spectrum of vascular surgeries into low, intermediate, and high.

2022 ESC Guidelines on cardiovascular assessment and management of patients undergoing non-cardiac surgery

“…49 The AUB-HAS2 Index has been tested in a broad spectrum of surgical subpopulations and demonstrated superior discriminatory power compared with the commonly utilized RCRI (Table 6). 50,51,81 There is significant variability in the predicted risk of cardiac complications using different risk-prediction tools; none can be disqualified with current evidence. 82…”

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

“…PLA2G2A [61], CCL23 [62], CD53 [63], TREML4 [64], TREM2 [65], CD180 [66], HPSE (heparanase) [67], CELA2A [68], TNFRSF4 [69], AMBP (alpha-1-microglobulin/bikunin precursor) [70], SOX18 [71], PANX2 [72], RSPO2 [73], COMP (cartilage oligomeric matrix protein) [74], ASGR1 [75] and NOXA1 [76] are involved in progression of atherosclerosis. A previous study reported that S100A9 [77], ADORA3 [78], IL1R2 [79], FPR1 [80], CCL20 [81], CD163 [82], S100A8 [83], TLR2 [84], HAS2 [85], PTX3 [86], TIMP4 [87], AREG (amphiregulin) [88], LBP (lipopolysaccharide binding protein) [89], IL18R1 [90], ALOX5AP [91], RETN (resistin) [92], F13A1 [93], FPR2 [94], SAA1 [95], FLT3 [96], AQP4 [97], FCER1G [98], CCL18 [99], HP (haptoglobin) [100], CDK1 [101], SLC7A11 [102], CFTR (CF transmembrane conductance regulator) [103], F8 [104], STC1[44], IL18RAP [90], TIMP3 [105], PDE4D [106], CYP4A11 [107], SCN10A [108], APOB (apolipoprotein B) [109], ACE (angiotensin I converting enzyme) [110], PENK (proenkephalin) [111], HSPB6 [112], TLR9 [113], EGR1 [114], CACNG8 [115], FOXD3 [116], DBH (dopamine beta-hydroxylase) [117], FOXP3 [118], GLP1R [119], IL34 [120], CCN1 [121], ADRA2A [122], BGN (biglycan) [123], NOS2 [124], AGRN (agrin) [125], DRD1 [126], GNB3 [127], EGR2 [128], MDK (midkine) [129], NOTCH3 [130], AZIN2 [131], NOTCH1 [132], LOX...…”

Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

“…LGR5 [61], GREM1 [62], GLRA3 [63], NEUROD4 [64], CYP2J2 [65], KCNH6 [66], LBP (lipopolysaccharide binding protein) [67], CXCL14 [68], RGN (regucalcin) [69], NPY2R [70], SERPINB13 [71], WNT5A [72], EDA (ectodysplasin A) [73], HSD11B2 [74], ACVR1C [75], NEUROD1 [76], SLIT2 [77], PPARGC1A [78], IGF1 [79], OSR1 [80], CYP46A1 [81], TLR3 [82], BMP7 [83], SELP (selectin P) [84], HLA-A [85], NOTCH2 [86], LRP1 [87], CLU (clusterin) [88], FCN1 [89], CDKN1A [90], SMAD3 [91], HLA-E [92], PTPRC (protein tyrosine phosphatase receptor type C) [93], MYH9 [94], JAK3 [95], IL6R [96], TIMP1 [97], DOCK8 [98], TNFRSF1B [99], ITGAL (integrin subunit alpha L) [100], CD47 [101], RARA (retinoic acid receptor alpha) [102], DGKD (diacylglycerol kinase delta) [103], PLEK (pleckstrin) [104], PREX1 [105], BSCL2 [106], PANX1 [107], IRF7 [108], NOTCH1 [109], STIM1 [110], TRIM13 [111], LRBA (LPS responsive beige-like anchor protein) [112], CXCR4 [113], MDM4 [114], MYO9B [115] and PDE5A [116] were revealed to be expressed in diabetes mellitus, but these genes might be novel targets for GDM. SIX1 [117], GREM1 [118], GHRHR (growth hormone releasing hormone receptor) [119], GPR37L1 [120], CYP2J2 [121], AQP4 [122], ROS1 [123], LBP (lipopolysaccharide binding protein) [124], SGCD (sarcoglycan delta) [125], CXCL14 [126], RGN (regucalcin) [127], F9 [128], KCND2 [129], AZGP1 [130], HAS2 [131], CNTN4 [132], WNT5A [...…”