Trans-Clot MAP gradient: A novel innovative technique during thrombectomy for acute ischemic stroke with potential applications for assessing collateral circulation, determining clot etiology, informing device selection, and predicting first-pass success.

Endovascular mechanical thrombectomy has significantly improved recovery in acute ischemic stroke (AIS). While traditional patient selection has relied on factors such as last known well and penumbra volume, emerging research highlights the importance of collateral circulation in influencing thrombectomy success. However, current methods to assess collateral circulation are often unreliable and lack standardization, limiting their integration into clinical decision-making and prompting the need for innovative approaches. This study introduces trans-clot manometry as a promising approach for quantitatively assessing collateral blood flow before thrombectomy. Two patients were included in this study: a 64-year-old female with a left M1 near-complete occlusion and an 81-year-old male with a left P1 occlusion. After receiving intravenous tenecteplase, each patient underwent emergent thrombectomy where intraoperative trans-clot manometry revealed significant trans-clot mean arterial pressure (MAP) gradients (66.7% for Patient 1 and 96.9% for Patient 2). Both patients had successful first-pass thrombectomy (Patient 1: TICI 3; Patient 2: TICI 3), with substantial clinical improvement (Patient 1: NIHSS 11 to 1; Patient 2: NIHSS 19 to 8). Intraoperative trans-clot manometry offers a simple yet powerful, objective, and generalizable measure of collateral circulation, applicable to a wide range of AIS cases regardless of clot location or vessel size. In addition, real-time correlations with heart-rate variability and radial artery pressures provide an intrinsic quality control, ensuring proper execution of the technique and accuracy of the resulting MAP gradient. Future research will focus on validating this approach, determining its generalizability, and establishing MAP gradient thresholds to enhance device selection and predict first-pass success.