A novel clinical nomogram for predicting 3-month unfavorable outcomes of stroke patients treated with mechanical thrombectomy

Background: Mechanical thrombectomy (MT) is an effective treatment for large-vessel occlusion in acute ischemic stroke, however, only some revascularized patients have a good prognosis. For stroke patients undergoing MT, predicting the risk of unfavorable outcomes and adjusting the treatment strategies accordingly can greatly improve prognosis. Therefore, we aimed to develop and validate a nomogram that can predict 3-month unfavorable outcomes for individual stroke patient treated with MT. Methods: We analyzed 238 patients with acute ischemic stroke who underwent MT from January 2018 to October 2020. The primary outcome was a 3-month unfavorable outcome, assessed using the modified Rankin Scale (mRS), 3-6. A nomogram was generated based on a multivariable logistic model. We used the area under the receiver-operating characteristic curve to evaluate the discriminative performance and used the calibration curve and Spiegelhalter’s Z-test to assess the calibration performance of the risk prediction model. Results: After multivariable logistic regression, six variables (gender, bridging therapy, postoperative mTICI, stroke-associated pneumonia, preoperative creatinine and Na) remained independent predictors of 3-month unfavorable outcomes in stroke patients treated with MT, thus forming a nomogram. The area under the nomogram curve was 0.848 with good calibration performance (P = 0.946 for the Spiegelhalter’s Z-test). Conclusions: A novel nomogram consisting of gender, bridging therapy, postoperative mTICI, stroke-associated pneumonia, preoperative creatinine and Na can predict the 3-month unfavorable outcomes in stroke patients treated with MT.