Construction and evaluation of nomogram model for short-term prognosis of elderly patients with acute cerebral infarction treated with mechanical thrombectomy
LIU Peihui1, ZHAO Jinyan1, JIANG Yang2, CAO Xiaopan2, BA Yi2, CHEN Ying1, SHEN Di1, SHEN Daguang1
1. Department of Neurointervention, Huludao Central Hospital, Huludao 125000, China; 2. Department of Neurology, First People's Hospital of Shenyang, Shenyang 110041, China; 3. Department of Neurology, Benxi Central Hospital, Benxi 117099, China
Abstract：Objective To construct a nomogram model for the short-term prognosis of elderly patients with acute cerebral infarction treated with mechanical thrombectomy, and to evaluate the accuracy and clinical applicability of the model. Methods The clinical data of elderly patients with acute cerebral infarction who underwent mechanical thrombectomy in three high-end stroke centers were retrospectively analyzed. They were divided into good prognosis group and poor prognosis group according to the mRS score after 90 days. Logistics analysis was used to analyze the causes of acute cerebral infarction in the elderly. Independent risk factors for patients’ short-term poor prognosis after mechanical thrombectomy. Based on the above independent risk factors, a nomogram model was constructed using R language. The ROC curve, Bootstrap method and clinical decision curve were used to verify the accuracy of the model and the benefits of clinical decision-making. Results The independent risk factors for poor short-term prognosis in elderly patients with acute cerebral infarction after mechanical thrombectomy are: NIHSS score on admission, diabetes mellitus, femoral artery puncture to recanalization time (PTR), postoperative pulmonary infection, postoperative complications Later, symptomatic cerebral hemorrhage occurred and collateral circulation was poor. The ROC curve validation nomogram model shows that the constructed nomogram model has strong predictive ability to predict the short-term prognosis of elderly patients with acute cerebral infarction treated with mechanical thrombectomy (AUC=0.876, 95%CI 0.71-0.94). The Bootstrap method shows the calibration curve. The average absolute error is 0.015, indicating that the calibration curve fits well with the ideal curve. The clinical decision curve shows that the nomogram model has the best applicability when the incidence threshold for predicting the short-term prognosis of elderly patients with acute cerebral infarction after mechanical thrombectomy is between 0.04 and 0.95. Conclusion The constructed nomogram model has high prediction accuracy and has wide clinical applicability.
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2024, Vol. 21 
