Construction and evaluation of nomogram model for short-term prognosis of elderly patients with acute cerebral infarction treated with mechanical thrombectomy

LIU Peihui; ZHAO Jinyan; JIANG Yang; CAO Xiaopan; BA Yi; CHEN Ying; SHEN Di; SHEN Daguang

PDF(2326 KB)

Journal of Hunan Normal University(Medical Science) ›› 2024, Vol. 21 ›› Issue (1) : 103-110.

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

Author information +

History +

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.

Key words

advanced age / acute cerebral infarction / mechanical thrombectomy / nomogram model / prognosis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

LIU Peihui, ZHAO Jinyan, JIANG Yang, CAO Xiaopan, BA Yi, CHEN Ying, SHEN Di, SHEN Daguang. Construction and evaluation of nomogram model for short-term prognosis of elderly patients with acute cerebral infarction treated with mechanical thrombectomy[J]. Journal of Hunan Normal University(Medical Science). 2024, 21(1): 103-110

References

[1] TSIVGOULIS G, KATSANOS A H, ORNELLO R, et al.Ischemic stroke epidemiology during the COVID-19 pandemic: navigating uncharted waters with changing tides[J]. Stroke, 2020, 51(7): 1924-1926.
[2] WU S, WU B O, LIU M, et al.Stroke in China: advances and challenges in epidemiology, prevention, and management[J]. The Lancet Neurology, 2019, 18(4): 394-405.
[3] 陈韵, 陈坤, 王桂斌, 等. 急性缺血性脑卒中患者卒中后抑郁发病率与危险因素分析[J]. 湖南师范大学学报 (医学版), 2021, 18(2): 20-23.
[4] ZHAO W, MA P, ZHANG P, et al.Mechanical thrombectomy for acute ischemic stroke in octogenarians: a systematic review and meta-analysis[J]. Frontiers in neurology, 2020, 10: 1355.
[5] SWEID A, WEINBERG J H, XU V, et al.Mechanical thrombectomy in acute ischemic stroke patients greater than 90 years of age: experience in 26 patients in a large tertiary care center and outcome comparison with younger patients[J]. World Neurosurgery, 2020, 133: e835-e841.
[6] 中华医学会神经病学分会, 中华医学会神经病学分会脑血管病学组, 中华医学会神经病学分会神经血管介入协作组. 中国急性缺血性脑卒中早期血管内介入诊疗指南2018[J]. 中华神经科杂志, 2018, 51(9): 683-691.
[7] 陈竺. 全国第三次死因回顾抽样调查报告[M]. 北京: 中国协和医科大学出版社, 2008.
[8] 吴亚哲, 陈伟伟. 中国脑卒中流行概况[J]. 心脑血管病防治, 2016, 16(6): 410-414.
[9] JIE G U, HAIYING C, XIAOQING G U, et al.Frailty and associated risk factors in elderly people with health examination in rural areas of China[J]. Iranian Journal of Public Health, 2019, 48(9): 1663.
[10] ANDREWS C E, MOUCHTOURIS N, FITCHETT E M, et al.Revascularization and functional outcomes after mechanical thrombectomy for acute ischemic stroke in elderly patients[J]. Journal of neurosurgery, 2019, 132(4): 1182-1187.
[11] SLAWSKI D E, SALAHUDDIN H, SHAWVER J, et al.Mechanical thrombectomy in elderly stroke patients with mild-to-moderate baseline disability[J]. Interventional neurology, 2018, 7(5): 246-255.
[12] HUANG X, YANG Q, SHI X, et al.Predictors of malignant brain edema after mechanical thrombectomy for acute ischemic stroke[J]. J Neurointerv Surg, 2019, 11(10): 994-998.
[13] BARRAL M, LASSALLE L, DARGAZANLI C, et al.Predictors of favorable outcome after mechanical thrombectomy for anterior circulation acute ischemic stroke in octogenarians[J]. J Neuroradiol, 2018, 45(4): 211-216.
[14] 范天晴, 胡国潢. 胃印戒细胞癌生存预测的列线图模型的建立和验证[J]. 湖南师范大学学报 (医学版), 2020, 17(4): 14-17.
[14] 湛构灵, 彭红华, 何雨, 等. 原发性肝细胞癌患者预后预测动态列线图的构建及应用[J]. 湖南师范大学学报 (医学版), 2022, 19(6): 43-49.
[15] OSPEL J M, BROWN S, KAPPELHOF M, et al.Comparing the prognostic impact of age and baseline national institutes of health stroke scale in acute stroke due to large vessel occlusion[J]. Stroke, 2021, 52(9): 2839-2845.
[16] ZHAO X J, LI Q X, LIU T J, et al.Predictive values of CSS and NIHSS in the prognosis of patients with acute cerebral infarction: A comparative analysis[J]. Medicine, 2018, 97(39): e12419.
[17] QUINN T J, DAWSON J, WALTERS M R.Sugar and stroke: cerebrovascular disease and blood glucose control[J]. Cardiovascular therapeutics, 2011, 29(6): e31-e42.
[18] YUAN M, LI F, TIAN X, et al.Risk factors for lung infection in stroke patients: a meta-analysis of observational studies[J]. Expert review of anti-infective therapy, 2015, 13(10): 1289-1298.
[19] GHELANI D P, KIM H A, ZHANG S R, et al.Ischemic stroke and infection: A brief update on mechanisms and potential therapies[J]. Biochemical Pharmacology, 2021, 193: 114768.
[20] HAO Y, YANG D, WANG H, et al.Predictors for symptomatic intracranial hemorrhage after endovascular treatment of acute ischemic stroke[J]. Stroke, 2017, 48(5): 1203-1209.
[21] GINSBERG M D.The cerebral collateral circulation: relevance to pathophysiology and treatment of stroke[J]. Neuropharmacology, 2018, 134: 280-292.
[22] 王旭, 赵冬, 代林志, 等. 急性脑梗死患者机械取栓术后补救性支架置入的预后影响因素分析[J]. 山东医药, 2023, 63(26): 20-25.
[23] 张红艳, 张灿, 高艳, 等. rt-PA静脉溶栓前基线NIHSS评分、D-二聚体水平对急性脑梗死患者预后的预测价值[J]. 淮海医药, 2022, 40(5): 466-469.