Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models

被引:10
|
作者
Ozkara, Burak B. [1 ]
Karabacak, Mert [2 ]
Hamam, Omar [3 ]
Wang, Richard [3 ]
Kotha, Apoorva [3 ]
Khalili, Neda [3 ]
Hoseinyazdi, Meisam [3 ]
Chen, Melissa M. [1 ]
Wintermark, Max [1 ]
Yedavalli, Vivek S. [3 ]
机构
[1] MD Anderson Canc Ctr, Dept Neuroradiol, Houston, TX 77030 USA
[2] Mt Sinai Hlth Syst, Dept Neurosurg, New York, NY 10029 USA
[3] Johns Hopkins Univ Hosp, Russell H Morgan Dept Radiol & Radiol Sci, Baltimore, MD 21287 USA
关键词
ischemic stroke; machine learning; medical decision making; middle cerebral artery; artificial intelligence; ACUTE ISCHEMIC-STROKE; MECHANICAL THROMBECTOMY; VESSEL OCCLUSION; SCORE;
D O I
10.3390/jcm12030839
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as inputs. Included were consecutive AIS patients with MCA M1 and proximal M2 occlusions. The XGBoost, LightGBM, CatBoost, and Random Forest were used to predict the outcome. Minimum redundancy maximum relevancy was used for selecting features. The primary outcomes were the National Institutes of Health Stroke Scale (NIHSS) shift and the modified Rankin Score (mRS) at 90 days. The algorithm with the highest area under the receiver operating characteristic curve (AUROC) for predicting the favorable and unfavorable outcome groups at 90 days was LightGBM. Random Forest had the highest AUROC when predicting the favorable and unfavorable groups based on the NIHSS shift. Using clinical, laboratory, and imaging parameters in conjunction with machine learning, we accurately predicted the functional outcome of AIS patients with proximal MCA occlusions.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study
    Lin, Mengqi
    Xia, Nengzhi
    Lin, Ru
    Xu, Liuhui
    Chen, Yongchun
    Zhou, Jiafeng
    Lin, Boli
    Zheng, Kuikui
    Wang, Hao
    Jia, Xiufen
    Liu, Jinjin
    Zhu, Dongqin
    Chen, Chao
    Yang, Yunjun
    Su, Na
    QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2023, 13 (08) : 4867 - +
  • [32] Machine Learning in Action: Stroke Diagnosis and Outcome Prediction
    Mainali, Shraddha
    Darsie, Marin E.
    Smetana, Keaton S.
    FRONTIERS IN NEUROLOGY, 2021, 12
  • [33] MACHINE LEARNING FOR AUTOMATIC STROKE ASSESSMENT AND OUTCOME PREDICTION
    Laksari, K.
    Tahsili-Fahadan, P.
    Deshpande, A.
    INTERNATIONAL JOURNAL OF STROKE, 2023, 18 (03) : 56 - 57
  • [34] The Prediction of Clinical Outcome Using HbA1c in Acute Ischemic Stroke of the Deep Branch of Middle Cerebral Artery
    Shin, Sung Bong
    Kim, Tae Uk
    Hyun, Jung Keun
    Kim, Jung Yoon
    ANNALS OF REHABILITATION MEDICINE-ARM, 2015, 39 (06): : 1011 - 1017
  • [35] Prediction of perinatal outcome in preeclampsia using middle cerebral artery and umbilical artery pulsatility and resistance indices
    Rani, Shikha
    Huria, Anju
    Kaur, Ravinder
    HYPERTENSION IN PREGNANCY, 2016, 35 (02) : 210 - 216
  • [36] Mechanical Thrombectomy Significantly Improves Functional Outcome in a Stroke Cohort With Middle Cerebral Artery (MCA) Occlusion
    Frei, Don
    Bellon, Richard
    Kulcsar, Zsolt
    Bonvin, Christophe
    Rufenacht, Daniel
    Alfke, Karsten
    Stingele, Robert
    Jansen, Olav
    Madison, Michael
    Struffert, Tobias
    Dorfler, Arnd
    Grunwald, Iris Q.
    Reith, Wolfgang
    Haass, Anton
    Hsu, Daniel
    Tarr, Robert
    STROKE, 2010, 41 (04) : E282 - E282
  • [37] Predictors for Good Collaterals in 857 Patients With Acute Ischemic Stroke and Proximal Middle Cerebral Artery Occlusion
    Nannoni, Stefania
    Cereda, Carlo W.
    Sirimarco, Gaia
    Lambrou, Dimitris
    Eskandari, Ashraf
    Maghraoui, Ali
    Puccinelli, Francesco
    Mosimann, Pascal
    Wintermark, Max
    Michel, Patrik
    STROKE, 2017, 48
  • [38] Analysis of angiographic findings and postoperative stroke in proximal middle cerebral artery aneurysms
    Park, D-H
    Kang, S-H
    Lim, D-J
    Kwon, T-H
    Lee, J.
    Chung, Y-G
    EUROPEAN JOURNAL OF NEUROLOGY, 2010, 17 : 164 - 164
  • [39] Factors predicting poor outcome at discharge in stroke patients with middle cerebral artery branch occlusion
    Naganuma, Masaki
    Inatomi, Yuichiro
    Nakajima, Makoto
    Yonehara, Toshiro
    Ando, Yukio
    INTERDISCIPLINARY NEUROSURGERY-ADVANCED TECHNIQUES AND CASE MANAGEMENT, 2020, 21
  • [40] Is hyperdense middle cerebral artery sign reliably indicative of arterial occlusion at the level of proximal middle cerebral artery in acute ischemic stroke?
    Xavier, AR
    Ionita, CC
    Utukuri, PS
    Kirmani, JF
    Qureshi, AI
    NEUROLOGY, 2004, 62 (07) : A283 - A284