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Zhao H, Li C, Jin Z, Duan W, Shang L, Chang Y, Xu J, Ren J, Lin S, Wang Y, Zhu L, Wang G, Chen X, He C, Zheng M. Risk prediction of preoperative acute ischemic stroke in acute type A aortic dissection. Eur Radiol 2023; 33:7250-7259. [PMID: 37178204 DOI: 10.1007/s00330-023-09691-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES To predict preoperative acute ischemic stroke (AIS) in acute type A aortic dissection (ATAAD). METHODS In this multi-center retrospective study, 508 consecutive patients diagnosed as ATAAD between April 2020 and March 2021 were considered for inclusion. The patients were divided into a development cohort and two validation cohorts based on time periods and centers. Clinical data and imaging findings obtained were analyzed. Univariable and multivariable logistic regression analyses were performed to identify predictors associated with preoperative AIS. The performance of resulting nomogram was evaluated in discrimination and calibration on all cohorts. RESULTS A total of 224 patients were in the development cohort, 94 in the temporal validation cohort, and 118 in the geographical validation cohort. Six predictors were identified: age, syncope, D-dimer, moderate to severe aortic valve insufficiency, diameter ratio of true lumen in ascending aorta < 0.33, and common carotid artery dissection. The nomogram established showed good discrimination (area under the receiver operating characteristic curve [AUC], 0.803; 95% CI: 0.742, 0.864) and calibration (Hosmer-Lemeshow test p = 0.300) in the development cohort. External validation showed good discrimination and calibration abilities in both temporal (AUC, 0.778; 95% CI: 0.671, 0.885; Hosmer-Lemeshow test p = 0.161) and geographical cohort (AUC, 0.806; 95% CI: 0.717, 0.895; Hosmer-Lemeshow test p = 0.100). CONCLUSIONS A nomogram, based on simple imaging and clinical variables collected on admission, showed good discrimination and calibration abilities in predicting preoperative AIS for ATAAD patients. KEY POINTS • A nomogram based on simple imaging and clinical findings may predict preoperative acute ischemic stroke in patients with acute type A aortic dissection in emergencies. • The nomogram showed good discrimination and calibration abilities in validation cohorts.
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Affiliation(s)
- Hongliang Zhao
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi'an, China
| | - Chengxiang Li
- Department of Cardiovascular Surgery, Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, China
| | - Zhenxiao Jin
- Department of Cardiovascular Surgery, Xijing Hospital, Fourth Military University, 127 Changle West Road, Xi'an, China.
| | - Weixun Duan
- Department of Cardiovascular Surgery, Xijing Hospital, Fourth Military University, 127 Changle West Road, Xi'an, China
| | - Lei Shang
- Department of Health Statistics, Fourth Military Medical University, 127 Changle West Road, Xi'an, China
| | - Yingjuan Chang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi'an, China
| | - Jingji Xu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi'an, China
| | - Jialiang Ren
- GE Healthcare China, 2 Yongchang North Road, Beijing, China
| | - Shushen Lin
- Siemens Healthineers Ltd., 278 Zhou Zhugong Road, Shanghai, China
| | - Yan Wang
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Urumqi, China
| | - Li Zhu
- Department of Radiology, General Hospital of Ningxia Medical University, 804 Shengli Road, Yinchuan, China
| | - Gang Wang
- Department of Radiology, The First Hospital of Lanzhou University, 1 Donggang West Road, Lanzhou, China
| | - Xin Chen
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, 157 Xiwu Road, Xi'an, China
| | - Chao He
- Department of Radiology, The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, 5 Weiyang West Road, Xianyang, China
| | - Minwen Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi'an, China.
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Wu Z, Li Y, Xu Z, Liu H, Liu K, Qiu P, Chen T, Lu X. Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study. BMJ Open 2023; 13:e066782. [PMID: 37012019 PMCID: PMC10083797 DOI: 10.1136/bmjopen-2022-066782] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
OBJECTIVES To conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques. DESIGN Retrospective cohort study. SETTING Data were collected from the electronic records and the databases of Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018. PARTICIPANTS 380 inpatients diagnosed with acute AD were included in the study. PRIMARY OUTCOME Preoperative in-hospital mortality rate. RESULTS A total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level. CONCLUSION In the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database. TRIAL REGISTRATION NUMBER ChiCTR1900025818.
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Affiliation(s)
- Zhaoyu Wu
- Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China
| | - Yixuan Li
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
- Department of Economics, University of Waterloo, Waterloo, Ontario, Canada
| | - Zhijue Xu
- Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China
| | - Haichun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Kai Liu
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
- School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Peng Qiu
- Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
- Department of Economics, University of Waterloo, Waterloo, Ontario, Canada
| | - Xinwu Lu
- Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China
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Kokkotis C, Giarmatzis G, Giannakou E, Moustakidis S, Tsatalas T, Tsiptsios D, Vadikolias K, Aggelousis N. An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data. Diagnostics (Basel) 2022; 12:2392. [PMID: 36292081 PMCID: PMC9600473 DOI: 10.3390/diagnostics12102392] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients' class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments.
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Affiliation(s)
- Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Georgios Giarmatzis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Erasmia Giannakou
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | | | - Themistoklis Tsatalas
- Department of Physical Education and Sport Science, University of Thessaly, 38221 Trikala, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
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