Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study
Abstract
:1. Introduction
2. Methods
2.1. Study Population
2.2. Patient Variables and Data Definitions
2.3. Statistical Analysis
2.4. Data Processing and Feature Selection
2.5. Modeling Strategies
2.6. Model Evaluation
2.7. Explanation of the Model
3. Results
3.1. Study Population
3.2. Feature Selection
3.3. Model Building and Evaluation
3.4. Explanation of the Model at the Feature Level
3.5. Explanation of the Model at the Individual Level
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | All Patients, n = 985 | with END, n = 157 | without END, n = 828 | p Value |
---|---|---|---|---|
Demographic characteristics | ||||
Age, years, median (IQR) | 71.0(63.0–79.0) | 71.0(63.5–80.0) | 71.0(63.0–79.0) | 0.620 |
Female, n (%) | 377(38.3) | 61(38.9) | 316(38.2) | 0.871 |
Vascular risk factors, n (%) | ||||
Hypertension | 677(68.7) | 117(74.5) | 560(67.6) | 0.088 |
Diabetes mellitus | 232(23.6) | 46(29.3) | 186(22.5) | 0.064 |
Hyperlipidemia | 17(1.7) | 3(1.9) | 14(1.7) | 0.846 |
Coronary artery disease | 142(14.4) | 13(8.3) | 129(15.6) | 0.017 |
Atrial fibrillation | 311(31.6) | 43(27.4) | 268(32.4) | 0.219 |
Previous stroke or TIA | 215(21.8) | 30(19.1) | 185(22.3) | 0.368 |
Smoking | 303(30.8) | 45(28.7) | 258(31.2) | 0.524 |
Drinking | 232(23.6) | 28(17.8) | 204(24.6) | 0.065 |
Clinical data, median (IQR) | ||||
Systolic blood pressure, mmHg | 138.0(123.0–155.0) | 140.0(125.0–160.0) | 138.0(123.0–154.0) | 0.196 |
Diastolic blood pressure, mmHg | 82.0(73.0–93.0) | 82.0(71.5–93.5) | 82.5(73.0–93.0) | 0.991 |
NIHSS at baseline | 14.0(11.0–18.0) | 14.0(8.5–18.0) | 14.0(11.0–18.0) | 0.186 |
Interval from onset to treatment, min | 270.0(190.0–410.0) | 300.0(189.5–500.0) | 270.0(190.0–404.3) | 0.426 |
Interval from groin puncture to recanalization, min | 70.0(50.0–95.0) | 72.0(55.0–104.5) | 68.0(50.0–94.3) | 0.089 |
Cause of stroke, n (%) | 0.067 | |||
Atherosclerotic | 448(45.5) | 76(48.4) | 372(44.9) | |
Cardioembolic | 433(44) | 58(36.9) | 375(45.3) | |
Others | 104(10.6) | 23(14.6) | 81(9.8) | |
Endovascular therapy, n (%) | ||||
Intravenous thrombolysis | 379(38.5) | 64(40.8) | 315(38) | 0.521 |
Tirofiban | 425(43.1) | 74(47.1) | 351(42.4) | 0.271 |
sICH | 139(14.1) | 39(24.8) | 100(12.1) | 0.000 |
Recanalization | 883(89.6) | 134(85.4) | 749(90.5) | 0.054 |
Lesion location, n (%) | ||||
Anterior circulation | 790(80.2) | 117(74.5) | 673(81.3) | 0.051 |
Posterior circulation | 197(20) | 40(25.5) | 157(19) | 0.061 |
Procedural modes | ||||
Aspiration only, n (%) | 34(3.5) | 8(5.1) | 26(3.1) | 0.218 |
Stent retriever only, n (%) | 740(75.1) | 104(66.2) | 636(76.8) | 0.005 |
Stent retriever/aspiration with rescue therapy, n (%) | 228(23.1) | 47(29.9) | 181(21.9) | 0.028 |
Passes of Stent retriever, median (IQR) | 2.0(1.0–3.0) | 2.0(1.0–3.0) | 2.0(1.0–3.0) | 0.830 |
Laboratory data, median (IQR) | ||||
Platelets, μmol/L | 180.0(145.0–220.0) | 185.0(145.5–228.5) | 180.0(145.0–219.0) | 0.433 |
Serum creatinine, μmol/L | 69.0(58.0–83.5) | 69.7(55.8–90.5) | 69.0(58.0–83.0) | 0.825 |
Blood glucose, mmol/L | 6.6(5.4–8.2) | 7.9(6.1–9.8) | 6.4(5.3–7.8) | 0.000 |
Total cholesterol, mmol/L | 4.1(3.4–5.0) | 4.1(3.5–5.0) | 4.1(3.4–4.9) | 0.476 |
Triglyceride, mmol/L | 1.0(0.8–1.5) | 1.1(0.8–1.5) | 1.0(0.7–1.4) | 0.074 |
High-density lipoprotein, mmol/L | 1.1(0.9–1.3) | 1.1(0.9–1.3) | 1.1(0.9–1.3) | 0.391 |
Low-density lipoprotein, mmol/L | 2.5(1.9–3.1) | 2.4(1.8–3.2) | 2.5(1.9–3.1) | 0.973 |
UA, μmol/L | 314.0(241.5–383.0) | 321.0(246.0–382.5) | 313.0(241.0–383.0) | 0.604 |
Glycated hemoglobin, mmol/L | 5.9(5.6–6.6) | 6.2(5.7–6.8) | 5.9(5.6–6.5) | 0.004 |
Homocysteine, μmol/L | 13.1(10.9–15.7) | 13.1(10.4–15.7) | 13.1(11.0–15.7) | 0.697 |
Model | AUC | Sensitivity | Specificity | Accuracy | Brier | Youden Index |
---|---|---|---|---|---|---|
LR | 0.644 | 0.387 | 0.793 | 0.587 | 0.234 | 0.565 |
RF | 0.819 | 0.827 | 0.689 | 0.759 | 0.190 | 0.480 |
SVM | 0.643 | 0.482 | 0.713 | 0.596 | 0.235 | 0.520 |
XGBoost | 0.826 | 0.798 | 0.713 | 0.756 | 0.184 | 0.509 |
Model | AUC | Sensitivity | Specificity | Accuracy |
---|---|---|---|---|
XGBoost | 0.846 | 0.750 | 0.836 | 0.815 |
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Yang, T.; Hu, Y.; Pan, X.; Lou, S.; Zou, J.; Deng, Q.; Zhang, Q.; Zhou, J.; Zhu, J. Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study. Brain Sci. 2023, 13, 557. https://doi.org/10.3390/brainsci13040557
Yang T, Hu Y, Pan X, Lou S, Zou J, Deng Q, Zhang Q, Zhou J, Zhu J. Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study. Brain Sciences. 2023; 13(4):557. https://doi.org/10.3390/brainsci13040557
Chicago/Turabian StyleYang, Tongtong, Yixing Hu, Xiding Pan, Sheng Lou, Jianjun Zou, Qiwen Deng, Qingxiu Zhang, Junshan Zhou, and Junrong Zhu. 2023. "Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study" Brain Sciences 13, no. 4: 557. https://doi.org/10.3390/brainsci13040557