Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Setting
2.2. Definitions
2.3. Feature Selection for ML
2.4. Imputation for Missing Values
2.5. Prediction Models
2.6. Model Training, Evaluation, and Performance Metrics
3. Results
3.1. Characteristics of Study Population
3.2. Comparing Machine Learning Model Performance
4. Discussion
4.1. Evaluation of four Models and Feature Selection Considerations
4.2. Comparing Different ML Algorithms
4.3. Translating the Model into Clinical Practice and Validation Steps
4.4. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Value |
---|---|
Age (mean ± standard deviation) | 32.2 y ± 15.0 |
Males | 863 (93.6%) |
Mechanism of injury (n, %) | |
• Falls | 230 (24.9%) |
• Road traffic injuries | 545 (59.1%) |
• Other | 147 (15.9%) |
Types of head injury (n, %) | |
• Epidural hematoma | 204 (22.1%) |
• Subdural hematoma | 321 (34.8%) |
• Subarachnoid hemorrhage | 387 (42.0%) |
• Compression of basal cisterns | 110 (11.9%) |
• Effacement of Sulci | 171 (18.5%) |
• Midline Shifts | 206 (22.3%) |
Glasgow Coma Scale (GCS) classification (n, %) | |
• Mild (GCS 14–15) | 66 (7.2%) |
• Moderate (GCS 9–13) | 166 (18.0%) |
• Severe (GCS 3–8) | 681 (73.9%) |
• Injury Severity Score (ISS) (median, IQR) | 27 (18–34) |
Initial Serum Electrolyte Levels [median, interquartile range (IQR)] | |
• Initial serum sodium | 141.0 (139–143) |
• Initial serum potassium | 3.8 (3.4–4.1) |
• Initial serum calcium | 2.0 (1.8–2.1) |
• Initial serum magnesium | 0.7 (0.6–0.8) |
• Initial serum phosphate | 0.9 (0.7–1.2) |
Other Clinical Parameters (median, IQR) | |
• Bicarbonate level | 19.6 (16.7–23.0) |
• Lactic acid level | 2.9 (2.0–4.3) |
• Prothrombin time | 12.0 (11.1–13.5) |
• Activated partial thromboplastin time | 26.2 (24.0–31.0) |
• International normalized ratio | 1.1 (1.1–1.3) |
• Hemoglobin | 13.0 (11.3–14.4) |
• Glucose | 8.0 (6.7–10.1) |
Interventions and Procedures (n, %) | |
• Intubation | 827 (89.7%) |
• Blood transfusion | 480 (52.1%) |
• Massive transfusion protocol activation | 138 (15.0%) |
• Intracranial pressure monitoring | 208 (24.6%) |
• Craniotomy/craniectomy | 192 (20.8%) |
Length of stay in days (LOS) (median, IQR) | |
• Mechanical ventilator | 5 (2–11) |
• Intensive care unit | 9 (4–17) |
• Hospital | 17 (7–32) |
In-hospital mortality | 204 (22.1%) |
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Mekkodathil, A.; El-Menyar, A.; Naduvilekandy, M.; Rizoli, S.; Al-Thani, H. Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department. Diagnostics 2023, 13, 2605. https://doi.org/10.3390/diagnostics13152605
Mekkodathil A, El-Menyar A, Naduvilekandy M, Rizoli S, Al-Thani H. Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department. Diagnostics. 2023; 13(15):2605. https://doi.org/10.3390/diagnostics13152605
Chicago/Turabian StyleMekkodathil, Ahammed, Ayman El-Menyar, Mashhood Naduvilekandy, Sandro Rizoli, and Hassan Al-Thani. 2023. "Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department" Diagnostics 13, no. 15: 2605. https://doi.org/10.3390/diagnostics13152605