Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Framework
2.2. Study Population and Data Collection
2.3. Outcomes
2.4. Model Development
2.5. Statistical Analysis
2.6. ML Model Interpretation
2.7. Performance Evaluation
3. Results
3.1. Feature Importance Outputs
3.2. ML Methods’ Performance
3.3. SHAP Values
4. Discussion
4.1. Comparison with Previous Studies
4.2. Important Features
4.2.1. Blood Type
4.2.2. Body Mass Index
4.2.3. Serum Creatinine
4.2.4. State of Residency
4.3. Strengths and Limitations
4.4. Future Research Directions
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Glossary
ALF | Acute Liver Failure |
CVD | Cardiovascular Death |
DT | Decision Tree |
GNB | Gaussian Naïve Bayes |
GFR | Glomerular Filtration Rate |
KNN | K-Nearest Neighbors |
LT | Liver Transplant |
ML | Machine Learning |
NAFLD | Nonalcoholic Fatty Liver Disease |
OPTN | Organ Procurement and Transplant Network |
RF | Random Forest |
RFE | Recursive Feature Elimination |
RCRI | Revised Cardiac Risk Index |
SFM | Select From Model |
SHAP | Shapley Additive Explanations |
SVM | Support Vector Machine |
UNOS | United Network for Organ Sharing |
VWF | Von Willebrand Factor |
XGB | XGBoost |
References
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Characteristics | All Recipients (n = 10,871) | Alive (n = 8261) | CVD (n = 449) | Non-CVD Death (n = 2161) | p-Value | Adjusted p-Value |
---|---|---|---|---|---|---|
Recipient characteristics | ||||||
Age (years) | 58.91 | 58.50 | 60.85 | 60.07 | < 0.001 | <0.001 * |
BMI (kg/m) | 32.70 | 32.80 | 32.91 | 32.25 | 0.002 | 0.004 * |
Ethnicity | <0.001 | <0.001 * | ||||
Non-Hispanic | 9334 (85.86%) | 7010 (84.85%) | 390 (86.86%) | 1934 (89.50%) | ||
Hispanic | 1537 (14.14%) | 1251 (15.14%) | 59 (13.14%) | 227 (10.50%) | ||
Gender | 0.470278 | 0.470278 | ||||
Female | 5160 (47.46%) | 3939 (47.68%) | 219 (48.77%) | 1002 | ||
Male | 5711 (52.54%) | 4322 (52.31%) | 230 (51.22%) | 1159 | ||
Blood Type | 0.257287 | 0.257287 | ||||
O | 4780 (43.97%) | 3619 (43.80%) | 202 (45.00%) | 959 (44.38%) | ||
A | 4037 (37.14%) | 3033 (36.71%) | 179 (40.00%) | 825 (38.18%) | ||
B | 1436 (13.21%) | 1108 (13.41%) | 57 (12.50%) | 271 (12.55%) | ||
AB | 600 (5.50%) | 486 (5.88%) | 11 (2.50%) | 103 (4.76%) | ||
A1 | 14 (1.28%) | 11 (0.14%) | 0 | 3 (0.13%) | ||
A1B | 2 (0.018%) | 2 (0.03%) | 0 | 0 | ||
A2B | 1 (0.009%) | 1 (0.015%) | 0 | 0 | ||
A2 | 1 (0.009%) | 1 (0.015%) | 0 | 0 | ||
Diabetes | <0.001 * | <0.00 * | ||||
No | 4836 (44.48%) | 3829 (46.35%) | 161 (35.85%) | 846 (39.14%) | ||
Type I | 234 (2.15%) | 133 (1.60%) | 16 (3.56%) | 85 (3.93%) | ||
Type II | 5462 (50.24%) | 4082 (49.42%) | 252 (56.12%) | 1128 (52.19%) | ||
Other type | 105 (0.96%) | 82 (0.99%) | 4 (0.89%) | 19 (0.87%) | ||
Unknown type | 190 (1.74%) | 109 (1.32%) | 14 (3.14%) | 67 (3.10%) | ||
Diabetes status unknown | 44 (40%) | 26 (0.32%) | 2 (0.44%) | 16 (0.74%) | ||
Albumin (g/dL) | 3.11 | 3.13 | 3.13 | 3.05 | <0.001 * | <0.001 * |
BILIRUBIN (μmol/L) | 7.67 | 7.63 | 8.57 | 7.63 | 0.03 * | 0.05828 |
INR | 1.97 | 1.99 | 1.98 | 1.89 | <0.001 * | <0.001 * |
MELD score | 25.21 | 25.21 | 26.19 | 25.00 | 0.04 | 0.06 |
SERUM CREAT (mg/dL) | 1.83 | 1.77 | 2.10 | 2.00 | <0.001 * | <0.001 * |
SERUM SODIUM (mmol/L) | 135.12 | 134.99 | 135.22 | 135.60 | <0.001 * | <0.001 * |
Donor characteristics | ||||||
Age (years) | 42.61 | 42.48 | 42.27 | 43.15 | 0.27 | 0.36 |
Gender | 0.47 | 0.47 | ||||
Female | 4363 (40.13%) | 3294 (39.87%) | 177 (39.42%) | 892 (41.28%) | ||
Male | 6508 (59.86%) | 4967 (60.13%) | 272 (60.58%) | 1269 (58.72%) | ||
Diabetes | 0.43 | 0.43 | ||||
No | 9399 (86.46%) | 7135 (86.64%) | 389 (86.67%) | 1875 (86.77%) | ||
Yes | 105 (0.96%) | 87 (0.010%) | 5 (1.11%) | 13 (0.60%) | ||
Unknown | 1367 (12.58%) | 1039 (12.57%) | 55 (12.24%) | 273 (12.63%) | ||
Blood Type | 0.06 | 0.06 | ||||
O | 1588 (14.60%) | 1193 (14.44%) | 72 (16.03%) | 323 (14.96%) | ||
A | 2210 (20.32%) | 1636 (19.80%) | 98 (21.82%) | 476 (22.02%) | ||
B | 156 (1.44%) | 123 (1.49%) | 4 (0.89%) | 29 (1.34%) | ||
AB | 364 (3.35%) | 293 (3.54%) | 143 (3.11%) | 57 (2.63%) | ||
A1 | 54 (0.49%) | 47 (0.56%) | 1 (0.22%) | 6 (0.27%) | ||
A1B | 165 (1.51%) | 134 (1.62%) | 2 (0.44%) | 29 (1.35%) | ||
A2B | 1307 (12.02%) | 1020 (12.35%) | 46 (10.24%) | 241 (11.15%) | ||
A2 | 5027 (46.24%) | 3815 (46.18%) | 212 (47.21%) | 1000 (46.27%) |
Feature Number | Feature | RFE_LR | SFM_LR | SFM_RF | RFE_RF | RFE_DT | SFM_DT | SFM_XGB | RFE_XGB | Number of Time Selected |
---|---|---|---|---|---|---|---|---|---|---|
1 | Recipient blood type | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 8 |
2 | Recipient height | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 6 | ||
3 | Actual year the registrant | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 6 | ||
4 | Donor blood type | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 6 | ||
5 | Donor home state | ✓ | ✓ | ✓ | ✓ | ✓ | 5 | |||
6 | Recipient bilirubin | ✓ | ✓ | ✓ | 3 | |||||
7 | Age | ✓ | ✓ | ✓ | 3 | |||||
8 | Recipient weight (kg) | ✓ | ✓ | ✓ | 3 | |||||
9 | Recipient dialysis | ✓ | ✓ | ✓ | 3 | |||||
10 | Donor hematocrit level | ✓ | ✓ | 2 | ||||||
11 | Recipient serum creatinine | ✓ | ✓ | 2 | ||||||
12 | Recipient state of residency | ✓ | ✓ | 2 | ||||||
13 | MELD score | ✓ | ✓ | 2 | ||||||
14 | Deceased donor cardiac arrest | ✓ | ✓ | 2 | ||||||
15 | BMI | ✓ | ✓ | 2 | ||||||
16 | Recipient serum sodium level | ✓ | 1 | |||||||
17 | Recipient work for income | ✓ | 1 | |||||||
18 | Recipient INR | ✓ | 1 | |||||||
19 | Recipient albumin level | ✓ | 1 | |||||||
20 | Total cold ischemic time | ✓ | 1 |
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Fatemi, Y.; Nikfar, M.; Oladazimi, A.; Zheng, J.; Hoy, H.; Ali, H. Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients. Healthcare 2024, 12, 1165. https://doi.org/10.3390/healthcare12121165
Fatemi Y, Nikfar M, Oladazimi A, Zheng J, Hoy H, Ali H. Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients. Healthcare. 2024; 12(12):1165. https://doi.org/10.3390/healthcare12121165
Chicago/Turabian StyleFatemi, Yasin, Mohsen Nikfar, Amir Oladazimi, Jingyi Zheng, Haley Hoy, and Haneen Ali. 2024. "Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients" Healthcare 12, no. 12: 1165. https://doi.org/10.3390/healthcare12121165
APA StyleFatemi, Y., Nikfar, M., Oladazimi, A., Zheng, J., Hoy, H., & Ali, H. (2024). Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients. Healthcare, 12(12), 1165. https://doi.org/10.3390/healthcare12121165