Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach
Abstract
:1. Introduction
Literature Review
2. Materials and Methods
2.1. Study Design and Participants
Inclusion and Exclusion Criteria
- −
- Sepsis Group:
- Suspected/proven infection (Organ dysfunction was identified as an acute change SOFA score ≥ 2 points consequent to the infection.) [1].
- Any two of the four systemic inflammatory response criteria present in the ED [46].
- Must be at least eighteen years old.
- At least 2.0 mmol/L of lactate.
- Enrollment started two hours after a quantitative resuscitation protocol was started.
- −
- Control Group:
- Admitted to the ED.
- No medical problems requiring chronic treatment impacting platelet function (e.g., aspirin, P2Y12 inhibitors).
- −
- Exclusion Criteria:
- Apart from sepsis as the primary diagnosis.
- Declared the status of “Do Not Resuscitate.”
- Transfer with previous sepsis treatment from another hospital.
- Cardiopulmonary resuscitation before enrollment.
2.2. Sample Collection
- Centrifugation at 200× g for 6 min at room temperature to separate platelet-rich plasma.
- A further centrifugation at 4500× g for 5 min at 4 °C to pellet the platelets.
2.3. Data Preprocessing and Machine Learning Modeling
2.4. Explainable Artificial Intelligence Approach and Interpretation of Sepsis Biomarkers and Prediction
2.5. Biostatistical Analysis
3. Results
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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Variable | Group | p-Value | ||
---|---|---|---|---|
Control | Sepsis | |||
Gender * | Male | 8 (57.1) | 15 (60) | 0.862 |
Female | 6 (42.9) | 10 (40) | ||
Age ** | 44 (14) | 55 (17) | 0.056 |
Metabolite Name * | Group | p-Value | |
---|---|---|---|
Control | Sepsis | ||
ADP | 41.95 (16.875) | 27.8 (25.8) | 0.058 |
AMP | 8.75 (3.825) | 7 (3.8) | 0.429 |
ATP | 57.2 (29.15) | 47.7 (36) | 0.183 |
Alanine | 11.9 (5.5) | 12.2 (5.1) | 0.781 |
Carnitine | 3.33 (1.075) | 1.9 (2) | 0.067 |
Choline | 4.725 (1.9) | 4.1 (3.6) | 0.446 |
Creatine | 3.25 (2.225) | 4.1 (2.82) | 0.379 |
Formate | 19 (6.6) | 18 (5.7) | 0.693 |
GTP | 16.05 (2.347) | 16.93 (5.3) | 0.507 |
Glucose | 56.47 (17.65) | 56.47 (15.6) | 0.426 |
Glutamate | 83.2 (19.225) | 85.7 (61.6) | 0.988 |
Glutamine | 34.95 (8.68) | 36.83 (16.6) | 0.669 |
Glycine | 12 (5.35) | 8.6 (6.9) | 0.183 |
Lactate | 64.5 (19.85) | 65.6 (48.4) | 0.965 |
O-acetylcholine | 3.95 (2.65) | 3.1 (1.9) | 0.027 |
O-phosphocholine | 9.55 (4.85) | 6.6 (6.1) | 0.224 |
O-phosphoethanolamine | 54.435 (17.9) | 38.2 (32.3) | 0.047 |
Taurine | 578.3 (239.225) | 443.2 (344.5) | 0.141 |
myo-Inositol | 22.9 (9.05) | 32.21 (29.4) | 0.377 |
Metric/Model | KTBoost | XGBoost | LightGBM |
---|---|---|---|
Accuracy | 0.900 (0.817–0.983) | 0.860 (0.764–0.956) | 0.800 (0.689–0.911) |
F1-score | 0.894 (0.808–0.979) | 0.851 (0.752–0.950) | 0.792 (0.679–0.904) |
Sensitivity | 0.840 (0.639–0.955) | 0.800 (0.593–0.932) | 0.760 (0.549–0.906) |
Specificity | 0.960 (0.796–0.999) | 0.920 (0.740–0.990) | 0.840 (0.639–0.955) |
AUC | 0.943 (0.900–0.987) | 0.914 (0.860–0.968) | 0.861 (0.800–0.923) |
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Yagin, F.H.; Aygun, U.; Algarni, A.; Colak, C.; Al-Hashem, F.; Ardigò, L.P. Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach. J. Clin. Med. 2024, 13, 5002. https://doi.org/10.3390/jcm13175002
Yagin FH, Aygun U, Algarni A, Colak C, Al-Hashem F, Ardigò LP. Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach. Journal of Clinical Medicine. 2024; 13(17):5002. https://doi.org/10.3390/jcm13175002
Chicago/Turabian StyleYagin, Fatma Hilal, Umran Aygun, Abdulmohsen Algarni, Cemil Colak, Fahaid Al-Hashem, and Luca Paolo Ardigò. 2024. "Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach" Journal of Clinical Medicine 13, no. 17: 5002. https://doi.org/10.3390/jcm13175002