To analyze serum amino acid levels in patients with sepsis and develop a machine learning-based prognostic model for outcome prediction, emphasizing the need for novel biomarkers.
Key Findings:
Significant differences in serum amino acid profiles were observed between healthy controls and sepsis patients.
The Deephit model demonstrated the best ability to predict survival probability among the tested machine learning models.
Amino acid alterations were strongly associated with clinical outcomes in sepsis patients.
Interpretation:
Alterations in serum amino acid profiles can distinguish sepsis patients from healthy individuals, correlating with clinical outcomes.
Limitations:
The study's sample size may limit the generalizability of the findings.
Further validation in larger cohorts is necessary to confirm the prognostic utility of the identified amino acids.
Conclusion:
The study suggests that serum amino acid profiles may serve as biomarkers for prognostic prediction in sepsis, pending further validation.
