To systematically review the current status and effectiveness of various biomarkers in predicting anastomotic leakage (AL) after colorectal surgery and propose a comprehensive multimodal prediction framework.
Key Findings:
Anastomotic leakage has an incidence of 2% to 19% and is associated with significant complications.
Traditional diagnostic methods are reactive and often delayed, increasing adverse outcomes.
Biomarkers from inflammation, ischemia, microbiome, and tissue repair show potential for early detection of AL.
Machine learning algorithms may help integrate diverse data sources, but external validation is lacking.
Interpretation:
The review highlights the fragmentation in existing research on biomarkers for AL, indicating a lack of systematic integration of findings into clinical tools.
Limitations:
Existing studies are heterogeneous in endpoints, timing, and methodology, with examples including variations in biomarker types and measurement techniques.
Most research is based on small-sample, single-center cohorts with limited external validation.
Conclusion:
A multimodal prediction framework integrating inflammation, ischemia, microbiome, and tissue repair is proposed, emphasizing the urgent need for future research focused on validation and standardization.
A VHA study across 11 vendors finds AI-generated primary care notes score lower than clinician-written notes, with the largest deficits in thoroughness, organization, and usefulness