From static thresholds to dynamic trends: Reassessing serum calcium in anastomotic leakage prediction
Year: 2025
Type: article
Abstract: introducing an interpretable machine learning model to predict anastomotic leakage after rectal cancer surgery, highlighting postoperative serum calcium as a key predictive feature. While this represents a significant advancement, we argue that reliance on a static calcium threshold may limit clinical applicability. We advocate for a dynamic, trajectory-based assessment of serum calcium levels across perioperative time points, using modeling approaches such as time-series regression or mixed-effects models. Furthermore, the model's robustness could be improved by incorporating systemic inflammation and nutritional indices such as C-reactive protein, procalcitonin, the neutrophil-to-lymphocyte ratio, and the systemic immune-inflammation index, supported by recent prospective studies. Finally, generalizability remains a concern, warranting broader validation and clearer clinical deployment strategies. By addressing these aspects, the model's clinical translation and decision-making impact could be substantially enhanced.
Authors: Jiang-Peng Zhu, Yu-Tong Chen, Guang-Yao Li
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4415465249