Additional considerations on a combination of inflammatory markers and scoring systems for early severity stratification of acute pancreatitis

Abstract

A recent single-center retrospective study proposed novel combinations of hematological parameters and scoring systems for predicting severe acute pancreatitis. While these combinations showed promising predictive performance, several limitations warrant consideration, including the lack of calibration, the absence of key inflammatory markers such as procalcitonin, and practical challenges in integrating these models into routine clinical workflows. To improve predictive accuracy and clinical applicability, prospective validation and the inclusion of additional variables are recommended.

Key Words: Acute pancreatitis; Procalcitonin; Calibration; Severity stratification; Prognostic model

Core Tip: A novel combination of hematological parameters and scoring systems demonstrated excellent predictive performance for severe acute pancreatitis. Despite its promise, limitations—including the absence of key inflammatory markers (e.g., procalcitonin) and the need for calibration analysis, as well as practical implementation challenges—necessitate further validation and incorporation of additional variables to enhance clinical applicability.

TO THE EDITOR

We read with great interest the recent article by Shi et al[1], which systematically evaluated the prognostic value of inflammatory markers and scoring systems in acute pancreatitis (AP). The study provides valuable insights by identifying key predictors for severe AP (SAP) and demonstrating the utility of combined predictive models. While this work represents a significant contribution to the field, we would like to highlight several aspects that warrant further discussion.

The authors reported area under the curve (AUC) values for various parameters and models, yet crucially omitted calibration analysis to evaluate predictive accuracy. While high AUC values indicate good discriminatory ability, a model with poor calibration may systematically overpredict SAP risk, potentially leading to unnecessary clinical interventions. This oversight is particularly consequential in AP, where urgent therapeutic decisions are required.

As emphasized in prior research[2], neglecting calibration can yield misleading conclusions even for models with excellent discrimination. Yang et al[3] demonstrated this principle by rigorously evaluating their SAP prediction model's calibration, while Jiang et al[4] established their AP mortality model's reliability through comprehensive calibration assessment (incorporating calibration curves, Brier score, and Hosmer-Lemeshow testing). These methodological approaches highlight how calibration strengthens a model's clinical validity.

We noted the absence of procalcitonin (PCT) evaluation in this study. PCT holds established prognostic value in AP, with elevated levels strongly correlating with infected pancreatic necrosis, organ failure, and mortality[5]. A recent meta-analysis of 18 studies confirmed PCT's strong diagnostic performance for SAP, demonstrating both good sensitivity and accuracy[6]. This is further supported by a retrospective study that developed a predictive model combining PCT with C-reactive protein (CRP) and D-dimer, achieving superior performance (AUC = 0.853) and significantly higher sensitivity (84.71%) compared to traditional scoring systems[7].

The exclusion of PCT may consequently limit the clinical applicability of the proposed models. PCT's unique kinetic profile, with levels rising within 3-24 hours of inflammation onset and peaking earlier than CRP in bacterial infections[8], makes it particularly valuable for early risk stratification. We therefore suggest that incorporating PCT alongside CRP, calcium (Ca²⁺), and prognostic nutritional index (PNI) could significantly improve SAP prediction accuracy and facilitate more timely antibiotic administration decisions.

While the combination models (CRP₄₈ + Ca²⁺ + PNI₄₈ and PNI₄₈ + Ranson) demonstrated high diagnostic accuracy, their clinical implementation faces practical challenges. The CRP₄₈ + Ca²⁺ + PNI₄₈ model requires the simultaneous measurement of four biomarkers, whereas the Ranson score necessitates evaluating 11 variables over 48 hours. This complexity may limit their utility in routine clinical practice, where simpler models with fewer parameters are generally preferred.

To enhance clinical applicability, we recommend employing variable selection techniques such as LASSO regression to identify key predictors and reduce parameter redundancy[9]. Furthermore, innovative approaches such as the machine learning-based metamodels developed by Weyant et al[10] could help simplify complex models while maintaining predictive accuracy, thereby improving their clinical adoption.

CONCLUSION

While acknowledging these limitations, the study makes a valuable contribution to the risk stratification of AP. To advance this work, we recommend: (1) Prospective validation of PCT-integrated predictive models; and (2) Incorporation of multicenter collaborative studies to enhance generalizability. Future research could benefit from multi-omics approaches to further improve predictive accuracy and clinical utility. Such developments would significantly strengthen the translation of these models into routine clinical practice.