# Predictive Role of Systemic Inflammatory Indices in Surgically Managed Postpericardiotomy Syndrome Following Cardiac Surgery

## TL;DR

This study shows that certain blood markers can help predict which patients are at risk of needing surgery for postpericardiotomy syndrome after heart surgery.

## Contribution

The study identifies SIRI and MLR as novel early biomarkers for predicting surgically managed postpericardiotomy syndrome.

## Key findings

- SIRI and MLR showed high sensitivity and specificity for predicting PPS at specific cut-off values.
- SII increased only in late stages, suggesting it reflects disease progression rather than early prediction.
- NLR was not a reliable predictor of PPS at any time point.

## Abstract

Objective: This study aimed to evaluate the prognostic utility of systemic inflammatory markers, such as the Systemic Immune-Inflammation Index (SII), Systemic Inflammatory Response Index (SIRI), Neutrophil–Lymphocyte Ratio (NLR), Monocyte–Lymphocyte Ratio (MLR), and Platelet–Lymphocyte Ratio (PLR), to identify patients at risk of developing surgically treated postpericardiotomy syndrome (PPS). Methods: A total of 150 patients were retrospectively analyzed. In total, 75 patients who developed postpericardiotomy syndrome requiring surgical drainage constituted the postpericardiotomy group, whereas 75 age- and surgically matched non-PPS patients served as the control group. Blood samples were collected at four time points: preoperative (T1), 24 h postoperative (T2), 7 days postoperative (T3), and 24 h before secondary intervention in the PPS group and the closest matched outpatient follow-up (T4) in the control group. Inflammatory marker values were compared within and between the groups at the four defined time points. Logistic regression and receiver operating characteristic (ROC) analyses were used to determine the diagnostic and predictive accuracy of each marker. Results: Significant increases in the SIRI, MLR, and CRP levels were observed in patients who developed PPS and required surgical intervention. MLR on postoperative day 7 had the highest sensitivity (84%) with a cut-off of 0.575, whereas SIRI demonstrated the highest specificity (81.3%) at a cut-off of 3.34. SII increased significantly only in the late stage, indicating disease progression. The NLR lacked predictive power across all time points. Conclusions: The SIRI and MLR are promising early-stage biomarkers for identifying patients at high risk of developing PPS. Their integration into routine postoperative follow-up could facilitate earlier diagnosis and reduce surgical burden. A multi-marker approach may enhance the diagnostic precision of PPS beyond that of traditional inflammatory measures.