# The incremental value of a novel immuno-inflammatory index (SIICI) in predicting sepsis after ureteroscopic lithotripsy: development and validation of a nomogram

## TL;DR

A new immune-inflammatory index (SIICI) improves sepsis prediction after a kidney stone procedure, especially when traditional infection markers are not helpful.

## Contribution

The novel SIICI index and a validated nomogram offer better sepsis prediction after ureteroscopic lithotripsy than existing indices.

## Key findings

- The SIICI-based model outperformed others with an AUC of 0.863 in predicting post-URSL sepsis.
- The model showed meaningful improvements in reclassification and discrimination compared to the baseline.
- The nomogram performed well even in patients with negative preoperative urine cultures (AUC = 0.850).

## Abstract

Sepsis continues to be a life-threatening complication following ureteroscopic lithotripsy (URSL). Available clinical prediction tools tend to be inadequate in their capacity to depict the underlying pathophysiology of sepsis-systemic immune-inflammatory imbalance. It is particularly difficult in patients who lack obvious preoperative microbiological findings. The study aims to evaluate the new Systemic Immune-Inflammatory Complex Index (SIICI) as well as other indices such as (SII, SIRI, PIV) in predicting post-URSL sepsis.

We performed a single-center retrospective study of 803 patients who underwent URSL. Multivariate logistic regression was used to create a clinical baseline model. To assess the incremental predictive value, each inflammatory index was added separately to the baseline model. The model performance was compared using the area under the ROC curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), likelihood ratio test (LRT) and decision curve analysis (DCA).

The “Base + SIICI” model was found to be the most effective among the four indices. It had the highest degree of discrimination (AUC = 0.863, 95% CI: 0.819-0.908), which is a considerable improvement over the baseline model (AUC = 0.807, p<0.001). There were meaningful improvements in reclassification (NRI = 0.133, p=0.001) and discrimination (IDI = 0.058, p=0.002), a significant likelihood ratio test (p<0.001) backed up these findings. The decision curve analysis confirmed that higher net clinical benefit was found at a larger variety of probability thresholds. Notably, the model performed well in individuals with negative preoperative urine cultures (AUC = 0.850). A visual nomogram was developed and validated based on this model, showing good calibration and a bootstrap-corrected AUC of 0.849. An online calculator was also created to facilitate clinical application.

SIICI is a new index that offers high incremental value in predicting sepsis after URSL compared to traditional indices like SII, SIRI and PIV. Nomogram based on SIICI presents a strong and useful instrument of early stratification of risks of development and can assist in making proactive clinical decisions, particularly where standard infection indicators cannot be used.