A model for sepsis prediction after retrograde intrarenal surgery and the use of the preoperative/postoperative white blood cell ratio to predict progression from sepsis to septic shock.

OBJECTIVES

To study the predictors of sepsis and progression to septic shock after RIRS; to establish and validate predictive models accordingly.

METHODS

In total, 1220 patients were included in the study during. Eight hundred forty-eight patients were assigned to the development cohort and 372 to the validation cohort according to medical record. Univariate and multivariate logistic regression analyses were used to screen independent risk factors for post-RIRS (Retrograde intrarenal surgery) sepsis and progression to septic shock. Nomogram prediction models were established according to the related independent risk factors. Areas under the receiver operating characteristic curves, calibration plots, and DCA (Decision curve analysis) were used to estimate the discrimination, calibration and clinical usefulness of the prediction model, respectively.

RESULTS

In the development cohort, sepsis occurred in 59 patients, 16 of whom developed septic shock. Multivariate logistic regression analyses showed that the independent risk factors for sepsis after RIRS were preoperative D-J stent implantation, hydronephrosis > 6.25 HU (Hounsfield units), AGR (Albumin/globulin ratio) < 1.95, hs-CRP/Alb (High-sensitivity C-reactive protein/albumin ratio) > 0.060, operating time > 67.5 min, and urinary nitrite positivity. The preoperative/postoperative WBC ratio > 1.5 was an independent risk factor for progression from sepsis to septic shock. In the development cohort, the AUC (Area under curve) for predicting sepsis risk was 0.845, and the AUC for predicting septic shock risk was 0.896; in the validation cohort, the corresponding values were 0.896 and 0.974, respectively. In the development cohort, the calibration test P values in the sepsis and septic shock cohorts, respectively, were 0.921 and 0.817; in the validation cohort, these values were 0.882 and 0.859. DCA of the model in the sepsis and septic shock cohorts showed threshold probabilities of 10-90% in the development cohort and 10-50% and 10-20% in the validation cohort.

CONCLUSION

These individualized nomogram prediction models can improve the early identification of patients at risk for developing sepsis after RIRS or progressing from sepsis to septic shock.