A Novel infrared spectroscopy biomarker to predict postoperative infection and sepsis in patients with upper urinary tract calculus

Abstract Background Urinary tract infection, especially sepsis, is a common and severe postoperative complication for patients with upper urinary tract calculus (UUTC). Infrared wavelengths are widely used to identify calculus mineral composition, but their role in predicting postoperative infections remains unknown. This study aimed to develop an Infrared-infection score (IR-infection score) system based on calculi infrared wavelengths to indicate postoperative infection in UUTC patients. Methods From March 2018 to March 2023, 328 patients from Fujian Provincial Hospital South Branch were included in constructing the IR-infection score and nomogram, and 152 patients from Fujian Provincial Hospital were used as the validation cohort. Then IR-infection score was constructed based on infection-related infrared wavelengths by univariate and multivariate analysis. Continually, logistic regression with the stepwise backward method was used to build a nomogram. Afterward, the IR-infection score and nomogram were evaluated by the ROC curve, Hosmer-Lemeshow test, calibration curves, the decision curves. Finally, the Delong test was used to compare the predictive power of the IR-infection score with traditional indicators. Results Our study showed that the IR-infection score could precisely predict postoperative infection and sepsis in UUTC patients (postoperative infection: AUC construction cohort = 0.708, AUC validation cohort = 0.707; postoperative sepsis: AUC construction cohort = 0.690, AUC validation cohort = 0.824), with better predictive values than calculus composition and clinical indicators including blood white blood cell, procalcitonin, urine white blood cells, urine bacterial count, and urine culture. Moreover, the nomogram based on IR-infection score had high predictive power (all AUCs ＞ 0.8). Conclusion Our study first identified infrared wavelengths related to infection and offered new insights into extending the application of the infrared spectrum in predicting postoperative infection in UUTC patients.