Development of a Predictive Nomogram for Sepsis in Patients with Urolithiasis-Related Obstructive Pyelonephritis

Abstract

Background and Objectives: In patients with urolithiasis-related obstructive pyelonephritis (UROP), sepsis represents a critical and concerning complication that can substantially increase the mortality rate. This study aimed to identify the risk factors for sepsis in UROP patients and to develop a predictive nomogram model. Materials and Methods: We analyzed data from 148 patients who met the UROP criteria and were admitted to Chang Gung Memorial Hospital between 1 January 2016 and 31 December 2021. The primary outcome evaluated was the incidence of sepsis, as defined by the most recent Sepsis-3 guidelines. To identify potential risk factors for sepsis, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression technique. Subsequently, we utilized multivariable logistic regression to construct the predictive model. Results: There was a total of 102 non-sepsis cases and 46 sepsis cases. Risk factors for sepsis in multivariable analysis were a history of diabetes mellitus (DM) (OR = 4.24, p = 0.007), shock index (SI) (×10^-1) (OR = 1.55, p < 0.001), C-reactive protein (CRP) (mg/dL) (OR = 1.08, p = 0.005), and neutrophil to lymphocyte ratio (NLR) (×10) (OR = 1.58, p = 0.007). The nomogram exhibited an area under the receiver operating characteristic curve of 0.890 (95% CI 0.830-0.949). Conclusions: Our study demonstrated that patients with UROP who have DM, higher SI, higher NLR, and elevated CRP levels are significantly more likely to develop sepsis. These insights may aid in risk stratification, and it is imperative that clinicians promptly initiate treatment for those identified as high risk.

Keywords: nomogram; sepsis; uolithiasis-related obstructive pyelonephritis.

Figure 1

Flow diagram of eligible patients. UROP: urolithiasis-related obstructive pyelonephritis.

Figure 2

Multivariable logistic regression analysis identifying predictors of sepsis in patients with urolithiasis-related obstructive pyelonephritis. OR: Odds ratio.

Figure 3

Nomogram for estimating probability of sepsis. Predictor points are identified for each subject variable on the top scale, summed, and the total projected onto the bottom scale to determine the sepsis probability.

Figure 4

(A) Calibration curve for the sepsis nomogram. The x-axis depicts the predicted sepsis risk, while the y-axis shows the actual diagnosed cases of sepsis. The diagonal dotted line illustrates a perfect prediction from an ideal model. The short-dashed line represents the nomogram’s apparent prediction, with the solid line indicating the nomogram’s performance after bias-correction through bootstrapping (B = 1000 repetitions). (B) Decision curve analysis for the nomogram. The x-axis displays the threshold probability, while the y-axis quantifies the net benefit. The blue solid line represents the nomogram. The red line assumes that all subjects had sepsis, and the green line assumes that none of the subjects had sepsis.

Figure 5

Comparison of the area under the curve (AUC) for the multivariable model and each selected variable.