Development of a Predictive Nomogram for Sepsis in Patients with Urolithiasis-Related Obstructive Pyelonephritis.
Yi-Chun TsaiYu-Hsuan HuangKuang-Yu NiuYu-Chen TsaiChen-Bin ChenChieh-Ching YenPublished in: Medicina (Kaunas, Lithuania) (2024)
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.
Keyphrases
- septic shock
- acute kidney injury
- intensive care unit
- end stage renal disease
- healthcare
- cardiovascular disease
- type diabetes
- emergency department
- adipose tissue
- chronic kidney disease
- risk factors
- newly diagnosed
- metabolic syndrome
- skeletal muscle
- coronary artery disease
- cardiovascular events
- tyrosine kinase
- risk assessment
- climate change
- artificial intelligence
- human health
- acute care