Clinical Determinants Predicting Clostridioides difficile Infection among Patients with Chronic Kidney Disease

Abstract

The majority of recently published studies indicate a greater incidence rate and mortality due to Clostridioides difficile infection (CDI) in patients with chronic kidney disease (CKD). The aim of this study was to assess the clinical determinants predicting CDI among hospitalized patients with CKD and refine methods of prevention. We evaluated the medical records of 279 patients treated at a nephrological department with symptoms suggesting CDI, of whom 93 tested positive for CDI. The survey showed that age, poor kidney function, high Padua prediction score (PPS) and patients’ classification of care at admission, treatment with antibiotics, and time of its duration were significantly higher or more frequent among patients who suffered CDI. Whereas BMI, Norton scale (ANSS) and serum albumin concentration were significantly lowered among CDI patients. In a multivariate analysis we proved the stage of CKD and length of antibiotics use increased the risk of CDI, whereas higher serum albumin concentration and ANSS have a protective impact.

Clostridioides Difficile Infection before and during Coronavirus Disease 2019 Pandemic—Similarities and Differences

“…15.6%., p = 0.038 and 39.6% vs. 11.7%, p ≤ 0.001 for nervous system disease) [17]. According to data published by Lis et al, risk factors that significantly contribute to the increase of CDI in patients with chronic renal failure are the advanced stage of chronic kidney disease, the length of antibiotic use, as well as lower albumin concentration [30].…”

The Artificial Neural Network as a Diagnostic Tool of the Risk of Clostridioides difficile Infection among Patients with Chronic Kidney Disease

“…In the study by Lis et al the length of antibiotic therapy and the Norton class were among the key parameters associated with the risk of CDI [3]. In our experience, these factors also turned out to be the most important, which in a way indicates the convergence of traditional statistical analysis with modeling based on machine learning.…”