Risk Factors of Severe Clostridioides difficile Infection; Sequential Organ Failure Assessment Score, Antibiotics, and Ribotypes

Shifting trends in bloodstream infection-causing microorganisms and their clinical impact in patients with haematologic malignancies in South Korea: A propensity score-matched study

BACKGROUND

This study aimed to identify recent trends in the epidemiology of bloodstream infection (BSI)-causing microorganisms among patients with haematologic malignancies (HMs) between 2011 and 2021, and to determine their impact on patient outcomes.

METHODS

This retrospective study included 6792 patients with HMs, of whom 1308 (19.3%) developed BSI within 1 y of diagnosis. The incidence of BSI-causing microorganisms was determined, and a propensity score-matched study was performed to identify risk factors for 28-d all-cause mortality in patients with HM.

RESULTS

A total of 6792 patients with HMs were enrolled. The cumulative incidence of BSI and neutropenia was significantly higher in the acute myeloid leukaemia and acute lymphoblastic leukaemia groups compared to other groups, and neutropenia and type of HMs were risk factors for the development of BSI. The annual incidence of coagulase-negative staphylococci (CoNS)-BSI decreased significantly (P < 0.001), whereas Klebsiella pneumoniae-BSI increased (P = 0.01). Carbapenem nonsusceptibility rates in K. pneumoniae isolates increased from 0.0% to 76.5% (P < 0.001). BSI caused by K. pneumoniae (adjusted odds ratio 2.17; 95% confidence interval 1.12-4.21) was associated with higher 28-d all-cause mortality compared to that caused by CoNS (adjusted odds ratio 0.86; 95% confidence interval 0.48-1.55).

CONCLUSION

The pathogenic spectrum of BSI-causing bacteria in patients with HMs gradually shifted from Gram-positive to Gram-negative, especially from CoNS to K. pneumoniae. Considering that K. pneumoniae-BSI had a significantly higher 28-d mortality rate than CoNS-BSI, this evolving trend could adversely impact the clinical outcomes of patients with HMs.

Machine Learning-Based Prediction Models for Clostridioides difficile Infection: A Systematic Review

INTRODUCTION

Despite research efforts, predicting Clostridioides difficile incidence and its outcomes remains challenging. The aim of this systematic review was to evaluate the performance of machine learning (ML) models in predicting C. difficile infection (CDI) incidence and complications using clinical data from electronic health records.

METHODS

We conducted a comprehensive search of databases (OVID, Embase, MEDLINE ALL, Web of Science, and Scopus) from inception up to September 2023. Studies employing ML techniques for predicting CDI or its complications were included. The primary outcome was the type and performance of ML models assessed using the area under the receiver operating characteristic curve.

RESULTS

Twelve retrospective studies that evaluated CDI incidence and/or outcomes were included. The most commonly used ML models were random forest and gradient boosting. The area under the receiver operating characteristic curve ranged from 0.60 to 0.81 for predicting CDI incidence, 0.59 to 0.80 for recurrence, and 0.64 to 0.88 for predicting complications. Advanced ML models demonstrated similar performance to traditional logistic regression. However, there was notable heterogeneity in defining CDI and the different outcomes, including incidence, recurrence, and complications, and a lack of external validation in most studies.

DISCUSSION

ML models show promise in predicting CDI incidence and outcomes. However, the observed heterogeneity in CDI definitions and the lack of real-world validation highlight challenges in clinical implementation. Future research should focus on external validation and the use of standardized definitions across studies.

Antibiotic Resistance Profile of RT 027/176 Versus Other Clostridioides difficile Isolates in Silesia, Southern Poland

Clostridioides difficile is an important health care-associated pathogen. The aim of this study was to analyze the antibiotic susceptibility of C. difficile isolates from feces of patients from 13 hospitals in Silesia, Poland. The incidence of CDI per 100.000 people in Silesia in 2018−2019 was higher than the average in Poland (39.3−38.7 vs. 30.2−29.5, respectively). The incidence doubled from 26.4 in 2020 to 55.1 in 2021. Two hundred and thirty stool samples tested positive for GDH (glutamate dehydrogenase) and toxins were cultured anaerobically for C. difficile. The isolates were characterized, typed, and tested for susceptibility to 11 antibiotics by E-test (EUCAST, 2021). The genes of toxins A/B and binary were detected by mPCR. Of 215 isolates, 166 (77.2%) were classified as RT 027 and 6 (2.8%) as related RT 176. Resistance to ciprofloxacin (96.7%), moxifloxacin (79.1%), imipenem (78.1%), penicillin (67%), and rifampicin (40.5%) was found. The ermB gene was detected in 79 (36.7%) strains. Multidrug resistance (MDR) was confirmed in 50 (23.3%) strains of RT 027 (94%). We concluded that a high prevalence of MDR among hypervirulent RT 027/176 C. difficile was found in the Silesian region of Poland, emphasizing the need to enhance regional infection control on CDI and antibiotic stewardships.