Systematic review on the definition and predictors of severe Clostridiodes difficile infection

Abstract

Clostridiodes difficile infection (CDI) is one of the most common hospital‐acquired infections with high mortality rates. Optimal management of CDI depends on early recognition of severity. However, currently, there is no acceptable standard of prediction. We reviewed severe CDI predictors in published literature and its definition according to clinical guidelines. We systematically reviewed studies describing clinical predictors for severe CDI in medical databases (Cochrane, EMBASE, Global Health Library, and MEDLINE/PubMed). They were independently evaluated by two reviewers. Six hundred thirty‐three titles and abstracts were screened, and 31 studies were included. We excluded studies that were restricted to a specific patient population. There were 16 articles that examined mortality in CDI, as compared with 15 articles investigating non‐mortality outcomes of CDI. The commonest risk factors identified were comorbidities, white blood cell count, serum albumin level, age, serum creatinine level and intensive care unit admission. Generally, the studies had small patient populations, were retrospective in nature, and mostly from Western centers. The commonest severe CDI criteria in clinical guidelines were raised white blood cell count, followed by low serum albumin and raised serum creatinine levels. There was no commonly agreed upon definition of severe CDI severity in the literature. Current clinical guidelines' definitions for severe CDI are heterogeneous. Hence, there is a need for prospective multi‐center studies using standardized protocol for biospecimen investigation collection and shared data on outcomes of patients in order to devise a universally accepted definition for severe CDI.

GRADING prognostic factors for severe and recurrentClostridioides difficileinfection: expected and unexpected findings. A systematic review

“…Our findings are in line with two recently published systematic reviews on predictors of sCDI that reported age and presence of comorbidities as the two most important severity predictors for CDI (162,165). Similarly, previous reviews on prognostic factors for rCDI found that older age, prior CDI and PPI therapy were risk factors for rCDI (162)(163)(164)(165). However, PPI therapy in general did not show a clear association in our study.…”

“…Our systematic review on prognostic factors resulted in several expected and unexpected findings when compared with previous reviews rCDI (162)(163)(164). Our findings are in line with two recently published systematic reviews on predictors of sCDI that reported age and presence of comorbidities as the two most important severity predictors for CDI (162,165). Similarly, previous reviews on prognostic factors for rCDI found that older age, prior CDI and PPI therapy were risk factors for rCDI (162)(163)(164)(165).…”

Prediction of in-hospital mortality ofClostriodiodes difficileinfection using critical care database: a big data-driven, machine learning approach

“…The finding of serum albumin being a predictor of mortality is concordant with our previous systematic review, which showed that at least five of the 31 articles identified prior comorbidities, age, white blood cell count, serum albumin, serum creatinine and ICU admission as predictors of severity. 14 Interestingly, none of the 31 studies on C. difficile severity predictors included lactate and bicarbonate levels. Furthermore, all international clinical guidelines on severe CDI, such as American College of Gastroenterology and European Society of Clinical Microbiology and Infectious Diseases, do not include lactate and bicarbonate levels.…”

“… 13 We recently published a systematic review of severe CDI predictors, but found that present risk scoring systems have been limited by small sample size and heterogeneity in definition of severe CDI. 14 Proposed severity scores such as Clostridiodes difficile Associated Risk of Death Score (CARDS) 15 and ATLAS Score 16 17 (combination of age, treatment with systemic antibiotics, leucocyte count, serum albumin and serum creatinine) have not been widely adopted in current clinical practice.…”

Healthcare Transformation in Singapore With Artificial Intelligence

“…Among all the teams in healthcare datathon over the years, we found that on average, 10% of teams remained active after the event. They were able to formalize their clinical problems into research projects and produce impactful publications (16)(17)(18)(19). For example, van den Boom et al (16) were a group of researchers who participated in healthcare datathon 2018 in Singapore and formed a research team to study the optimal oxygen saturation target for critically ill patients.…”