Fecal microbiota composition is a better predictor of recurrent Clostridioides difficile infection than clinical factors in a prospective, multicentre cohort study

Abstract

Introduction: Clostridioides difficile infection (CDI) is the most common cause of antibiotic-associated diarrhoea. Fidaxomicin and fecal microbiota transplantation (FMT) are effective, but expensive therapies to treat recurrent CDI (reCDI). Our objective was to develop a prediction model for reCDI based on the gut microbiota composition and clinical characteristics, to identify patients who could benefit from early treatment with fidaxomicin or FMT.

Methods: Multicentre, prospective, observational study in adult patients diagnosed with a primary episode of CDI. Fecal samples and clinical data were collected prior to, and after 5 days of CDI treatment. Follow-up duration was 8 weeks. Microbiota composition was analysed by IS-pro, a bacterial profiling technique based on phylum- and species-specific differences in the 16-23 S interspace regions of ribosomal DNA. Bayesian additive regression trees (BART) and adaptive group-regularized logistic ridge regression (AGRR) were used to construct prediction models for reCDI.

Results: 209 patients were included, of which 25% developed reCDI. Variables related to microbiota composition provided better prediction of reCDI and were preferentially selected over clinical factors in joint prediction models. Bacteroidetes abundance and diversity after start of CDI treatment, and the increase in Proteobacteria diversity relative to baseline, were the most robust predictors of reCDI. The sensitivity and specificity of a BART model including these factors were 95% and 78%, but these dropped to 67% and 62% in out-of-sample prediction.

Conclusion: Early microbiota response to CDI treatment is a better predictor of reCDI than clinical prognostic factors, but not yet sufficient enough to predict reCDI in daily practice.

Fig. 1

Boxplots (depicting median, interquartile ranges and outliers) of microbial abundance (A), diversity (B) and changes in abundance (C) and diversity (D) between D0-D5 in patients with treatment failure (dark bars) and patients with treatment success (light bars). Statistically significant differences between reCDI and non-reCDI patients are indicated (*). D0: at baseline, before start of CDI treatment; D5: 5 days after start of CDI treatment; FAFV: Firmicutes, Actinobacteria, Fusobacteria, Verrucomicrobia; BACT: Bacteroidetes; PROT: Proteobacteria

Fig. 2

The twenty most important clinical factors for reCDI prediction. Inclusion proportion refers to the proportion of decision nodes in which the clinical factor was included; the higher the inclusion proportion, the more important the clinical factor is for predicting reCDI (vs. no reCDI). The blue bar indicates that the association between heart frequency and reCDI is not linear, having an optimum at intermediate values (see partial effect plots in Figure S1. *within 3 months before start of CDI treatment

Fig. 3

A The twenty most important microbial abundance/diversity factors for reCDI prediction. Inclusion proportion refers to the proportion of decision nodes in which the clinical factor is included; the higher the inclusion proportion, the more important the factor is for predicting reCDI. The blue bars indicate nonlinear associations, having an optimum at intermediate values (see Fig. 3B and Figure S2). B Partial effect plots of the three most important microbial factors for prediction of reCDI, provided by BART. These plots show the association between a predictor (in this case, a specific microbial abundance/diversity) and the outcome (reCDI risk) for any given value of the predictor. Therefore, in case of non-linear associations this model provides more accurate predictions than for example logistic regression, which can only identify linear associations (described by regression coefficients). The higher the partial effect (Y-axis), the higher the chance of reCDI. For all partial effect plots, see Figure S2

Fig. 4

The twenty most important clinical and microbial abundance/diversity factors for reCDI prediction. Inclusion proportion refers to the proportion of decision nodes in which the clinical factor is included; the higher the inclusion proportion, the more important the factor is for predicting reCDI. The blue bars indicate nonlinear associations, having an optimum at intermediate values

Fig. 5

Receiver operating characteristic (ROC) curves of the two best performing AGRR models for the prediction of reCDI. For each model, the performance based on all factors (black) and based on a panel of the 25 most important factors via elastic-net (EN) feature selection (red) are shown. In blue, the sensitivity and corresponding specificity of the BART model based on microbial abundance/diversity is indicated