Relationship between bacterial strain type, host biomarkers, and mortality in Clostridium difficile infection

Abstract

Background: Despite substantial interest in biomarkers, their impact on clinical outcomes and variation with bacterial strain has rarely been explored using integrated databases.

Methods: From September 2006 to May 2011, strains isolated from Clostridium difficile toxin enzyme immunoassay (EIA)-positive fecal samples from Oxfordshire, United Kingdom (approximately 600,000 people) underwent multilocus sequence typing. Fourteen-day mortality and levels of 15 baseline biomarkers were compared between consecutive C. difficile infections (CDIs) from different clades/sequence types (STs) and EIA-negative controls using Cox and normal regression adjusted for demographic/clinical factors.

Results: Fourteen-day mortality was 13% in 2222 adults with 2745 EIA-positive samples (median, 78 years) vs 5% in 20,722 adults with 27,550 EIA-negative samples (median, 74 years) (absolute attributable mortality, 7.7%; 95% CI, 6.4%-9.0%). Mortality was highest in clade 5 CDIs (25% [16 of 63]; polymerase chain reaction (PCR) ribotype 078/ST 11), then clade 2 (20% [111 of 560]; 99% PCR ribotype 027/ST 1) versus clade 1 (12% [137 of 1168]; adjusted P < .0001). Within clade 1, 14-day mortality was only 4% (3 of 84) in ST 44 (PCR ribotype 015) (adjusted P = .05 vs other clade 1). Mean baseline neutrophil counts also varied significantly by genotype: 12.4, 11.6, and 9.5 × 10(9) neutrophils/L for clades 5, 2 and 1, respectively, vs 7.0 × 10(9) neutrophils/L in EIA-negative controls (P < .0001) and 7.9 × 10(9) neutrophils/L in ST 44 (P = .08). There were strong associations between C. difficile-type-specific effects on mortality and neutrophil/white cell counts (rho = 0.48), C-reactive-protein (rho = 0.43), eosinophil counts (rho = -0.45), and serum albumin (rho = -0.47). Biomarkers predicted 30%-40% of clade-specific mortality differences.

Conclusions: C. difficile genotype predicts mortality, and excess mortality correlates with genotype-specific changes in biomarkers, strongly implicating inflammatory pathways as a major influence on poor outcome after CDI. PCR ribotype 078/ST 11 (clade 5) leads to severe CDI; thus ongoing surveillance remains essential.

Keywords: C. difficile; biomarkers; mortality; strain-specific variation.

Figure 1.

Fourteen-day mortality after enzyme immunoassay (EIA) tests for Clostridium difficile, overall and by strain. A, Fourteen-day mortality by EIA-negative control vs EIA-positive case and multilocus sequencing type clade if culture positive. B, Fourteen-day mortality by sequence type within clade 1. C, Fourteen-day mortality by age (all tests). Most common ribotypes of isolates from each clade (A) or sequence type (B) shown in brackets. Dashed line in (B) shows overall clade 1 mortality. Clade 4 not shown in (C) due to small numbers (n = 29). Abbreviations: EIA, enzyme immunoassay.

Figure 2.

One-year mortality after first-ever Clostridium difficile enzyme immunoassay–positive test or first negative before positive test by strain. Abbreviation: EIA, enzyme immunoassay.

Figure 3.

Variation in 14-day mortality risks according to Clostridium difficile clade. Abbreviations: adj, adjusted; CI, confidence interval; cult, culture; EIA, enzyme immunoassay; het, heterogeneity test.

Figure 4.

Variation in 7 biomarkers at diagnosis according to Clostridium difficile clade and association with mortality. A, Neutrophils (×10⁹/L). B, White cell count (×10⁹/L). C, C-reactive protein (mg/L). D, Eosinophils (×10⁹/L). E, Albumin (g/dL). F, Sodium (mmol/L). G, Hemoglobin (g/dL). For each biomarker, left-hand panels show mean (95% confidence interval) values at sample collection for enzyme immunoassay (EIA)–negative controls vs EIA-positive cases; then subdividing EIA-positive cases into culture-negative, not cultured, and culture-positive cases; then subdividing culture-positive cases by clade and comparing sequence type (ST) 44 vs other STs within clade 1; with P values testing for heterogeneity across each group. Means are calculated on BoxCox-transformed values and back-transformed for presentation (see Supplementary Methods). For each clade and EIA-positive/culture-negative cases, the right-hand panels plot the standardized adjusted mean difference vs EIA-negative controls from the left-hand panel (on the BoxCox-transformed scale,±standard error) against the adjusted hazard ratio for mortality vs EIA-negative controls from Table 1. The correlation, ρ, between biomarker and mortality risk excesses was estimated using multivariable random effects meta-analysis (see Supplementary Methods). Diagonal lines show the line of best fit (ie, the best prediction of excess mortality for any given excess in biomarkers compared with EIA-negative controls). If differences in biomarkers across clades completely explained mortality differences (ie, the biomarker was a perfect surrogate for mortality), all the points would lie on the diagonal line. The closer the points are to the diagonal line, the stronger the relationship between biomarker differences and excess mortality risks. Points lying far from the diagonal line indicate a mismatch, either high excess mortality with little difference in biomarkers from EIA-negative controls or vice versa. Abbreviations: CRP, C-reactive protein; cult, culture; EIA, enzyme immunoassay; OUH, Oxford University Hospitals; SE, standard error.

Figure 5.

Impact of Clostridium difficile clade and individual sequence type (ST) on biomarkers compared with mortality. A, Neutrophils (×10⁹/L). B, C-reactive protein (mg/L). C, Albumin (g/dL). D, Sodium (mmol/L). For clades 2–5 (labelled C2, C3, C4, C5) and each clade 1 ST with >20 isolates, the panels plot the standardized adjusted mean difference vs enzyme immunoassay (EIA)–negative controls (on the BoxCox-transformed scale,±standard error) against the hazard ratio for mortality vs EIA-negative controls, adjusted as in Table 1. The correlation, ρ, between biomarker and mortality risk excesses across STs/clades was estimated using multivariable random effects meta-analysis (see Supplementary Methods). Diagonal lines show the line of best fit (ie, the best prediction of excess mortality for any given excess in biomarkers compared with EIA-negative controls), together with a 95% credibility region indicated by the shaded region. If a biomarker was a perfect surrogate for mortality (ie, differences in biomarkers across STs/clades completely explained mortality differences), all the points would lie on the diagonal line. The closer the points are to the diagonal line, the stronger the relationship between biomarker differences and excess mortality risks. Points lying far from the diagonal line indicate a mismatch, either high excess mortality with little difference in biomarkers from EIA-negative controls or vice versa. All clade 1 STs lying outside the 95% credibility region on any of the 4 panels are labelled on each panel; ST 58, which had high mortality in [6], is also labelled. Abbreviations: CRP, C-reactive protein; cult, culture; EIA, enzyme immunoassay; HR, hazard ratio; SE, standard error; ST, sequence type.