Novel Biomarkers to Predict Outcome in Clostridium Difficile Infection

Overview

Acquiring diarrhoea in hospital is a serious problem and most frequently occurs when susceptible patients receive antibiotics as part of their (often life-saving) care. The commonest cause is Clostridium difficile - a bacterium that normally lives in up to a third of us but causes no problems. Rates of infection had been falling with increased awareness and improved hygiene but they are starting to creep up again. Clostridium difficile can cause a range of disease from a short-lived mild diarrhoea to severe disease of the bowel with major effects on the whole body and even death. This study aims to identify substances in the stool and in the blood to enable doctors to predict how severe that individual's disease will be. These tests can easily be performed. If they prove accurate in identifying the subsequent severity of the patient's illness due to Clostridium difficile, patients predicted to develop the worst disease can receive the most intensive treatments before they become too unwell to benefit. On the other hand, patients whose disease is predicted by these markers to run its course without causing serious consequences can be spared the side effects and risks of more intensive treatment.

Clostridium difficile infection (CDI), which is often a consequence of antibiotic therapy, is the most common and one of the most serious hospital-acquired illnesses. It causes a range of intestinal upset from mild diarrhoea to a life-threatening severe colitis. The incidence, recurrence and mortality rates of CDI have increased dramatically in recent years. Metronidazole is the recommended first-line antibiotic, with vancomycin and colectomy reserved for severe cases. Although risk factors for developing CDI are well known, factors that predict outcome and/or recurrence of CDI are uncertain and lack specificity for intestinal inflammation. We wish to investigate if careful prospective monitoring of standard clinical and biochemical measures will be able to identify patients who will go on to fail primary treatment. If one or more of these factors proves able to predict outcome, they could ultimately be used for early escalation of therapy with the aim of reducing morbidity, mortality and length of hospital stay.

Conditions