Assessment of clinical risk predictive rules for invasive candidiasis in a prospective multicentre cohort of ICU patients

• Published in: Intensive care medicine Vol. 35; no. 12; pp. 2141 - 2145
• Main Authors: Playford, Elliott Geoffrey, Lipman, Jeff, Kabir, Masrura, McBryde, Emma S., Nimmo, Graeme R., Lau, Anna, Sorrell, Tania C.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.12.2009; Springer; Springer Nature B.V
• Subjects: Adult; Algorithms; Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Anesthesiology; Antibiotics; Biological and medical sciences; Brief Report; Candida; Candidiasis - diagnosis; Candidiasis - epidemiology; Candidiasis - microbiology; Clinical death. Palliative care. Organ gift and preservation; Cohort Studies; Critical Care Medicine; Emergency Medicine; Female; Hospitals; Humans; Intensive; Intensive care; Intensive care medicine; Intensive Care Units - statistics & numerical data; Male; Medical sciences; Medicine; Medicine & Public Health; Middle Aged; Mortality; Pain Medicine; Patients; Pediatrics; Peritonitis; Pneumology/Respiratory System; Predictive Value of Tests; Prospective Studies; Risk Assessment; Risk Factors; Australia; Risk; ICU; Candidaemia; Prediction; Human; Candidiasis; Intensive care; Mycosis; Infection; Candid; Risks; Fungemia; Risk factor; Resuscitation
• ISSN: 0342-4642; 1432-1238
• DOI: 10.1007/s00134-009-1619-9

Purpose To assess the generalisability of published clinical risk predictive models for invasive candidiasis in ICU patients. Methods The performance characteristics of published clinical risk factor-only and Candida colonisation-only predictive models for invasive candidiasis were assessed in a multicentre cohort of Australian ICU patients. Clinical risk factors and Candida colonisation parameters were collected prospectively from patients. Results The two clinical risk factor-only predictive models applied to an Australian patient cohort ( n  = 615) performed less well than in published studies involving derivation populations. Model performance characteristics improved when Candida colonisation parameters were added post-hoc. Conclusions Risk predictive models should factor in both clinical risk factors and Candida colonisation parameters. Integrating these models into therapeutic algorithms first requires external validation in different patient populations and settings.