Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study. (22nd December 2018)

• Record Type: Journal Article
• Authors: Rawson, T M; Hernandez, B; Moore, L S P; Blandy, O; Herrero, P; Gilchrist, M; Gordon, A; Toumazou, C; Sriskandan, S; Georgiou, P; Holmes, A H
• Is Part Of: Journal of antimicrobial chemotherapy. Volume 74:Number 4(2019)
• Page Start: 1108
• Page End: 1115
• Publication Date: 2018-12-22
• Subjects: Anti-infective agents -- Periodicals; Chemotherapy -- Periodicals; 615.58
• DOI: 10.1093/jac/dky514
• Languages: English
• ISSNs: 0305-7453

Abstract

Abstract: Background: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital. Methods: An SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored. Results: One hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21–98) years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) ( P < 0.01; 95% CI: 0.20–0.40). ROC AUC was 0.84 (95% CI: 0.76–0.91). Conclusions: An SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.