T M Rawson1,2, B Hernandez3, L S P Moore1,2, O Blandy1, P Herrero3, M Gilchrist2, A Gordon4, C Toumazou3, S Sriskandan1,2, P Georgiou3, A H Holmes1,2. 1. National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, UK. 2. Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London, UK. 3. Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK. 4. Section of Anaesthetics, Pain Medicine & Intensive Care, Imperial College London, South Kensington Campus, London, UK.
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.
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.
Authors: Conor K Corbin; Lillian Sung; Arhana Chattopadhyay; Morteza Noshad; Amy Chang; Stanley Deresinksi; Michael Baiocchi; Jonathan H Chen Journal: Commun Med (Lond) Date: 2022-04-08
Authors: Timothy M Rawson; Richard C Wilson; Danny O'Hare; Pau Herrero; Andrew Kambugu; Mohammed Lamorde; Matthew Ellington; Pantelis Georgiou; Anthony Cass; William W Hope; Alison H Holmes Journal: Nat Rev Microbiol Date: 2021-06-22 Impact factor: 60.633
Authors: Ahmed Al-Hindawi; Ahmed Abdulaal; Timothy M Rawson; Saleh A Alqahtani; Nabeela Mughal; Luke S P Moore Journal: Front Digit Health Date: 2021-12-23
Authors: Timothy M Rawson; Bernard Hernandez; Richard C Wilson; Damien Ming; Pau Herrero; Nisha Ranganathan; Keira Skolimowska; Mark Gilchrist; Giovanni Satta; Pantelis Georgiou; Alison H Holmes Journal: JAC Antimicrob Resist Date: 2021-02-03