Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.
Adult
Aged
Aged, 80 and over
Algorithms
Biomarkers
Clinical Decision-Making
Cohort Studies
Decision Support Systems, Clinical
Diagnostic Tests, Routine
/ methods
Disease Management
Female
Follow-Up Studies
Hematologic Tests
Humans
Infections
/ diagnosis
Male
Middle Aged
Patient Admission
Prognosis
ROC Curve
Supervised Machine Learning
Young Adult
Journal
The Journal of antimicrobial chemotherapy
ISSN: 1460-2091
Titre abrégé: J Antimicrob Chemother
Pays: England
ID NLM: 7513617
Informations de publication
Date de publication:
01 04 2019
01 04 2019
Historique:
received:
23
06
2018
revised:
11
10
2018
accepted:
14
11
2018
pubmed:
28
12
2018
medline:
26
6
2020
entrez:
28
12
2018
Statut:
ppublish
Résumé
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. 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. 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). An SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.
Sections du résumé
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.
Identifiants
pubmed: 30590545
pii: 5258037
doi: 10.1093/jac/dky514
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1108-1115Subventions
Organisme : Department of Health
ID : II-LA-0214-20008
Pays : United Kingdom
Organisme : Department of Health
ID : RP-2015-06-018
Pays : United Kingdom
Informations de copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please email: journals.permissions@oup.com.