Hardy, F;
Heyl, J;
Tucker, K;
Hopper, A;
Marchã, MJ;
Navaratnam, AV;
Briggs, TWR;
... Gray, WK; + view all
(2023)
Estimating nosocomial infection and its outcomes in hospital patients in England with a diagnosis of COVID-19 using machine learning.
International Journal of Data Science and Analytics
10.1007/s41060-023-00419-3.
(In press).
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Abstract
Our aim was to provide a comprehensive account of COVID-19 nosocomial infections (NIs) in England and identify their characteristics and outcomes using machine learning. From the Hospital Episodes Statistics database, 374,244 adult hospital patients in England with a diagnosis of COVID-19 and discharged between March 1, 2020, and March 31, 2021, were identified. A cohort of suspected COVID-19 NIs was identified using four empirical methods linked to hospital coding. A random forest classifier was designed to model the characteristics of these infections. The model estimated a mean NI rate of 10.5%, with a peak close to 18% during the first wave, but much lower rates (7%) thereafter. NIs were highly correlated with longer lengths of stay, high trust capacity strain, greater age and a higher degree of patient frailty, and associated with higher mortality rates and more severe COVID-19 sequelae, including pneumonia, kidney disease and sepsis. Identification of the characteristics of patients who acquire NIs should help trusts to identify those most at risk. The evolution of the NI rate over time may reflect the impact of changes in hospital management practises and vaccination efforts. Variations in NI rates across trusts may partly reflect different data recording and coding practise.
| Type: | Article |
|---|---|
| Title: | Estimating nosocomial infection and its outcomes in hospital patients in England with a diagnosis of COVID-19 using machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s41060-023-00419-3 |
| Publisher version: | https://doi.org/10.1007/s41060-023-00419-3 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Information Systems, Computer Science, COVID-19, Coronavirus, Mortality, Hospital acquired infection, Nosocomial infection |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10181053 |
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