Affiliation:
1. University College London
2. Guy’s and St Thomas’ NHS Foundation Trust
3. Getting It Right First Time, NHS England and NHS Improvement
4. Science and Technology Facilities Council Distributed Research Utilising Advanced Computing High Performance Computing Facility
Abstract
Abstract
Purpose.
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.
Methods.
From the Hospital Episodes Statistics database, 374,244 adult hospital patients in England with a diagnosis of COVID-19 and discharged between March 1st 2020 and March 31st 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.
Results.
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.
Conclusions.
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 practices and vaccination efforts. Variations in NI rates across trusts may partly reflect different data recording and coding practice.
Publisher
Research Square Platform LLC