T Phuong Quan1,2,3, Berit Muller-Pebody4, Nicola Fawcett5,6,7, Bernadette C Young6, Mehdi Minaji4, Jonathan Sandoe8, Susan Hopkins4, Derrick Crook5,6,9,7, Timothy Peto5,6,9,7, Alan P Johnson4, A Sarah Walker5,6,9. 1. National Institute for Health Research (NIHR) Health Protection Research Unit on Healthcare Associated Infections and Antimicrobial Resistance, John Radcliffe Hospital, Microbiology Level 7, Headley Way, Oxford, OX3 9DU, UK. phuong.quan@ndm.ox.ac.uk. 2. Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK. phuong.quan@ndm.ox.ac.uk. 3. NIHR Biomedical Research Centre, Oxford, OX3 9DU, UK. phuong.quan@ndm.ox.ac.uk. 4. National Infection Service, Public Health England, Colindale, London, UK. 5. National Institute for Health Research (NIHR) Health Protection Research Unit on Healthcare Associated Infections and Antimicrobial Resistance, John Radcliffe Hospital, Microbiology Level 7, Headley Way, Oxford, OX3 9DU, UK. 6. Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK. 7. Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK. 8. Department of Microbiology, Leeds Teaching Hospitals NHS Trust and University of Leeds, Leeds, LS1 3EX, UK. 9. NIHR Biomedical Research Centre, Oxford, OX3 9DU, UK.
Abstract
BACKGROUND: Infective endocarditis is an uncommon but serious infection, where evidence for giving antibiotic prophylaxis before invasive dental procedures is inconclusive. In England, antibiotic prophylaxis was offered routinely to patients at risk of infective endocarditis until March 2008, when new guidelines aimed at reducing unnecessary antibiotic use were issued. We investigated whether changes in infective endocarditis incidence could be detected using electronic health records, assessing the impact of inclusion criteria/statistical model choice on inferences about the timing/type of any change. METHODS: Using national data from Hospital Episode Statistics covering 1998-2017, we modelled trends in infective endocarditis incidence using three different sets of inclusion criteria plus a range of regression models, identifying the most likely date for a change in trends if evidence for one existed. We also modelled trends in the proportions of different organism groups identified during infection episodes, using secondary diagnosis codes and data from national laboratory records. Lastly, we applied non-parametric local smoothing to visually inspect any changes in trend around the guideline change date. RESULTS: Infective endocarditis incidence increased markedly over the study (22.2-41.3 per million population in 1998 to 42.0-67.7 in 2017 depending on inclusion criteria). The most likely dates for a change in incidence trends ranged from September 2001 (uncertainty interval August 2000-May 2003) to May 2015 (March 1999-January 2016), depending on inclusion criteria and statistical model used. For the proportion of infective endocarditis cases associated with streptococci, the most likely change points ranged from October 2008 (March 2006-April 2010) to August 2015 (September 2013-November 2015), with those associated with oral streptococci decreasing in proportion after the change point. Smoothed trends showed no notable changes in trend around the guideline date. CONCLUSIONS: Infective endocarditis incidence has increased rapidly in England, though we did not detect any change in trends directly following the updated guidelines for antibiotic prophylaxis, either overall or in cases associated with oral streptococci. Estimates of when changes occurred were sensitive to inclusion criteria and statistical model choice, demonstrating the need for caution in interpreting single models when using large datasets. More research is needed to explore the factors behind this increase.
BACKGROUND:Infective endocarditis is an uncommon but serious infection, where evidence for giving antibiotic prophylaxis before invasive dental procedures is inconclusive. In England, antibiotic prophylaxis was offered routinely to patients at risk of infective endocarditis until March 2008, when new guidelines aimed at reducing unnecessary antibiotic use were issued. We investigated whether changes in infective endocarditis incidence could be detected using electronic health records, assessing the impact of inclusion criteria/statistical model choice on inferences about the timing/type of any change. METHODS: Using national data from Hospital Episode Statistics covering 1998-2017, we modelled trends in infective endocarditis incidence using three different sets of inclusion criteria plus a range of regression models, identifying the most likely date for a change in trends if evidence for one existed. We also modelled trends in the proportions of different organism groups identified during infection episodes, using secondary diagnosis codes and data from national laboratory records. Lastly, we applied non-parametric local smoothing to visually inspect any changes in trend around the guideline change date. RESULTS:Infective endocarditis incidence increased markedly over the study (22.2-41.3 per million population in 1998 to 42.0-67.7 in 2017 depending on inclusion criteria). The most likely dates for a change in incidence trends ranged from September 2001 (uncertainty interval August 2000-May 2003) to May 2015 (March 1999-January 2016), depending on inclusion criteria and statistical model used. For the proportion of infective endocarditis cases associated with streptococci, the most likely change points ranged from October 2008 (March 2006-April 2010) to August 2015 (September 2013-November 2015), with those associated with oral streptococci decreasing in proportion after the change point. Smoothed trends showed no notable changes in trend around the guideline date. CONCLUSIONS:Infective endocarditis incidence has increased rapidly in England, though we did not detect any change in trends directly following the updated guidelines for antibiotic prophylaxis, either overall or in cases associated with oral streptococci. Estimates of when changes occurred were sensitive to inclusion criteria and statistical model choice, demonstrating the need for caution in interpreting single models when using large datasets. More research is needed to explore the factors behind this increase.
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Authors: Khawaja M Talha; Larry M Baddour; Martin H Thornhill; Verda Arshad; Wajeeha Tariq; Imad M Tleyjeh; Christopher G Scott; Meredith C Hyun; Kent R Bailey; Nandan S Anavekar; Raj Palraj; M Rizwan Sohail; Daniel C DeSimone; Mark J Dayer Journal: Open Heart Date: 2021-10
Authors: Khawaja M Talha; Mark J Dayer; Martin H Thornhill; Wajeeha Tariq; Verda Arshad; Imad M Tleyjeh; Kent R Bailey; Raj Palraj; Nandan S Anavekar; M Rizwan Sohail; Daniel C DeSimone; Larry M Baddour Journal: Open Forum Infect Dis Date: 2021-09-25 Impact factor: 4.423