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1. Real-time modelling of the SARS-CoV-2 pandemic in England 2020-2023: a challenging data integration

Author

Birrell, Paul J, Blake, Joshua, Kandiah, Joel, Alexopoulos, Angelos, van Leeuwen, Edwin, Pouwels, Koen, Ghosh, Sanmitra, Starr, Colin, Walker, Ann Sarah, House, Thomas A, Gay, Nigel, Finnie, Thomas, Gent, Nick, Charlett, André, and De Angelis, Daniela

Subjects
Statistics - Applications
Abstract

A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission and associated healthcare burden. Here we present a detailed deconstruction of one of the 'real-time' models that was key contributor to this response, focussing on the model adaptations required over three pandemic years characterised by the imposition of lockdowns, mass vaccination campaigns and the emergence of new pandemic strains. The Bayesian model integrates an array of surveillance and other data sources including a novel approach to incorporating prevalence estimates from an unprecedented large-scale household survey. We present a full range of estimates of the epidemic history and the changing severity of the infection, quantify the impact of the vaccination programme and deconstruct contributing factors to the reproduction number. We further investigate the sensitivity of model-derived insights to the availability and timeliness of prevalence data, identifying its importance to the production of robust estimates., Comment: 35 pages, 7 figures, 3 tables

Published
2024

4. Improving the representativeness of UK’s national COVID-19 Infection Survey through spatio-temporal regression and post-stratification

Author

Pouwels, Koen B., Eyre, David W., House, Thomas, Aspey, Ben, Matthews, Philippa C., Stoesser, Nicole, Newton, John N., Diamond, Ian, Studley, Ruth, Taylor, Nick G. H., Bell, John I., Farrar, Jeremy, Kolenchery, Jaison, Marsden, Brian D., Hoosdally, Sarah, Jones, E. Yvonne, Stuart, David I., Crook, Derrick W., Peto, Tim E. A., and Walker, A. Sarah

Published
2024
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5. Assessment of three antibiotic combination regimens against Gram-negative bacteria causing neonatal sepsis in low- and middle-income countries

Author

Kakaraskoska Boceska, Biljana, Vilken, Tuba, Xavier, Basil Britto, Kostyanev, Tomislav, Lin, Qiang, Lammens, Christine, Ellis, Sally, O’Brien, Seamus, da Costa, Renata Maria Augusto, Cook, Aislinn, Russell, Neal, Bielicki, Julia, Riddell, Amy, Stohr, Wolfgang, Walker, Ann Sarah, Berezin, Eitan Naaman, Roilides, Emmanuel, De Luca, Maia, Romani, Lorenza, Ballot, Daynia, Dramowski, Angela, Wadula, Jeannette, Lochindarat, Sorasak, Boonkasidecha, Suppawat, Namiiro, Flavia, Ngoc, Hoang Thi Bich, Tran, Minh Dien, Cressey, Tim R., Preedisripipat, Kanchana, Berkley, James A., Musyimi, Robert, Zarras, Charalampos, Nana, Trusha, Whitelaw, Andrew, da Silva, Cely Barreto, Jaglal, Prenika, Ssengooba, Willy, Saha, Samir K., Islam, Mohammad Shahidul, Mussi-Pinhata, Marisa Marcia, Carvalheiro, Cristina Gardonyi, Piddock, Laura J. V., Heath, Paul T., Malhotra-Kumar, Surbhi, Sharland, Michael, Glupczynski, Youri, and Goossens, Herman

Published
2024
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6. Author Correction: The plasmidome associated with Gram-negative bloodstream infections: a large-scale observational study using complete plasmid assemblies

Author

Lipworth, Samuel, Matlock, William, Shaw, Liam, Vihta, Karina-Doris, Rodger, Gillian, Chau, Kevin, Barker, Leanne, George, Sophie, Kavanagh, James, Davies, Timothy, Vaughan, Alison, Andersson, Monique, Jeffery, Katie, Oakley, Sarah, Morgan, Marcus, Hopkins, Susan, Peto, Timothy, Crook, Derrick, Walker, A. Sarah, and Stoesser, Nicole

Published
2024
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7. SARS-CoV-2, influenza A/B and respiratory syncytial virus positivity and association with influenza-like illness and self-reported symptoms, over the 2022/23 winter season in the UK: a longitudinal surveillance cohort

Author

Dietz, Elisabeth, Pritchard, Emma, Pouwels, Koen, Ehsaan, Muhammad, Blake, Joshua, Gaughan, Charlotte, Haduli, Eric, Boothe, Hugh, Vihta, Karina-Doris, Peto, Tim, Stoesser, Nicole, Matthews, Philippa, Taylor, Nick, Diamond, Ian, Studley, Ruth, Rourke, Emma, Birrell, Paul, De Angelis, Daniela, Fowler, Tom, Watson, Conall, Eyre, David, House, Thomas, and Walker, Ann Sarah

Published
2024
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8. The plasmidome associated with Gram-negative bloodstream infections: A large-scale observational study using complete plasmid assemblies

Author

Lipworth, Samuel, Matlock, William, Shaw, Liam, Vihta, Karina-Doris, Rodger, Gillian, Chau, Kevin, Barker, Leanne, George, Sophie, Kavanagh, James, Davies, Timothy, Vaughan, Alison, Andersson, Monique, Jeffery, Katie, Oakley, Sarah, Morgan, Marcus, Hopkins, Susan, Peto, Timothy, Crook, Derrick, Walker, A. Sarah, and Stoesser, Nicole

Published
2024
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10. Prevalence of persistent SARS-CoV-2 in a large community surveillance study

Author

Ghafari, Mahan, Hall, Matthew, Golubchik, Tanya, Ayoubkhani, Daniel, House, Thomas, MacIntyre-Cockett, George, Fryer, Helen R., Thomson, Laura, Nurtay, Anel, Kemp, Steven A., Ferretti, Luca, Buck, David, Green, Angie, Trebes, Amy, Piazza, Paolo, Lonie, Lorne J., Studley, Ruth, Rourke, Emma, Smith, Darren L., Bashton, Matthew, Nelson, Andrew, Crown, Matthew, McCann, Clare, Young, Gregory R., Santos, Rui Andre Nunes dos, Richards, Zack, Tariq, Mohammad Adnan, Cahuantzi, Roberto, Barrett, Jeff, Fraser, Christophe, Bonsall, David, Walker, Ann Sarah, and Lythgoe, Katrina

Published
2024
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11. Cycle Threshold Values as Indication of Increasing SARS-CoV-2 New Variants, England, 2020-2022

Author

Harrison, Rebecca E., Hamada, Ahmed, Haswell, Nujcharee, Groves, Aigul, Vihta, Karina-Doris, Cella, Kerry, Garner, Sarah, Walker, Ann Sarah, and Seale, Anna C.

Subjects
Infection -- Health aspects -- Analysis, Health
Abstract

From identification of SARS-CoV-2 in December 2019 through September 22, 2022, [approximately equal to] 612 million confirmed cases and 6.5 million confirmed deaths were reported worldwide (1). During the COVID-19 [...]

Published
2023
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12. The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis

Author

Walker, Timothy M, Miotto, Paolo, Köser, Claudio U, Fowler, Philip W, Knaggs, Jeff, Iqbal, Zamin, Hunt, Martin, Chindelevitch, Leonid, Farhat, Maha R, Cirillo, Daniela Maria, Comas, Iñaki, Posey, James, Omar, Shaheed V, Peto, Timothy EA, Suresh, Anita, Uplekar, Swapna, Laurent, Sacha, Colman, Rebecca E, Nathanson, Carl-Michael, Zignol, Matteo, Walker, Ann Sarah, Crook, Derrick W, Ismail, Nazir, Rodwell, Timothy C, Consortium, the SeqTreat Consortium CRyPTIC, Walker, A Sarah, Steyn, Adrie JC, Lalvani, Ajit, Baulard, Alain, Christoffels, Alan, Mendoza-Ticona, Alberto, Trovato, Alberto, Skrahina, Alena, Lachapelle, Alexander S, Brankin, Alice, Piatek, Amy, Cruz, Ana Gibertoni, Koch, Anastasia, Cabibbe, Andrea Maurizio, Spitaleri, Andrea, Brandao, Angela P, Chaiprasert, Angkana, Barbova, Anna, Van Rie, Annelies, Ghodousi, Arash, Bainomugisa, Arnold, Mandal, Ayan, Roohi, Aysha, Javid, Babak, Zhu, Baoli, Letcher, Brice, Rodrigues, Camilla, Nimmo, Camus, NATHANSON, Carl-Michael, Duncan, Carla, Coulter, Christopher, Utpatel, Christian, Liu, Chunfa, Grazian, Clara, Kong, Clare, Wilson, Daniel J, Matias, Daniela, Jorgensen, Danielle, Zimenkov, Danila, Chetty, Darren, Moore, David AJ, Clifton, David A, van Soolingen, Dick, Liu, Dongxin, Kohlerschmidt, Donna, Barreira, Draurio, Ngcamu, Dumisani, Lazaro, Elias David Santos, Kelly, Ellis, Borroni, Emanuele, Roycroft, Emma, Andre, Emmanuel, Böttger, Erik C, Robinson, Esther, Menardo, Fabrizio, Mendes, Flavia F, Jamieson, Frances B, Coll, Francesc, Gao, George Fu, Kasule, George W, Rossolini, Gian Maria, Rodger, Gillian, Smith, E Grace, Meintjes, Graeme, Thwaites, Guy, Hoffmann, Harald, Albert, Heidi, Cox, Helen, Laurenson, Ian F, and Arandjelovic, Irena

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Microbiology, Clinical Sciences, Tuberculosis, Infectious Diseases, Rare Diseases, Antimicrobial Resistance, Orphan Drug, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Infection, Good Health and Well Being, Antitubercular Agents, Drug Resistance, Ethambutol, Microbial Sensitivity Tests, Mutation, Mycobacterium tuberculosis, World Health Organization, CRyPTIC Consortium, Seq&Treat Consortium, Immunology, Medical microbiology
Abstract

BackgroundMolecular diagnostics are considered the most promising route to achieving rapid, universal drug susceptibility testing for Mycobacterium tuberculosiscomplex (MTBC). We aimed to generate a WHO endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction.MethodsA candidate gene approach was used to identify mutations as associated with resistance, or consistent with susceptibility, for 13 WHO endorsed anti-tuberculosis drugs. 38,215 MTBC isolates with paired whole-genome sequencing and phenotypic drug susceptibility testing data were amassed from 45 countries. For each mutation, a contingency table of binary phenotypes and presence or absence of the mutation computed positive predictive value, and Fisher's exact tests generated odds ratios and Benjamini-Hochberg corrected p-values. Mutations were graded as Associated with Resistance if present in at least 5 isolates, if the odds ratio was >1 with a statistically significant corrected p-value, and if the lower bound of the 95% confidence interval on the positive predictive value for phenotypic resistance was >25%. A series of expert rules were applied for final confidence grading of each mutation.Findings15,667 associations were computed for 13,211 unique mutations linked to one or more drugs. 1,149/15,667 (7·3%) mutations were classified as associated with phenotypic resistance and 107/15,667 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was >80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were classified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs.InterpretationThis first WHO endorsed catalogue of molecular targets for MTBC drug susceptibility testing provides a global standard for resistance interpretation. Its existence should encourage the implementation of molecular diagnostics by National Tuberculosis Programmes.FundingUNITAID, Wellcome, MRC, BMGF.

Published
2022

13. Improving clinical trial interpretation with ACCEPT analyses

Author

Clements, Michelle N., White, Ian R., Copas, Andrew J., Cornelius, Victoria, Cro, Suzie, Dunn, David T, Quartagno, Matteo, Turner, Rebecca M., Tweed, Conor D., and Walker, A. Sarah

Subjects
Statistics - Applications
Abstract

Effective decision making from randomised controlled clinical trials relies on robust interpretation of the numerical results. However, the language we use to describe clinical trials can cause confusion both in trial design and in comparing results across trials. ACceptability Curve Estimation using Probability Above Threshold (ACCEPT) aids comparison between trials (even where of different designs) by harmonising reporting of results, acknowledging different interpretations of the results may be valid in different situations, and moving the focus from comparison to a pre-specified value to interpretation of the trial data. ACCEPT can be applied to historical trials or incorporated into statistical analysis plans for future analyses. An online tool enables ACCEPT on up to three trials simultaneously.

Published
2022

15. A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Author

Fowler, Philip W, Wright, Carla, Spiers, Helen, Zhu, Tingting, Baeten, Elisabeth ML, Hoosdally, Sarah W, Cruz, Ana L Gibertoni, Roohi, Aysha, Kouchaki, Samaneh, Walker, Timothy M, Peto, Timothy EA, Miller, Grant, Lintott, Chris, Clifton, David, Crook, Derrick W, Walker, A Sarah, Barilar, Ivan, Battaglia, Simone, Borroni, Emanuele, Brandao, Angela Pires, Brankin, Alice, Cabibbe, Andrea Maurizio, Carter, Joshua, Cirillo, Daniela Maria, Claxton, Pauline, Clifton, David A, Cohen, Ted, Coronel, Jorge, Earle, Sarah G, Escuyer, Vincent, Ferrazoli, Lucilaine, Gao, George F, Gardy, Jennifer, Gharbia, Saheer, Ghisi, Kelen Teixeira, Ghodousi, Arash, Cruz, Ana Luıza Gibertoni, Grazian, Clara, Guthrie, Jennifer L, He, Wencong, Hoffmann, Harald, Hoosdally, Sarah J, Hunt, Martin, Iqbal, Zamin, Ismail, Nazir Ahmed, Jarrett, Lisa, Joseph, Lavania, Jou, Ruwen, Kambli, Priti, Knaggs, Jeff, Koch, Anastasia, Kohlerschmidt, Donna, Lachapelle, Alexander S, Lalvani, Ajit, Lapierre, Simon Grandjean, Laurenson, Ian F, Letcher, Brice, Lin, Wan-Hsuan, Liu, Chunfa, Liu, Dongxin, Malone, Kerri M, Mandal, Ayan, Matias, Daniela, Meintjes, Graeme, Mendes, Flavia Freitas, Merker, Matthias, Mihalic, Marina, Millard, James, Miotto, Paolo, Mistry, Nerges, Moore, David, Dreyer, Viola, Chetty, Darren, Musser, Kimberlee A, Ngcamu, Dumisani, Nhung, Hoang Ngoc, Grandjean, Louis, Nilgiriwala, Kayzad Soli, Nimmo, Camus, Okozi, Nana, Oliveira, Rosangela Siqueira, Omar, Shaheed Vally, Paton, Nicholas, Pinhata, Juliana Maira Watanabe, Plesnik, Sara, Puyen, Zully M, Rabodoarivelo, Marie Sylvianne, Rakotosamimanana, Niaina, Rancoita, Paola MV, Rathod, Priti, Robinson, Esther, Rodger, Gillian, Rodrigues, Camilla, Rodwell, Timothy C, and Santos-Lazaro, David

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Emerging Infectious Diseases, Infectious Diseases, Prevention, Antimicrobial Resistance, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Infection, Good Health and Well Being, Antitubercular Agents, Humans, Microbial Sensitivity Tests, Mycobacterium tuberculosis, Tuberculosis, Volunteers, Zooniverse Volunteer Community, CRyPTIC Consortium, M. tuberculosis, antibiotics, citizen science, clinical microbiology, infectious disease, microbiology, tuberculosis, Biochemistry and Cell Biology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally, this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, each being present at a pre-determined concentration, thereby determining if the sample isresistant or susceptible to each sample. The minimum inhibitory concentration (MIC) of a drug is the lowestconcentration that inhibits growth and is a more useful quantity but requires each sample to be tested at a range ofconcentrations for each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is still an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11-17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing.

Published
2022

16. Total Effect Analysis of Vaccination on Household Transmission in the Office for National Statistics COVID-19 Infection Survey

Author

House, Thomas, Pellis, Lorenzo, Pritchard, Emma, McLean, Angela R., and Walker, A. Sarah

Subjects
Quantitative Biology - Populations and Evolution, Statistics - Applications, 62P10
Abstract

We investigate the distribution of numbers of secondary cases in households in the Office for National Statistics COVID-19 Infection Survey (ONS CIS), stratified by timing of vaccination and infection in the households. This shows a total effect of a statistically significant approximate halving of the secondary attack rate in households following vaccination., Comment: 5 pages, 2 figures

Published
2021

17. Planning a method for covariate adjustment in individually-randomised trials: a practical guide

Author

Morris, Tim P., Walker, A. Sarah, Williamson, Elizabeth J., and White, Ian R.

Subjects
Statistics - Methodology
Abstract

Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions, and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually-transmitted-infection testing and results service. Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely., Comment: 1 figure, 5 main tables, 2 appendices, 2 appendix tables

Published
2021

22. Multi-centre, randomised, open-label, blinded endpoint assessed, trial of corticosteroids plus intravenous immunoglobulin (IVIG) and aspirin, versus IVIG and aspirin for prevention of coronary artery aneurysms (CAA) in Kawasaki disease (KD): the KD CAA prevention (KD-CAAP) trial protocol

Author

Eleftheriou, Despina, Moraes, Yolanda Collaco, Purvis, Cara, Pursell, Molly, Morillas, Marta Merida, Kahn, Robin, Mossberg, Maria, Kucera, Filip, Tulloh, Robert, Standing, Joseph F., Swallow, Veronica, McCormack, Rachael, Herberg, Jethro, Levin, Michael, Wan, Mandy, Klein, Nigel, Connon, Roisin, Walker, Ann Sarah, and Brogan, Paul

Published
2023
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26. Inferring Risks of Coronavirus Transmission from Community Household Data

Author

House, Thomas, Riley, Heather, Pellis, Lorenzo, Pouwels, Koen B., Bacon, Sebastian, Eidukas, Arturas, Jahanshahi, Kaveh, Eggo, Rosalind M., and Walker, A. Sarah

Subjects
Statistics - Applications, Quantitative Biology - Populations and Evolution, 62P10
Abstract

The response of many governments to the COVID-19 pandemic has involved measures to control within- and between-household transmission, providing motivation to improve understanding of the absolute and relative risks in these contexts. Here, we perform exploratory, residual-based, and transmission-dynamic household analysis of the Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) data from 26 April 2020 to 15 July 2021 in England. This provides evidence for: (i) temporally varying rates of introduction of infection into households broadly following the trajectory of the overall epidemic and vaccination programme; (ii) Susceptible-Infectious Transmission Probabilities (SITPs) of within-household transmission in the 15-35% range; (iii) the emergence of the Alpha and Delta variants, with the former being around 50% more infectious than wildtype and 35% less infectious than Delta within households; (iv) significantly (in the range 25-300%) more risk of bringing infection into the household for workers in patient-facing roles pre-vaccine; (v) increased risk for secondary school-age children of bringing the infection into the household when schools are open; (vi) increased risk for primary school-age children of bringing the infection into the household when schools were open since the emergence of new variants., Comment: 27 pages, 7 figures

Published
2021

27. Safety and efficacy of whole-body chlorhexidine gluconate cleansing with or without emollient in hospitalised neonates (NeoCHG): a multicentre, randomised, open-label, factorial pilot trial

Author

Russell, Neal, Clements, Michelle N., Azmery, Kazi Shammin, Bekker, Adrie, Bielicki, Julia, Dramowski, Angela, Ellis, Sally, Fataar, Aaqilah, Hoque, Mahbubul, LeBeau, Kristen, O’Brien, Seamus, Schiavone, Francesca, Skoutari, Peter, Islam, Mohammad Shahidul, Saha, Samir K., Walker, Ann Sarah, Whitelaw, Andrew, and Sharland, Michael

Published
2024
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29. The DURATIONS randomised trial design: estimation targets, analysis methods and operating characteristics

Author

Quartagno, Matteo, Carpenter, James R., Walker, A. Sarah, Clements, Michelle, and Parmar, Mahesh K. B.

Subjects
Statistics - Methodology
Abstract

Background. Designing trials to reduce treatment duration is important in several therapeutic areas, including TB and antibiotics. We recently proposed a new randomised trial design to overcome some of the limitations of standard two-arm non-inferiority trials. This DURATIONS design involves randomising patients to a number of duration arms, and modelling the so-called duration-response curve. This article investigates the operating characteristics (type-1 and type-2 errors) of different statistical methods of drawing inference from the estimated curve. Methods. Our first estimation target is the shortest duration non-inferior to the control (maximum) duration within a specific risk difference margin. We compare different methods of estimating this quantity, including using model confidence bands, the delta method and bootstrap. We then explore the generalisability of results to estimation targets which focus on absolute event rates, risk ratio and gradient of the curve. Results. We show through simulations that, in most scenarios and for most of the estimation targets, using the bootstrap to estimate variability around the target duration leads to good results for DURATIONS design-appropriate quantities analogous to power and type-1 error. Using model confidence bands is not recommended, while the delta method leads to inflated type-1 error in some scenarios, particularly when the optimal duration is very close to one of the randomised durations. Conclusions. Using the bootstrap to estimate the optimal duration in a DURATIONS design has good operating characteristics in a wide range of scenarios, and can be used with confidence by researchers wishing to design a DURATIONS trial to reduce treatment duration. Uncertainty around several different targets can be estimated with this bootstrap approach., Comment: 4 figures, 1 table + additional material

Published
2020

30. Handling an uncertain control group event risk in non-inferiority trials: non-inferiority frontiers and the power-stabilising transformation

Author

Quartagno, Matteo, Walker, A. Sarah, Babiker, Abdel G., Turner, Rebecca M., Parmar, Mahesh K. B., Copas, Andrew, and White, Ian R.

Subjects
Statistics - Methodology
Abstract

Background. Non-inferiority (NI) trials are increasingly used to evaluate new treatments expected to have secondary advantages over standard of care, but similar efficacy on the primary outcome. When designing a NI trial with a binary primary outcome, the choice of effect measure for the NI margin has an important effect on sample size calculations; furthermore, if the control event risk observed is markedly different from that assumed, the trial can quickly lose power or the results become difficult to interpret. Methods. We propose a new way of designing NI trials to overcome the issues raised by unexpected control event risks by specifying a NI frontier, i.e. a curve defining the most appropriate non-inferiority margin for each possible value of control event risk. We propose a fixed arcsine difference frontier, the power-stabilising transformation for binary outcomes. We propose and compare three ways of designing a trial using this frontier. Results. Testing and reporting on the arcsine scale leads to results which are challenging to interpret clinically. Working on the arcsine scale generally requires a larger sample size compared to the risk difference scale. Therefore, working on the risk difference scale, modifying the margin after observing the control event risk, might be preferable, as it requires a smaller sample size. However, this approach tends to slightly inflate type I error rate; a solution is to use a lower significance level for testing. When working on the risk ratio scale, the same approach leads to power levels above the nominal one, maintaining type I error under control. Conclusions. Our proposed methods of designing NI trials using power-stabilising frontiers make trial design more resilient to unexpected values of the control event risk, at the only cost of requiring larger sample sizes when the goal is to report results on the risk difference scale., Comment: ~4000 words + 5 figures + 1 table + additional material (2 appendices, 1 table, 4 figures)

Published
2019

31. A global core outcome measurement set for snakebite clinical trials

Author

Abouyannis, Michael, Aggarwal, Dinesh, Alcoba, Gabriel, Alphan, Hassan J, Cammack, Nick, Casewell, Nicholas R, Chippaux, Jean-Philippe, Coldiron, Matthew E, Esmail, Hanif, Gutiérrez, José M, Habib, Abdulrazaq G, Hamaluba, Mainga, Harrison, Robert A, Isbister, Geoffrey K, Lalloo, David G, Lavonas, Eric J, Martins, Diogo, Mumba, Noni, Mwalukore, Salim, Mwangudzah, Hope, Ngama, Mwanajuma, Ribeiro, Isabela, Walker, A Sarah, Watson, James A, Williams, David J, Yeri, Betty K, Bammigatti, Chanaveerappa, Carter, Rebecca W, Gerardo, Charles J, de Silva, H Janaka, Lamb, Thomas, Lewin, Matthew R, Monteiro, Wuelton, Stienstra, Ymkje, and Walker, Sarah A

Published
2023
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32. Antibiotic review kit for hospitals (ARK-Hospital): a stepped-wedge cluster-randomised controlled trial

Author

Llewelyn, Martin J, Budgell, Eric P, Laskawiec-Szkonter, Magda, Cross, Elizabeth L A, Alexander, Rebecca, Bond, Stuart, Coles, Phil, Conlon-Bingham, Geraldine, Dymond, Samantha, Evans, Morgan, Fok, Rosemary, Frost, Kevin J, Garcia-Arias, Veronica, Glass, Stephen, Gormley, Cairine, Gray, Katherine, Hamson, Clare, Harvey, David, Hills, Tim, Iyer, Shabnam, Johnson, Alison, Jones, Nicola, Kang, Parmjit, Kiapi, Gloria, Mack, Damien, Makanga, Charlotte, Mawer, Damian, McCullagh, Bernie, Mirfenderesky, Mariyam, McEwen, Ruth, Nag, Sath, Nagar, Aaron, Northfield, John, O'Driscoll, Jean, Pegden, Amanda, Porter, Robert, Powell, Neil, Price, David, Sheridan, Elizabeth, Slatter, Mandy, Stewart, Bruce, Watson, Cassandra, Weichert, Immo, Sivyer, Katy, Wordsworth, Sarah, Quaddy, Jack, Santillo, Marta, Krusche, Adele, Roope, Laurence S J, Mowbray, Fiona, Hand, Kieran S, Dobson, Melissa, Crook, Derrick W, Vaughan, Louella, Hopkins, Susan, Yardley, Lucy, Peto, Timothy E A, and Walker, Ann Sarah

Published
2023
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33. Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

Author

Pouwels, Koen B., Walker, A. Sarah, Crook, Derrick, Matthews, Philippa C., Peto, Tim, Pritchard, Emma, Stoesser, Nicole, Vihta, Karina-Doris, Howarth, Alison, Doherty, George, Kavanagh, James, Chau, Kevin K., Hatch, Stephanie B., Ebner, Daniel, Martins Ferreira, Lucas, Christott, Thomas, Marsden, Brian D., Dejnirattisai, Wanwisa, Mongkolsapaya, Juthathip, Hoosdally, Sarah, Cornall, Richard, Stuart, David I., Screaton, Gavin, Eyre, David, Bell, John, Cox, Stuart, Paddon, Kevin, James, Tim, House, Thomas, Newton, John N., Robotham, Julie V., Birrell, Paul, Jordan, Helena, Sheppard, Tim, Athey, Graham, Moody, Dan, Curry, Leigh, Brereton, Pamela, Hay, Jodie, Vansteenhouse, Harper, Bell, Iain, Diamond, Ian, Lambert, Alex, Benton, Pete, Rourke, Emma, Hawkes, Stacey, Henry, Sarah, Scruton, James, Stokes, Peter, Thomas, Tina, Allen, John, Black, Russell, Bovill, Heather, Braunholtz, David, Brown, Dominic, Collyer, Sarah, Crees, Megan, Daglish, Colin, Davies, Byron, Donnarumma, Hannah, Douglas-Mann, Julia, Felton, Antonio, Finselbach, Hannah, Fordham, Eleanor, Ipser, Alberta, Jenkins, Joe, Jones, Joel, Kent, Katherine, Kerai, Geeta, Lloyd, Lina, Masding, Victoria, Osborn, Ellie, Patel, Alpi, Pereira, Elizabeth, Pett, Tristan, Randall, Melissa, Reeve, Donna, Shah, Palvi, Snook, Ruth, Studley, Ruth, Sutherland, Esther, Swinn, Eliza, Thomas, Heledd, Tudor, Anna, Weston, Joshua, Leib, Shayla, Tierney, James, Farkas, Gabor, Cobb, Raf, Van Galen, Folkert, Compton, Lewis, Irving, James, Clarke, John, Mullis, Rachel, Ireland, Lorraine, Airimitoaie, Diana, Nash, Charlotte, Cox, Danielle, Fisher, Sarah, Moore, Zoe, McLean, James, Kerby, Matt, Pouwels, Koen B, Robotham, Julie V, Birrell, Paul J, Gelman, Andrew, Bowers, Nikola, Boreham, Ian, Lewis, James, Bell, John I, Newton, John N, Farrar, Jeremy, and Walker, Ann Sarah

Published
2021
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34. Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar

Author

Pritchard, Emma, primary, Vihta, Karina-Doris, additional, Eyre, David W, additional, Hopkins, Susan, additional, Peto, Tim E A, additional, Matthews, Philippa C, additional, Stoesser, Nicole, additional, Studley, Ruth, additional, Rourke, Emma, additional, Diamond, Ian, additional, Pouwels, Koen B, additional, Walker, Ann Sarah, additional, and Infection Survey Team, the COVID-19, additional

Published
2024
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35. Antibody responses and correlates of protection in the general population after two doses of the ChAdOx1 or BNT162b2 vaccines

Author

Wei, Jia, Pouwels, Koen B., Stoesser, Nicole, Matthews, Philippa C., Diamond, Ian, Studley, Ruth, Rourke, Emma, Cook, Duncan, Bell, John I., Newton, John N., Farrar, Jeremy, Howarth, Alison, Marsden, Brian D., Hoosdally, Sarah, Jones, E. Yvonne, Stuart, David I., Crook, Derrick W., Peto, Tim E. A., Walker, A. Sarah, and Eyre, David W.

Published
2022
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36. Re-thinking non-inferiority: a practical trial design for optimising treatment duration

Author

Quartagno, Matteo, Walker, A. Sarah, Carpenter, James R., Phillips, Patrick P. J., and Parmar, Mahesh K. B.

Subjects
Statistics - Methodology
Abstract

Background: trials to identify the minimal effective treatment duration are needed in different therapeutic areas, including bacterial infections, TB and Hepatitis--C. However, standard non-inferiority designs have several limitations, including arbitrariness of non-inferiority margins, choice of research arms and very large sample sizes. Methods: we recast the problem of finding an appropriate non-inferior treatment duration in terms of modelling the entire duration-response curve within a pre-specified range. We propose a multi-arm randomised trial design, allocating patients to different treatment durations. We use fractional polynomials and spline-based methods to flexibly model the duration-response curve. We compare different methods in terms of a scaled version of the area between true and estimated prediction curves. We evaluate sensitivity to key design parameters, including sample size, number and position of arms. Results: a total sample size of $\sim 500$ patients divided into a moderate number of equidistant arms (5-7) is sufficient to estimate the duration-response curve within a $5\%$ error margin in $95\%$ of the simulations. Fractional polynomials provide similar or better results than spline-based methods in most scenarios. Conclusions: our proposed practical randomised trial design is an alternative to standard non-inferiority designs, avoiding many of their limitations, and yet being fairly robust to different possible duration-response curves. The trial outcome is the whole duration-response curve, which could be used by clinicians and policy makers to make informed decisions, facilitating a move away from a forced binary hypothesis testing paradigm.

Published
2018

38. Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing

Author

Allix-Béguec, Caroline, Arandjelovic, Irena, Bi, Lijun, Beckert, Patrick, Bonnet, Maryline, Bradley, Phelim, Cabibbe, Andrea M, Cancino-Muñoz, Irving, Caulfield, Mark J, Chaiprasert, Angkana, Cirillo, Daniela, Clifton, David, Comas, Iñaki, Crook, Derrick W, De Filippo, Maria Rosaria, de Neeling, Han, Diel, Roland, Drobniewski, Francis A, Faksri, Kiatichai, Farhat, Maha R, Fleming, Joy, Fowler, Philip, Fowler, Tom A, Gao, Qian, Gardy, Jennifer, Gascoyne-Binzi, Deborah, Cruz, Ana Gibertoni, Gil-Brusola, Ana, Golubchik, Tanya, Gonzalo, Ximena, Grandjean, Louis, He, Guangxue, Guthrie, Jennifer L, Hoosdally, Sarah, Hunt, Martin, Iqbal, Zamin, Ismail, Nazir, Johnston, James, Khanzada, Faisal Masood, Khor, Chiea Chuen, Kohl, Thomas A, Kong, Clare, Lipworth, Sam, Liu, Qingyun, Maphalala, Gugu, Martinez, Elena, Mathys, Vanessa, Merker, Matthias, Miotto, Paolo, Mistry, Nerges, Moore, David, Murray, Megan, Niemann, Stefan, Ong, Rick Twee-Hee, Peto, Tim EA, Posey, James E, Prammananan, Therdsak, Pym, Alexander, Rodrigues, Camilla, Rodrigues, Mabel, Rodwell, Timothy, Rossolini, Gian Maria, Padilla, Elisabeth Sánchez, Schito, Marco, Shen, Xin, Shendure, Jay, Sintchenko, Vitali, Sloutsky, Alex, Smith, E Grace, Snyder, Matthew, Soetaert, Karine, Starks, Angela M, Supply, Philip, Suriyapol, Prapat, Tahseen, Sabira, Tang, Patrick, Teo, Yik-Ying, Thuong, Thuong Nguyen Thuy, Thwaites, Guy, Tortoli, Enrico, Omar, Shaheed Vally, van Soolingen, Dick, Walker, A Sarah, Walker, Timothy M, Wilcox, Mark, Wilson, Daniel J, Wyllie, David, Yang, Yang, Zhang, Hongtai, Zhao, Yanlin, and Zhu, Baoli

Subjects
Genetics, Human Genome, Tuberculosis, Clinical Research, Patient Safety, Prevention, Rare Diseases, Infectious Diseases, Antimicrobial Resistance, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Infection, Good Health and Well Being, Antitubercular Agents, Drug Resistance, Bacterial, Ethambutol, Genome, Bacterial, Genotype, Humans, Isoniazid, Microbial Sensitivity Tests, Mutation, Mycobacterium tuberculosis, Phenotype, Pyrazinamide, Rifampin, Whole Genome Sequencing, CRyPTIC Consortium and the 100, 000 Genomes Project, Medical and Health Sciences, General & Internal Medicine
Abstract

BackgroundThe World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear.MethodsWe obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance.ResultsA total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted.ConclusionsGenotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.).

Published
2018

39. Rethinking non-inferiority: a practical trial design for optimising treatment duration

Author

Quartagno, Matteo, Walker, A Sarah, Carpenter, James R, Phillips, Patrick PJ, and Parmar, Mahesh KB

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, 8.4 Research design and methodologies (health services), Health and social care services research, Infection, Good Health and Well Being, Drug Resistance, Microbial, Equivalence Trials as Topic, Humans, Research Design, Antimicrobial resistance, design, randomised trial, flexible modelling, non-inferiority, duration of therapy, Statistics, Statistics & Probability, Clinical sciences, Clinical and health psychology
Abstract

Background Trials to identify the minimal effective treatment duration are needed in different therapeutic areas, including bacterial infections, tuberculosis and hepatitis C. However, standard non-inferiority designs have several limitations, including arbitrariness of non-inferiority margins, choice of research arms and very large sample sizes. Methods We recast the problem of finding an appropriate non-inferior treatment duration in terms of modelling the entire duration-response curve within a pre-specified range. We propose a multi-arm randomised trial design, allocating patients to different treatment durations. We use fractional polynomials and spline-based methods to flexibly model the duration-response curve. We call this a 'Durations design'. We compare different methods in terms of a scaled version of the area between true and estimated prediction curves. We evaluate sensitivity to key design parameters, including sample size, number and position of arms. Results A total sample size of ~ 500 patients divided into a moderate number of equidistant arms (5-7) is sufficient to estimate the duration-response curve within a 5% error margin in 95% of the simulations. Fractional polynomials provide similar or better results than spline-based methods in most scenarios. Conclusion Our proposed practical randomised trial 'Durations design' shows promising performance in the estimation of the duration-response curve; subject to a pending careful investigation of its inferential properties, it provides a potential alternative to standard non-inferiority designs, avoiding many of their limitations, and yet being fairly robust to different possible duration-response curves. The trial outcome is the whole duration-response curve, which may be used by clinicians and policymakers to make informed decisions, facilitating a move away from a forced binary hypothesis testing paradigm.

Published
2018

44. Whole blood versus red cell concentrates for children with severe anaemia: a secondary analysis of the Transfusion and Treatment of African Children (TRACT) trial

Author

Maitland, Kathryn, Walker, Sarah A, George, Elizabeth C, Williams, Thomas N, Gibb, Diana M, Mpoya, Ayub, Frost, Gary, Walsh, Kevin, Olupot-Olupot, Peter, Nteziyaremye, Julius, Namayanja, Cate, Ssenyondo, Tonny, Passi, George, Muhindo, Rita, Masifa, George, Adong, Ruth, Okalebo, Charles B, Atimango, Emmanuela, Thembo, Nobert, Odong, George, Kiluli, Godfrey, Kapsindet, Job, Kiguli, Sarah, Opoka, Robert O, Kayaga, Julianne, Nabawanuka, Eva, Kadama, Eva, Mukisa, Cynthia Williams, Engoru, Charles, Alaroker, Florence, Nakuya, Margaret, Amorut, Denis, Olupot, Moses, Onyas, Pius, Ariimi, Margaret, Itipe, Melda, Atim, Mary G, Abeno, Mary, Okwi, Steven, Kulume, Mary G, Among, Grace, Achipa, Dorreen E, Uyoga, Sophie, Macharia, Alex, Mellewa, Machpherson, Chagaluka, George, Kennedy, Neil, Kumwenda, Felistas, Sochera, Tiferanji Fatch, Malenga, Albert, Chimalizeni, Yamikani FG, Gushu, Benard, Phiri, Tusekile, Chesale, Amisa, Mitole, Ndaona, Chokani, Ellida, Munthali, Annie, Hensbroek, Michael Boele von, South, Annabelle, Thomason, Margaret J, Baptiste, David, Connon, Roisin, MacCabe, Leanne, Ali, Abdul, Khamis, Kibibi, Madoola, Macreen, Abongo, Grace, Bates, Imelda, Urban, Britta, Heydermann, Robert, Kyomuhendo, Flavia, Nakalanzi, Sarah, Chabuka, John, Mkandawire, Nkhafwire, Fitzgerald, Felicity, Evans, Jennifer A, Molyneux, Elizabeth, Lubega, Irene, Crawley, Jane, Kazembe, Peter, Murphy, Mike, Peto, Tim, Todd, Jim, Mirembe, Grace, Musoka, Philipa, Tenu, Filemoni, M'baya, Bridon, Kyeyune Byabazair, Dorothy, Mbanya, Dora, and Walker, A Sarah

Published
2022
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45. Determining sample size in a personalized randomized controlled (PRACTical) trial.

Author

Turner, Rebecca M., Lee, Kim May, Walker, A. Sarah, Ellis, Sally, Sharland, Michael, Bielicki, Julia A., Stöhr, Wolfgang, and White, Ian R.

Abstract

In clinical settings with no commonly accepted standard‐of‐care, multiple treatment regimens are potentially useful, but some treatments may not be appropriate for some patients. A personalized randomized controlled trial (PRACTical) design has been proposed for this setting. For a network of treatments, each patient is randomized only among treatments which are appropriate for them. The aim is to produce treatment rankings that can inform clinical decisions about treatment choices for individual patients. Here we propose methods for determining sample size in a PRACTical design, since standard power‐based methods are not applicable. We derive a sample size by evaluating information gained from trials of varying sizes. For a binary outcome, we quantify how many adverse outcomes would be prevented by choosing the top‐ranked treatment for each patient based on trial results rather than choosing a random treatment from the appropriate personalized randomization list. In simulations, we evaluate three performance measures: mean reduction in adverse outcomes using sample information, proportion of simulated patients for whom the top‐ranked treatment performed as well or almost as well as the best appropriate treatment, and proportion of simulated trials in which the top‐ranked treatment performed better than a randomly chosen treatment. We apply the methods to a trial evaluating eight different combination antibiotic regimens for neonatal sepsis (NeoSep1), in which a PRACTical design addresses varying patterns of antibiotic choice based on disease characteristics and resistance. Our proposed approach produces results that are more relevant to complex decision making by clinicians and policy makers. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Patterns of antibiotic use, pathogens, and prediction of mortality in hospitalized neonates and young infants with sepsis: A global neonatal sepsis observational cohort study (NeoOBS)

Author

Russell, Neal J., Stöhr, Wolfgang, Plakkal, Nishad, Cook, Aislinn, Berkley, James A., Adhisivam, Bethou, Agarwal, Ramesh, Ahmed, Nawshad Uddin, Balasegaram, Manica, Ballot, Daynia, Bekker, Adrie, Berezin, Eitan Naaman, Bilardi, Davide, Boonkasidecha, Suppawat, Carvalheiro, Cristina G., Chami, Neema, Chaurasia, Suman, Chiurchiu, Sara, Colas, Viviane Rinaldi Favarin, Cousens, Simon, Cressey, Tim R., de Assis, Ana Carolina Dantas, Dien, Tran Minh, Ding, Yijun, Dung, Nguyen Trong, Dong, Han, Dramowski, Angela, DS, Madhusudhan, Dudeja, Ajay, Feng, Jinxing, Glupczynski, Youri, Goel, Srishti, Goossens, Herman, Hao, Doan Thi Huong, Khan, Mahmudul Islam, Huertas, Tatiana Munera, Islam, Mohammad Shahidul, Jarovsky, Daniel, Khavessian, Nathalie, Khorana, Meera, Kontou, Angeliki, Kostyanev, Tomislav, Laoyookhon, Premsak, Lochindarat, Sorasak, Larsson, Mattias, Luca, Maia De, Malhotra-Kumar, Surbhi, Mondal, Nivedita, Mundhra, Nitu, Musoke, Philippa, Mussi-Pinhata, Marisa M., Nanavati, Ruchi, Nakwa, Firdose, Nangia, Sushma, Nankunda, Jolly, Nardone, Alessandra, Nyaoke, Borna, Obiero, Christina W., Owor, Maxensia, Ping, Wang, Preedisripipat, Kanchana, Qazi, Shamim, Qi, Lifeng, Ramdin, Tanusha, Riddell, Amy, Romani, Lorenza, Roysuwan, Praewpan, Saggers, Robin, Roilides, Emmanuel, Saha, Samir K., Sarafidis, Kosmas, Tusubira, Valerie, Thomas, Reenu, Velaphi, Sithembiso, Vilken, Tuba, Wang, Xiaojiao, Wang, Yajuan, Yang, Yonghong, Zunjie, Liu, Ellis, Sally, Bielicki, Julia A., Walker, A. Sarah, Heath, Paul T., and Sharland, Mike

Subjects
Infants -- Patient outcomes, Sepsis -- Diagnosis -- Care and treatment, Antibiotics -- Dosage and administration, Hospital patients -- Care and treatment, Market trend/market analysis, Biological sciences
Abstract

Background There is limited data on antibiotic treatment in hospitalized neonates in low- and middle-income countries (LMICs). We aimed to describe patterns of antibiotic use, pathogens, and clinical outcomes, and to develop a severity score predicting mortality in neonatal sepsis to inform future clinical trial design. Methods and findings Hospitalized infants A total of 3,204 infants were enrolled, with median birth weight of 2,500 g (IQR 1,400 to 3,000) and postnatal age of 5 days (IQR 1 to 15). 206 different empiric antibiotic combinations were started in 3,141 infants, which were structured into 5 groups based on the World Health Organization (WHO) AWaRe classification. Approximately 25.9% (n = 814) of infants started WHO first line regimens (Group 1-Access) and 13.8% (n = 432) started WHO second-line cephalosporins (cefotaxime/ceftriaxone) (Group 2-'Low' Watch). The largest group (34.0%, n = 1,068) started a regimen providing partial extended-spectrum beta-lactamase (ESBL)/pseudomonal coverage (piperacillin-tazobactam, ceftazidime, or fluoroquinolone-based) (Group 3-'Medium' Watch), 18.0% (n = 566) started a carbapenem (Group 4-'High' Watch), and 1.8% (n = 57) a Reserve antibiotic (Group 5, largely colistin-based), and 728/2,880 (25.3%) of initial regimens in Groups 1 to 4 were escalated, mainly to carbapenems, usually for clinical deterioration (n = 480; 65.9%). A total of 564/3,195 infants (17.7%) were blood culture pathogen positive, of whom 62.9% (n = 355) had a gram-negative organism, predominantly Klebsiella pneumoniae (n = 132) or Acinetobacter spp. (n = 72). Both were commonly resistant to WHO-recommended regimens and to carbapenems in 43 (32.6%) and 50 (71.4%) of cases, respectively. MRSA accounted for 33 (61.1%) of 54 Staphylococcus aureus isolates. Overall, 350/3,204 infants died (11.3%; 95% CI 10.2% to 12.5%), 17.7% if blood cultures were positive for pathogens (95% CI 14.7% to 21.1%, n = 99/564). A baseline NeoSep Severity Score had a C-index of 0.76 (0.69 to 0.82) in the validation sample, with mortality of 1.6% (3/189; 95% CI: 0.5% to 4.6%), 11.0% (27/245; 7.7% to 15.6%), and 27.3% (12/44; 16.3% to 41.8%) in low (score 0 to 4), medium (5 to 8), and high (9 to 16) risk groups, respectively, with similar performance across subgroups. A related NeoSep Recovery Score had an area under the receiver operating curve for predicting death the next day between 0.8 and 0.9 over the first week. There was significant variation in outcomes between sites and external validation would strengthen score applicability. Conclusion Antibiotic regimens used in neonatal sepsis commonly diverge from WHO guidelines, and trials of novel empiric regimens are urgently needed in the context of increasing antimicrobial resistance (AMR). The baseline NeoSep Severity Score identifies high mortality risk criteria for trial entry, while the NeoSep Recovery Score can help guide decisions on regimen change. NeoOBS data informed the NeoSep1 antibiotic trial (ISRCTN48721236), which aims to identify novel first- and second-line empiric antibiotic regimens for neonatal sepsis. Trial registration ClinicalTrials.gov, (NCT03721302)., Author(s): Neal J. Russell 1,*, Wolfgang Stöhr 2, Nishad Plakkal 3, Aislinn Cook 1, James A. Berkley 4,5,6, Bethou Adhisivam 3, Ramesh Agarwal 7, Nawshad Uddin Ahmed 8, Manica Balasegaram [...]

Published
2023
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48. Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches

Author

Carter, Joshua J, primary, Walker, Timothy M, additional, Walker, A Sarah, additional, Whitfield, Michael G, additional, Morlock, Glenn P, additional, Lynch, Charlotte I, additional, Adlard, Dylan, additional, Peto, Timothy E A, additional, Posey, James E, additional, Crook, Derrick W, additional, and Fowler, Philip W, additional

Published
2024
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