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7. Dynamic Updating of Clinical Survival Prediction Models in a Rapidly Changing Environment

Author

Tanner, Kamaryn, Keogh, Ruth H., Coupland, Carol A. C., Hippisley-Cox, Julia, and Diaz-Ordaz, Karla

Subjects
Statistics - Methodology
Abstract

Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to remain useful. Here, we investigate methods for discrete and dynamic model updating of clinical survival prediction models based on refitting, recalibration and Bayesian updating. In contrast to discrete or one-time updating, dynamic updating refers to a process in which a prediction model is repeatedly updated with new data. Motivated by infectious disease settings, our focus was on model performance in rapidly changing environments. We first compared the methods using a simulation study. We simulated scenarios with changing survival rates, the introduction of a new treatment and predictors of survival that are rare in the population. Next, the updating strategies were applied to patient data from the QResearch database, an electronic health records database from general practices in the UK, to study the updating of a model for predicting 70-day covid-19 related mortality. We found that a dynamic updating process outperformed one-time discrete updating in the simulations. Bayesian dynamic updating has the advantages of making use of knowledge from previous updates and requiring less data compared to refitting., Comment: 18 pages, 3 figures, 2 tables

Published
2023

9. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Author

Gleeson, Fergus, Baldwin, David, Batchkala, George, Buchanan, James, Burchardt, Judith, Chakraborty, Rohan, Chana, Ravi, Chen, Yan, Coupland, Carol, Crichton, Charles, Davies, Jim, Devaraj, Anand, Fan, Mengran, Hippisley-Cox, Julia, Koleva-Kolarova, Rositsa, Lee, Richard, Liao, Weiqi, Nair, Arjun, Pickup, Lyndsey, Powell, Anne, Rittscher, Jens, Shadmaan, Amied, Shanmugam, Kandavel, Stokes, Elizabeth, Verrill, Clare, Watkins, Johnathan, Wordsworth, Sarah, Coupland, Carol A C, Baldwin, David R, and Gleeson, Fergus V

Published
2023
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17. Disparities in care and outcomes for primary liver cancer in England during 2008–2018: a cohort study of 8.52 million primary care population using the QResearch database

Author

Barnes, Eleanor, Culver, Emma, Fischer, Roman, Hippisley-Cox, Julia, Innes, Hamish, Irving, William L., Jepsen, Peter, Kelly, Matt, Klenerman, Paul, Liao, Weiqi, Mann, Derek, Marshall, Aileen, Matthews, Philippa C., Pavlides, Michael, Peters, Rory J.R., Pickles, Elisabeth, Robineau, James, Schuster-Böckler, Benjamin, Song, Chunxiao, Tomlinson, Jeremy, Welberry, Christopher, Coupland, Carol A.C., and Campbell, Cori

Published
2023
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21. Helicobacter pylori eradication for primary prevention of peptic ulcer bleeding in older patients prescribed aspirin in primary care (HEAT): a randomised, double-blind, placebo-controlled trial

Author

Mant, David CA, Ford, Alexander C, Macdonald, Thomas M, Bradburn, Mike, Klauza, Claire A, Shone, Angela, Logan, Richard FA, Deeks, Jonathan J, McColl, Kenneth E.L., Goddard, Andrew F, Stevens, Richard, Sami, Sarned, Haughney, John, Cupples, Margaret E, Morar, Monique, O'Brien, Wendy, Hawkey, Chris, Avery, Anthony, Coupland, Carol A C, Crooks, Colin, Dumbleton, Jennifer, Hobbs, F D Richard, Kendrick, Denise, Moore, Michael, Morris, Clive, Rubin, Gregory, Smith, Murray, and Stevenson, Diane

Published
2022
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29. Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults : national derivation and validation cohort study

Author

Clift, Ash K, Coupland, Carol A C, Keogh, Ruth H, Diaz-Ordaz, Karla, Williamson, Elizabeth, Harrison, Ewen M, Hayward, Andrew, Hemingway, Harry, Horby, Peter, Mehta, Nisha, Benger, Jonathan, Khunti, Kamlesh, Spiegelhalter, David, Sheikh, Aziz, Valabhji, Jonathan, Lyons, Ronan A, Robson, John, Semple, Malcolm G, Kee, Frank, Johnson, Peter, Jebb, Susan, Williams, Tony, and Hippisley-Cox, Julia

Published
2020

34. The Use of Primary Care Big Data in Understanding the Pharmacoepidemiology of COVID-19: A Consensus Statement From the COVID-19 Primary Care Database Consortium

Author

Dambha-Miller, Hajira, Griffin, Simon J., Young, Duncan, Watkinson, Peter, Tan, Pui San, Clift, Ashley K., Payne, Rupert A., Coupland, Carol, Hopewell, Jemma C., Mant, Jonathan, Martin, Richard M., and Hippisley-Cox, Julia

Subjects
Medical research -- Methods, Medicine, Experimental -- Methods, Big data -- Usage -- Health aspects, Medical records -- Usage, Primary health care, Health, Science and technology
Abstract

The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic. Key words: big data; coronavirus; epidemiology; primary health care, INTRODUCTION Primary care big data refers to routinely collected anonymized general practitioner (GP) electronic health records that form large and complex longitudinal databases, often with hundreds of variables at an [...]

Published
2021
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36. Combined MRI, high-resolution manometry and a randomised trial of bisacodyl versus hyoscine show the significance of an enlarged colon in constipation: the RECLAIM study

Author

Wilkinson-Smith, Victoria, Scott, Mark, Menys, Alex, Wiklendt, Lukasz, Marciani, Luca, Atkinson, David, Sansone, Stefano, Zdanaviciene, Ausra, Coupland, Carol, Knowles, Charles H, Dinning, Philip, Taylor, Stuart A, Gowland, Penny, Hoad, Caroline Louise, Corsetti, Maura, and Spiller, Robin C

Abstract

BackgroundColonic motility in constipation can be assessed non-invasively using MRI.ObjectiveTo compare MRI with high-resolution colonic manometry (HRCM) for predicting treatment response.DesignPart 1: 44 healthy volunteers (HVs), 43 patients with irritable bowel syndrome with constipation (IBS-C) and 37 with functional constipation (FC) completed stool diaries and questionnaires and underwent oral macrogol (500–1000 mL) challenge. Whole gut transit time (WGTT), segmental colonic volumes (CV), MRI-derived Motility Index and chyme movement by ‘tagging’ were assessed using MRI and time to defecation after macrogol recorded. Left colonic HRCM was recorded before and after a 700 kcal meal. Patients then proceeded to Part 2: a randomised cross-over study of 10-days bisacodyl 10 mg daily versus hyoscine 20 mg three times per day, assessing daily pain and constipation.ResultsPart 1: Total CVs median (range) were significantly greater in IBS-C (776 (595–1033)) and FC (802 (633–951)) vs HV (645 (467–780)), p<0.001. Patients also had longer WGTT and delayed evacuation after macrogol. IBS-C patients showed significantly reduced tagging index and less propagated pressure wave (PPW) activity during HRCM versus HV. Compared with FC, IBS-C patients were more anxious and reported more pain. Abnormally large colons predicted significantly delayed evacuation after macrogol challenge (p<0.02), impaired manometric meal response and reduced pain with bisacodyl (p<0.05).Part 2: Bisacodyl compared with hyoscine increased bowel movements but caused more pain in both groups (p<0.03).ConclusionAn abnormally large colon is an important feature in constipation which predicts impaired manometric response to feeding and treatment responses. HRCM shows that IBS-C patients have reduced PPW activity.Trial registration numberThe study was preregistered on ClinicalTrials.gov, Reference: NCT03226145.

Published
2025
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38. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Author

Liao, Weiqi, primary, Coupland, Carol A C, additional, Burchardt, Judith, additional, Baldwin, David R, additional, Gleeson, Fergus V, additional, Hippisley-Cox, Julia, additional, Gleeson, Fergus, additional, Baldwin, David, additional, Batchkala, George, additional, Buchanan, James, additional, Chakraborty, Rohan, additional, Chana, Ravi, additional, Chen, Yan, additional, Coupland, Carol, additional, Crichton, Charles, additional, Davies, Jim, additional, Devaraj, Anand, additional, Fan, Mengran, additional, Koleva-Kolarova, Rositsa, additional, Lee, Richard, additional, Liao, Weiqi, additional, Nair, Arjun, additional, Pickup, Lyndsey, additional, Powell, Anne, additional, Rittscher, Jens, additional, Shadmaan, Amied, additional, Shanmugam, Kandavel, additional, Stokes, Elizabeth, additional, Verrill, Clare, additional, Watkins, Johnathan, additional, and Wordsworth, Sarah, additional

Published
2023
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46. Observational study assessing the frequency and impact of medication reviews in UK primary care for people aged ≥65 years

Author

Joseph, Rebecca, primary, Knaggs, Roger, additional, Coupland, Carol, additional, Taylor, Amy, additional, Vinogradova, Yana, additional, Butler, Debbie, additional, Waldram, David, additional, Iyen, Barbara, additional, Akyea, Ralph, additional, Ashcroft, Darren, additional, Avery, Anthony, additional, and Jack, Ruth, additional

Published
2023
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