Diagnostic and prognostic research最新文献

{"title":"Conducting invasive urodynamics in primary care: qualitative interview study examining experiences of patients and healthcare professionals.","authors":"Sarah Milosevic,&nbsp;Natalie Joseph-Williams,&nbsp;Bethan Pell,&nbsp;Elizabeth Cain,&nbsp;Robyn Hackett,&nbsp;Ffion Murdoch,&nbsp;Haroon Ahmed,&nbsp;A Joy Allen,&nbsp;Alison Bray,&nbsp;Samantha Clarke,&nbsp;Marcus J Drake,&nbsp;Michael Drinnan,&nbsp;Kerenza Hood,&nbsp;Tom Schatzberger,&nbsp;Yemisi Takwoingi,&nbsp;Emma Thomas-Jones,&nbsp;Raymond White,&nbsp;Adrian Edwards,&nbsp;Chris Harding","doi":"10.1186/s41512-021-00100-y","DOIUrl":"https://doi.org/10.1186/s41512-021-00100-y","url":null,"abstract":"<p><strong>Background: </strong>Invasive urodynamics is used to investigate the causes of lower urinary tract symptoms; a procedure usually conducted in secondary care by specialist practitioners. No study has yet investigated the feasibility of carrying out this procedure in a non-specialist setting. Therefore, the aim of this study was to explore, using qualitative methodology, the feasibility and acceptability of conducting invasive urodynamic testing in primary care.</p><p><strong>Methods: </strong>Semi-structured interviews were conducted during the pilot phase of the PriMUS study, in which men experiencing bothersome lower urinary tract symptoms underwent invasive urodynamic testing along with a series of simple index tests in a primary care setting. Interviewees were 25 patients invited to take part in the PriMUS study and 18 healthcare professionals involved in study delivery. Interviews were audio-recorded, transcribed verbatim and analysed using a framework approach.</p><p><strong>Results: </strong>Patients generally found the urodynamic procedure acceptable and valued the primary care setting due to its increased accessibility and familiarity. Despite some logistical issues, facilitating invasive urodynamic testing in primary care was also a positive experience for urodynamic nurses. Initial issues with general practitioners receiving and utilising the results of urodynamic testing may have limited the potential benefit to some patients. Effective approaches to study recruitment included emphasising the benefits of the urodynamic test and maintaining contact with potential participants by telephone. Patients' relationship with their general practitioner was an important influence on study participation.</p><p><strong>Conclusions: </strong>Conducting invasive urodynamics in primary care is feasible and acceptable and has the potential to benefit patients. Facilitating study procedures in a familiar primary care setting can impact positively on research recruitment. However, it is vital that there is a support network for urodynamic nurses and expertise available to help interpret urodynamic results.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s41512-021-00100-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39007458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: A study protocol for the development of a multivariable model predicting 6- and 12-month mortality for people with dementia living in residential aged care facilities (RACFs) in Australia","authors":"Ross Bicknell, W. Lim, A. Maier, D. Logiudice","doi":"10.1186/s41512-021-00099-2","DOIUrl":"https://doi.org/10.1186/s41512-021-00099-2","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48764842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRISMA-DTA for Abstracts: a new addition to the toolbox for test accuracy research.","authors":"Daniël A Korevaar,&nbsp;Patrick M Bossuyt,&nbsp;Matthew D F McInnes,&nbsp;Jérémie F Cohen","doi":"10.1186/s41512-021-00097-4","DOIUrl":"https://doi.org/10.1186/s41512-021-00097-4","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25540791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods for Evaluation of medical prediction Models, Tests And Biomarkers (MEMTAB) 2020 Symposium : Virtual. 10-11 December 2020.","authors":"","doi":"10.1186/s41512-021-00094-7","DOIUrl":"https://doi.org/10.1186/s41512-021-00094-7","url":null,"abstract":"","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"5 Suppl 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s41512-021-00094-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25535480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive sample size determination for the development of clinical prediction models.","authors":"Evangelia Christodoulou,&nbsp;Maarten van Smeden,&nbsp;Michael Edlinger,&nbsp;Dirk Timmerman,&nbsp;Maria Wanitschek,&nbsp;Ewout W Steyerberg,&nbsp;Ben Van Calster","doi":"10.1186/s41512-021-00096-5","DOIUrl":"https://doi.org/10.1186/s41512-021-00096-5","url":null,"abstract":"<p><strong>Background: </strong>We suggest an adaptive sample size calculation method for developing clinical prediction models, in which model performance is monitored sequentially as new data comes in.</p><p><strong>Methods: </strong>We illustrate the approach using data for the diagnosis of ovarian cancer (n = 5914, 33% event fraction) and obstructive coronary artery disease (CAD; n = 4888, 44% event fraction). We used logistic regression to develop a prediction model consisting only of a priori selected predictors and assumed linear relations for continuous predictors. We mimicked prospective patient recruitment by developing the model on 100 randomly selected patients, and we used bootstrapping to internally validate the model. We sequentially added 50 random new patients until we reached a sample size of 3000 and re-estimated model performance at each step. We examined the required sample size for satisfying the following stopping rule: obtaining a calibration slope ≥ 0.9 and optimism in the c-statistic (or AUC) < = 0.02 at two consecutive sample sizes. This procedure was repeated 500 times. We also investigated the impact of alternative modeling strategies: modeling nonlinear relations for continuous predictors and correcting for bias on the model estimates (Firth's correction).</p><p><strong>Results: </strong>Better discrimination was achieved in the ovarian cancer data (c-statistic 0.9 with 7 predictors) than in the CAD data (c-statistic 0.7 with 11 predictors). Adequate calibration and limited optimism in discrimination was achieved after a median of 450 patients (interquartile range 450-500) for the ovarian cancer data (22 events per parameter (EPP), 20-24) and 850 patients (750-900) for the CAD data (33 EPP, 30-35). A stricter criterion, requiring AUC optimism < = 0.01, was met with a median of 500 (23 EPP) and 1500 (59 EPP) patients, respectively. These sample sizes were much higher than the well-known 10 EPP rule of thumb and slightly higher than a recently published fixed sample size calculation method by Riley et al. Higher sample sizes were required when nonlinear relationships were modeled, and lower sample sizes when Firth's correction was used.</p><p><strong>Conclusions: </strong>Adaptive sample size determination can be a useful supplement to fixed a priori sample size calculations, because it allows to tailor the sample size to the specific prediction modeling context in a dynamic fashion.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s41512-021-00096-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25511209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes after pregnancy: a study protocol for the derivation and validation of a risk prediction model for 5-year risk of diabetes following pregnancy.","authors":"Stephanie H Read, Laura C Rosella, Howard Berger, Denice S Feig, Karen Fleming, Padma Kaul, Joel G Ray, Baiju R Shah, Lorraine L Lipscombe","doi":"10.1186/s41512-021-00095-6","DOIUrl":"10.1186/s41512-021-00095-6","url":null,"abstract":"<p><strong>Background: </strong>Pregnancy offers a unique opportunity to identify women at higher future risk of type 2 diabetes mellitus (DM). In pregnancy, a woman has greater engagement with the healthcare system, and certain conditions are more apt to manifest, such as gestational DM (GDM) that are important markers for future DM risk. This study protocol describes the development and validation of a risk prediction model (RPM) for estimating a woman's 5-year risk of developing type 2 DM after pregnancy.</p><p><strong>Methods: </strong>Data will be obtained from existing Ontario population-based administrative datasets. The derivation cohort will consist of all women who gave birth in Ontario, Canada between April 2006 and March 2014. Pre-specified predictors will include socio-demographic factors (age at delivery, ethnicity), maternal clinical factors (e.g., body mass index), pregnancy-related events (gestational DM, hypertensive disorders of pregnancy), and newborn factors (birthweight percentile). Incident type 2 DM will be identified by linkage to the Ontario Diabetes Database. Weibull accelerated failure time models will be developed to predict 5-year risk of type 2 DM. Measures of predictive accuracy (Nagelkerke's R²), discrimination (C-statistics), and calibration plots will be generated. Internal validation will be conducted using a bootstrapping approach in 500 samples with replacement, and an optimism-corrected C-statistic will be calculated. External validation of the RPM will be conducted by applying the model in a large population-based pregnancy cohort in Alberta, and estimating the above measures of model performance. The model will be re-calibrated by adjusting baseline hazards and coefficients where appropriate.</p><p><strong>Discussion: </strong>The derived RPM may help identify women at high risk of developing DM in a 5-year period after pregnancy, thus facilitate lifestyle changes for women at higher risk, as well as more frequent screening for type 2 DM after pregnancy.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7938478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25451708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid community point-of-care testing for COVID-19 (RAPTOR-C19): protocol for a platform diagnostic study.","authors":"Brian D Nicholson, Gail Hayward, Philip J Turner, Joseph J Lee, Alexandra Deeks, Mary Logan, Abigail Moore, Anna Seeley, Thomas Fanshawe, Jason Oke, Constantinos Koshiaris, James P Sheppard, Uy Hoang, Vaishnavi Parimalanathan, George Edwards, Harshana Liyange, Julian Sherlock, Rachel Byford, Maria Zambon, Joanna Ellis, Jamie Lopez Bernal, Gayatri Amirthalingam, Ezra Linley, Ray Borrow, Gary Howsam, Sophie Baines, Filipa Ferreira, Simon de Lusignan, Rafael Perera, F D Richard Hobbs","doi":"10.1186/s41512-021-00093-8","DOIUrl":"10.1186/s41512-021-00093-8","url":null,"abstract":"<p><strong>Background: </strong>The aim of RApid community Point-of-care Testing fOR COVID-19 (RAPTOR-C19) is to assess the diagnostic accuracy of multiple current and emerging point-of-care tests (POCTs) for active and past SARS-CoV2 infection in the community setting. RAPTOR-C19 will provide the community testbed to the COVID-19 National DiagnOstic Research and Evaluation Platform (CONDOR).</p><p><strong>Methods: </strong>RAPTOR-C19 incorporates a series of prospective observational parallel diagnostic accuracy studies of SARS-CoV2 POCTs against laboratory and composite reference standards in patients with suspected current or past SARS-CoV2 infection attending community settings. Adults and children with suspected current SARS-CoV2 infection who are having an oropharyngeal/nasopharyngeal (OP/NP) swab for laboratory SARS-CoV2 reverse transcriptase Digital/Real-Time Polymerase Chain Reaction (d/rRT-PCR) as part of clinical care or community-based testing will be invited to participate. Adults (≥ 16 years) with suspected past symptomatic infection will also be recruited. Asymptomatic individuals will not be eligible. At the baseline visit, all participants will be asked to submit samples for at least one candidate point-of-care test (POCT) being evaluated (index test/s) as well as an OP/NP swab for laboratory SARS-CoV2 RT-PCR performed by Public Health England (PHE) (reference standard for current infection). Adults will also be asked for a blood sample for laboratory SARS-CoV-2 antibody testing by PHE (reference standard for past infection), where feasible adults will be invited to attend a second visit at 28 days for repeat antibody testing. Additional study data (e.g. demographics, symptoms, observations, household contacts) will be captured electronically. Sensitivity, specificity, positive, and negative predictive values for each POCT will be calculated with exact 95% confidence intervals when compared to the reference standard. POCTs will also be compared to composite reference standards constructed using paired antibody test results, patient reported outcomes, linked electronic health records for outcomes related to COVID-19 such as hospitalisation or death, and other test results.</p><p><strong>Discussion: </strong>High-performing POCTs for community use could be transformational. Real-time results could lead to personal and public health impacts such as reducing onward household transmission of SARS-CoV2 infection, improving surveillance of health and social care staff, contributing to accurate prevalence estimates, and understanding of SARS-CoV2 transmission dynamics in the population. In contrast, poorly performing POCTs could have negative effects, so it is necessary to undertake community-based diagnostic accuracy evaluations before rolling these out.</p><p><strong>Trial registration: </strong>ISRCTN, ISRCTN14226970.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7868893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25345540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scoping review of causal methods enabling predictions under hypothetical interventions.","authors":"Lijing Lin, Matthew Sperrin, David A Jenkins, Glen P Martin, Niels Peek","doi":"10.1186/s41512-021-00092-9","DOIUrl":"10.1186/s41512-021-00092-9","url":null,"abstract":"<p><strong>Background: </strong>The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications, this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions.</p><p><strong>Aims: </strong>We aimed to identify published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and potential pitfalls and challenges with using the method. Finally, we aimed to highlight unresolved methodological challenges.</p><p><strong>Methods: </strong>We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used for predictions under hypothetical interventions. We included both methodologies proposed in statistical/machine learning literature and methodologies used in applied studies.</p><p><strong>Results: </strong>We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full-text screening, of which 13 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation.</p><p><strong>Conclusions: </strong>There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models: (1) enriching prediction models derived from observational studies with estimated causal effects from clinical trials and meta-analyses and (2) estimating prediction models and causal effects directly from observational data. These methods require extending to dynamic treatment regimes, and consideration of multiple interventions to operationalise a clinical decision support system. Techniques for validating 'causal prediction models' are still in their infancy.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"5 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10291708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation.","authors":"Jack Wilkinson,&nbsp;Andy Vail,&nbsp;Stephen A Roberts","doi":"10.1186/s41512-020-00091-2","DOIUrl":"https://doi.org/10.1186/s41512-020-00091-2","url":null,"abstract":"<p><p>In vitro fertilisation (IVF) comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patient's uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information about the reasons for success or failure. As such, the ability to predict not only the overall outcome of the cycle, but also the stage-specific responses, can be useful. This could be done by developing separate models for each response variable, but recent work has suggested that it may be advantageous to use a multivariate approach to model all outcomes simultaneously. Here, joint analysis of the sequential responses is complicated by mixed outcome types defined at two levels (patient and embryo). A further consideration is whether and how to incorporate information about the response at each stage in models for subsequent stages. We develop a case study using routinely collected data from a large reproductive medicine unit in order to investigate the feasibility and potential utility of multivariate prediction in IVF. We consider two possible scenarios. In the first, stage-specific responses are to be predicted prior to treatment commencement. In the second, responses are predicted dynamically, using the outcomes of previous stages as predictors. In both scenarios, we fail to observe benefits of joint modelling approaches compared to fitting separate regression models for each response variable.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s41512-020-00091-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38840971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?","authors":"David A Jenkins, Glen P Martin, Matthew Sperrin, Richard D Riley, Thomas P A Debray, Gary S Collins, Niels Peek","doi":"10.1186/s41512-020-00090-3","DOIUrl":"10.1186/s41512-020-00090-3","url":null,"abstract":"<p><p>Clinical prediction models (CPMs) have become fundamental for risk stratification across healthcare. The CPM pipeline (development, validation, deployment, and impact assessment) is commonly viewed as a one-time activity, with model updating rarely considered and done in a somewhat ad hoc manner. This fails to address the fact that the performance of a CPM worsens over time as natural changes in populations and care pathways occur. CPMs need constant surveillance to maintain adequate predictive performance. Rather than reactively updating a developed CPM once evidence of deteriorated performance accumulates, it is possible to proactively adapt CPMs whenever new data becomes available. Approaches for validation then need to be changed accordingly, making validation a continuous rather than a discrete effort. As such, \"living\" (dynamic) CPMs represent a paradigm shift, where the analytical methods dynamically generate updated versions of a model through time; one then needs to validate the system rather than each subsequent model revision.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2021-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38807699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}