Diagnostic and prognostic research最新文献

{"title":"Graphical calibration curves and the integrated calibration index (ICI) for competing risk models.","authors":"Peter C Austin,&nbsp;Hein Putter,&nbsp;Daniele Giardiello,&nbsp;David van Klaveren","doi":"10.1186/s41512-021-00114-6","DOIUrl":"https://doi.org/10.1186/s41512-021-00114-6","url":null,"abstract":"<p><strong>Background: </strong>Assessing calibration-the agreement between estimated risk and observed proportions-is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention.</p><p><strong>Methods: </strong>We propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of the cause-specific outcome of interest on the predicted outcome risk of the model whose calibration we want to assess. We provide modifications of the integrated calibration index (ICI), of E50 and of E90, which are numerical calibration metrics, for use with competing risk data. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and when the model was mis-specified and when the incidence of the cause-specific outcome differed between the derivation and validation samples. We illustrated the usefulness of calibration curves and the numerical calibration metrics by comparing the calibration of a Fine-Gray subdistribution hazards regression model with that of random survival forests for predicting cardiovascular mortality in patients hospitalized with heart failure.</p><p><strong>Results: </strong>The simulations indicated that the method for constructing graphical calibration curves and the associated calibration metrics performed as desired. We also demonstrated that the numerical calibration metrics can be used as optimization criteria when tuning machine learning methods for competing risk outcomes.</p><p><strong>Conclusions: </strong>The calibration curves and numeric calibration metrics permit a comprehensive comparison of the calibration of different competing risk models.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39828527","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":"Performance of binary prediction models in high-correlation low-dimensional settings: a comparison of methods.","authors":"Artuur M Leeuwenberg,&nbsp;Maarten van Smeden,&nbsp;Johannes A Langendijk,&nbsp;Arjen van der Schaaf,&nbsp;Murielle E Mauer,&nbsp;Karel G M Moons,&nbsp;Johannes B Reitsma,&nbsp;Ewoud Schuit","doi":"10.1186/s41512-021-00115-5","DOIUrl":"https://doi.org/10.1186/s41512-021-00115-5","url":null,"abstract":"<p><strong>Background: </strong>Clinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate.</p><p><strong>Methods: </strong>We compare different methods to address collinearity, including shrinkage, dimensionality reduction, and constrained optimization. The effectiveness of these methods is illustrated via simulations.</p><p><strong>Results: </strong>In the conducted simulations, no effect of collinearity was observed on predictive outcomes (AUC, R², Intercept, Slope) across methods. However, a negative effect of collinearity on the stability of predictor selection was found, affecting all compared methods, but in particular methods that perform strong predictor selection (e.g., Lasso). Methods for which the included set of predictors remained most stable under increased collinearity were Ridge, PCLR, LAELR, and Dropout.</p><p><strong>Conclusions: </strong>Based on the results, we would recommend refraining from data-driven predictor selection approaches in the presence of high collinearity, because of the increased instability of predictor selection, even in relatively high events-per-variable settings. The selection of certain predictors over others may disproportionally give the impression that included predictors have a stronger association with the outcome than excluded predictors.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39900592","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":"Urgent care study of the LumiraDx SARS-CoV-2 Ag Test for rapid diagnosis of COVID-19.","authors":"Jared Gresh, Harold Kisner, Brian DuChateau","doi":"10.1186/s41512-021-00113-7","DOIUrl":"10.1186/s41512-021-00113-7","url":null,"abstract":"<p><strong>Background: </strong>Testing individuals suspected of severe acute respiratory syndrome-like coronavirus 2 (SARS-CoV-2) infection is essential to reduce the spread of disease. The purpose of this retrospective study was to determine the false negativity rate of the LumiraDx SARS-CoV-2 Ag Test when utilized for testing individuals suspected of SARS-CoV-2 infection.</p><p><strong>Methods: </strong>Concurrent swab samples were collected from patients suspected of SARS-CoV-2 infection by their healthcare provider within two different urgent care centers located in Easton, MA, USA and East Bridgewater, MA, USA. One swab was tested using the LumiraDx SARS-CoV-2 Ag Test. Negative results in patients considered at moderate to high risk of SARS-CoV-2 infection were confirmed at a regional reference laboratory by polymerase chain reaction (PCR) using the additional swab sample. The data included in this study was collected retrospectively as an analysis of routine clinical practice.</p><p><strong>Results: </strong>From October 19, 2020 to January 3, 2021, a total of 2241 tests were performed using the LumiraDx SARS-CoV-2 Ag Test, with 549 (24.5%) testing positive and 1692 (75.5%) testing negative. A subset (800) of the samples rendering a negative LumiraDx SARS-CoV-2 Ag Test was also tested using a PCR-based test for SARS-CoV-2. Of this subset, 770 (96.3%) tested negative, and 30 (3.8%) tested positive. Negative results obtained with the LumiraDx SARS-CoV-2 Ag test demonstrated 96.3% agreement with PCR-based tests (CI 95%, 94.7-97.4%). A cycle threshold (C_T) was available for 17 of the 30 specimens that yielded discordant results, with an average C_T value of 31.2, an SD of 3.0, and a range of 25.2-36.3. C_T was > 30.0 in 11/17 specimens (64.7%).</p><p><strong>Conclusions: </strong>This study demonstrates that the LumiraDx SARS-CoV-2 Ag Test had a low false-negative rate of 3.8% when used in a community-based setting.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"24"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39873693","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":"Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia.","authors":"Orouba Almilaji, Gwilym Webb, Alec Maynard, Thomas P Chapman, Brian S F Shine, Antony J Ellis, John Hebden, Sharon Docherty, Elizabeth J Williams, Jonathon Snook","doi":"10.1186/s41512-021-00112-8","DOIUrl":"10.1186/s41512-021-00112-8","url":null,"abstract":"<p><strong>Background: </strong>Using two large datasets from Dorset, we previously reported an internally validated multivariable risk model for predicting the risk of GI malignancy in IDA-the IDIOM score. The aim of this retrospective observational study was to validate the IDIOM model using two independent external datasets.</p><p><strong>Methods: </strong>The external validation datasets were collected, in a secondary care setting, by different investigators from cohorts in Oxford and Sheffield derived under different circumstances, comprising 1117 and 474 patients with confirmed IDA respectively. The data were anonymised prior to analysis. The predictive performance of the original model was evaluated by estimating measures of calibration, discrimination and clinical utility using the validation datasets.</p><p><strong>Results: </strong>The discrimination of the original model using the external validation data was 70% (95% CI 65, 75) for the Oxford dataset and 70% (95% CI 61, 79) for the Sheffield dataset. The analysis of mean, weak, flexible and across the risk groups' calibration showed no tendency for under or over-estimated risks in the combined validation data. Decision curve analysis demonstrated the clinical value of the IDIOM model with a net benefit that is higher than 'investigate all' and 'investigate no-one' strategies up to a threshold of 18% in the combined validation data, using a risk cut-off of around 1.2% to categorise patients into the very low risk group showed that none of the patients stratified in this risk group proved to have GI cancer on investigation in the validation datasets.</p><p><strong>Conclusion: </strong>This external validation exercise has shown promising results for the IDIOM model in predicting the risk of underlying GI malignancy in independent IDA datasets collected in different clinical settings.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"23"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39601578","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":"Development and validation of ester impregnated pH strips for locating nasogastric feeding tubes in the stomach-a multicentre prospective diagnostic performance study.","authors":"Melody Ni, Mina E Adam, Fatima Akbar, Jeremy R Huddy, Simone Borsci, Peter Buckle, Francesca Rubulotta, Reuben Carr, Ian Fotheringham, Claire Wilson, Matthew Tsang, Susan Harding, Nichola White, George B Hanna","doi":"10.1186/s41512-021-00111-9","DOIUrl":"10.1186/s41512-021-00111-9","url":null,"abstract":"<p><strong>Background: </strong>NG (nasogastric) tubes are used worldwide as a means to provide enteral nutrition. Testing the pH of tube aspirates prior to feeding is commonly used to verify tube location before feeding or medication. A pH at or lower than 5.5 was taken as evidence for stomach intubation. However, the existing standard pH strips lack sensitivity, especially in patients receiving feeding and antacids medication. We developed and validated a first-generation ester-impregnated pH strip test to improve the accuracy towards gastric placements in adult population receiving routine NG-tube feeding. The sensitivity was improved by its augmentation with the action of human gastric lipase (HGL), an enzyme specific to the stomach.</p><p><strong>Methods: </strong>We carried out a multi-centred, prospective, two-gate diagnostic accuracy study on patients who require routine NG-tube feeding in 10 NHS hospitals comparing the sensitivity of the novel pH strip to the standard pH test, using either chest X-rays or, in its absence, clinical observation of the absence of adverse events as the reference standard. We also tested the novel pH strips in lung aspirates from patients undergoing oesophageal cancer surgeries using visual inspection as the reference standard. We simulated health economics using a decision analytic model and carried out adoption studies to understand its route to commercialisation. The primary end point is the sensitivity of novel and standard pH tests at the recommended pH cut-off of 5.5.</p><p><strong>Results: </strong>A total of 6400 ester-impregnated pH strips were prepared based on an ISO13485 quality management system. A total of 376 gastric samples were collected from adult patients in 10 NHS hospitals who were receiving routine NG-tube feeding. The sensitivities of the standard and novel pH tests were respectively 49.2% (95% CI 44.1‑54.3%) and 70.2% (95% CI 65.6‑74.8%) under pH cut-off of 5.5 and the novel test has a lung specificity of 89.5% (95% CI 79.6%, 99.4%). Our simulation showed that using the novel test can potentially save 132 unnecessary chest X-rays per check per every 1000 eligible patients, or direct savings of £4034 to the NHS.</p><p><strong>Conclusions: </strong>The novel pH test correctly identified significantly more patients with tubes located inside the stomach compared to the standard pH test used widely by the NHS.</p><p><strong>Trial registration: </strong>http://www.isrctn.com/ISRCTN11170249 , Registered 21 June 2017-retrospectively registered.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"22"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39834650","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":"Knowledge translation of prediction rules: methods to help health professionals understand their trade-offs.","authors":"K Hemming,&nbsp;M Taljaard","doi":"10.1186/s41512-021-00109-3","DOIUrl":"https://doi.org/10.1186/s41512-021-00109-3","url":null,"abstract":"<p><p>Clinical prediction models are developed with the ultimate aim of improving patient outcomes, and are often turned into prediction rules (e.g. classifying people as low/high risk using cut-points of predicted risk) at some point during the development stage. Prediction rules often have reasonable ability to either rule-in or rule-out disease (or another event), but rarely both. When a prediction model is intended to be used as a prediction rule, conveying its performance using the C-statistic, the most commonly reported model performance measure, does not provide information on the magnitude of the trade-offs. Yet, it is important that these trade-offs are clear, for example, to health professionals who might implement the prediction rule. This can be viewed as a form of knowledge translation. When communicating information on trade-offs to patients and the public there is a large body of evidence that indicates natural frequencies are most easily understood, and one particularly well-received way of depicting the natural frequency information is to use population diagrams. There is also evidence that health professionals benefit from information presented in this way.Here we illustrate how the implications of the trade-offs associated with prediction rules can be more readily appreciated when using natural frequencies. We recommend that the reporting of the performance of prediction rules should (1) present information using natural frequencies across a range of cut-points to inform the choice of plausible cut-points and (2) when the prediction rule is recommended for clinical use at a particular cut-point the implications of the trade-offs are communicated using population diagrams. Using two existing prediction rules, we illustrate how these methods offer a means of effectively and transparently communicating essential information about trade-offs associated with prediction rules.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"21"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666169/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39592971","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":"Comparison of dynamic updating strategies for clinical prediction models.","authors":"Erin M Schnellinger, Wei Yang, Stephen E Kimmel","doi":"10.1186/s41512-021-00110-w","DOIUrl":"10.1186/s41512-021-00110-w","url":null,"abstract":"<p><strong>Background: </strong>Prediction models inform many medical decisions, but their performance often deteriorates over time. Several discrete-time update strategies have been proposed in the literature, including model recalibration and revision. However, these strategies have not been compared in the dynamic updating setting.</p><p><strong>Methods: </strong>We used post-lung transplant survival data during 2010-2015 and compared the Brier Score (BS), discrimination, and calibration of the following update strategies: (1) never update, (2) update using the closed testing procedure proposed in the literature, (3) always recalibrate the intercept, (4) always recalibrate the intercept and slope, and (5) always refit/revise the model. In each case, we explored update intervals of every 1, 2, 4, and 8 quarters. We also examined how the performance of the update strategies changed as the amount of old data included in the update (i.e., sliding window length) increased.</p><p><strong>Results: </strong>All methods of updating the model led to meaningful improvement in BS relative to never updating. More frequent updating yielded better BS, discrimination, and calibration, regardless of update strategy. Recalibration strategies led to more consistent improvements and less variability over time compared to the other updating strategies. Using longer sliding windows did not substantially impact the recalibration strategies, but did improve the discrimination and calibration of the closed testing procedure and model revision strategies.</p><p><strong>Conclusions: </strong>Model updating leads to improved BS, with more frequent updating performing better than less frequent updating. Model recalibration strategies appeared to be the least sensitive to the update interval and sliding window length.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"20"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39692399","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 prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol.","authors":"Mariella Gregorich,&nbsp;Andreas Heinzel,&nbsp;Michael Kammer,&nbsp;Heike Meiselbach,&nbsp;Carsten Böger,&nbsp;Kai-Uwe Eckardt,&nbsp;Gert Mayer,&nbsp;Georg Heinze,&nbsp;Rainer Oberbauer","doi":"10.1186/s41512-021-00107-5","DOIUrl":"https://doi.org/10.1186/s41512-021-00107-5","url":null,"abstract":"<p><strong>Background: </strong>Chronic kidney disease (CKD) is a well-established complication in people with diabetes mellitus. Roughly one quarter of prevalent patients with diabetes exhibit a CKD stage of 3 or higher and the individual course of progression is highly variable. Therefore, there is a clear need to identify patients at high risk for fast progression and the implementation of preventative strategies. Existing prediction models of renal function decline, however, aim to assess the risk by artificially grouped patients prior to model building into risk strata defined by the categorization of the least-squares slope through the longitudinally fluctuating eGFR values, resulting in a loss of predictive precision and accuracy.</p><p><strong>Methods: </strong>This study protocol describes the development and validation of a prediction model for the longitudinal progression of renal function decline in Caucasian patients with type 2 diabetes mellitus (DM2). For development and internal-external validation, two prospective multicenter observational studies will be used (PROVALID and GCKD). The estimated glomerular filtration rate (eGFR) obtained at baseline and at all planned follow-up visits will be the longitudinal outcome. Demographics, clinical information and laboratory measurements available at a baseline visit will be used as predictors in addition to random country-specific intercepts to account for the clustered data. A multivariable mixed-effects model including the main effects of the clinical variables and their interactions with time will be fitted. In application, this model can be used to obtain personalized predictions of an eGFR trajectory conditional on baseline eGFR values. The final model will then undergo external validation using a third prospective cohort (DIACORE). The final prediction model will be made publicly available through the implementation of an R shiny web application.</p><p><strong>Discussion: </strong>Our proposed state-of-the-art methodology will be developed using multiple multicentre study cohorts of people with DM2 in various CKD stages at baseline, who have received modern therapeutic treatment strategies of diabetic kidney disease in contrast to previous models. Hence, we anticipate that the multivariable prediction model will aid as an additional informative tool to determine the patient-specific progression of renal function and provide a useful guide to early on identify individuals with DM2 at high risk for rapid progression.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8600780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39633267","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":"The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene-a protocol.","authors":"Jamie Miles,&nbsp;Richard Jacques,&nbsp;Janette Turner,&nbsp;Suzanne Mason","doi":"10.1186/s41512-021-00108-4","DOIUrl":"https://doi.org/10.1186/s41512-021-00108-4","url":null,"abstract":"<p><strong>Background: </strong>Demand for both the ambulance service and the emergency department (ED) is rising every year and when this demand is excessive in both systems, ambulance crews queue at the ED waiting to hand patients over. Some transported ambulance patients are 'low-acuity' and do not require the treatment of the ED. However, paramedics can find it challenging to identify these patients accurately. Decision support tools have been developed using expert opinion to help identify these low acuity patients but have failed to show a benefit beyond regular decision-making. Predictive algorithms may be able to build accurate models, which can be used in the field to support the decision not to take a low-acuity patient to an ED.</p><p><strong>Methods and analysis: </strong>All patients in Yorkshire who were transported to the ED by ambulance between July 2019 and February 2020 will be included. Ambulance electronic patient care record (ePCR) clinical data will be used as candidate predictors for the model. These will then be linked to the corresponding ED record, which holds the outcome of a 'non-urgent attendance'. The estimated sample size is 52,958, with 4767 events and an EPP of 7.48. An XGBoost algorithm will be used for model development. Initially, a model will be derived using all the data and the apparent performance will be assessed. Then internal-external validation will use non-random nested cross-validation (CV) with test sets held out for each ED (spatial validation). After all models are created, a random-effects meta-analysis will be undertaken. This will pool performance measures such as goodness of fit, discrimination and calibration. It will also generate a prediction interval and measure heterogeneity between clusters. The performance of the full model will be updated with the pooled results.</p><p><strong>Discussion: </strong>Creating a risk prediction model in this area will lead to further development of a clinical decision support tool that ensures every ambulance patient can get to the right place of care, first time. If this study is successful, it could help paramedics evaluate the benefit of transporting a patient to the ED before they leave the scene. It could also reduce congestion in the urgent and emergency care system.</p><p><strong>Trial registration: </strong>This study was retrospectively registered with the ISRCTN: 12121281.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39601419","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":"Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis.","authors":"Konstantina Chalkou,&nbsp;Ewout Steyerberg,&nbsp;Patrick Bossuyt,&nbsp;Suvitha Subramaniam,&nbsp;Pascal Benkert,&nbsp;Jens Kuhle,&nbsp;Giulio Disanto,&nbsp;Ludwig Kappos,&nbsp;Chiara Zecca,&nbsp;Matthias Egger,&nbsp;Georgia Salanti","doi":"10.1186/s41512-021-00106-6","DOIUrl":"10.1186/s41512-021-00106-6","url":null,"abstract":"<p><strong>Background: </strong>Prognosis for the occurrence of relapses in individuals with relapsing-remitting multiple sclerosis (RRMS), the most common subtype of multiple sclerosis (MS), could support individualized decisions and disease management and could be helpful for efficiently selecting patients for future randomized clinical trials. There are only three previously published prognostic models on this, all of them with important methodological shortcomings.</p><p><strong>Objectives: </strong>We aim to present the development, internal validation, and evaluation of the potential clinical benefit of a prognostic model for relapses for individuals with RRMS using real-world data.</p><p><strong>Methods: </strong>We followed seven steps to develop and validate the prognostic model: (1) selection of prognostic factors via a review of the literature, (2) development of a generalized linear mixed-effects model in a Bayesian framework, (3) examination of sample size efficiency, (4) shrinkage of the coefficients, (5) dealing with missing data using multiple imputations, (6) internal validation of the model. Finally, we evaluated the potential clinical benefit of the developed prognostic model using decision curve analysis. For the development and the validation of our prognostic model, we followed the TRIPOD statement.</p><p><strong>Results: </strong>We selected eight baseline prognostic factors: age, sex, prior MS treatment, months since last relapse, disease duration, number of prior relapses, expanded disability status scale (EDSS) score, and number of gadolinium-enhanced lesions. We also developed a web application that calculates an individual's probability of relapsing within the next 2 years. The optimism-corrected c-statistic is 0.65 and the optimism-corrected calibration slope is 0.92. For threshold probabilities between 15 and 30%, the \"treat based on the prognostic model\" strategy leads to the highest net benefit and hence is considered the most clinically useful strategy.</p><p><strong>Conclusions: </strong>The prognostic model we developed offers several advantages in comparison to previously published prognostic models on RRMS. Importantly, we assessed the potential clinical benefit to better quantify the clinical impact of the model. Our web application, once externally validated in the future, could be used by patients and doctors to calculate the individualized probability of relapsing within 2 years and to inform the management of their disease.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8549310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39563779","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}