Diagnostic and prognostic research最新文献

{"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}

{"title":"Advanced cardiovascular risk prediction in the emergency department: updating a clinical prediction model - a large database study protocol.","authors":"Charles Reynard,&nbsp;Glen P Martin,&nbsp;Evangelos Kontopantelis,&nbsp;David A Jenkins,&nbsp;Anthony Heagerty,&nbsp;Brian McMillan,&nbsp;Anisa Jafar,&nbsp;Rajendar Garlapati,&nbsp;Richard Body","doi":"10.1186/s41512-021-00105-7","DOIUrl":"https://doi.org/10.1186/s41512-021-00105-7","url":null,"abstract":"<p><strong>Background: </strong>Patients presenting with chest pain represent a large proportion of attendances to emergency departments. In these patients clinicians often consider the diagnosis of acute myocardial infarction (AMI), the timely recognition and treatment of which is clinically important. Clinical prediction models (CPMs) have been used to enhance early diagnosis of AMI. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid is currently in clinical use across Greater Manchester. CPMs have been shown to deteriorate over time through calibration drift. We aim to assess potential calibration drift with T-MACS and compare methods for updating the model.</p><p><strong>Methods: </strong>We will use routinely collected electronic data from patients who were treated using TMACS at two large NHS hospitals. This is estimated to include approximately 14,000 patient episodes spanning June 2016 to October 2020. The primary outcome of acute myocardial infarction will be sourced from NHS Digital's admitted patient care dataset. We will assess the calibration drift of the existing model and the benefit of updating the CPM by model recalibration, model extension and dynamic updating. These models will be validated by bootstrapping and one step ahead prequential testing. We will evaluate predictive performance using calibrations plots and c-statistics. We will also examine the reclassification of predicted probability with the updated TMACS model.</p><p><strong>Discussion: </strong>CPMs are widely used in modern medicine, but are vulnerable to deteriorating calibration over time. Ongoing refinement using routinely collected electronic data will inevitably be more efficient than deriving and validating new models. In this analysis we will seek to exemplify methods for updating CPMs to protect the initial investment of time and effort. If successful, the updating methods could be used to continually refine the algorithm used within TMACS, maintaining or even improving predictive performance over time.</p><p><strong>Trial registration: </strong>ISRCTN number: ISRCTN41008456.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8499458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39497261","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":"Management of superficial venous thrombosis based on individual risk profiles: protocol for the development and validation of three prognostic prediction models in large primary care cohorts.","authors":"F S van Royen,&nbsp;M van Smeden,&nbsp;K G M Moons,&nbsp;F H Rutten,&nbsp;G J Geersing","doi":"10.1186/s41512-021-00104-8","DOIUrl":"https://doi.org/10.1186/s41512-021-00104-8","url":null,"abstract":"<p><strong>Background: </strong>Superficial venous thrombosis (SVT) is considered a benign thrombotic condition in most patients. However, it also can cause serious complications, such as clot progression to deep venous thrombosis (DVT) and pulmonary embolism (PE). Although most SVT patients are encountered in primary healthcare, studies on SVT nearly all were focused on patients seen in the hospital setting. This paper describes the protocol of the development and external validation of three prognostic prediction models for relevant clinical outcomes in SVT patients seen in primary care: (i) prolonged (painful) symptoms within 14 days since SVT diagnosis, (ii) for clot progression to DVT or PE within 45 days and (iii) for clot recurrence within 12 months.</p><p><strong>Methods: </strong>Data will be used from four primary care routine healthcare registries from both the Netherlands and the UK; one UK registry will be used for the development of the prediction models and the remaining three will be used as external validation cohorts. The study population will consist of patients ≥18 years with a diagnosis of SVT. Selection of SVT cases will be based on a combination of ICPC/READ/Snowmed coding and free text clinical symptoms. Predictors considered are sex, age, body mass index, clinical SVT characteristics, and co-morbidities including (history of any) cardiovascular disease, diabetes, autoimmune disease, malignancy, thrombophilia, pregnancy or puerperium and presence of varicose veins. The prediction models will be developed using multivariable logistic regression analysis techniques for models i and ii, and for model iii, a Cox proportional hazards model will be used. They will be validated by internal-external cross-validation as well as external validation.</p><p><strong>Discussion: </strong>There are currently no prediction models available for predicting the risk of serious complications for SVT patients presenting in primary care settings. We aim to develop and validate new prediction models that should help identify patients at highest risk for complications and to support clinical decision making for this understudied thrombo-embolic disorder. Challenges that we anticipate to encounter are mostly related to performing research in large, routine healthcare databases, such as patient selection, endpoint classification, data harmonisation, missing data and avoiding (predictor) measurement heterogeneity.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39320778","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 development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example.","authors":"Kara-Louise Royle, David A Cairns","doi":"10.1186/s41512-021-00103-9","DOIUrl":"10.1186/s41512-021-00103-9","url":null,"abstract":"<p><strong>Background: </strong>The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology.</p><p><strong>Methods: </strong>Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin's rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin's rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin's rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin's rules.</p><p><strong>Results: </strong>The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716-0.964) in the training dataset and 0.654 (95% CI 0.497-0.811) in the test dataset and the corrected D-Statistic was 0.801.</p><p><strong>Conclusion: </strong>The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model.</p><p><strong>Trial registration: </strong>Both trials were registered; Myeloma IX- ISRCTN68454111 , registered 21 September 2000. Myeloma XI- ISRCTN49407852 , registered 24 June 2009.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8335879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39280878","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 relationship between the incremental values of area under the ROC curve and of area under the precision-recall curve.","authors":"Qian M Zhou,&nbsp;Lu Zhe,&nbsp;Russell J Brooke,&nbsp;Melissa M Hudson,&nbsp;Yan Yuan","doi":"10.1186/s41512-021-00102-w","DOIUrl":"https://doi.org/10.1186/s41512-021-00102-w","url":null,"abstract":"<p><strong>Background: </strong>Incremental value (IncV) evaluates the performance change between an existing risk model and a new model. Different IncV metrics do not always agree with each other. For example, compared with a prescribed-dose model, an ovarian-dose model for predicting acute ovarian failure has a slightly lower area under the receiver operating characteristic curve (AUC) but increases the area under the precision-recall curve (AP) by 48%. This phenomenon of disagreement is not uncommon, and can create confusion when assessing whether the added information improves the model prediction accuracy.</p><p><strong>Methods: </strong>In this article, we examine the analytical connections and differences between the AUC IncV (ΔAUC) and AP IncV (ΔAP). We also compare the true values of these two IncV metrics in a numerical study. Additionally, as both are semi-proper scoring rules, we compare them with a strictly proper scoring rule: the IncV of the scaled Brier score (ΔsBrS) in the numerical study.</p><p><strong>Results: </strong>We demonstrate that ΔAUC and ΔAP are both weighted averages of the changes (from the existing model to the new one) in separating the risk score distributions between events and non-events. However, ΔAP assigns heavier weights to the changes in higher-risk regions, whereas ΔAUC weights the changes equally. Due to this difference, the two IncV metrics can disagree, and the numerical study shows that their disagreement becomes more pronounced as the event rate decreases. In the numerical study, we also find that ΔAP has a wide range, from negative to positive, but the range of ΔAUC is much smaller. In addition, ΔAP and ΔsBrS are highly consistent, but ΔAUC is negatively correlated with ΔsBrS and ΔAP when the event rate is low.</p><p><strong>Conclusions: </strong>ΔAUC treats the wins and losses of a new risk model equally across different risk regions. When neither the existing or new model is the true model, this equality could attenuate a superior performance of the new model for a sub-region. In contrast, ΔAP accentuates the change in the prediction accuracy for higher-risk regions.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s41512-021-00102-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39184419","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 development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study.","authors":"Andrew S Moriarty,&nbsp;Lewis W Paton,&nbsp;Kym I E Snell,&nbsp;Richard D Riley,&nbsp;Joshua E J Buckman,&nbsp;Simon Gilbody,&nbsp;Carolyn A Chew-Graham,&nbsp;Shehzad Ali,&nbsp;Stephen Pilling,&nbsp;Nick Meader,&nbsp;Bob Phillips,&nbsp;Peter A Coventry,&nbsp;Jaime Delgadillo,&nbsp;David A Richards,&nbsp;Chris Salisbury,&nbsp;Dean McMillan","doi":"10.1186/s41512-021-00101-x","DOIUrl":"10.1186/s41512-021-00101-x","url":null,"abstract":"<p><strong>Background: </strong>Most patients who present with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common (at least 50% of patients treated for depression will relapse after a single episode) and leads to considerable morbidity and decreased quality of life for patients. The majority of patients will relapse within 6 months, and those with a history of relapse are more likely to relapse in the future than those with no such history. GPs see a largely undifferentiated case-mix of patients, and once patients with depression reach remission, there is limited guidance to help GPs stratify patients according to risk of relapse. We aim to develop a prognostic model to predict an individual's risk of relapse within 6-8 months of entering remission. The long-term objective is to inform the clinical management of depression after the acute phase.</p><p><strong>Methods: </strong>We will develop a prognostic model using secondary analysis of individual participant data drawn from seven RCTs and one longitudinal cohort study in primary or community care settings. We will use logistic regression to predict the outcome of relapse of depression within 6-8 months. We plan to include the following established relapse predictors in the model: residual depressive symptoms, number of previous depressive episodes, co-morbid anxiety and severity of index episode. We will use a \"full model\" development approach, including all available predictors. Performance statistics (optimism-adjusted C-statistic, calibration-in-the-large, calibration slope) and calibration plots (with smoothed calibration curves) will be calculated. Generalisability of predictive performance will be assessed through internal-external cross-validation. Clinical utility will be explored through net benefit analysis.</p><p><strong>Discussion: </strong>We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. Assuming the model has sufficient predictive performance, we outline the next steps including independent external validation and further assessment of clinical utility and impact.</p><p><strong>Study registration: </strong>ClinicalTrials.gov ID: NCT04666662.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"5 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s41512-021-00101-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9518162","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":"Purines for Rapid Identification of Stroke Mimics (PRISM): study protocol for a diagnostic accuracy study.","authors":"Lisa Shaw, Sara Graziadio, Clare Lendrem, Nicholas Dale, Gary A Ford, Christine Roffe, Craig J Smith, Philip M White, Christopher I Price","doi":"10.1186/s41512-021-00098-3","DOIUrl":"10.1186/s41512-021-00098-3","url":null,"abstract":"<p><strong>Background: </strong>Rapid treatment of stroke improves outcomes, but accurate early recognition can be challenging. Between 20 and 40% of patients suspected to have stroke by ambulance and emergency department staff later receive a non-stroke 'mimic' diagnosis after stroke specialist investigation. This early diagnostic uncertainty results in displacement of mimic patients from more appropriate services, inappropriate demands on stroke specialist resources and delayed access to specialist therapies for stroke patients. Blood purine concentrations rise rapidly during hypoxic tissue injury, which is a key mechanism of damage during acute stroke but is not typical in mimic conditions. A portable point of care fingerprick test has been developed to measure blood purine concentration which could be used to triage patients experiencing suspected stroke symptoms into those likely to have a non-stroke mimic condition and those likely to have true stroke. This study is evaluating test performance for identification of stroke mimic conditions.</p><p><strong>Methods: </strong>Design: prospective observational cohort study Setting: regional UK ambulance and acute stroke services Participants: a convenience series of two populations will be tested: adults with a label of suspected stroke assigned (and tested) by attending ambulance personnel and adults with a label of suspected stroke assigned at hospital (who have not been tested by ambulance staff).</p><p><strong>Index test: </strong>SMARTChip Purine assay Reference standard tests: expert clinician opinion informed by brain imaging and/or other investigations will assign the following diagnoses which constitute the suspected stroke population: ischaemic stroke, haemorrhagic stroke, TIA and stroke mimic conditions.</p><p><strong>Sample size: </strong>ambulance population (powered for mimic sensitivity) 935 participants; hospital population (powered for mimic specificity) 377 participants.</p><p><strong>Analyses: </strong>area under the receiver operating curve (ROC) and optimal sensitivity, specificity, and negative and positive predictive values for identification of mimic conditions. Optimal threshold for the ambulance population will maximise sensitivity, minimum 80%, and aim to keep specificity above 70%. Optimal threshold for the hospital population will maximise specificity, minimum 80%, and aim to keep sensitivity above 70%.</p><p><strong>Discussion: </strong>The results from this study will determine how accurately the SMARTChip purine assay test can identify stroke mimic conditions within the suspected stroke population. If acceptable performance is confirmed, deployment of the test in ambulances or emergency departments could enable more appropriate direction of patients to stroke or non-stroke services.</p><p><strong>Trial registration: </strong>Registered with ISRCTN (identifier: ISRCTN22323981) on 13/02/2019 http://www.isrctn.com/ISRCTN22323981.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":" ","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s41512-021-00098-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39003884","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}