scholarly journals Prognostic Models for Predicting Remission of Diabetes Following Bariatric Surgery: A Systematic Review and Meta-analysis

2021 ◽  
Author(s):  
Pushpa Singh ◽  
Nicola J Adderley ◽  
Jonathan Hazlehurst ◽  
Malcolm Price ◽  
Abd A Tahrani ◽  
...  
Keyword(s):  
Systematic Review ◽  
Bariatric Surgery ◽  
Prediction Models ◽  
Data Extraction ◽  
External Validation ◽  
Model Development ◽  
Diabetes Remission ◽  
Development Studies ◽  
Cochrane Central Register

<p>Background</p> <p>Remission of type 2 diabetes following bariatric surgery is well established but identifying patients who will go into remission is challenging. </p> <p>Purpose</p> <p>To perform a systematic review of currently available diabetes remission prediction models, compare their performance, and evaluate their applicability in clinical settings.</p> <p>Data sources</p> <p>A comprehensive systematic literature search of MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE and Cochrane Central Register of Controlled Trials was undertaken. The search was restricted to studies published in the last 15 years and in the English language. </p> <p>Study selection and data extraction</p> <p>All studies developing or validating a prediction model for diabetes remission in adults after bariatric surgery were included. The search identified 4165 references of which 38 were included for data extraction. We identified 16 model development and 22 validation studies. </p> <p>Data synthesis</p> <p>Of the 16 model development studies, 11 developed scoring systems and 5 proposed logistic regression models. In model development studies, 10 models showed excellent discrimination with area under curve (AUC) ≥ 0.800. Two of these prediction models, ABCD and DiaRem, were widely externally validated in different populations, a variety of bariatric procedures, and for both short- and long-term diabetes remission. Newer prediction models showed excellent discrimination in test studies, but external validation was limited.</p> <p>Limitations and Conclusions</p> Amongst the prediction models identified, the ABCD and DiaRem models were the most widely validated and showed acceptable to excellent discrimination. More studies validating newer models and focusing on long-term diabetes remission are needed.

scholarly journals Prognostic Models for Predicting Remission of Diabetes Following Bariatric Surgery: A Systematic Review and Meta-analysis

2021 ◽  
Author(s):  
Pushpa Singh ◽  
Nicola J Adderley ◽  
Jonathan Hazlehurst ◽  
Malcolm Price ◽  
Abd A Tahrani ◽  
...  
Keyword(s):  
Systematic Review ◽  
Bariatric Surgery ◽  
Prediction Models ◽  
Data Extraction ◽  
External Validation ◽  
Model Development ◽  
Diabetes Remission ◽  
Development Studies ◽  
Cochrane Central Register

<p>Background</p> <p>Remission of type 2 diabetes following bariatric surgery is well established but identifying patients who will go into remission is challenging. </p> <p>Purpose</p> <p>To perform a systematic review of currently available diabetes remission prediction models, compare their performance, and evaluate their applicability in clinical settings.</p> <p>Data sources</p> <p>A comprehensive systematic literature search of MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE and Cochrane Central Register of Controlled Trials was undertaken. The search was restricted to studies published in the last 15 years and in the English language. </p> <p>Study selection and data extraction</p> <p>All studies developing or validating a prediction model for diabetes remission in adults after bariatric surgery were included. The search identified 4165 references of which 38 were included for data extraction. We identified 16 model development and 22 validation studies. </p> <p>Data synthesis</p> <p>Of the 16 model development studies, 11 developed scoring systems and 5 proposed logistic regression models. In model development studies, 10 models showed excellent discrimination with area under curve (AUC) ≥ 0.800. Two of these prediction models, ABCD and DiaRem, were widely externally validated in different populations, a variety of bariatric procedures, and for both short- and long-term diabetes remission. Newer prediction models showed excellent discrimination in test studies, but external validation was limited.</p> <p>Limitations and Conclusions</p> Amongst the prediction models identified, the ABCD and DiaRem models were the most widely validated and showed acceptable to excellent discrimination. More studies validating newer models and focusing on long-term diabetes remission are needed.

P–419 On prognosis after unexplained recurrent pregnancy losses (RPL); a systematic review and external validation of clinical prediction models

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Youssef
Keyword(s):  
Systematic Review ◽  
Supportive Care ◽  
Prediction Model ◽  
Pregnancy Outcome ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Predictive Performance ◽  
Pregnancy Rates ◽  
Cochrane Library

Abstract Study question Which models that predict pregnancy outcome in couples with unexplained RPL exist and what is the performance of the most used model? Summary answer We identified seven prediction models; none followed the recommended prediction model development steps. Moreover, the most used model showed poor predictive performance. What is known already RPL remains unexplained in 50–75% of couples For these couples, there is no effective treatment option and clinical management rests on supportive care. Essential part of supportive care consists of counselling on the prognosis of subsequent pregnancies. Indeed, multiple prediction models exist, however the quality and validity of these models varies. In addition, the prediction model developed by Brigham et al is the most widely used model, but has never been externally validated. Study design, size, duration We performed a systematic review to identify prediction models for pregnancy outcome after unexplained RPL. In addition we performed an external validation of the Brigham model in a retrospective cohort, consisting of 668 couples with unexplained RPL that visited our RPL clinic between 2004 and 2019. Participants/materials, setting, methods A systematic search was performed in December 2020 in Pubmed, Embase, Web of Science and Cochrane library to identify relevant studies. Eligible studies were selected and assessed according to the TRIPOD) guidelines, covering topics on model performance and validation statement. The performance of predicting live birth in the Brigham model was evaluated through calibration and discrimination, in which the observed pregnancy rates were compared to the predicted pregnancy rates. Main results and the role of chance Seven models were compared and assessed according to the TRIPOD statement. This resulted in two studies of low, three of moderate and two of above average reporting quality. These studies did not follow the recommended steps for model development and did not calculate a sample size. Furthermore, the predictive performance of neither of these models was internally- or externally validated. We performed an external validation of Brigham model. Calibration showed overestimation of the model and too extreme predictions, with a negative calibration intercept of –0.52 (CI 95% –0.68 – –0.36), with a calibration slope of 0.39 (CI 95% 0.07 – 0.71). The discriminative ability of the model was very low with a concordance statistic of 0.55 (CI 95% 0.50 – 0.59). Limitations, reasons for caution None of the studies are specifically named prediction models, therefore models may have been missed in the selection process. The external validation cohort used a retrospective design, in which only the first pregnancy after intake was registered. Follow-up time was not limited, which is important in counselling unexplained RPL couples. Wider implications of the findings: Currently, there are no suitable models that predict on pregnancy outcome after RPL. Moreover, we are in need of a model with several variables such that prognosis is individualized, and factors from both the female as the male to enable a couple specific prognosis. Trial registration number Not applicable

scholarly journals Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e044687
Author(s):  
Lauren S. Peetluk ◽  
Felipe M. Ridolfi ◽  
Peter F. Rebeiro ◽  
Dandan Liu ◽  
Valeria C Rolla ◽  
...  
Keyword(s):  
Systematic Review ◽  
Treatment Outcomes ◽  
Prediction Models ◽  
Assessment Tool ◽  
Data Extraction ◽  
External Validation ◽  
Included Study ◽  
Risk Of Bias ◽  
Pulmonary Tb ◽  
Tb Treatment

ObjectiveTo systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.DesignSystematic review.Data sourcesPubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.Study selection and data extractionStudies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures.Results14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68–0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis.ConclusionsTB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models.Trial registrationThe study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782)

scholarly journals Evaluating Methodological Quality of Prognostic Prediction Models on Patient Reported Outcome Measurements after Total Hip Replacement and Total Knee Replacement Surgery: a systematic review protocol

2021 ◽  
Author(s):  
Wei-Ju Chang ◽  
Justine Naylor ◽  
Pragadesh Natarajan ◽  
Spiro Menounos ◽  
Masiath Monuja ◽  
...  
Keyword(s):  
Systematic Review ◽  
Knee Osteoarthritis ◽  
Prediction Model ◽  
Methodological Quality ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Patient Reported ◽  
Modelling Studies

Abstract Background Prediction models for poor patient-reported surgical outcomes after total hip replacement (THR) and total knee replacement (TKR) may provide a method for improving appropriate surgical care for hip and knee osteoarthritis. There are concerns about methodological issues and the risk of bias of studies producing prediction models. A critical evaluation of the methodological quality of prediction modelling studies in THR and TKR is needed to ensure their clinical usefulness. This systematic review aims to: 1) evaluate and report the quality of risk stratification and prediction modelling studies that predict patient-reported outcomes after THR and TKR; 2) identify areas of methodological deficit and provide recommendations for future research; and 3) synthesise the evidence on prediction models associated with post-operative patient-reported outcomes after THR and TKR surgeries. Methods MEDLINE, EMBASE and CINAHL electronic databases will be searched to identify relevant studies. Title and abstract and full-text screening will be performed by two independent reviewers. We will include: 1) prediction model development studies without external validation; 2) prediction model development studies with external validation of independent data; 3) external model validation studies; and 4) studies updating a previously developed prediction model. Data extraction spreadsheets will be developed based on the CHARMS checklist and TRIPOD statement and piloted on two relevant studies. Study quality and risk of bias will be assessed using the PROBAST tool. Prediction models will be summarised qualitatively. Meta-analyses on the predictive performance of included models will be conducted if appropriate. Discussion This systematic review will evaluate the methodological quality and usefulness of prediction models for poor outcomes after THR or TKR. This information is essential to provide evidence-based healthcare for end-stage hip and knee osteoarthritis. Findings of this review will contribute to the identification of key areas for improvement in conducting prognostic research in this field and facilitate the progress in evidence-based tailored treatments for hip and knee osteoarthritis. Systematic review registration: Submitted to PROSPERO on 30 August 2021.

scholarly journals Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e025579 ◽  
Author(s):  
Mohammad Ziaul Islam Chowdhury ◽  
Fahmida Yeasmin ◽  
Doreen M Rabi ◽  
Paul E Ronksley ◽  
Tanvir C Turin
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Data Extraction ◽  
Meta Analysis ◽  
External Validation ◽  
Predictive Performance ◽  
Eligibility Criteria ◽  
C Statistic ◽  
Increased Risk ◽  
Patients With Diabetes

ObjectiveStroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis.DesignSystematic review and meta-analysis.Data sourcesA detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models.Eligibility criteriaAll studies that developed stroke prediction models in populations with diabetes were included.Data extraction and synthesisTwo reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic.ResultsOur search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68.ConclusionsOverall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models’ predictive ability and further external validation of the existing models in diverse population to improve generalisability.

scholarly journals Metabolomic signatures after bariatric surgery – a systematic review

2021 ◽  
Author(s):  
Matilde Vaz ◽  
Sofia S. Pereira ◽  
Mariana P. Monteiro
Keyword(s):  
Systematic Review ◽  
Bariatric Surgery ◽  
Diabetes Remission ◽  
The Body ◽  
Therapeutic Interventions ◽  
Eligibility Criteria ◽  
Type 2 Diabetes Remission ◽  
Meta Analyses

AbstractMetabolomics emerged as an important tool to gain insights on how the body responds to therapeutic interventions. Bariatric surgery is the most effective treatment for severe obesity and obesity-related co-morbidities. Our aim was to conduct a systematic review of the available data on metabolomics profiles that characterize patients submitted to different bariatric surgery procedures, which could be useful to predict clinical outcomes including weight loss and type 2 diabetes remission. For that, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - PRISMA guidelines were followed. Data from forty-seven original study reports addressing metabolomics profiles induced by bariatric surgery that met eligibility criteria were compiled and summarized. Amino acids, lipids, energy-related and gut microbiota-related were the metabolite classes most influenced by bariatric surgery. Among these, higher pre-operative levels of specific lipids including phospholipids, long-chain fatty acids and bile acids were associated with post-operative T2D remission. As conclusion, metabolite profiling could become a useful tool to predict long term response to different bariatric surgery procedures, allowing more personalized interventions and improved healthcare resources allocation.

scholarly journals Predicting inhospital admission at the emergency department: a systematic review

2021 ◽  
pp. emermed-2020-210902
Author(s):  
Anniek Brink ◽  
Jelmer Alsma ◽  
Lodewijk AAM van Attekum ◽  
Wichor M Bramer ◽  
Robert Zietse ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Data Extraction ◽  
Model Development ◽  
Model Performance ◽  
Area Under The Curve ◽  
Registration Number ◽  
Modelling Studies ◽  
Assess Quality ◽  
The Uk

BackgroundED crowding has potential detrimental consequences for both patient care and staff. Advancing disposition can reduce crowding. This may be achieved by using prediction models for admission. This systematic review aims to present an overview of prediction models for admission at the ED. Furthermore, we aimed to identify the best prediction tool based on its performance, validation, calibration and clinical usability.MethodsWe included observational studies published in Embase.com, Medline Ovid, Cochrane CENTRAL, Web of Science Core Collection or Google scholar, in which admission models were developed or validated in a general medical population in European EDs including the UK. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to assess quality of model development. Model performance was presented as discrimination and calibration. The search was performed on 11 October 2020.ResultsIn total, 18 539 articles were identified. We included 11 studies, describing 16 different models, comprising the development of 9 models and 12 external validations of 11 models. The risk of bias of the development studies was considered low to medium. Discrimination, as represented by the area under the curve ranged from 0.630 to 0.878. Calibration was assessed in seven models and was strong. The best performing models are the models of Lucke et al and Cameron et al. These models combine clinical applicability, by inclusion of readily available parameters, and appropriate discrimination, calibration and validation.ConclusionNone of the models are yet implemented in EDs. Further research is needed to assess the applicability and implementation of the best performing models in the ED.Systematic review registration numberPROSPERO CRD42017057975.

scholarly journals Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Samaneh Asgari ◽  
Davood Khalili ◽  
Farhad Hosseinpanah ◽  
Farzad Hadaegh
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Model ◽  
Prediction Models ◽  
Assessment Tool ◽  
Data Extraction ◽  
External Validation ◽  
Model Development ◽  
Selection Procedure ◽  
Poor Quality

Objectives: This study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST). Data Sources: Both PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage. Study Selection: Articles published between December 2011 and October 2019 were considered. Data Extraction: For each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported. Results: The median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively. Conclusions: Among prediction models, an intermediate to poor quality were reassessed in several aspects of model development and validation, even though there was a comprehensive protocol. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.

scholarly journals Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review

2021 ◽  
Author(s):  
Cynthia Yang ◽  
Jan A. Kors ◽  
Solomon Ioannou ◽  
Luis H. John ◽  
Aniek F. Markus ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Final Model ◽  
Development And Validation ◽  
Over Time

Objectives This systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators. Materials and Methods We searched Embase, Medline, Web-of-Science, Cochrane Library and Google Scholar to identify studies that developed one or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009-2019. Results We identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009-2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented. Discussion Overall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented. Conclusion Improvement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.

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