Predicting inhospital admission at the emergency department: a systematic review

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.

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Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis

EP Europace ◽

10.1093/europace/euaa005 ◽

2020 ◽

Vol 22 (5) ◽

pp. 684-694 ◽

Cited By ~ 1

Author(s):

Jelle C L Himmelreich ◽

Lieke Veelers ◽

Wim A M Lucassen ◽

Renate B Schnabel ◽

Michiel Rienstra ◽

...

Keyword(s):

Atrial Fibrillation ◽

Systematic Review ◽

Prediction Models ◽

Prediction Interval ◽

Data Extraction ◽

Meta Analysis ◽

Aging Research ◽

Genomic Epidemiology ◽

C Statistic ◽

Modelling Studies

Abstract Aims Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing efficiency of future primary AF screening efforts. Methods and results We performed a systematic review on multivariable models derived, validated, and/or augmented for AF prediction in community cohorts using Pubmed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) through 1 August 2019. We performed meta-analysis of model discrimination with the summary C-statistic as the primary expression of associations using a random effects model. In case of high heterogeneity, we calculated a 95% prediction interval. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist for risk of bias assessment. We included 27 studies with a total of 2 978 659 unique participants among 20 cohorts with mean age ranging from 42 to 76 years. We identified 21 risk models used for incident AF risk in community cohorts. Three models showed significant summary discrimination despite high heterogeneity: CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [summary C-statistic 0.71; 95% confidence interval (95% CI) 0.66–0.76], FHS-AF (Framingham Heart Study risk score for AF) (summary C-statistic 0.70; 95% CI 0.64–0.76), and CHA2DS2-VASc (summary C-statistic 0.69; 95% CI 0.64–0.74). Of these, CHARGE-AF and FHS-AF had originally been derived for AF incidence prediction. Only CHARGE-AF, which comprises easily obtainable measurements and medical history elements, showed significant summary discrimination among cohorts that had applied a uniform (5-year) risk prediction window. Conclusion CHARGE-AF appeared most suitable for primary screening purposes in terms of performance and applicability in older community cohorts of predominantly European descent.

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Accuracy of potential diagnostic indicators for coeliac disease: a systematic review protocol

BMJ Open ◽

10.1136/bmjopen-2020-038994 ◽

2020 ◽

Vol 10 (10) ◽

pp. e038994

Author(s):

Martha Maria Christine Elwenspoek ◽

Joni Jackson ◽

Sarah Dawson ◽

Hazel Everitt ◽

Peter Gillett ◽

...

Keyword(s):

Systematic Review ◽

Coeliac Disease ◽

Prediction Models ◽

Data Extraction ◽

Meta Analysis ◽

Case Finding ◽

Endoscopic Biopsy ◽

Cochrane Library ◽

Ethical Approval ◽

Registration Number

IntroductionCoeliac disease (CD) is a systemic immune-mediated disorder triggered by gluten in genetically predisposed individuals. CD is diagnosed using a combination of serology tests and endoscopic biopsy of the small intestine. However, because of non-specific symptoms and heterogeneous clinical presentation, diagnosing CD is challenging. Early detection of CD through improved case-finding strategies can improve the response to a gluten-free diet, patients’ quality of life and potentially reduce the risk of complications. However, there is a lack of consensus in which groups may benefit from active case-finding.Methods and analysisWe will perform a systematic review to determine the accuracy of diagnostic indicators (such as symptoms and risk factors) for CD in adults and children, and thus can help identify patients who should be offered CD testing. MEDLINE, Embase, Cochrane Library and Web of Science will be searched from 1997 until 2020. Screening will be performed in duplicate. Data extraction will be performed by one and checked by a second reviewer. Disagreements will be resolved through discussion or referral to a third reviewer. We will produce a narrative summary of identified prediction models. Studies, where 2×2 data can be extracted or reconstructed, will be treated as diagnostic accuracy studies, that is, the diagnostic indicators are the index tests and CD serology and/or biopsy is the reference standard. For each diagnostic indicator, we will perform a bivariate random-effects meta-analysis of the sensitivity and specificity.Ethics and disseminationResults will be reported in peer-reviewed journals, academic and public presentations and social media. We will convene an implementation panel to advise on the optimum strategy for enhanced dissemination. We will discuss findings with Coeliac UK to help with dissemination to patients. Ethical approval is not applicable, as this is a systematic review and no research participants will be involved.PROSPERO registration numberCRD42020170766.

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

10.2337/figshare.15173232 ◽

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

Background Remission of type 2 diabetes following bariatric surgery is well established but identifying patients who will go into remission is challenging. Purpose To perform a systematic review of currently available diabetes remission prediction models, compare their performance, and evaluate their applicability in clinical settings. Data sources 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. Study selection and data extraction 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. Data synthesis 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. Limitations and Conclusions 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.

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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

10.21203/rs.3.rs-903247/v1 ◽

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.

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

10.2337/figshare.15173232.v1 ◽

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

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Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BMJ Open ◽

10.1136/bmjopen-2020-044500 ◽

2021 ◽

Vol 11 (2) ◽

pp. e044500

Author(s):

Yauhen Statsenko ◽

Fatmah Al Zahmi ◽

Tetiana Habuza ◽

Klaus Neidl-Van Gorkom ◽

Nazar Zaki

Keyword(s):

Lymphocyte Count ◽

Prediction Models ◽

Model Performance ◽

Area Under The Curve ◽

Predictive Biomarkers ◽

Supervised Machine Learning ◽

Study Cohort ◽

Threshold Values ◽

Laboratory Findings ◽

Study Results

BackgroundDespite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.ObjectivesTo identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).MethodsThe study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.ResultsWith the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×109/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.ConclusionThe performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

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Factors affecting consultant attitudes to undertaking undergraduate medical student teaching in the UK: a systematic review

BMJ Open ◽

10.1136/bmjopen-2020-042653 ◽

2021 ◽

Vol 11 (1) ◽

pp. e042653

Author(s):

Isobel Marion Harris ◽

Heather McNeilly ◽

Hani Benamer ◽

Derek J Ward ◽

Alice J Sitch ◽

...

Keyword(s):

Systematic Review ◽

Medical Students ◽

Student Teaching ◽

Data Extraction ◽

Undergraduate Medical Students ◽

Systematic Review Methodology ◽

Factors Affecting ◽

Persistent Problem ◽

Medical Student Teaching ◽

The Uk

ObjectiveThis systematic review aimed to explore consultant attitudes towards teaching undergraduate medical students in the UK.DesignSystematic review.MethodologyStandard systematic review methodology was followed. MEDLINE, EMBASE and OpenGrey were searched from inception to August 2019 to identify studies exploring senior doctors’ attitudes towards teaching undergraduate medical students. Two reviewers independently carried out key methodological steps including study screening/selection, quality assessment and data extraction. A narrative synthesis was undertaken.ResultsFive studies were included in the review dating 2003–2015. Two studies used questionnaires, and three used focus groups/semistructured interviews. Key findings identified across all studies were consultants generally found teaching undergraduate medical students enjoyable, and consultants identified time constraints as a barrier to teaching. Other findings were consultants feeling there was a lack of recognition for time spent teaching, and a lack of training/guidance regarding teaching students.ConclusionsThis is the first systematic review to explore senior hospital doctors’ attitudes towards teaching undergraduate medical students. Despite these five studies spanning 12 years, the same attitudes and issues regarding teaching are identified by all, suggesting lack of time particularly is a persistent problem regarding consultant-based teaching. An anecdotal impression is that consultants are no longer as enthusiastic about teaching as they once were, but it is evident over the 12 years of these studies that enjoyment levels, and presumably enthusiasm, have not changed significantly.

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What does my neighbourhood have to do with my weight? A protocol for systematic review and meta-analysis of the association between neighbourhood socioeconomic status and body weight

BMJ Open ◽

10.1136/bmjopen-2017-017567 ◽

2017 ◽

Vol 7 (9) ◽

pp. e017567

Author(s):

Shimels Hussien Mohammed ◽

Mulugeta Molla Birhanu ◽

Tesfamichael Awoke Sissay ◽

Tesfa Dejenie Habtewold ◽

Balewgizie Sileshi Tegegn ◽

...

Keyword(s):

Systematic Review ◽

Socioeconomic Status ◽

Data Extraction ◽

Meta Analysis ◽

Cross Sectional ◽

Registration Number ◽

Newcastle Ottawa Scale ◽

Neighbourhood Socioeconomic Status ◽

Using Data

IntroductionIndividuals living in poor neighbourhoods are at a higher risk of overweight/obesity. There is no systematic review and meta-analysis study on the association of neighbourhood socioeconomic status (NSES) with overweight/obesity. We aimed to systematically review and meta-analyse the existing evidence on the association of NSES with overweight/obesity.Methods and analysisCross-sectional, case–control and cohort studies published in English from inception to 15 May 2017 will be systematically searched using the following databases: PubMed, EMBASE, Web of Sciences and Google Scholar. Selection, screening, reviewing and data extraction will be done by two reviewers, independently and in duplicate. The Newcastle–Ottawa Scale (NOS) will be used to assess the quality of evidence. Publication bias will be checked by visual inspection of funnel plots and Egger’s regression test. Heterogeneity will be checked by Higgins’s method (I2statistics). Meta-analysis will be done to estimate the pooled OR. Narrative synthesis will be performed if meta-analysis is not feasible due to high heterogeneity of studies.Ethics and disseminationEthical clearance is not required as we will be using data from published articles. Findings will be communicated through a publication in a peer-reviewed journal and presentations at professional conferences.PROSPERO registration numberCRD42017063889.

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Pharmacogenetics of Type 2 diabetes mellitus: A Systematic Review Protocol (Preprint)

10.2196/preprints.13605 ◽

2019 ◽

Author(s):

Hamid Reza Aghaei Meybodi ◽

Negar Sarhangi ◽

Anoosh Naghavi ◽

Marzieh Rahbaran ◽

Maryam Hassani Doabsari ◽

...

Keyword(s):

Systematic Review ◽

Type 2 Diabetes ◽

Data Extraction ◽

Relevant Information ◽

Risk Of Bias ◽

Cochrane Database ◽

Registration Number ◽

Newcastle Ottawa Scale ◽

Review Protocol

UNSTRUCTURED The objective of this systematic review is to determine the effect of genetic variants that associate with antidiabetic medications and their efficacy and toxicity in T2DM patients. The understanding may allow interventions for improving management of T2DM and later systematically evaluated in more in-depth studies. We will have performed a comprehensive search using PubMed, Scopus, EMBASE, Web of Sciences and Cochrane database from 1990 to 2018. Relevant journals and references of all included studies will be hand searched to find the additional studied. Eligible studies such as pharmacogenetics studies in terms of drug response and toxicity in the type 2 diabetes patients and performed just on human will be included. Data extraction and quality assessment will be carried out by two independent reviewers and disagreements will be resolved through third expert reviewer. Risk of bias will be assessed with the Cochrane Risk of Bias tool for randomized studies and Newcastle-Ottawa Scale (NOS) for observational Studies. Narrative synthesis will be conducted by the combination of key findings. The results of this study will be submitted to a peer-reviewed journal for publication and also presented at PROSPERO. We expect this review will provide highly relevant information for clinicians, pharmaceutical industry that will benefit from the summary of the best available data regarding the efficacy of antidiabetic medication in the aspect of pharmacogenetics. PROSPERO Registration number (CRD42018104843)

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

Human Reproduction ◽

10.1093/humrep/deab130.418 ◽

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

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