Systematic scoping review protocol for clinical prediction rules (CPRs) in the management of patients with spinal cord injuries

Boggenpoel, B.; Madasa, Vuyolwethu; Jeftha, Tarryn; Joseph, Conran

doi:10.1136/bmjopen-2018-025076

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…In some epidemiological contexts, such as the one we are focusing on, it is important to assess studies' qualities even if it does not add to the methodological strength of the scoping review itself. For example, in an ongoing scoping review, authors aimed to assess the number of validated prediction rules that exist for spinal cord injury management and to provide evidence of the psychometric properties of these prediction rules, especially with regard to its clinical impact [17]. Although their scoping approach does not aim to assess the overall effectiveness of these prediction rules in their respective settings, their systematic appraisal of data quality will help readers make informed use of their findings.…”

Section: Methodsmentioning

confidence: 99%

“…A third reviewer (RN, GN) will review any studies where there is a discrepancy between the two independent reviewers that they are not able to resolve. Although scoping reviews do not usually include quality assessment, when dealing with epidemiological models, it is important to pay attention to the methodology and the design of original studies [17]. Two independent reviewers trained in epidemiology (RN, IF, GN) will be involved in assessing potential selection and information bias in selected studies and will discuss the potential impact of bias on the features and accuracy of selected models.…”

Section: Data Extractionmentioning

confidence: 99%

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Predictive models of diabetes complications: protocol for a scoping review

et al. 2020

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Background: Diabetes is a highly prevalent chronic disease that places a large burden on individuals and health care systems. Models predicting the risk (also called predictive models) of other conditions often compare people with and without diabetes, which is of little to no relevance for people already living with diabetes (called patients). This review aims to identify and synthesize findings from existing predictive models of physical and mental health diabetes-related conditions. Methods: We will use the scoping review frameworks developed by the Joanna Briggs Institute and Levac and colleagues. We will perform a comprehensive search for studies from Ovid MEDLINE and Embase databases. Studies involving patients with prediabetes and all types of diabetes will be considered, regardless of age and gender. We will limit the search to studies published between 2000 and 2018. There will be no restriction of studies based on country or publication language. Abstracts, full-text screening, and data extraction will be done independently by two individuals. Data abstraction will be conducted using a standard methodology. We will undertake a narrative synthesis of findings while considering the quality of the selected models according to validated and wellrecognized tools and reporting standards. Discussion: Predictive models are increasingly being recommended for risk assessment in treatment decisionmaking and clinical guidelines. This scoping review will provide an overview of existing predictive models of diabetes complications and how to apply them. By presenting people at higher risk of specific complications, this overview may help to enhance shared decision-making and preventive strategies concerning diabetes complications. Our anticipated limitation is potentially missing models because we will not search grey literature.

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Section: Methodsmentioning

confidence: 99%

Section: Data Extractionmentioning

confidence: 99%

Predictive models of diabetes complications: protocol for a scoping review

et al. 2020

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“…We only included prospective studies with quantitative data for at least one prediction prognostic model of diabetes complications and for which there was reported evidence of internal and/or external validation 18. We explored potential biases to better inform readers and knowledge users who may be concerned with the reliability and consistency of diverse data 19 20…”

Section: Eligibility Criteriamentioning

confidence: 99%

Prediction models of diabetes complications: a scoping review

Ndjaboué

Ngueta

Rochefort-Brihay

et al. 2022

J Epidemiol Community Health

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BackgroundDiabetes often places a large burden on people with diabetes (hereafter ‘patients’) and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.MethodsBuilding on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.ResultsOverall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.ConclusionThis rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics.Scoping review registrationhttps://osf.io/fjubt/

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“…23,24 Because diabetes risk complications are often evaluated in epidemiological studies, we additionally explored potential biases in original studies. Previous work using a similar approach suggested that it may help readers make informed use of findings 25 and alleviate the potential challenge of evaluating reliability and consistency in data of diverse nature. 26…”

Section: Eligibility Criteriamentioning

confidence: 99%