Design, methods, and reporting of impact studies of cardiovascular clinical prediction rules are suboptimal: a systematic review

Ban, Jong-Wook; Chan, Mei Sum; Muthee, Tonny Brian; Páez, Arsenio; Stevens, R J; Perera, Rafael

doi:10.1016/j.jclinepi.2021.01.016

Cited by 4 publications

(4 citation statements)

References 68 publications

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“…Many reports mentioned that repetition may generate waste, as excessive work may not contribute knowledge or value. [47][48][49][50] The execution of studies that seek to address questions that have already been adequately answered by existing evidence is excessive, 51 which some reports considered unethical behavior. 48,52 Therefore, some reports recommended that researchers should precede a clinical study or research project with a systematic review.…”

Section: Negligible Research Wastementioning

confidence: 99%

Five aspects of research waste in biomedicine: A scoping review

Rosengaard,

Andersen,

Rosenberg

et al. 2024

J Evidence Based Medicine

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BackgroundThe number of published journal articles has grown exponentially during the last 30 years, which may have led to some wasteful research. However, the terminology associated with research waste remains unclear. To address this, we aimed to identify, define, and categorize the aspects of research waste in published biomedical reports.MethodsIn this scoping review, we systematically searched for biomedical literature reports from 1993 to 2023 in two databases, focusing on those addressing and defining research waste. Through data charting, we analyzed and categorized the aspects of research waste.ResultsBased on 4285 initial records in the searches, a total of 832 reports were included in the analysis. The included reports were primarily narrative reviews (26%) and original reports (21%). We categorized research waste into five aspects: methodological, invisible, negligible, underreported, and structural (MINUS) research waste. More than half of the reports (56%) covered methodological research waste concerning flaws in study design, study conduct, or analysis. Invisible research waste covered nonpublication, discontinuation, and lack of data‐sharing. Negligible research waste primarily concerned unnecessary repetition, for example, stemming from the absence of preceding a trial with a systematic review of the literature. Underreported research waste mainly included poor reporting, resulting in a lack of transparency. Structural research waste comprised inadequate management, collaboration, prioritization, implementation, and dissemination.ConclusionMINUS encapsulates the five main aspects of research waste. Recognizing these aspects of research waste is important for addressing and preventing further research waste and thereby ensuring efficient resource allocation and scientific integrity.

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Section: Negligible Research Wastementioning

confidence: 99%

Five aspects of research waste in biomedicine: A scoping review

Rosengaard,

Andersen,

Rosenberg

et al. 2024

J Evidence Based Medicine

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“…With so many obstacles, it is not surprising that in the end almost all CDRs do not make it to the end 2 . A small subset of CDRs has undergone an appropriate prospective external validation, while only a few have undergone a formal impact analysis (Ban et al., 2021 ; Ebell et al., 2021 ; Laupacis et al., 1997 ; Reilly and Evans, 2006 ; Toll et al., 2008 ) 3 . However, once a CDR has passed the entire process, clinicians have an additional tool at their disposal that can be very helpful for them and their patients.…”

Section: From Derivation To Implementation Of a Viable Cdrmentioning

confidence: 99%

“… 3 A recent study has demonstrated that in cardiology, a field in which traditionally many CDRs are being used, only 110 impact studies have been performed, from which a small minority has been described as ‘studies of sufficient quality’ (Ban et al., 2021 ). …”

mentioning

confidence: 99%

“…Almost 20 years ago, estimates of scientific articles discussing CDRs already exceeded 15 000(Toll et al, 2008). Nowadays, numbers have probably been increased to a multiple of that amount.3 A recent study has demonstrated that in cardiology, a field in which traditionally many CDRs are being used, only 110 impact studies have been performed, from which a small minority has been described as 'studies of sufficient quality'(Ban et al, 2021).Primary Health Care Research & Development…”

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confidence: 99%

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Clinical decision rules in primary care: necessary investments for sustainable healthcare

Heerink

Oudega

Hopstaken

et al. 2023

Prim Health Care Res Dev

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Clinical judgement in primary care is more often decisive than in the hospital. Clinical decision rules (CDRs) can help general practitioners facilitating the work-through of differentials that follows an initial suspicion, resulting in a concrete ‘course of action’: a ‘rule-out’ without further testing, a need for further testing, or a specific treatment. However, in daily primary care, the use of CDRs is limited to only a few isolated rules. In this paper, we aimed to provide insight into the laborious path required to implement a viable CDR. At the same time, we noted that the limited use of CDRs in primary care cannot be explained by implementation barriers alone. Through the case study of the Oudega rule for the exclusion of deep vein thrombosis, we concluded that primary care CDRs come out best if they are tailor-made, taking into consideration the specific context of primary health care. Current CDRs should be evaluated frequently, and future decision rules should anticipate the latest developments such as the use of point-of-care (POC) tests. Hence, such new powerful diagnostic CDRs could improve and expand the possibilities for patient-oriented primary care.

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Prediction of cardiovascular markers and diseases using retinal fundus images and deep learning: a systematic scoping review

Li,

Isaksen,

Lebiecka-Johansen

et al. 2024

European Heart Journal - Digital Health

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Rapid development in deep learning for image analysis inspired studies to focus on predicting cardiovascular risk using retinal fundus images. This scoping review aimed to identify and describe studies using retinal fundus images and deep learning to predict cardiovascular risk markers and diseases. We searched MEDLINE and Embase on 17 November 2023. Abstracts and relevant full-text articles were independently screened by two reviewers. We included studies that used deep learning for the analysis of retinal fundus images to predict cardiovascular risk markers or cardiovascular diseases (CVDs) and excluded studies only using predefined characteristics of retinal fundus images. Study characteristics were presented using descriptive statistics. We included 24 articles published between 2018 and 2023. Among these, 23 (96%) were cross-sectional studies and eight (33%) were follow-up studies with clinical CVD outcomes. Seven studies included a combination of both designs. Most studies (96%) used convolutional neural networks to process images. We found nine (38%) studies that incorporated clinical risk factors in the prediction and four (17%) that compared the results to commonly used clinical risk scores in a prospective setting. Three of these reported improved discriminative performance. External validation of models was rare (21%). There is increasing interest in using retinal fundus images in cardiovascular risk assessment with some studies demonstrating some improvements in prediction. However, more prospective studies, comparisons of results to clinical risk scores, and models augmented with traditional risk factors can strengthen further research in the field.

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Design, methods, and reporting of impact studies of cardiovascular clinical prediction rules are suboptimal: a systematic review

Cited by 4 publications

References 68 publications

Five aspects of research waste in biomedicine: A scoping review

Five aspects of research waste in biomedicine: A scoping review

Clinical decision rules in primary care: necessary investments for sustainable healthcare

Prediction of cardiovascular markers and diseases using retinal fundus images and deep learning: a systematic scoping review

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