Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Snell, Kym I E; Levis, Brooke; Damen, Johanna Aag; Dhiman, Paula; Debray, Thomas P. A.; Hooft, Lotty; Reitsma, Johannes B.; Moons, Karel G.M.; Collins, Gary S.; Riley, Richard D

doi:10.1136/bmj-2022-073538

Cited by 40 publications

(23 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 17 TRIPOD 2015 mainly focused on models developed using regression modelling, which was the prevailing approach at the time. Additional guidance has since been created for reporting abstracts of prediction model studies (TRIPOD for Abstracts 18 ), studies developing or validating prediction models using clustered data (TRIPOD-Cluster 19 20 ), systematic reviews and meta-analyses of prediction model studies (TRIPOD-SRMA 21 ), and guidance in preparation for study protocols (TRIPOD-P 22 ). All available guidance, as well as template checklists for filling out separately, can also be found on the TRIPOD website ( https://www.tripod-statement.org/ ).…”

mentioning

confidence: 99%

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins,

Moons,

Dhiman

et al. 2024

BMJ

Self Cite

124

View full text Add to dashboard Cite

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed. TRIPOD+AI provides harmonised guidance for reporting prediction model studies, irrespective of whether regression modelling or machine learning methods have been used. The new checklist supersedes the TRIPOD 2015 checklist, which should no longer be used. This article describes the development of TRIPOD+AI and presents the expanded 27 item checklist with more detailed explanation of each reporting recommendation, and the TRIPOD+AI for Abstracts checklist. TRIPOD+AI aims to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. Complete reporting will facilitate study appraisal, model evaluation, and model implementation.

show abstract

mentioning

confidence: 99%

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins,

Moons,

Dhiman

et al. 2024

BMJ

Self Cite

124

View full text Add to dashboard Cite

show abstract

“…We also adhered to the 2023 Transparent Reporting of a multivariable prediction models for Individual Prognosis Or Diagnosis: checklist for Systematic Reviews and Meta-Analyses (TRIPOD-SRMA -Supplementary S1). [23] Search strategy A comprehensive unrestricted search of Medline Ovid (Pubmed), Embase and Googlescholar was performed by the primary author (A.S.D) using prespeci ed search syntax and keywords (see Supplementary S2) on the 1st of May 2023. Individual case reports were excluded though case series were included if they met the inclusion criteria.…”

Section: Methodsmentioning

confidence: 99%

Pre-treatment re-bleeding following aneurysmal subarachnoid hemorrhage: a systematic review of prediction models

Dissanayake

Phillips

et al. 2023

Preprint

View full text Add to dashboard Cite

Pre-treatment rebleeding following aneurysmal subarachnoid hemorrhage (aSAH) increases the risk of a poor outcome. Treatment as early as practicable is recommend to mitigate this risk though the benefit of emergency treatment is debated, emphasising the need for individualised risk prediction. Predictive models have been recently described incorporating established risk factors. Following prospective registration on the International prospective register of systematic reviews (PROSPERO) CRD 42023421235; Ovid Medline (Pubmed), Embase and Googlescholar were searched for English language studies describing clinical prediction models between May 2002 and May 2023 for pre-treatment rebleed prediction following aSAH in adults ³18 years. Of 763 unique records, 17 full texts were scrutinised with 5 publications describing 4 models reviewed. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were used. Reported performance varied from 0.790 to 0.939. The Intracranial Aneurysm Risk Score demonstrated a high risk of bias and low clinical applicability. The risk score of Oppong et al. was not validated or calibrated and the Clinical + Morphological model of Liu et al. did not have a sufficient event-to-predictor ratio. The ARISE extended model was formulated using patient data from multiple centres and time periods increasing the risk of non-standardised predictor assessment. Furthermore, patient recruitment overlapped the period of the endovascular paradigm shift in aneurysm treatment, limiting the applicability of this model to patients treated according to modern conditions. Thus, no published predictive model could be recommended for clinical use.

show abstract

“…This systematic review was reported by the recommendations by the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement 16 and Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta‐analyses (TRIPOD‐SRMA) 17 . A detailed study protocol was available on the PROSPERO website under the registration number CRD42022303937.…”

Section: Methodsmentioning

confidence: 99%

Critical evaluation of established risk prediction models for acute respiratory distress syndrome in adult patients: A systematic review and meta‐analysis

Wei,

Peng,

et al. 2023

J Evidence Based Medicine

View full text Add to dashboard Cite

AimTo assess the performance of validated prediction models for acute respiratory distress syndrome (ARDS) by systematic review and meta‐analysis.MethodsEight databases (Medline, CINAHL, Embase, The Cochrane Library, CNKI, WanFang Data, Sinomed, and VIP) were searched up to March 26, 2023. Studies developed and validated a prediction model for ARDS in adult patients were included. Items on study design, incidence, derivation methods, predictors, discrimination, and calibration were collected. The risk of bias was assessed by the Prediction model Risk of Bias Assessment Tool. Models with a reported area under the curve of the receiver operating characteristic (AUC) metric were analyzed.ResultsA total of 25 studies were retrieved, including 48 unique prediction models. Discrimination was reported in all studies, with AUC ranging from 0.701 to 0.95. Emerged AUC value of the logistic regression model was 0.837 (95% CI: 0.814 to 0.859). Besides, the value in the ICU group was 0.856 (95% CI: 0.812 to 0.899), the acute pancreatitis group was 0.863 (95% CI: 0.844 to 0.882), and the postoperation group was 0.835 (95% CI: 0.808 to 0.861). In total, 24 of the included studies had a high risk of bias, which was mostly due to the improper methods in predictor screening (13/24), model calibration assessment (9/24), and dichotomization of continuous predictors (6/24).ConclusionsThis study shows that most prediction models for ARDS are at high risk of bias, and the discrimination ability of the model is excellent. Adherence to standardized guidelines for model development is necessary to derive a prediction model of value to clinicians.

show abstract

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Cited by 40 publications

References 46 publications

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Pre-treatment re-bleeding following aneurysmal subarachnoid hemorrhage: a systematic review of prediction models

Critical evaluation of established risk prediction models for acute respiratory distress syndrome in adult patients: A systematic review and meta‐analysis

Contact Info

Product

Resources

About