Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

Munguía‐Realpozo, Pamela; Etchegaray‐Morales, Ivet; Mendoza‐Pinto, Claudia; Méndez‐Martínez, Socorro; Osorio-Peña, Ángel David; Ayón-Aguilar, Jorge; Garcı́a-Carrasco, Mario

doi:10.1016/j.autrev.2023.103294

Cited by 6 publications

(6 citation statements)

References 64 publications

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“…However, systematic reviews evaluating studies of prediction models have shown that they are often poorly conducted (including deficiencies in study design or data collection 37 38 ); use poor methodology 37 38 ; are incompletely reported with key details missing 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 ; are consequently at high risk of bias 41 49 55 56 57 ; rarely adhere to open science practices, 58 and are susceptible to overinterpretation or so-called spin. 59 60 These deficiencies cast considerable doubt on models’ usefulness and safety, and raises concerns about their potential to create or widen healthcare disparities.…”

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

124

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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.

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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

124

View full text Add to dashboard Cite

show abstract

“…Besides, reporting guidelines on the use of AI in healthcare to ensure fair and transparent research have been recently defined, such as TRIPOD-AI and PROBAST-AI [161], SPIRIT-AI and CONSORT-AI [162, 163] or STARD-AI [164]. A recently published review article has tackled the adherence in diagnostic and prognostic applications of ML in SLE patients using TRIPOD and PROBAST [165]. A more detailed description of these reporting guidelines is addressed in [166].…”

Section: Discussionmentioning

confidence: 99%

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

Madrid

Merino‐Barbancho

Rodríguez-González

et al. 2022

Preprint

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The outstanding and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five-year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease classification, disease prediction, predictors identification, patient stratification and disease subtype identification, disease progression and activity, and treatment response. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed are highlighted in a discussion. Therefore, the present review arises with the aim of characterising how researchers are employing data science techniques in the rheumatology medical field.

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“…Since ML methods are relatively new to the field, assessing the quality or technical aspects of these reports may be challenging to most non-ML researchers. A recent systematic review by Munguía-Realpozo et al 14 assessed 45 SLE reports that used ML to build diagnostic and/or predictive algorithms and determined whether they adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting standards. 15 The review concluded that most reports were deficient in multiple domains of the TRIPOD recommendations, often under-reporting relevant details about their data preprocessing, model-building process, model specification and model performance.…”

Section: Reports In Slementioning

confidence: 99%

“…While the aim of the study was not to systematically evaluate the reporting adherences of these reports, in general, we found similar limitations identified by Munguía-Realpozo et al . 14 This highlights that there is a need to improve transparency and reporting of prediction models in future ML SLE studies.…”

Section: Reports In Slementioning

confidence: 99%

See 1 more Smart Citation

Systemic lupus in the era of machine learning medicine

Zhan,

Buhler,

Chen

et al. 2024

Lupus Sci Med

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Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

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Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

Cited by 6 publications

References 64 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

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

Systemic lupus in the era of machine learning medicine

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