TRIPOD provides guidance on the key items to report when describing studies developing, evaluating (or validating), or updating clinical prediction models.10,11 Although TRIPOD aims primarily to ...improve reporting, it also leads to more comprehensive understanding, conduct, and analysis of prediction model studies, ensuring that prediction models can be picked up by subsequent researchers and users to be studied further and used to guide health care, thus encouraging reproducible research and reduce research waste. ...concerns have been raised that artificial intelligence in clinical medicine is overhyped and, if not used with proper guidance, knowledge, or expertise, has methodological shortcomings, poor transparency, and poor reproducibility.12 Methodological concerns include an often incorrect focus on classification over prediction, overfitting (whereby too many predictors or features are included for the sample size), lack of robust assessment of predictive accuracy when used with other data than those from which they were developed (validation), weak and unbiased comparison with simpler modelling approaches, and lack of transparency of the artificial intelligence and machine learning algorithm, which limits independent evaluation. Clearly, the consequences of making a wrong or inaccurate prediction are substantial for the clinical application of a machine learning prediction model, such as the deep learning models for detection of stroke or wrist fractures approved by the US Food and Drug Administration.13 Therefore, the clinical community must not get mesmerised by the artificial intelligence and machine learning revolution, and artificial intelligence and machine learning prediction models must be appropriately developed, evaluated, and—if needed—tailored to different situations before they are used in daily medical practice.
Causal directed acyclic graphs (cDAGs) have become popular tools for researchers to better examine biases related to causal questions. DAGs comprise a series of arrows connecting nodes that represent ...variables and in doing so can demonstrate the causal relation between different variables. cDAGs can provide researchers with a blueprint of the exposure and outcome relation and the other variables that play a role in that causal question. cDAGs can be helpful in the design and interpretation of observational studies in pulmonary, critical care, sleep, and cardiovascular medicine. They can also help clinicians and researchers to better identify the structure of different biases that can affect the validity of observational studies. Most of the available literature on cDAGs and their function use language that might be unfamiliar to clinicians. This article explains cDAG terminology and the principles behind how they work. We use cDAGs and clinical examples that are mostly focused in the area of pulmonary medicine to describe the structure of confounding, selection bias, overadjustment bias, and detection bias. These principles are then applied to a more complex published case study on the use of statins and COPD mortality. We also introduce readers to other resources for a more in-depth discussion of causal inference principles.
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will ...occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
The updated TRIPOD+AI reporting guidelines can help guide the writing of research on artificial intelligence in healthcare to improve the transparency and usefulness of reporting
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will ...occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Summary Measurements of health indicators are rarely available for every population and period of interest, and available data may not be comparable. The Guidelines for Accurate and Transparent ...Health Estimates Reporting (GATHER) define best reporting practices for studies that calculate health estimates for multiple populations (in time or space) using multiple information sources. Health estimates that fall within the scope of GATHER include all quantitative population-level estimates (including global, regional, national, or subnational estimates) of health indicators, including indicators of health status, incidence and prevalence of diseases, injuries, and disability and functioning; and indicators of health determinants, including health behaviours and health exposures. GATHER comprises a checklist of 18 items that are essential for best reporting practice. A more detailed explanation and elaboration document, describing the interpretation and rationale of each reporting item along with examples of good reporting, is available on the GATHER website.
To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert ...clinicians.
Systematic review.
Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019.
Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax.
Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies.
Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required.
Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions.
PROSPERO CRD42019123605.
The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature.
We conducted a Medline literature ...search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes.
We included 71 of 927 studies. The median sample size was 1,250 (range 72–3,994,872), with 19 predictors considered (range 5–563) and eight events per predictor (range 0.3–6,697). The most common ML methods were classification trees, random forests, artificial neural networks, and support vector machines. In 48 (68%) studies, we observed potential bias in the validation procedures. Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between an LR and ML model (AUC range, 0.52–0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, −0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20–0.47) higher for ML.
We found no evidence of superior performance of ML over LR. Improvements in methodology and reporting are needed for studies that compare modeling algorithms.
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