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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
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Northern Cape , South Africa , United States , United Kingdom , University College , New South Wales , Dan Adler , Richiardi Jonas , Victor Castro , Stacey Fisher , Boris Janssen , Laure Wynants , Vinyas Harish , Daniel Stahl , Viknesh Sounderajah , Evangelos Kanoulas , Conrad Harrison , Alex Garaiman , Junfeng Wang , Brooke Levis , Frank Rademakers , Adrian Barnett , Van Leeuwen , Jie Ma , Zane Perkins , Rachel Kuo ,

AI models show promise in predicting heart disease risks, but lack validation

Review highlights the potential of AI models in predicting cardiovascular disease risks but emphasizes the need for independent external validation to ensure their clinical applicability. ....

United States , Summit Art Creations , North America , South American , Transparent Reporting , Individual Prognosis Or Diagnosis , Cardiovascular Disease , Artificial Intelligence , Deep Learning , Machine Learning ,

Evaluation of clinical prediction models (part 2): how to undertake an external validation study

External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on model evaluation, Riley and colleagues explain what an external validation study entails and describe the key steps involved, from establishing a high quality dataset to evaluating a model’s predictive performance and clinical usefulness.

A clinical prediction model is used to calculate predictions for an individual conditional on their characteristics. Such predictions might be of a continuous value (eg, blood pressure, fat mass) or the probability of a particular event occurring (eg, disease recurrence), and are often in the context of a particular time point (eg, probability of disease recurrence within the next 12 months). Clinical prediction models are traditionally based on a regression equation but are increasingly derived using artificial intelligence or machine learning methods (eg, random forests, neural networks). Regardless ....

United Kingdom , Lucinda Archer , Lauraj Bonnett , Glenp Martin , Paula Dhiman , Richardd Riley , Kym Ie Snell , Garys Collins , University Of Birmingham , Duke Clinical Research Institute , Cancer Research United Kingdom , Better Methods Research , Health Improvement Network , Research Council , Sciences Research Council , United Kingdom Department Of Health , Birmingham Biomedical Research Centre , University Hospitals Birmingham , National Institute For Health , Clinical Practice Research Datalink , Care Research , Transparent Reporting , Individual Prognosis Or Diagnosis , United Kingdom Biobank , Risk Of Bias , Participant Selection ,

Evaluation of clinical prediction models (part 1): from development to external validation

Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance.

Healthcare decisions for individuals are routinely made on the basis of risk or probability.1 Whether this probability is that a specific outcome or disease is present (diagnostic) or that a specific outcome will occur in the future (prognostic), it is important to know how these probabilities are estimated and whether they are accurate. Clinical prediction models estimate outcome risk for an individual conditional on their characteristics of multiple predictors (eg, age, family history, symptoms, blood pressure). Examples include the ISARIC (International ....

Glasgow City , United Kingdom , Administrative Coordinating Center , National Institutes Of Health , Care Research Birmingham Biomedical Centre , Research Foundation , National Heart , National Institute For Health , University Of Birmingham , Infection Consortium , Cancer Research United Kingdom , Research Council Better Methods , Sciences Research Council , International Severe Acute , Translational Sciences Clinical Science Award , Research Council , Blood Institute , National Center , University Hospitals Birmingham , International Severe Acute Respiratory , Emerging Infection Consortium , Transparent Reporting , Individual Prognosis Or Diagnosis , National Institute , Care Excellence , Physical Sciences Research Council ,

Patterns of antibiotic use, pathogens, and prediction of mortality in hospitalized neonates and young infants with sepsis: A global neonatal sepsis observational cohort study (NeoOBS)

James Neal Russell and colleagues report a global neonatal sepsis observational cohort study (NeoOBS) exploring patterns of antibiotic use, pathogens and prediction of mortality in hospitalised neonates and young infants with sepsis. ....

United Kingdom , United States , Principality Of Monaco , Research Ethics Committee , World Health Organization , Global Antimicrobial Resistance Innovation Fund , Indian Council For Medical Research , German Federal Ministry Of Education , Leo Model Foundation , Sustainable Development Goal , Development Partnership , Melinda Gates Foundation , University Of Antwerp , Laboratory Standard Institute , United Kingdom Department For International Development , Laboratory Of Medical Microbiology , Netherlands Ministry Of Health , United Kingdom Medical Research Council , Luxembourg Ministry Of Development Cooperation , Swiss Federal Office Of Public Health , Luxembourg Ministry Of Health , German Federal Ministry Of Health , Japanese Ministry Of Health , University Of London , United Kingdom Department Of Health , Global Antibiotic Research ,