Artificial intelligence in cardiology
Artificial intelligence and machine learning are poised to influence nearly every aspect of the
human condition, and cardiology is not an exception to this trend. This paper provides a …
human condition, and cardiology is not an exception to this trend. This paper provides a …
Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges
Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk
models have been based on regression models. While useful and robust, these statistical …
models have been based on regression models. While useful and robust, these statistical …
2022 American College of Rheumatology/EULAR classification criteria for giant cell arteritis
Objective To develop and validate updated classification criteria for giant cell arteritis (GCA).
Methods Patients with vasculitis or comparator diseases were recruited into an international …
Methods Patients with vasculitis or comparator diseases were recruited into an international …
2022 American College of Rheumatology/EULAR classification criteria for Takayasu arteritis
PC Grayson, C Ponte, R Suppiah… - Arthritis & …, 2022 - Wiley Online Library
Objective To develop and validate new classification criteria for Takayasu arteritis (TAK).
Methods Patients with vasculitis or comparator diseases were recruited into an international …
Methods Patients with vasculitis or comparator diseases were recruited into an international …
Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C …
Objective To develop and validate a pragmatic risk score to predict mortality in patients
admitted to hospital with coronavirus disease 2019 (covid-19). Design Prospective …
admitted to hospital with coronavirus disease 2019 (covid-19). Design Prospective …
Calculating the sample size required for developing a clinical prediction model
Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or
prognosis in healthcare. Hundreds of prediction models are published in the medical …
prognosis in healthcare. Hundreds of prediction models are published in the medical …
Minimum sample size for external validation of a clinical prediction model with a binary outcome
In prediction model research, external validation is needed to examine an existing model's
performance using data independent to that for model development. Current external …
performance using data independent to that for model development. Current external …
PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration
KGM Moons, RF Wolff, RD Riley, PF Whiting… - Annals of internal …, 2019 - acpjournals.org
Prediction models in health care use predictors to estimate for an individual the probability
that a condition or disease is already present (diagnostic model) or will occur in the future …
that a condition or disease is already present (diagnostic model) or will occur in the future …
Minimum sample size for developing a multivariable prediction model: PART II‐binary and time‐to‐event outcomes
When designing a study to develop a new prediction model with binary or time‐to‐event
outcomes, researchers should ensure their sample size is adequate in terms of the number …
outcomes, researchers should ensure their sample size is adequate in terms of the number …
Sample size for binary logistic prediction models: beyond events per variable criteria
M van Smeden, KGM Moons… - … methods in medical …, 2019 - journals.sagepub.com
Binary logistic regression is one of the most frequently applied statistical approaches for
developing clinical prediction models. Developers of such models often rely on an Events …
developing clinical prediction models. Developers of such models often rely on an Events …