Increased endorsement of TRIPOD and other reporting guidelines by high impact factor journals: survey of instructions to authors.

Objectives: To assess the endorsement of reporting guidelines by high impact factor journals over the period 2017-2022, with a specific focus on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

Study Design And Setting: We searched the online 'instructions to authors' of high impact factor medical journals in February 2017 and in January 2022 for any reference to reporting guidelines and TRIPOD in particular.

Results: In 2017, 205 out of 337 (61%) journals mentioned any reporting guideline in their instructions to authors and in 2022 this increased to 245 (73%) journals.

One Heartbeat Away from a Prediction Model for Cardiovascular Diseases in Patients with Chronic Kidney Disease: A Systematic Review.

Introduction: Patients with chronic kidney disease (CKD) have a high risk of cardiovascular disease (CVD). Prediction models, combining clinical and laboratory characteristics, are commonly used to estimate an individual's CVD risk. However, these models are not specifically developed for patients with CKD and may therefore be less accurate.

Effectiveness of contact tracing apps for SARS-CoV-2: a rapid systematic review.

Objective: To systematically review evidence on effectiveness of contact tracing apps (CTAs) for SARS-CoV-2 on epidemiological and clinical outcomes.

Design: Rapid systematic review.

Data Sources: EMBASE (OVID), MEDLINE (PubMed), BioRxiv and MedRxiv were searched up to 28 October 2020.

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.

Introduction: Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further.

Transparent Reporting of Multivariable Prediction Models in Journal and Conference Abstracts: TRIPOD for Abstracts.

Clear and informative reporting in titles and abstracts is essential to help readers and reviewers identify potentially relevant studies and decide whether to read the full text. Although the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement provides general recommendations for reporting titles and abstracts, more detailed guidance seems to be desirable. The authors present TRIPOD for Abstracts, a checklist and corresponding guidance for reporting prediction model studies in abstracts.

The increasing need for systematic reviews of prognosis studies: strategies to facilitate review production and improve quality of primary research.

Personalized, precision, and risk-based medicine are becoming increasingly important in medicine. These involve the use of information about the prognosis of a patient, to make individualized treatment decisions. This has led to an accumulating amount of literature available on prognosis studies.

Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies.

To promote uniformity in measuring adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, a reporting guideline for diagnostic and prognostic prediction model studies, and thereby facilitate comparability of future studies assessing its impact, we transformed the original 22 TRIPOD items into an adherence assessment form and defined adherence scoring rules. TRIPOD specific challenges encountered were the existence of different types of prediction model studies and possible combinations of these within publications. More general issues included dealing with multiple reporting elements, reference to information in another publication, and non-applicability of items.

Empirical evidence of the impact of study characteristics on the performance of prediction models: a meta-epidemiological study.

Objectives: To empirically assess the relation between study characteristics and prognostic model performance in external validation studies of multivariable prognostic models.

Design: Meta-epidemiological study.

Data Sources And Study Selection: On 16 October 2018, we searched electronic databases for systematic reviews of prognostic models.

Cardiovascular risk prediction models for women in the general population: A systematic review.

Aim: To provide a comprehensive overview of cardiovascular disease (CVD) risk prediction models for women and models that include female-specific predictors.

Methods: We performed a systematic review of CVD risk prediction models for women in the general population by updating a previous review. We searched Medline and Embase up to July 2017 and included studies in which; (a) a new model was developed, (b) an existing model was validated, or (c) a predictor was added to an existing model.

Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement.

Background: As complete reporting is essential to judge the validity and applicability of multivariable prediction models, a guideline for the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) was introduced. We assessed the completeness of reporting of prediction model studies published just before the introduction of the TRIPOD statement, to refine and tailor its implementation strategy.

Methods: Within each of 37 clinical domains, 10 journals with the highest journal impact factor were selected.

Treatment use in prognostic model research: a systematic review of cardiovascular prognostic studies.

Background: Ignoring treatments in prognostic model development or validation can affect the accuracy and transportability of models. We aim to quantify the extent to which the effects of treatment have been addressed in existing prognostic model research and provide recommendations for the handling and reporting of treatment use in future studies.

Methods: We first describe how and when the use of treatments by individuals in a prognostic study can influence the development or validation of a prognostic model.

Erratum to: Methods for evaluating medical tests and biomarkers.

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

Prediction models for cardiovascular disease risk in the general population: systematic review.

Objective:  To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population.

Design:  Systematic review.

Data Sources:  Medline and Embase until June 2013.