BMJ : British medical journal
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Meta Analysis
Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts.
To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations. ⋯ Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates.
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Multicenter Study Clinical Trial
Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study.
To develop and validate a delirium prediction model for adult intensive care patients and determine its additional value compared with prediction by caregivers. ⋯ The PRE-DELIRIC model for intensive care patients consists of 10 risk factors that are readily available within 24 hours after intensive care admission and has a high predictive value. Clinical prediction by nurses and physicians performed significantly worse. The model allows for early prediction of delirium and initiation of preventive measures. Trial registration Clinical trials NCT00604773 (development study) and NCT00961389 (validation study).
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Randomized Controlled Trial Multicenter Study
Facilitated physical activity as a treatment for depressed adults: randomised controlled trial.
To investigate the effectiveness of facilitated physical activity as an adjunctive treatment for adults with depression presenting in primary care. ⋯ The addition of a facilitated physical activity intervention to usual care did not improve depression outcome or reduce use of antidepressants compared with usual care alone.
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To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort. ⋯ Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.