Prioritising patients at high-risk of medication harm: development and validation of risk prediction models

2020 ◽  
Author(s):  
Nazanin Ghahreman Falconer
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Development And Validation

Tu1812 DEVELOPMENT AND VALIDATION OF RISK PREDICTION MODELS FOR HIGH-RISK SERRATED POLYPS AT SCREENING COLONOSCOPY

2020 ◽  
Vol 158 (6) ◽  
pp. S-1171-S-1172
Author(s):  
Zhangyan Lyu ◽  
Xiaosheng He ◽  
Dong Hang ◽  
Kana Wu ◽  
Yin Cao ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Screening Colonoscopy ◽  
Risk Prediction Models ◽  
Serrated Polyps ◽  
Development And Validation

scholarly journals Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Maomao Cao ◽  
He Li ◽  
Dianqin Sun ◽  
Siyi He ◽  
Yadi Zheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Liver Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Prediction Risk

Abstract Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

scholarly journals 1227Colorectal cancer risk prediction models incorporating lifestyle and biomarker data: Results from the EPIC cohort

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Krasimira Aleksandrova ◽  
Robin Reichmann ◽  
Mazda Jenab ◽  
Sabina Rinaldi ◽  
Rudolf Kaaks ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Lifestyle Factors ◽  
Health Concern ◽  
Incidence Density ◽  
Processed Meat ◽  
Risk Prediction Models ◽  
Biomarker Data

Abstract Background Colorectal cancer represents a major public health concern and there is a worrying tendency of increasing incidence rates among younger people in the last decades. Risk stratification of high-risk individuals may aid targeted disease prevention. We therefore aimed to evaluate the predictive value of a wide range of lifestyle and biomarker variables using data within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods A range of lifestyle, anthropometric and dietary variables in 329,885 participants in the EPIC cohort were evaluated as potential predictors for risk of colorectal cancer over 10 years. Biomarker measurements of 41 parameters were available for 1,320 CRC cases and 1,320 controls selected using incidence density matching. Best sets of predictors were selected using elastic net regularization with bootstrapping. Random survival forest was applied as a novel technique to validate the set of selected predictors taking variable interactions into account. Results The results suggested a set of lifestyle factors including age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary that showed good discrimination (Harrell's C-index: 0.710) and excellent calibration. The analyses further revealed a set of biomarkers that increased the predictive performance beyond age, sex and lifestyle factors. Conclusions Risk prediction models based on lifestyle and biomarker data may prove useful in the identification of individuals at high risk for colorectal cancer. Key messages Risk prediction models incorporating lifestyle and biomarker data could contribute to developing strategies for targeted colorectal cancer prevention.

Development and validation of modified risk prediction models for cardiovascular disease and its subtypes: The Hisayama Study

2018 ◽  
Vol 279 ◽  
pp. 38-44 ◽  
Author(s):  
Takanori Honda ◽  
Daigo Yoshida ◽  
Jun Hata ◽  
Yoichiro Hirakawa ◽  
Yuki Ishida ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Development And Validation

scholarly journals Development and validation of risk prediction models for COVID-19 positivity in a hospital setting

2020 ◽  
Vol 101 ◽  
pp. 74-82 ◽  
Author(s):  
Ming-Yen Ng ◽  
Eric Yuk Fai Wan ◽  
Ho Yuen Frank Wong ◽  
Siu Ting Leung ◽  
Jonan Chun Yin Lee ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Hospital Setting ◽  
Risk Prediction Models ◽  
Development And Validation
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