scholarly journals Attributable risk of all-cause mortality in hypertensive adults based on disease risk prediction model: A Chinese cohort study

2020 ◽  
Vol 13 (9) ◽  
pp. 1290-1296
Author(s):  
Weiwei Sun ◽  
Yanhong Huo ◽  
Qingqing Liu ◽  
Andrew C. Ahn ◽  
Jingwei Zhou ◽  
...  
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Disease Risk ◽  
Attributable Risk ◽  
Risk Prediction Model ◽  
All Cause Mortality ◽  
Chinese Cohort

scholarly journals P161 Crohn's disease risk prediction model appropriately stratifies patients' risk for developing disease related complications

2017 ◽  
Vol 11 (suppl_1) ◽  
pp. S159-S159 ◽  
Author(s):  
K.D. Thompson ◽  
L.S. Siegel ◽  
T. MacKenzie ◽  
M.C. Dubinsky ◽  
C.A. Siegel
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Disease Risk ◽  
Risk Prediction Model

A risk prediction model for xerostomia: a retrospective cohort study

Gerodontology ◽  
2015 ◽  
Vol 33 (4) ◽  
pp. 562-568 ◽  
Author(s):  
Alessandro Villa ◽  
Francesco Nordio ◽  
Anita Gohel
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Risk Prediction Model

Development of a Risk Prediction Model for Carbapenem-Resistant Enterobacteriaceae Infection after Liver Transplantation: A Multinational Cohort Study

2021 ◽  
Author(s):  
Maddalena Giannella ◽  
Maristela Freire ◽  
Matteo Rinaldi ◽  
Edson Abdala ◽  
Arianna Rubin ◽  
...  
Keyword(s):  
Liver Transplantation ◽  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Univariate Analysis ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
High Morbidity ◽  
Carbapenem Resistant ◽  
Carbapenem Resistant Enterobacteriaceae

Abstract Background Patients colonized with carbapenem resistant Enterobacteriaceae (CRE) are at higher risk of developing CRE infection after liver transplantation (LT) with associated high morbidity and mortality. Prediction model for CRE infection after LT among carriers could be useful to target preventive strategies. Methods Multinational multicenter cohort study of consecutive adult patients underwent LT and colonized with CRE before or after LT, from January 2010 to December 2017. Risk factors for CRE infection were analyzed by univariate analysis and by Fine-Gray sub-distribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created. Results 840 LT recipients found to be colonized with CRE before (n=203) or after (n=637) LT were enrolled. CRE infection was diagnosed in 250 (29.7%) patients within 19 (IQR 9-42) days after LT. Pre-and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical re-intervention were retained in the prediction model. Median 30 and 60-day predicted risk was 15% (IQR 11-24%) and 21% (IQR 15-33%), respectively. Discrimination and prediction accuracy for CRE infection was acceptable on derivation (AUC 74.6, Brier index 16.3) and bootstrapped validation dataset (AUC 73.9, Brier index 16.6). Decision-curve analysis suggested net benefit of model-directed intervention over default strategies (treat all, treat none) when CRE infection probability exceeded 10%. The risk prediction model is freely available as mobile application at https://idbologna.shinyapps.io/CREPostOLTPredictionModel/. Conclusions Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant.

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