Dynamic Risk Prediction for Cardiovascular Disease: An Illustration Using the ARIC Study

2017 ◽  
pp. 47-65 ◽  
Author(s):  
Jessica K. Barrett ◽  
Michael J. Sweeting ◽  
Angela M. Wood
Keyword(s):  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Dynamic Risk ◽  
Dynamic Risk Prediction ◽  
Aric Study

scholarly journals Development and Validation of a Dynamic Risk Prediction Model to Forecast Psychosis Onset in Patients at Clinical High Risk

2019 ◽  
Author(s):  
Erich Studerus ◽  
Katharina Beck ◽  
Paolo Fusar-Poli ◽  
Anita Riecher-Rössler
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Clinical High Risk ◽  
Risk Calculator ◽  
Joint Models ◽  
Prognostic Performance ◽  
Dynamic Risk ◽  
Online Risk ◽  
Prognostic Accuracy ◽  
Dynamic Risk Prediction

Abstract The prediction of outcomes in patients at Clinical High Risk for Psychosis (CHR-P) almost exclusively relies on static data obtained at a single snapshot in time (ie, baseline data). Although the CHR-P symptoms are intrinsically evolving over time, available prediction models cannot be dynamically updated to reflect these changes. Hence, the aim of this study was to develop and internally validate a dynamic risk prediction model (joint model) and to implement this model in a user-friendly online risk calculator. Furthermore, we aimed to explore the prognostic performance of extended dynamic risk prediction models and to compare static with dynamic prediction. One hundred ninety-six CHR-P patients were recruited as part of the “Basel Früherkennung von Psychosen” (FePsy) study. Psychopathology and transition to psychosis was assessed at regular intervals for up to 5 years using the Brief Psychiatric Rating Scale-Expanded (BPRS-E). Various specifications of joint models were compared with regard to their cross-validated prognostic performance. We developed and internally validated a joint model that predicts psychosis onset from BPRS-E disorganization and years of education at baseline and BPRS-E positive symptoms during the follow-up with good prognostic performance. The model was implemented as online risk calculator (http://www.fepsy.ch/DPRP/). The use of extended joint models slightly increased the prognostic accuracy compared to basic joint models, and dynamic models showed a higher prognostic accuracy than static models. Our results confirm that extended joint modeling could improve the prediction of psychosis in CHR-P patients. We implemented the first online risk calculator that can dynamically update psychosis risk prediction.

Dynamic risk prediction based on discriminant analysis for maize drought disaster

2012 ◽  
Vol 65 (3) ◽  
pp. 1275-1284 ◽  
Author(s):  
Qi Zhang ◽  
Jiquan Zhang ◽  
Denghua Yan ◽  
Yulong Bao
Keyword(s):  
Discriminant Analysis ◽  
Risk Prediction ◽  
Dynamic Risk ◽  
Drought Disaster ◽  
Dynamic Risk Prediction

scholarly journals A tutorial on dynamic risk prediction of a binary outcome based on a longitudinal biomarker

2019 ◽  
Vol 62 (2) ◽  
pp. 398-413
Author(s):  
Rana Dandis ◽  
Steven Teerenstra ◽  
Leon Massuger ◽  
Fred Sweep ◽  
Yalck Eysbouts ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Binary Outcome ◽  
Dynamic Risk ◽  
Dynamic Risk Prediction ◽  
Longitudinal Biomarker

Dynamic risk prediction in Primary Biliary Cholangitis using the UK-PBC cohort

2017 ◽  
Vol 49 (1) ◽  
pp. e32-e33
Author(s):  
M. Carbone ◽  
A. Nardi ◽  
H.F. Ainsworth ◽  
M.A. Heneghan ◽  
G.M. Hirschfield ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Primary Biliary Cholangitis ◽  
Dynamic Risk ◽  
Dynamic Risk Prediction ◽  
The Uk

scholarly journals Feasibility of dynamic risk prediction for hepatocellular carcinoma development in patients with chronic hepatitis B

2017 ◽  
Vol 38 (4) ◽  
pp. 676-686 ◽  
Author(s):  
Mi Young Jeon ◽  
Hye Won Lee ◽  
Seung Up Kim ◽  
Beom Kyung Kim ◽  
Jun Yong Park ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Risk Prediction ◽  
Dynamic Risk ◽  
Dynamic Risk Prediction

scholarly journals Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients

2021 ◽  
Vol 3 (11) ◽  
pp. e0580
Author(s):  
Amy M. Shui ◽  
Phillip Kim ◽  
Vamsi Aribindi ◽  
Chiung-Yu Huang ◽  
Mi-Ok Kim ◽  
...  
Keyword(s):  
Critical Care ◽  
Risk Prediction ◽  
Dynamic Risk ◽  
Pressure Injury ◽  
Dynamic Risk Prediction ◽  
Hospital Acquired ◽  
Critical Care Patients

Dynamic risk prediction in Primary Biliary Cholangitis using the UK-PBC cohort

2017 ◽  
Vol 66 (1) ◽  
pp. S547-S548
Author(s):  
M. Carbone ◽  
A. Nardi ◽  
H. Ainsworth ◽  
M.A. Heneghan ◽  
G.M. Hirschfield ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Primary Biliary Cholangitis ◽  
Dynamic Risk ◽  
Dynamic Risk Prediction ◽  
The Uk
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