A dynamic prediction model for time‐to‐peak

2021 ◽  
Author(s):  
Mistaya Langridge ◽  
Ed McBean ◽  
Hossein Bonakdari ◽  
Bahram Gharabaghi
Keyword(s):  
Prediction Model ◽  
Dynamic Prediction ◽  
Time To Peak

scholarly journals Dynamic prediction for clinically relevant pancreatic fistula: a novel prediction model for laparoscopic pancreaticoduodenectomy

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Runwen Liu ◽  
Yunqiang Cai ◽  
He Cai ◽  
Yajia Lan ◽  
Lingwei Meng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pancreatic Fistula ◽  
Postoperative Pancreatic Fistula ◽  
Laparoscopic Pancreaticoduodenectomy ◽  
C Reactive Protein ◽  
Dynamic Prediction ◽  
Albumin Ratio ◽  
Drainage Fluid ◽  
Reactive Protein ◽  
Baseline Factors

Abstract Background With the recent emerge of dynamic prediction model on the use of diabetes, cardiovascular diseases and renal failure, and its advantage of providing timely predicted results according to the fluctuation of the condition of the patients, we aim to develop a dynamic prediction model with its corresponding risk assessment chart for clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by combining baseline factors and postoperative time-relevant drainage fluid amylase level and C-reactive protein-to-albumin ratio. Methods We collected data of 251 patients undergoing LPD at West China Hospital of Sichuan University from January 2016 to April 2019. We extracted preoperative and intraoperative baseline factors and time-window of postoperative drainage fluid amylase and C-reactive protein-to-albumin ratio relevant to clinically relevant pancreatic fistula by performing univariate and multivariate analyses, developing a time-relevant logistic model with the evaluation of its discrimination ability. We also established a risk assessment chart in each time-point. Results The proportion of the patients who developed clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy was 7.6% (19/251); preoperative albumin and creatine levels, as well as drainage fluid amylase and C-reactive protein-to-albumin ratio on postoperative days 2, 3, and 5, were the independent risk factors for clinically relevant postoperative pancreatic fistula. The cut-off points of the prediction value of each time-relevant logistic model were 14.0% (sensitivity: 81.9%, specificity: 86.5%), 8.3% (sensitivity: 85.7%, specificity: 79.1%), and 7.4% (sensitivity: 76.9%, specificity: 85.9%) on postoperative days 2, 3, and 5, respectively, the area under the receiver operating characteristic curve was 0.866 (95% CI 0.737–0.996), 0.896 (95% CI 0.814–0.978), and 0.888 (95% CI 0.806–0.971), respectively. Conclusions The dynamic prediction model for clinically relevant postoperative pancreatic fistula has a good to very good discriminative ability and predictive accuracy. Patients whose predictive values were above 14.0%, 8.3%, and 7.5% on postoperative days 2, 3, and 5 would be very likely to develop clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

scholarly journals S136. A NOVEL APPROACH FOR DEVELOPING PREDICTION MODEL OF TRANSITION TO PSYCHOSIS: DYNAMIC PREDICTION USING JOINT MODELLING

2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S378-S379 ◽  
Author(s):  
Hok Pan Yuen ◽  
Andrew Mackinnon ◽  
Jessica Hartmann ◽  
Paul Amminger ◽  
Connie Markulev ◽  
...  
Keyword(s):  
Prediction Model ◽  
Dynamic Prediction ◽  
Joint Modelling ◽  
Novel Approach

scholarly journals Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study

2020 ◽  
Vol 15 (9) ◽  
Author(s):  
Marieke Welten ◽  
Alet H. Wijga ◽  
Marleen Hamoen ◽  
Ulrike Gehring ◽  
Gerard H. Koppelman ◽  
...  
Keyword(s):  
Cohort Study ◽  
High Risk ◽  
Young Children ◽  
Prediction Model ◽  
Dynamic Prediction ◽  
Internal Validation

Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

2014 ◽  
Vol 986-987 ◽  
pp. 524-528 ◽  
Author(s):  
Ting Jing Ke ◽  
Min You Chen ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Model Parameters ◽  
Short Term ◽  
Dynamic Prediction ◽  
Power Dynamic ◽  
Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power forecasting.

scholarly journals Application Study on the Dynamic Prediction Model for Determining the Mining Subsidence

2016 ◽  
Vol 9 (2) ◽  
pp. 80-85 ◽  
Author(s):  
H. F. HU ◽  
◽  
X. G. LIAN ◽  
Y. LI ◽  
◽  
...  
Keyword(s):  
Prediction Model ◽  
Mining Subsidence ◽  
Dynamic Prediction ◽  
Application Study

scholarly journals Path Tracking of Wheeled Mobile Robots Based on Dynamic Prediction Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39690-39701 ◽  
Author(s):  
Guoxing Bai ◽  
Li Liu ◽  
Yu Meng ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Mobile Robots ◽  
Path Tracking ◽  
Wheeled Mobile Robots ◽  
Dynamic Prediction
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