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

Development and internal validation of a clinical prediction model for acute adjacent vertebral fracture after vertebral augmentation

2022 ◽  
Vol 104-B (1) ◽  
pp. 97-102
Author(s):  
Yasukazu Hijikata ◽  
Tsukasa Kamitani ◽  
Masayuki Nakahara ◽  
Shinji Kumamoto ◽  
Tsubasa Sakai ◽  
...  
Keyword(s):  
Decision Making ◽  
Vertebral Fracture ◽  
High Risk ◽  
Prediction Model ◽  
Vertebral Augmentation ◽  
Clinical Prediction ◽  
Internal Validation ◽  
Duration Of Symptoms ◽  
Clinical Prediction Model ◽  
Adjacent Vertebral Fracture

Aims To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score. Methods In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism. Results Of the 377 patients used for model derivation, 58 (15%) had an acute AVF postoperatively. The following preoperative measures on multivariable analysis were summarized in the five-point AVA score: intravertebral instability (≥ 5 mm), focal kyphosis (≥ 10°), duration of symptoms (≥ 30 days), intravertebral cleft, and previous history of vertebral fracture. Internal validation showed a mean optimism of 0.019 with a corrected AUC of 0.77. A cut-off of ≤ one point was chosen to classify a low risk of AVF, for which only four of 137 patients (3%) had AVF with 92.5% sensitivity and 45.6% specificity. A cut-off of ≥ four points was chosen to classify a high risk of AVF, for which 22 of 38 (58%) had AVF with 41.5% sensitivity and 94.5% specificity. Conclusion In this study, the AVA score was found to be a simple preoperative method for the identification of patients at low and high risk of postoperative acute AVF. This model could be applied to individual patients and could aid in the decision-making before vertebral augmentation. Cite this article: Bone Joint J 2022;104-B(1):97–102.

scholarly journals Short clinically-based prediction model to forecast transition to psychosis in individuals at clinical high risk state

2019 ◽  
Vol 58 ◽  
pp. 72-79 ◽  
Author(s):  
Magdalena Kotlicka-Antczak ◽  
Michał S. Karbownik ◽  
Konrad Stawiski ◽  
Agnieszka Pawełczyk ◽  
Natalia Żurner ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Risk Estimation ◽  
Clinical High Risk ◽  
Clinical Predictors ◽  
Individual Subject ◽  
Internal Validation ◽  
Replication Studies

AbstractObjective:The predictive accuracy of the Clinical High Risk criteria for Psychosis (CHR-P) regarding the future development of the disorder remains suboptimal. It is therefore necessary to incorporate refined risk estimation tools which can be applied at the individual subject level. The aim of the study was to develop an easy-to use, short refined risk estimation tool to predict the development of psychosis in a new CHR-P cohort recruited in European country with less established early detection services.Methods:A cohort of 105 CHR-P individuals was assessed with the Comprehensive Assessment of At Risk Mental States12/2006, and then followed for a median period of 36 months (25th-75th percentile:10–59 months) for transition to psychosis. A multivariate Cox regression model predicting transition was generated with preselected clinical predictors and was internally validated with 1000 bootstrap resamples.Results:Speech disorganization and unusual thought content were selected as potential predictors of conversion on the basis of published literature. The prediction model was significant (p < 0.0001) and confirmed that both speech disorganization (HR = 1.69; 95%CI: 1.39–2.05) and unusual thought content (HR = 1.51; 95%CI: 1.27–1.80) were significantly associated with transition. The prognostic accuracy of the model was adequate (Harrell’s c- index = 0.79), even after optimism correction through internal validation procedures (Harrell’s c-index = 0.78).Conclusions:The clinical prediction model developed, and internally validated, herein to predict transition from a CHR-P to psychosis may be a promising tool for use in clinical settings. It has been incorporated into an online tool available at:https://link.konsta.com.pl/psychosis. Future external replication studies are needed.

scholarly journals Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study

BMJ Open ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. e023912 ◽  
Author(s):  
Marleen Hamoen ◽  
Yvonne Vergouwe ◽  
Alet H Wijga ◽  
Martijn W Heymans ◽  
Vincent W V Jaddoe ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Prediction Model ◽  
High Blood Pressure ◽  
Characteristic Curve ◽  
Model Development ◽  
Population Based ◽  
The Body ◽  
Dynamic Prediction ◽  
Internal Validation ◽  
Primordial Prevention

ObjectivesTo develop a dynamic prediction model for high blood pressure at the age of 9–10 years that could be applied at any age between birth and the age of 6 years in community-based child healthcare.Design, setting and participantsData were used from 5359 children in a population-based prospective cohort study in Rotterdam, the Netherlands.Outcome measureHigh blood pressure was defined as systolic and/or diastolic blood pressure ≥95th percentile for gender, age and height. Using multivariable pooled logistic regression, the predictive value of characteristics at birth, and of longitudinal information on the body mass index (BMI) of the child until the age of 6 years, was assessed. Internal validation was performed using bootstrapping.Results227 children (4.2%) had high blood pressure at the age of 9–10 years. Final predictors were maternal hypertensive disease during pregnancy, maternal educational level, maternal prepregnancy BMI, child ethnicity, birth weight SD score (SDS) and the most recent BMI SDS. After internal validation, the area under the receiver operating characteristic curve ranged from 0.65 (prediction at age 3 years) to 0.73 (prediction at age 5–6 years).ConclusionsThis prediction model may help to monitor the risk of developing high blood pressure in childhood which may allow for early targeted primordial prevention of cardiovascular disease.

Risk and resilience in high-risk samples: How to promote positive outcomes for young children

2010 ◽  
Author(s):  
Margo Candelaria ◽  
Kathy Katz ◽  
Anna Quigg ◽  
Sarah Oberlander ◽  
Margo Candelaria
Keyword(s):  
High Risk ◽  
Young Children ◽  
Positive Outcomes ◽  
Risk And Resilience

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 A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

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
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