Prediction Model Hadoop-based for High-risk Students

(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.

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Cross-Age Tutoring and the High Risk Students

The Clearing House A Journal of Educational Strategies Issues and Ideas ◽

10.1080/00098655.1974.11478511 ◽

1974 ◽

Vol 48 (5) ◽

pp. 300-302

Author(s):

Joe D. Dallas

Keyword(s):

High Risk ◽

Cross Age Tutoring ◽

High Risk Students

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Academic Support Program for High-Risk Students

Journal of Nursing Education ◽

10.3928/0148-4834-19961001-11 ◽

1996 ◽

Vol 35 (7) ◽

pp. 331-333

Author(s):

Brenda S Parkes ◽

Sharon M Kirkpatrick

Keyword(s):

High Risk ◽

Academic Support ◽

Support Program ◽

High Risk Students

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Standardized Tests and Timed Curriculum-Based Assessments: A Comparison of Two Methods for Screening High-Risk Students

Journal of Psychoeducational Assessment ◽

10.1177/073428298700500301 ◽

1987 ◽

Vol 5 (3) ◽

pp. 185-193 ◽

Cited By ~ 3

Author(s):

Bonnie G. Joyce ◽

William D. Wolking

Keyword(s):

High Risk ◽

Standardized Tests ◽

High Risk Students

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Polygenic risk scores predict diabetic complications and their response to therapy

10.1101/19010785 ◽

2019 ◽

Author(s):

J. Tremblay ◽

M. Haloui ◽

F. Harvey ◽

R. Tahir ◽

F.-C. Marois-Blanchet ◽

...

Keyword(s):

High Risk ◽

Prediction Model ◽

Risk Group ◽

Intensive Therapy ◽

Cardiovascular Death ◽

Response To Therapy ◽

Low Risk ◽

Number Needed To Treat ◽

Genome Wide Association Studies ◽

Increased Risk

AbstractType 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction can lead to timely intervention and better outcomes. Through summary statistics of meta-analyses of published genome-wide association studies performed in over 1.2 million of individuals, we combined 9 PRS gathering genomic variants associated to cardiovascular and renal diseases and their key risk factors into one logistic regression model, to predict micro- and macrovascular endpoints of diabetes. Its clinical utility in predicting complications of diabetes was tested in 4098 participants with diabetes of the ADVANCE trial followed during a period of 10 years and replicated it in three independent non-trial cohorts. The prediction model adjusted for ethnicity, sex, age at onset and diabetes duration, identified the top 30% of ADVANCE participants at 3.1-fold increased risk of major micro- and macrovascular events (p=6.3×10−21 and p=9.6×10−31, respectively) and at 4.4-fold (p=6.8×10−33) increased risk of cardiovascular death compared to the remainder of T2D subjects. While in ADVANCE overall, combined intensive therapy of blood pressure and glycaemia decreased cardiovascular mortality by 24%, the prediction model identified a high-risk group in whom this therapy decreased mortality by 47%, and a low risk group in whom the therapy had no discernable effect. Patients with high PRS had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. This novel polygenic prediction model identified people with diabetes at low and high risk of complications and improved targeting those at greater benefit from intensive therapy while avoiding unnecessary intensification in low-risk subjects.

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First term probation: models for identifying high risk students

30th Annual Frontiers in Education Conference. Building on A Century of Progress in Engineering Education. Conference Proceedings (IEEE Cat. No.00CH37135) ◽

10.1109/fie.2000.897696 ◽

2002 ◽

Cited By ~ 5

Author(s):

A. Scalise ◽

M. Besterfield-Sacre ◽

L. Shuman ◽

H. Wolfe

Keyword(s):

High Risk ◽

High Risk Students

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Development and Validation of a Clostridium difficile Infection Risk Prediction Model

Infection Control and Hospital Epidemiology ◽

10.1086/658944 ◽

2011 ◽

Vol 32 (4) ◽

pp. 360-366 ◽

Cited By ~ 62

Author(s):

Erik R. Dubberke ◽

Yan Yan ◽

Kimberly A. Reske ◽

Anne M. Butler ◽

Joshua Doherty ◽

...

Keyword(s):

High Risk ◽

Clostridium Difficile ◽

Prediction Model ◽

Risk Prediction ◽

Predictive Accuracy ◽

Medical Center ◽

Tertiary Care ◽

Risk Prediction Model ◽

Brier Score ◽

Tertiary Care Medical Center

Objective.To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.Design and Setting.Retrospective cohort study in a tertiary care medical center.Patients.Patients admitted to the hospital for at least 48 hours during the calendar year 2003.Methods.Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.Results.A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).Conclusions.The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.

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Abstract 2701: Multicenter Study For Prediction And Validation Of Intracranial Hemorrhage Risk In The Natural History Of Brain Arteriovenous Malformation Patients

Stroke ◽

10.1161/str.43.suppl_1.a2701 ◽

2012 ◽

Vol 43 (suppl_1) ◽

Author(s):

Helen Kim ◽

Tony Pourmohamad ◽

Charles E McCulloch ◽

Michael T Lawton ◽

Jay P Mohr ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Natural History ◽

Prediction Model ◽

Intracranial Hemorrhage ◽

Prediction Models ◽

Statistical Significance ◽

Venous Drainage ◽

Cox Proportional Hazards ◽

Natural Course

Background: BAVM is an important cause of intracranial hemorrhage (ICH) in younger persons. Accurate and reliable prediction models for determining ICH risk in the natural history course of BAVM patients are needed to help guide management. The purpose of this study was to develop a prediction model of ICH risk, and validate the performance independently using the Multicenter AVM Research Study (MARS). Methods: We used 3 BAVM cohorts from MARS: the UCSF Brain AVM Study Project (n=726), Columbia AVM Study (COL, n=640), and Scottish Intracranial Vascular Malformation Study (SIVMS, n=218). Cox proportional hazards analysis of time-to-ICH in the natural course after diagnosis was performed, censoring patients at first treatment, death, or last visit, up to 10 years. UCSF served as the model development cohort. We chose a simple model, including known risk factors that are reliably measured across cohorts (age at diagnosis, gender, initial hemorrhagic presentation, and deep venous drainage); variables were included without regard to statistical significance. Tertiles of predicted probabilities corresponding to low, medium, and high risk were obtained from UCSF and risk thresholds were validated in COL and SIVMS using Kaplan-Meier survival curves and log-rank tests (to assess whether the model discriminated between risk categories). Results: Overall, 82 ICH events occurred during the natural course: 28 in UCSF, 41 in COL, and 13 in SIVMS. Effects in the prediction model (estimated from UCSF data) were: age in decades (HR=1.1, 95% CI=0.9-1.4, P=0.41), initial hemorrhagic presentation (HR=3.6, 95% CI=1.5-8.6, P=0.01), male gender (HR=1.1, 95% CI=0.48-2.6; P=0.81), and deep venous drainage (HR=0.8, 95% CI=0.2-2.8 P=0.72). Tertiles of ICH risk are shown in the Figure , demonstrating good separation of curves into low, medium and high risk after 3 years in UCSF (left, log-rank P=0.05). The model validated well in the COL referral cohort with better discrimination of curves (middle, P<0.001). In SIMVS, a population-based study, the model separated curves in the earlier years but a consistent pattern was not observed (right, P=0.51), possibly due to the small number of ICH events. Conclusion: Our current prediction model for predicting ICH risk in the natural history course validates well in another referral population, but not as well in a population cohort. Inclusion of additional cohorts and risk factors after data harmonization may improve overall prediction and discrimination of ICH risk, and provide a generalizable model for clinical application.

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