Predicting Mortality after Coronary Artery Bypass Surgery

1998 ◽  
Vol 18 (2) ◽  
pp. 229-235 ◽  
Author(s):  
◽  
Jack V. Tu ◽  
Milton C. Weinstein ◽  
Barbara J. McNeil ◽  
C. David Naylor
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Coronary Artery ◽  
Regression Model ◽  
Regression Models ◽  
Artery Bypass ◽  
Training Set ◽  
Cabg Surgery ◽  
Logistic Regression Models ◽  
Test Sets

Objective. To compare the abilities of artificial neural network and logistic regression models to predict the risk of in-hospital mortality after coronary artery bypass graft (CABG) surgery. Methods. Neural network and logistic regression models were developed using a training set of 4,782 patients undergoing CABG surgery in Ontario, Canada, in 1991, and they were validated in two test sets of 5,309 and 5,517 patients having CABG surgery in 1992 and 1993, respectively. Results. The probabilities predicted from a fully trained neural network were similar to those of a “saturated” regression model, with both models detecting all possible interactions in the training set and validating poorly in the two test sets. A second neural network was developed by cross-validating a network against a new set of data and terminating network training early to create a more generalizable model. A simple “main effects” regression model without any interaction terms was also developed. Both of these models validated well, with areas under the receiver operating characteristic curves of 0.78 and 0.77 (p > 0.10) in the 1993 test set. The predictions from the two models were very highly correlated (r = 0.95). Conclusions. Artificial neural networks and logistic regression models learn similar relationships between patient characteristics and mortality after CABG surgery.

Prediction of Prolonged Ventilation after Coronary Artery Bypass Grafting: Data from an Artificial Neural Network

2017 ◽  
Vol 20 (1) ◽  
pp. 007 ◽  
Author(s):  
Eric Stephen Wise ◽  
David P. Stonko ◽  
Zachary A. Glaser ◽  
Kelly L. Garcia ◽  
Jennifer J. Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Ventilation ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Cardiopulmonary Bypass ◽  
Coronary Artery ◽  
Early Identification ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Prolonged Ventilation

Objectives: The need for mechanical ventilation 24 hours after coronary artery bypass grafting (CABG) is considered a morbidity by the Society of Thoracic Surgeons. The purpose of this investigation was twofold: to identify simple preoperative patient factors independently associated with prolonged ventilation and to optimize prediction and early identification of patients prone to prolonged ventilation using an artificial neural network (ANN).Methods: Using the institutional Adult Cardiac Database, 738 patients who underwent CABG since 2005 were reviewed for preoperative factors independently associated with prolonged postoperative ventilation. Prediction of prolonged ventilation from the identified variables was modeled using both “traditional” multiple logistic regression and an ANN. The two models were compared using Pearson r2 and area under the curve (AUC) parameters.Results: Of 738 included patients, 14% (104/738) required mechanical ventilation ≥ 24 hours postoperatively. Upon multivariate analysis, higher body-mass index (BMI; odds ratio [OR] 1.10 per unit, P < 0.001), lower ejection fraction (OR 0.97 per %, P = 0.01) and use of cardiopulmonary bypass (OR 2.59, P = 0.02) were independently predictive of prolonged ventilation. The Pearson r2 and AUC of the multivariate nominal logistic regression model were 0.086 and 0.698 ± 0.05, respectively; analogous statistics of the ANN model were 0.159 and 0.732 ± 0.05, respectively.BMI, ejection fraction and cardiopulmonary bypass represent three simple factors that may predict prolonged ventilation after CABG. Early identification of these patients can be optimized using an ANN, an emerging paradigm for clinical outcomes modeling that may consider complex relationships among these variables.

scholarly journals Aplicación de modelos de regresión logística en metodología observacional: modalidades de competición en la iniciación al fútbol.

2015 ◽  
Vol 32 (1) ◽  
pp. 288 ◽  
Author(s):  
Daniel Lapresa ◽  
Javier Arana ◽  
M.Teresa Anguera ◽  
J.Ignacio Pérez-Castellanos ◽  
Mario Amatria
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Multiple Logistic Regression ◽  
Observational Methodology ◽  
Logistic Regression Models ◽  
Game Format ◽  
Simple Logistic

This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.

Ordinal regression model and the linear regression model were superior to the logistic regression models

2006 ◽  
Vol 59 (5) ◽  
pp. 448-456 ◽  
Author(s):  
Colleen M. Norris ◽  
William A. Ghali ◽  
L. Duncan Saunders ◽  
Rollin Brant ◽  
Diane Galbraith ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Ordinal Regression ◽  
Logistic Regression Models ◽  
Ordinal Regression Model

scholarly journals Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1517
Author(s):  
Hao Yang Teng ◽  
Zhengjun Zhang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Treatment Effects ◽  
Real Data ◽  
Medical Data ◽  
Severe Problem ◽  
New Model ◽  
Logistic Regression Models

Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications, the model provides both significant and more meaningful results.

scholarly journals Predicting Early Transplant Failure: Neural Network Versus Logistic Regression Models

2009 ◽  
Vol 3 (1) ◽  
pp. 14-21 ◽  
Author(s):  
Vicente Ibanez ◽  
Eugenia Pareja ◽  
Antonio J. Serrano ◽  
Juan Jose Vila ◽  
Santiago Perez ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Transplant Failure

scholarly journals Prediction of independence in bowel function after spinal cord injury: validation of a logistic regression model

Spinal Cord ◽  
2020 ◽  
Author(s):  
Omar Khan ◽  
Jetan H. Badhiwala ◽  
Michael G. Fehlings
Keyword(s):  
Spinal Cord ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Clinical Utility ◽  
Logistic Regression Model ◽  
Bowel Function ◽  
Motor Score ◽  
Logistic Regression Models ◽  
Cord Injury

Abstract Study design Retrospective analysis of prospectively collected data. Objectives Recently, logistic regression models were developed to predict independence in bowel function 1 year after spinal cord injury (SCI) on a multicenter European SCI (EMSCI) dataset. Here, we evaluated the external validity of these models against a prospectively accrued North American SCI dataset. Setting Twenty-five SCI centers in the United States and Canada. Methods Two logistic regression models developed by the EMSCI group were applied to data for 277 patients derived from three prospective multicenter SCI studies based in North America. External validation was evaluated for both models by assessing their discrimination, calibration, and clinical utility. Discrimination and calibration were assessed using ROC curves and calibration curves, respectively, while clinical utility was assessed using decision curve analysis. Results The simplified logistic regression model, which used baseline total motor score as the predictor, demonstrated the best performance, with an area under the ROC curve of 0.869 (95% confidence interval: 0.826–0.911), a sensitivity of 75.5%, and a specificity of 88.5%. Moreover, the model was well calibrated across the full range of observed probabilities and displayed superior clinical benefit on the decision curve. Conclusions A logistic regression model using baseline total motor score as a predictor of independent bowel function 1 year after SCI was successfully validated against an external dataset. These findings provide evidence supporting the use of this model to enhance the care for individuals with SCI.

scholarly journals A-140 Single-Item Screening for Anxiety and Depression using the Neurobehavioral Symptom Inventory

2020 ◽  
Vol 35 (6) ◽  
pp. 933-933
Author(s):  
Rolin S ◽  
Kitchen Andren K ◽  
Mullen C ◽  
Kurniadi N ◽  
Davis J
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Anxiety And Depression ◽  
Average Score ◽  
Characteristic Analysis ◽  
Logistic Regression Models ◽  
Symptom Inventory

Abstract Objective Previous research in a Veterans Affairs sample proposed using single items on the Neurobehavioral Symptom Inventory (NSI) to screen for anxiety (item 19) and depression (item 20). This study examined the approach in an outpatient physical medicine and rehabilitation sample. Method Participants (N = 84) underwent outpatient neuropsychological evaluation using the NSI, BDI-II, GAD-7, MMPI-2-RF, and Memory Complaints Inventory (MCI) among other measures. Anxiety and depression were psychometrically determined via cutoffs on the GAD-7 (&gt;4) and MMPI-2-RF ANX (&gt;64 T), and BDI-II (&gt;13) and MMPI-2-RF RC2 (&gt;64 T), respectively. Analyses included receiver operating characteristic analysis (ROC) and logistic regression. Logistic regression models used dichotomous anxiety and depression as outcomes and relevant NSI items and MCI average score as predictors. Results ROC analysis using NSI items to classify cases showed area under the curve (AUC) values of .77 for anxiety and .85 for depression. The logistic regression model predicting anxiety correctly classified 80% of cases with AUC of .86. The logistic regression model predicting depression correctly classified 79% of cases with AUC of .88. Conclusion Findings support the utility of NSI anxiety and depression items as screening measures in a rehabilitation population. Consideration of symptom validity via the MCI improved classification accuracy of the regression models. The approach may be useful in other clinical settings for quick assessment of psychological issues warranting further evaluation.

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