# Clinical Decision Support: Using Logistic Regression to Diagnose COPD and CHF ©2012 Wayne G. Fischer, PhD 1 COPD patient inclusion criteria: Discharged.

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Clinical Decision Support: Using Logistic Regression to Diagnose COPD and CHF ©2012 Wayne G. Fischer, PhD 1 COPD patient inclusion criteria: Discharged 01Feb  31Oct2011 > 40 years of age Primary dx of COPD, or any one or more of the 11 “indicator” variables All multiple encounters, no matter when COPD first diagnosed

What is Logistic Regression? 2 ©2012 Wayne G. Fischer, PhD Start with the sigmoid…the “s-curve”: Probability X (predictor or indicator variable) 0 = absent 1 = present Condition / Event f

©2012 Wayne G. Fischer, PhD 3 What is Logistic Regression? (cont’d)

©2012 Wayne G. Fischer, PhD 4 Stepwise LR  7 of 15 predictors significant

©2012 Wayne G. Fischer, PhD Diagnostics: 7-term model 5

©2012 Wayne G. Fischer, PhD (more) Diagnostics: 7-term model 6

©2012 Wayne G. Fischer, PhD Receiver Operating Characteristic (ROC) Curve Explained (sensitivity vs. specificity + cutoff) 7 A B

©2012 Wayne G. Fischer, PhD Receiver Operating Characteristic (ROC) Curve Explained (cont’d) 8 ROC curve generated using various cutoff points (e.g., A and B are two different cutoff points)

©2012 Wayne G. Fischer, PhD ROC Curve: 7-term model 9 Power = 1 - β α False Positive (1 – Specificity)

©2012 Wayne G. Fischer, PhD ROC Table: 7-term model (see Word file) Choosing a Cutoff Point  10 False + True +

©2012 Wayne G. Fischer, PhD 3-term model: CC + Bronch + Methylpred 11

©2012 Wayne G. Fischer, PhD But, significant Lack of Fit (LOF) 12

©2012 Wayne G. Fischer, PhD ROC Curve: 3-term model 13 Power = 1 - β α False Positive (1 – Specificity)

©2012 Wayne G. Fischer, PhD ROC Table: 3-term model  Choosing a Cutoff Point 14 False + True +

CHF Prediction using Logistic Regression ©2012 Wayne G. Fischer, PhD 15 CHF patient inclusion criteria: Discharged 01Feb  30Nov2011 Primary dx of CHF, or any one or more of the “indicator” variables Age > 40 years, up to 100 years

©2012 Wayne G. Fischer, PhD “All In” Model – 11 predictors 16

©2012 Wayne G. Fischer, PhD Parameter Estimates – “All In” model 17

©2012 Wayne G. Fischer, PhD ROC Curve: “All In” model 18 Power = 1 - β False Positive (1 – Specificity) α

©2012 Wayne G. Fischer, PhD ROC Table: “All In” model (see Word file) 19 False + True + Choosing a Cutoff Point 

©2012 Wayne G. Fischer, PhD 3-term Model: CC + Lasix + NT-proBNP 20

©2012 Wayne G. Fischer, PhD LoF and Param Estimates: 3-Term Model 21

©2012 Wayne G. Fischer, PhD ROC Curve: 3-Term Model 22 Power = 1 - β False Positive (1 – Specificity) α

©2012 Wayne G. Fischer, PhD ROC Table Values: 3-Term Model Choosing a cutoff point 23 False + True +

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