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Table 2 Univariate and multivariate COX regression of factors associated with disease progression

From: A multimodal nomogram for predicting disease progression in diabetic patients with coronary artery disease: integrating clinical, ultrasound, and angiographic data

characteristics

Univariate COX regression

multivariate COX regression

VIF

OR

CI

P

OR

CI

P

Lesion

1.997

1.171–3.404

0.011

2.259

1.304–3.915

0.004

1.07

MI

1.657

1.062–2.587

0.026

1.623

1.035–2.544

0.035

1.02

Alcoholism

0.812

0.431–1.532

0.521

    

smoke

1.11

0.719–1.713

0.638

    

Hypertension

1.003

0.601–1.674

0.99

    

K

2.233

1.16–4.302

0.016

    

Cr

1.01

1.006–1.013

< 0.001

1.006

1.002–1.01

0.002

1.41

BUN

1.116

1.069–1.165

< 0.001

    

CKMB

0.998

0.989–1.007

0.622

    

CK

0.999

0.998–1.001

0.31

    

LDL

0.838

0.666–1.054

0.131

    

HDL

0.506

0.18–1.426

0.198

    

TC

0.875

0.726–1.054

0.161

    

EF

0.259

0.153–0.439

< 0.001

0.265

0.148–0.477

< 0.001

1.25

SV

1.015

0.997–1.033

0.104

    

LVDs

1.049

1.02–1.078

0.001

    

LVDd

1.066

1.03–1.104

< 0.001

    

IVS

1.064

0.92–1.23

0.402

    

AO

1.005

0.945–1.07

0.866

    

years

1.004

0.983–1.026

0.699

    

genders

1.104

0.703–1.733

0.668

    
  1. Note: AO: Aortic Diameter; IVS: Interventricular Septum; LVDd: Left Ventricular Diastolic Diamete; LVDs: Left Ventricular Systolic Diameter; SV: Stroke Volume; EF: Ejection Fraction; TC: Total Cholesterol; HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; CK: Creatine Kinase; CKMB: Creatine Kinase Isoenzyme MB; BUN: Blood Urea Nitrogen; Cr: Creatinine; K: Potassium; MI: History of myocardial infarction; smoke: smoking history; Alcoholism: drinking history; Lesion: Number of coronary lesions; CI: Confidence Interval; OR: Odds Ratio; VIF: Variance Inflation Factor;
  2. VIF values were calculated to assess multicollinearity among predictors retained in the final multivariate model. All VIF values were < 2, indicating no significant multicollinearity