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Table 4 Logistic regression for predicting risk stratifications of PCa based on clinic features

From: Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte

Clinic Features

Univariate logistic regression

Multivariate logistic regression

OR (95%CL)

p-value

OR (95%CL)

p-value

Age

1.286 (1.129–1.465)

0.000*

1.137 (1.008–1.284)

0.037 *

Alkaline phosphatase

1.171 (1.024–1.339)

0.021*

1.121 (0.998–1.260)

0.054

B cells (CD3−CD19+)

0.870 (0.761–0.995)

0.043*

0.844 (0.746–0.956)

0.008 *

Interleukin-1β

1.148 (1.003–1.313)

0.045*

1.095 (0.972–1.234)

0.133

Interleukin-2R

1.189 (1.041 1.359)

0.011*

1.120 (0.996–1.260)

0.059

Lactate dehydrogenase

1.147 (1.003–1.313)

0.045*

1.110 (0.987–1.248)

0.082

Neutrophil percentage

0.839 (0.734–0.959)

0.010*

0.799 (0.708–0.902)

0.000 *

PSA

1.379 (1.215–1.564)

0.000*

1.228 (1.084–1.391)

0.001 *

Th/Ts

1.155 (1.009–1.320)

0.036*

1.200 (1.069–1.346)

0.002 *

  1. *p < 0.05. Values in bold indicate independent predictors in the multivariate logistic regression