Table 3

Prognostic factors associated with PFS and OS in multivariate models

VariableHR95% CIP value
LowerUpper
Multivariate Cox regression for PFS     
 Clb-R vs Clb 0.385 0.217 0.684 .001 
 Clb-G vs Clb 0.149 0.081 0.274 <.001 
 Unmutated IGHV 3.278 2.081 5.163 <.001 
 Del(17p) and/or TP53mut 3.079 1.550 6.116 .001 
Multivariate Cox regression for OS     
 β2M >3.5 mg/L 1.879 1.020 3.459 .043 
 Complex karyotypes 2.682 1.366 5.264 .004 
 Unmutated IGHV 3.061 1.482 6.321 .003 
POT1mut 3.997 1.581 10.106 .003 
 Binet C vs A/B 4.251 2.193 8.240 <.001 
VariableHR95% CIP value
LowerUpper
Multivariate Cox regression for PFS     
 Clb-R vs Clb 0.385 0.217 0.684 .001 
 Clb-G vs Clb 0.149 0.081 0.274 <.001 
 Unmutated IGHV 3.278 2.081 5.163 <.001 
 Del(17p) and/or TP53mut 3.079 1.550 6.116 .001 
Multivariate Cox regression for OS     
 β2M >3.5 mg/L 1.879 1.020 3.459 .043 
 Complex karyotypes 2.682 1.366 5.264 .004 
 Unmutated IGHV 3.061 1.482 6.321 .003 
POT1mut 3.997 1.581 10.106 .003 
 Binet C vs A/B 4.251 2.193 8.240 <.001 

The table lists independent variables associated with time-to-event outcome (PFS or OS) in multivariate analysis. Briefly, all parameters with a univariate P < .10 in univariate analysis (supplemental Table 3) were entered into multivariate Cox proportional hazards models to identify variables with significant and independent prognostic value.

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