EHA Library - The official digital education library of European Hematology Association (EHA)

THE INTERNATIONAL PROGNOSTIC INDEX FOR PATIENTS WITH CHRONIC LYMPHOCYTIC LEUKEMIA (CLL-IPI) – AN INTERNATIONAL META-ANALYSIS
(Abstract release date: 05/21/15) EHA Library. Bahlo J. 06/14/15; 103401; S791
Jasmin Bahlo
Jasmin Bahlo
Contributions
Abstract
Abstract: S791

Type: Oral Presentation

Presentation during EHA20: From 14.06.2015 08:15 to 14.06.2015 08:30

Location: Room A7

Background

Chronic lymphocytic leukemia (CLL) is a disease with a highly heterogeneous course, ranging from mild to aggressive. Therefore, prediction of outcome is important in clinical practice to avoid too aggressive treatment in patients with a rather mild course of disease and to identify patients in whom standard treatment is likely to fail. In the era of more effective treatments for CLL, the established clinical staging systems [Binet/Rai] do not accurately discriminate between prognostic subgroups. Despite the introduction of several new prognostic markers, there is no system integrating the major clinical, biological and genetic variables into one widely accepted prognostic system.

 



Aims

To develop an internationally applicable prognostic index for CLL patients [CLL-IPI], we performed a comprehensive analysis of 26 prognostic factors including clinical, biological and genetic markers.

 



Methods


Results

Based on 1192 (52%) patients from the training dataset, 5 independent predictors for OS were identified: age [cut off, 65 yr], clinical stage [Binet A/Rai 0 vs. Binet B-C/Rai I-IV], del(17p) and/or TP53 mutation, IGHV mutation status (MS) and serum β2-microglobulin (B2M) [cut off, 3.5 mg/L]. Applying weighted grading of the independent factors based on the regression parameters, a prognostic index was derived separating 4 different risk groups [low (score 0-1), intermediate (score 2-3), high (score 4-6) and very high risk (score 7-10)] with significantly different OS [93%, 79%, 64% and 23% OS at 5 yr for the low, intermediate, high and very high risk group respectively, p<0.001; C-statistic c=0.724 (95% confidence interval (CI), 0.69-0.76)] (figure 1A). This multivariable model was confirmed on the internal validation dataset [575 (49%) patients; 91%, 80%, 52% and 19% 5-yr OS, p<0.001; c=0.777 (95% CI, 0.73-0.82)] (figure 1B). In the external Mayo dataset the 5-yr OS for the CLL-IPI risk groups were 97%, 91%, 68% and 21% [p<0.001; c=0.790 (95% CI, 0.74 – 0.85)] (figure 1C). Further, the CLL-IPI provided accurate estimation regarding time to first treatment within this cohort [81%, 47%, 30% and 19% patients free from treatment at 5 yr, p<0.001].

 



Summary

We report the development and validation of a weighted, integrated prognostic score and index derived from a broad number of established prognostic markers. The resulting CLL-IPI combines the most important genetic risk factors (IGHV MS, del(17p)/TP53) with traditional clinical stage, age, and B2M into an easily applicable prognostic index for CLL patients. Moreover, it both discriminates between prognostic subgroups and may help to inform current treatment recommendations in the future.



Session topic: CLL: Refining outcomes
Abstract: S791

Type: Oral Presentation

Presentation during EHA20: From 14.06.2015 08:15 to 14.06.2015 08:30

Location: Room A7

Background

Chronic lymphocytic leukemia (CLL) is a disease with a highly heterogeneous course, ranging from mild to aggressive. Therefore, prediction of outcome is important in clinical practice to avoid too aggressive treatment in patients with a rather mild course of disease and to identify patients in whom standard treatment is likely to fail. In the era of more effective treatments for CLL, the established clinical staging systems [Binet/Rai] do not accurately discriminate between prognostic subgroups. Despite the introduction of several new prognostic markers, there is no system integrating the major clinical, biological and genetic variables into one widely accepted prognostic system.

 



Aims

To develop an internationally applicable prognostic index for CLL patients [CLL-IPI], we performed a comprehensive analysis of 26 prognostic factors including clinical, biological and genetic markers.

 



Methods


Results

Based on 1192 (52%) patients from the training dataset, 5 independent predictors for OS were identified: age [cut off, 65 yr], clinical stage [Binet A/Rai 0 vs. Binet B-C/Rai I-IV], del(17p) and/or TP53 mutation, IGHV mutation status (MS) and serum β2-microglobulin (B2M) [cut off, 3.5 mg/L]. Applying weighted grading of the independent factors based on the regression parameters, a prognostic index was derived separating 4 different risk groups [low (score 0-1), intermediate (score 2-3), high (score 4-6) and very high risk (score 7-10)] with significantly different OS [93%, 79%, 64% and 23% OS at 5 yr for the low, intermediate, high and very high risk group respectively, p<0.001; C-statistic c=0.724 (95% confidence interval (CI), 0.69-0.76)] (figure 1A). This multivariable model was confirmed on the internal validation dataset [575 (49%) patients; 91%, 80%, 52% and 19% 5-yr OS, p<0.001; c=0.777 (95% CI, 0.73-0.82)] (figure 1B). In the external Mayo dataset the 5-yr OS for the CLL-IPI risk groups were 97%, 91%, 68% and 21% [p<0.001; c=0.790 (95% CI, 0.74 – 0.85)] (figure 1C). Further, the CLL-IPI provided accurate estimation regarding time to first treatment within this cohort [81%, 47%, 30% and 19% patients free from treatment at 5 yr, p<0.001].

 



Summary

We report the development and validation of a weighted, integrated prognostic score and index derived from a broad number of established prognostic markers. The resulting CLL-IPI combines the most important genetic risk factors (IGHV MS, del(17p)/TP53) with traditional clinical stage, age, and B2M into an easily applicable prognostic index for CLL patients. Moreover, it both discriminates between prognostic subgroups and may help to inform current treatment recommendations in the future.



Session topic: CLL: Refining outcomes

By clicking “Accept Terms & all Cookies” or by continuing to browse, you agree to the storing of third-party cookies on your device to enhance your user experience and agree to the user terms and conditions of this learning management system (LMS).

Cookie Settings
Accept Terms & all Cookies