DECODING THE RISK OF THROMBOEMBOLIC EVENTS IN LYMPHOMA PATIENTS
(Abstract release date: 05/19/16)
EHA Library. Antic D. 06/10/16; 135171; S138
Dr. Darko Antic
Contributions
Contributions
Abstract
Abstract: S138
Type: Oral Presentation
Presentation during EHA21: On Friday, June 10, 2016 from 11:45 - 12:00
Location: Room H4
Background
Considering data about increased incidence of thrombosis in lymphoma patients and the impact of thrombosis on the survival of lymphoma patients, the main question was “Which lymphoma patients are candidates for thromboprophylaxis after establishing the diagnosis of lymphoma and during chemotherapy?” Owing to risk of thrombocytopenia due to disease or chemotherapy, patients with hematologic malignancies are often excluded from thromboprophylaxis and those with nonhematologic malignancies are preferred
Aims
The aim of this study was to develop the prognostic risk score based on individual clinical and laboratory parameters that would allow physicians to designate patients at risk for thromboembolic event.
Methods
We developed prognostic Thrombosis Lymphoma – ThroLy score based on the study population including 1820 lymphoma patients, who received at least one cycle of chemotherapy. The study population was divided based on a split-sample random method into the model developing and validation cohorts. The model was developed using data from a derivation, and further assessed in the validation cohort.
Results
99 patients (5.4%) developed thromboembolic events. The variables independently associated with risk of thromboembolism were: previous venous and/or arterial events, mediastinum involvement, BMI>30 kg/m2, reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. Based on the risk model score the population was divided into the following risk categories: low (score 0-1), intermediate (score 2-3), and high (score >3). For patients in the derivation cohort classified as at risk for TE (score >1), the model produced a negative predictive value (probability of not experiencing TE in patients designated low risk) of 98.5%, and the positive predictive value (probability of TE occurring in patient designated at risk) of 25.1%. The sensitivity (probability of being classified as at risk in patients experiencing TE) was 75.4%, and the specificity (probability of being classified as low risk in patients not experiencing TE) was 87.5%. Interestingly, a high-risk score ≥ 4 had a positive predictive value (probability of TE occurring in patient designated at high risk) of 65.2%. The risk model was then applied to the validation cohort (n=584) in which 34 patients (5.8%) developed TE. Similarly, in the validation cohort, the negative predictive value was 97.6%; the positive predictive value was 28.9%; the sensitivity was 64.7%; and the specificity was 90.2%.
Conclusion
ThroLy score is more specific for lymphoma patients than any other available score targeting cancer patients. Moreover, it is dynamic, can be changed during the different phases of therapy, does not require non-routine laboratory analysis and is not limited to hospitalized or outpatient settings.
Session topic: Thrombosis
Type: Oral Presentation
Presentation during EHA21: On Friday, June 10, 2016 from 11:45 - 12:00
Location: Room H4
Background
Considering data about increased incidence of thrombosis in lymphoma patients and the impact of thrombosis on the survival of lymphoma patients, the main question was “Which lymphoma patients are candidates for thromboprophylaxis after establishing the diagnosis of lymphoma and during chemotherapy?” Owing to risk of thrombocytopenia due to disease or chemotherapy, patients with hematologic malignancies are often excluded from thromboprophylaxis and those with nonhematologic malignancies are preferred
Aims
The aim of this study was to develop the prognostic risk score based on individual clinical and laboratory parameters that would allow physicians to designate patients at risk for thromboembolic event.
Methods
We developed prognostic Thrombosis Lymphoma – ThroLy score based on the study population including 1820 lymphoma patients, who received at least one cycle of chemotherapy. The study population was divided based on a split-sample random method into the model developing and validation cohorts. The model was developed using data from a derivation, and further assessed in the validation cohort.
Results
99 patients (5.4%) developed thromboembolic events. The variables independently associated with risk of thromboembolism were: previous venous and/or arterial events, mediastinum involvement, BMI>30 kg/m2, reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. Based on the risk model score the population was divided into the following risk categories: low (score 0-1), intermediate (score 2-3), and high (score >3). For patients in the derivation cohort classified as at risk for TE (score >1), the model produced a negative predictive value (probability of not experiencing TE in patients designated low risk) of 98.5%, and the positive predictive value (probability of TE occurring in patient designated at risk) of 25.1%. The sensitivity (probability of being classified as at risk in patients experiencing TE) was 75.4%, and the specificity (probability of being classified as low risk in patients not experiencing TE) was 87.5%. Interestingly, a high-risk score ≥ 4 had a positive predictive value (probability of TE occurring in patient designated at high risk) of 65.2%. The risk model was then applied to the validation cohort (n=584) in which 34 patients (5.8%) developed TE. Similarly, in the validation cohort, the negative predictive value was 97.6%; the positive predictive value was 28.9%; the sensitivity was 64.7%; and the specificity was 90.2%.
Conclusion
ThroLy score is more specific for lymphoma patients than any other available score targeting cancer patients. Moreover, it is dynamic, can be changed during the different phases of therapy, does not require non-routine laboratory analysis and is not limited to hospitalized or outpatient settings.
Session topic: Thrombosis
Abstract: S138
Type: Oral Presentation
Presentation during EHA21: On Friday, June 10, 2016 from 11:45 - 12:00
Location: Room H4
Background
Considering data about increased incidence of thrombosis in lymphoma patients and the impact of thrombosis on the survival of lymphoma patients, the main question was “Which lymphoma patients are candidates for thromboprophylaxis after establishing the diagnosis of lymphoma and during chemotherapy?” Owing to risk of thrombocytopenia due to disease or chemotherapy, patients with hematologic malignancies are often excluded from thromboprophylaxis and those with nonhematologic malignancies are preferred
Aims
The aim of this study was to develop the prognostic risk score based on individual clinical and laboratory parameters that would allow physicians to designate patients at risk for thromboembolic event.
Methods
We developed prognostic Thrombosis Lymphoma – ThroLy score based on the study population including 1820 lymphoma patients, who received at least one cycle of chemotherapy. The study population was divided based on a split-sample random method into the model developing and validation cohorts. The model was developed using data from a derivation, and further assessed in the validation cohort.
Results
99 patients (5.4%) developed thromboembolic events. The variables independently associated with risk of thromboembolism were: previous venous and/or arterial events, mediastinum involvement, BMI>30 kg/m2, reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. Based on the risk model score the population was divided into the following risk categories: low (score 0-1), intermediate (score 2-3), and high (score >3). For patients in the derivation cohort classified as at risk for TE (score >1), the model produced a negative predictive value (probability of not experiencing TE in patients designated low risk) of 98.5%, and the positive predictive value (probability of TE occurring in patient designated at risk) of 25.1%. The sensitivity (probability of being classified as at risk in patients experiencing TE) was 75.4%, and the specificity (probability of being classified as low risk in patients not experiencing TE) was 87.5%. Interestingly, a high-risk score ≥ 4 had a positive predictive value (probability of TE occurring in patient designated at high risk) of 65.2%. The risk model was then applied to the validation cohort (n=584) in which 34 patients (5.8%) developed TE. Similarly, in the validation cohort, the negative predictive value was 97.6%; the positive predictive value was 28.9%; the sensitivity was 64.7%; and the specificity was 90.2%.
Conclusion
ThroLy score is more specific for lymphoma patients than any other available score targeting cancer patients. Moreover, it is dynamic, can be changed during the different phases of therapy, does not require non-routine laboratory analysis and is not limited to hospitalized or outpatient settings.
Session topic: Thrombosis
Type: Oral Presentation
Presentation during EHA21: On Friday, June 10, 2016 from 11:45 - 12:00
Location: Room H4
Background
Considering data about increased incidence of thrombosis in lymphoma patients and the impact of thrombosis on the survival of lymphoma patients, the main question was “Which lymphoma patients are candidates for thromboprophylaxis after establishing the diagnosis of lymphoma and during chemotherapy?” Owing to risk of thrombocytopenia due to disease or chemotherapy, patients with hematologic malignancies are often excluded from thromboprophylaxis and those with nonhematologic malignancies are preferred
Aims
The aim of this study was to develop the prognostic risk score based on individual clinical and laboratory parameters that would allow physicians to designate patients at risk for thromboembolic event.
Methods
We developed prognostic Thrombosis Lymphoma – ThroLy score based on the study population including 1820 lymphoma patients, who received at least one cycle of chemotherapy. The study population was divided based on a split-sample random method into the model developing and validation cohorts. The model was developed using data from a derivation, and further assessed in the validation cohort.
Results
99 patients (5.4%) developed thromboembolic events. The variables independently associated with risk of thromboembolism were: previous venous and/or arterial events, mediastinum involvement, BMI>30 kg/m2, reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. Based on the risk model score the population was divided into the following risk categories: low (score 0-1), intermediate (score 2-3), and high (score >3). For patients in the derivation cohort classified as at risk for TE (score >1), the model produced a negative predictive value (probability of not experiencing TE in patients designated low risk) of 98.5%, and the positive predictive value (probability of TE occurring in patient designated at risk) of 25.1%. The sensitivity (probability of being classified as at risk in patients experiencing TE) was 75.4%, and the specificity (probability of being classified as low risk in patients not experiencing TE) was 87.5%. Interestingly, a high-risk score ≥ 4 had a positive predictive value (probability of TE occurring in patient designated at high risk) of 65.2%. The risk model was then applied to the validation cohort (n=584) in which 34 patients (5.8%) developed TE. Similarly, in the validation cohort, the negative predictive value was 97.6%; the positive predictive value was 28.9%; the sensitivity was 64.7%; and the specificity was 90.2%.
Conclusion
ThroLy score is more specific for lymphoma patients than any other available score targeting cancer patients. Moreover, it is dynamic, can be changed during the different phases of therapy, does not require non-routine laboratory analysis and is not limited to hospitalized or outpatient settings.
Session topic: Thrombosis
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