An international prognostic index for patients with chronic lymphocytic leukaemia (CLL-IPI): a meta-analysis of individual patient data

医学 人口 肿瘤科 单变量 队列 内科学 临床试验 多元分析 单变量分析 多元统计 统计 数学 环境卫生
出处
期刊:Lancet Oncology [Elsevier BV]
卷期号:17 (6): 779-790 被引量:640
标识
DOI:10.1016/s1470-2045(16)30029-8
摘要

Background The management of patients with chronic lymphocytic leukaemia is currently undergoing improvements due to novel therapies and a plethora of biological and genetic variables that add prognostic information to the classic clinical staging systems. We established an international consortium with the aim to create an international prognostic index for chronic lymphocytic leukaemia (CLL-IPI) that integrates the major prognostic parameters. Methods We used results from a systematic search of the Cochrane Haematological Malignancies Group of MEDLINE, Embase, and Central databases for prospective, clinical phase 2 and 3 trials of chronic lymphocytic leukaemia, published between Jan 1, 1950, and Dec 31, 2010, which identified 13 trials. We contacted the principal investigators of these 13 trials, of which eight agreed to include individual patient data. We used the individual patient data from these phase 3 trials from France, Germany, Poland, the UK, and the USA to create the full analysis dataset. The full analysis dataset was randomly divided, using a random sample procedure, into training and internal-validation datasets. We did a univariate analysis and multivariate analyses using 27 baseline factors and overall survival as an endpoint. We assigned weighted risk scores to each factor included in the final multivariable model. We assessed the discriminatory value using C-statistics and also the validity and reproducibility of the CLL-IPI by subgroup analysis. We used two additional datasets from the Mayo Clinic (Rochester, MN, USA; MAYO cohort) and the SCALE Scandinavian population-based case-control study (SCAN cohort) as the external-validation datasets. Findings 3472 treatment-naive patients were included in the full analysis dataset; 2308 were randomly segregated into the training dataset and 1164 into the internal-validation dataset. 838 patients were included in the MAYO cohort and 416 in the SCAN cohort. Median age of patients in the full analysis dataset was 61 years (range 27–86). Five independent prognostic factors were identified in the training dataset: TP53 status (no abnormalities vs del[17p] or TP53 mutation or both), IGHV mutational status (mutated vs unmutated), serum β2-microglobulin concentration (≤3·5 mg/L vs >3·5 mg/L), clinical stage (Binet A or Rai 0 vs Binet B–C or Rai I–IV), and age (≤65 years vs >65 years). Using a weighted grading of the independent factors, a prognostic index was derived that identified four risk groups within the training dataset with significantly different overall survival at 5 years: low (93·2% [95% CI 90·5–96·0]), intermediate (79·3% [75·5–83·2]), high (63·3% [57·9–68·8]), and very high risk (23·3% [12·5–34·1]; log-rank test comparing survival across the four risk groups p<0·0001; C-statistic, c=0·723 [95% CI 0·684–0·752]). These risk groups were confirmed in the internal-validation and external-validation datasets. Interpretation The CLL-IPI combines genetic, biochemical, and clinical parameters into a prognostic model, discriminating four prognostic subgroups. The CLL-IPI will allow a more targeted management of patients with chronic lymphocytic leukaemia in clinical practice and in trials testing novel drugs. Funding José Carreras Leukaemia Foundation The management of patients with chronic lymphocytic leukaemia is currently undergoing improvements due to novel therapies and a plethora of biological and genetic variables that add prognostic information to the classic clinical staging systems. We established an international consortium with the aim to create an international prognostic index for chronic lymphocytic leukaemia (CLL-IPI) that integrates the major prognostic parameters. We used results from a systematic search of the Cochrane Haematological Malignancies Group of MEDLINE, Embase, and Central databases for prospective, clinical phase 2 and 3 trials of chronic lymphocytic leukaemia, published between Jan 1, 1950, and Dec 31, 2010, which identified 13 trials. We contacted the principal investigators of these 13 trials, of which eight agreed to include individual patient data. We used the individual patient data from these phase 3 trials from France, Germany, Poland, the UK, and the USA to create the full analysis dataset. The full analysis dataset was randomly divided, using a random sample procedure, into training and internal-validation datasets. We did a univariate analysis and multivariate analyses using 27 baseline factors and overall survival as an endpoint. We assigned weighted risk scores to each factor included in the final multivariable model. We assessed the discriminatory value using C-statistics and also the validity and reproducibility of the CLL-IPI by subgroup analysis. We used two additional datasets from the Mayo Clinic (Rochester, MN, USA; MAYO cohort) and the SCALE Scandinavian population-based case-control study (SCAN cohort) as the external-validation datasets. 3472 treatment-naive patients were included in the full analysis dataset; 2308 were randomly segregated into the training dataset and 1164 into the internal-validation dataset. 838 patients were included in the MAYO cohort and 416 in the SCAN cohort. Median age of patients in the full analysis dataset was 61 years (range 27–86). Five independent prognostic factors were identified in the training dataset: TP53 status (no abnormalities vs del[17p] or TP53 mutation or both), IGHV mutational status (mutated vs unmutated), serum β2-microglobulin concentration (≤3·5 mg/L vs >3·5 mg/L), clinical stage (Binet A or Rai 0 vs Binet B–C or Rai I–IV), and age (≤65 years vs >65 years). Using a weighted grading of the independent factors, a prognostic index was derived that identified four risk groups within the training dataset with significantly different overall survival at 5 years: low (93·2% [95% CI 90·5–96·0]), intermediate (79·3% [75·5–83·2]), high (63·3% [57·9–68·8]), and very high risk (23·3% [12·5–34·1]; log-rank test comparing survival across the four risk groups p<0·0001; C-statistic, c=0·723 [95% CI 0·684–0·752]). These risk groups were confirmed in the internal-validation and external-validation datasets. The CLL-IPI combines genetic, biochemical, and clinical parameters into a prognostic model, discriminating four prognostic subgroups. The CLL-IPI will allow a more targeted management of patients with chronic lymphocytic leukaemia in clinical practice and in trials testing novel drugs.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
wwt发布了新的文献求助10
2秒前
2秒前
22发布了新的文献求助30
3秒前
胖头鱼发布了新的文献求助30
4秒前
4秒前
6秒前
6秒前
英姑应助vv采纳,获得10
7秒前
PP发布了新的文献求助30
7秒前
7秒前
7秒前
8秒前
2re完成签到,获得积分10
9秒前
淡淡天宇发布了新的文献求助10
9秒前
10秒前
jay发布了新的文献求助50
10秒前
辛勤牛青完成签到,获得积分10
11秒前
小马甲应助LWJ采纳,获得10
11秒前
12秒前
王佳倩完成签到,获得积分20
12秒前
上官若男应助iW采纳,获得10
13秒前
WangYZ发布了新的文献求助10
13秒前
F1nka应助文艺千琴采纳,获得10
14秒前
大个应助半盏采纳,获得10
14秒前
二号发布了新的文献求助10
15秒前
淡淡天宇完成签到,获得积分10
15秒前
YY完成签到,获得积分10
16秒前
17秒前
18秒前
有魅力的大白菜真实的钥匙完成签到,获得积分20
18秒前
完美世界应助二号采纳,获得10
18秒前
shan完成签到,获得积分10
19秒前
七月份的风完成签到,获得积分10
19秒前
20秒前
Ava应助活力寄云采纳,获得10
20秒前
20秒前
22秒前
23秒前
高分求助中
Adhesion Science: Principles & Practice 1234
Signals, Systems, and Signal Processing 610
The Resilient Mindset 400
Impact of Storage Orientation and Duration on Prefilled Syringe Performance: Break-Loose and Glide Forces, and Injection Time Across Multiple Time Points 360
Programming for Chemical Engineers Using C, C++, and MATLAB 300
Upland Kenya wild flowers and ferns: a flora of the flowers, ferns, grasses, and sedges of highland Kenya 300
Disturbing the Quiet Life? Competition and CEO Incentives 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6654382
求助须知:如何正确求助?哪些是违规求助? 8407618
关于积分的说明 17977135
捐赠科研通 5851042
什么是DOI,文献DOI怎么找? 2972283
邀请新用户注册赠送积分活动 1948057
关于科研通互助平台的介绍 1869116