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Xu W, Yi SH, Feng R, Wang X, Jin J, Mi JQ, Ding KY, Yang W, Niu T, Wang SY, Zhou KS, Peng HL, Huang L, Liu LH, Ma J, Luo J, Su LP, Bai O, Liu L, Li F, He PC, Zeng Y, Gao D, Jiang M, Wang JS, Yao HX, Qiu LG, Li JY. [Current status of diagnosis and treatment of chronic lymphocytic leukemia in China: A national multicenter survey research]. ZHONGHUA XUE YE XUE ZA ZHI = ZHONGHUA XUEYEXUE ZAZHI 2023; 44:380-387. [PMID: 37550187 PMCID: PMC10440613 DOI: 10.3760/cma.j.issn.0253-2727.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Indexed: 08/09/2023]
Abstract
Objective: To understand the current status of diagnosis and treatment of chronic lymphocytic leukemia (CLL) /small lymphocytic lymphoma (SLL) among hematologists, oncologists, and lymphoma physicians from hospitals of different levels in China. Methods: This multicenter questionnaire survey was conducted from March 2021 to July 2021 and included 1,000 eligible physicians. A combination of face-to-face interviews and online questionnaire surveys was used. A standardized questionnaire regarding the composition of patients treated for CLL/SLL, disease diagnosis and prognosis evaluation, concomitant diseases, organ function evaluation, treatment selection, and Bruton tyrosine kinase (BTK) inhibitor was used. Results: ①The interviewed physicians stated that the proportion of male patients treated for CLL/SLL is higher than that of females, and the age is mainly concentrated in 61-70 years old. ②Most of the interviewed physicians conducted tests, such as bone marrow biopsies and immunohistochemistry, for patient diagnosis, in addition to the blood test. ③Only 13.7% of the interviewed physicians fully grasped the initial treatment indications recommended by the existing guidelines. ④In terms of cognition of high-risk prognostic factors, physicians' knowledge of unmutated immunoglobulin heavy-chain variable and 11q- is far inferior to that of TP53 mutation and complex karyotype, which are two high-risk prognostic factors, and only 17.1% of the interviewed physicians fully mastered CLL International Prognostic Index scoring system. ⑤Among the first-line treatment strategy, BTK inhibitors are used for different types of patients, and physicians have formed a certain understanding that BTK inhibitors should be preferentially used in patients with high-risk factors and elderly patients, but the actual use of BTK inhibitors in different types of patients is not high (31.6%-46.0%). ⑥BTK inhibitors at a reduced dose in actual clinical treatment were used by 69.0% of the physicians, and 66.8% of the physicians had interrupted the BTK inhibitor for >12 days in actual clinical treatment. The use of BTK inhibitors is reduced or interrupted mainly because of adverse reactions, such as atrial fibrillation, severe bone marrow suppression, hemorrhage, and pulmonary infection, as well as patients' payment capacity and effective disease progression control. ⑦Some differences were found in the perceptions and behaviors of hematologists and oncologists regarding the prognostic assessment of CLL/SLL, the choice of treatment options, the clinical use of BTK inhibitors, etc. Conclusion: At present, a gap remains between the diagnosis and treatment of CLL/SLL among Chinese physicians compared with the recommendations in the guidelines regarding the diagnostic criteria, treatment indications, prognosis assessment, accompanying disease assessment, treatment strategy selection, and rational BTK inhibitor use, especially the proportion of dose reduction or BTK inhibitor discontinuation due to high adverse events.
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Affiliation(s)
- W Xu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - S H Yi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - R Feng
- Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - X Wang
- Shandong Provincial Hospital, Jinan 250021, China
| | - J Jin
- The First Affiliated Hospital of Medical College of Zhejiang University, Hangzhou 310003, China
| | - J Q Mi
- Ruijin Hospital Affiliated to Medical College of Shanghai Jiaotong University, Shanghai 200025, China
| | - K Y Ding
- Anhui Province Cancer Hospital, Hefei 230031, China
| | - W Yang
- Shengjing Hospital Affiliated to China Medical University, Shenyang 117004, China
| | - T Niu
- West China Hospital of Sichuan University, Chengdu 610044, China
| | - S Y Wang
- Union Hospital Affiliated to Fujian Medical University, Fuzhou 350001, China
| | - K S Zhou
- Henan Cancer Hospital (Affiliated Cancer Hospital of Zhengzhou University), Zhengzhou 450003, China
| | - H L Peng
- Xiangya Second Hospital of Central South University, Changsha 410008, China
| | - L Huang
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - L H Liu
- The Fourth Hospital of Hebei Medical University (Hebei Tumor Hospital), Shijiazhuang 050011, China
| | - J Ma
- Harbin Institute of hematological oncology, Harbin 150001, China
| | - J Luo
- The First Affiliated Hospital of Guangxi Medical University, Nanchang 530021, China
| | - L P Su
- Shanxi Cancer Hospital, Taiyuan 030013, China
| | - O Bai
- The first hospital of Jilin University, Changchun 130061, China
| | - L Liu
- The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
| | - F Li
- The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - P C He
- The First Affiliated Hospital of Xi' an Jiaotong University, Xi' an 710061, China
| | - Y Zeng
- The First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - D Gao
- Affiliated Hospital of Inner Mongolia Medical University, Hohhot 750306, China
| | - M Jiang
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - J S Wang
- Affiliated hospital of Guizhou Medical University, Guiyang 550004, China
| | - H X Yao
- Hainan Provincial People's Hospital, Haikou 570311, China
| | - L G Qiu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - J Y Li
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
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Yang S, Varghese AM, Sood N, Chiattone C, Akinola NO, Huang X, Gale RP. Ethnic and geographic diversity of chronic lymphocytic leukaemia. Leukemia 2020; 35:433-439. [PMID: 33077870 DOI: 10.1038/s41375-020-01057-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 09/02/2020] [Accepted: 10/05/2020] [Indexed: 12/16/2022]
Abstract
East Asians, Asian Indians and Amerindians have a five to ten-fold lower age-adjusted incidence rate (AAIR) of chronic lymphocytic leukaemia (CLL) compared with persons of predominately European descent. The data we review suggest a genetic rather than environmental basis for this discordance. All these populations arose from a common African Black ancestor but different clades have different admixture with archaic hominins including Neanderthals, Denisovans and Homo erectus, which may explain different CLL incidences. There are also some differences in clinical laboratory and molecular co-variates of CLL between these populations. Because the true age-adjusted incidence rate in African Blacks is unknown it is not possible to determine whether modern Europeans acquired susceptibility to CLL or the other populations lost susceptibility and/or developed resistance to developing CLL. We also found other B-cell lymphomas and T- and NK-cell cancers had different incidences in the populations we studied. These data provide clues to determining the cause(s) of CLL.
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Affiliation(s)
- Shenmiao Yang
- Peking University Peoples Hospital; Peking University Institute of Hematology, Beijing, China
| | - Abraham M Varghese
- Little Flower Hospital and Research Centre, Kerala, India.,St James University Hospital, Leeds, UK
| | - Nitin Sood
- Clinical Haematology and Stem Cell Transplant, Medanta-Medicity, Gurgaon, India
| | - Carlos Chiattone
- Department of Hematology and Oncology, Santa Casa Medical School, Sao Paulo, Brazil
| | - Norah O Akinola
- Department of Haematology and Immunology, Obafemi Awolowo University and Teaching Hospitals Complex, Ile-Ife, Osun State, Nigeria
| | - Xiaojun Huang
- Peking University Peoples Hospital; Peking University Institute of Hematology, Beijing, China
| | - Robert Peter Gale
- Haematology Research Centre, Department of Immunology and Inflammation, Imperial College London, London, UK.
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Kreuzberger N, Damen JA, Trivella M, Estcourt LJ, Aldin A, Umlauff L, Vazquez-Montes MD, Wolff R, Moons KG, Monsef I, Foroutan F, Kreuzer KA, Skoetz N. Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis. Cochrane Database Syst Rev 2020; 7:CD012022. [PMID: 32735048 PMCID: PMC8078230 DOI: 10.1002/14651858.cd012022.pub2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Chronic lymphocytic leukaemia (CLL) is the most common cancer of the lymphatic system in Western countries. Several clinical and biological factors for CLL have been identified. However, it remains unclear which of the available prognostic models combining those factors can be used in clinical practice to predict long-term outcome in people newly-diagnosed with CLL. OBJECTIVES To identify, describe and appraise all prognostic models developed to predict overall survival (OS), progression-free survival (PFS) or treatment-free survival (TFS) in newly-diagnosed (previously untreated) adults with CLL, and meta-analyse their predictive performances. SEARCH METHODS We searched MEDLINE (from January 1950 to June 2019 via Ovid), Embase (from 1974 to June 2019) and registries of ongoing trials (to 5 March 2020) for development and validation studies of prognostic models for untreated adults with CLL. In addition, we screened the reference lists and citation indices of included studies. SELECTION CRITERIA We included all prognostic models developed for CLL which predict OS, PFS, or TFS, provided they combined prognostic factors known before treatment initiation, and any studies that tested the performance of these models in individuals other than the ones included in model development (i.e. 'external model validation studies'). We included studies of adults with confirmed B-cell CLL who had not received treatment prior to the start of the study. We did not restrict the search based on study design. DATA COLLECTION AND ANALYSIS We developed a data extraction form to collect information based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Independent pairs of review authors screened references, extracted data and assessed risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For models that were externally validated at least three times, we aimed to perform a quantitative meta-analysis of their predictive performance, notably their calibration (proportion of people predicted to experience the outcome who do so) and discrimination (ability to differentiate between people with and without the event) using a random-effects model. When a model categorised individuals into risk categories, we pooled outcome frequencies per risk group (low, intermediate, high and very high). We did not apply GRADE as guidance is not yet available for reviews of prognostic models. MAIN RESULTS From 52 eligible studies, we identified 12 externally validated models: six were developed for OS, one for PFS and five for TFS. In general, reporting of the studies was poor, especially predictive performance measures for calibration and discrimination; but also basic information, such as eligibility criteria and the recruitment period of participants was often missing. We rated almost all studies at high or unclear risk of bias according to PROBAST. Overall, the applicability of the models and their validation studies was low or unclear; the most common reasons were inappropriate handling of missing data and serious reporting deficiencies concerning eligibility criteria, recruitment period, observation time and prediction performance measures. We report the results for three models predicting OS, which had available data from more than three external validation studies: CLL International Prognostic Index (CLL-IPI) This score includes five prognostic factors: age, clinical stage, IgHV mutational status, B2-microglobulin and TP53 status. Calibration: for the low-, intermediate- and high-risk groups, the pooled five-year survival per risk group from validation studies corresponded to the frequencies observed in the model development study. In the very high-risk group, predicted survival from CLL-IPI was lower than observed from external validation studies. Discrimination: the pooled c-statistic of seven external validation studies (3307 participants, 917 events) was 0.72 (95% confidence interval (CI) 0.67 to 0.77). The 95% prediction interval (PI) of this model for the c-statistic, which describes the expected interval for the model's discriminative ability in a new external validation study, ranged from 0.59 to 0.83. Barcelona-Brno score Aimed at simplifying the CLL-IPI, this score includes three prognostic factors: IgHV mutational status, del(17p) and del(11q). Calibration: for the low- and intermediate-risk group, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of four external validation studies (1755 participants, 416 events) was 0.64 (95% CI 0.60 to 0.67); 95% PI 0.59 to 0.68. MDACC 2007 index score The authors presented two versions of this model including six prognostic factors to predict OS: age, B2-microglobulin, absolute lymphocyte count, gender, clinical stage and number of nodal groups. Only one validation study was available for the more comprehensive version of the model, a formula with a nomogram, while seven studies (5127 participants, 994 events) validated the simplified version of the model, the index score. Calibration: for the low- and intermediate-risk groups, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of the seven external validation studies for the index score was 0.65 (95% CI 0.60 to 0.70); 95% PI 0.51 to 0.77. AUTHORS' CONCLUSIONS Despite the large number of published studies of prognostic models for OS, PFS or TFS for newly-diagnosed, untreated adults with CLL, only a minority of these (N = 12) have been externally validated for their respective primary outcome. Three models have undergone sufficient external validation to enable meta-analysis of the model's ability to predict survival outcomes. Lack of reporting prevented us from summarising calibration as recommended. Of the three models, the CLL-IPI shows the best discrimination, despite overestimation. However, performance of the models may change for individuals with CLL who receive improved treatment options, as the models included in this review were tested mostly on retrospective cohorts receiving a traditional treatment regimen. In conclusion, this review shows a clear need to improve the conducting and reporting of both prognostic model development and external validation studies. For prognostic models to be used as tools in clinical practice, the development of the models (and their subsequent validation studies) should adapt to include the latest therapy options to accurately predict performance. Adaptations should be timely.
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MESH Headings
- Adult
- Age Factors
- Bias
- Biomarkers, Tumor
- Calibration
- Confidence Intervals
- Discriminant Analysis
- Disease-Free Survival
- Female
- Genes, p53/genetics
- Humans
- Immunoglobulin Heavy Chains/genetics
- Immunoglobulin Variable Region/genetics
- Leukemia, Lymphocytic, Chronic, B-Cell/mortality
- Leukemia, Lymphocytic, Chronic, B-Cell/pathology
- Male
- Models, Theoretical
- Neoplasm Staging
- Prognosis
- Progression-Free Survival
- Receptors, Antigen, B-Cell/genetics
- Reproducibility of Results
- Tumor Suppressor Protein p53/genetics
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Affiliation(s)
- Nina Kreuzberger
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Johanna Aag Damen
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Lise J Estcourt
- Haematology/Transfusion Medicine, NHS Blood and Transplant, Oxford, UK
| | - Angela Aldin
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lisa Umlauff
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Karel Gm Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ina Monsef
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Farid Foroutan
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Karl-Anton Kreuzer
- Center of Integrated Oncology Cologne-Bonn, Department I of Internal Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Nicole Skoetz
- Cochrane Cancer, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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