Abstract：
BACKGROUND: Myelodysplastic syndromes (MDS) are clonal hematopoietic diseases of the elderly characterized by chronic cytopenias, ineffective and dysplastic haematopoiesis, recurrent genetic abnormalities and increased risk of progression to acute myeloid leukemia. A challenge of routine laboratory Complete Blood Counts (CBC) is to correctly identify MDS patients while simultaneously avoiding excess smear reviews. To optimize smear review, the latest generations of hematology analyzers provide new cell population data (CPD) parameters with an increased ability to screen MDS, among which the previously described MDS-CBC Score, based on Absolute Neutrophil Count (ANC), structural neutrophil dispersion (Ne-WX) and mean corpuscular volume (MCV). Ne-WX is increased in the presence of hypogranulated/degranulated neutrophils, a hallmark of dysplasia in the context of MDS or chronic myelomonocytic leukemia. Ne-WX and MCV are CPD derived from leukocytes and red blood cells, therefore the MDS-CBC score does not include any platelet-derived CPD. We asked whether this score could be improved by adding the immature platelet fraction (IPF), a CPD used as a surrogate marker of dysplastic thrombopoiesis.
METHODS: Here, we studied a cohort of more than 500 individuals with cytopenias, including 168 MDS patients. In a first step, we used Breiman\'s random forests algorithm, a machine-learning approach, to identify the most relevant parameters for MDS prediction. We then designed Classification And Regression Trees (CART) to evaluate, using resampling, the effect of model tuning parameters on performance and choose the \"optimal\" model across these parameters.
RESULTS: Using random forests algorithm, we identified Ne-WX and IPF as the strongest discriminatory predictors, explaining 37 and 33% of diagnoses respectively. To obtain \"simplified\" trees, which could be easily implemented into laboratory middlewares, we designed CART combining MDS-CBC score and IPF. Optimal results were obtained using a MDS-CBC score threshold equal to 0.23, and an IPF threshold equal to 3%.
CONCLUSIONS: We propose an extended MDS-CBC score, including CPD from the three myeloid lineages, to improve MDS diagnosis on routine laboratory CBCs and optimize smear reviews.
METHODS: Here, we studied a cohort of more than 500 individuals with cytopenias, including 168 MDS patients. In a first step, we used Breiman\'s random forests algorithm, a machine-learning approach, to identify the most relevant parameters for MDS prediction. We then designed Classification And Regression Trees (CART) to evaluate, using resampling, the effect of model tuning parameters on performance and choose the \"optimal\" model across these parameters.
RESULTS: Using random forests algorithm, we identified Ne-WX and IPF as the strongest discriminatory predictors, explaining 37 and 33% of diagnoses respectively. To obtain \"simplified\" trees, which could be easily implemented into laboratory middlewares, we designed CART combining MDS-CBC score and IPF. Optimal results were obtained using a MDS-CBC score threshold equal to 0.23, and an IPF threshold equal to 3%.
CONCLUSIONS: We propose an extended MDS-CBC score, including CPD from the three myeloid lineages, to improve MDS diagnosis on routine laboratory CBCs and optimize smear reviews.
摘要：
背景：骨髓增生异常综合征（MDS）是以慢性血细胞减少为特征的老年人的克隆造血疾病，无效和发育不良的造血，复发性遗传异常和进展为急性髓细胞性白血病的风险增加。常规实验室全血计数（CBC）的挑战是正确识别MDS患者，同时避免过多的涂片检查。为了优化涂片检查，最新一代的血液学分析仪提供了新的细胞群数据（CPD）参数，提高了筛选MDS的能力，其中前面描述的MDS-CBC评分，基于绝对中性粒细胞计数（ANC），中性粒细胞结构性弥散（Ne-WX）和平均红细胞体积（MCV）。Ne-WX在低粒化/去粒化中性粒细胞的存在下增加，在MDS或慢性粒单核细胞白血病的背景下，发育不良的标志。Ne-WX和MCV是来源于白细胞和红细胞的CPD,因此,MDS-CBC评分不包括任何血小板来源的CPD.我们询问是否可以通过添加未成熟的血小板分数（IPF）来改善该分数，CPD用作增生性血小板生成异常的替代标记。
方法：这里，我们研究了500多名血细胞减少症患者，包括168例MDS患者。第一步,我们使用了Breiman的随机森林算法，机器学习方法，确定MDS预测最相关的参数。然后，我们设计了分类和回归树(CART)来评估，使用重采样，模型调整参数对性能的影响，并在这些参数中选择“最佳”模型。
结果：使用随机森林算法，我们确定Ne-WX和IPF是最强的歧视性预测因子，分别解释了37%和33%的诊断。要获得“简化”树，这可以很容易地应用到实验室中间件中，我们设计了结合MDS-CBC评分和IPF的CART。使用等于0.23的MDS-CBC评分阈值和等于3%的IPF阈值获得最佳结果。
结论：我们提出了一个扩展的MDS-CBC评分，包括来自三个髓系的CPD，改善常规实验室CBCs的MDS诊断并优化涂片检查。
方法：这里，我们研究了500多名血细胞减少症患者，包括168例MDS患者。第一步,我们使用了Breiman的随机森林算法，机器学习方法，确定MDS预测最相关的参数。然后，我们设计了分类和回归树(CART)来评估，使用重采样，模型调整参数对性能的影响，并在这些参数中选择“最佳”模型。
结果：使用随机森林算法，我们确定Ne-WX和IPF是最强的歧视性预测因子，分别解释了37%和33%的诊断。要获得“简化”树，这可以很容易地应用到实验室中间件中，我们设计了结合MDS-CBC评分和IPF的CART。使用等于0.23的MDS-CBC评分阈值和等于3%的IPF阈值获得最佳结果。
结论：我们提出了一个扩展的MDS-CBC评分，包括来自三个髓系的CPD，改善常规实验室CBCs的MDS诊断并优化涂片检查。
