Do we Need to Perform Bone Marrow Examination in all Subjects Suspected of MDS? Evaluation and Validation of Non-Invasive (Web-Based) Diagnostic Algorithm

IF 2.3 3区医学 Q2 HEMATOLOGY European Journal of Haematology Pub Date : 2025-01-04 DOI:10.1111/ejh.14379

Howard S. Oster, Ariel M. Polakow, Roi Gat, Noa Goldschmidt, Jonathan Ben-Ezra, Moshe Mittelman

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Abstract

Background

Bone marrow examination (BME) is the gold standard of diagnosing myelodysplastic syndromes (MDS). Problems: it is invasive, painful, causing possible bleeding, inaccurate (aspirate hemodilution), and subjective (inter-observer interpretation discordance). We developed non-invasive diagnostic tools: A logistic regression formula [LeukRes 2018], then a web algorithm using 10 variables (age, gender, Hb, MCV, WBC, ANC, monocytes, PLT, glucose, creatinine) to diagnose/exclude MDS [BldAdv 2021]. Here, we perform external validation of the model.

Methods

From the TASMC BM registry (2019–22) we identified and compared the model performance between MDS patients and controls (> 50 year with unexplained anemia, not MDS), all BME diagnosed, and not used in model building.

Results

The model was accurate and predicted MDS in 63% of 103 patients, and excluded (correctly) in 83% of 101 controls. It miss-classified in 11%/7% respectively, and was indeterminate in 26%/10% respectively. The positive predictive value (PPV), NPV, sensitivity, and specificity (excluding the indeterminate group) were 90%, 88%, 86%, and 92%, respectively. Subgroup (Lower/higher risk, LR/HR) analysis results were similar.

Conclusions

The MDS diagnostic model was validated and can be used, mainly for MDS exclusion, especially in suspected LR-MDS, avoiding BME in some patients. In the future incorporating peripheral blood genetics and morphometry can further improve the model.

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是否所有疑似MDS的患者都需要进行骨髓检查？无创（基于web）诊断算法的评估与验证。

背景：骨髓检查（BME）是诊断骨髓增生异常综合征（MDS）的金标准。问题：它是侵入性的，痛苦的，可能引起出血，不准确（吸入性血液稀释），主观（观察者之间的解释不一致）。我们开发了非侵入性诊断工具：逻辑回归公式[LeukRes 2018]，然后使用10个变量（年龄、性别、Hb、MCV、WBC、ANC、单核细胞、PLT、葡萄糖、肌酐）的网络算法来诊断/排除MDS [BldAdv 2021]。在这里，我们执行模型的外部验证。方法：从TASMC BM注册表（2019-22）中，我们确定并比较了MDS患者和对照组（bbb50岁，不明原因贫血，非MDS）的模型性能，所有BME诊断，未用于模型构建。结果：该模型准确预测了103例患者中63%的MDS，并正确排除了101例对照中83%的MDS。漏分率分别为11%/7%，不确定率分别为26%/10%。阳性预测值（PPV）、NPV、敏感性和特异性（不包括不确定组）分别为90%、88%、86%和92%。亚组（低/高风险，LR/HR）分析结果相似。结论：MDS诊断模型是有效的，可用于排除MDS，特别是疑似LR-MDS，避免了部分患者的BME。将来加入外周血遗传学和形态学可以进一步改进模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Haematology 医学-血液学

CiteScore

5.50

自引率

0.00%

发文量

168

审稿时长

4-8 weeks

期刊介绍： European Journal of Haematology is an international journal for communication of basic and clinical research in haematology. The journal welcomes manuscripts on molecular, cellular and clinical research on diseases of the blood, vascular and lymphatic tissue, and on basic molecular and cellular research related to normal development and function of the blood, vascular and lymphatic tissue. The journal also welcomes reviews on clinical haematology and basic research, case reports, and clinical pictures.