背景:患有中性粒细胞占优势的白细胞增多症(白细胞计数>50,000/μL)的成年人通常需要紧急医疗处理。这些患者被诊断为慢性骨髓性恶性肿瘤或白血病反应的急性表现。然而,准确的模型来区分这些实体并不存在。我们使用人口统计和实验室数据来构建能够区分这些诊断的机器学习模型。
方法:查询三级医疗中心的医疗记录,以确定2000-2021年间白细胞计数大于50,000/μL且中性粒细胞>50%的成年人。对于每个病人来说,在首次出现时提取完整的人口统计和实验室值,其中白计数>50,000/μL.我们建立了一系列模型,其中确定了最能预测骨髓恶性肿瘤的参数,并将监督机器学习方法应用于数据集。
结果:我们的最佳模型-使用支持向量机算法-在识别髓系恶性肿瘤方面产生了96.0%的灵敏度和95.9%的特异性(曲线下面积=0.982)。我们还确定了基于诊断结果的临床意义和显着差异-诊断为白血病反应的患者的12个月死亡率增加了六倍。
结论:这些发现需要验证,但在深刻的背景下,这些发现填补了对及时准确诊断的未满足需求。中性粒细胞占优势的白细胞增多,并支持使用预测模型作为改善患者预后的手段。
Adults presenting with a neutrophil-predominant leukocytosis (white cell count >50,000/μL) often necessitate urgent medical management. These patients are diagnosed with either acute presentations of chronic myeloid malignancies or leukemoid reactions, yet accurate models to distinguish between these entities do not exist. We used demographic and lab data to build a machine learning model capable of discriminating between these diagnoses.
The medical record at a tertiary care medical center was queried to identify adults with instances of white counts greater than 50,000/μL and >50% neutrophils from 2000 to 2021. For each patient, a full set of demographic and lab values were extracted at the time of their first presentation with a white count >50,000/μL. We generated a series of models in which the parameters most predictive of myeloid malignancies were identified, and a supervised machine learning approach was applied to the dataset.
Our best model-using a support vector machine algorithm-produced a sensitivity of 96% and a specificity of 95.9% (area under the curve = 0.982) for identifying myeloid malignancies. We also identified a clinically meaningful and significant disparity in outcomes based on diagnosis-a 6-fold increase in 12-month mortality in those diagnosed with leukemoid reactions.
These findings need to be validated but fill an unmet need for timely and accurate diagnosis in the setting of profound, neutrophil-predominant leukocytosis and support the use of predictive models as a means to improve patient outcomes.