用孕期常规化验数据，提前预测罕见致命妊娠病。
Interpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Data
- 用梯度提升模型分析长期化验数据，捕捉潜在风险信号。
- 预测准确率AUROC达0.872，敏感性75%、特异性81.2%。
- 发现孕6周胱抑素C水平或可作为早期预警指标。
妊娠相关血栓性微血管病（P-TMA）虽罕见但危及生命。早期风险预测困难，因实验室异常细微且常被生理性改变如妊娠性血小板减少和蛋白尿掩盖，与良性产科及肾病重叠严重。本研究回顾性纳入300例妊娠，含142例P-TMA病例与158例对照。剔除标识符与非信息变量后保留146个纵向实验室指标。采用分层抽样将样本分为训练集（80%）与保留测试集（20%）。评估了逻辑回归、支持向量机、随机森林、极端随机树与梯度提升五种算法，以平均交叉验证AUROC选择最终模型，在全训练集上重新拟合，并在保留测试集中单次评估。结果表明，梯度提升在训练集表现最优。模型在保留测试集中达到AUROC 0.872（95% CI: 0.769–0.952），AUPRC 0.883（95% CI: 0.780–0.959），敏感性0.750，特异性0.812。结果显示，常规护理中获取的纵向实验室数据蕴含可解释且临床合理的P-TMA风险信号。值得注意的是，孕6周时的胱抑素C水平展现出早期监测潜力。
原文摘要 · Abstract (English)
Background: Pregnancy-associated thrombotic microangiopathy (P-TMA) is rare but life-threatening. Early risk prediction before overt clinical presentation remains challenging, as the associated laboratory abnormalities are subtle, multidimensional, and frequently masked by common physiological changes such as gestational thrombocytopenia and pregnancy-related proteinuria, thus overlapping heavily with benign obstetric and renal conditions. This complexity is poorly captured by univariate or rule-based approaches; however, it is addressable by machine learning, which can extract latent, time-dependent risk signatures from longitudinal clinical tests. Methods: This retrospective study included 300 pregnancies comprising 142 P-TMA cases and 158 controls. After exclusion of identifiers and non-informative variables, 146 longitudinal laboratory predictors were retained. Participants were divided into a training cohort (80%) and a held-out test cohort (20%) using stratified sampling. Five algorithms were evaluated: logistic regression, support vector machine with radial basis function kernel, random forest, extra trees, and gradient boosting. The final model was selected by mean cross-validated AUROC, refitted on the full training cohort, and evaluated once in the held-out test cohort. Interpretability analyses examined global feature importance and distributional patterns of leading predictors. Results: Gradient boosting was prespecified by cross-validation in the training cohort. The model achieved an AUROC of 0.872 (95% CI: 0.769-0.952) and an AUPRC of 0.883 (95% CI: 0.780-0.959) in a held-out test cohort, with sensitivity of 0.750 and specificity of 0.812. Conclusions: Longitudinal clinical laboratory tests obtained during routine care contained informative and clinically plausible signals for P-TMA risk. Notably, cystatin C at week 6 showed promise as an early monitoring indicator.
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