Soares Ferreira Junior Alexandre, Lessa Morgana Pinheiro Maux, Sanborn Kate, Gordee Alexander, Kuchibhatla Maragatha, Karafin Matthew S, Onwuemene Oluwatoyosi A
Department of Medicine, Faculdade de Medicina de São José do Rio Preto, São Paulo, Brazil.
General and Applied Biology Program, Institute of Biosciences (IBB), Sao Paulo State University (UNESP), Botucatu, Brazil.
J Clin Apher. 2025 Apr;40(2):e70013. doi: 10.1002/jca.70013.
Although therapeutic plasma exchange (TPE) can be associated with bleeding, there are currently no known strategies to reliably predict bleeding risk. This study developed a TPE bleeding risk prediction model for hospitalized patients. To develop the prediction model, we undertook a secondary analysis of public use files from the Recipient Epidemiology and Donor Evaluation Study-III. First, we used a literature review to identify potential predictors. Second, we used Multiple Imputation by Chained Equations to impute variables with < 30% missing data. Third, we performed a 10-fold Cross-Validated Least Absolute Shrinkage and Selection Operator to optimize variable selection. Finally, we fitted a logistic regression model. The model identified 10 unique predictors and seven interactions. Among those with the highest odds ratios (OR) were the following: > 10 TPE procedures and antiplatelet agents (OR 3.26); nephrogenic systemic sclerosis (OR 3.15); and intensive care unit stay (OR 3.08). Among those with the lowest OR were the following: albumin-only TPE (OR 0.50); male sex (OR 0.82); and heart failure (OR 0.85). The model indicated an acceptable performance with a C-statistic of 0.71 (95% CI 0.699-0.717). A model to predict bleeding risk among hospitalized patients undergoing TPE identified key predictors and interactions. Although the model achieved acceptable performance, future studies are needed to validate and operationalize it.
尽管治疗性血浆置换（TPE）可能与出血有关，但目前尚无可靠的策略来预测出血风险。本研究为住院患者开发了一种TPE出血风险预测模型。为了开发该预测模型，我们对“受者流行病学和供者评估研究III”的公共使用文件进行了二次分析。首先，我们通过文献综述确定潜在的预测因素。其次，我们使用链式方程多重填补法对缺失数据<30%的变量进行填补。第三，我们进行了10倍交叉验证的最小绝对收缩和选择算子以优化变量选择。最后，我们拟合了一个逻辑回归模型。该模型确定了10个独特的预测因素和7种相互作用。比值比（OR）最高的因素包括：>10次TPE操作和抗血小板药物（OR 3.26）；肾源性系统性硬化症（OR 3.15）；以及入住重症监护病房（OR 3.08）。OR最低的因素包括：仅使用白蛋白的TPE（OR 0.50）；男性（OR 0.82）；以及心力衰竭（OR 0.85）。该模型的C统计量为0.71（95%CI 0.699 - 0.717），显示出可接受的性能。一个用于预测接受TPE的住院患者出血风险的模型确定了关键的预测因素和相互作用。尽管该模型取得了可接受的性能，但仍需要未来的研究对其进行验证和实施。
