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Abstract：
This study aimed to identify highly valuable blood indicators for predicting the clinical outcomes of patients with aortic aneurysms (AA). Baseline data of 1180 patients and 16 blood indicators were obtained from the public Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The association of blood indicators with 4 types of clinical outcomes was analyzed, and the prediction performance of core indicators on different outcomes was next evaluated. Then, we explored the detailed association between core indicators and key outcomes among subgroups. Finally, a machine learning model was established to improve the prediction performance. Generalized linear regression analysis indicated that only red cell volume distribution width (RDW) was commonly associated with 4 end-points including surgery requirement, ICU stay requirement, length of hospital stay, and in-hospital death (all P < .05). Further, RDW showed the best performance for predicting in-hospital death by receiver operating characteristic (ROC) analysis. The significant association between RDW and in-hospital death was then determined by 3 logistic regression models adjusting for different variables (all P < .05). Stratification analysis showed that their association was mainly observed in unruptured AA and abdominal AA (AAA, all P < .05). We subsequently established an RDW-based model for predicting the in-hospital death only in patients with unruptured AAA. The favorable prediction performance of the RDW-based model was verified in training, validation, and test sets. RDW was found to make the greatest contribution to in-hospital death within the model. RDW had favorable clinical value for predicting the in-hospital death of patients, especially in unruptured AAA.
摘要：
这项研究旨在确定非常有价值的血液指标，以预测主动脉瘤（AA）患者的临床结局。1180名患者的基线数据和16项血液指标从公共医学信息集市重症监护IV（MIMIC-IV）数据库中获得。分析血液指标与4种临床结局的相关性,接下来评估核心指标对不同结果的预测性能。然后，我们探讨了亚组间核心指标与关键结局之间的详细关联.最后,建立了机器学习模型以提高预测性能。广义线性回归分析表明，只有红细胞体积分布宽度（RDW）通常与包括手术要求在内的4个终点相关。ICU停留要求,住院时间,住院死亡（均P<0.05）。Further,RDW通过受试者工作特征（ROC）分析显示出预测院内死亡的最佳性能。RDW和院内死亡之间的显著关联，然后通过3个逻辑回归模型调整不同的变量（所有P<0.05）。分层分析表明，它们的关联主要在未破裂AA和腹部AA（AAA，所有P<.05)。随后，我们建立了基于RDW的模型，仅用于预测未破裂AAA患者的院内死亡。在训练中验证了基于RDW的模型的良好预测性能，验证,和测试集。在该模型中，RDW对院内死亡做出了最大的贡献。RDW对预测患者院内死亡具有良好的临床价值,尤其是在未破裂的AAA中。
