Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
Department of Cardiovascular Surgery, Tianjin Chest Hospital, Tianjin, China.
Front Public Health. 2023 Mar 29;11:1143160. doi: 10.3389/fpubh.2023.1143160. eCollection 2023.
Our goal was to create a prediction model for in-hospital death in Chinese patients with acute type A aortic dissection (ATAAD).
A retrospective derivation cohort was made up of 340 patients with ATAAD from Tianjin, and the retrospective validation cohort was made up of 153 patients with ATAAD from Nanjing. For variable selection, we used least absolute shrinkage and selection operator analysis, and for risk scoring, we used logistic regression coefficients. We categorized the patients into low-, middle-, and high-risk groups and looked into the correlation with in-hospital fatalities. We established a risk classifier based on independent baseline data using a multivariable logistic model. The prediction performance was determined based on the receiver operating characteristic curve (ROC). Individualized clinical decision-making was conducted by weighing the net benefit in each patient by decision curve analysis (DCA).
We created a risk prediction model using risk scores weighted by five preoperatively chosen variables [AUC: 0.7039 (95% CI, 0.643-0.765)]: serum creatinine (Scr), D-dimer, white blood cell (WBC) count, coronary heart disease (CHD), and blood urea nitrogen (BUN). Following that, we categorized the cohort's patients as low-, intermediate-, and high-risk groups. The intermediate- and high-risk groups significantly increased hospital death rates compared to the low-risk group [adjusted OR: 3.973 (95% CI, 1.496-10.552), < 0.01; 8.280 (95% CI, 3.054-22.448), < 0.01, respectively). The risk score classifier exhibited better prediction ability than the triple-risk categories classifier [AUC: 0.7039 (95% CI, 0.6425-0.7652) vs. 0.6605 (95% CI, 0.6013-0.7197); = 0.0022]. The DCA showed relatively good performance for the model in terms of clinical application if the threshold probability in the clinical decision was more than 10%.
A risk classifier is an effective strategy for predicting in-hospital death in patients with ATAAD, but it might be affected by the small number of participants.
我们的目标是建立一个预测模型，用于预测中国急性 A 型主动脉夹层（ATAAD）患者的院内死亡。
回顾性推导队列由来自天津的 340 名 ATAAD 患者组成，回顾性验证队列由来自南京的 153 名 ATAAD 患者组成。我们使用最小绝对收缩和选择算子分析进行变量选择，使用逻辑回归系数进行风险评分。我们将患者分为低危、中危和高危组，并研究与院内死亡的相关性。我们基于多变量逻辑模型使用独立的基线数据建立了风险分类器。根据接收者操作特征曲线（ROC）确定预测性能。通过决策曲线分析（DCA）对每个患者的净收益进行加权，以进行个体化的临床决策。
我们使用术前选择的五个变量的风险评分（AUC：0.7039（95%CI，0.643-0.765））创建了一个风险预测模型：血清肌酐（Scr）、D-二聚体、白细胞（WBC）计数、冠心病（CHD）和血尿素氮（BUN）。随后，我们将队列中的患者分为低危、中危和高危组。与低危组相比，中危组和高危组的院内死亡率显著增加[调整后的 OR：3.973（95%CI，1.496-10.552）， < 0.01；8.280（95%CI，3.054-22.448）， < 0.01]。风险评分分类器的预测能力优于三重风险分类器分类器[AUC：0.7039（95%CI，0.6425-0.7652）与 0.6605（95%CI，0.6013-0.7197）； = 0.0022]。在临床决策的阈值概率大于 10%的情况下，DCA 显示模型在临床应用方面具有相对较好的性能。
风险分类器是预测 ATAAD 患者院内死亡的有效策略，但可能受到参与者数量少的影响。
