Liu Hong, Li Haiyang, Han Lu, Zhang Yingyuan, Wu Ying, Hong Liang, Yang Jinong, Zhong Jisheng, Wang Yuqi, Wu Dongkai, Fan Guoliang, Chen Junquan, Zhang Shengqiang, Peng Xingxing, Zeng Zhihua, Tang Zhiwei, Lu Zhanjie, Sun Lizhong, Qian Sichong, Shao Yongfeng, Zhang Hongjia
Department of Cardiovascular Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
Innovation (Camb). 2023 May 25;4(4):100448. doi: 10.1016/j.xinn.2023.100448. eCollection 2023 Jul 10.
The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016-2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%-41.6%; RR 1.196 [0.698-2.049] for the predicted risk of <23.5%; RR 0.922 [95% CI 0.816-1.042] for the predicted risk of >41.6%). By using artificial intelligence to define an individual's benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.
在临床实践中，抗炎药物治疗的系统益处因心血管疾病而异。我们旨在评估人工智能在急性A型主动脉夹层（ATAAD）患者中的应用，以确定能从使用尿胰蛋白酶抑制剂（乌司他丁）中获益的最佳目标人群。利用中国多中心5A研究数据库（2016 - 2022年）中患者入院时的特征，建立了一个炎症风险模型来预测多器官功能障碍综合征（MODS）。将该人群（来自15家医院的5126名患者）分为60%的样本用于模型推导，其余40%用于模型验证。接下来，我们训练了一种极端梯度提升算法（XGBoost），以建立一个简洁的患者水平炎症风险模型来预测MODS。最后，构建了一个由估计肾小球滤过率、白细胞计数、血小板计数、德瑞蒂斯比值、血红蛋白和白蛋白组成的前六项特征工具，该工具在推导和验证队列中的鉴别、校准和临床效用方面显示出足够的预测性能。通过个体风险概率和治疗效果分析，我们确定了使用乌司他丁获益存在差异的个体（预测风险为23.5% - 41.6%时，MODS的风险比[RR]为0.802[95%置信区间（CI）0.656，0.981]；预测风险<23.5%时，RR为1.196[0.698 - 2.049]；预测风险>41.6%时，RR为0.922[95% CI 0.816 - 1.042]）。通过使用人工智能根据风险概率和治疗效果预测来定义个体获益情况，我们发现风险概率的个体差异可能对乌司他丁治疗及结果产生重要影响，这突出了为ATAAD患者个体化选择最佳抗炎治疗目标的必要性。
