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机器学习模型预测 ICU 血流动力学不稳定的外部验证

External validation of a machine learning model to predict hemodynamic instability in intensive care unit.

机构信息

Department of Critical Care Medicine, Taipei Veteran General Hospital, No. 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan.

School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.

出版信息

Crit Care. 2022 Jul 14;26(1):215. doi: 10.1186/s13054-022-04088-9.

Abstract

BACKGROUND

Early prediction model of hemodynamic instability has the potential to improve the critical care, whereas limited external validation on the generalizability. We aimed to independently validate the Hemodynamic Stability Index (HSI), a multi-parameter machine learning model, in predicting hemodynamic instability in Asian patients.

METHOD

Hemodynamic instability was marked by using inotropic, vasopressor, significant fluid therapy, and/or blood transfusions. This retrospective study included among 15,967 ICU patients who aged 20 years or older (not included 20 years) and stayed in ICU for more than 6 h admitted to Taipei Veteran General Hospital (TPEVGH) between January 1, 2010, and March 31, 2020, of whom hemodynamic instability occurred in 3053 patients (prevalence = 19%). These patients in unstable group received at least one intervention during their ICU stays, and the HSI score of both stable and unstable group was calculated in every hour before intervention. The model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and was compared to single indicators like systolic blood pressure (SBP) and shock index. The hemodynamic instability alarm was set by selecting optimal threshold with high sensitivity, acceptable specificity, and lead time before intervention was calculated to indicate when patients were firstly identified as high risk of hemodynamic instability.

RESULTS

The AUROC of HSI was 0.76 (95% CI, 0.75-0.77), which performed significantly better than shock Index (0.7; 95% CI, 0.69-0.71) and SBP (0.69; 95% CI, 0.68-0.70). By selecting 0.7 as a threshold, HSI predicted 72% of all 3053 patients who received hemodynamic interventions with 67% in specificity. Time-varying results also showed that HSI score significantly outperformed single indicators even up to 24 h before intervention. And 95% unstable patients can be identified more than 5 h in advance.

CONCLUSIONS

The HSI has acceptable discrimination but underestimates the risk of stable patients in predicting the onset of hemodynamic instability in an external cohort.

摘要

背景

早期预测血流动力学不稳定的模型有可能改善重症监护,但在通用性方面的外部验证有限。我们旨在独立验证血流动力学稳定性指数(HSI),这是一种多参数机器学习模型,用于预测亚洲患者的血流动力学不稳定。

方法

血流动力学不稳定的标志是使用正性肌力药、血管加压药、大量液体治疗和/或输血。这项回顾性研究纳入了 2010 年 1 月 1 日至 2020 年 3 月 31 日期间在台北荣民总医院(TPEVGH)入住 ICU 超过 6 小时的 15967 例年龄在 20 岁或以上的 ICU 患者(不包括 20 岁),其中 3053 例患者发生血流动力学不稳定(发生率为 19%)。不稳定组患者在 ICU 期间至少接受了一次干预,在干预前每小时计算稳定组和不稳定组的 HSI 评分。使用接受者操作特征曲线下面积(AUROC)评估模型性能,并与单一指标(如收缩压[SBP]和休克指数)进行比较。血流动力学不稳定警报通过选择高灵敏度、可接受特异性和干预前提前时间的最佳阈值进行设置,以指示何时首次将患者识别为血流动力学不稳定的高风险。

结果

HSI 的 AUROC 为 0.76(95%CI,0.75-0.77),明显优于休克指数(0.7;95%CI,0.69-0.71)和 SBP(0.69;95%CI,0.68-0.70)。选择 0.7 作为阈值,HSI 预测了 3053 例接受血流动力学干预的患者中的 72%,特异性为 67%。时变结果还表明,HSI 评分甚至在干预前 24 小时内显著优于单一指标。并且 95%的不稳定患者可以提前 5 小时以上被识别。

结论

HSI 在预测外部队列血流动力学不稳定的发生时具有可接受的区分能力,但低估了稳定患者的风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d376/9281065/8386ea837403/13054_2022_4088_Fig1_HTML.jpg

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