Incidence and pathophysiology of gastrointestinal bleeding during mechanical circulatory support: A retrospective analysis using machine learning algorithms.

The journal of extra-corporeal technology Pub Date : 2026-03-01 Epub Date: 2026-03-13 DOI:10.1051/ject/2025061
Kelsey Gore, Dean Linder, Juan Jose Martinez Duque, Junxi Wang, Connor Rudnicki, Adrian Alexis Ruiz, Shaun Yockelson, Bobby Nossaman
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Abstract

Background: End-organ hypoperfusion from cardiopulmonary shock may require mechanical circulatory support (MCS). However, patients receiving MCS risk the development of hemorrhagic complications, including gastrointestinal bleeding (GI). Examining potential risk factors for these complications improves clinical understanding. The purpose of this investigation was to study the risk for GI bleeding in MCS patients.

Methods: Following IRB approval, patient characteristics, previously reported comorbidities, and the incidence of GI bleeding were reviewed from January 2017 to October 2023. Clinical variables underwent machine learning with autovalidation. Support vector machine modeling provided the best performance among the ensemble models tested.

Results: In this study of 156 patients who underwent 284 MCS procedures, the incidence of GI bleeding was 6.0% CI 3.3-10.4%. Following machine learning, patients with insulin-dependent diabetes were associated with GI bleeding. The Receiver Operating Characteristic (ROC) curve demonstrated an area under the curve (AUC) of 0.85 with a misclassification rate of 7.5%. The relative risk of the need for major transfusion (>2 packed red blood cell units/episode) was 1.7 CI 1.1-2.5. The majority (87%), but not all, of these patients received unfractionated heparin therapy. Finally, hospital length of stay was increased in patients with GI bleeding.

Conclusion: Insulin-dependent diabetes was associated with increased risk for GI bleeding during MCS, and these patients more often required major transfusions. Further evaluation of continuous anticoagulation therapy is warranted. Knowledge derived from this analytical study may guide the development of institutional protocols to improve care in this patient population.

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机械循环支持期间消化道出血的发生率和病理生理学:使用机器学习算法的回顾性分析。
背景:心肺休克引起的终末器官灌注不足可能需要机械循环支持(MCS)。然而,接受MCS的患者有发生出血性并发症的风险,包括胃肠道出血(GI)。检查这些并发症的潜在危险因素可以提高临床认识。本研究的目的是研究MCS患者发生消化道出血的风险。方法:在获得IRB批准后,对2017年1月至2023年10月期间的患者特征、先前报告的合并症和胃肠道出血发生率进行了回顾。临床变量通过机器学习进行自动验证。在测试的集成模型中,支持向量机建模提供了最好的性能。结果:在这项研究中,156例患者接受了284例MCS手术,胃肠道出血的发生率为6.0%,CI为3.3-10.4%。在机器学习之后,胰岛素依赖型糖尿病患者与胃肠道出血相关。受试者工作特征(ROC)曲线下面积(AUC)为0.85,误分类率为7.5%。需要大量输血的相对风险(血红细胞单位/次)为1.7 CI 1.1-2.5。大多数(87%),但不是全部,这些患者接受了不分段肝素治疗。最后,消化道出血患者的住院时间增加。结论:胰岛素依赖型糖尿病与MCS期间消化道出血风险增加相关,这些患者更经常需要大量输血。进一步评估持续抗凝治疗是必要的。从分析性研究中获得的知识可以指导机构方案的发展,以改善对这一患者群体的护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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