Development and external validation of temporal fusion transformer models for continuous intraoperative blood pressure forecasting.

IF 9.6 1区 医学 Q1 MEDICINE, GENERAL & INTERNAL
EClinicalMedicine Pub Date : 2024-08-30 eCollection Date: 2024-09-01 DOI:10.1016/j.eclinm.2024.102797
Lorenz Kapral, Christoph Dibiasi, Natasa Jeremic, Stefan Bartos, Sybille Behrens, Aylin Bilir, Clemens Heitzinger, Oliver Kimberger
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引用次数: 0

Abstract

Background: During surgery, intraoperative hypotension is associated with postoperative morbidity and should therefore be avoided. Predicting the occurrence of hypotension in advance may allow timely interventions to prevent hypotension. Previous prediction models mostly use high-resolution waveform data, which is often not available.

Methods: We utilised a novel temporal fusion transformer (TFT) algorithm to predict intraoperative blood pressure trajectories 7 min in advance. We trained the model with low-resolution data (sampled every 15 s) from 73,009 patients who were undergoing general anaesthesia for non-cardiothoracic surgery between January 1, 2017, and December 30, 2020, at the General Hospital of Vienna, Austria. The data set contained information on patient demographics, vital signs, medication, and ventilation. The model was evaluated using an internal (n = 8113) and external test set (n = 5065) obtained from the openly accessible Vital Signs Database.

Findings: In the internal test set, the mean absolute error for predicting mean arterial blood pressure was 0.376 standard deviations-or 4 mmHg-and 0.622 standard deviations-or 7 mmHg-in the external test set. We also adapted the TFT model to binarily predict the occurrence of hypotension as defined by mean arterial blood pressure < 65 mmHg in the next one, three, five, and 7 min. Here, model discrimination was excellent, with a mean area under the receiver operating characteristic curve (AUROC) of 0.933 in the internal test set and 0.919 in the external test set.

Interpretation: Our TFT model is capable of accurately forecasting intraoperative arterial blood pressure using only low-resolution data showing a low prediction error. When used for binary prediction of hypotension, we obtained excellent performance.

Funding: No external funding.

用于术中连续血压预测的时间融合变换器模型的开发和外部验证。
背景:在手术过程中,术中低血压与术后发病率有关,因此应该避免。提前预测低血压的发生可以及时采取干预措施预防低血压。以往的预测模型大多使用高分辨率波形数据,而这种数据通常无法获得:方法:我们利用一种新颖的时间融合变换器(TFT)算法提前 7 分钟预测术中血压轨迹。我们利用奥地利维也纳总医院在 2017 年 1 月 1 日至 2020 年 12 月 30 日期间接受非心胸手术全身麻醉的 73,009 名患者的低分辨率数据(每 15 秒采样一次)对模型进行了训练。数据集包含患者人口统计学、生命体征、用药和通气信息。使用从公开的生命体征数据库中获取的内部测试集(n = 8113)和外部测试集(n = 5065)对模型进行了评估:在内部测试集中,预测平均动脉血压的平均绝对误差为 0.376 个标准差,即 4 mmHg;在外部测试集中,预测平均动脉血压的平均绝对误差为 0.622 个标准差,即 7 mmHg。我们还调整了 TFT 模型,以二元方式预测未来 1 分钟、3 分钟、5 分钟和 7 分钟内平均动脉血压小于 65 mmHg 的低血压发生率。在这里,模型的辨别能力非常出色,内部测试集的接收者操作特征曲线下的平均面积(AUROC)为 0.933,外部测试集的接收者操作特征曲线下的平均面积(AUROC)为 0.919:我们的 TFT 模型仅使用低分辨率数据就能准确预测术中动脉血压,且预测误差较小。当用于二元预测低血压时,我们取得了优异的成绩:无外部资助。
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来源期刊
EClinicalMedicine
EClinicalMedicine Medicine-Medicine (all)
CiteScore
18.90
自引率
1.30%
发文量
506
审稿时长
22 days
期刊介绍: eClinicalMedicine is a gold open-access clinical journal designed to support frontline health professionals in addressing the complex and rapid health transitions affecting societies globally. The journal aims to assist practitioners in overcoming healthcare challenges across diverse communities, spanning diagnosis, treatment, prevention, and health promotion. Integrating disciplines from various specialties and life stages, it seeks to enhance health systems as fundamental institutions within societies. With a forward-thinking approach, eClinicalMedicine aims to redefine the future of healthcare.
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