Development and Validation of a Novel Deep Learning Model to Predict Pharmacologic Closure of Patent Ductus Arteriosus in Premature Infants.

IF 5.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Puneet Sharma, Addison Gearhart, Guangze Luo, Anil Palepu, Cindy Wang, Joshua Mayourian, Kristyn Beam, Fotios Spyropoulos, Andrew J Powell, Philip Levy, Andrew Beam
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引用次数: 0

Abstract

Background: Patent ductus arteriosus (PDA) is associated with significant morbidity and mortality in preterm infants. Although pharmacotherapy can be effective, it is difficult to predict whether a patient will respond, leading to delays in care. Machine learning has emerged as a powerful tool to interpret clinical data to predict clinical outcomes but has not yet been applied to this question. The aim of this study was to train and validate a novel deep learning model to predict the likelihood of PDA closure after an initial course of pharmacotherapy in preterm infants.

Methods: A retrospective cohort of 174 preterm infants who received pharmacologic treatment for PDA was identified. After collecting relevant perinatal data and pretreatment echocardiograms, the subjects were randomized into training and validation sets in a 70:30 split. Two distinct convolutional neural networks (CNN) were trained, one based on echocardiograms alone and the other on both echocardiograms and perinatal data. The performance of the CNNs was compared against controls of random forest and logistic regression models trained on perinatal data alone.

Results: The rate of PDA closure after an initial course of pharmacotherapy was 60% in this cohort. The 174 echocardiograms collected for all subjects included 1,926 clips. A total of 121 infants (1,387 clips) were successfully randomized into the training set and 53 (539 clips) into the validation set. The multimodal CNN had an area under the curve (AUC) of 0.82, outperforming the imaging-only model (AUC = 0.66). Additionally, the multimodal CNN outperformed logistic regression (AUC = 0.66) and random forest (AUC = 0.74) models.

Conclusions: This novel, multimodal CNN shows promise for clinicians, who do not currently have a reliable tool to predict the success of PDA closure after an initial course of pharmacotherapy. This investigation represents the first attempt to use deep learning methodology to predict this outcome.

一种预测早产儿动脉导管未闭药理学关闭的新型深度学习模型的开发和验证。
背景:动脉导管未闭(PDA)与早产儿显著的发病率和死亡率相关。虽然药物治疗可能是有效的,但很难预测患者是否会有反应,导致护理延误。机器学习已经成为解释临床数据以预测临床结果的强大工具,但尚未应用于这个问题。本研究的目的是训练和验证一种新的深度学习模型,以预测早产儿在初始药物治疗后PDA关闭的可能性。方法:对174例接受药物治疗的PDA早产儿进行回顾性研究。收集围产期相关资料及预处理超声心动图后,将受试者按70:30的比例随机分为训练组和验证组。两个不同的卷积神经网络(CNN)被训练,一个基于超声心动图单独和另一个超声心动图和围产期数据。cnn的性能与随机森林和单独训练围产期数据的逻辑回归模型的对照进行了比较。结果:在该队列中,初始药物治疗后PDA闭合率为60%。收集的所有受试者的174张超声心动图包括1,926个片段。共有121名婴儿(1,387个片段)被成功随机分配到训练集,53名婴儿(539个片段)被随机分配到验证集。多模态CNN的曲线下面积(AUC)为0.82,优于单纯成像模型(AUC = 0.66)。此外,多模态CNN优于逻辑回归(AUC = 0.66)和随机森林(AUC = 0.74)模型。结论:这种新颖的、多模式的CNN为临床医生带来了希望,他们目前还没有一个可靠的工具来预测在初始药物治疗过程后PDA关闭的成功。这项研究首次尝试使用深度学习方法来预测这一结果。
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来源期刊
CiteScore
9.50
自引率
12.30%
发文量
257
审稿时长
66 days
期刊介绍: The Journal of the American Society of Echocardiography(JASE) brings physicians and sonographers peer-reviewed original investigations and state-of-the-art review articles that cover conventional clinical applications of cardiovascular ultrasound, as well as newer techniques with emerging clinical applications. These include three-dimensional echocardiography, strain and strain rate methods for evaluating cardiac mechanics and interventional applications.
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