Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.

IF 2.3 4区医学 Q3 BIOPHYSICS Physiological measurement Pub Date : 2024-06-03 DOI:10.1088/1361-6579/ad4e91

Jiaxing Qiu, Juliann M Di Fiore, Narayanan Krishnamurthi, Premananda Indic, John L Carroll, Nelson Claure, James S Kemp, Phyllis A Dennery, Namasivayam Ambalavanan, Debra E Weese-Mayer, Anna Maria Hibbs, Richard J Martin, Eduardo Bancalari, Aaron Hamvas, J Randall Moorman, Douglas E Lake, Katy N Krahn, Amanda M Zimmet, Bradley S Hopkins, Erin K Lonergan, Casey M Rand, Arlene Zadell, Arie Nakhmani, Waldemar A Carlo, Deborah Laney, Colm P Travers, Silvia Vanbuskirk, Carmen D'Ugard, Ana Cecilia Aguilar, Alini Schott, Julie Hoffmann, Laura Linneman

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Abstract

Objective.Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes.Approach. We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90_DPE (duration per event of intermittent hypoxemia events below 90%).Main Results.The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850).Significance. These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

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高度比较氧饱和度和心率的时间序列分析，以预测极早产儿的呼吸系统预后。

目的：高度比较时间序列分析（HCTSA）是一种新颖的方法，涉及到利用许多学科的公开代码进行大规模特征提取。早产相关通气控制（Pre-Vent）多中心前瞻性观察研究收集了超过 700 名极度早产儿的床旁监护仪数据，以确定预测呼吸结果的生理特征。我们计算了来自 PreVent 队列的大于 700 万个 10 分钟氧饱和度 (SPO2) 和心率 (HR) 窗口的 33 个 HCTSA 特征子集，以量化预测性能。该子集包括之前在大于 3500 个 HCTSA 算法上使用无监督聚类确定的代表特征。每个特征的性能是通过生命不同天数和二元呼吸结果下的单个接收者操作曲线下面积（AUC）来衡量的。我们假设，最佳 HCTSA 算法将优于最佳 PreVent 生理预测指标 IH90_DPE（间歇性低氧血症事件每次持续时间低于 90%）：最高的 HCTSA 特征来自一组与 SPO2 时间序列自相关性相关的算法，并将低频率的饱和度降低模式识别为高风险。这些特征与 IH90_DPE 的性能相当且高度相关，但也许能以更稳健的方式测量婴儿的生理状态，值得进一步研究。HR HCTSA 的首要特征是符号转换指标，这些指标之前已被确定为新生儿死亡率的有力预测指标。HR指标仅是生命早期的重要预测指标，这可能是由于较大比例的婴儿因任何原因死亡。使用 3 个顶级特征的简单 HCTSA 模型在生命第 7 天的表现优于 IH90_DPE（0.778 对 0.729），但在生命第 28 天的表现基本相当（0.849 对 0.850）。这些结果验证了具有代表性的 HCTSA 方法的实用性，同时也提供了更多证据支持 IH90_DPE 作为呼吸结局的最佳预测指标。

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来源期刊

Physiological measurement 生物-工程：生物医学

CiteScore

5.50

自引率

9.40%

发文量

124

审稿时长

3 months

期刊介绍： Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.