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
{"title":"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。","authors":"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","doi":"10.1088/1361-6579/ad4e91","DOIUrl":null,"url":null,"abstract":"<p><p><i>Objective.</i>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.<i>Approach</i>. 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%).<i>Main Results.</i>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).<i>Significance</i>. 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.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11485323/pdf/","citationCount":"0","resultStr":"{\"title\":\"Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.\",\"authors\":\"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\",\"doi\":\"10.1088/1361-6579/ad4e91\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Objective.</i>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.<i>Approach</i>. 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%).<i>Main Results.</i>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).<i>Significance</i>. 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Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.
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.
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.