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Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.
Qiu, Jiaxing; Di Fiore, Juliann M; Krishnamurthi, Narayanan; Indic, Premananda; Carroll, John L; Claure, Nelson; Kemp, James S; Dennery, Phyllis A; Ambalavanan, Namasivayam; Weese-Mayer, Debra E; Hibbs, Anna Maria; Martin, Richard J; Bancalari, Eduardo; Hamvas, Aaron; Randall Moorman, J; Lake, Douglas E.
Affiliation
  • Qiu J; Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA.
  • Di Fiore JM; Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH.
  • Krishnamurthi N; Department of Pediatrics, Division of Autonomic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.
  • Indic P; Department of Electrical Engineering, University of Texas at Tyler, Tyler, TX.
  • Carroll JL; Department of Pediatrics, University of Arkansas for Medical Sciences and Arkansas Children's Hospital, Little Rock, AK.
  • Claure N; Department of Pediatrics, Division of Neonatology, University of Miami Miller School of Medicine, Miami, FL.
  • Kemp JS; Department of Pediatrics, Division of Pediatric Pulmonology, Washington University School of Medicine, St. Louis, MO.
  • Dennery PA; Department of Pediatrics, Division of Newborn Medicine, Washington University School of Medicine, St. Louis, MO.
  • Ambalavanan N; Department of Pediatrics, Division of Neonatology, University of Alabama at Birmingham, Birmingham, AL.
  • Weese-Mayer DE; Department of Pediatrics, Division of Autonomic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.
  • Hibbs AM; Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH.
  • Martin RJ; Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH.
  • Bancalari E; Department of Pediatrics, Division of Neonatology, University of Miami Miller School of Medicine, Miami, FL.
  • Hamvas A; Ann and Robert H. Lurie Children's Hospital and Northwestern University Department of Pediatrics, Chicago, IL.
  • Randall Moorman J; Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA.
  • Lake DE; Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA.
medRxiv ; 2024 Jan 24.
Article in En | MEDLINE | ID: mdl-38343830
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 > 700 extremely preterm infants to identify physiologic features that predict respiratory outcomes. We calculated a subset of 33 HCTSA features on > 7M 10-minute 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 > 3500 HCTSA algorithms. Performance of each feature was measured by individual area under the receiver operating curve (AUC) at various days of life and binary respiratory outcomes. These were compared to optimal PreVent physiologic predictor IH90 DPE, the duration per event of intermittent hypoxemia events with threshold of 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). 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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: MedRxiv Year: 2024 Document type: Article Affiliation country: Ciudad del Vaticano

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: MedRxiv Year: 2024 Document type: Article Affiliation country: Ciudad del Vaticano
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