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Qiu J, Di Fiore JM, Krishnamurthi N, Indic P, Carroll JL, Claure N, Kemp JS, Dennery PA, Ambalavanan N, Weese-Mayer DE, Maria Hibbs A, Martin RJ, Bancalari E, Hamvas A, Randall Moorman J, Lake DE. Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants. Physiol Meas 2024; 45:055025. [PMID: 38772400 DOI: 10.1088/1361-6579/ad4e91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 05/21/2024] [Indexed: 05/23/2024]
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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Affiliation(s)
- Jiaxing Qiu
- Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA, United States of America
| | - Juliann M Di Fiore
- Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH, United States of America
| | - Narayanan Krishnamurthi
- Department of Pediatrics, Division of Autonomic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Premananda Indic
- Department of Electrical Engineering, University of Texas at Tyler, Tyler, TX, United States of America
| | - John L Carroll
- Department of Pediatrics, University of Arkansas for Medical Sciences and Arkansas Children's Hospital, Little Rock, AR, United States of America
| | - Nelson Claure
- Department of Pediatrics, Division of Neonatology, University of Miami Miller School of Medicine, Miami, FL, United States of America
| | - James S Kemp
- Department of Pediatrics, Division of Pediatric Pulmonology, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Phyllis A Dennery
- Department of Pediatrics, Brown University School of Medicine, Department of Pediatrics, Providence, RI, United States of America
| | - Namasivayam Ambalavanan
- Department of Pediatrics, Division of Neonatology, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Debra E Weese-Mayer
- Department of Pediatrics, Division of Autonomic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Anna Maria Hibbs
- Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH, United States of America
| | - Richard J Martin
- Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH, United States of America
| | - Eduardo Bancalari
- Department of Pediatrics, Division of Neonatology, University of Miami Miller School of Medicine, Miami, FL, United States of America
| | - Aaron Hamvas
- Ann and Robert H. Lurie Children's Hospital and Northwestern University Department of Pediatrics, Chicago, IL, United States of America
| | - J Randall Moorman
- Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA, United States of America
| | - Douglas E Lake
- Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA, United States of America
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Qiu J, Di Fiore JM, Krishnamurthi N, Indic P, Carroll JL, Claure N, Kemp JS, Dennery PA, Ambalavanan N, Weese-Mayer DE, Hibbs AM, Martin RJ, Bancalari E, Hamvas A, Randall Moorman J, Lake DE. Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.24.24301724. [PMID: 38343830 PMCID: PMC10854343 DOI: 10.1101/2024.01.24.24301724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
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.
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Affiliation(s)
- Jiaxing Qiu
- Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA
| | - Juliann M Di Fiore
- Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH
| | - Narayanan Krishnamurthi
- Department of Pediatrics, Division of Autonomic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Premananda Indic
- Department of Electrical Engineering, University of Texas at Tyler, Tyler, TX
| | - John L Carroll
- Department of Pediatrics, University of Arkansas for Medical Sciences and Arkansas Children's Hospital, Little Rock, AK
| | - Nelson Claure
- Department of Pediatrics, Division of Neonatology, University of Miami Miller School of Medicine, Miami, FL
| | - James S Kemp
- Department of Pediatrics, Division of Pediatric Pulmonology, Washington University School of Medicine, St. Louis, MO
| | - Phyllis A Dennery
- Department of Pediatrics, Division of Newborn Medicine, Washington University School of Medicine, St. Louis, MO
| | - Namasivayam Ambalavanan
- Department of Pediatrics, Division of Neonatology, University of Alabama at Birmingham, Birmingham, AL
| | - Debra E Weese-Mayer
- Department of Pediatrics, Division of Autonomic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Anna Maria Hibbs
- Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH
| | - Richard J Martin
- Department of Pediatrics, Case Western Reserve University School of Medicine, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, OH
| | - Eduardo Bancalari
- Department of Pediatrics, Division of Neonatology, University of Miami Miller School of Medicine, Miami, FL
| | - Aaron Hamvas
- Ann and Robert H. Lurie Children's Hospital and Northwestern University Department of Pediatrics, Chicago, IL
| | - J Randall Moorman
- Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA
| | - Douglas E Lake
- Department of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA
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Improving child health through Big Data and data science. Pediatr Res 2023; 93:342-349. [PMID: 35974162 PMCID: PMC9380977 DOI: 10.1038/s41390-022-02264-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/10/2022] [Accepted: 06/28/2022] [Indexed: 12/04/2022]
Abstract
Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.
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Machine Learning Algorithms to Predict Mortality of Neonates on Mechanical Intubation for Respiratory Failure. Biomedicines 2021; 9:biomedicines9101377. [PMID: 34680497 PMCID: PMC8533201 DOI: 10.3390/biomedicines9101377] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Early identification of critically ill neonates with poor outcomes can optimize therapeutic strategies. We aimed to examine whether machine learning (ML) methods can improve mortality prediction for neonatal intensive care unit (NICU) patients on intubation for respiratory failure. METHODS A total of 1734 neonates with respiratory failure were randomly divided into training (70%, n = 1214) and test (30%, n = 520) sets. The primary outcome was the probability of NICU mortality. The areas under the receiver operating characteristic curves (AUCs) of several ML algorithms were compared with those of the conventional neonatal illness severity scoring systems including the NTISS and SNAPPE-II. RESULTS For NICU mortality, the random forest (RF) model showed the highest AUC (0.939 (0.921-0.958)) for the prediction of neonates with respiratory failure, and the bagged classification and regression tree model demonstrated the next best results (0.915 (0.891-0.939)). The AUCs of both models were significantly better than the traditional NTISS (0.836 (0.800-0.871)) and SNAPPE-II scores (0.805 (0.766-0.843)). The superior performances were confirmed by higher accuracy and F1 score and better calibration, and the superior and net benefit was confirmed by decision curve analysis. In addition, Shapley additive explanation (SHAP) values were utilized to explain the RF prediction model. CONCLUSIONS Machine learning algorithms increase the accuracy and predictive ability for mortality of neonates with respiratory failure compared with conventional neonatal illness severity scores. The RF model is suitable for clinical use in the NICU, and clinicians can gain insights and have better communication with families in advance.
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