Blood pressure and heart rate from the arterial blood pressure waveform can reliably estimate cardiac output in a conscious sheep model of multiple hemorrhages and resuscitation using computer machine learning approaches

Nehemiah T. Liu, George Kramer, Muzna Khan, Michael Kinsky, José Salinas

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    Abstract

    BACKGROUND: This study was a first step to facilitate the development of automated decision support systems using cardiac output (CO) for combat casualty care. Such systems remain a practical challenge in battlefield and prehospital settings. In these environments, reliable CO estimation using blood pressure (BP) and heart rate (HR) may provide additional capabilities for diagnosis and treatment of trauma patients. The aim of this study was to demonstrate that continuous BP and HR from the arterial BP waveform coupled with machine learning (ML) can reliably estimate CO in a conscious sheep model of multiple hemorrhages and resuscitation.

    METHODS: Hemodynamic parameters (BPs, HR) were derived from 100-Hz arterial BP waveforms of 10 sheep records, 3 hours to 4 hours long. Two models (mean arterial pressure, Windkessel) were then applied and merged to estimate COVS. ML was used to develop a rule for identifying when models required calibration. All records contained 100-Hz recording of pulmonary arterial blood flow using Doppler transit time (COFP). COFP and COVS were analyzed using equivalence tests and Bland-Altman analysis, as well as waveform and concordance plots.

    RESULTS: Baseline COFP varied from 3.0 L/min to 5.4 L/min, while posthemorrhage COFP varied from 1.0 L/min to 1.8 L/min. A total of 315,196 pairs of data were obtained. Equivalence tests for individual records showed that COVS was statistically equivalent to COFP (p <0.05). Smaller equivalence thresholds (

    CONCLUSION: This study showed that CO can be reliably estimated using BPs and HR from the arterial BP waveform in combination with ML. A next step will be to test this approach using noninvasive BPs and HR.

    Original languageEnglish (US)
    Pages (from-to)S85-S92
    JournalThe journal of trauma and acute care surgery
    Volume79
    Issue number4
    DOIs
    StatePublished - Oct 1 2015

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    Resuscitation
    Cardiac Output
    Sheep
    Arterial Pressure
    Heart Rate
    Hemorrhage
    Blood Pressure
    Calibration
    Hemodynamics
    Machine Learning
    Lung
    Wounds and Injuries
    Therapeutics

    ASJC Scopus subject areas

    • Medicine(all)

    Cite this

    @article{ddb2faccf7e44466af8bdb0d80503804,
    title = "Blood pressure and heart rate from the arterial blood pressure waveform can reliably estimate cardiac output in a conscious sheep model of multiple hemorrhages and resuscitation using computer machine learning approaches",
    abstract = "BACKGROUND: This study was a first step to facilitate the development of automated decision support systems using cardiac output (CO) for combat casualty care. Such systems remain a practical challenge in battlefield and prehospital settings. In these environments, reliable CO estimation using blood pressure (BP) and heart rate (HR) may provide additional capabilities for diagnosis and treatment of trauma patients. The aim of this study was to demonstrate that continuous BP and HR from the arterial BP waveform coupled with machine learning (ML) can reliably estimate CO in a conscious sheep model of multiple hemorrhages and resuscitation.METHODS: Hemodynamic parameters (BPs, HR) were derived from 100-Hz arterial BP waveforms of 10 sheep records, 3 hours to 4 hours long. Two models (mean arterial pressure, Windkessel) were then applied and merged to estimate COVS. ML was used to develop a rule for identifying when models required calibration. All records contained 100-Hz recording of pulmonary arterial blood flow using Doppler transit time (COFP). COFP and COVS were analyzed using equivalence tests and Bland-Altman analysis, as well as waveform and concordance plots.RESULTS: Baseline COFP varied from 3.0 L/min to 5.4 L/min, while posthemorrhage COFP varied from 1.0 L/min to 1.8 L/min. A total of 315,196 pairs of data were obtained. Equivalence tests for individual records showed that COVS was statistically equivalent to COFP (p <0.05). Smaller equivalence thresholds (CONCLUSION: This study showed that CO can be reliably estimated using BPs and HR from the arterial BP waveform in combination with ML. A next step will be to test this approach using noninvasive BPs and HR.",
    author = "Liu, {Nehemiah T.} and George Kramer and Muzna Khan and Michael Kinsky and Jos{\'e} Salinas",
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    doi = "10.1097/TA.0000000000000671",
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    TY - JOUR

    T1 - Blood pressure and heart rate from the arterial blood pressure waveform can reliably estimate cardiac output in a conscious sheep model of multiple hemorrhages and resuscitation using computer machine learning approaches

    AU - Liu, Nehemiah T.

    AU - Kramer, George

    AU - Khan, Muzna

    AU - Kinsky, Michael

    AU - Salinas, José

    PY - 2015/10/1

    Y1 - 2015/10/1

    N2 - BACKGROUND: This study was a first step to facilitate the development of automated decision support systems using cardiac output (CO) for combat casualty care. Such systems remain a practical challenge in battlefield and prehospital settings. In these environments, reliable CO estimation using blood pressure (BP) and heart rate (HR) may provide additional capabilities for diagnosis and treatment of trauma patients. The aim of this study was to demonstrate that continuous BP and HR from the arterial BP waveform coupled with machine learning (ML) can reliably estimate CO in a conscious sheep model of multiple hemorrhages and resuscitation.METHODS: Hemodynamic parameters (BPs, HR) were derived from 100-Hz arterial BP waveforms of 10 sheep records, 3 hours to 4 hours long. Two models (mean arterial pressure, Windkessel) were then applied and merged to estimate COVS. ML was used to develop a rule for identifying when models required calibration. All records contained 100-Hz recording of pulmonary arterial blood flow using Doppler transit time (COFP). COFP and COVS were analyzed using equivalence tests and Bland-Altman analysis, as well as waveform and concordance plots.RESULTS: Baseline COFP varied from 3.0 L/min to 5.4 L/min, while posthemorrhage COFP varied from 1.0 L/min to 1.8 L/min. A total of 315,196 pairs of data were obtained. Equivalence tests for individual records showed that COVS was statistically equivalent to COFP (p <0.05). Smaller equivalence thresholds (CONCLUSION: This study showed that CO can be reliably estimated using BPs and HR from the arterial BP waveform in combination with ML. A next step will be to test this approach using noninvasive BPs and HR.

    AB - BACKGROUND: This study was a first step to facilitate the development of automated decision support systems using cardiac output (CO) for combat casualty care. Such systems remain a practical challenge in battlefield and prehospital settings. In these environments, reliable CO estimation using blood pressure (BP) and heart rate (HR) may provide additional capabilities for diagnosis and treatment of trauma patients. The aim of this study was to demonstrate that continuous BP and HR from the arterial BP waveform coupled with machine learning (ML) can reliably estimate CO in a conscious sheep model of multiple hemorrhages and resuscitation.METHODS: Hemodynamic parameters (BPs, HR) were derived from 100-Hz arterial BP waveforms of 10 sheep records, 3 hours to 4 hours long. Two models (mean arterial pressure, Windkessel) were then applied and merged to estimate COVS. ML was used to develop a rule for identifying when models required calibration. All records contained 100-Hz recording of pulmonary arterial blood flow using Doppler transit time (COFP). COFP and COVS were analyzed using equivalence tests and Bland-Altman analysis, as well as waveform and concordance plots.RESULTS: Baseline COFP varied from 3.0 L/min to 5.4 L/min, while posthemorrhage COFP varied from 1.0 L/min to 1.8 L/min. A total of 315,196 pairs of data were obtained. Equivalence tests for individual records showed that COVS was statistically equivalent to COFP (p <0.05). Smaller equivalence thresholds (CONCLUSION: This study showed that CO can be reliably estimated using BPs and HR from the arterial BP waveform in combination with ML. A next step will be to test this approach using noninvasive BPs and HR.

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    U2 - 10.1097/TA.0000000000000671

    DO - 10.1097/TA.0000000000000671

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    VL - 79

    SP - S85-S92

    JO - Journal of Trauma and Acute Care Surgery

    JF - Journal of Trauma and Acute Care Surgery

    SN - 2163-0755

    IS - 4

    ER -