Key result
Machine learning estimation of cardiac output from arterial blood pressure waveforms was statistically equivalent to Doppler measurements (agreement -0.13 L/min; p < 0.05).
Why the study?
Does continuous BP and HR from the arterial BP waveform coupled with machine learning reliably estimate cardiac output in a conscious sheep model of hemorrhage and resuscitation?
Does continuous BP and HR from the arterial BP waveform coupled with machine learning reliably estimate cardiac output in a conscious sheep model of hemorrhage and resuscitation?
Machine learning applied to arterial blood pressure waveforms can reliably estimate cardiac output during hemorrhage and resuscitation in a preclinical model.
Preclinical equivalence supports ML waveform CO estimation in hemorrhage; leaves open human translation and prospective validation.
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 (<0.3 L/min) indicated an overall high COFP accuracy. The agreement between COFP and COVS was -0.13 (0.69) L/min (Bland-Altman). In an exclusion zone of 12%, trending analysis found a 92% concordance between 5-minute changes in COFP and COVS. 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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Liu et al. (2015) studied multiple hemorrhages and resuscitation (n=10). Machine learning estimation of cardiac output from arterial BP waveform vs. Pulmonary arterial blood flow using Doppler transit time was evaluated on Cardiac output estimation equivalence and agreement (Mean agreement -0.13 L/min, p=<0.05). Machine learning estimation of cardiac output from arterial blood pressure waveforms was statistically equivalent to Doppler measurements (agreement -0.13 L/min; p < 0.05).
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