Detection of Hemodynamic Status Using an Analytic Based on an Electrocardiogram Lead Waveform

Abstract

Delayed identification of hemodynamic deterioration remains a persistent issue for in-hospital patient care. Clinicians continue to rely on vital signs associated with tachycardia and hypotension to identify hemodynamically unstable patients. A novel, noninvasive technology, the Analytic for Hemodynamic Instability (AHI), uses only the continuous electrocardiogram (ECG) signal from a typical hospital multiparameter telemetry monitor to monitor hemodynamics. The intent of this study was to determine if AHI is able to predict hemodynamic instability without the need for continuous direct measurement of blood pressure. DESIGN: Retrospective cohort study.SETTING: Single quaternary care academic health system in Michigan. PATIENTS:Hospitalized adult patients between November 2019 and February 2020 undergoing continuous ECG and intra-arterial blood pressure monitoring in an intensive care setting. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:One million two hundred fifty-two thousand seven hundred forty-two 5-minute windows of the analytic output were analyzed from 597 consecutive adult patients. AHI outputs were compared with vital sign indications of hemodynamic instability (heart rate > 100 beats/min, systolic blood pressure < 90 mm Hg, and shock index of > 1) in the same window. The observed sensitivity and specificity of AHI were 96.9% and 79.0%, respectively, with an area under the curve (AUC) of 0.90 for heart rate and systolic blood pressure. For the shock index analysis, AHI's sensitivity was 72.0% and specificity was 80.3% with an AUC of 0.81. CONCLUSIONS:The AHI-derived hemodynamic status appropriately detected the various gold standard indications of hemodynamic instability (hypotension, tachycardia and hypotension, and shock index > 1). AHI may provide continuous dynamic hemodynamic monitoring capabilities in patients who traditionally have intermittent static vital sign measurements.

Prediction of Episode of Hemodynamic Instability Using an Electrocardiogram Based Analytic: A Retrospective Cohort Study

“…AHI automates the extraction and analysis of ECG patterns that re ect the compensatory burden of the autonomic nervous system including signal quality assessment and processing of extracted patterns through a pretrained classi cation model that embeds nonlinear HRV complexity and ECG morphologic features into a signal output. 11,18,19 AHI-PI builds on this output and updates every two minutes, producing one of three types of outputs: red, yellow, or green, indicating high, moderate, or low risk respectively of a future episode of hemodynamic instability.…”

Use of a Continuous Single Lead Electrocardiogram Analytic to Predict Patient Deterioration Requiring Rapid Response Team Activation

“…In this study, we evaluate the Analytic for Hemodynamic Instability-Predictive Index (AHI-PI: Fifth Eye, Ann Arbor, MI), a FDA approved software as a medical device (SaMD) that uses a continuous ECG waveform and extracted heart rate variability (HRV) to predict hemodynamic instability. [13][14][15] Changes in HRV have been demonstrated to reflect changes in the autonomic nervous system in the setting of many states of critical illness and injury including hemorrhage, sepsis, cardiogenic shock, respiratory failure, and others with these changes occurring prior to overt decompensation. [16][17][18][19][20][21][22][23] In previous studies, we demonstrated its ability to predict hemodynamic instability (a combination of tachycardia and hypotension) with high sensitivity and specificity with average lead times greater than 3 hours.…”

“…[16][17][18][19][20][21][22][23] In previous studies, we demonstrated its ability to predict hemodynamic instability (a combination of tachycardia and hypotension) with high sensitivity and specificity with average lead times greater than 3 hours. 15,24 AHI-PI automates the extraction and analysis of ECG patterns to reflect the compensatory burden on the autonomic nervous system to provide information regarding the patient's predicted future risk for clinical deterioration based on the known physiologic relationship of HRV, the autonomic nervous system, and the cardiovascular system. 16,19 It also includes a signal quality assessment and processing of extracted patterns through a pretrained classification model that embeds nonlinear HRV complexity and ECG morphologic features into a single output.…”

“…16-23 In previous studies, we demonstrated its ability to predict hemodynamic instability (a combination of tachycardia and hypotension) with high sensitivity and specificity with average lead times greater than 3 hours. 15, 24…”

“…16, 19 It also includes a signal quality assessment and processing of extracted patterns through a pretrained classification model that embeds nonlinear HRV complexity and ECG morphologic features into a single output. 13-15…”