Machine Learning Algorithm to Predict Cardiac Output Using Arterial Pressure Waveform Analysis

Liu, Ke; Elibol, Armağan; Xiao, Wei; Cenyu, Liao; Wang, Wei; Chong, Nak Young

doi:10.1109/bibm55620.2022.9995429

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Drawing a comparison with the work of Liao Ke et al [15], they also explored the prediction of CO from arterial pressure waveform analysis (APWA) using various machine learning models. Their study incorporated features based on traditional hemodynamic models and waveform or time series features.…”

Section: Discussionmentioning

confidence: 99%

“…Rüschen et al [14] developed an online estimation method for total cardiac output during left ventricular assistance using optical pressure sensors mounted on an Impella CP pump. Liao Ke et al [15] investigated the application of machine learning and feature engineering techniques to predict cardiac output based on arterial pressure waveform analysis, incorporating time-domain, frequency-domain, and other characteristics of time series data. Petrou et al [16] proposed a pipeline that combines algorithms and pump inlet pressure sensors, along with machine learning models, to estimate cardiac output by predicting aortic valve opening status.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Left Ventricular Assist Device Therapy: A Machine Learning Approach for Predicting Cardiac Output

Belkacem,

Jemili,

Ellouze

et al. 2023

Preprint

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Left Ventricular Assist Device Therapy: A Machine Learning Approach for Predicting Cardiac Output

Belkacem,

Jemili,

Ellouze

et al. 2023

Preprint

View full text Add to dashboard Cite

“…Ayana Dvir et al [16] in 2022 compared the CO obtained from monitors using PPG with the CO obtained by thermodilution, resulting in a correlation coefficient of 0.906. Ke et al [17] used arterial pressure waveforms and created a regression model to predict CO in 2022. Using the results of the random forest model, the MSE was 1.421 L/min, the 95% limit of agreement was −2.35 L/min and 2.32 L/min, the percentage error was 39.44%, being higher than 30%, and the result using the XGBoost model was 28.89%.…”

Section: Introductionmentioning

confidence: 99%

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

Xu,

Tang,

Chen

2023

Sensors

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We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was −0.04 L/min with a −1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between −20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

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A multimodal stacked ensemble model for cardiac output prediction utilizing cardiorespiratory interactions during general anesthesia

Dervishi

2024

Sci Rep

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This study examined the possibility of estimating cardiac output (CO) using a multimodal stacking model that utilizes cardiopulmonary interactions during general anesthesia and outlined a retrospective application of machine learning regression model to a pre-collected dataset. The data of 469 adult patients (obtained from VitalDB) with normal pulmonary function tests who underwent general anesthesia were analyzed. The hemodynamic data in this study included non-invasive blood pressure, plethysmographic heart rate, and SpO2. CO was recorded using Vigileo and EV1000 (pulse contour technique devices). Respiratory data included mechanical ventilation parameters and end-tidal CO2 levels. A generalized linear regression model was used as the metalearner for the multimodal stacking ensemble method. Random forest, generalized linear regression, gradient boosting machine, and XGBoost were used as base learners. A Bland–Altman plot revealed that the multimodal stacked ensemble model for CO prediction from 327 patients had a bias of − 0.001 L/min and − 0.271% when calculating the percentage of difference using the EV1000 device. Agreement of model CO prediction and measured Vigileo CO in 142 patients reported a bias of − 0.01 and − 0.333%. Overall, this model predicts CO compared to data obtained by the pulse contour technique CO monitors with good agreement.

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Machine Learning Algorithm to Predict Cardiac Output Using Arterial Pressure Waveform Analysis

Cited by 6 publications

References 25 publications

Optimizing Left Ventricular Assist Device Therapy: A Machine Learning Approach for Predicting Cardiac Output

Optimizing Left Ventricular Assist Device Therapy: A Machine Learning Approach for Predicting Cardiac Output

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

A multimodal stacked ensemble model for cardiac output prediction utilizing cardiorespiratory interactions during general anesthesia

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