• Jiho Han, Kyo Jin Ahn, Kyoung-Chul Cha, Sun Ju Kim, Woo Jin Jung, Young-Il Roh, YoonYoung RoYRDepartment of Biomedical Engineering, Yonsei University, South Korea. Electronic address: yoon@yonsei.ac.kr., and Sung Oh Hwang.
• Department of Biomedical Engineering, Yonsei University, South Korea. Electronic address: ronda06@yonsei.ac.kr.
• Resuscitation. 2024 Jul 23; 202: 110331110331.

ObjectivesThis study aimed to predict blood pressure during CPR using chest compression waveform information obtained from a CPR feedback device.MethodsQuantitative data including chest compression waveforms from a CPR feedback device and the blood pressure measured by arterial cannulation in patients with cardiac arrest during CPR were used. Forty-one features to predict blood pressure were selected from chest compression waveform and demographic characteristics with neighborhood component analysis algorithm. Optimized Gaussian process regression was used as a machine learning algorithm.ResultsA total of 14,619 datasets from 19 patients with cardiac arrest (mean age: 66 ± 13 years, 14 men) were used in the analysis. The model could predict blood pressure with high precision and low bias for almost the whole range of systolic (SBP), diastolic (DBP), and mean arterial blood pressure (MAP). The correlation coefficients (r) between the predicted and actual values were 0.954 (95% confidence interval: 0.951-0.957, p < 0.001) for SBP, 0.926 (95% confidence interval: 0.921-0.931, p < 0.001) for DBP, and 0.958 (95% confidence interval: 0.955-0.961, p < 0.001) for MBP, which all indicated a very good agreement.ConclusionsBlood pressure generated by chest compressions can be predicted with high accuracy by a machine learning method using chest compression waveform information obtained from a CPR feedback device and the patient's demographic characteristics. Real-time provision of the predicted blood pressure can be used to monitor the quality and efficacy of CPR.Copyright © 2024 Elsevier B.V. All rights reserved.

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