Prediction of blood pressure using chest compression waveform during cardiopulmonary resuscitation.

ABSTRACT

OBJECTIVES: This study aimed to predict blood pressure during CPR using chest compression waveform information obtained from a CPR feedback device. METHODS: Quantitative 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. RESULTS: A 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. CONCLUSIONS: Blood 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.