Pulse Rate and Transit Time Analysis to Predict Hypotension Events After Spinal Anesthesia During Programmed Cesarean Labor

Intraoperative Beat-to-Beat Pulse Transit Time (PTT) Monitoring via Non-Invasive Piezoelectric/Piezocapacitive Peripheral Sensors Can Predict Changes in Invasively Acquired Blood Pressure in High-Risk Surgical Patients

BACKGROUND

Non-invasive tracking of beat-to-beat pulse transit time (PTT) via piezoelectric/piezocapacitive sensors (PES/PCS) may expand perioperative hemodynamic monitoring. This study evaluated the ability for PTT via PES/PCS to correlate with systolic, diastolic, and mean invasive blood pressure (SBP_IBP, DBP_IBP, and MAP_IBP, respectively) and to detect SBP_IBP fluctuations.

METHODS

PES/PCS and IBP measurements were performed in 20 patients undergoing abdominal, urological, and cardiac surgery. A Pearson's correlation analysis (r) between 1/PTT and IBP was performed. The predictive ability of 1/PTT with changes in SBP_IBP was determined by area under the curve (reported as AUC, sensitivity, specificity).

RESULTS

Significant correlations between 1/PTT and SBP_IBP were found for PES (r = 0.64) and PCS (r = 0.55) (p < 0.01), as well as MAP_IBP/DBP_IBP for PES (r = 0.6/0.55) and PCS (r = 0.5/0.45) (p < 0.05). A 7% decrease in 1/PTT_PES predicted a 30% SBP_IBP decrease (0.82, 0.76, 0.76), while a 5.6% increase predicted a 30% SBP_IBP increase (0.75, 0.7, 0.68). A 6.6% decrease in 1/PTT_PCS detected a 30% SBP_IBP decrease (0.81, 0.72, 0.8), while a 4.8% 1/PTT_PCS increase detected a 30% SBP_IBP increase (0.73, 0.64, 0.68).

CONCLUSIONS

Non-invasive beat-to-beat PTT via PES/PCS demonstrated significant correlations with IBP and detected significant changes in SBP_IBP. Thus, PES/PCS as a novel sensor technology may augment intraoperative hemodynamic monitoring during major surgery.

Preoperative heart rate variability as a predictor of perioperative outcomes: a systematic review without meta-analysis

Heart rate variability (HRV) is a predictor of mortality and morbidity after non-lethal cardiac ischemia, but the relation between preoperatively measured HRV and intra- and postoperative complications is sparsely studied and most recently reviewed in 2007. We, therefore, reviewed the literature regarding HRV as a predictor for intra- and postoperative complications and outcomes. We carried out a systematic review without meta-analysis. A PICO model was set up, and we searched PubMed, EMBASE, and CENTRAL. The screening was done by one author, but all authors performed detailed review of the included studies. We present data from studies on intraoperative and postoperative complications, which were too heterogeneous to warrant formal meta-analysis, and we provide a pragmatic review of HRV indices to facilitate understanding our findings. The review was registered in PROSPERO (CRD42021230641). We screened 2337 records for eligibility. 131 records went on to full-text assessment, 63 were included. In frequency analysis of HRV, low frequency to high frequency ratio could be a predictor for intraoperative hypotension in spinal anesthesia and lower total power could possibly predict intraoperative hypotension under general anesthesia. Detrended fluctuation analysis of HRV is a promising candidate for predicting postoperative atrial fibrillation. This updated review of the relation between preoperative HRV and surgical outcome suggests a clinically relevant role of HRV but calls for high quality studies due to methodological heterogeneity in the current literature. Areas for future research are suggested.

Classification of blood pressure in critically ill patients using photoplethysmography and machine learning

OBJECTIVE

The aim of this study was to evaluate the capability of features extracted from photoplethysmography (PPG) based Pulse Rate Variability (PRV) to classify hypertensive, normotensive and hypotensive events, and to estimate mean arterial, systolic and diastolic blood pressure in critically ill patients.

METHODS

Time-domain, frequency-domain and non-linear indices from PRV were extracted from 5-min and 1-min segments obtained from PPG signals. These features were filtered using machine learning algorithms in order to obtain the optimal combination for the classification of hypertensive, hypotensive and normotensive events, and for the estimation of blood pressure.

RESULTS

5-min segments allowed for an improved performance in both classification and estimation tasks. Classification of blood pressure states showed around 70% accuracy and around 75% specificity. The sensitivity, precision and F1 scores were around 50%. In estimating mean arterial, systolic, and diastolic blood pressure, mean absolute errors as low as 2.55 ± 0.78 mmHg, 4.74 ± 2.33 mmHg, and 1.78 ± 0.14 mmHg were obtained, respectively. Bland-Altman analysis and Wilcoxon rank sum tests showed good agreement between real and estimated values, especially for mean and diastolic arterial blood pressures.

CONCLUSION

PRV-based features could be used for the classification of blood pressure states and the estimation of blood pressure values, although including additional features from the PPG waveform could improve the results.

SIGNIFICANCE

PRV contains information related to blood pressure, which may aid in the continuous, noninvasive, non-intrusive estimation of blood pressure and detection of hypertensive and hypotensive events in critically ill subjects.

Learning and non-learning algorithms for cuffless blood pressure measurement: a review

The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.

Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

Heart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

Prediction of spinal anesthesia-induced hypotension during elective cesarean section: a systematic review of prospective observational studies

BACKGROUND

Spinal anesthesia is the standard for elective cesarean section but spinal anesthesia-induced hypotension remains an important problem. Accurate prediction of hypotension could enhance clinical decision-making, alter management, and facilitate early intervention. We performed a systematic review of predictors of spinal anesthesia-induced hypotension and their predictive value during cesarean section.

METHODS

PubMed, Embase, Cochrane Library, Google Scholar and Web of Science databases were searched for prospective observational studies assessing the diagnostic accuracy of predictors of spinal anesthesia-induced hypotension in elective cesarean section. The quality of studies was assessed and predictors were grouped in domains based on the type of predictor.

RESULTS

Thirty-eight studies (n=3086 patients) were included. In most studies, patients received 500-1000 mL crystalloid preload or 500-2000 mL crystalloid coload. Vasopressors for post-spinal hypotension were boluses of ephedrine 5-15 mg and/or phenylephrine 25-100 µg in most studies. The hypotension rate varied from 29% to 80% based on the definition. For analysis, >30 predictors were classified into seven domains: demographic characteristics, baseline hemodynamic variables, baseline sympathovagal balance, postural stress testing, peripheral perfusion indices, blood volume and fluid responsiveness indices, and genetic polymorphism.

CONCLUSIONS

Environmental and individual factors increased outcome variability, which restricted the value of the autonomic nervous system and peripheral perfusion indices for prediction of spinal anesthesia-induced hypotension. Supine stress tests may reflect parturients' cardiovascular tolerance during hemodynamic fluctuations and may optimize the predictive value of static state predictors. Future research for predicting spinal anesthesia-induced hypotension should focus on composite and dynamic parameters during the supine stress tests.

Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses

Introduction: Heart Rate Variability (HRV) and Pulse Rate Variability (PRV), are non-invasive techniques for monitoring changes in the cardiac cycle. Both techniques have been used for assessing the autonomic activity. Although highly correlated in healthy subjects, differences in HRV and PRV have been observed under various physiological conditions. The reasons for their disparities in assessing the degree of autonomic activity remains unknown.

Methods: To investigate the differences between HRV and PRV, a whole-body cold exposure (CE) study was conducted on 20 healthy volunteers (11 male and 9 female, 30.3 ± 10.4 years old), where PRV indices were measured from red photoplethysmography signals acquired from central (ear canal, ear lobe) and peripheral sites (finger and toe), and HRV indices from the ECG signal. PRV and HRV indices were used to assess the effects of CE upon the autonomic control in peripheral and core vasculature, and on the relationship between HRV and PRV. The hypotheses underlying the experiment were that PRV from central vasculature is less affected by CE than PRV from the peripheries, and that PRV from peripheral and central vasculature differ with HRV to a different extent, especially during CE.

Results: Most of the PRV time-domain and Poincaré plot indices increased during cold exposure. Frequency-domain parameters also showed differences except for relative-power frequency-domain parameters, which remained unchanged. HRV-derived parameters showed a similar behavior but were less affected than PRV. When PRV and HRV parameters were compared, time-domain, absolute-power frequency-domain, and non-linear indices showed differences among stages from most of the locations. Bland-Altman analysis showed that the relationship between HRV and PRV was affected by CE, and that it recovered faster in the core vasculature after CE.

Conclusion: PRV responds to cold exposure differently to HRV, especially in peripheral sites such as the finger and the toe, and may have different information not available in HRV due to its non-localized nature. Hence, multi-site PRV shows promise for assessing the autonomic activity on different body locations and under different circumstances, which could allow for further understanding of the localized responses of the autonomic nervous system.

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

BACKGROUND AND SIGNIFICANCE

Mechanical alternans (MA) is a biomarker associated with mortality and life-threatening arrhythmias in heart failure patients. Despite showing prognostic value, its use is limited by the requirement of measuring the continuous blood pressure (BP), which is costly and impractical.

OBJECTIVE

To develop and test, for the first time, non-invasive MA surrogates based on photoplethysmography (PPG).

METHODS

Continuous BP and PPG were recorded during clinical procedures and tests in 35 patients. MA was induced either by ventricular pacing (Group A, N = 19) or exercise (Group B, N = 16). MA was categorized as sustained or intermittent if MA episodes were observed in at least 20 or between 12 and 20 consecutive beats, respectively. Eight features characterizing the pulse morphology were derived from the PPG, and MA surrogates were evaluated.

RESULTS

Sustained alternans was observed in 9 patients (47%) from Group A, whereas intermittent alternans was observed in 13 patients (68%) from Group A and in 10 patients (63%) from Group B. The PPG-based MA surrogate showing the highest accuracy, V^'_M, was based on the maximum of the first derivative of the PPG pulse. It detected both sustained and intermittent MA with 100% sensitivity and 100% specificity in Group A and intermittent MA with 100% sensitivity and 83% specificity in Group B. Furthermore, the magnitudes of MA and its PPG-based surrogate were linearly correlated (R² = 0.83, p < 0.001).

CONCLUSION

MA can be accurately identified non-invasively through PPG analysis. This may have important clinical implications for risk stratification and remote monitoring.