Impact of acute changes in perfusion index and blood pressure on the accuracy of non-invasive continuous hemoglobin concentration measurements during induction of anesthesia

Introduction: Laryngoscopy and intubation is always associated with a short term reflex sympathetic pressor response. The perfusion index is an indirect, non-invasive, and continuous measure of peripheral perfusion by pulse oximeter which can detect the stress response to intubation similar to heart rate, systolic blood pressure and diastolic blood pressure. Methods: This prospective observational study enrolled sixty-five normotensive patients of American society of anesthesiologists physical status grade I and II scheduled for elective surgery under general anaesthesia. Tracheal intubation was performed after induction with intravenous fentanyl, propofol and vecuronium. Heart rate, Systolic and Diastolic Blood Pressure and Perfusion Index were measured before induction of anesthesia, before intubation and one minute, three minutes, five minutes after the insertion of the endotracheal tube. Increase in heart rate by ?10 beats per minute, systolic and diastolic blood pressure by ?15 millimeters of mercury and decrease in Perfusion index ?10% after endotracheal intubation as compared to preintubationvalue were considered positive haemodynamic changes. Results: Endotracheal intubation produced a significant increase in heart rate and blood pressure whereas perfusion index decreased significantly. Our study showed that perfusion index response criterion achieved 97.7% (Confidence interval 97.58-97.86) sensitivity in detecting the stress response to insertion of endotracheal tube whereas systolic and diastolic blood pressure achieved sensitivity of 90% and 92% respectively. Conclusion: Perfusion Index is easier, reliable and non-invasive alternative to conventional haemodynamic criteria for detection of stress response to endotracheal intubation.

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

npj Digital Medicine ◽

10.1038/s41746-021-00447-y ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Elisa Mejía-Mejía ◽

James M. May ◽

Mohamed Elgendi ◽

Panayiotis A. Kyriacou

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Heart Rate Variability ◽

Pulse Rate ◽

Pulse Transit Time ◽

Pulse Rate Variability ◽

Physiological Factors ◽

Ecg Signals ◽

Non Invasive ◽

Electrocardiogram Ecg

AbstractHeart 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.

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Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

Optical and Quantum Electronics ◽

10.1007/s11082-020-02667-0 ◽

2021 ◽

Vol 53 (2) ◽

Author(s):

Sen Yang ◽

Yaping Zhang ◽

Siu-Yeung Cho ◽

Ricardo Correia ◽

Stephen P. Morgan

Keyword(s):

Machine Learning ◽

Blood Pressure ◽

Deep Learning ◽

Absolute Error ◽

Learning Model ◽

Physiological Measurement ◽

British Hypertension Society ◽

Non Invasive ◽

Blood Pressure Estimation ◽

Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

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Association between central non-dipping pattern and platelet morphology in adults with type 1 diabetes without cardiovascular disease: a cross-sectional study

Scientific Reports ◽

10.1038/s41598-021-94414-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Michal Kulecki ◽

Dariusz Naskret ◽

Mikolaj Kaminski ◽

Dominika Kasprzak ◽

Pawel Lachowski ◽

...

Keyword(s):

Blood Pressure ◽

Type 1 Diabetes ◽

Smoking Status ◽

Cross Sectional Study ◽

Cross Sectional ◽

Distribution Width ◽

Non Invasive ◽

Platelet Morphology ◽

Oscillometric Device

AbstractThe non-dipping pattern is nighttime systolic blood pressure (SBP) fall of less than 10%. Several studies showed that the non-dipping pattern, increased mean platelet volume (MPV), and platelet distribution width (PDW) are associated with elevated cardiovascular risk. Hypertensives with the non-dipping pattern have higher MPV than the dippers but this relationship was never investigated among people with type 1 diabetes mellitus (T1DM). This study aimed to investigate the association between the central dipping pattern and platelet morphology in T1DM subjects. We measured the central and brachial blood pressure with a validated non-invasive brachial oscillometric device—Arteriograph 24—during twenty-four-hour analysis in T1DM subjects without diagnosed hypertension. The group was divided based on the central dipping pattern for the dippers and the non-dippers. From a total of 62 subjects (32 males) aged 30.1 (25.7–37) years with T1DM duration 15.0 (9.0–20) years, 36 were non-dippers. The non-dipper group had significantly higher MPV (MPV (10.8 [10.3–11.5] vs 10.4 [10.0–10.7] fl; p = 0.041) and PDW (13.2 [11.7–14.9] vs 12.3 [11.7–12.8] fl; p = 0.029) than dipper group. Multivariable logistic regression revealed that MPV (OR 3.74; 95% CI 1.48–9.45; p = 0.005) and PDW (OR 1.91; 95% CI 1.22–3.00; p = 0.005) were positively associated with central non-dipping pattern adjusting for age, sex, smoking status, daily insulin intake, and height. MPV and PDW are positively associated with the central non-dipping pattern among people with T1DM.

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Does non‐invasive blood pressure measurement increase the complications and decrease indwelling time of peripheral intravenous catheters: A cohort study

Journal of Clinical Nursing ◽

10.1111/jocn.15751 ◽

2021 ◽

Author(s):

Qiling Tan ◽

Shangping Zhao ◽

Xiaoqin Li ◽

Bo Gu ◽

Zhao Ni

Keyword(s):

Blood Pressure ◽

Cohort Study ◽

Pressure Measurement ◽

Blood Pressure Measurement ◽

Invasive Blood Pressure ◽

Non Invasive ◽

Indwelling Time ◽

Intravenous Catheters ◽

Invasive Blood Pressure Measurement

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Non-invasive diffuse optical neuromonitoring during cardiopulmonary resuscitation predicts return of spontaneous circulation

Scientific Reports ◽

10.1038/s41598-021-83270-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tiffany S. Ko ◽

Constantine D. Mavroudis ◽

Ryan W. Morgan ◽

Wesley B. Baker ◽

Alexandra M. Marquez ◽

...

Keyword(s):

Cardiac Arrest ◽

Cardiopulmonary Resuscitation ◽

Hemoglobin Concentration ◽

Spontaneous Circulation ◽

Youden Index ◽

Swine Model ◽

Return Of Spontaneous Circulation ◽

Non Invasive ◽

Tissue Optical Properties ◽

Tenfold Cross Validation

AbstractNeurologic injury is a leading cause of morbidity and mortality following pediatric cardiac arrest. In this study, we assess the feasibility of quantitative, non-invasive, frequency-domain diffuse optical spectroscopy (FD-DOS) neuromonitoring during cardiopulmonary resuscitation (CPR), and its predictive utility for return of spontaneous circulation (ROSC) in an established pediatric swine model of cardiac arrest. Cerebral tissue optical properties, oxy- and deoxy-hemoglobin concentration ([HbO2], [Hb]), oxygen saturation (StO2) and total hemoglobin concentration (THC) were measured by a FD-DOS probe placed on the forehead in 1-month-old swine (8–11 kg; n = 52) during seven minutes of asphyxiation followed by twenty minutes of CPR. ROSC prediction and time-dependent performance of prediction throughout early CPR (< 10 min), were assessed by the weighted Youden index (Jw, w = 0.1) with tenfold cross-validation. FD-DOS CPR data was successfully acquired in 48/52 animals; 37/48 achieved ROSC. Changes in scattering coefficient (785 nm), [HbO2], StO2 and THC from baseline were significantly different in ROSC versus No-ROSC subjects (p < 0.01) after 10 min of CPR. Change in [HbO2] of + 1.3 µmol/L from 1-min of CPR achieved the highest weighted Youden index (0.96) for ROSC prediction. We demonstrate feasibility of quantitative, non-invasive FD-DOS neuromonitoring, and stable, specific, early ROSC prediction from the third minute of CPR.

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An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽

10.3390/s21051867 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1867

Author(s):

Tasbiraha Athaya ◽

Sunwoong Choi

Keyword(s):

Blood Pressure ◽

Cardiovascular Health ◽

Estimation Method ◽

Absolute Error ◽

British Hypertension Society ◽

Non Invasive ◽

Arterial Blood ◽

Blood Pressure Waveform ◽

The Mean ◽

Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

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