Recovery of arterial blood pressure after chest compression pauses in patients with out-of-hospital cardiac arrest.

BACKGROUND AND AIMS: Chest compressions generating good perfusion during cardiopulmonary resuscitation (CPR) in cardiac arrest patients are critical for positive patient outcomes. Conventional wisdom advises minimizing compression pauses because several compressions are required to recover arterial blood pressure (ABP) back to pre-pause values. Our study examines how compression pauses influence ABP recovery post-pause in out-of-hospital cardiac arrest. METHODS: We analyzed data from a subset of a prospective, randomized LUCAS 2 Active Decompression trial. Patients were treated by an anesthesiologist-staffed rapid response car program in Oslo, Norway (2015-2017) with mechanical chest compressions using the LUCAS device at 102 compressions/min. Patients with an ABP signal during CPR and at least one compression pause >2 sec were included. Arterial cannulation, compression pauses, and ECG during the pause were verified by physician review of patient records and physiological signals. Pauses were excluded if return of spontaneous circulation occurred during the pause (pressure pulses associated with ECG complexes). Compression, mean, and decompression ABP for 10 compressions before/after each pause and the mean ABP during the pause were measured with custom MATLAB code. The relationship between pause duration and ABP recovery was investigated using linear regression. RESULTS: We included 56 patients with a total of 271 pauses (pause duration: median = 11 sec, Q1 = 7 sec, Q3 = 18 sec). Mean ABP dropped from 53 ± 10 mmHg for the last pre-pause compression to 33 ± 7 mmHg during the pause. Compression and mean ABP recovered to >90% of pre-pause pressure within 2 compressions, or 1.7 sec. Pause duration did not affect the recovery of ABP post-pause (R2: 0.05, 0.03, 0.01 for compression, mean, and decompression ABP, respectively). CONCLUSIONS: ABP generated by mechanical CPR recovered quickly after pauses. Recovery of ABP after a pause was independent of pause duration.

Arterial hypotension "magnitude" and neurological outcome during mechanical thrombectomy under general anesthesia.

INTRODUCTION: Mechanical thrombectomy (MT) is the standard of care for the treatment of acute ischemic stroke (AIS) with large vessel occlusion (LVO), but unfavorable outcomes remain common. Procedural arterial hypotension is associated with poor patient outcome. This study aimed to assess the impact of arterial hypotension "magnitude" (a combination of the depth, defined as the percentage relative to baseline arterial blood pressure, and the duration of hypotensive episodes)" during MT on neurological outcome. METHODS: This is a monocentric retrospective study. Charts were reviewed between January 2018 and June 2021. "Patients were eligible if they were 18 years or older, anterior LVO was diagnosed on cerebral imaging" and MT performed under general anesthesia. Mean arterial pressure (MAP) was recorded every 5 minutes throughout the procedure, and the arterial hypotension "magnitude" was estimated by the area under the curve (AUC) for different thresholds of MAP drops. MAIN OUTCOME MEASURE: The modified Rankin Scale (mRS) at 90 days. MAIN RESULTS: Among the 117 patients analyzed, 46% had poor neurological outcome. Our study showed correlations between poor outcome and a greater procedural AUC of arterial hypotension for the different thresholds: 5% (k 0.18; 95% CI 0.06-0.30; P = 0.007), 10% (k 0.18; 95% CI 0.05-0.30; P = 0.008), 15% (k 0.18; 95% CI 0.04-0.30; P = 0.011), 20% (k 0.18; 95% CI 0.05-0.30; P = 0.010) and 30% (k 0.19; 95% CI 0.05-0.31; P = 0.010). This association persisted after controlling for age, baseline NIHSS score, and ASPECT score. CONCLUSION: The magnitude of hypotension during MT under general anesthesia for AIS is an independent factor of poor outcome at 90 days. These associations have been observed in patients with mild and severe hypotensive episodes.

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG). We evaluated over 600 h of high-frequency (125 Hz) simultaneously acquired ICP, ABP, ECG, and PPG waveform data in 10 patients admitted to the ICU with critical brain disorders. The data were segmented in non-overlapping 10-s windows, and ABP, ECG, and PPG waveforms were used to train deep learning (DL) models to re-create concurrent ICP. The predictive performance of six different DL models was evaluated in single- and multi-patient iterations. The mean average error (MAE) ± SD of the best-performing models was 1.34 ± 0.59 mmHg in the single-patient and 5.10 ± 0.11 mmHg in the multi-patient analysis. Ablation analysis was conducted to compare contributions from single physiologic sources and demonstrated statistically indistinguishable performances across the top DL models for each waveform (MAE±SD 6.33 ± 0.73, 6.65 ± 0.96, and 7.30 ± 1.28 mmHg, respectively, for ECG, PPG, and ABP; p = 0.42). Results support the preliminary feasibility and accuracy of DL-enabled continuous noninvasive ICP waveform computation using extracranial physiological waveforms. With refinement and further validation, this method could represent a safer and more accessible alternative to invasive ICP, enabling assessment and treatment in low-resource settings.

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.

BACKGROUND AND OBJECTIVE: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. METHODS: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm against marked DN detection, while box plots and Bland-Altman plots were used to compare its performance with both marked DN detection and an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy Python package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. RESULTS: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87,343) =0.99, p<.001) and PPG (R2(86,764) =0.98, p<.001) waveforms. The algorithm had a lower mean error of DN detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high rate of detectability of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. CONCLUSION: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.

Haemosync: A synchronisation algorithm for multimodal haemodynamic signals.

BACKGROUND: Synchronous acquisition of haemodynamic signals is crucial for their multimodal analysis, such as dynamic cerebral autoregulation (DCA) analysis of arterial blood pressure (ABP) and transcranial Doppler (TCD)-derived cerebral blood velocity (CBv). Several technical problems can, however, lead to (varying) time-shifts between the different signals. These can be difficult to recognise and can strongly influence the multimodal analysis results. METHODS: We have developed a multistep, cross-correlation-based time-shift detection and synchronisation algorithm for multimodal pulsatile haemodynamic signals. We have developed the algorithm using ABP and CBv measurements from a dataset that contained combinations of several time-shifts. We validated the algorithm on an external dataset with time-shifts. We additionally quantitatively validated the algorithm's performance on a dataset with artificially added time-shifts, consisting of sample clock differences ranging from -0.2 to 0.2 s/min and sudden time-shifts between -4 and 4 s. The influence of superimposed noise and variation in waveform morphology on the time-shift estimation was quantified, and their influence on DCA-indices was determined. RESULTS: The instantaneous median absolute error (MedAE) between the artificially added time-shifts and the estimated time-shifts was 12 ms (median, IQR 12-12, range 11-14 ms) for drifts between -0.1 and 0.1 s/min and sudden time-shifts between -4 and 4 s. For drifts above 0.1 s/min, MedAE was higher (median 753, IQR 19 - 766, range 13 - 772 ms). When a certainty threshold was included (peak cross-correlation > 0.9), MedAE for all drifts-shift combinations decreased to 12 ms, with smaller variability (IQR 12 - 13, range 8 - 22 ms, p < 0.001). The time-shift estimation is robust to noise, as the MedAE was similar for superimposed white noise with variance equal to the signal variance. After time-shift correction, DCA-indices were similar to the original, non-time-shifted signals. Phase shift differed by 0.17° (median, IQR 0.13-0.2°, range 0.0038-1.1°) and 0.54° (median, IQR 0.23-1.7°, range 0.0088-5.6°) for the very low frequency and low frequency ranges, respectively. DISCUSSION: This algorithm allows visually interpretable detection and accurate correction of time-shifts between pulsatile haemodynamic signals (ABP and CBv).

Comparison of Noninvasive Oscillometric and Intra-Arterial Blood Pressure Measurements in Children Admitted to the Pediatric Intensive Care Unit.

Intra-arterial blood pressure (IABP) measurement, although considered the gold standard in critically ill children, is associated with certain risks and lacks widespread availability. This study was conducted to determine the differences and agreements between oscillometric non-invasive blood pressure (NIBP) and invasive IABP measurements in children. Inclusion criteria consisted of children (from 1 month to 18 years) admitted to the pediatric intensive care unit (PICU) of a teaching hospital who required arterial catheter insertion for blood pressure (BP) monitoring. The comparison between IABP and NIBP was studied using paired t -test, Bland-Altman analysis, and Pearson's correlation coefficient. In total, 4,447 pairs of simultaneously recorded hourly NIBP and IABP measurements were collected from 65 children. Mean differences between IABP and NIBP were -3.6 ± 12.85, -4.7 ± 9.3, and -3.12 ± 9.30 mm Hg for systolic, diastolic, and mean arterial BP, respectively ( p < 0.001), with wide limits of agreement. NIBP significantly overestimated BP ( p < 0.001) in all three BP states (hypotensive, normotensive, and hypertensive), except systolic blood pressure (SBP) during hypertension where IABP was significantly higher. The difference in SBP was most pronounced during hypotension. The difference in SBP was significant in children <10 years ( p < 0.001), with the maximum difference being in infants. It was insignificant in adolescents ( p = 0.28) and underweight children ( p = 0.55). NIBP recorded significantly higher BP in all states of BP except SBP in the hypertensive state. SBP measured by NIBP tended to be the most reliable in adolescents and underweight children. NIBP was the most unreliable in infants, obese children, and during hypotension.

Adherence to international guidelines in neurocritical care of cervical traumatic spinal cord injury-a retrospective study.

Introduction: The American Association of Neurologic Surgeons guidelines on the management of traumatic spinal cord injury (SCI), updated in 2013, focus on spinal cord perfusion, early decompressive surgery, and venous thromboembolism (VTE) prophylaxis to improve neurological outcome. Research question: How neurocritical care and initial management have changed with the implementation of updated management guidelines, focusing on guidelines adherence and neurological outcome. Material and methods: Systemic physiological variables, time to neurosurgical treatment and VTE prophylaxis, and neurological outcome, were retrospectively collected from adult patients treated for cervical SCI 2001-2021. Results: Fifty-two patients were included. Mean arterial blood pressure (MAP) was significantly higher after 2013 (86±9.9 mmHg) when compared to before 2013 (79±9.9 mmHg), p = 0.041. Median time to surgery was 41 h before, and 20 h after 2013 (p = 0.029). Time to VTE prophylaxis was six days before and four days after 2013. Most neurocritical care complications were less commonly observed after 2013. Despite improved adherence to treatment goals, 44 % of MAP levels were below target, and 33% of patients were operated beyond 24h post-injury. Neurological outcome was not improved after implementation of the revised guidelines. Discussion and conclusion: While implementation of the revised 2013 guidelines improved most aspects of the acute SCI management, many guideline targets were not met in a large subset of patients. Strict adherence to the acute neurocritical management goals, and early surgical treatment, is likely crucial when aiming to improve SCI outcome.

Effect of limb and cuff positioning on measurement of arterial blood pressure with an oscillometry device (PetMAP) in anaesthetized cats.

Arterial blood pressure (ABP) is often measured with oscillometry during anaesthesia. Changing the height of the measuring cuff with respect to the level of the heart is known to affect oscillometry accuracy in some species; however, this effect has not been investigated in cats. The objective of this study was to determine the effects of raising and lowering the measuring cuff from standard position (level of the heart) on ABP, measured with PetMAP, in anaesthetised cats. ABP readings were obtained from 29 cats with the cuff at standard position (baseline), and 5 cm above and below the heart. The end-tidal isoflurane concentrations were maintained constant during data acquisition. There were no differences between baseline values and those measured below the heart, while ABP measured above the heart was consistently lower than baseline for both the thoracic and pelvic limbs (P < 0.001), with absolute differences of 8.2 (2.5 - 14) mmHg and 6.5 (3.0 - 15.0) mmHg, respectively. Systolic ABP readings at the pelvic limb were consistently higher than those at the thoracic limb at standard position (112 ± 26 versus 103 ± 21 mmHg, p = 0.010), above (106 ± 22 versus 95 ± 20 mmHg, p = 0.003), and below the heart (116 ± 26 versus 107 ± 22 mmHg, p = 0.011). This study shows that raising the cuff by 5 cm above the heart, which may become necessary during procedural positioning, results in clinically significant underestimation of ABP measured with PetMAP.

Associations between anxiety, sleep, and blood pressure parameters in pregnancy: a prospective pilot cohort study.

BACKGROUND: The potential effect modification of sleep on the relationship between anxiety and elevated blood pressure (BP) in pregnancy is understudied. We evaluated the relationship between anxiety, insomnia, and short sleep duration, as well as any interaction effects between these variables, on BP during pregnancy. METHODS: This was a prospective pilot cohort of pregnant people between 23 to 36 weeks' gestation at a single institution between 2021 and 2022. Standardized questionnaires were used to measure clinical insomnia and anxiety. Objective sleep duration was measured using a wrist-worn actigraphy device. Primary outcomes were systolic (SBP), diastolic (DBP), and mean (MAP) non-invasive BP measurements. Separate sequential multivariable linear regression models fit with generalized estimating equations (GEE) were used to separately assess associations between anxiety (independent variable) and each BP parameter (dependent variables), after adjusting for potential confounders (Model 1). Additional analyses were conducted adding insomnia and the interaction between anxiety and insomnia as independent variables (Model 2), and adding short sleep duration and the interaction between anxiety and short sleep duration as independent variables (Model 3), to evaluate any moderating effects on BP parameters. RESULTS: Among the 60 participants who completed the study, 15 (25%) screened positive for anxiety, 11 (18%) had subjective insomnia, and 34 (59%) had objective short sleep duration. In Model 1, increased anxiety was not associated with increases in any BP parameters. When subjective insomnia was included in Model 2, increased DBP and MAP was significantly associated with anxiety (DBP: ß 6.1, p = 0.01, MAP: ß 6.2 p < 0.01). When short sleep was included in Model 3, all BP parameters were significantly associated with anxiety (SBP: ß 9.6, p = 0.01, DBP: ß 8.1, p < 0.001, and MAP: ß 8.8, p < 0.001). No moderating effects were detected between insomnia and anxiety (p interactions: SBP 0.80, DBP 0.60, MAP 0.32) or between short sleep duration and anxiety (p interactions: SBP 0.12, DBP 0.24, MAP 0.13) on BP. CONCLUSIONS: When including either subjective insomnia or objective short sleep duration, pregnant people with anxiety had 5.1-9.6 mmHg higher SBP, 6.1-8.1 mmHg higher DBP, and 6.2-8.8 mmHg higher MAP than people without anxiety.

Oral bioavailability enhancement of doxazosin mesylate: Nanosuspension versus self-nanoemulsifying drug delivery systems.

Background and purpose: Doxazosin mesylate (DOX) is an antihypertensive drug that possesses poor water solubility and, hence, poor release properties. Both nanosuspensions and self-nanoemulsifying drug delivery systems (SNEDDS) are becoming nanotechnology techniques for the enhancement of water solubility of different drugs. Experimental approach: The study's goal was to identify the best drug delivery system able to enhance the release and antihypertensive effect of DOX by comparing the physical characteristics such as particle size, zeta potential, entrapment efficiency, release rate, and main arterial blood pressure of DOX-loaded nanosuspensions and SNEDDS in liquid and solid form. Key results: DOX nanosuspension preparation had a particle size of 385±13 nm, poly-dispersity index of 0.049±3, zeta potential of 50 ± 4 mV and drug release after 20 min (91±0.43 %). Liquid SNEDDS had a droplet size of 224±15 nm, poly-dispersity index of (0.470±0.01), zeta potential of -5±0.10 mV and DR20min of 93±4 %. Solid SEDDS showed particle size of 79±14 nm, poly-dispersity index of 1±0.00, a zeta potential of -18 ±0.26 mv and DR20min of 100 ±2.72 %. Conclusion: Finally, in terms of the mean arterial blood pressure lowering, solid SNEDDS performed better effect than both liquid SNEDDS and nanosuspension (P >0.05).

Autonomic Function and Baroreflex Control in COVID-19 Patients Admitted to the Intensive Care Unit.

Background: Autonomic function and baroreflex control might influence the survival rate of coronavirus disease 2019 (COVID-19) patients admitted to the intensive care unit (ICU) compared to respiratory failure patients without COVID-19 (non-COVID-19). This study describes physiological control mechanisms in critically ill COVID-19 patients admitted to the ICU in comparison to non-COVID-19 individuals with the aim of improving stratification of mortality risk. Methods: We evaluated autonomic and baroreflex control markers extracted from heart period (HP) and systolic arterial pressure (SAP) variability acquired at rest in the supine position (REST) and during a modified head-up tilt (MHUT) in 17 COVID-19 patients (age: 63 ± 10 years, 14 men) and 33 non-COVID-19 patients (age: 60 ± 12 years, 23 men) during their ICU stays. Patients were categorized as survivors (SURVs) or non-survivors (non-SURVs). Results: We found that COVID-19 and non-COVID-19 populations exhibited similar vagal and sympathetic control markers; however, non-COVID-19 individuals featured a smaller baroreflex sensitivity and an unexpected reduction in the HP-SAP association during the MHUT compared to the COVID-19 group. Nevertheless, none of the markers of the autonomic and baroreflex functions could distinguish SURVs from non-SURVs in either population. Conclusions: We concluded that COVID-19 patients exhibited a more preserved baroreflex control compared to non-COVID-19 individuals, even though this information is ineffective in stratifying mortality risk.

Letter to the editor: Deriving transfer function analysis metrics from driven methods.

Driven and spontaneous methods have been used to quantify the cerebral pressure-flow relationship via transfer function analysis (TFA). Commonly, TFA derived estimates are assessed using band averages within the very-low (0.02-0.07 Hz) and low (0.07-0.20 Hz) frequency during spontaneous oscillations but are quantified at frequencies of interest where blood pressure oscillations are driven (e.g., 0.05 and/or 0.10 Hz). Driven estimates more closely resemble the autoregulatory challenges individuals experience on a daily basis, while also eliciting higher levels of reliability. While driven estimates with point-estimates are not feasible for all clinical populations, these approaches increase the ability to understand pathophysiological changes.

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.

Background and Objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. Methods: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm with an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy PYTHON package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87343) =.99, p<.001) and PPG (R2(86764) =.98, p<.001) waveforms. The algorithm had a lower mean error of dicrotic notch detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high accuracy of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. Conclusion: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.