Complexity of preoperative blood pressure dynamics: possible utility in cardiac surgical risk assessment

Blood pressure fragmentation as a new measure of blood pressure variability: association with predictors of cardiac surgery outcomes

Background: Fluctuations in beat-to-beat blood pressure variability (BPV) encode untapped information of clinical utility. A need exists for developing new methods to quantify the dynamical properties of these fluctuations beyond their mean and variance. Objectives: Introduction of a new beat-to-beat BPV measure, termed blood pressure fragmentation (BPF), and testing of whether increased preoperative BPF is associated with (i) older age; (ii) higher cardiac surgical risk, assessed using the Society of Thoracic Surgeons' (STS) Risk of Morbidity and Mortality index and the European System for Cardiac Operative Risk Evaluation Score (EuroSCORE II); and (iii) longer ICU length of stay (LOS) following cardiac surgery. The secondary objective was to use standard BPV measures, specifically, mean, SD, coefficient of variation (CV), average real variability (ARV), as well a short-term scaling index, the detrended fluctuation analysis (DFA) ⍺1 exponent, in the same type of analyses to compare the results with those obtained using BPF. Methods: Consecutive sample of 497 adult patients (72% male; age, median [inter-quartile range]: 67 [59-75] years) undergoing cardiac surgery with cardiopulmonary bypass. Fragmentation, standard BPV and DFA ⍺1 measures were derived from preoperative systolic blood pressure (SBP) time series obtained from radial artery recordings. Results: Increased preoperative systolic BPF was associated with older age, higher STS Risk of Morbidity and Mortality and EuroSCORE II values, and longer ICU LOS in all models. Specifically, a one-SD increase in systolic BPF (9%) was associated with a 26% (13%-40%) higher likelihood of longer ICU LOS (>2 days). Among the other measures, only ARV and DFA ⍺1 tended to be associated with longer ICU LOS. However, the associations did not reach significance in the most adjusted models. Conclusion: Preoperative BPF was significantly associated with preoperative predictors of cardiac surgical outcomes as well as with ICU LOS. Our findings encourage future studies of preoperative BPF for assessment of health status and risk stratification of surgical and non-surgical patients.

Multiscale Analysis of Runoff Complexity in the Yanhe Watershed

Runoff complexity is an important indicator reflecting the sustainability of a watershed ecosystem. In order to explore the multiscale characteristics of runoff complexity and analyze its variation and influencing factors in the Yanhe watershed in China during the period 1991-2020, we established a new analysis method for watershed runoff complexity based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method for the decomposition of multiscale characteristics and the refined composite multiscale entropy (RCMSE) method for the quantification of the system complexity. The results show that runoff and its components all present multiscale complexity characteristics that are different from random signals, and the intermediate frequency modes contribute the most to runoff complexity. The runoff complexity of the Yanhe watershed has decreased gradually since 1991, and 2010 was a turning point of runoff complexity, when it changed from a decline to an increase, indicating that the ecological sustainability of this basin has improved since 2010, which was mainly related to the ecological restoration measures of the Grain for Green Project. This study expands the research perspective for analyzing the variation characteristics of runoff at the multiscale, and provides a reference for the study of watershed ecological sustainability and ecological management.

Blood Pressure Complexity Discriminates Pathological Beat-to-Beat Variability as a Marker of Vascular Aging

Background

Beat‐to‐beat blood pressure variability (BPV) is associated with an increased risk of stroke but can be driven by both healthy physiological processes and failure of compensatory mechanisms. Blood pressure (BP) complexity measures structured, organized variations in BP, as opposed to random fluctuations, and its reduction may therefore identify pathological beat‐to‐beat BPV.

Methods and Results

In the prospective, population‐based OXVASC (Oxford Vascular Study) Phenotyped Cohort with transient ischemic attack or minor stroke, patients underwent at least 5 minutes of noninvasive beat‐to‐beat monitoring of BP (Finometer) and ECG to derive the following: BPV (coefficient of variation) and complexity (modified multiscale entropy) of systolic BP and diastolic BP, heart rate variability (SD of R‐R intervals), and baroreflex sensitivity (BRS; Welch's method), in low‐ (0.04–0.15 Hz) and high‐frequency (0.15–0.4 Hz) bands. Associations between BPV or BP complexity with autonomic indexes and arterial stiffness were determined (linear regression), unadjusted, and adjusted for age, sex, and cardiovascular risk factors. In 908 consecutive, consenting patients, BP complexity was inversely correlated with BPV coefficient of variation (P<0.001) and was similarly reduced in patients with hypertension or diabetes (P<0.001). However, although BPV coefficient of variation had a U‐shaped relationship with age, BP complexity fell systematically across age quintiles (quintile 1: 15.1 [14.0–16.1] versus quintile 5: 13.8 [12.4–15.1]) and was correlated with markers of autonomic dysfunction (heart rate variability SD of R‐R intervals: r = 0.20; BRS low frequency: 0.19; BRS high frequency: 0.26) and arterial stiffness (pulse wave velocity: −0.21; all P<0.001), even after adjustment for clinical variables (heart rate variability SD of R‐R intervals: 0.12; BRS low frequency and BRS high frequency: 0.13 and 0.17; and pulse wave velocity: −0.07; all P<0.05).

Conclusions

Loss of BP complexity discriminates BPV because of pathological failure of compensatory mechanisms and may represent a less confounded and potentially modifiable risk factor for stroke.

Multiscale Dynamics of Blood Pressure Fluctuation Is Associated With White Matter Lesion Burden in Older Adults With and Without Hypertension: Observations From a Pilot Study

Background: White matter lesions (WMLs) are highly prevalent in older adults, and hypertension is one of the main contributors to WMLs. The blood pressure (BP) is regulated by complex underlying mechanisms over multiple time scales, thus the continuous beat-to-beat BP fluctuation is complex. The association between WMLs and hypertension may be manifested as diminished complexity of BP fluctuations. The aim of this pilot study is to explore the relationships between hypertension, BP complexity, and WMLs in older adults. Method: Fifty-three older adults with clinically diagnosed hypertension and 47 age-matched older adults without hypertension completed one MRI scan and one BP recording of 10-15 min when sitting quietly. Their cerebral WMLs were assessed by two neurologists using the Fazekas scale based on brain structural MRI of each of their own. Greater score reflected higher WML grade. The complexity of continuous systolic (SBP) and diastolic (DBP) BP series was quantified using multiscale entropy (MSE). Lower MSE reflected lower complexity. Results: Compared to the non-hypertensive group, hypertensives had significantly greater Fazekas scores (F > 5.3, p < 0.02) and lower SBP and DBP complexity (F > 8.6, p < 0.004). Both within each group (β < -0.42, p < 0.01) and across groups (β < -0.47, p < 0.003), those with lower BP complexity had higher Fazekas score. Moreover, complexity of both SBP and DBP mediated the influence of hypertension on WMLs (indirect effects > 0.25, 95% confidence intervals = 0.06 - 0.50). Conclusion: These results suggest that diminished BP complexity is associated with WMLs and may mediate the influence of hypertension on WMLs. Future longitudinal studies are needed to examine the causal relationship between BP complexity and WMLs.

Beat-to-beat blood pressure variability: an early predictor of disease and cardiovascular risk

Blood pressure (BP) varies on the long, short and very-short term. Owing to the hidden physiological and pathological information present in BP time-series, increasing interest has been given to the study of continuous, beat-to-beat BP variability (BPV) using invasive and noninvasive methods. Different linear and nonlinear parameters of variability are employed in the characterization of BP signals in health and disease. Although linear parameters of beat-to-beat BPV are mainly measures of dispersion, such as standard deviation (SD), nonlinear parameters of BPV quantify the degree of complexity/irregularity- using measures of entropy or self-similarity/correlation. In this review, we summarize the value of linear and nonlinear parameters in reflecting different information about the pathophysiology of changes in beat-to-beat BPV independent of or superior to mean BP. We then provide a comparison of the relative power of linear and nonlinear parameters of beat-to-beat BPV in detecting early and subtle differences in various states. The practical advantage and utility of beat-to-beat BPV monitoring support its incorporation into routine clinical practices.

Is pelvic floor muscle training able to alter the response of cardiovascular autonomic modulation and provide a possible cardiovascular benefit to pregnant women?

AIMS

To evaluate the acute and chronic effect of an exercise protocol of pelvic floor muscles (PFMs) contraction on the heart period (HP) and systolic arterial pressure (SAP) variabilities and baroreflex sensitivity (BRS) at rest in pregnant women; and to evaluate if this progressive exercise protocol was well-tolerated by the pregnant women studied.

METHODS

We evaluated 48 women at 18 weeks of pregnancy by vaginal palpation, vaginal manometry, and cardiopulmonary exercise test. They were divided in control (CG; 31.75 ± 3.91 years) and training groups (TG; 30.71 ± 3.94 years). At 19 and 36 weeks of pregnancy, electrocardiogram and noninvasive peripheral SAP data were collected at rest before and after 10 PFM contractions. TG performed PFMT from the 20th to the 36th week. HP and SAP variabilities were analyzed by spectral and symbolic analysis. The baroreflex was evaluated by cross-spectral analysis between the HP and SAP series.

RESULTS

The groups did not differ in relation to VO₂ , HP and SAP variabilities, and BRS at the beginning of the protocol. TG increased the endurance of the PFM after training. PFM contraction did not change the HP and SAP variabilities, and BRS at the 18th week. After the training, the TG presented lower SAP mean, lower BF of SAP variability, and higher BRS than CG.

CONCLUSIONS

Acute PFM contractions did not alter HP and SAP variabilities and BRS, but PFMT resulted in a lower SAP mean and higher BRS in trained pregnant when compared to the untrained.

Comparison of Invasive and Noninvasive Blood Pressure Measurements for Assessing Signal Complexity and Surgical Risk in Cardiac Surgical Patients

BACKGROUND

Continuous arterial blood pressure (ABP) is typically recorded by placement of an intraarterial catheter. Recently, noninvasive ABP monitors have been shown to be comparable in accuracy to invasive measurements. In a previous study, we showed that the fluctuations in beat-to-beat ABP measurements were not random variations but had a complex dynamical structure, and that ABP dynamical complexity was inversely associated with surgical risk estimated using the Society of Thoracic Surgeons (STS) index. Dynamical complexity is a mathematical construct that reflects the capacity of a physiological system to adapt to stimuli. The objectives of present study were to: (1) determine whether noninvasive beat-to-beat ABP measurements also exhibit a complex temporal structure; (2) compare the complexity of noninvasive versus invasive ABP time series; and (3) quantify the relationship between the complexity of noninvasive ABP time series and the STS risk scores.

METHODS

Fifteen adult patients undergoing coronary artery bypass graft, valve, or combined coronary artery bypass graft/valve surgery were enrolled in this observational study. Preoperative ABP waveforms were simultaneously recorded for ≥15 minutes using a radial artery catheter (invasive) and a continuous noninvasive arterial pressure monitor. Beat-to-beat systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP) time series were extracted from the continuous waveforms. Complexity was assessed using the multiscale entropy method. The Wilcoxon signed-rank test was used to compare the mean ranks of indices derived from invasive versus noninvasive ABP time series. Spearman correlation coefficients were used to quantify the relationship between invasive and noninvasive indices. Linear regression analysis was used to quantify the association between each of the complexity indices and the STS risk scores.

RESULTS

Beat-to-beat fluctuations in noninvasive ABP measurements were not random but complex; however, their degree of complexity was lower than that of fluctuations in invasively obtained ABP signals (SBP: 7.05 vs 8.66, P < .001; DBP: 7.40 vs 8.41, P < .001; PP: 6.83 vs 8.82, P < .001; and MAP: 7.17 vs 8.68, P < .005). Invasive and noninvasive indices for MSEΣ·slope showed good correlation (rs) (0.53 for SBP, 0.79 for DBP, 0.42 for PP, 0.60 for MAP). The complexity of noninvasive ABP time series (-0.70 [-1.28 to -0.11]; P = .023 for DBP), like that of invasive time series (-0.94 [-1.52 to -0.35]; P = .004 for DBP), was inversely associated with estimated surgical risk in patients undergoing cardiovascular operations.

CONCLUSIONS

Our results support the use of noninvasive ABP monitoring in computations of complexity-based indices that correlate with estimated surgical risk.

Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study

Background

Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We explored the ability of Poincaré plots and coefficient of variation (CV) by measuring intraoperative BPV in predicting adverse outcomes.

Methods

In this retrospective, observational, cohort study, 3687 adult patients (> 18 years) undergoing cardiac surgery requiring cardio-pulmonary bypass from 2008 to 2014 were included. Blood pressure variability was computed by Poincare plots and CV. Standard descriptors (SD) SD1, SD2 were measured with Poincare plots by ellipse fitting technique. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability.

Results

Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (standard descriptors from Poincare plots and CV) performed poorly in predicting postoperative 30-day mortality and renal failure [Concordance(C)-Statistic around 0.5]. They did not add any significant value to the standard STS risk score [C-statistic: STS alone 0.7, STS + BPV parmeters 0.7].

Conclusions

In conclusion, BP variability computed from Poincare plots and CV were not predictive of mortality and renal failure in cardiac surgical patients. Patient comorbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly.

Preoperative Blood Pressure Complexity Indices as a Marker for Frailty in Patients Undergoing Cardiac Surgery

OBJECTIVE

Frailty, a state of decreased physiological reserve, increases the risk of adverse outcomes. There is no standard tool for frailty during perioperative period. Autonomic dysfunction, an underlying process in frailty, could result in hemodynamic fluctuations. Complexity, the physiological adaptability of a system can quantify these fluctuations. The authors hypothesized that complexity could be a marker for frailty and explored their relationship in cardiac surgical patients.

DESIGN

Prospective, observational study.

SETTING

Single-center teaching hospital.

PARTICIPANTS

Three hundred and sixty-four adult patients undergoing cardiac surgery.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

Preoperative beat-to-beat systolic arterial pressure (SAP) and mean arterial pressure (MAP) time series were obtained. Complexity indices were calculated using multiscale entropy (MSE) analysis. Frailty was assessed from: age >70 years, body mass index <18.5, hematocrit <35%, albumin <3.4 g/dL, and creatinine >2.0 mg/dL. The association between complexity indices and frailty was explored by logistic regression and predictive ability by C-statistics. In total, 190 (52%) patients had frailty. The complexity index (MSE_Σ) median (quartile 1, quartile 3) of SAP and MAP time series decreased significantly in frail patients (SAP: 8.32 [7.27, 9.24] v 9.13 [8.00, 9.72], p < 0.001 and MAP: 8.56 [7.56; 9.27] v 9.18 [8.26; 9.83], p < 0.001). MSE _Σ demonstrated a fair predictive ability of frailty (C-statistic: SAP 0.62 and MAP 0.64).

CONCLUSION

Preoperative BP complexity indices correlate and predict frailty. Impaired autonomic control is the underlying mechanism to explain this finding. A simple automated measure of preoperative BP complexity in the surgeon's office has the potential to reliably assess frailty.

An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals

Medical devices generate huge amounts of continuous time series data. However, missing values commonly found in these data can prevent us from directly using analytic methods such as sample entropy to reveal the information contained in these data. To minimize the influence of missing points on the calculation of sample entropy, we propose a new method to handle missing values in continuous time series data. We use both experimental and simulated datasets to compare the performance (in percentage error) of our proposed method with three currently used methods: skipping the missing values, linear interpolation, and bootstrapping. Unlike the methods that involve modifying the input data, our method modifies the calculation process. This keeps the data unchanged which is less intrusive to the structure of the data. The results demonstrate that our method has a consistent lower average percentage error than other three commonly used methods in multiple common physiological signals. For missing values in common physiological signal type, different data size and generating mechanism, our method can more accurately extract the information contained in continuously monitored data than traditional methods. So it may serve as an effective tool for handling missing values and may have broad utility in analyzing sample entropy for common physiological signals. This could help develop new tools for disease diagnosis and evaluation of treatment effects.