Arterial Waveform Analysis Research Articles

Machine Learning Models for Tracking Blood Loss and Resuscitation in a Hemorrhagic Shock Swine Injury Model

Hemorrhage leading to life-threatening shock is a common and critical problem in both civilian and military medicine. Due to complex physiological compensatory mechanisms, traditional vital signs may fail to detect patients’ impending hemorrhagic shock in a timely manner when life-saving interventions are still viable. To address this shortcoming of traditional vital signs in detecting hemorrhagic shock, we have attempted to identify metrics that can predict blood loss. We have previously combined feature extraction and machine learning methodologies applied to arterial waveform analysis to develop advanced metrics that have enabled the early and accurate detection of impending shock in a canine model of hemorrhage, including metrics that estimate blood loss such as the Blood Loss Volume Metric, the Percent Estimated Blood Loss metric, and the Hemorrhage Area metric. Importantly, these metrics were able to identify impending shock well before traditional vital signs, such as blood pressure, were altered enough to identify shock. Here, we apply these advanced metrics developed using data from a canine model to data collected from a swine model of controlled hemorrhage as an interim step towards showing their relevance to human medicine. Based on the performance of these advanced metrics, we conclude that the framework for developing these metrics in the previous canine model remains applicable when applied to a swine model and results in accurate performance in these advanced metrics. The success of these advanced metrics in swine, which share physiological similarities to humans, shows promise in developing advanced blood loss metrics for humans, which would result in increased positive casualty outcomes due to hemorrhage in civilian and military medicine.

Association Between Arterial Pulse Waveform Analysis and Mortality in Patients With Septic Shock: A Retrospective Cohort Study Using Japanese Diagnosis Procedure Combination Data.

Purpose: Specialized pressure transducers for arterial pulse waveform analysis (S-APWA) devices are dedicated kits connected to an arterial pressure catheter that monitors hemodynamic parameters, such as cardiac output, pulse pressure variation, and stroke volume variation, less invasively. While the association between the use of S-APWA devices and clinical outcomes in perioperative patients has been previously evaluated, its assessment in patients with septic shock remains inadequate. Materials and Methods: This retrospective cohort study utilized a nationwide Diagnosis Procedure Combination database in Japan. Adult patients with septic shock admitted to the intensive care unit (ICU) with arterial pressure catheter placement on the admission day from August 2012 to February 2021 were included. Hospitalizations meeting the eligibility criteria were categorized into groups based on S-APWA device usage. The primary outcome, evaluated using Cox regression analysis, was 30-day all-cause mortality in the propensity score overlap-weighted population. Secondary outcomes included in-hospital mortality, ICU duration, and overall hospital stay. Results: Among 5130 eligible hospitalizations, 643 were in the S-APWA group and 4487 were in the conventional pressure transducer group. Cox regression analysis within the propensity score overlap-weighted population showed no significant difference in 30-day mortality (adjusted hazard ratio: 0.94; 95% confidence interval: 0.9-1.38; P = .58). Logistic regression analysis indicated no significant differences in the in-hospital mortality. While the S-APWA group had prolonged ICU stays, no significant difference in the overall hospital stay was observed according to linear regression analyses. Conclusions: Our study found no significant association between S-APWA use and 30-day mortality in patients with septic shock. These findings offer insights into optimizing monitoring systems in ICUs.

Predicting stroke volume variation using central venous pressure waveform: a deep learning approach

Objective. This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.Approach. Long short-term memory (LSTM) and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the LSTM consisted of 10 s CVP waveforms sampled at 2 s intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of LSTM and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000.Main results. The model hyperparameters consisted of 12 memory cells in the LSTM layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717 978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval, 0.992-0.993) for SVV measured by EV1000.Significance. Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis.

Stroke Volume and Stroke Volume Variation, but not Cardiac Index Is Associated With Survival of Majorly Burned Patients in Early Burn Shock.

Adequate fluid therapy is crucial to maintain organ function after burn trauma. Major burns lead to a systemic response with fluid loss and cardiac dysfunction. To guide fluid therapy, measurement of cardiac pre- and afterload is helpful. Whereas cardiac function is usually measured after admission to intensive care unit (ICU), in this study, hemodynamic monitoring was performed directly after arrival at hospital. We conducted a prospective cohort study with inclusion of 19 patients (male/female 13/6, 55 ± 18 years, mean total body surface area 36 ± 19%). Arterial waveform analysis (PulsioFlexProAqt®, Getinge) was implemented immediately after admission to hospital to measure cardiac pre- and afterload and to guide resuscitation therapy. Cardiac parameters 3.75 (2.67-6.0) h after trauma were normal regarding cardiac index (3.45 ± 0.82) L/min/m², systemic vascular resistance index (1749 ± 533) dyn sec/cm5 m2, and stroke volume (SV; 80 ± 20) mL. Stroke volume variation (SVV) was increased (21 ± 7) % and associated with mortality (mean SVV survivors vs nonsurvivors 18.92 (±6.37) % vs 27.6 (±5.68) %, P = .017). Stroke volume was associated with mortality at the time of ICU-admission (mean SV survivors vs nonsurvivors 90 (±20) mL vs 50 (±0) mL, P = .004). Changes after volume challenge were significant for SVV (24 ± 9 vs19 ± 8%, P = .01) and SV (68 ± 24 vs 76 ± 26 mL, P = .03). We described association of SVV and SV with survival of severely burned patients in an observational study. This indicates high valence of those parameters in the early postburn period. The use of an autocalibrated device enables a very early monitoring of parameters relevant to burn shock survival.

Beyond the debut: unpacking six years of Hypotension Prediction Index software in intraoperative hypotension prevention - a systematic review and meta-analysis.

Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension. A systematic search was performed in Pubmed and Scopus to retrieve articles published from January 2018 to February 2024 regarding the impact of the HPI software on reducing IOH in adult patients undergoing non-cardio/thoracic surgery. Excluded were case series, case reports, meta-analyses, systematic reviews, and studies using non-invasive arterial waveform analysis. The risk of bias was assessed by the Cochrane risk-of-bias tool (RoB 2) and the Risk Of Bias In Non-randomised Studies (ROBINS-I). A meta-analysis was undertaken solely for outcomes where sufficient data were available from the included studies. 9 RCTs and 5 cohort studies were retrieved. The overall median differences between the HPI-guided and the control groups were - 0.21 (95% CI:-0.33, -0.09) - p < 0.001 for the Time-Weighted Average (TWA) of Mean Arterial Pressure (MAP) < 65mmHg, -3.71 (95% CI= -6.67, -0.74)-p = 0.014 for the incidence of hypotensive episodes per patient, and - 10.11 (95% CI= -15.82, -4.40)-p = 0.001 for the duration of hypotension. Notably a large amount of heterogeneity was detected among the studies. While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation.

Intraoperative Hypotension in Patients Having Major Noncardiac Surgery Under General Anesthesia: A Systematic Review of Blood Pressure Optimization Strategies.

Intraoperative hypotension is associated with increased risks of postoperative complications. Consequently, a variety of blood pressure optimization strategies have been tested to prevent or promptly treat intraoperative hypotension. We performed a systematic review to summarize randomized controlled trials that evaluated the efficacy of blood pressure optimization interventions in either mitigating exposure to intraoperative hypotension or reducing risks of postoperative complications. Medline, Embase, PubMed, and Cochrane Controlled Register of Trials were searched from database inception to August 2, 2023, for randomized controlled trials (without language restriction) that evaluated the impact of any blood pressure optimization intervention on intraoperative hypotension and/or postoperative outcomes. The review included 48 studies (N = 46,377), which evaluated 10 classes of blood pressure optimization interventions. Commonly assessed interventions included hemodynamic protocols using arterial waveform analysis, preoperative withholding of antihypertensive medications, continuous blood pressure monitoring, and adjuvant agents (vasopressors, anticholinergics, anticonvulsants). These same interventions reduced intraoperative exposure to hypotension. Conversely, low blood pressure alarms had an inconsistent impact on exposure to hypotension. Aside from limited evidence that higher prespecified intraoperative blood pressure targets led to a reduced risk of complications, there were few data suggesting that these interventions prevented postoperative complications. Heterogeneity in interventions and outcomes precluded meta-analysis. Several different blood pressure optimization interventions show promise in reducing exposure to intraoperative hypotension. Nonetheless, the impact of these interventions on clinical outcomes remains unclear. Future trials should assess promising interventions in samples sufficiently large to identify clinically plausible treatment effects on important outcomes.

Multicenter randomized controlled trial of exercise in aortic dissection survivors: rationale, design, and initial hemodynamic data

Aims: There are currently no evidence-based guidelines for exercise after thoracic aortic dissection (TAD), leading to highly variable recommendations that frequently lead patients to restrict their physical activities. This multicenter randomized controlled trial was intended to evaluate the safety and efficacy of a moderate intensity guided exercise program for TAD survivors. Methods: Participants were eligible if they had a Type A or Type B dissection at least 90 days before enrollment and could attend two in-person study visits. The guided exercise circuit consisted of six aerobic, isotonic, or isometric exercises that participants continued at home with virtual follow-up sessions. The primary endpoint is the change in the composite anxiety and depression PROMIS-29 T-score at 12 months. Secondary endpoints include changes in grip strength, weight, 24-h ambulatory blood pressure, and arterial biomechanical properties measured by central arterial waveform analysis. Results: Preliminary analysis of the first 81 enrolled participants demonstrated that the guided exercise circuit was completed safely and was not associated with severe hypertension, injury, or adverse cardiovascular events. At enrollment, adverse central waveform or ABPM characteristics were prevalent and were significantly associated with exertional hypertension. Conclusions: Guided exercise is safe for aortic dissection survivors. Follow-up of enrolled participants will conclude in October 2024.

Hypotension Prediction Index Is Equally Effective in Predicting Intraoperative Hypotension during Noncardiac Surgery Compared to a Mean Arterial Pressure Threshold: A Prospective Observational Study.

The Hypotension Prediction Index is designed to predict intraoperative hypotension in a timely manner and is based on arterial waveform analysis using machine learning. It has recently been suggested that this algorithm is highly correlated with the mean arterial pressure itself. Therefore, the aim of this study was to compare the index with mean arterial pressure-based prediction methods, and it is hypothesized that their ability to predict hypotension is comparable. In this observational study, the Hypotension Prediction Index was used in addition to routine intraoperative monitoring during moderate- to high-risk elective noncardiac surgery. The agreement in time between the default Hypotension Prediction Index alarm (greater than 85) and different concurrent mean arterial pressure thresholds was evaluated. Additionally, the predictive performance of the index and different mean arterial pressure-based methods were assessed within 5, 10, and 15 min before hypotension occurred. A total of 100 patients were included. A mean arterial pressure threshold of 73 mmHg agreed 97% of the time with the default index alarm, whereas a mean arterial pressure threshold of 72 mmHg had the most comparable predictive performance. The areas under the receiver operating characteristic curve of the Hypotension Prediction Index (0.89 [0.88 to 0.89]) and concurrent mean arterial pressure (0.88 [0.88 to 0.89]) were almost identical for predicting hypotension within 5 min, outperforming both linearly extrapolated mean arterial pressure (0.85 [0.84 to 0.85]) and delta mean arterial pressure (0.66 [0.65 to 0.67]). The positive predictive value was 31.9 (31.3 to 32.6)% for the default index alarm and 32.9 (32.2 to 33.6)% for a mean arterial pressure threshold of 72 mmHg. In clinical practice, the Hypotension Prediction Index alarms are highly similar to those derived from mean arterial pressure, which implies that the machine learning algorithm could be substituted by an alarm based on a mean arterial pressure threshold set at 72 or 73 mmHg. Further research on intraoperative hypotension prediction should therefore include comparison with mean arterial pressure-based alarms and related effects on patient outcome.

AcumenTM hypotension prediction index guidance for prevention and treatment of hypotension in noncardiac surgery: a prospective, single-arm, multicenter trial

BackgroundIntraoperative hypotension is common during noncardiac surgery and is associated with postoperative myocardial infarction, acute kidney injury, stroke, and severe infection. The Hypotension Prediction Index software is an algorithm based on arterial waveform analysis that alerts clinicians of the patient’s likelihood of experiencing a future hypotensive event, defined as mean arterial pressure < 65 mmHg for at least 1 min.MethodsTwo analyses included (1) a prospective, single-arm trial, with continuous blood pressure measurements from study monitors, compared to a historical comparison cohort. (2) A post hoc analysis of a subset of trial participants versus a propensity score-weighted contemporaneous comparison group, using external data from the Multicenter Perioperative Outcomes Group (MPOG). The trial included 485 subjects in 11 sites; 406 were in the final effectiveness analysis. The post hoc analysis included 457 trial participants and 15,796 comparison patients. Patients were eligible if aged 18 years or older, American Society of Anesthesiologists (ASA) physical status 3 or 4, and scheduled for moderate- to high-risk noncardiac surgery expected to last at least 3 h. Measurements: minutes of mean arterial pressure (MAP) below 65 mmHg and area under MAP < 65 mmHg.ResultsAnalysis 1: Trial subjects (n = 406) experienced a mean of 9 ± 13 min of MAP below 65 mmHg, compared with the MPOG historical control mean of 25 ± 41 min, a 65% reduction (p < 0.001). Subjects with at least one episode of hypotension (n = 293) had a mean of 12 ± 14 min of MAP below 65 mmHg compared with the MPOG historical control mean of 28 ± 43 min, a 58% reduction (p< 0.001). Analysis 2: In the post hoc inverse probability treatment weighting model, patients in the trial demonstrated a 35% reduction in minutes of hypotension compared to a contemporaneous comparison group [exponentiated coefficient: − 0.35 (95%CI − 0.43, − 0.27); p < 0.001].ConclusionsThe use of prediction software for blood pressure management was associated with a clinically meaningful reduction in the duration of intraoperative hypotension. Further studies must investigate whether predictive algorithms to prevent hypotension can reduce adverse outcomes.Trial registrationClinical trial number: NCT03805217. Registry URL: https://clinicaltrials.gov/ct2/show/NCT03805217. Principal investigator: Xiaodong Bao, MD, PhD. Date of registration: January 15, 2019.

The Incidence of Perioperative Hypotension in Patients Undergoing Major Abdominal Surgery with the Use of Arterial Waveform Analysis and the Hypotension Prediction Index Hemodynamic Monitoring-A Retrospective Analysis.

Intraoperative hypotension (IH) is common in patients receiving general anesthesia and can lead to serious complications such as kidney failure, myocardial injury and increased mortality. The Hypotension Prediction Index (HPI) algorithm is a machine learning system that analyzes the arterial pressure waveform and alerts the clinician of an impending hypotension event. The purpose of the study was to compare the frequency of perioperative hypotension in patients undergoing major abdominal surgery with different types of hemodynamic monitoring. The study included 61 patients who were monitored with the arterial pressure-based cardiac output (APCO) technology (FloTrac group) and 62 patients with the Hypotension Prediction Index algorithm (HPI group). Our primary outcome was the time-weighted average (TWA) of hypotension below < 65 mmHg. The median TWA of hypotension in the FloTrac group was 0.31 mmHg versus 0.09 mmHg in the HPI group (p = 0.000009). In the FloTrac group, the average time of hypotension was 27.9 min vs. 8.1 min in the HPI group (p = 0.000023). By applying the HPI algorithm in addition to an arterial waveform analysis alone, we were able to significantly decrease the frequency and duration of perioperative hypotension events in patients who underwent major abdominal surgery.

Early Autocalibrated Arterial Waveform Analysis for the Management of Burn Shock-A Cohort Study.

Adequate fluid therapy is crucial for resuscitation after major burns. To adapt this to individual patient demands, standard is adjustment of volume to laboratory parameters and values of enhanced hemodynamic monitoring. To implement calibrated parameters, patients must have reached the intensive care unit (ICU). The aim of this study was, to evaluate the use of an auto-calibrated enhanced hemodynamic monitoring device to improve fluid management before admission to ICU. We used PulsioflexProAqt® (Getinge) during initial treatment and burn shock resuscitation. Analysis was performed regarding time of measurement, volume management, organ dysfunction, and mortality. We conducted a monocentre, prospective cohort study of 20 severely burned patients, >20% total body surface area (TBSA), receiving monitoring immediately after admission. We compared to 57 patients, matched in terms of TBSA, age, sex, and existence of inhalation injury out of a retrospective control group, who received standard care. Hemodynamic measurement with autocalibrated monitoring started significantly earlier: 3.75(2.67-6.0) hours (h) after trauma in the study group versus 13.6(8.1-17.5) h in the control group (P < .001). Study group received less fluid after 6 h: 1.7(1.2-2.2) versus 2.3(1.6-2.8) ml/TBSA%/kg, P = .043 and 12 h: 3.0(2.5-4.0) versus 4.2(3.1-5.0) ml/TBSA%/kg, P = .047. Dosage of norepinephrine was higher after 18 h in the study group: 0.20(0.12-0.3) versus 0.08(0.02-0.18) µg/kg/min, P = .014. The study group showed no adult respiratory distress syndrome versus 21% in the control group, P = .031. There was no difference in other organ failures, organ replacement therapy, and mortality. The use of auto-calibrated enhanced hemodynamic monitoring is a fast and feasible way to guide early fluid therapy after burn trauma. It reduces the time to reach information about patient's volume capacity. Management of fluid application changed to a more restrictive fluid use in the early period of burn shock and led to a reduction of pulmonary complications.

Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms

This paper intends to investigate the feasibility of peripheral artery disease (PAD) diagnosis based on the analysis of non-invasive arterial pulse waveforms. We generated realistic synthetic arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals pertaining to PAD present at the abdominal aorta with a wide range of severity levels using a mathematical model that simulates arterial blood circulation and arterial BP-PVR relationships. We developed a deep learning (DL)-enabled algorithm that can diagnose PAD by analyzing brachial and tibial PVR waveforms, and evaluated its efficacy in comparison with the same DL-enabled algorithm based on brachial and tibial arterial BP waveforms as well as the ankle-brachial index (ABI). The results suggested that it is possible to detect PAD based on DL-enabled PVR waveform analysis with adequate accuracy, and its detection efficacy is close to when arterial BP is used (positive and negative predictive values at 40 % abdominal aorta occlusion: 0.78 vs 0.89 and 0.85 vs 0.94; area under the ROC curve (AUC): 0.90 vs 0.97). On the other hand, its efficacy in estimating PAD severity level is not as good as when arterial BP is used (r value: 0.77 vs 0.93; Bland-Altman limits of agreement: −32%-+32 % vs −20%-+19 %). In addition, DL-enabled PVR waveform analysis significantly outperformed ABI in both detection and severity estimation. In sum, the findings from this paper suggest the potential of DL-enabled non-invasive arterial pulse waveform analysis as an affordable and non-invasive means for PAD diagnosis.

Low cardiac index and high total peripheral resistance are independent predictors of mortality for acutely admitted medical patients

Abstract Introduction Identification of critically ill and high-risk patients upon hospital admission represents a significant triage task in the emergency department. Rapid identification and treatment of these patients improves survival, reduces complications during admission, decrease hospitalization duration, and lower treatment cost. Hemodynamic variables such as cardiac index (CI) and total peripheral resistance (TPR) hold the potential to improve the diagnosis of critically ill patients by providing better prognostic value. Using arterial waveform and the model flow method, CI and TPR can be measured non-invasively. These methods are validated against thermodilution, which is the golden standard. Purpose The purpose of the study was to examine the association between CI, TPR, and all-cause mortality within 30 and 90-day among patients admitted to acutely the emergency department or cardiac care unit. Methods In this observational prospective cohort study, all patients were included from the emergency department and cardiac care unit. Patients of age 18 years or older admitted to the emergency department or cardiac care unit due to acute medical disease, were included. Patients with a life expectance of 3months or less and patients unable to give informed consent were excluded. Within the first 24 hours of admission, patients underwent a 10-minute non-invasive hemodynamic measurement using arterial waveform analysis and the model-flow method. Patients were followed up to 90 days after discharge. The endpoints 30- and 90-day mortality were obtained through electronic patient journals. Cox proportional-hazard models examined the association between CI, TPR, and outcomes. All hazard models were adjusted for age, sex, BMI, heart failure, early warning score, and Charlson comorbidity score. Results Between May 2019 and January 2023, we included 920 unselected patients from the department of emergency medicine (n=433) and cardiac care unit (n=487) from a metropolitan hospital. We observed a 3% 30-days mortality while 90-days mortality was 4.9%. The median value of CI was 3.3L, the lowest decile was 2L, and the highest decile was 4.7L. Patients with the lowest CI were older, had higher incidents of heart failure, and had more comorbidities according to Charlson’s comorbidity score (table 1). In Cox proportional hazard models, lowest decile of CI at admission was associated with a significantly higher risk of 30- and 90-days mortality. Total peripheral resistance was significantly associated with 90-day mortality (table 2). Conclusion Low CI ≤2.0 liter/min/m2, and high peripheral resistance measured non-invasively were associated with an increased risk of mortality among patients admitted to the emergency department or cardiac care unit. Non-invasive estimation of CI and TPR may assist in identifying high-risk patients.Table 1Table 2

Abstract 15854: Pulmonary Arterial Waveform Analysis and Supervised Machine Learning Predicts Outcomes in Heart Failure: An Analysis of the CHAMPION Trial

Introduction: Pulmonary artery (PA) pressure monitors have been shown to prevent hospitalizations in heart failure patients using mean pressure values. PA waveform analysis offers an overall assessment of right ventricular load that combines resistance and compliance. Hypothesis: We hypothesised that clinical outcomes can be predicted from PA waveform analyses from patients with an implanted PA monitor. Methods: The CHAMPION trial database was analysed. Information about the demographics and data collection methods for this cohort have been published previously. All waveforms were analysed automatically using Python. Tau or RC time, respiratory rate, respiratory range, P asymptote, mean pulmonary arterial pressure (mPAP), diastolic pulmonary arterial pressure (dPAP) and systolic pulmonary arterial pressure (sPAP) were derived from the raw waveforms. Tau and P asymptote were calculated using phase plane analysis. Outcomes of hospitalization or mortality were adjudicated as part of the original trial. Statistics were performed in R. A decision tree statistical approach using the XGBoost algorithm was used. Contribution to the model was assessed using Shapley Additive Explanations (SHAP) values. The dataset was split into 70% train and 30% test sets. Results: 296,948 PA pressure traces from 547 patients were analysed. 3 patients had insufficient quality of trace to analyse. There were 1340 outcome events throughout the follow up period, median 664 days. The trained algorithm was able to predict clinical outcomes in the test dataset with 100% accuracy. The most significant contributors according the mean SHAP value were RC time (SHAP = 1.1), respiratory range (SHAP = 1.1) and heart rate (SHAP = 1.1). Conclusions: Detailed analysis of the PA waveform from PA pressure monitors is feasible and yields clinically relevant information. A supervised machine learning algorithm trained on this data can accurately predict hospitalization or mortality events in heart failure patients.

Central-to-peripheral blood pressure amplification: role of the recording site, technology, analysis approach, and calibration scheme in invasive and non-invasive data agreement.

Systolic blood pressure amplification (SBPA) and pulse pressure amplification (PPA) can independently predict cardiovascular damage and mortality. A wide range of methods are used for the non-invasive estimation of SBPA and PPA. The most accurate non-invasive method for obtaining SBPA and/or PPA remains unknown. This study aims to evaluate the agreement between the SBPA and PPA values that are invasively and non-invasively obtained using different (1) measurement sites (radial, brachial, carotid), (2) measuring techniques (tonometry, oscillometry/plethysmography, ultrasound), (3) pulse waveform analysis approaches, and (4) calibration methods [systo-diastolic vs. approaches using brachial diastolic and mean blood pressure (BP)], with the latter calculated using different equations or measured by oscillometry. Invasive aortic and brachial pressure (catheterism) and non-invasive aortic and peripheral (brachial, radial) BP were simultaneously obtained from 34 subjects using different methodologies, analysis methods, measuring sites, and calibration methods. SBPA and PPA were quantified. Concordance correlation and the Bland-Altman analysis were performed. (1) In general, SBPA and PPA levels obtained with non-invasive approaches were not associated with those recorded invasively. (2) The different non-invasive approaches led to (extremely) dissimilar results. In general, non-invasive measurements underestimated SBPA and PPA; the higher the invasive SBPA (or PPA), the greater the underestimation. (3) None of the calibration schemes, which considered non-invasive brachial BP to estimate SBPA or PPA, were better than the others. (4) SBPA and PPA levels obtained from radial artery waveform analysis (tonometry) (5) and common carotid artery ultrasound recordings and brachial artery waveform analysis, respectively, minimized the mean errors. Overall, the findings showed that (i) SBPA and PPA indices are not "synonymous" and (ii) non-invasive approaches would fail to accurately determine invasive SBPA or PPA levels, regardless of the recording site, analysis, and calibration methods. Non-invasive measurements generally underestimated SBPA and PPA, and the higher the invasive SBPA or PPA, the higher the underestimation. There was not a calibration scheme better than the others. Consequently, our study emphasizes the strong need to be critical of measurement techniques, to have methodological transparency, and to have expert consensus for non-invasive assessment of SBPA and PPA.

Hypotension Prediction Index and Incidence of Perioperative Hypotension: A Single-Center Propensity-Score-Matched Analysis.

(1) Background: Intraoperative hypotension is common and is associated with increased morbidity and mortality. The Hypotension Prediction Index (HPI) is an advancement of arterial waveform analysis and allows preventive treatments. We used a propensity-score-matched study design to test whether application of the HPI reduces hypotensive events in non-cardiac surgery patients; (2) Methods: 769 patients were selected for propensity score matching. After matching, both HPI and non-HPI groups together comprised n = 136 patients. A goal-directed treatment protocol was applied in both groups. The primary endpoint was the incidence and duration of hypotensive events defined as MAP < 65 mmHg, evaluated by the time-weighted average (TWA) of hypotension. (3) Results: The median TWA of hypotension below 65 mmHg in the matched cohort was 0.180 mmHg (IQR 0.060, 0.410) in the non-HPI group vs. 0.070 mmHg (IQR 0.020, 0.240) in the HPI group (p < 0.001). TWA was higher in patients with ASA classification III/IV (0.170 mmHg; IQR 0.035, 0.365) than in patients with ASA status II (0.100; IQR 0.020, 0.250; p = 0.02). Stratification by intervention group showed no differences in the HPI group while TWA values in the non-HPI group were more than twice as high in patients with ASA status III/IV (p = 0.01); (4) Conclusions: HPI reduces intraoperative hypotension in a matched cohort seen for TWA below 65 mmHg and relative time in hypotension. In addition, non-HPI patients with ASA status III/IV showed a higher TWA compared with HPI-patients, indicating an advantageous effect of using HPI in patients at higher risk.

The relationship between combat-related traumatic amputation and subclinical cardiovascular risk

BackgroundThe relationship between acute combat-related traumatic injury (CRTI) to coronary flow reserve (CFR) and subclinical cardiovascular risk have not been examined and was the primary aim of this study. Methods and resultsUK combat veterans from the ADVANCE cohort study (UK-Afghanistan War 2003–14) with traumatic limb amputations were compared to injured non-amputees and to a group of uninjured veterans from the same conflict. Subclinical cardiovascular risk measures included fasted blood atherogenic index of plasma (AIP), triglyceride-glucose index (TyG; insulin resistance), the neutrophil-lymphocyte ratio (NLR) and high-sensitivity C-reactive protein (hs-CRP; vascular inflammation), body mass index (BMI) and visceral fat volume (dual-energy X-ray absorptiometry) and 6-min walk distance (6MWD; physical performance). The subendocardial viability ratio (SEVR), to estimate CFR, was calculated using arterial pulse waveform analysis (Vicorder device). In total 1144 adult male combat veterans were investigated, comprising 579 injured (161 amputees, 418 non-amputees) and 565 uninjured men. AIP, TyG, NLR, hs-CRP, BMI, total body fat and visceral fat volume were significantly higher and the SEVR and 6MWD significantly lower in the amputees versus the injured-non-amputees and uninjured groups. The SEVR was lowest in those with above knee and multiple limb amputations. CRTI (ExpB 0.96; 95% CI 0.94–0.98: p < 0.0001) and amputation (ExpB 0.94: 95% CI 0.91–0.97: p < 0.0001) were independently associated with lower SEVR after adjusting for age, rank, ethnicity and time from injury. ConclusionCRTI, traumatic amputation and its worsening physical deficit are associated with lower coronary flow reserve and heightened subclinical cardiovascular risk.

ARTERY ELASTICITY INDICES AND TESTOSTERONE- LONGITUDINAL STUDY IN VARA- SKÖVDE COHORT

Objective: To determine the associations between testosterone, sex hormone-binding globulin (SHBG) and arterial elasticity in men in a longitudinal observational study. Design and method: 2816 randomly selected individuals were examined (Men = 1400) between 2002 and 2005. After excluding participants with known hypertension and without data for arterial elasticity and testosterone, we include 1202 men in cross-sectional analyses. After a mean follow-up of 9.7 ± 1.4 years, a representative sample of 1327 original participants was re-examined. In the longitudinal analysis, 473 men were included. Body height, weight, heart rate and blood pressure were measured. Total testosterone (TT) and SHBG were analyzed using RIA. Bioavailable testosterone (BioT) is calculated by the Vermeulen coefficient. Arterial elasticity was measured based on pulse contour analysis of arterial waveform using the HDI/Pulse WaveTM CR2000 and C1 and C2 were obtained. Linear regression models were used to investigate these associations in three models: in model 1 adjusting for age, in model 2 we added heart rate, systolic blood pressure and in model 3 we added BMI. Multivariable general linear models were used to investigate the differences in C1 and C2 based on testosterone deficiency. Results: Mean age at baseline was 46.2±10.9 years. In cross-sectional analysis, an increase in TT and BioT was associated with an increase of C1 (beta = 0.08, CI: 0.02-0,14, p = 0.01 resp beta = 0.14, CI: 0.001-0.272, p = 0.05) in model 1, but not in the other models. In model 1 participants with testosterone deficiency had significantly lower C1 (C1 in TD: 17.6±5, C1 in the normal level of TT: 18.2±5, delta C1: 0.6, CI: 0.06- 1.1; p = 0.05), but not in the other models. Other associations between TT, BioT, SHBG, TD and C1 respective C2 were not significant in the models. Conclusions: We found weak associations between artery elasticity indices and testosterone levels. Surprisingly these associations were not significant after adjustments for pulse and blood pressure, even though the sample was large enough to reveal clinically significant associations.

Abdominal aortic aneurysm monitoring via arterial waveform analysis: towards a convenient point-of-care device

Abdominal aortic aneurysms (AAAs) are lethal but treatable yet substantially under-diagnosed and under-monitored. Hence, new AAA monitoring devices that are convenient in use and cost are needed. Our hypothesis is that analysis of arterial waveforms, which could be obtained with such a device, can provide information about AAA size. We aim to initially test this hypothesis via tonometric waveforms. We study noninvasive carotid and femoral blood pressure (BP) waveforms and reference image-based maximal aortic diameter measurements from 50 AAA patients as well as the two noninvasive BP waveforms from these patients after endovascular repair (EVAR) and from 50 comparable control patients. We develop linear regression models for predicting the maximal aortic diameter from waveform or non-waveform features. We evaluate the models in out-of-training data in terms of predicting the maximal aortic diameter value and changes induced by EVAR. The best model includes the carotid area ratio (diastolic area divided by systolic area) and normalized carotid-femoral pulse transit time ((age·diastolic BP)/(height/PTT)) as input features with positive model coefficients. This model is explainable based on the early, negative wave reflection in AAA and the Moens-Korteweg equation for relating PTT to vessel diameter. The predicted maximal aortic diameters yield receiver operating characteristic area under the curves of 0.83 ± 0.04 in classifying AAA versus control patients and 0.72 ± 0.04 in classifying AAA patients before versus after EVAR. These results are significantly better than a baseline model excluding waveform features as input. Our findings could potentially translate to convenient devices that serve as an adjunct to imaging.

Vascular alterations among male elite athletes recovering from SARS-CoV-2 infection

SARS-CoV-2 may affect the cardiovascular system and vascular impairment has been reported in healthy young adults recovering from COVID-19. However, the impact of SARS-CoV-2 infection on the vascular function of elite athletes is unknown. We examined 30 healthy male elite athletes (age 25.8 ± 4.6 years) pre-season and at a 6-month follow-up (182 ± 10 days). Vascular function and central blood pressure were calculated using transfer function-based analysis of peripheral arterial waveforms obtained by oscillometry. We performed a two-way repeated-measures ANOVA on the biomarker data, with SARS-CoV-2 status as the between-groups factor and time as the within-groups factor. Subjects who tested positive for SARS-CoV-2 were studied 18 ± 4 days after their positive testing date at follow-up. Of 30 athletes, 15 tested positive for SARS-CoV-2 after the first examination and prior to the follow-up. None had severe COVID-19 or reported any persisting symptoms. The results of the two-way repeated measures ANOVA revealed that there was no significant main effect of COVID-19 on any of the investigated biomarkers. However, there was a significant interaction between the effects of SARS-CoV-2 exposure and time on augmentation index (Aix) (p = 0.006) and augmentation index normalized to a heart rate of 75 beats per minute (Aix@75), (p = 0.0018). The observation of an interaction effect on Aix and Aix@75 in the absence of any main effect indicates a cross-over interaction. Significant vascular alterations in male elite athletes recovering from COVID-19 were observed that suggest vascular impairment. Whether these alterations affect athletic performance should be evaluated in future studies.