Detecting aortic valve stenosis based on the non-invasive blood pressure waveform-a proof of concept study.

The incidence of aortic valve stenosis (AoS) increases with age, and once diagnosed, symptomatic severe AoS has a yearly mortality rate of 25%. AoS is diagnosed with transthoracic echocardiography (TTE), however, this gold standard is time consuming and operator and acoustic window dependent. As AoS affects the arterial blood pressure waveform, AoS-specific waveform features might serve as a diagnostic tool. Aim of the present study was to develop a novel, non-invasive, AoS detection model based on blood pressures waveforms. This cross-sectional study included patients with AoS undergoing elective transcatheter or surgical aortic valve replacement. AoS was determined using TTE, and patients with no or mild AoS were labelled as patients without AoS, while patients with moderate or severe AoS were labelled as patients with AoS. Non-invasive blood pressure measurements were performed in awake patients. Ten minutes of consecutive data was collected. Several blood pressure-based features were derived, and the median, interquartile range, variance, and the 1st and 9th decile of the change of these features were calculated. The primary outcome was the development of a machine-learning model for AoS detection, investigating multiple classifiers and training on the area under the receiver-operating curve (AUROC). In total, 101 patients with AoS and 48 patients without AoS were included. Patients with AoS showed an increase in left ventricular ejection time (0.02 s, p = 0.001), a delayed maximum upstroke in the systolic phase (0.015 s, p < 0.001), and a delayed maximal systolic pressure (0.03 s, p < 0.001) compared to patients without AoS. With the logistic regression model, a sensitivity of 0.81, specificity of 0.67, and AUROC of 0.79 were found. The majority of the population without AoS was male (85%), whereas in the population with AoS this was evenly distributed (54% males). Age was significantly (5 years, p < 0.001) higher in the population with AoS. In the present study, we developed a novel model able to distinguish no to mild AoS from moderate to severe AoS, based on blood pressure features with high accuracy. Clinical registration number: The study entailing patients with TAVR treatment was registered at ClinicalTrials.gov (NCT03088787, https://clinicaltrials.gov/ct2/show/NCT03088787 ). The study with elective cardiac surgery patients was registered with the Netherland Trial Register (NL7810, https://trialsearch.who.int/Trial2.aspx?TrialID=NL7810 ).

Performance of a Machine Learning Algorithm to Predict Hypotension in Spontaneously Breathing Non-Ventilated Post-Anesthesia and ICU Patients.

Background: Hypotension is common in the post-anesthesia care unit (PACU) and intensive care unit (ICU), and is associated with adverse patient outcomes. The Hypotension Prediction Index (HPI) algorithm has been shown to accurately predict hypotension in mechanically ventilated patients in the OR and ICU and to reduce intraoperative hypotension (IOH). Since positive pressure ventilation significantly affects patient hemodynamics, we performed this validation study to examine the performance of the HPI algorithm in a non-ventilated PACU and ICU population. Materials & Methods: The performance of the HPI algorithm was assessed using prospectively collected blood pressure (BP) and HPI data from a PACU and a mixed ICU population. Recordings with sufficient time (≥3 h) spent without mechanical ventilation were selected using data from the electronic medical record. All HPI values were evaluated for sensitivity, specificity, predictive value, and time-to-event, and a receiver operating characteristic (ROC) curve was constructed. Results: BP and HPI data from 282 patients were eligible for analysis, of which 242 (86%) were ICU patients. The mean age (standard deviation) was 63 (13.5) years, and 186 (66%) of the patients were male. Overall, the HPI predicted hypotension accurately, with an area under the ROC curve of 0.94. The most used HPI threshold cutoff in research and clinical use, 85, showed a sensitivity of 1.00, specificity of 0.79, median time-to-event of 160 s [60-380], PPV of 0.85, and NPV of 1.00. Conclusion: The absence of positive pressure ventilation and the influence thereof on patient hemodynamics does not negatively affect the performance of the HPI algorithm in predicting hypotension in the PACU and ICU. Future research should evaluate the feasibility and influence on hypotension and outcomes following HPI implementation in non-ventilated patients at risk of hypotension.

Dynamic cerebral autoregulation during step-wise increases in blood pressure during anaesthesia: A nonrandomised interventional trial.

BACKGROUND: Classically, cerebral autoregulation (CA) entails cerebral blood flow (CBF) remaining constant by cerebrovascular tone adapting to fluctuations in mean arterial pressure (MAP) between ∼60 and ∼150 mmHg. However, this is not an on-off mechanism; previous work has suggested that vasomotor tone is proportionally related to CA function. During propofol-based anaesthesia, there is cerebrovascular vasoconstriction, and static CA remains intact. Sevoflurane-based anaesthesia induces cerebral vasodilation and attenuates CA dose-dependently. It is unclear how this translates to dynamic CA across a range of blood pressures in the autoregulatory range. OBJECTIVE: The aim of this study was to quantify the effect of step-wise increases in MAP between 60 and 100 mmHg, using phenylephrine, on dynamic CA during propofol- and sevoflurane-based anaesthesia. DESIGN: A nonrandomised interventional trial. SETTING: Single centre enrolment started on 11 January 2019 and ended on 23 September 2019. PATIENTS: We studied American Society of Anesthesiologists (ASA) I/II patients undergoing noncardiothoracic, nonneurosurgical and nonlaparoscopic surgery under general anaesthesia. INTERVENTION: In this study, cerebrovascular tone was manipulated in the autoregulatory range by increasing MAP step-wise using phenylephrine in patients receiving either propofol- or sevoflurane-based anaesthesia. MAP and mean middle cerebral artery blood velocity (MCA Vmean ) were measured in ASA I and II patients, anaesthetised with either propofol ( n  = 26) or sevoflurane ( n  = 28), during 10 mmHg step-wise increments of MAP between 60 and 100 mmHg. Static CA was determined by plotting 2-min averaged MCA Vmean versus MAP. Dynamic CA was determined using transfer function analysis and expressed as the phase lead (°) between MAP and MCA Vmean oscillations, created with positive pressure ventilation with a frequency of 6 min -1 . MAIN OUTCOMES: The primary outcome of this study was the response of dynamic CA during step-wise increases in MAP during propofol- and sevoflurane-based anaesthesia. RESULTS: MAP levels achieved per step-wise increments were comparable between anaesthesia regiment (63 ±â€Š3, 72 ±â€Š2, 80 ±â€Š2, 90 ±â€Š2, 100 ±â€Š3 mmHg, and 61 ±â€Š4, 71 ±â€Š2, 80 ±â€Š2, 89 ±â€Š2, 98 ±â€Š4 mmHg for propofol and sevoflurane, respectively). MCA Vmean increased more during step-wise MAP increments for sevoflurane compared to propofol ( P ≤0.001). Dynamic CA improved during propofol (0.73° mmHg -1 , 95% CI 0.51 to 0.95; P  ≤ 0.001)) and less pronounced during sevoflurane-based anaesthesia (0.21°â€ŠmmHg -1 (95% CI 0.01 to 0.42, P  = 0.04). CONCLUSIONS: During general anaesthesia, dynamic CA is dependent on MAP, also within the autoregulatory range. This phenomenon was more pronounced during propofol anaesthesia than during sevoflurane. TRIAL REGISTRATION: NCT03816072 ( https://clinicaltrials.gov/ct2/show/NCT03816072 ).

Immediate reduction in left ventricular ejection time following TAVI is associated with improved quality of life.

Background: TAVI has shown to result in immediate and sustained hemodynamic alterations and improvement in health-related quality of life (HRQoL), but previous studies have been suboptimal to predict who might benefit from TAVI. The relationship between immediate hemodynamic changes and outcome has not been studied before. This study sought to assess whether an immediate hemodynamic change, reflecting myocardial contractile reserve, following TAVI is associated with improved HRQoL. Furthermore, it assessed whether pre-procedural cardiac power index (CPI) and left ventricular ejection fraction (LVEF) could predict these changes. Methods: During the TAVI procedure, blood pressure and systemic hemodynamics were prospectively collected with a Nexfin® non-invasive monitor. HRQoL was evaluated pre-procedurally and 12 weeks after the procedure, using the EQ-5D-5L classification tool. Results: Overall, 97/114 (85%) of the included patients were eligible for analyses. Systolic, diastolic and mean arterial pressure, heart rate, and stroke volume increased immediately after TAVI (all p < 0.005), and left ventricular ejection time (LVET) immediately decreased with 10 ms (95%CI = -4 to -16, p < 0.001). Overall HRQoLindex increased from 0.810 [0.662-0.914] before to 0.887 [0.718-0.953] after TAVI (p = 0.016). An immediate decrease in LVET was associated with an increase in HRQoLindex (0.02 index points per 10 ms LVET decrease, p = 0.041). Pre-procedural CPI and LVEF did not predict hemodynamic changes or change in HRQoL. Conclusion: TAVI resulted in an immediate hemodynamic response and increase in HRQoL. Immediate reduction in LVET, suggesting unloading of the ventricle, was associated with an increase in HRQoL, but neither pre-procedural CPI nor LVEF predicted these changes. Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT03088787.

Clinical agreement of a novel algorithm to estimate radial artery blood pressure from the non-invasive finger blood pressure.

STUDY OBJECTIVE: A new algorithm was developed that transforms the non-invasive finger blood pressure (BP) into a radial artery BP (B̂PRad), whereas the original algorithm estimated brachial BP (B̂PBra). In this study we determined whether this new algorithm shows better agreement with invasive radial BP than the original one and whether in the operating room this algorithm can be used safely. DESIGN, SETTING AND PATIENTS: This observational study was conducted on thirty-three non-cardiac surgery patients. INTERVENTION AND MEASUREMENTS: Invasive radial and non-invasive finger BP were measured, of the latter B̂PRad and B̂PBra were transformed. Agreement of systolic, mean, and diastolic arterial BP (SAP, MAP, and DAP, respectively) was assessed traditionally with Bland-Altman and trend analysis and clinically safety was quantified with error grid analyses. A bias (precision) of 5 (8) mmHg or less was considered adequate. MAIN RESULTS: Thirty-three patients were included with an average of 676 (314) 20 s segments. For both comparisons, bias (precision) of MAP was within specified criteria, whereas for SAP, precision was higher than 8 mmHg. B̂PRad showed a better agreement than B̂PBra with BPRad for DAP values (bias (precision): 0.7 (6.0) and - 6.4 (4.3) mmHg, respectively). B̂PRad and B̂PBra both showed good concordance in following changes in BPRad (for all parameters overall degree was <7°). There were slightly more measurement pairs of MAP within the no-risk zone for B̂PRad than for B̂PBra (96 vs 77%, respectively). CONCLUSIONS: In this cohort of non-cardiac surgery patients, we found good agreement between BPRad and B̂PRad. Compared to B̂PBra, B̂PRad shows better agreement although clinical implications are small. This trial was registered with ClinicalTrials.gov (https://clinicaltrials.gov/ct2/show/NCT03795831).

Cerebral hemodynamics during sustained intraoperative hypotension.

Static cerebral autoregulation (CA) maintains cerebral blood flow (CBF) relatively constant above a mean arterial blood pressure (BPmean) of 60-65 mmHg. Below this lower limit of CA (LLCA), CBF declines along with BPmean. Data are lacking in describing how CA reacts to sustained hypotension since hypotension is usually avoided. In this study, we took advantage of a procedure requiring sustained hypotension. We assessed static CA for LLCA determination, and a more continuous CA, which counters short-term blood pressure variations. With these data, we analyzed CA during longstanding hypotension. Continuous arterial blood pressure and middle cerebral artery blood flow velocity (MCAVmean) were monitored in 23 patients that required deep intraoperative hypotension. The LLCA was determined for every patient, and BPmean below this LLCA was classified as the patient-specific hypotension. With the mean flow index (Mxa), continuous CA (Mxa-CA) was quantified. Mxa was calculated and averaged after induction of general anesthesia (baseline), every 15 min during, and 15 min after 1 h of hypotension. Functioning CA was defined as Mxa < 0.4. Data are expressed as median (25th-75th percentile). The LLCA was located at 56 (47-74) mmHg. At baseline, Mxa was 0.21 (0.14-0.32) and 0.61 (0.48-0.78) during hypotension (P < 0.01), with no appreciable change over time, n = 12. After blood pressure restoration, Mxa improved, 0.25 (0.06-0.35, n = 9). Mxa-CA became and remained disturbed during the 1 h of hypotension, and improved after blood pressure restoration. This completely reversible situation suggests no ischemic hyperemia occurs and renders an adaptation mechanism during sustained hypotension unlikely.NEW & NOTEWORTHY Intraoperative hypotension is normally avoided by anesthesiologists. However, for the Personalized External Aortic Root Support (PEARS) procedure, deep-induced hypotension is an essential requirement for the surgeon to be able to manipulate the aortic root. In this procedure, blood pressure and middle cerebral artery blood flow velocity were monitored. In this study, we assessed cerebral autoregulation during sustained hypotension, to give an insight into its behavior during hypotension.

Cardiorespiratory monitoring in the delivery room using transcutaneous electromyography.

OBJECTIVE: To assess feasibility of transcutaneous electromyography of the diaphragm (dEMG) as a monitoring tool for vital signs and diaphragm activity in the delivery room (DR). DESIGN: Prospective observational study. SETTING: Delivery room. PATIENTS: Newborn infants requiring respiratory stabilisation after birth. INTERVENTIONS: In addition to pulse oximetry (PO) and ECG, dEMG was measured with skin electrodes for 30 min after birth. OUTCOME MEASURES: We assessed signal quality of dEMG and ECG recording, agreement between heart rate (HR) measured by dEMG and ECG or PO, time between sensor application and first HR read-out and agreement between respiratory rate (RR) measured with dEMG and ECG, compared with airway flow. Furthermore, we analysed peak, tonic and amplitude diaphragmatic activity from the dEMG-based respiratory waveform. RESULTS: Thirty-three infants (gestational age: 31.7±2.8 weeks, birth weight: 1525±661 g) were included.18%±14% and 22%±21% of dEMG and ECG data showed poor quality, respectively. Monitoring HR with dEMG was fast (median 10 (IQR 10-11) s) and accurate (intraclass correlation coefficient (ICC) 0.92 and 0.82 compared with ECG and PO, respectively). RR monitoring with dEMG showed moderate (ICC 0.49) and ECG low (ICC 0.25) agreement with airway flow. Diaphragm activity started high with a decreasing trend in the first 15 min and subsequent stabilisation. CONCLUSION: Monitoring vital signs with dEMG in the DR is feasible and fast. Diaphragm activity can be detected and described with dEMG, making dEMG promising for future DR studies.