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 ).

Effect of the machine learning-derived Hypotension Prediction Index (HPI) combined with diagnostic guidance versus standard care on depth and duration of intraoperative and postoperative hypotension in elective cardiac surgery patients: HYPE-2 - study protocol of a randomised clinical trial.

INTRODUCTION: Hypotension is common during cardiac surgery and often persists postoperatively in the intensive care unit (ICU). Still, treatment is mainly reactive, causing a delay in its management. The Hypotension Prediction Index (HPI) can predict hypotension with high accuracy. Using the HPI combined with a guidance protocol resulted in a significant reduction in the severity of hypotension in four non-cardiac surgery trials. This randomised trial aims to evaluate the effectiveness of the HPI in combination with a diagnostic guidance protocol on reducing the occurrence and severity of hypotension during coronary artery bypass grafting (CABG) surgery and subsequent ICU admission. METHODS AND ANALYSIS: This is a single-centre, randomised clinical trial in adult patients undergoing elective on-pump CABG surgery with a target mean arterial pressure of 65 mm Hg. One hundred and thirty patients will be randomly allocated in a 1:1 ratio to either the intervention or control group. In both groups, a HemoSphere patient monitor with embedded HPI software will be connected to the arterial line. In the intervention group, HPI values of 75 or above will initiate the diagnostic guidance protocol, both intraoperatively and postoperatively in the ICU during mechanical ventilation. In the control group, the HemoSphere patient monitor will be covered and silenced. The primary outcome is the time-weighted average of hypotension during the combined study phases. ETHICS AND DISSEMINATION: The medical research ethics committee and the institutional review board of the Amsterdam UMC, location AMC, the Netherlands, approved the trial protocol (NL76236.018.21). No publication restrictions apply, and the study results will be disseminated through a peer-reviewed journal. TRIAL REGISTRATION NUMBER: The Netherlands Trial Register (NL9449), ClinicalTrials.gov (NCT05821647).

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 ).

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).

Effect of Hypotension Prediction Index-guided intraoperative haemodynamic care on depth and duration of postoperative hypotension: a sub-study of the Hypotension Prediction trial.

BACKGROUND: Intraoperative and postoperative hypotension are associated with morbidity and mortality. The Hypotension Prediction (HYPE) trial showed that the Hypotension Prediction Index (HPI) reduced the depth and duration of intraoperative hypotension (IOH), without excess use of intravenous fluid, vasopressor, and/or inotropic therapies. We hypothesised that intraoperative HPI-guided haemodynamic care would reduce the severity of postoperative hypotension in the PACU. METHODS: This was a sub-study of the HYPE study, in which 60 adults undergoing elective noncardiac surgery were allocated randomly to intraoperative HPI-guided or standard haemodynamic care. Blood pressure was measured using a radial intra-arterial catheter, which was connected to a FloTracIQ sensor. Hypotension was defined as MAP <65 mm Hg, and a hypotensive event was defined as MAP <65 mm Hg for at least 1 min. The primary outcome was the time-weighted average (TWA) of postoperative hypotension. Secondary outcomes were absolute incidence, area under threshold for hypotension, and percentage of time spent with MAP <65 mm Hg. RESULTS: Overall, 54/60 (90%) subjects (age 64 (8) yr; 44% female) completed the protocol, owing to failure of the FloTracIQ device in 6/60 (10%) patients. Intraoperative HPI-guided care was used in 28 subjects; 26 subjects were randomised to the control group. Postoperative hypotension occurred in 37/54 (68%) subjects. HPI-guided care did not reduce the median duration (TWA) of postoperative hypotension (adjusted median difference, vs standard of care: 0.118; 95% confidence interval [CI], 0-0.332; P=0.112). HPI-guidance reduced the percentage of time with MAP <65 mm Hg by 4.9% (adjusted median difference: -4.9; 95% CI, -11.7 to -0.01; P=0.046). CONCLUSIONS: Intraoperative HPI-guided haemodynamic care did not reduce the TWA of postoperative hypotension.

Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: A cohort study.

BACKGROUND: Intra-operative hypotension is associated with adverse postoperative outcomes. A machine-learning-derived algorithm developed to predict hypotension based on arterial blood pressure (ABP) waveforms significantly reduced intra-operative hypotension. The algorithm calculates the likelihood of hypotension occurring within minutes, expressed as the Hypotension Prediction Index (HPI) which ranges from 0 to 100. Currently, HPI is only available for patients monitored with invasive ABP, which is restricted to high-risk procedures and patients. In this study, the performance of HPI, employing noninvasive continuous ABP measurements, is assessed. OBJECTIVES: The first aim was to compare the performance of the HPI algorithm, using noninvasive versus invasive ABP measurements, at a mathematically optimal HPI alarm threshold (Youden index). The second aim was to assess the performance of the algorithm using a HPI alarm threshold of 85 that is currently used in clinical trials. Hypotension was defined as a mean arterial pressure (MAP) below 65 mmHg for at least 1 min. The predictive performance of the algorithm at different HPI alarm thresholds (75 and 95) was studied. DESIGN: Observational cohort study. SETTING: Tertiary academic medical centre. PATIENTS: Five hundred and seven adult patients undergoing general surgery. RESULTS: The performance of the algorithm with invasive and noninvasive ABP input was similar. A HPI alarm threshold of 85 showed a median [IQR] time from alarm to hypotension of 2.7 [1.0 to 7.0] min with a sensitivity of 92.7 (95% confidence interval [CI], 91.2 to 94.3), specificity of 87.6 (95% CI, 86.2 to 89.0), positive predictive value of 79.9 (95% CI, 77.7 to 82.1) and negative predictive value of 95.8 (95% CI, 94.9 to 96.7). A HPI alarm threshold of 75 provided a lower positive predictive value but a prolonged time from prediction to actual hypotension. CONCLUSION: This study demonstrated that the algorithm can be employed using continuous noninvasive ABP waveforms. This opens up the potential to predict and prevent hypotension in a larger patient population. TRIAL REGISTRATION: Clinical trials registration number NCT03533205.

One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making.

This review describes the steps and conclusions from the development and validation of an artificial intelligence algorithm (the Hypotension Prediction Index), one of the first machine learning predictive algorithms used in the operating room environment. The algorithm has been demonstrated to reduce intraoperative hypotension in two randomized controlled trials via real-time prediction of upcoming hypotensive events prompting anesthesiologists to act earlier, more often, and differently in managing impending hypotension. However, the algorithm entails no dynamic learning process that evolves from use in clinical patient care, meaning the algorithm is fixed, and furthermore provides no insight into the decisional process that leads to an early warning for intraoperative hypotension, which makes the algorithm a "black box." Many other artificial intelligence machine learning algorithms have these same disadvantages. Clinical validation of such algorithms is relatively new and requires more standardization, as guidelines are lacking or only now start to be drafted. Before adaptation in clinical practice, impact of artificial intelligence algorithms on clinical behavior, outcomes and economic advantages should be studied too.