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.

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 ).

The Influence of Carbon Dioxide on Cerebral Autoregulation During Sevoflurane-based Anesthesia in Patients With Type 2 Diabetes.

BACKGROUND: Cerebral autoregulation (CA) continuously adjusts cerebrovascular resistance to maintain cerebral blood flow (CBF) constant despite changes in blood pressure. Also, CBF is proportional to changes in arterial carbon dioxide (CO 2 ) (cerebrovascular CO 2 reactivity). Hypercapnia elicits cerebral vasodilation that attenuates CA efficacy, while hypocapnia produces cerebral vasoconstriction that enhances CA efficacy. In this study, we quantified the influence of sevoflurane anesthesia on CO 2 reactivity and the CA-CO 2 relationship. METHODS: We studied patients with type 2 diabetes mellitus (DM), prone to cerebrovascular disease, and compared them to control subjects. In 33 patients (19 DM, 14 control), end-tidal CO 2 , blood pressure, and CBF velocity were monitored awake and during sevoflurane-based anesthesia. CA, calculated with transfer function analysis assessing phase lead (degrees) between low-frequency oscillations in CBF velocity and mean arterial blood pressure, was quantified during hypocapnia, normocapnia, and hypercapnia. RESULTS: In both control and DM patients, awake CO 2 reactivity was smaller (2.8%/mm Hg CO 2 ) than during sevoflurane anesthesia (3.9%/mm Hg; P <0.005). Hyperventilation increased CA efficacy more (3 deg./mm Hg CO 2 ) in controls than in DM patients (1.8 deg./mm Hg CO 2 ; P <0.001) in both awake and sevoflurane-anesthetized states. CONCLUSIONS: The CA-CO 2 relationship is impaired in awake patients with type 2 DM. Sevoflurane-based anesthesia does not further impair this relationship. In patients with DM, hypocapnia induces cerebral vasoconstriction, but CA efficacy does not improve as observed in healthy subjects.

Incidence, Severity and Clinical Factors Associated with Hypotension in Patients Admitted to an Intensive Care Unit: A Prospective Observational Study.

Background: The majority of patients admitted to the intensive care unit (ICU) experience severe hypotension which is associated with increased morbidity and mortality. At present, prospective studies examining the incidence and severity of hypotension using continuous waveforms are missing. Methods: This study is a prospective observational cohort study in a mixed surgical and non-surgical ICU population. All patients over 18 years were included and continuous arterial pressure waveforms data were collected. Mean arterial pressure (MAP) below 65 mmHg for at least 10 s was defined as hypotension and a MAP below 45 mmHg as severe hypotension. The primary outcome was the incidence of hypotension. Secondary outcomes were the severity of hypotension expressed in time-weighted average (TWA), factors associated with hypotension, the number and duration of hypotensive events. Results: 499 patients were included. The incidence of hypotension (MAP < 65 mmHg) was 75% (376 out of 499) and 9% (46 out of 499) experienced severe hypotension. Median TWA was 0.3 mmHg [0−1.0]. Associated clinical factors were age, male sex, BMI and cardiogenic shock. There were 5 (1−12) events per patients with a median of 52 min (5−170). Conclusions: In a mixed surgical and non-surgical ICU population the incidence of hypotension is remarkably high.

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.

Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care unit: a cohort study.

The Hypotension Prediction Index (HPI) is a commercially available machine-learning algorithm that provides warnings for impending hypotension, based on real-time arterial waveform analysis. The HPI was developed with arterial waveform data of surgical and intensive care unit (ICU) patients, but has never been externally validated in the latter group. In this study, we evaluated diagnostic ability of the HPI with invasively collected arterial blood pressure data in 41 patients with COVID-19 admitted to the ICU for mechanical ventilation. Predictive ability was evaluated at HPI thresholds from 0 to 100, at incremental intervals of 5. After exceeding the studied threshold, the next 20 min were screened for positive (mean arterial pressure (MAP) < 65 mmHg for at least 1 min) or negative (absence of MAP < 65 mmHg for at least 1 min) events. Subsequently, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and time to event were determined for every threshold. Almost all patients (93%) experienced at least one hypotensive event. Median number of events was 21 [7-54] and time spent in hypotension was 114 min [20-303]. The optimal threshold was 90, with a sensitivity of 0.91 (95% confidence interval 0.81-0.98), specificity of 0.87 (0.81-0.92), PPV of 0.69 (0.61-0.77), NPV of 0.99 (0.97-1.00), and median time to event of 3.93 min (3.72-4.15). Discrimination ability of the HPI was excellent, with an area under the curve of 0.95 (0.93-0.97). This validation study shows that the HPI correctly predicts hypotension in mechanically ventilated COVID-19 patients in the ICU, and provides a basis for future studies to assess whether hypotension can be reduced in ICU patients using this algorithm.

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.

Central Hypovolemia Detection During Environmental Stress-A Role for Artificial Intelligence?

The first step to exercise is preceded by the required assumption of the upright body position, which itself involves physical activity. The gravitational displacement of blood from the chest to the lower parts of the body elicits a fall in central blood volume (CBV), which corresponds to the fraction of thoracic blood volume directly available to the left ventricle. The reduction in CBV and stroke volume (SV) in response to postural stress, post-exercise, or to blood loss results in reduced left ventricular filling, which may manifest as orthostatic intolerance. When termination of exercise removes the leg muscle pump function, CBV is no longer maintained. The resulting imbalance between a reduced cardiac output (CO) and a still enhanced peripheral vascular conductance may provoke post-exercise hypotension (PEH). Instruments that quantify CBV are not readily available and to express which magnitude of the CBV in a healthy subject should remains difficult. In the physiological laboratory, the CBV can be modified by making use of postural stressors, such as lower body "negative" or sub-atmospheric pressure (LBNP) or passive head-up tilt (HUT), while quantifying relevant biomedical parameters of blood flow and oxygenation. Several approaches, such as wearable sensors and advanced machine-learning techniques, have been followed in an attempt to improve methodologies for better prediction of outcomes and to guide treatment in civil patients and on the battlefield. In the recent decade, efforts have been made to develop algorithms and apply artificial intelligence (AI) in the field of hemodynamic monitoring. Advances in quantifying and monitoring CBV during environmental stress from exercise to hemorrhage and understanding the analogy between postural stress and central hypovolemia during anesthesia offer great relevance for healthy subjects and clinical populations.

Cerebral Blood Flow in Patients with Severe Aortic Valve Stenosis Undergoing Transcatheter Aortic Valve Implantation.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) is a minimally invasive, life-saving treatment for patients with severe aortic valve stenosis that improves quality of life. We examined cardiac output and cerebral blood flow in patients undergoing TAVI to test the hypothesis that improved cardiac output after TAVI is associated with an increase in cerebral blood flow. DESIGN: Prospective cohort study. SETTING: European high-volume tertiary multidisciplinary cardiac care. PARTICIPANTS: Thirty-one patients (78.3 ± 4.6 years; 61% female) with severe symptomatic aortic valve stenosis. MEASUREMENTS: Noninvasive prospective assessment of cardiac output (L/min) by inert gas rebreathing and cerebral blood flow of the total gray matter (mL/100 g per min) using arterial spin labeling magnetic resonance imaging in resting state less than 24 hours before TAVI and at 3-month follow-up. Cerebral blood flow change was defined as the difference relative to baseline. RESULTS: On average, cardiac output in patients with severe aortic valve stenosis increased from 4.0 ± 1.1 to 5.4 ± 2.4 L/min after TAVI (P = .003). The increase in cerebral blood flow after TAVI strongly varied between patients (7% ± 24%; P = .41) and related to the increase in cardiac output, with an 8.2% (standard error = 2.3%; P = .003) increase in cerebral blood flow per every additional liter of cardiac output following the TAVI procedure. CONCLUSION: Following TAVI, there was an association of increase in cardiac output with increase in cerebral blood flow. These findings encourage future larger studies to determine the influence of TAVI on cerebral blood flow and cognitive function.

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.

Cerebral vs. Cardiovascular Responses to Exercise in Type 2 Diabetic Patients.

The human brain is constantly active and even small limitations to cerebral blood flow (CBF) may be critical for preserving oxygen and substrate supply, e.g., during exercise and hypoxia. Exhaustive exercise evokes a competition for the supply of oxygenated blood between the brain and the working muscles, and inability to increase cardiac output sufficiently during exercise may jeopardize cerebral perfusion of relevance for diabetic patients. The challenge in diabetes care is to optimize metabolic control to slow progression of vascular disease, but likely because of a limited ability to increase cardiac output, these patients perceive aerobic exercise to be more strenuous than healthy subjects and that limits the possibility to apply physical activity as a preventive lifestyle intervention. In this review, we consider the effects of functional activation by exercise on the brain and how it contributes to understanding the control of CBF with the limited exercise tolerance experienced by type 2 diabetic patients. Whether a decline in cerebral oxygenation and thereby reduced neural drive to working muscles plays a role for "central" fatigue during exhaustive exercise is addressed in relation to brain's attenuated vascular response to exercise in type 2 diabetic subjects.

Detecting central hypovolemia in simulated hypovolemic shock by automated feature extraction with principal component analysis.

Assessment of the volume status by blood pressure (BP) monitoring is difficult, since baroreflex control of BP makes it insensitive to blood loss up to about one liter. We hypothesized that a machine learning model recognizes the progression of central hypovolemia toward presyncope by extracting information of the noninvasive blood pressure waveform parametrized through principal component analysis. This was tested in healthy volunteers exposed to simulated hemorrhage by lower body negative pressure (LBNP). Fifty-six healthy volunteers were subjected to progressive central hypovolemia. A support vector machine was trained on the blood pressure waveform. Three classes of progressive stages of hypovolemia were defined. The model was optimized for the number of principal components and regularization parameter for penalizing misclassification (cost): C. Model performance was expressed as accuracy, mean squared error (MSE), and kappa statistic (inter-rater agreement). Forty-six subjects developed presyncope of which 41 showed an increase in model classification severity from baseline to presyncope. In five of the remaining nine subjects (1 was excluded) it stagnated. Classification of samples during baseline and end-stage LBNP had the highest accuracy (95% and 50%, respectively). Baseline and first stage of LBNP demonstrated the lowest MSE (0.01 respectively 0.32). Model MSE and accuracy did not improve for C values exceeding 0.01. Adding more than five principal components did not further improve accuracy or MSE. Increment in kappa halted after 10 principal components had been added. Automated feature extraction of the blood pressure waveform allows modeling of progressive hypovolemia with a support vector machine. The model distinguishes classes between baseline and presyncope.