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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)., Competing Interests: Competing interests: The Departments of Anaesthesiology and Intensive Care of the Amsterdam UMC, location AMC, receive financial support from Edwards Lifesciences to perform this study. Alexander P.J. Vlaar reports receipt of unrestricted research grants and lecture fees from Edwards Lifesciences and Philips paid to the institution. Denise P. Veelo reports receipt of research grants from Philips as well as consultancy, lecture, and travel expenses fees from Edwards Lifesciences. Bart F. Geerts reports receipt of research grants from Edwards Lifesciences and Philips and additional consultancy fees from Philips. Finally, Markus Hollmann reports receipt of a research grant from the Dutch healthcare financing organization ZonMW paid to the institution, consulting fees from Commonwealth Serum Laboratories (CSL) Behring and Interventional Drug Development (IDD) Pharma, and he serves as an executive section editor for Anesthesia Analgesia, section editor for Journal of Clinical Medicine, and editor for Frontiers in Physiology. The other authors have no conflicts of interest to declare., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© 2021. The Author(s).)

Introduction: Hypotension in the ICU is common, yet management is challenging and variable. Insight in management by ICU physicians and nurses may improve patient care and guide future hypotension treatment trials and guidelines., Methods: We conducted an international survey among ICU personnel to provide insight in monitoring, management, and perceived consequences of hypotension., Results: Out of 1464 respondents, 1197 (81.7%) were included (928 physicians (77.5%) and 269 nurses (22.5%)). The majority indicated that hypotension is underdiagnosed (55.4%) and largely preventable (58.8%). Nurses are primarily in charge of monitoring changes in blood pressure, physicians are in charge of hypotension treatment. Balanced crystalloids, dobutamine, norepinephrine, and Trendelenburg position were the most frequently reported fluid, inotrope, vasopressor, and positional maneuver used to treat hypotension. Reported complications believed to be related to hypotension were AKI and myocardial injury. Most ICUs do not have a specific hypotension treatment guideline or protocol (70.6%), but the majority would like to have one in the future (58.1%)., Conclusions: Both physicians and nurses report that hypotension in ICU patients is underdiagnosed, preventable, and believe that hypotension influences morbidity. Hypotension management is generally not protocolized, but the majority of respondents would like to have a specific hypotension management protocol., Competing Interests: Declaration of Competing Interest TWLS received research grants and honoraria from Edwards Lifesciences (Irvine, CA, USA) and Masimo Inc. (Irvine, CA, USA) for consulting and lecturing and from Pulsion Medical Systems SE (Feldkirchen, Germany) for lecturing (all payments made to institution). DPV reported receipt of personal fees and other from Edwards Lifesciences (Irvine, CA, USA) as well as consultancy fees and research grants from Philips (Eindhoven, the Netherlands) and Hemologic (Amersfoort, the Netherlands). APJV reported receipt of grants from Edwards Lifesciences (Irvine, CA, USA) and Philips (Eindhoven, the Netherlands). WHV, JS, JS, SR, TGVC, WKL, PT, PWGE, PRT, JB, BFG, and FP declare that they have no conflicts of interest., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declarations of interest The Department of Anaesthesiology of the Amsterdam UMC, location ‘AMC' received financial support for this project from Edwards Lifesciences. DPV, APV, MW, RVI, and BFG received consultancy fees from Edwards Lifesciences. The Hypotension Prediction Index algorithm is proprietary to Edwards Lifesciences LLC. None of the investigators of the Amsterdam UMC have any form of (in)direct ownership in the algorithm, software or hardware of Edwards, and/or subject of this study. Also, no rights or claims to rights exist that might lead to financial gains for any of the authors or the Amsterdam UMC as an institution. BFG and DPV received consultancy fees and research grants from Philips outside the submitted word. MWH is executive section editor Pharmacology with Anesthesia & Analgesia. He received consultancy fees from Eurocept BV and speakers fees from Eurocept and CSL Behring. JS, JMM, BJPS, and LH declare that they have no conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Although hypotension in ICU patients is associated with adverse outcome, currently used definitions are unknown and no universally accepted definition exists., Methods: We conducted an international, peer-reviewed survey among ICU physicians and nurses to provide insight in currently used definitions, estimations of incidence, and duration of hypotension., Results: Out of 1394 respondents (1055 physicians (76%) and 339 nurses (24%)), 1207 (82%) completed the questionnaire. In all patient categories, hypotension definitions were predominantly based on an absolute MAP of 65 mmHg, except for the neuro(trauma) category (75 mmHg, p < 0.001), without differences between answers from physicians and nurses. Hypotension incidence was estimated at 55%, and time per day spent in hypotension at 15%, both with nurses reporting higher percentages than physicians (estimated mean difference 5%, p = 0.01; and 4%, p < 0.001)., Conclusions: An absolute MAP threshold of 65 mmHg is most frequently used to define hypotension in ICU patients. In neuro(trauma) patients a higher threshold was reported. The majority of ICU patients are estimated to endure hypotension during their ICU admission for a considerable amount of time, with nurses reporting a higher estimated incidence and time spent in hypotension than physicians., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2021 European Society of Anaesthesiology and Intensive Care. Unauthorized reproduction of this article is prohibited.)

Background: Intraoperative hypotension, with varying definitions in literature, may be associated with postoperative complications. The aim of this meta-analysis was to assess the association of intraoperative hypotension with postoperative morbidity and mortality., Methods: MEDLINE, Embase and Cochrane databases were searched for studies published between January 1990 and August 2018. The primary endpoints were postoperative overall morbidity and mortality. Secondary endpoints were postoperative cardiac outcomes, acute kidney injury, stroke, delirium, surgical outcomes and combined outcomes. Subgroup analyses, sensitivity analyses and a meta-regression were performed to test the robustness of the results and to explore heterogeneity., Results: The search identified 2931 studies, of which 29 were included in the meta-analysis, consisting of 130 862 patients. Intraoperative hypotension was associated with an increased risk of morbidity (odds ratio (OR) 2.08, 95 per cent confidence interval 1.56 to 2.77) and mortality (OR 1.94, 1.32 to 2.84). In the secondary analyses, intraoperative hypotension was associated with cardiac complications (OR 2.44, 1.52 to 3.93) and acute kidney injury (OR 2.69, 1.31 to 5.55). Overall heterogeneity was high, with an I2 value of 88 per cent. When hypotension severity, outcome severity and study population variables were added to the meta-regression, heterogeneity was reduced to 50 per cent., Conclusion: Intraoperative hypotension during non-cardiac surgery is associated with postoperative cardiac and renal morbidity, and mortality. A universally accepted standard definition of hypotension would facilitate further research into this topic., (© The Author(s) 2021. Published by Oxford University Press on behalf of BJS Society Ltd.)

Importance: Intraoperative hypotension is associated with increased morbidity and mortality. A machine learning-derived early warning system to predict hypotension shortly before it occurs has been developed and validated., Objective: To test whether the clinical application of the early warning system in combination with a hemodynamic diagnostic guidance and treatment protocol reduces intraoperative hypotension., Design, Setting, and Participants: Preliminary unblinded randomized clinical trial performed in a tertiary center in Amsterdam, the Netherlands, among adult patients scheduled for elective noncardiac surgery under general anesthesia and an indication for continuous invasive blood pressure monitoring, who were enrolled between May 2018 and March 2019. Hypotension was defined as a mean arterial pressure (MAP) below 65 mm Hg for at least 1 minute., Interventions: Patients were randomly assigned to receive either the early warning system (n = 34) or standard care (n = 34), with a goal MAP of at least 65 mm Hg in both groups., Main Outcomes and Measures: The primary outcome was time-weighted average of hypotension during surgery, with a unit of measure of millimeters of mercury. This was calculated as the depth of hypotension below a MAP of 65 mm Hg (in millimeters of mercury) × time spent below a MAP of 65 mm Hg (in minutes) divided by total duration of operation (in minutes)., Results: Among 68 randomized patients, 60 (88%) completed the trial (median age, 64 [interquartile range {IQR}, 57-70] years; 26 [43%] women). The median length of surgery was 256 minutes (IQR, 213-430 minutes). The median time-weighted average of hypotension was 0.10 mm Hg (IQR, 0.01-0.43 mm Hg) in the intervention group vs 0.44 mm Hg (IQR, 0.23-0.72 mm Hg) in the control group, for a median difference of 0.38 mm Hg (95% CI, 0.14-0.43 mm Hg; P = .001). The median time of hypotension per patient was 8.0 minutes (IQR, 1.33-26.00 minutes) in the intervention group vs 32.7 minutes (IQR, 11.5-59.7 minutes) in the control group, for a median difference of 16.7 minutes (95% CI, 7.7-31.0 minutes; P < .001). In the intervention group, 0 serious adverse events resulting in death occurred vs 2 (7%) in the control group., Conclusions and Relevance: In this single-center preliminary study of patients undergoing elective noncardiac surgery, the use of a machine learning-derived early warning system compared with standard care resulted in less intraoperative hypotension. Further research with larger study populations in diverse settings is needed to understand the effect on additional patient outcomes and to fully assess safety and generalizability., Trial Registration: ClinicalTrials.gov Identifier: NCT03376347.

Background: Intraoperative hypotension is associated with increased morbidity and mortality. Current treatment is mostly reactive. The Hypotension Prediction Index (HPI) algorithm is able to predict hypotension minutes before the blood pressure actually decreases. Internal and external validation of this algorithm has shown good sensitivity and specificity. We hypothesize that the use of this algorithm in combination with a personalized treatment protocol will reduce the time weighted average (TWA) in hypotension during surgery spent in hypotension intraoperatively., Methods/design: We aim to include 100 adult patients undergoing non-cardiac surgery with an anticipated duration of more than 2 h, necessitating the use of an arterial line, and an intraoperatively targeted mean arterial pressure (MAP) of > 65 mmHg. This study is divided into two parts; in phase A baseline TWA data from 40 patients will be collected prospectively. A device (HemoSphere) with HPI software will be connected but fully covered. Phase B is designed as a single-center, randomized controlled trial were 60 patients will be randomized with computer-generated blocks of four, six or eight, with an allocation ratio of 1:1. In the intervention arm the HemoSphere with HPI will be used to guide treatment; in the control arm the HemoSphere with HPI software will be connected but fully covered. The primary outcome is the TWA in hypotension during surgery., Discussion: The aim of this trial is to explore whether the use of a machine-learning algorithm intraoperatively can result in less hypotension. To test this, the treating anesthesiologist will need to change treatment behavior from reactive to proactive., Trial Registration: This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT03376347 . The trial was submitted on 4 November 2017 and accepted for registration on 18 December 2017.