Use of the Hypotension Prediction Index During Cardiac Surgery

Comparison of Differences in Cohort (Forward) and Case Control (Backward) Methodologic Approaches for Validation of the Hypotension Prediction Index

BACKGROUND

The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiologic changes that may lead to hypotension. The original validation used a case control (backward) analysis that has been suggested to be biased. This study therefore conducted a cohort (forward) analysis and compared this to the original validation technique.

METHODS

A retrospective analysis of data from previously reported studies was conducted. All data were analyzed identically with two different methodologies, and receiver operating characteristic curves were constructed. Both backward and forward analyses were performed to examine differences in area under the receiver operating characteristic curves for the Hypotension Prediction Index and other hemodynamic variables to predict a mean arterial pressure (MAP) less than 65 mmHg for at least 1 min 5, 10, and 15 min in advance.

RESULTS

The analysis included 2,022 patients, yielding 4,152,124 measurements taken at 20-s intervals. The area under the curve for the index predicting hypotension analyzed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947 to 0.964) versus 0.923 (95% CI, 0.912 to 0.933) 5 min in advance, 0.933 (95% CI, 0.924 to 0.942) versus 0.923 (95% CI, 0.911 to 0.933) 10 min in advance, and 0.929 (95% CI, 0.918 to 0.938) versus 0.926 (95% CI, 0.914 to 0.937) 15 min in advance. No variable other than MAP had an area under the curve greater than 0.7. The areas under the curve using forward analysis for MAP predicting hypotension 5, 10, and 15 min in advance were 0.932 (95% CI, 0.920 to 0.940), 0.929 (95% CI, 0.918 to 0.938), and 0.932 (95% CI, 0.921 to 0.940), respectively. The R2 for the variation in the index due to MAP was 0.77.

CONCLUSIONS

Using an updated methodology, the study found that the utility of the Hypotension Prediction Index to predict future hypotensive events is high, with an area under the receiver operating characteristics curve similar to that of the original validation method.

EDITOR’S PERSPECTIVE

Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis

INTRODUCTION

Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to improve patient outcomes. This systematic review and meta analysis explores the intersection of AI and IOH prediction, addressing the crucial need for effective monitoring in surgical settings.

METHOD

A search of Pubmed, Scopus, Web of Science, and Embase was conducted. Screening involved two-phase assessments by independent reviewers, ensuring adherence to predefined PICOS criteria. Included studies focused on AI models predicting IOH in any type of surgery. Due to the high number of studies evaluating the hypotension prediction index (HPI), we conducted two sets of meta-analyses: one involving the HPI studies and one including non-HPI studies. In the HPI studies the following outcomes were analyzed: cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC). In the non-HPI studies, we examined the pooled AUROC of all AI models other than HPI.

RESULTS

43 studies were included in this review. Studies showed significant reduction in IOH duration, TWA-MAP < 65 mmHg, and AUT-MAP < 65 mmHg in groups where HPI was used. AUROC for HPI algorithms demonstrated strong predictive performance (AUROC = 0.89, 95CI). Non-HPI models had a pooled AUROC of 0.79 (95CI: 0.74, 0.83).

CONCLUSION

HPI demonstrated excellent ability to predict hypotensive episodes and hence reduce the duration of hypotension. Other AI models, particularly those based on deep learning methods, also indicated a great ability to predict IOH, while their capacity to reduce IOH-related indices such as duration remains unclear.

A retrospective study of the effects of a vasopressor bolus on systolic slope (dP/dt) and dynamic arterial elastance (Eadyn)

BACKGROUND

To enhance the utility of functional hemodynamic monitoring, the variables systolic slope (dP/dt) and dynamic arterial elastance (Eadyn) are calculated by the Hypotension Prediction Index (HPI) Acumen® Software. This study was designed to characterize the effects of phenylephrine and ephedrine on dP/dt and Eadyn.

METHODS

This was a retrospective, non-randomized analysis of data collected during two clinical studies. All patients required intra-operative controlled mechanical ventilation and had an indwelling radial artery catheter connected to an Acumen IQ sensor. Raw arterial pressure waveform data was downloaded from the patient monitor and all hemodynamic measurements were calculated off-line. The anesthetic record was reviewed for bolus administrations of either phenylephrine or ephedrine. Cardiovascular variables prior to drug administration were compared to those following vasopressor administrations. The primary outcome was the difference for dP/dt and Eadyn at baseline compared with the average after the bolus administration. All data sets demonstrated non-normal distributions so statistical analysis of paired and unpaired data followed the Wilcoxon matched pairs signed-rank test or Mann-Whitney U test, respectively.

RESULTS

201 doses of phenylephrine and 100 doses of ephedrine were analyzed. All data sets are reported as median [95% CI]. Mean arterial pressure (MAP) increased from 62 [54,68] to 78 [76,80] mmHg following phenylephrine and from 59 [55,62] to 80 [77,83] mmHg following ephedrine. Stroke volume and cardiac output both increased. Stroke volume variation and pulse pressure variation decreased. Both drugs produced significant increases in dP/dt, from 571 [531, 645] to 767 [733, 811] mmHg/sec for phenylephrine and from 537 [509, 596] to 848 [779, 930] mmHg/sec for ephedrine. No significant changes in Eadyn were observed.

CONCLUSION

Bolus administration of phenylephrine or ephedrine increases dP/dt but does not change Eadyn. dP/dt demonstrates potential for predicting the inotropic response to phenylephrine or ephedrine, providing guidance for the most efficacious vasopressor when treating hypotension.

TRIAL REGISTRATION

Data was collected from two protocols. The first was deemed to not require written, informed consent by the Institutional Review Board (IRB). The second was IRB-approved (Effect of Diastolic Dysfunction on Dynamic Cardiac Monitors) and registered on ClinicalTrials.gov (NCT04177225).

Anesthesia for Minimal Invasive Cardiac Surgery: The Bonn Heart Center Protocol

The development and adoption of minimally invasive techniques has revolutionized various surgical disciplines and has also been introduced into cardiac surgery, offering patients less invasive options with reduced trauma and faster recovery time compared to traditional open-heart procedures with sternotomy. This article provides a comprehensive overview of the anesthesiologic management for minimally invasive cardiac surgery (MICS), focusing on preoperative assessment, intraoperative anesthesia techniques, and postoperative care protocols. Anesthesia induction and airway management strategies are tailored to each patient's needs, with meticulous attention to maintaining hemodynamic stability and ensuring adequate ventilation. Intraoperative monitoring, including transesophageal echocardiography (TEE), processed EEG monitoring, and near-infrared spectroscopy (NIRS), facilitates real-time assessment of cardiac and cerebral perfusion, as well as function, optimizing patient safety and improving outcomes. The peripheral cannulation techniques for cardiopulmonary bypass (CPB) initiation are described, highlighting the importance of cannula placement to minimize tissue as well as vessel trauma and optimize perfusion. This article also discusses specific MICS procedures, detailing anesthetic considerations and surgical techniques. The perioperative care of patients undergoing MICS requires a multidisciplinary approach including surgeons, perfusionists, and anesthesiologists adhering to standardized treatment protocols and pathways. By leveraging advanced monitoring techniques and tailored anesthetic protocols, clinicians can optimize patient outcomes and promote early extubation and enhanced recovery.

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

BACKGROUND

Intraoperative 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.

METHODS

Two 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.

RESULTS

Analysis 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].

CONCLUSIONS

The 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 REGISTRATION

Clinical trial number: NCT03805217. Registry URL: https://clinicaltrials.gov/ct2/show/NCT03805217 . Principal investigator: Xiaodong Bao, MD, PhD. Date of registration: January 15, 2019.

Hypotension prediction index for the prevention of hypotension during surgery and critical care: A narrative review

Surgeons and anesthesia clinicians commonly face a hemodynamic disturbance known as intraoperative hypotension (IOH), which has been linked to more severe postoperative outcomes and increases mortality rates. Increased occurrence of IOH has been positively associated with mortality and incidence of myocardial infarction, stroke, and organ dysfunction hypertension. Hence, early detection and recognition of IOH is meaningful for perioperative management. Currently, when hypotension occurs, clinicians use vasopressor or fluid therapy to intervene as IOH develops but interventions should be taken before hypotension occurs; therefore, the Hypotension Prediction Index (HPI) method can be used to help clinicians further react to the IOH process. This literature review evaluates the HPI method, which can reliably predict hypotension several minutes before a hypotensive event and is beneficial for patients' outcomes.

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.

Perioperative hemodynamic monitoring in cardiac surgery

PURPOSE OF REVIEW

Cardiac surgery has traditionally relied upon invasive hemodynamic monitoring, including regular use of pulmonary artery catheters. More recently, there has been advancement in our understanding as well as broader adoption of less invasive alternatives. This review serves as an outline of the key perioperative hemodynamic monitoring options for cardiac surgery.

RECENT FINDINGS

Recent study has revealed that the use of invasive monitoring such as pulmonary artery catheters or transesophageal echocardiography in low-risk patients undergoing low-risk cardiac surgery is of questionable benefit. Lesser invasive approaches such a pulse contour analysis or ultrasound may provide a useful alternative to assess patient hemodynamics and guide resuscitation therapy. A number of recent studies have been published to support broader indication for these evolving technologies.

SUMMARY

More selective use of indwelling catheters for cardiac surgery has coincided with greater application of less invasive alternatives. Understanding the advantages and limitations of each tool allows the bedside clinician to identify which hemodynamic monitoring modality is most suitable for which patient.

American society of anesthesiologists physical status classification significantly affects the performances of machine learning models in intraoperative hypotension inference

STUDY OBJECTIVE

To explore how American Society of Anesthesiologists (ASA) physical status classification affects different machine learning models in hypotension prediction and whether the prediction uncertainty could be quantified.

DESIGN

Observational Studies SETTING: UofL health hospital PATIENTS: This study involved 562 hysterectomy surgeries performed on patients (≥ 18 years) between June 2020 and July 2021.

INTERVENTIONS

None MEASUREMENTS: Preoperative and intraoperative data is collected. Three parametric machine learning models, including Bayesian generalized linear model (BGLM), Bayesian neural network (BNN), a newly proposed BNN with multivariate mixed responses (BNNMR), and one nonparametric model, Gaussian Process (GP), were explored to predict patients' diastolic and systolic blood pressures (continuous responses) and patients' hypotensive event (binary response) for the next five minutes. Data was separated into American Society of Anesthesiologists (ASA) physical status class 1- 4 before being read in by four machine learning models. Statistical analysis and models' constructions are performed in Python. Sensitivity, specificity, and the confidence/credible intervals were used to evaluate the prediction performance of each model for each ASA physical status class.

MAIN RESULTS

ASA physical status classes require distinct models to accurately predict intraoperative blood pressures and hypotensive events. Overall, high sensitivity (above 0.85) and low uncertainty can be achieved by all models for ASA class 4 patients. In contrast, models trained without controlling ASA classes yielded lower sensitivity (below 0.5) and larger uncertainty. Particularly, in terms of predicting binary hypotensive event, for ASA physical status class 1, BNNMR yields the highest sensitivity of 1. For classes 2 and 3, BNN has the highest sensitivity of 0.429 and 0.415, respectively. For class 4, BNNMR and GP are tied with the highest sensitivity of 0.857. On the other hand, the sensitivity is just 0.031, 0.429, 0.165 and 0.305 for BNNMR, BNN, GBLM and GP models respectively, when training data is not divided by ASA physical status classes. In terms of predicting systolic blood pressure, the GP regression yields the lowest root mean squared errors (RMSE) of 2.072, 7.539, 9.214 and 0.295 for ASA physical status classes 1, 2, 3 and 4, respectively, but a RMSE of 126.894 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. RMSEs are 2.175, 13.861, 17.560 and 22.426 for classes 1, 2, 3 and 4 respectively for the BGLM. In terms of predicting diastolic blood pressure, the GP regression yields the lowest RMSEs of 2.152, 6.573, 5.371 and 0.831 for ASA physical status classes 1, 2, 3 and 4, respectively; RMSE of 8.084 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. Finally, in terms of the width of the 95% confidence interval of the mean prediction for systolic and diastolic blood pressures, GP regression gives narrower confidence interval with much smaller margin of error across all four ASA physical status classes.

CONCLUSIONS

Different ASA physical status classes present different data distributions, and thus calls for distinct machine learning models to improve prediction accuracy and reduce predictive uncertainty. Uncertainty quantification enabled by Bayesian inference provides valuable information for clinicians as an additional metric to evaluate performance of machine learning models for medical decision making.

Advanced artificial intelligence-guided hemodynamic management within cardiac enhanced recovery after surgery pathways: A multi-institution review

Objective

The study objective was to report early outcomes of integrating Hypotension Prediction Index-guided hemodynamic management within a cardiac enhanced recovery pathway on total initial ventilation hours and length of stay in the intensive care unit.

Methods

A multicenter, historical control, observational analysis of implementation of a hemodynamic management tool within enhanced recovery pathways was conducted by identifying cardiac surgery cases from 3 sites during 2 time periods, August 1 to December 31, 2019 (preprogram), and April 1 to August 31, 2021 (program). Reoperations, emergency (salvage), or cases requiring mechanical assist were excluded. Data were extracted from electronic medical records and chart reviews. Two primary outcome variables were length of stay in the intensive care unit (using Society of Thoracic Surgeons definitions) and acute kidney injury (using modified Kidney Disease Improving Global Outcomes criteria). One secondary outcome variable, total initial ventilation hours, used Society of Thoracic Surgeons definitions. Differences in length of stay in the intensive care unit and total ventilation time were analyzed using Kruskal-Wallis and stepwise multiple linear regression. Acute kidney injury stage used chi-square and stepwise cumulative logistic regression.

Results

A total of 1404 cases (795 preprogram; 609 program) were identified. Overall reductions of 6.8 and 4.4 hours in intensive care unit length of stay (P = .08) and ventilation time (P = .03) were found, respectively. No significant association between proportion of patients identified with acute kidney injury by stage and period was found.

Conclusions

Adding artificial intelligence-guided hemodynamic management to cardiac enhanced recovery pathways resulted in associated reduced time in the intensive care unit for patients undergoing nonemergency cardiac surgery across institutions in a real-world setting.

Revisiting the promise, practice and progress of resuscitative endovascular balloon occlusion of the aorta

PURPOSE OF REVIEW

The use of Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) to temporarily control bleeding and improve central perfusion in critically injured trauma patients remains a controversial topic. In the last decade, select trauma services around the world have gained experience with REBOA. We discuss the recent observational data together with the initial results of the first randomized control trial and provide a view on the next steps for REBOA in trauma resuscitation.

RECENT FINDINGS

While the observational data continue to be conflicting, the first randomized control trial signals that in the UK, in-hospital REBOA is associated with harm. Likely a result of delays to haemorrhage control, views are again split on whether to abandon complex interventions in bleeding trauma patients and to only prioritize transfer to the operating room or to push REBOA earlier into the post injury phase, recognizing that some patients will not survive without intervention.

SUMMARY

Better understanding of cardiac shock physiology provides a new lens in which to evaluate REBOA through. Patient selection remains a huge challenge. Invasive blood pressure monitoring, combined with machine learning aided decision support may assist clinicians and their patients in the future. The use of REBOA should not delay definitive haemorrhage control in those patients without impending cardiac arrest.

Dynamic Arterial Elastance as a Predictor of Supine-to-Prone Hypotension (SuProne Study): An Observational Study

Background and Objectives: Supine-to-prone hypotension is caused by increased intrathoracic pressure and decreased venous return in the prone position. Dynamic arterial elastance (Eadyn) indicates fluid responsiveness and can be used to predict hypotension. This study aimed to investigate whether Eadyn can predict supine-to-prone hypotension. Materials and Methods: In this prospective, observational study, 47 patients who underwent elective spine surgery in the prone position were enrolled. Supine-to-prone hypotension is defined as a decrease in Mean Arterial Pressure (MAP) by more than 20% in the prone position compared to the supine position. Hemodynamic parameters, including systolic blood pressure (SAP), diastolic blood pressure, MAP, stroke volume variation (SVV), pulse pressure variation (PPV), stroke volume index, cardiac index, dP/dt, and hypotension prediction index (HPI), were collected in the supine and prone positions. Supine-to-prone hypotension was also assessed using two different definitions: MAPprone < 65 mmHg and SAPprone < 100 mmHg. Hemodynamic parameters were analyzed to determine the predictability of supine-to-prone hypotension. Results: Supine-to-prone hypotension occurred in 13 (27.7%) patients. Eadyn did not predict supine-to-prone hypotension [Area under the curve (AUC), 0.569; p = 0.440]. SAPsupine > 139 mmHg (AUC, 0.760; p = 0.003) and dP/dtsupine > 981 mmHg/s (AUC, 0.765; p = 0.002) predicted supine-to-prone hypotension. MAPsupine, SAPsupine, PPVsupine, and HPIsupine predicted MAPprone <65 mm Hg. MAPsupine, SAPsupine, SVVsupine, PPVsupine, and HPIsupine predicted SAPprone < 100 mm Hg. Conclusions: Dynamic arterial elastance did not predict supine-to-prone hypotension in patients undergoing spine surgery. Systolic arterial pressure > 139 mmHg and dP/dt > 981 mmHg/s in the supine position were predictors for supine-to-prone hypotension. When different definitions were employed (mean arterial pressure < 65 mmHg in the prone position or systolic arterial pressure < 100 mmHg in the prone position), low blood pressures in the supine position were related to supine-to-prone hypotension.

The Impact of Intraoperative Haemodynamic Monitoring, Prediction of Hypotension and Goal-Directed Therapy on the Outcomes of Patients Treated with Posterior Fusion Due to Adolescent Idiopathic Scoliosis

A prospective, single-centre, non-randomised, case-control study aimed to evaluate the effectiveness of intraoperative haemodynamic monitoring, prediction of hypotension and goal-directed therapy on the outcomes of patients undergoing posterior fusion for adolescent idiopathic scoliosis (AIS). The control group (n = 35, mean age: 15 years) received standard blood pressure control during surgery, while the intervention group (n = 24, mean age: 14 years) underwent minimally invasive haemodynamic monitoring and goal-directed therapy. The intervention group showed significantly shorter durations of hypotension (mean arterial pressure < 60 mmHg), reduced hospital stays and smaller decreases in post-surgery haemoglobin levels. Additionally, the intervention group experienced shorter times from the end of surgery to extubation. These findings suggest that incorporating targeted interventions during intraoperative care for AIS patients undergoing posterior fusion can lead to improved outcomes.

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).

Effective hemodynamic monitoring

Hemodynamic monitoring is the centerpiece of patient monitoring in acute care settings. Its effectiveness in terms of improved patient outcomes is difficult to quantify. This review focused on effectiveness of monitoring-linked resuscitation strategies from: (1) process-specific monitoring that allows for non-specific prevention of new onset cardiovascular insufficiency (CVI) in perioperative care. Such goal-directed therapy is associated with decreased perioperative complications and length of stay in high-risk surgery patients. (2) Patient-specific personalized resuscitation approaches for CVI. These approaches including dynamic measures to define volume responsiveness and vasomotor tone, limiting less fluid administration and vasopressor duration, reduced length of care. (3) Hemodynamic monitoring to predict future CVI using machine learning approaches. These approaches presently focus on predicting hypotension. Future clinical trials assessing hemodynamic monitoring need to focus on process-specific monitoring based on modifying therapeutic interventions known to improve patient-centered outcomes.

Prediction and Prevention of Intraoperative Hypotension with the Hypotension Prediction Index: A Narrative Review

Intraoperative hypotension is common and has been associated with adverse events. Although association does not imply causation, predicting and preventing hypotension may improve postoperative outcomes. This review summarizes current evidence on the development and validation of an artificial intelligence predictive algorithm, the Hypotension Prediction (HPI) (formerly known as the Hypotension Probability Indicator). This machine learning model can arguably predict hypotension up to 15 min before its occurrence. Several validation studies, retrospective cohorts, as well as a few prospective randomized trials, have been published in the last years, reporting promising results. Larger trials are needed to definitively assess the usefulness of this algorithm in optimizing postoperative outcomes.

Performance of the Hypotension Prediction Index May Be Overestimated Due to Selection Bias

The Hypotension Prediction Index is a proprietary prediction model incorporated into a commercially available intraoperative hemodynamic monitoring system. The Hypotension Prediction Index uses multiple features of the arterial blood pressure waveform to predict hypotension. The index publication introducing the Hypotension Prediction Index describes the selection of training and validation data. Although precise details of the Hypotension Prediction Index algorithm are proprietary, the authors describe a selection process whereby a mean arterial pressure (MAP) less than 75 mmHg will always predict hypotension. We hypothesize that the data selection process introduced a systematic bias that resulted in an overestimation of the current MAP value's ability to predict future hypotension. Since current MAP is a predictive variable contributing to Hypotension Prediction Index, this exaggerated predictive performance likely also applies to the corresponding Hypotension Prediction Index value. Other existing validation studies appear similarly problematic, suggesting that additional validation work and, potentially, updates to the Hypotension Prediction Index model may be necessary.

Clinical Implication of the Acumen Hypotension Prediction Index for Reducing Intraoperative Haemorrhage in Patients Undergoing Lumbar Spinal Fusion Surgery: A Prospective Randomised Controlled Single-Blinded Trial

We investigated the clinical implication of the Hypotension Prediction Index (HPI) in decreasing amount of surgical haemorrhage and requirements of blood transfusion compared to the conventional method (with vs. without HPI monitoring). A prospective, randomised controlled-trial of 19- to 73-year-old patients (n = 76) undergoing elective lumbar spinal fusion surgery was performed. According to the exclusion criteria, the patients were divided into the non-HPI (n = 33) and HPI (n = 35) groups. The targeted-induced hypotension systolic blood pressure was 80−100 mmHg (in both groups), with HPI > 85 (in the HPI group). Intraoperative bleeding was lower in the HPI group (299.3 ± 219.8 mL) than in the non-HPI group (532 ± 232.68 mL) (p = 0.001). The non-HPI group had a lower level of haemoglobin at the end of the surgery with a larger decline in levels. The incidence of postoperative transfusion of red blood cells was higher in the non-HPI group than in the HPI group (9 (27.3%) vs. 1 (2.9%)). The use of HPI monitoring may play a role in providing timely haemodynamic information that leads to improving the quality of induced hypotension care and to ameliorate intraoperative surgical blood loss and postoperative demand for blood transfusion in patients undergoing lumbar fusion surgery.

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

Background

Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients.

Methods

We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted.

Results

Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results.

Conclusions

ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.

Proactive Management of Intraoperative Hypotension Reduces Biomarkers of Organ Injury and Oxidative Stress during Elective Non-Cardiac Surgery: A Pilot Randomized Controlled Trial

Background: Intraoperative hypotension is associated with increased postoperative morbidity and mortality. Methods: We randomly assigned patients undergoing major general surgery to early warning system (EWS) and hemodynamic algorithm (intervention group, n = 20) or standard care (n = 20). The primary outcome was the difference in hypotension (defined as mean arterial pressure < 65 mmHg) and as secondary outcome surrogate markers of organ injury and oxidative stress. Results: The median number of hypotensive episodes was lower in the intervention group (−5.0 (95% CI: −9.0, −0.5); p < 0.001), with lower time spent in hypotension (−12.8 min (95% CI: −38.0, −2.3 min); p = 0.048), correspondent to −4.8% of total surgery time (95% CI: −12.7, 0.01%; p = 0.048).The median time-weighted average of hypotension was 0.12 mmHg (0.35) in the intervention group and 0.37 mmHg (1.11) in the control group, with a median difference of −0.25 mmHg (95% CI: −0.85, −0.01; p = 0.025). Neutrophil Gelatinase-Associated Lipocalin (NGAL) correlated with time-weighted average of hypotension (R = 0.32; p = 0.038) and S100B with number of hypotensive episodes, absolute time of hypotension, relative time of hypotension and time-weighted average of hypotension (p < 0.001 for all). The intervention group showed lower Neuronal Specific Enolase (NSE) and higher reduced glutathione when compared to the control group. Conclusions: The use of an EWS coupled with a hemodynamic algorithm resulted in reduced intraoperative hypotension, reduced NSE and oxidative stress.

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.

Hypotension Prediction Index with non-invasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery

Intraoperative hypotension (IOH) is common during major surgery and is associated with a poor postoperative outcome. Hypotension Prediction Index (HPI) is an algorithm derived from machine learning that uses the arterial waveform to predict IOH. The aim of this study was to assess the diagnostic ability of HPI working with non-invasive ClearSight system in predicting impending hypotension in patients undergoing major gynaecologic oncologic surgery (GOS). In this retrospective analysis hemodynamic data were downloaded from an Edwards Lifesciences HemoSphere platform and analysed. Receiver operating characteristic curves were constructed to evaluate the performance of HPI working on the ClearSight pressure waveform in predicting hypotensive events, defined as mean arterial pressure < 65 mmHg for > 1 min. Sensitivity, specificity, positive predictive value and negative predictive value were computed at a cutpoint (the value which minimizes the difference between sensitivity and specificity). Thirty-one patients undergoing GOS were included in the analysis, 28 of which had complete data set. The HPI predicted hypotensive events with a sensitivity of 0.85 [95% confidence interval (CI) 0.73-0.94] and specificity of 0.85 (95% CI 0.74-0.95) 15 min before the event [area under the curve (AUC) 0.95 (95% CI 0.89-0.99)]; with a sensitivity of 0.82 (95% CI 0.71-0.92) and specificity of 0.83 (95% CI 0.71-0.93) 10 min before the event [AUC 0.9 (95% CI 0.83-0.97)]; and with a sensitivity of 0.86 (95% CI 0.78-0.93) and specificity 0.86 (95% CI 0.77-0.94) 5 min before the event [AUC 0.93 (95% CI 0.89-0.97)]. HPI provides accurate and continuous prediction of impending IOH before its occurrence in patients undergoing GOS in general anesthesia.