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

Artificial intelligence and nonoperating room anesthesia

PURPOSE OF REVIEW

The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future.

RECENT FINDINGS

AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems.

SUMMARY

The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

Hypotension prediction index for prevention of intraoperative hypotension in patients undergoing general anesthesia: a randomized controlled trial

BACKGROUND

Intraoperative hypotension is a common side effect of general anesthesia. Here we examined whether the Hypotension Prediction Index (HPI), a novel warning system, reduces the severity and duration of intraoperative hypotension during general anesthesia.

METHODS

This randomized controlled trial was conducted in a tertiary referral hospital. We enrolled patients undergoing general anesthesia with invasive arterial monitoring. Patients were randomized 1:1 either to receive hemodynamic management with HPI guidance (intervention) or standard of care (control) treatment. Intraoperative hypotension treatment was initiated at HPI > 85 (intervention) or mean arterial pressure (MAP) < 65 mmHg (control). The primary outcome was hypotension severity, defined as a time-weighted average (TWA) MAP < 65 mmHg. Secondary outcomes were TWA MAP < 60 and < 55 mmHg.

RESULTS

Of the 60 patients who completed the study, 30 were in the intervention group and 30 in the control group. The patients' median age was 62 years, and 48 of them were male. The median duration of surgery was 490 min. The median MAP before surgery presented no significant difference between the two groups. The intervention group showed significantly lower median TWA MAP < 65 mmHg than the control group (0.02 [0.003, 0.08] vs. 0.37 [0.20, 0.58], P < 0.001). Findings were similar for TWA MAP < 60 mmHg and < 55 mmHg. The median MAP during surgery was significantly higher in the intervention group than that in the control group (87.54 mmHg vs. 77.92 mmHg, P < 0.001).

CONCLUSIONS

HPI guidance appears to be effective in preventing intraoperative hypotension during general anesthesia. Further investigation is needed to assess the impact of HPI on patient outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT04966364); 202105065RINA; Date of registration: July 19, 2021; The recruitment date of the first patient: July 22, 2021.

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.

Personalized Predictive Hemodynamic Management for Gynecologic Oncologic Surgery: Feasibility of Cost-Benefit Derivatives of Digital Medical Devices

BACKGROUND

Intraoperative hypotension is associated with increased perioperative complications, hospital length of stay (LOS) and healthcare expenditure in gynecologic surgery. We tested the hypothesis that the adoption of a machine learning-based warning algorithm (hypotension prediction index-HPI) might yield an economic advantage, with a reduction in adverse outcomes that outweighs the costs for its implementation as a medical device.

METHODS

A retrospective-matched cohort cost-benefit Italian study in gynecologic surgery was conducted. Sixty-six female patients treated with standard goal-directed therapy (GDT) were matched in a 2:1 ratio with thirty-three patients treated with HPI based on ASA status, diagnosis, procedure, surgical duration and age.

RESULTS

The most relevant contributor to medical costs was operating room occupation (46%), followed by hospital stay (30%) and medical devices (15%). Patients in the HPI group had EURO 300 greater outlay for medical devices without major differences in total costs (GDT 5425 (3505, 8127), HPI 5227 (4201, 7023) p = 0.697). A pre-specified subgroup analysis of 50% of patients undergoing laparotomic surgery showed similar medical device costs and total costs, with a non-significant saving of EUR 1000 in the HPI group (GDT 8005 (5961, 9679), HPI 7023 (5227, 11,438), p = 0.945). The hospital LOS and intensive care unit stay were similar in the cohorts and subgroups.

CONCLUSIONS

Implementation of HPI is associated with a scenario of cost neutrality, with possible economic advantage in high-risk settings.

Non-Invasive Monitoring during Caesarean Delivery: Prevalence of Hypotension and Impact on the Newborn

BACKGROUND

The aim of our study was to investigate the prevalence of perioperative hypotension after spinal anesthesia for cesarean section using non-invasive continuous hemodynamic monitoring and its correlation with neonatal well-being.

METHODS

We included 145 patients. Spinal anesthesia was performed with a combination of hyperbaric bupivacaine 0.5% (according to a weight/height scheme) and fentanyl 20 μg. Hypotension was defined as a mean arterial pressure (MAP) < 65 mmHg or <60 mmHg. We also evaluated the impact of hypotension on neonatal well-being.

RESULTS

Perioperative maternal hypotension occurred in 54.5% of cases considering a MAP < 65 mmHg and in 42.1% with the more conservative cut-off (<60 mmHg). Severe neonatal acidosis occurred in 1.4% of neonates, while an Apgar score ≥ 9 was observed in 95.9% at 1 min and 100% at 5 min.

CONCLUSIONS

Continuous non-invasive hemodynamic monitoring allowed an early detection of maternal hypotension leading to a prompt treatment with satisfactory results considering neonatal well-being.

Patent landscape review of non-invasive medical sensors for continuous monitoring of blood pressure and their validation in critical care practice

Objectives

Continuous non-invasive monitoring of blood pressure is one of the main factors in ensuring the safety of the patient's condition in anesthesiology, intensive care, surgery, and other areas of medicine. The purpose of this work was to analyze the current patent situation and identify directions and trends in the application of non-invasive medical sensors for continuous blood pressure monitoring, with a focus on clinical experience in critical care and validation thereof.

Materials and methods

The research results reflect data collected up to September 30, 2022. Patent databases, Google Scholar, the Lens database, Pubmed, Scopus databases were used to search for patent and clinical information.

Results

An analysis of the patent landscape indicates a significant increase in interest in the development of non-invasive devices for continuous blood pressure monitoring and their implementation in medical practice, especially in the last 10 years. The key players in the intellectual property market are the following companies: Cnsystems Medizintechnik; Sotera Wireless INC; Tensys Medical INC; Healthstats Int Pte LTD; Edwards Lifesciences Corp, among others. Systematization of data from validation and clinical studies in critical care practice on patients with various pathological conditions and ages, including children and newborns, revealed that a number of non-invasive medical sensor technologies are quite accurate and comparable to the "gold standard" continuous invasive blood pressure monitoring. They are approved by the FDA for medical applications and certified according to ISO 81060-2, ISO 81060-3, and ISO/TS 81060-5. Unregistered and uncertified medical sensors require further clinical trials.

Conclusion

Non-invasive medical sensors for continuous blood pressure monitoring do not replace, but complement, existing methods of regular blood pressure measurement, and it is expected to see more of these technologies broadly implemented in the practice in the near future.

New technologies in anesthesia and intensive care: take your ticket for the future

The modern world runs all around hi-tech, which surrounds us in our everyday life. The medical field is no less; the introduction of the novel disruptive technologies are transforming every healthcare system. Anesthesia, intensive care, and pain medicine are fields in which the application of new technologies is proving to have great potential. However, it is crucial that this digital medical transformation always takes place under the coordination of natural (human) intelligence.

Hypotension Prediction Index guided Goal Directed therapy and the amount of Hypotension during Major Gynaecologic Oncologic Surgery: a Randomized Controlled clinical Trial

Intraoperative hypotension (IOH) is associated with increased morbidity and mortality. Hypotension Prediction Index (HPI) is a machine learning derived algorithm that predicts IOH shortly before it occurs. We tested the hypothesis that the application of the HPI in combination with a pre-defined Goal Directed Therapy (GDT) hemodynamic protocol reduces IOH during major gynaecologic oncologic surgery. We enrolled women scheduled for major gynaecologic oncologic surgery under general anesthesia with invasive arterial pressure monitoring. Patients were randomized to a GDT protocol aimed at optimizing stroke volume index (SVI) or hemodynamic management based on HPI guidance in addition to GDT. The primary outcome was the amount of IOH, defined as the timeweighted average (TWA) mean arterial pressure (MAP) < 65 mmHg. Secondary outcome was the TWA-MAP < 65 mmHg during the first 20 min after induction of GA. After exclusion of 10 patients the final analysis included 60 patients (30 in each group). The median (25-75th IQR) TWA-MAP < 65 mmHg was 0.14 (0.04-0.66) mmHg in HPI group versus 0.77 (0.36-1.30) mmHg in Control group, P < 0.001. During the first 20 min after induction of GA, the median TWA-MAP < 65 mmHg was 0.53 (0.06-1.8) mmHg in the HPI group and 2.15 (0.65-4.2) mmHg in the Control group, P = 0.001. Compared to a GDT protocol aimed to SVI optimization, a machine learning-derived algorithm for prediction of IOH combined with a GDT hemodynamic protocol, reduced IOH and hypotension after induction of general anesthesia in patients undergoing major gynaecologic oncologic surgery.Trial registration number: NCT04547491. Date of registration: 10/09/2020.

Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial

PURPOSE

Intraoperative hypotension is linked to increased incidence of perioperative adverse events such as myocardial and cerebrovascular infarction and acute kidney injury. Hypotension prediction index (HPI) is a novel machine learning guided algorithm which can predict hypotensive events using high fidelity analysis of pulse-wave contour. Goal of this trial is to determine whether use of HPI can reduce the number and duration of hypotensive events in patients undergoing major thoracic procedures.

METHODS

Thirty four patients undergoing esophageal or lung resection were randomized into 2 groups -"machine learning algorithm" (AcumenIQ) and "conventional pulse contour analysis" (Flotrac). Analyzed variables were occurrence, severity and duration of hypotensive events (defined as a period of at least one minute of MAP below 65 mmHg), hemodynamic parameters at 9 different timepoints interesting from a hemodynamics viewpoint and laboratory (serum lactate levels, arterial blood gas) and clinical outcomes (duration of mechanical ventilation, ICU and hospital stay, occurrence of adverse events and in-hospital and 28-day mortality).

RESULTS

Patients in the AcumenIQ group had significantly lower area below the hypotensive threshold (AUT, 2 vs 16.7 mmHg x minutes) and time-weighted AUT (TWA, 0.01 vs 0.08 mmHg). Also, there were less patients with hypotensive events and cumulative duration of hypotension in the AcumenIQ group. No significant difference between groups was found in terms of laboratory and clinical outcomes.

CONCLUSIONS

Hemodynamic optimization guided by machine learning algorithm leads to a significant decrease in number and duration of hypotensive events compared to traditional goal directed therapy using pulse-contour analysis hemodynamic monitoring in patients undergoing major thoracic procedures. Further, larger studies are needed to determine true clinical utility of HPI guided hemodynamic monitoring.

TRIAL REGISTRATION

Date of first registration: 14/11/2022 Registration number: 04729481-3a96-4763-a9d5-23fc45fb722d.

Feasibility and Accuracy of Noninvasive Continuous Arterial Pressure Monitoring during Transcatheter Atrial Fibrillation Ablation

Introduction: Transcatheter atrial fibrillation (AF) ablation is still carried out with continuous invasive radial arterial blood pressure (IBP) monitoring in many centers. Continuous noninvasive blood pressure (CNBP) measurement using the volume-clamp method is a noninvasive alternative method used in ICU. No data on CNBP reliability are available in the electrophysiology lab during AF ablation, where rhythm variations are common. Background: The objective of the present study was to compare continuous noninvasive arterial pressure measured with the ClearSight device (Edwards Lifesciences, Irvine, CA, USA) with invasive radial artery pressure used as the reference method during AF ablation. Methods: We prospectively enrolled 55 consecutive patients (age 62 ± 11 years, 80% male) undergoing transcatheter AF ablation (62% paroxysmal, 38% persistent) at our center. Standard of care IBP monitoring via a radial cannula and a contralateral noninvasive finger volume-clamp CNBP measurement device were positioned simultaneously in all patients for the entire procedure. Bland-Altman analysis was used to analyze the agreement between the two techniques. Results: A total of 1219 paired measurements for systolic, diastolic, and mean arterial pressure were obtained in 55 subjects, with a mean (SD) of 22 (9) measurements per patient. The mean bias (SD) was −12.97 (13.89) mmHg for systolic pressure (level of agreement −14.24–40.20; correlation coefficient 0.84), −1.85 (8.52) mmHg for diastolic pressure (level of agreement −18.54–14.84; correlation coefficient 0.77) and 2.31 (8.75) mmHg for mean pressure (level of agreement −14.84–19.46; correlation coefficient 0.85). Conclusion: In patients undergoing AF ablation, CNBP monitoring with the ClearSight device showed acceptable agreement with IBP monitoring. Larger studies are needed to confirm the potential clinical implications of continuous noninvasive BP monitoring during AF ablation.

Intraoperative Hypotension Is Associated with Postoperative Nausea and Vomiting in the PACU: A Retrospective Database Analysis

Multiple risk factors for postoperative nausea and vomiting (PONV)-a very distressing and outcome-related complication-have been identified, including female sex, absence of a history of smoking, history of PONV, and postoperative opioid use. Evidence of association of intraoperative hypotension with PONV is contradictory. A retrospective analysis of the perioperative documentation of 38,577 surgeries was conducted. The associations between different characterizations of intraoperative hypotension and PONV in the postoperative care unit (PACU) were investigated. First, the relationship between different characterizations of intraoperative hypotension with regard to PONV in the PACU was investigated. Secondly, the performance of the optimal characterization was assessed in an independent dataset derived via random split. The vast majority of characterizations showed an association of hypotension with the incidence of PONV in the PACU. In a multivariable regression, time with a MAP under 50 mmHg showed the strongest association with PONV in terms of the cross-validated Brier score. The adjusted odds for PONV in the PACU were estimated to be 1.34 times higher (95% CI: 1.33-1.35) when a MAP was under 50 mmHg for at least 1.8 min than when a MAP remained above 50 mmHg. The finding indicates that intraoperative hypotension may yet be another risk factor for PONV and therefore emphasizes the importance of intraoperative blood pressure management not only in patients at risk for cardiovascular complications but also in young and healthy patients at risk of PONV.

New Developments in Continuous Hemodynamic Monitoring of the Critically Ill Patient

Hemodynamic monitoring is a cornerstone in the assessment of patients with circulatory shock. Timely recognition of hemodynamic compromise and proper optimisation is essential to ensure adequate tissue perfusion and maintain renal, hepatic, abdominal, and cerebral functions. Hemodynamic monitoring has significantly evolved since the first inception of the pulmonary artery catheter more than 50 years ago. Bedside echocardiography, when combined with noninvasive and minimally invasive technologies, provides tools to monitor and quantify the cardiac output to promptly react and improve hemodynamic management in an acute care setting. Commonly used technologies include noninvasive pulse-wave analysis, pulse-wave transit time, thoracic bioimpedance and bioreactance, esophageal Doppler, minimally invasive pulse-wave analysis, transpulmonary thermodilution, and pulmonary artery catheter. These monitoring strategies are reviewed here, along with detailed analysis of their operating mode, particularities, and limitations. The use of artificial intelligence to enhance performance and effectiveness of hemodynamic monitoring is reviewed to apprehend future possibilities.

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.

The Use of Non-Invasive Continuous Blood Pressure Measuring (ClearSight®) during Central Neuraxial Anaesthesia for Caesarean Section—A Retrospective Validation Study

The close monitoring of blood pressure during a caesarean section performed under central neuraxial anaesthesia should be the standard of safe anaesthesia. As classical oscillometric and invasive blood pressure measuring have intrinsic disadvantages, we investigated a novel, non-invasive technique for continuous blood pressure measuring. Methods: In this monocentric, retrospective data analysis, the reliability of continuous non-invasive blood pressure measuring using ClearSight^® (Edwards Lifesciences Corporation) is validated in 31 women undergoing central neuraxial anaesthesia for caesarean section. In addition, patients and professionals evaluated ClearSight^® through questioning. Results: 139 measurements from 11 patients were included in the final analysis. Employing Bland–Altman analyses, we identified a bias of −10.8 mmHg for systolic, of −0.45 mmHg for diastolic and of +0.68 mmHg for mean arterial blood pressure measurements. Pooling all paired measurements resulted in a Pearson correlation coefficient of 0.7 for systolic, of 0.67 for diastolic and of 0.75 for mean arterial blood pressure. Compensating the interindividual differences in linear regressions of the paired measurements provided improved correlation coefficients of 0.73 for systolic, of 0.9 for diastolic and of 0.89 for mean arterial blood pressure measurements. Discussion: Diastolic and mean arterial blood pressure are within an acceptable range of deviation from the reference method, according to the Association for the Advancement of Medical Instrumentation (AAMI) in the patient collective under study. Both patients and professionals prefer ClearSight^® to oscillometric blood pressure measurement in regard of comfort and handling.