The Use of the Hypotension Prediction Index Integrated in an Algorithm of Goal Directed Hemodynamic Treatment during Moderate and High-Risk Surgery

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.

Intraoperative Hypotension in Patients Having Major Noncardiac Surgery Under General Anesthesia: A Systematic Review of Blood Pressure Optimization Strategies

INTRODUCTION

Intraoperative hypotension is associated with increased risks of postoperative complications. Consequently, a variety of blood pressure optimization strategies have been tested to prevent or promptly treat intraoperative hypotension. We performed a systematic review to summarize randomized controlled trials that evaluated the efficacy of blood pressure optimization interventions in either mitigating exposure to intraoperative hypotension or reducing risks of postoperative complications.

METHODS

Medline, Embase, PubMed, and Cochrane Controlled Register of Trials were searched from database inception to August 2, 2023, for randomized controlled trials (without language restriction) that evaluated the impact of any blood pressure optimization intervention on intraoperative hypotension and/or postoperative outcomes.

RESULTS

The review included 48 studies (N = 46,377), which evaluated 10 classes of blood pressure optimization interventions. Commonly assessed interventions included hemodynamic protocols using arterial waveform analysis, preoperative withholding of antihypertensive medications, continuous blood pressure monitoring, and adjuvant agents (vasopressors, anticholinergics, anticonvulsants). These same interventions reduced intraoperative exposure to hypotension. Conversely, low blood pressure alarms had an inconsistent impact on exposure to hypotension. Aside from limited evidence that higher prespecified intraoperative blood pressure targets led to a reduced risk of complications, there were few data suggesting that these interventions prevented postoperative complications. Heterogeneity in interventions and outcomes precluded meta-analysis.

CONCLUSIONS

Several different blood pressure optimization interventions show promise in reducing exposure to intraoperative hypotension. Nonetheless, the impact of these interventions on clinical outcomes remains unclear. Future trials should assess promising interventions in samples sufficiently large to identify clinically plausible treatment effects on important outcomes.

KEY POINTS

Pilot Study to Optimize Goal-directed Hemodynamic Management During Pancreatectomy

INTRODUCTION

Intraoperative goal-directed hemodynamic therapy (GDHT) is a cornerstone of enhanced recovery protocols. We hypothesized that use of an advanced noninvasive intraoperative hemodynamic monitoring system to guide GDHT may decrease intraoperative hypotension (IOH) and improve perfusion during pancreatic resection.

METHODS

The monitor uses machine learning to produce the Hypotension Prediction Index to predict hypotensive episodes. A clinical decision-making algorithm uses the Hypotension Prediction Index and hemodynamic data to guide intraoperative fluid versus pressor management. Pre-implementation (PRE), patients were placed on the monitor and managed per usual. Post-implementation (POST), anesthesia teams were educated on the algorithm and asked to use the GDHT guidelines. Hemodynamic data points were collected every 20 s (8942 PRE and 26,638 POST measurements). We compared IOH (mean arterial pressure <65 mmHg), cardiac index >2, and stroke volume variation <12 between the two groups.

RESULTS

10 patients were in the PRE and 24 in the POST groups. In the POST group, there were fewer minimally invasive resections (4.2% versus 30.0%, P = 0.07), more pancreaticoduodenectomies (75.0% versus 20.0%, P < 0.01), and longer operative times (329.0 + 108.2 min versus 225.1 + 92.8 min, P = 0.01). After implementation, hemodynamic parameters improved. There was a 33.3% reduction in IOH (5.2% ± 0.1% versus 7.8% ± 0.3%, P < 0.01, a 31.6% increase in cardiac index >2.0 (83.7% + 0.2% versus 63.6% + 0.5%, P < 0.01), and a 37.6% increase in stroke volume variation <12 (73.2% + 0.3% versus 53.2% + 0.5%, P < 0.01).

CONCLUSIONS

Advanced intraoperative hemodynamic monitoring to predict IOH combined with a clinical decision-making tree for GDHT may improve intraoperative hemodynamic parameters during pancreatectomy. This warrants further investigation in larger studies.

Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.

Artificial intelligence in surgery

Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.

Towards the automatic detection and correction of abnormal arterial pressure waveforms

Both over and underdamping of the arterial pressure waveform are frequent during continuous invasive radial pressure monitoring. They may influence systolic blood pressure measurements and the accuracy of cardiac output monitoring with pulse wave analysis techniques. It is therefore recommended to regularly perform fast flush tests to unmask abnormal damping. Smart algorithms have recently been developed for the automatic detection of abnormal damping. In case of overdamping, air bubbles, kinking, and partial obstruction of the arterial catheter should be suspected and eliminated. In the case of underdamping, resonance filters may be necessary to normalize the arterial pressure waveform and ensure accurate hemodynamic measurements.

Impact of clinicians' behavior, an educational intervention with mandated blood pressure and the hypotension prediction index software on intraoperative hypotension: a mixed methods study

PURPOSE

Intraoperative hypotension (IOH) is associated with adverse outcomes. We therefore explored beliefs regarding IOH and barriers to its treatment. Secondarily, we assessed if an educational intervention and mandated mean arterial pressure (MAP), or the implementation of the Hypotension Prediction Index-software (HPI) were associated with a reduction in IOH.

METHODS

Structured interviews (n = 27) and questionnaires (n = 84) were conducted to explore clinicians' beliefs and barriers to IOH treatment, in addition to usefulness of HPI questionnaires (n = 14). 150 elective major surgical patients who required invasive blood pressure monitoring were included in three cohorts to assess incidence and time-weighted average (TWA) of hypotension (MAP < 65 mmHg). Cohort one received standard care (baseline), the clinicians of cohort two had a training on hypotension and a mandated MAP > 65 mmHg, and patients of the third cohort received protocolized care using the HPI.

RESULTS

Clinicians felt challenged to manage IOH in some patients, yet they reported sufficient knowledge and skills. HPI-software was considered useful and beneficial. No difference was found in incidence of IOH between cohorts. TWA was comparable between baseline and education cohort (0.15 mmHg [0.05-0.41] vs. 0.11 mmHg [0.02-0.37]), but was significantly lower in the HPI cohort (0.04 mmHg [0.00 to 0.11], p < 0.05 compared to both).

CONCLUSIONS

Clinicians believed they had sufficient knowledge and skills, which could explain why no difference was found after the educational intervention. In the HPI cohort, IOH was significantly reduced compared to baseline, therefore HPI-software may help prevent IOH.

TRIAL REGISTRATION

ISRCTN 17,085,700 on May 9th, 2019.

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.

The Incidence of Perioperative Hypotension in Patients Undergoing Major Abdominal Surgery with the Use of Arterial Waveform Analysis and the Hypotension Prediction Index Hemodynamic Monitoring-A Retrospective Analysis

Intraoperative hypotension (IH) is common in patients receiving general anesthesia and can lead to serious complications such as kidney failure, myocardial injury and increased mortality. The Hypotension Prediction Index (HPI) algorithm is a machine learning system that analyzes the arterial pressure waveform and alerts the clinician of an impending hypotension event. The purpose of the study was to compare the frequency of perioperative hypotension in patients undergoing major abdominal surgery with different types of hemodynamic monitoring. The study included 61 patients who were monitored with the arterial pressure-based cardiac output (APCO) technology (FloTrac group) and 62 patients with the Hypotension Prediction Index algorithm (HPI group). Our primary outcome was the time-weighted average (TWA) of hypotension below < 65 mmHg. The median TWA of hypotension in the FloTrac group was 0.31 mmHg versus 0.09 mmHg in the HPI group (p = 0.000009). In the FloTrac group, the average time of hypotension was 27.9 min vs. 8.1 min in the HPI group (p = 0.000023). By applying the HPI algorithm in addition to an arterial waveform analysis alone, we were able to significantly decrease the frequency and duration of perioperative hypotension events in patients who underwent major abdominal surgery.

CON: The hypotension prediction index is not a validated predictor of hypotension

The Hypotension Prediction Index (HPI) algorithm is a commercial prediction algorithm developed to predict hypotension, a mean arterial pressure (MAP) below 65 mmHg. Although HPI has been investigated in several studies, recent concerns of have been raised regarding HPI's predictive abilities, which may have been overstated. A selection bias may have forced the HPI algorithm to learn almost exclusively from MAP. This CON position paper describes the selection bias further and summarises the scientific status of HPI's predictive abilities, including the meaning of a recent erratum retracting the primary conclusion of a published HPI validation study. We argue that the HPI algorithm needs re-validation or complete re-development to achieve a clinically relevant 'added value' in comparison with the predictive performance of a simple and costless MAP alarm threshold in the range of 70 to 75 mmHg.

Bibliometric and visual analysis of intraoperative hypotension from 2004 to 2022

Background

Intraoperative hypotension (IOH) is a common complication occurring in surgical practice. This study aims to comprehensively review the collaboration and impact of countries, institutions, authors, journals, keywords, and critical papers on intraoperative hypotension from the perspective of bibliometric, and to evaluate the evolution of knowledge structure clustering and identify research hotspots and emerging topics.

Methods

Articles and reviews related to IOH published from 2004 to 2022 were retrieved from the Web of Science Core Collection. Bibliometric analyses and visualization were conducted on Excel, CiteSpace, VOSviewer, and Bibliometrix (R-Tool of R-Studio).

Results

A total of 1,784 articles and reviews were included from 2004 to 2022. The number of articles on IOH gradually increased in the past few years, and peaked in 2021. These publications were chiefly from 1,938 institutions in 40 countries, led by America and China in publications. Sessler Daniel I published the most papers and enjoyed the highest number of citations. Analysis of the journals with the most outputs showed that most journals concentrated on perioperative medicine and clinical anesthesiology. Delirium, acute kidney injury and vasoconstrictor agents are the current and developing research hotspots. The keywords "Acute kidney injury", "postoperative complication", "machine learning", "risk factors" and "hemodynamic instability" may also become new trends and focuses of the near future research.

Conclusion

This study uses bibliometrics and visualization methods to comprehensively review the research on intraoperative hypotension, which is helpful for scholars to better understand the dynamic evolution of IOH and provide directions for future research.

Hypotension Prediction Index and Incidence of Perioperative Hypotension: A Single-Center Propensity-Score-Matched Analysis

(1) Background: Intraoperative hypotension is common and is associated with increased morbidity and mortality. The Hypotension Prediction Index (HPI) is an advancement of arterial waveform analysis and allows preventive treatments. We used a propensity-score-matched study design to test whether application of the HPI reduces hypotensive events in non-cardiac surgery patients; (2) Methods: 769 patients were selected for propensity score matching. After matching, both HPI and non-HPI groups together comprised n = 136 patients. A goal-directed treatment protocol was applied in both groups. The primary endpoint was the incidence and duration of hypotensive events defined as MAP < 65 mmHg, evaluated by the time-weighted average (TWA) of hypotension. (3) Results: The median TWA of hypotension below 65 mmHg in the matched cohort was 0.180 mmHg (IQR 0.060, 0.410) in the non-HPI group vs. 0.070 mmHg (IQR 0.020, 0.240) in the HPI group (p < 0.001). TWA was higher in patients with ASA classification III/IV (0.170 mmHg; IQR 0.035, 0.365) than in patients with ASA status II (0.100; IQR 0.020, 0.250; p = 0.02). Stratification by intervention group showed no differences in the HPI group while TWA values in the non-HPI group were more than twice as high in patients with ASA status III/IV (p = 0.01); (4) Conclusions: HPI reduces intraoperative hypotension in a matched cohort seen for TWA below 65 mmHg and relative time in hypotension. In addition, non-HPI patients with ASA status III/IV showed a higher TWA compared with HPI-patients, indicating an advantageous effect of using HPI in patients at higher risk.

Impact of Intraoperative Fluid Balance and Norepinephrine on Postoperative Acute Kidney Injury after Cystectomy and Urinary Diversion over Two Decades: A Retrospective Observational Cohort Study

The use of norepinephrine and the restriction of intraoperative hydration have gained increasing acceptance over the last few decades. Recently, there have been concerns regarding the impact of this approach on renal function. The objective of this study was to examine the influence of norepinephrine, intraoperative fluid administration and their interaction on acute kidney injury (AKI) after cystectomy. In our cohort of 1488 consecutive patients scheduled for cystectomies and urinary diversions, the overall incidence of AKI was 21.6% (95%-CI: 19.6% to 23.8%) and increased by an average of 0.6% (95%-CI: 0.1% to 1.1%, p = 0.025) per year since 2000. The fluid and vasopressor regimes were characterized by an annual decrease in fluid balance (-0.24 mL·kg^-1·h^-1, 95%-CI: -0.26 to -0.22, p < 0.001) and an annual increase in the amount of norepinephrine of 0.002 µg·kg^-1·min^-1 (95%-CI: 0.0016 to 0.0024, p < 0.001). The interaction between the fluid balance and norepinephrine levels resulted in a U-shaped association with the risk of AKI; however, the magnitude and shape depended on the reference categories of confounders (age and BMI). We conclude that decreased intraoperative fluid balance combined with increased norepinephrine administration was associated with an increased risk of AKI. However, other potential drivers of the observed increase in AKI incidence need to be further investigated in the future.

Intraoperative hypotension when using hypotension prediction index software during major noncardiac surgery: a European multicentre prospective observational registry (EU HYPROTECT)

Background

Intraoperative hypotension is associated with organ injury. Current intraoperative arterial pressure management is mainly reactive. Predictive haemodynamic monitoring may help clinicians reduce intraoperative hypotension. The Acumen™ Hypotension Prediction Index software (HPI-software) (Edwards Lifesciences, Irvine, CA, USA) was developed to predict hypotension. We built up the European multicentre, prospective, observational EU HYPROTECT Registry to describe the incidence, duration, and severity of intraoperative hypotension when using HPI-software monitoring in patients having noncardiac surgery.

Methods

We enrolled 749 patients having elective major noncardiac surgery in 12 medical centres in five European countries. Patients were monitored using the HPI-software. We quantified hypotension using the time-weighted average MAP <65 mm Hg (primary endpoint), the proportion of patients with at least one ≥1 min episode of a MAP <65 mm Hg, the number of ≥1 min episodes of a MAP <65 mm Hg, and duration patients spent below a MAP of 65 mm Hg.

Results

We included 702 patients in the final analysis. The median time-weighted average MAP <65 mm Hg was 0.03 (0.00-0.20) mm Hg. In addition, 285 patients (41%) had no ≥1 min episode of a MAP <65 mm Hg; 417 patients (59%) had at least one. The median number of ≥1 min episodes of a MAP <65 mm Hg was 1 (0-3). Patients spent a median of 2 (0-9) min below a MAP of 65 mm Hg.

Conclusions

The median time-weighted average MAP <65 mm Hg was very low in patients in this registry. This suggests that using HPI-software monitoring may help reduce the duration and severity of intraoperative hypotension in patients having noncardiac surgery.

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).

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.

The artificial intelligence evidence-based medicine pyramid

Several studies exist in the literature regarding the exploitation of artificial intelligence in intensive care. However, an important gap between clinical research and daily clinical practice still exists that can only be bridged by robust validation studies carried out by multidisciplinary teams.

Intraoperative Hypotension Prediction—A Proactive Perioperative Hemodynamic Management—A Literature Review

Intraoperative hypotension (IH) is a frequent phenomenon affecting a substantial number of patients undergoing general anesthesia. The occurrence of IH is related to significant perioperative complications, including kidney failure, myocardial injury, and even increased mortality. Despite advanced hemodynamic monitoring and protocols utilizing goal directed therapy, our management is still reactive; we intervene when the episode of hypotension has already occurred. This literature review evaluated the Hypotension Prediction Index (HPI), which is designed to predict and reduce the incidence of IH. The HPI algorithm is based on a machine learning algorithm that analyzes the arterial pressure waveform as an input and the occurrence of hypotension with MAP <65 mmHg for at least 1 min as an output. There are several studies, both retrospective and prospective, showing a significant reduction in IH episodes with the use of the HPI algorithm. However, the level of evidence on the use of HPI remains very low, and further studies are needed to show the benefits of this algorithm on perioperative outcomes.

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.

Effect of hypotension prediction index in the prevention of intraoperative hypotension during noncardiac surgery: A systematic review

Intraoperative hypotension (IOH) is common in noncardiac surgery and is associated with serious postoperative complications. Hypotension Prediction Index (HPI) has shown high sensitivity and specificity for predicting hypotension and may reduce IOH in noncardiac surgery. We conducted a systematic review of randomized controlled trials (RCTs) to evaluate the applications and effects of HPI in reducing hypotension during noncardiac surgery. We comprehensively searched the PubMed, Embase, Cochrane Library, Google Scholar, and http://ClinicalTrials.gov databases to identify RCTs conducted before May 2022. The primary outcome measures were the time-weighted average (TWA) of hypotension and the area under the hypotensive threshold (65 mmHg). Secondary outcomes were the incidence and duration of hypotension and the percentage of hypotensive time during surgery. The Cochrane Risk of Bias (RoB) tool was used to assess the quality of selected studies. We conducted data synthesis for median differences and assessed the certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. We included five studies with a total of 461 patients. Limited evidence suggested that HPI-guided intraoperative hemodynamics management leads to lower a) TWA of hypotension (median of difference of medians [MDM], -0.27 mmHg; 95% confidence interval [CI], -0.38, -0.01), b) area under the hypotensive threshold (MDM, -60.28 mmHg*min; 95% CI, -74.00, -1.30), c) incidence of hypotension (MDM, -4.50; 95% CI, -5.00, -4.00), d) total duration of hypotension (MDM, -12.80 min; 95% CI, -16.11, -3.39), and e) percentage of hypotension (MDM, -5.80; 95% CI, -6.65, -4.82) than routine hemodynamic management during noncardiac surgery. However, only very low- to low-quality evidence on the benefit of intraoperative HPI-based hemodynamic management is available. Our review revealed that HPI has the potential to reduce the occurrence, duration, and severity of IOH during noncardiac surgery compared to standard intraoperative care with proper adherence to the protocol. Systematic review registration PROSPERO CRD42022333834.

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.