Effect of Hypotension Prediction Index-guided intraoperative haemodynamic care on depth and duration of postoperative hypotension: a sub-study of the Hypotension Prediction trial

The Hypotension Prediction Index in Free Flap Transplant in Head and Neck Surgery: Protocol of a Prospective Randomized Controlled Trial

INTRODUCTION

Microvascular free flap surgery is a treatment method for patients with head and neck cancer requiring reconstruction surgery. Patients undergoing this complex, long-lasting surgery are prone to prolonged episodes of intraoperative hypotension, which is associated with increased incidence of postoperative mortality, morbidity, and free flap failure. A new technology recently approved, named the Hypotension Prediction Index (HPI), allows precise hemodynamic monitoring of patients under general anesthesia, with a significant reduction of intraoperative hypotension events. This study aims to assess the impact of the Hypotension Prediction Index (HPI) on the incidence and severity of intraoperative hypotension in patients undergoing free flap surgery.

METHODS AND ANALYSIS

Eligible patients will be randomly assigned to one of two groups: Group A, receiving invasive blood pressure monitoring with standard medical therapy, or Group B, undergoing hemodynamic monitoring using the Hypotension Prediction Index (HPI) software. The primary outcome is the time-weighted average (TWA) of mean arterial pressure (MAP) < 65 mmHg. Secondary outcomes include free flap viability and perioperative complications.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Poznan University of Medical Sciences Ethics Committee (KB-560/22; date 1 July 2022). Results will be submitted for publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

NCT05738603.

Perioperative goal-directed therapy with artificial intelligence to reduce the incidence of intraoperative hypotension and renal failure in patients undergoing lung surgery: A pilot study

STUDY OBJECTIVE

The aim of this study was to investigate whether goal-directed treatment using artificial intelligence, compared to standard care, can reduce the frequency, duration, and severity of intraoperative hypotension in patients undergoing single lung ventilation, with a potential reduction of postoperative acute kidney injury (AKI).

DESIGN

single center, single-blinded randomized controlled trial.

SETTING

University hospital operating room.

PATIENTS

150 patients undergoing lung surgery with single lung ventilation were included.

INTERVENTIONS

Patients were randomly assigned to two groups: the Intervention group, where a goal-directed therapy based on the Hypotension Prediction Index (HPI) was implemented; the Control group, without a specific hemodynamic protocol.

MEASUREMENTS

The primary outcome measures include the frequency, duration of intraoperative hypotension, furthermore the Area under MAP 65 and the time-weighted average (TWA) of MAP of 65. Other outcome parameters are the incidence of AKI and myocardial injury after non-cardiac surgery (MINS).

MAIN RESULTS

The number of hypotensive episodes was lower in the intervention group compared to the control group (0 [0-1] vs. 1 [0-2]; p = 0.01), the duration of hypotension was shorter in the intervention group (0 min [0-3.17] vs. 2.33 min [0-7.42]; p = 0.01). The area under the MAP of 65 (0 mmHg * min [0-12] vs. 10.67 mmHg * min [0-44.16]; p < 0.01) and the TWA of MAP of 65 (0 mmHg [0-0.08] vs. 0.07 mmHg [0-0.25]; p < 0.01) were lower in the intervention group. The incidence of postoperative AKI showed no differences between the groups (6.7 % vs.4.2 %; p = 0.72). There was a trend to lower incidence of MINS in the intervention group (17.1 % vs. 31.8 %; p = 0.07). A tendency towards reduced postoperative infection was seen in the intervention group (16.0 % vs. 26.8 %; p = 0.16).

CONCLUSIONS

The implementation of a treatment algorithm based on HPI allowed us to decrease the duration and severity of hypotension in patients undergoing lung surgery. It did not result in a significant reduction in the incidence of AKI, however we observed a tendency towards lower incidence of MINS in the intervention group, along with a slight reduction in postoperative infections.

Intraoperative hemodynamic management in abdominal aortic surgery guided by the Hypotension Prediction Index: the Hemas multicentric observational study

BACKGROUND

Intraoperative hypotension (IOH) during non-cardiac surgery is closely associated with postoperative complications. Hypotensive events are more likely during major open vascular surgery. We prospectively investigated whether our institutional algorithm of cardiocirculatory management, which included the Hypotension Prediction Index (HPI), a predictive model of hypotension of the Hemosphere™ platform (Edwards Lifescience, Irwin, CA, USA), was able to reduce the incidence and severity of intraoperative hypotension during open abdominal aortic aneurysm repair.

METHODS

A multi-center observational study was conducted at IRCCS-Humanitas Research Hospital (Milan) and AO Mauriziano Umberto I Hospital (Turin) between July 2022 and September 2023, enrolling patients undergoing elective open abdominal aortic aneurysm repair. A hemodynamic protocol based on the Acumen-HPI Hemosphere™ platform was employed, integrating advanced parameters (e.g., HPI, Ea-dyn, dP/dt) and tailored interventions to minimize intraoperative hypotension. The primary endpoint was cumulative intraoperative hypotension time < 10% of surgical time, with secondary endpoints including incidence of hypotensive events, time-weighted averages of MAP < 65 mmHg (TWA65) and < 50 mmHg (TWA50), and postoperative complications.

RESULTS

We enrolled 53 patients submitted to open abdominal aortic repair. The primary endpoint (time in hypotension < 10%) was successfully reached: 5 [1-10] %. The targeted time-weighted average (< 0.40 mmHg) both for MAP < 65 mmHg (TWA65) and MAP < 50 mmHg (severe hypotension; TWA50) were reached: TWA65 = 0.26 [0.04-0.65] mmHg and TWA50 = 0.00 [0.00-0.01].

CONCLUSIONS

Our hemodynamic management algorithm based on the HPI and other parameters of the Hemosphere™ platform was able to limit the incidence and severity of intraoperative hypotension during open abdominal aortic repair.

TRIAL REGISTRATION

NCT05478564.

Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis

BACKGROUND

We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes.

METHODS

Ovid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes.

RESULTS

Among 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n=5), Nociception Level Index (NoL) (n=7), and a scheduling system (n=1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n=421, P=0.003, I2=75%) and relative hypotension (n=208, P<0.0001, I2=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n=191, P=0.004, I2=19%). NoL showed no significant impact on intraoperative opioid consumption (n=339, P=0.31, I2=92%) or PACU opioid consumption (n=339, P=0.11, I2=0%). No significant difference in hospital length of stay (n=361, P=0.81, I2=0%) and PACU stay (n=267, P=0.44, I2=0) was found between HPI and NoL.

CONCLUSIONS

HPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions.

SYSTEMATIC REVIEW PROTOCOL

CRD42023433163 (PROSPERO).

Definition of clinically relevant intraoperative hypotension: A data-driven approach

BACKGROUND

Associations between intraoperative hypotension (IOH) and various postoperative outcomes were shown in retrospective trials using a variety of different definitions of IOH. This complicates the comparability of these trials and makes clinical application difficult. Information about the best performing definitions of IOH regarding 30-day mortality, hospital length of stay (hLOS), and postanesthesia care unit length of stay (PACU-LOS) is missing.

METHODS

A retrospective cohort trial was conducted using data from patients undergoing noncardiothoracic surgery. We split the obtained dataset into two subsets. First, we used one subset to choose the best fitting definitions of IOH for the outcomes 30-day mortality, hLOS, and PACU-LOS. The other subset was used to independently assess the performance of the chosen definitions of IOH.

RESULTS

The final cohort consisted of 65,454 patients. In the shaping subset, nearly all tested definitions of IOH showed associations with the three outcomes, where the risk of adverse outcomes often increased continuously with decreasing MAP. The best fitting definitions were relative time with a MAP (mean arterial pressure) of <80 mmHg for 30-day mortality, lowest MAP for one minute for hLOS, and lowest MAP for one cumulative minute for PACU-LOS. Testing these three definitions of IOH in the independent second subset confirmed the associations of IOH with 30-day mortality, hLOS, and PACU-LOS.

CONCLUSIONS

Using a data-driven approach, we identified the best fitting definitions of IOH for 30-day mortality, hLOS, and PACU-LOS. Our results demonstrate the need for careful selection of IOH definitions. Clinical trial number: n/a, EC #2245/2020.

Effect of standardized EHR-integrated handoff report on intraoperative communication outcomes

OBJECTIVES

We evaluated the effectiveness and implementability of a standardized EHR-integrated handoff report to support intraoperative handoffs.

MATERIALS AND METHODS

A pre-post intervention study was used to compare the quality of intraoperative handoffs supported by unstructured notes (pre) to structured, standardized EHR-integrated handoff reports (post). Participants included anesthesia clinicians involved in intraoperative handoffs. A mixed-method approach was followed, supported by general observations, shadowing, surveys, and interviews.

RESULTS

One hundred and fifty-one intraoperative permanent handoffs (78 pre, 73 post) were included. One hundred percent of participants in the post-intervention cohort utilized the report. Compared to unstructured, structured handoffs using the EHR-integrated handoff report led to: (1) significant increase in the transfer of information about airway management (55%-78%, P < .001), intraoperative course (63%-86%, P < .001), and potential concerns (64%-88%, P < .001); (2) significant improvement in clinician satisfaction scores, with regards to information clarity and succinctness (4.5-4.7, P = .002), information transfer (3.8-4.2, P = .011), and opportunities for fewer errors reported by senders (3.3-2.5, P < .001) and receivers (3.2-2.4, P < .001); and (3) significant decrease in handoff duration (326.2-262.3 s, P = .016). Clinicians found the report implementation highly acceptable, appropriate, and feasible but noted a few areas for improvement to enhance its usability and integration within the intraoperative workflow.

DISCUSSION AND CONCLUSION

A standardized EHR-integrated handoff report ensures the effectiveness and efficiency of intraoperative handoffs with its structured, consistent format that-promotes up-to-date and pertinent intraoperative information transfer; reduces opportunities for errors; and streamlines verbal communication. Handoff standardization can promote safe and high-quality intraoperative care.

Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis

BACKGROUND

Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to identify the gap between AI research and its implementation in anesthesiology via a systematic review of randomized controlled trials with meta-analysis (CRD42022353727).

METHODS

We searched the databases of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), Institute of Electrical and Electronics Engineers Xplore (IEEE), and Google Scholar and retrieved randomized controlled trials comparing conventional and AI-assisted anesthetic management published between the date of inception of the database and August 31, 2023.

RESULTS

Eight randomized controlled trials were included in this systematic review (n = 568 patients), including 286 and 282 patients who underwent anesthetic management with and without AI-assisted interventions, respectively. AI-assisted interventions used in the studies included fuzzy logic control for gas concentrations (one study) and the Hypotension Prediction Index (seven studies; adding only one indicator). Seven studies had small sample sizes (n = 30 to 68, except for the largest), and meta-analysis including the study with the largest sample size (n = 213) showed no difference in a hypotension-related outcome (mean difference of the time-weighted average of the area under the threshold 0.22, 95% confidence interval -0.03 to 0.48, P = 0.215, I2 93.8%).

CONCLUSIONS

This systematic review and meta-analysis revealed that randomized controlled trials on AI-assisted interventions in anesthesiology are in their infancy, and approaches that take into account complex clinical practice should be investigated in the future.

TRIAL REGISTRATION

This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).

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.

Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis

Background: Published evidence indicates that mean arterial pressure (MAP) below a goal range (hypotension) is associated with worse outcomes, though MAP management failures are common. We sought to characterize hypotension occurrences in ICUs and consider the implications for MAP management. Methods: Retrospective analysis of 3 hospitals' cohorts of adult ICU patients during continuous vasopressor infusion. Two cohorts were general, mixed ICU patients and one was exclusively acute spinal cord injury patients. "Hypotension-clusters" were defined where there were ≥10 min of cumulative hypotension over a 60-min period and "constant hypotension" was ≥10 continuous minutes. Trend analysis was performed (predicting future MAP using 14 min of preceding MAP data) to understand which hypotension-clusters could likely have been predicted by clinician awareness of MAP trends. Results: In cohorts of 155, 66, and 16 ICU stays, respectively, the majority of hypotension occurred within the hypotension-clusters. Failures to keep MAP above the hypotension threshold were notable in the bottom quartiles of each cohort, with hypotension durations of 436, 167, and 468 min, respectively, occurring within hypotension-clusters per day. Mean arterial pressure trend analysis identified most hypotension-clusters before any constant hypotension occurred (81.2%-93.6% sensitivity, range). The positive predictive value of hypotension predictions ranged from 51.4% to 72.9%. Conclusions: Across 3 cohorts, most hypotension occurred in temporal clusters of hypotension that were usually predictable from extrapolation of MAP trends.

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.

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

BACKGROUND

The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation.

METHODS

A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies.

RESULTS

A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications.

CONCLUSIONS

The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice.

EDITOR’S PERSPECTIVE

Postoperative arterial hypotension: the unnoticed enemy

Postoperative hypotension is a frequently underestimated health problem associated with high morbidity and mortality and increased use of health care resources. It also poses significant clinical, technological, and human challenges for healthcare. As it is a modifiable and avoidable risk factor, this document aims to increase its visibility, defining its clinical impact and the technological challenges involved in optimizing its management, taking clinical-technological, humanistic, and economic aspects into account.

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.

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 serum lactate levels as a prognostic predictor of outcome for emergency abdominal surgery: a retrospective study

BACKGROUND

The relationship between intraoperative lactate levels and prognosis after emergency gastrointestinal surgery remains unclear. The purpose of this study was to investigate the prognostic value of intraoperative lactate levels for predicting in-hospital mortality, and to examine intraoperative hemodynamic managements.

METHODS

We conducted a retrospective observational study of emergency GI surgeries performed at our institution between 2011 and 2020. The study group comprised patients admitted to intensive care units postoperatively, and whose intraoperative and postoperative lactate levels were available. Intraoperative peak lactate levels (intra-LACs) were selected for analysis, and in-hospital mortality was set as the primary outcome. The prognostic value of intra-LAC was assessed using logistic regression and receiver operating characteristic (ROC) curve analysis.

RESULTS

Of the 551 patients included in the study, 120 died postoperatively. Intra-LAC in the group who survived and the group that died was 1.80 [interquartile range [IQR], 1.19-3.01] mmol/L and 4.22 [IQR, 2.15-7.13] mmol/L (P < 0.001), respectively. Patients who died had larger volumes of red blood cell (RBC) transfusions and fluid administration, and were administered higher doses of vasoactive drugs. Logistic regression analysis showed that intra-LAC was an independent predictor of postoperative mortality (odds ratio [OR] 1.210, 95% CI 1.070 -1.360, P = 0.002). The volume of RBCs, fluids transfused, and the amount of vasoactive agents administered were not independent predictors. The area under the curve (AUC) of the ROC curve for intra-LAC for in-hospital mortality was 0.762 (95% confidence interval [CI], 0.711-0.812), with a cutoff value of 3.68 mmol/L by Youden index.

CONCLUSIONS

Intraoperative lactate levels, but not hemodynamic management, were independently associated with increased in-hospital mortality after emergency GI 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).

Association between mean arterial pressure and sublingual microcirculation during major non-cardiac surgery: Post hoc analysis of a prospective cohort

OBJECTIVE

To test the hypothesis that there is an association between mean arterial pressure (MAP) and sublingual perfusion during major surgery, and perhaps an identifiable harm threshold.

METHODS

This post hoc analysis of a prospective cohort included patients who had elective major non-cardiac surgery with a duration of ≥2 h under general anesthesia. We assessed sublingual microcirculation every 30 min using SDF+ imaging and determined the De Backer score, Consensus Proportion of Perfused Vessels (Consensus PPV), and the Consensus PPV (small). Our primary outcome was the relationship between MAP and sublingual perfusion which was evaluated with linear mixed effects modeling.

RESULTS

A total of 100 patients were included, with MAP ranging between 65 mmHg and 120 mmHg during anesthesia and surgery. Over a range of intraoperative MAPs between 65 and 120 mmHg, there were no meaningful associations between blood pressure and various measures of sublingual perfusion. There were also no meaningful changes in microcirculatory flow over 4.5 h of surgery.

CONCLUSIONS

In patients having elective major non-cardiac surgery with general anesthesia, sublingual microcirculation is well maintained when MAP ranges between 65 and 120 mmHg. It remains possible that sublingual perfusion will be a useful marker of tissue perfusion when MAP is lower than 65 mmHg.

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.

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.

Flavonoids regulate tumor-associated macrophages - From structure-activity relationship to clinical potential (Review)

In recent years, the strategy for tumor therapy has changed from focusing on the direct killing effect of different types of therapeutic agents on cancer cells to the new mainstream of multi-mode and -pathway combined interventions in the microenvironment of the developing tumor. Flavonoids, with unique tricyclic structures, have diverse and extensive immunomodulatory and anti-cancer activities in the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the most abundant immunosuppressive cells in the TME. The regulation of macrophages to fight cancer is a promising immunotherapeutic strategy. This study covers the most comprehensive cognition of flavonoids in regulating TAMs so far. Far more than a simple list of studies, we try to dig out evidence of crosstalk at the molecular level between flavonoids and TAMs from literature, in order to discuss the most relevant chemical structure and its possible relationship with the multimodal pharmacological activity, as well as systematically build a structure-activity relationship between flavonoids and TAMs. Additionally, we point out the advantages of the macro-control of flavonoids in the TME and discuss the potential clinical implications as well as areas for future research of flavonoids in regulating TAMs. These results will provide hopeful directions for the research of antitumor drugs, while providing new ideas for the pharmaceutical industry to develop more effective forms of flavonoids.

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.

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

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