Predicting hypotension in perioperative and intensive care medicine

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.

Closed-Loop Pharmacologic Control of Blood Pressure: A Review of Existing Systems

Blood pressure management is a critical aspect of patient care, particularly in surgical and critical care settings. Closed-loop systems, which utilize real-time data and feedback to adjust treatment interventions, have gained attention for their potential to enhance blood pressure control. This review explores the application of closed-loop systems in blood pressure management. We discuss various closed-loop approaches, including their mechanisms, benefits, and limitations. By harnessing real-time patient data and feedback, closed-loop systems can tailor interventions dynamically, thus enhancing blood pressure regulation. Additionally, we examine the integration of advanced monitoring technologies and artificial intelligence algorithms in closed-loop systems. The review highlights recent studies and their findings, emphasizing the evolving landscape of closed-loop blood pressure management across different clinical scenarios. From the perioperative period to critical care settings, closed-loop systems hold the potential to optimize patient outcomes by precisely adjusting vasopressor administration in response to continuous blood pressure fluctuations. By providing insights into the current state of closed-loop systems for blood pressure control, this review offers a comprehensive overview of their potential contributions to improved patient outcomes and future directions for research and implementation.

Intraoperative arterial pressure management: knowns and unknowns

Preventing postoperative organ dysfunction is integral to the practice of anaesthesia. Although intraoperative hypotension is associated with postoperative end organ dysfunction, there remains ambiguity with regards to its definition, targets, thresholds for initiating treatment, and ideal treatment modalities.

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.

Continuous Blood Pressure Monitoring in Patients Having Surgery: A Narrative Review

Hypotension can occur before, during, and after surgery and is associated with postoperative complications. Anesthesiologists should thus avoid profound and prolonged hypotension. A crucial part of avoiding hypotension is accurate and tight blood pressure monitoring. In this narrative review, we briefly describe methods for continuous blood pressure monitoring, discuss current evidence for continuous blood pressure monitoring in patients having surgery to reduce perioperative hypotension, and expand on future directions and innovations in this field. In summary, continuous blood pressure monitoring with arterial catheters or noninvasive sensors enables clinicians to detect and treat hypotension immediately. Furthermore, advanced hemodynamic monitoring technologies and artificial intelligence-in combination with continuous blood pressure monitoring-may help clinicians identify underlying causes of hypotension or even predict hypotension before it occurs.

Surgical Pharmacy for Optimizing Medication Therapy Management Services within Enhanced Recovery after Surgery (ERAS®) Programs

Drug-related problems (DRPs) are common among surgical patients, especially older patients with polypharmacy and underlying diseases. DRPs can potentially lead to morbidity, mortality, and increased treatment costs. The enhanced recovery after surgery (ERAS) system has shown great advantages in managing surgical patients. Medication therapy management for surgical patients (established as "surgical pharmacy" by Guangdong Province Pharmaceutical Association (GDPA)) is an important part of the ERAS system. Improper medication therapy management can lead to serious consequences and even death. In order to reduce DRPs further, and promote the rapid recovery of surgical patients, the need for pharmacists in the ERAS program is even more pressing. However, the medication therapy management services of surgical pharmacy and how surgical pharmacists should participate in ERAS programs are still unclear worldwide. Therefore, this article reviews the main perioperative medical management strategies and precautions from several aspects, including antimicrobial agents, antithrombotic agents, pain medication, nutritional therapy, blood glucose monitoring, blood pressure treatment, fluid management, treatment of nausea and vomiting, and management of postoperative delirium. Additionally, the way surgical pharmacists participate in perioperative medication management, and the relevant medication pathways are explored for optimizing medication therapy management services within the ERAS programs. This study will greatly assist surgical pharmacists' work, contributing to surgeons accepting that pharmacists have an important role in the multidisciplinary team, benefitting medical workers in treating, counseling, and advocating for their patients, and further improving the effectiveness, safety and economy of medication therapy for patients and promoting patient recovery.

Hemodynamic Instability during Surgery for Pheochromocytoma: A Retrospective Cohort Analysis

BACKGROUND

Perioperative hemodynamic instability is one of the most common adverse events in patients undergoing adrenalectomy for pheochromocytoma. The aim of this study was to analyze the impact of perioperative severe hemodynamic instability.

METHODS

We present a retrospective, single-center analysis in a major tertiary hospital of all consecutive patients undergoing elective adrenalectomy from 2005 to 2019 for pheochromocytoma. Severe perioperative hypertension and hypotension were evaluated, defined as changes in blood pressure larger than 30% of the preoperative patient-specific mean arterial pressure (MAP).

RESULTS

Unilateral adrenalectomy was performed in 67 patients. Intraoperative episodes of hemodynamic instability occurred in 97% of all patients (n = 65), severe hypertension occurred in 24 patients (36%), and severe hypotensive episodes occurred in 62 patients (93%). Patients with more than five severe hypotensive episodes (n = 29) received higher preoperative alpha-adrenergic blockades (phenoxybenzamine 51 ± 50 mg d^-1 vs. 29 ± 27 mg d^-1; p = 0.023) and had a longer mean ICU stay (39.6 ± 41.5 h vs. 20.6 ± 19.1 h, p = 0.015).

CONCLUSION

Intraoperative hypotensive, rather than hypertensive, episodes occurred during adrenalectomy. The occurrence of more than five hypotensive episodes correlated well with a significantly longer hospital stay and ICU time.

Hypotension Prediction Index Software to Prevent Intraoperative Hypotension during Major Non-Cardiac Surgery: Protocol for a European Multicenter Prospective Observational Registry (EU-HYPROTECT)

Background: Intraoperative hypotension is common in patients having non-cardiac surgery and associated with postoperative acute myocardial injury, acute kidney injury, and mortality. Avoiding intraoperative hypotension is a complex task for anesthesiologists. Using artificial intelligence to predict hypotension from clinical and hemodynamic data is an innovative and intriguing approach. The AcumenTM Hypotension Prediction Index (HPI) software (Edwards Lifesciences; Irvine, CA, USA) was developed using artificial intelligence—specifically machine learning—and predicts hypotension from blood pressure waveform features. We aimed to describe the incidence, duration, severity, and causes of intraoperative hypotension when using HPI monitoring in patients having elective major non-cardiac surgery. Methods: We built up a European, multicenter, prospective, observational registry including at least 700 evaluable patients from five European countries. The registry includes consenting adults (≥18 years) who were scheduled for elective major non-cardiac surgery under general anesthesia that was expected to last at least 120 min and in whom arterial catheter placement and HPI monitoring was planned. The major objectives are to quantify and characterize intraoperative hypotension (defined as a mean arterial pressure [MAP] < 65 mmHg) when using HPI monitoring. This includes the time-weighted average (TWA) MAP < 65 mmHg, area under a MAP of 65 mmHg, the number of episodes of a MAP < 65 mmHg, the proportion of patients with at least one episode (1 min or more) of a MAP < 65 mmHg, and the absolute maximum decrease below a MAP of 65 mmHg. In addition, we will assess causes of intraoperative hypotension and investigate associations between intraoperative hypotension and postoperative outcomes. Discussion: There are only sparse data on the effect of using HPI monitoring on intraoperative hypotension in patients having elective major non-cardiac surgery. Therefore, we built up a European, multicenter, prospective, observational registry to describe the incidence, duration, severity, and causes of intraoperative hypotension when using HPI monitoring in patients having elective major non-cardiac surgery.

Intraoperative prediction of postanaesthesia care unit hypotension

BACKGROUND

Postoperative hypotension is associated with adverse outcomes, but intraoperative prediction of postanaesthesia care unit (PACU) hypotension is not routine in anaesthesiology workflow. Although machine learning models may support clinician prediction of PACU hypotension, clinician acceptance of prediction models is poorly understood.

METHODS

We developed a clinically informed gradient boosting machine learning model using preoperative and intraoperative data from 88 446 surgical patients from 2015 to 2019. Nine anaesthesiologists each made 192 predictions of PACU hypotension using a web-based visualisation tool with and without input from the machine learning model. Questionnaires and interviews were analysed using thematic content analysis for model acceptance by anaesthesiologists.

RESULTS

The model predicted PACU hypotension in 17 029 patients (area under the receiver operating characteristic [AUROC] 0.82 [95% confidence interval {CI}: 0.81-0.83] and average precision 0.40 [95% CI: 0.38-0.42]). On a random representative subset of 192 cases, anaesthesiologist performance improved from AUROC 0.67 (95% CI: 0.60-0.73) to AUROC 0.74 (95% CI: 0.68-0.79) with model predictions and information on risk factors. Anaesthesiologists perceived more value and expressed trust in the prediction model for prospective planning, informing PACU handoffs, and drawing attention to unexpected cases of PACU hypotension, but they doubted the model when predictions and associated features were not aligned with clinical judgement. Anaesthesiologists expressed interest in patient-specific thresholds for defining and treating postoperative hypotension.

CONCLUSIONS

The ability of anaesthesiologists to predict PACU hypotension was improved by exposure to machine learning model predictions. Clinicians acknowledged value and trust in machine learning technology. Increasing familiarity with clinical use of model predictions is needed for effective integration into perioperative workflows.

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.

Hemodynamic monitoring with Hypotension Prediction Index versus arterial waveform analysis alone and incidence of perioperative hypotension

BACKGROUND

Intraoperative hypotension is associated with increased morbidity and mortality. The Hypotension Prediction Index (HPI) is an advancement of the arterial waveform analysis to predict intraoperative hypotension minutes before episodes occur enabling preventive treatments. We tested the hypothesis that the HPI combined with a personalized treatment protocol reduces intraoperative hypotension when compared to arterial waveform analysis alone.

METHODS

We conducted a retrospective analysis of 100 adult consecutive patients undergoing moderate- or high-risk noncardiac surgery with invasive arterial pressure monitoring using either index guidance (HPI) or arterial waveform analysis (FloTrac) depending on availability (FloTrac, n = 50; HPI, n = 50). A personalized 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 time-weighted average of hypotension.

RESULTS

In the FloTrac group, 42 patients (84%) experienced a hypotension while in the HPI group 26 patients (52%) were hypotensive (p = 0.001). The median (IQR) time-weighted average of hypotension in the FloTrac group was 0.27 (0.42) mmHg versus 0.10 (0.19) mmHg in the HPI group (p = 0.001). Finally, the median duration of each hypotensive event (IQR) was 2.75 (2.40) min in the FloTrac group compared to 1.00 (2.06) min in the HPI group (p = 0.002).

CONCLUSIONS

The application of the HPI combined with a personalized treatment protocol can reduce incidence and duration of hypotension when compared to arterial waveform analysis alone. This study therefore provides further evidence of the transition from prediction to actual prevention of hypotension using HPI.

Perioperative hemodynamic optimization: from guidelines to implementation-an experts' opinion paper

Despite a large body of evidence, the implementation of guidelines on hemodynamic optimization and goal-directed therapy remains limited in daily routine practice. To facilitate/accelerate this implementation, a panel of experts in the field proposes an approach based on six relevant questions/answers that are frequently mentioned by clinicians, using a critical appraisal of the literature and a modified Delphi process. The mean arterial pressure is a major determinant of organ perfusion, so that the authors unanimously recommend not to tolerate absolute values below 65 mmHg during surgery to reduce the risk of postoperative organ dysfunction. Despite well-identified limitations, the authors unanimously propose the use of dynamic indices to rationalize fluid therapy in a large number of patients undergoing non-cardiac surgery, pending the implementation of a “validity criteria checklist” before applying volume expansion. The authors recommend with a good agreement mini- or non-invasive stroke volume/cardiac output monitoring in moderate to high-risk surgical patients to optimize fluid therapy on an individual basis and avoid volume overload. The authors propose to use fluids and vasoconstrictors in combination to achieve optimal blood flow and maintain perfusion pressure above the thresholds considered at risk. Although purchase of disposable sensors and stand-alone monitors will result in additional costs, the authors unanimously acknowledge that there are data strongly suggesting this may be counterbalanced by a sustained reduction in postoperative morbidity and hospital lengths of stay. Beside existing guidelines, knowledge and explicit clinical reasoning tools followed by decision algorithms are mandatory to implement individualized hemodynamic optimization strategies and reduce postoperative morbidity and duration of hospital stay in high-risk surgical patients.

[Perioperative pharmacological circulatory support in daily clinical routine]

Perioperative phases of hypotension are associated with an increase in postoperative complications and organ damage. Whereas some years ago hemodynamic stabilization was primarily carried out by volume supplementation, in recent years the use and dosing of cardiovascular-active substances has significantly increased. But like intravascular volume therapy, also substances with a cardiovascular effect have therapeutic margins, and thus, potential side effects. This review article discusses indications for each cardiovascular-active agent, weighing up advantages and disadvantages. Special attention is paid to the question how to administrate them: central venous catheter vs. peripheral indwelling venous cannula. The authors come to the conclusion that it is not a question of whether it is principally allowed to apply cardiovascular-active drugs via peripheral veins but more importantly, what should be taken into consideration if a peripheral venous access is used. This article provides concise recommendations.

Intraoperative hypotension: Pathophysiology, clinical relevance, and therapeutic approaches

Intraoperative hypotension (IOH) i.e., low arterial blood pressure (AP) during surgery is common in patients having non-cardiac surgery under general anaesthesia. It has a multifactorial aetiology, and is associated with major postoperative complications including acute kidney injury, myocardial injury and death. Therefore, IOH may be a modifiable risk factor for postoperative complications. However, there is no uniform definition for IOH. IOH not only occurs during surgery but also after the induction of general anaesthesia before surgical incision. However, the optimal therapeutic approach to IOH remains elusive. There is evidence from one small randomised controlled trial that individualising AP targets may reduce the risk of postoperative organ dysfunction compared with standard care. More research is needed to define individual AP harm thresholds, to develop therapeutic strategies to treat and avoid IOH, and to integrate new technologies for continuous AP monitoring.

Intraoperative hypotension and its prediction

Intraoperative hypotension (IOH) very commonly accompanies general anaesthesia in patients undergoing major surgical procedures. The development of IOH is unwanted, since it is associated with adverse outcomes such as acute kidney injury and myocardial injury, stroke and mortality. Although the definition of IOH is variable, harm starts to occur below a mean arterial pressure (MAP) threshold of 65 mmHg. The odds of adverse outcome increase for increasing duration and/or magnitude of IOH below this threshold, and even short periods of IOH seem to be associated with adverse outcomes. Therefore, reducing the hypotensive burden by predicting and preventing IOH through proactive appropriate treatment may potentially improve patient outcome. In this review article, we summarise the current state of the prediction of IOH by the use of so-called machine-learning algorithms. Machine-learning algorithms that use high-fidelity data from the arterial pressure waveform, may be used to reveal 'traits' that are unseen by the human eye and are associated with the later development of IOH. These algorithms can use large datasets for 'training', and can subsequently be used by clinicians for haemodynamic monitoring and guiding therapy. A first clinically available application, the hypotension prediction index (HPI), is aimed to predict an impending hypotensive event, and additionally, to guide appropriate treatment by calculated secondary variables to asses preload (dynamic preload variables), contractility (dP/dtmax), and afterload (dynamic arterial elastance, Eadyn). In this narrative review, we summarise the current state of the prediction of hypotension using such novel, automated algorithms and we will highlight HPI and the secondary variables provided to identify the probable origin of the (impending) hypotensive event.