Perioperative stroke - Prediction, Prevention, and Protection

The hypotension prediction index in major abdominal surgery - A prospective randomised clinical trial protocol

Background

Patients undergoing major abdominal surgery are at increased risk of developing perioperative hypotension, which is associated with increased mortality and morbidity. Despite using advanced technologies such as evaluating arterial pressure derived cardiac output, anaesthetic management to maintain hemodynamic stability is still reactive when the clinical decision is made after hypotension has developed. Previous perioperative goal-directed studies have not proven the benefits of this approach with high certainty. A new, approved technology called the Hypotension Prediction Index (HPI) aims to prevent hypotension occurrence by allowing the precise hemodynamic monitoring of patients under general anaesthesia, significantly reducing intraoperative hypotension events. This prospective randomised clinical trial aims to compare the rate of perioperative hypotension in patients undergoing major abdominal surgery according to their type of hemodynamic monitoring.

Methods

and Analysis: Patients meeting the inclusion criteria will be randomly assigned to receive hemodynamic assessment with arterial pressure cardiac output (APCO) monitoring (group A) or hemodynamic monitoring with the HPI software (group B). The primary outcome is a time-weighted average (TWA) mean arterial pressure (MAP) of <65 mmHg: TWA MAP = (depth of hypotension [in mmHg] below a MAP of 65 mmHg × time [in minutes] spent below a MAP of 65 mmHg)/total duration of the operation (in minutes). Its secondary outcomes include perioperative hemodynamic management and the rate of postoperative complications.

Ethics and dissemination

This trial was approved by the Ethics Committee of the Poznan University of Medical Sciences (KB-559/220; date: 01/07/2022). Its results will be submitted for publication in a peer-reviewed journal.

Trial registration number

NCT06247384.

Stroke outcomes following cardiac and aortic surgery are improved by the involvement of a stroke team

OBJECTIVES

Post-cardiac and aortic surgery stroke is often underreported. We detail our single-centre experience the following introduction of comprehensive consultant-led daily stroke service, to demonstrate the efficacy of a stroke team in recovery from stroke following cardiac and aortic surgeries.

METHODS

This retrospective, single-centre observational cohort study analysed consecutive patients undergoing cardiac and aortic surgery at our institution from August 2014 to December 2020. Main outcomes included stroke rate, predictors of stroke, and neurological deficit resolution or persistence at discharge and clinic follow-up.

RESULTS

A total of 12,135 procedures were carried out in the reference period. Among these, 436 (3.6%) suffered a stroke. Overall survival to discharge and follow-up were 86.0% and 84.0% respectively. Independent risk factors for post-operative stroke included advanced age (OR 1.033, 95% CI [1.023, 1.044], p < .001), female sex (OR 1.491, 95% [1.212, 1.827], p < .001), history of previous cardiac surgeries (OR 1.670, 95% CI [1.239, 2.218], p < .001), simultaneous coronary artery bypass graft + valve procedures (OR 1.825, 95% CI [1.382, 2.382], p < .001) and CPB time longer than 240 min (OR 3.384, 95% CI [2.413, 4.705], p < .001). Stroke patients managed by the multidisciplinary team demonstrated significantly higher rates of survival at discharge (87.3% vs. 61.9%, p = .001).

CONCLUSIONS

Perioperative stroke can be debilitating immediately long term. The involvement of specialist stroke teams plays a key role in reducing the long-term burden and mortality of this condition.

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.

Multitask Attention-Based Neural Network for Intraoperative Hypotension Prediction

Timely detection and response to Intraoperative Hypotension (IOH) during surgery is crucial to avoid severe postoperative complications. Although several methods have been proposed to predict IOH using machine learning, their performance still has space for improvement. In this paper, we propose a ResNet-BiLSTM model based on multitask training and attention mechanism for IOH prediction. We trained and tested our proposed model using bio-signal waveforms obtained from patient monitoring of non-cardiac surgery. We selected three models (WaveNet, CNN, and TCN) that process time-series data for comparison. The experimental results demonstrate that our proposed model has optimal MSE (43.83) and accuracy (0.9224) compared to other models, including WaveNet (51.52, 0.9087), CNN (318.52, 0.5861), and TCN (62.31, 0.9045), which suggests that our proposed model has better regression and classification performance. We conducted ablation experiments on the multitask and attention mechanisms, and the experimental results demonstrated that the multitask and attention mechanisms improved MSE and accuracy. The results demonstrate the effectiveness and superiority of our proposed model in predicting IOH.

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.

Neurofilament Light Chain: A Candidate Biomarker of Perioperative Stroke

Perioperative stroke is defined as a brain infarction of ischemic or hemorrhagic etiology that occurs during surgery or within 30 days after surgery. However, identifying perioperative stroke is challenging. Thus, the discovery and validation of neurological biomarkers for perioperative stroke are urgently needed. Neurofilament forms part of the neuronal cytoskeleton and is exclusively expressed in neurons. After disease-related neuroaxonal damage occurs, neurofilament light chain protein is released into the cerebrospinal fluid and blood. Blood neurofilament light chain has recently been shown to serve as a potential marker of interest during the perioperative period. Therefore, the aim of the present review was to give an overview of the current understanding and knowledge of neurofilament light chain as a potential biomarker of perioperative stroke.

Factors associated with long-term survival in patients with stroke after coronary artery bypass grafting

Objective

Occurrence of a stroke within 30 days following coronary artery bypass grafting (CABG) is an uncommon, but often devastating, complication. This study aimed to identify factors associated with long-term survival (beyond 30 days) in patients with stroke after CABG.

Methods

De-identified patients’ records from the Veterans Affairs Surgical Quality Improvement Program database were used to identify risk factors and perioperative complications associated with survival for up to 20 years in patients with post-CABG stroke. The multivariable Cox proportional hazards model was used for analyzing survival.

Results

The median survival time for patients with stroke (n = 1422) was 6.7 years. The mortality rate for these patients was highest in the first year post-CABG and was significantly elevated compared with non-stroke patients. Survival rates at 1, 5, and 10 years for stroke versus non-stroke patients were 79% vs. 96%, 58% vs. 83%, and 36% vs. 63%, respectively. High preoperative serum creatinine levels, postoperative occurrence of renal failure, prolonged ventilation, coma, and reoperation for bleeding were important predictors of 1-year mortality of patients with post-CABG stroke.

Conclusions

Veterans with post-CABG stroke have a considerably higher risk for mortality during the first year compared with patients without stroke.

Last Electrically Well: Intraoperative Neurophysiological Monitoring for Identification and Triage of Large Vessel Occlusions

INTRODUCTION

Intra-operative stroke (IOS) is associated with poor clinical outcome as detection is often delayed and time of symptom onset or patient's last known well (LKW) is uncertain. Intra-operative neurophysiological monitoring (IONM) is uniquely capable of detecting onset of neurological dysfunction in anesthetized patients, thereby precisely defining time last electrically well (LEW). This novel parameter may aid in the detection of large vessel occlusion (LVO) and prompt treatment with endovascular thrombectomy (EVT).

METHODS

We performed a retrospective analysis of a prospectively maintained AIS and LVO database from May 2018-August 2019. Inclusion criteria required any surgical procedure under general anesthesia (GA) utilizing EEG (electroencephalography) and/or SSEP (somatosensory evoked potentials) monitoring with development of intraoperative focal persistent changes using predefined alarm criteria and who were considered for EVT.

RESULT

Five cases were identified. LKW to closure time ranged from 66 to 321 minutes, while LEW to closure time ranged from 43 to 174 min. All LVOs were in the anterior circulation. Angiography was not pursued in two cases due to large established infarct (both patients expired in the hospital). EVT was pursued in two cases with successful recanalization and spontaneous recanalization was noted in one patient (mRS 0-3 at 90 days was achieved in all 3 cases).

CONCLUSIONS

This study demonstrates that significant IONM changes can accurately identify patients with an acute LVO in the operative setting. Given the challenges of recognizing peri-operative stroke, LEW may be an appropriate surrogate to quickly identify and treat IOS.

Perioperative Stroke

PURPOSE OF REVIEW

This review overviews perioperative stroke as it pertains to specific surgical procedures.

RECENT FINDINGS

As awareness of perioperative stroke increases, so does the opportunity to potentially improve outcomes for these patients by early stroke recognition and intervention. Perioperative stroke is defined to be any stroke that occurs within 30 days of the initial surgical procedure. The incidence of perioperative stroke varies and is dependent on the specific type of surgery performed. This chapter overviews the risks, mechanisms, and acute evaluation and management of perioperative stroke in four surgical populations: cardiac surgery, carotid endarterectomy, neurosurgery, and non-cardiac/non-carotid/non-neurological surgeries.

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.