Hypotension Prediction Index software for management of hypotension during moderate- to high-risk noncardiac surgery: protocol for a randomized trial

Goal-directed therapy guided by the FloTrac sensor in major surgery: a systematic review and meta-analysis

OBJECTIVE

To provide insights into the potential benefits of goal-directed therapy guided by FloTrac in reducing postoperative complications and improving outcomes.

METHODS

We performed a systematic review and meta-analysis of randomized controlled trials to evaluate goal-directed therapy guided by FloTrac in major surgery, comparing goal-directed therapy with usual care or invasive monitoring in cardiac and noncardiac surgery subgroups. The quality of the articles and evidence were evaluated with a risk of bias tool and GRADE.

RESULTS

We included 29 randomized controlled trials with 3,468 patients. Goal-directed therapy significantly reduced the duration of hospital stay (mean difference -1.43 days; 95%CI 2.07 to -0.79; I2 81%), intensive care unit stay (mean difference -0.77 days; 95%CI -1.18 to -0.36; I2 93%), and mechanical ventilation (mean difference -2.48 hours, 95%CI -4.10 to -0.86, I2 63%). There was no statistically significant difference in mortality, myocardial infarction, acute kidney injury or hypotension, but goal-directed therapy significantly reduced the risk of heart failure or pulmonary edema (RR 0.46; 95%CI 0.23 - 0.92; I2 0%).

CONCLUSION

Goal-directed therapy guided by the FloTrac sensor improved clinical outcomes and shortened the length of stay in the hospital and intensive care unit in patients undergoing major surgery. Further research can validate these results using specific protocols and better understand the potential benefits of FloTrac beyond these outcomes.

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.

Advanced Variables to Optimize Hemodynamic Monitoring

Measuring hemodynamic parameters has become safer and more precise than in the past. Accurately monitoring and evaluating the effectiveness of fluid, inotrope, and vasoactive medication administration can improve patient outcomes. Arbitrary fluid administration without stroke volume measurement can be detrimental to patient outcomes. Early detection and prompt treatment of shock states is essential to combat deleterious effects on critically ill patients. In addition to measuring traditional hemodynamic variables, the use of advanced variables such as hypotension prediction index, dynamic arterial elastance, and systolic slope can improve the precision of treat ment for critically ill patients. Using predictive analytics can help the bedside critical care nurse provide patient care that is proactive rather than reactive.

Ambulatory anesthesia: restructuring for success

PURPOSE OF REVIEW

We review current evidence about organizational structures, patient selection criteria, safety measures, economic considerations, quality management, and staffing challenges in ambulatory anesthesia. The focus is on the facilitators and barriers related to the peri-interventional period and the potential concepts and innovations for the future development of ambulatory anesthesia services.

RECENT FINDINGS

Recent findings shed light on organizational structures in ambulatory anesthesia, including hospital-based centers, freestanding ambulatory centers, and office-based practices. Patient selection for ambulatory anesthesia involves a two-step process, considering both surgical and anesthetic factors. Safety measures, such as standardized guidelines and scoring systems, aim to ensure patient well being during the perioperative course. Economic considerations pose challenges due to the complexities of managing operating room efficiency and the variations in reimbursement systems. Quality management in ambulatory anesthesia emphasizes the need for outcome studies and patient-centered quality indicators. Staffing requirements necessitate highly skilled professionals with both technical and nontechnical skills, and structured education and training are essential.

SUMMARY

Ambulatory anesthesia is gaining importance due to advancements in surgical techniques and peri-interventional care. The review highlights the need for addressing challenges related to organizational structures, patient selection, patient safety, economic considerations, quality management, and staffing in ambulatory anesthesia. Understanding and addressing these factors are crucial for promoting the further development and improvement of ambulatory anesthesia services.

Systems Anesthesiology: Systems of Care Delivery and Optimization in the Operating Room

Anesthesiology presents a challenge to a traditional simplifying approach given the ever-increasing amount of medical data and a more demanding environment. Systems anesthesiology is a modern approach to perioperative care, integrating the complexity of multifactorial knowledge and data to achieve a more adequate representation of reality, while including both patient-related medical aspects as well as economic and organizational challenges. We discuss the value of some innovative technologies such as the emergence of anesthesia information systems, the use of tele-medicine, predictive monitoring, or closed-loop systems as it pertains to the changes in the current standards of care in anesthesiology. Furthermore, we highlight the importance of systems anesthesiology in operating room planning, anesthesia research, and education.

Development of the Modern Cardiothoracic Intensive Care Unit and Current Management

The modern cardiothoracic intensive care unit (CTICU) developed as a result of advances in critical care, cardiology, and cardiac surgery. Patients undergoing cardiac surgery today are sicker, frailer, and have more complex cardiac and noncardiac morbidities. CTICU providers need to understand postoperative implications of different surgical procedures, complications that can occur in CTICU patients, resuscitation protocols for cardiac arrest, and diagnostic and therapeutic interventions such as transesophageal echocardiography and mechanical circulatory support. Optimum CTICU care requires a multidisciplinary team with collaboration between cardiac surgeons and critical care physicians with training and experience in the care of CTICU patients.

Integrating machine learning predictions for perioperative risk management: Towards an empirical design of a flexible-standardized risk assessment tool

BACKGROUND

Surgical patients are complex, vulnerable, and prone to postoperative complications that can potentially be mitigated with quality perioperative risk assessment and management. Several institutions have incorporated machine learning (ML) into their patient care to improve awareness and support clinician decision-making along the perioperative spectrum. Recent research suggests that ML risk prediction can support perioperative patient risk monitoring and management across several situations, including the operating room (OR) to intensive care unit (ICU) handoffs.

OBJECTIVES

Our study objectives were threefold: (1) evaluate whether ML-generated postoperative predictions are concordant with clinician-generated risk rankings for acute kidney injury, delirium, pneumonia, deep vein thrombosis, and pulmonary embolism, and establish their associated risk factors; (2) ascertain clinician end-user suggestions to improve adoption of ML-generated risks and their integration into the perioperative workflow; and (3) develop a user-friendly visualization format for a tool to display ML-generated risks and risk factors to support postoperative care planning, for example, within the context of OR-ICU handoffs.

METHODS

Graphical user interfaces for postoperative risk prediction models were assessed for end-user usability through cognitive walkthroughs and interviews with anesthesiologists, surgeons, certified registered nurse anesthetists, registered nurses, and critical care physicians. Thematic analysis relying on an explanation design framework was used to identify feedback and suggestions for improvement.

RESULTS

17 clinicians participated in the evaluation. ML estimates of complication risks aligned with clinicians' independent rankings, and related displays were perceived as valuable for decision-making and care planning for postoperative care. During OR-ICU handoffs, the tool could speed up report preparation and remind clinicians to address patient-specific complications, thus providing more tailored care information. Suggestions for improvement centered on electronic tool delivery; methods to build trust in ML models; modifiable risks and risk mitigation strategies; and additional patient information based on individual preferences (e.g., surgical procedure).

CONCLUSIONS

ML estimates of postoperative complication risks can provide anticipatory guidance, potentially increasing the efficiency of care planning. We have offered an ML visualization framework for designing future ML-augmented tools and anticipate the development of tools that recommend specific actions to the user based on ML model output.

An ethnographic study on the impact of a novel telemedicine-based support system in the operating room

OBJECTIVE

The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)-risk-inducing or risk-mitigating workflow deviations-and ascertain ACT's impact on clinical workflow and patient safety.

MATERIALS AND METHODS

We used ethnographic methods including shadowing ACT and OR clinicians during 83 surgeries, artifact collection, chart reviews for decision alerts sent to the OR, and 10 clinician interviews. We used hybrid thematic analysis informed by a human-factors systems-oriented approach to assess ACT's role and impact on safety, conducting content analysis to assess NREs.

RESULTS

Across 83 cases, 469 risk alerts were triggered, and the ACT sent 280 care recommendations to the OR. 135 NREs were observed. Critical factors facilitating ACT's role in supporting patient safety included providing backup support and offering a fresh-eye perspective on OR decisions. Factors impeding ACT included message timing and ACT and OR clinician cognitive lapses. Suggestions for improvement included tailoring ACT message content (structure, timing, presentation) and incorporating predictive analytics for advanced planning.

DISCUSSION

ACT served as a safety net with remote surveillance features and as a learning healthcare system with feedback/auditing features. Supporting strategies include adaptive coordination and harnessing clinician/patient support to improve ACT's sustainability. Study insights inform future intraoperative telemedicine design considerations to mitigate safety risks.

CONCLUSION

Incorporating similar remote technology enhancement into routine perioperative care could markedly improve safety and quality for millions of surgical patients.

Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials

Objective

Given the complexities of testing the translational capability of new artificial intelligence (AI) tools, we aimed to map the pathways of training/validation/testing in development process and external validation of AI tools evaluated in dedicated randomised controlled trials (AI-RCTs).

Methods

We searched for peer-reviewed protocols and completed AI-RCTs evaluating the clinical effectiveness of AI tools and identified development and validation studies of AI tools. We collected detailed information, and evaluated patterns of development and external validation of AI tools.

Results

We found 23 AI-RCTs evaluating the clinical impact of 18 unique AI tools (2009–2021). Standard-of-care interventions were used in the control arms in all but one AI-RCT. Investigators did not provide access to the software code of the AI tool in any of the studies. Considering the primary outcome, the results were in favour of the AI intervention in 82% of the completed AI-RCTs (14 out of 17). We identified significant variation in the patterns of development, external validation and clinical evaluation approaches among different AI tools. A published development study was found only for 10 of the 18 AI tools. Median time from the publication of a development study to the respective AI-RCT was 1.4 years (IQR 0.2–2.2).

Conclusions

We found significant variation in the patterns of development and validation for AI tools before their evaluation in dedicated AI-RCTs. Published peer-reviewed protocols and completed AI-RCTs were also heterogeneous in design and reporting. Upcoming guidelines providing guidance for the development and clinical translation process aim to improve these aspects.

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.

The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients

Critical care clinicians are trained to analyze simultaneously multiple physiological parameters to predict critical conditions such as hemodynamic instability. We developed the Multi-task Learning Physiological Deep Learner (MTL-PDL), a deep learning algorithm that predicts simultaneously the mean arterial pressure (MAP) and the heart rate (HR). In an external validation dataset, our model exhibited very good calibration: R² of 0.747 (95% confidence interval, 0.692 to 0.794) and 0.850 (0.815 to 0.879) for respectively, MAP and HR prediction 60-minutes ahead of time. For acute hypotensive episodes defined as a MAP below 65 mmHg for 5 min, our MTL-PDL reached a predictive value of 90% for patients at very high risk (predicted MAP ≤ 60 mmHg) and 2‰ for patients at low risk (predicted MAP >70 mmHg). Based on its excellent prediction performance, the Physiological Deep Learner has the potential to help the clinician proactively adjust the treatment in order to avoid hypotensive episodes and end-organ hypoperfusion.

Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: A cohort study

BACKGROUND

Intra-operative hypotension is associated with adverse postoperative outcomes. A machine-learning-derived algorithm developed to predict hypotension based on arterial blood pressure (ABP) waveforms significantly reduced intra-operative hypotension. The algorithm calculates the likelihood of hypotension occurring within minutes, expressed as the Hypotension Prediction Index (HPI) which ranges from 0 to 100. Currently, HPI is only available for patients monitored with invasive ABP, which is restricted to high-risk procedures and patients. In this study, the performance of HPI, employing noninvasive continuous ABP measurements, is assessed.

OBJECTIVES

The first aim was to compare the performance of the HPI algorithm, using noninvasive versus invasive ABP measurements, at a mathematically optimal HPI alarm threshold (Youden index). The second aim was to assess the performance of the algorithm using a HPI alarm threshold of 85 that is currently used in clinical trials. Hypotension was defined as a mean arterial pressure (MAP) below 65 mmHg for at least 1 min. The predictive performance of the algorithm at different HPI alarm thresholds (75 and 95) was studied.

DESIGN

Observational cohort study.

SETTING

Tertiary academic medical centre.

PATIENTS

Five hundred and seven adult patients undergoing general surgery.

RESULTS

The performance of the algorithm with invasive and noninvasive ABP input was similar. A HPI alarm threshold of 85 showed a median [IQR] time from alarm to hypotension of 2.7 [1.0 to 7.0] min with a sensitivity of 92.7 (95% confidence interval [CI], 91.2 to 94.3), specificity of 87.6 (95% CI, 86.2 to 89.0), positive predictive value of 79.9 (95% CI, 77.7 to 82.1) and negative predictive value of 95.8 (95% CI, 94.9 to 96.7). A HPI alarm threshold of 75 provided a lower positive predictive value but a prolonged time from prediction to actual hypotension.

CONCLUSION

This study demonstrated that the algorithm can be employed using continuous noninvasive ABP waveforms. This opens up the potential to predict and prevent hypotension in a larger patient population.

TRIAL REGISTRATION

Clinical trials registration number NCT03533205.

Hypotension Prediction Index for Prevention of Hypotension during Moderate- to High-risk Noncardiac Surgery

Background

The Hypotension Prediction Index is a commercially available algorithm, based on arterial waveform features, that predicts hypotension defined as mean arterial pressure less than 65 mmHg for at least 1 min. We therefore tested the primary hypothesis that index guidance reduces the duration and severity of hypotension during noncardiac surgery.

Methods

We enrolled adults having moderate- or high-risk noncardiac surgery with invasive arterial pressure monitoring. Participating patients were randomized to hemodynamic management with or without index guidance. Clinicians caring for patients assigned to guidance were alerted when the index exceeded 85 (range, 0 to 100) and a treatment algorithm based on advanced hemodynamic parameters suggested vasopressor administration, fluid administration, inotrope administration, or observation. Primary outcome was the amount of hypotension, defined as time-weighted average mean arterial pressure less than 65 mmHg. Secondary outcomes were time-weighted mean pressures less than 60 and 55 mmHg.

Results

Among 214 enrolled patients, guidance was provided for 105 (49%) patients randomly assigned to the index guidance group. The median (first quartile, third quartile) time-weighted average mean arterial pressure less than 65 mmHg was 0.14 (0.03, 0.37) in guided patients versus 0.14 (0.03, 0.39) mmHg in unguided patients: median difference (95% CI) of 0 (–0.03 to 0.04), P = 0.757. Index guidance therefore did not reduce amount of hypotension less than 65 mmHg, nor did it reduce hypotension less than 60 or 55 mmHg. Post hoc, guidance was associated with less hypotension when analysis was restricted to episodes during which clinicians intervened.

Conclusions

In this pilot trial, index guidance did not reduce the amount of intraoperative hypotension. Half of the alerts were not followed by treatment, presumably due to short warning time, complex treatment algorithm, or clinicians ignoring the alert. In the future we plan to use a lower index alert threshold and a simpler treatment algorithm that emphasizes prompt treatment.

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Data-driven ICU management: Using Big Data and algorithms to improve outcomes

The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term "Big Data" can be used to refer to the analysis of these datasets that collect enormous amount of data of different origin and format. Complexity and variety define the value of Big Data. In fact, the retrospective analysis of these datasets allows to generate new knowledge, with consequent potential improvements in the clinical practice. Despite the promising start of Big Data analysis in medical research, which has seen a rising number of peer-reviewed articles, very limited applications have been used in ICU clinical practice. A close future effort should be done to validate the knowledge extracted from clinical Big Data and implement it in the clinic. In this article, we provide an introduction to Big Data in the ICU, from data collection and data analysis, to the main successful examples of prognostic, predictive and classification models based on ICU data. In addition, we focus on the main challenges that these models face to reach the bedside and effectively improve ICU care.

Dynamic Arterial Elastance as a Ventriculo-Arterial Coupling Index: An Experimental Animal Study

Dynamic arterial elastance (Ea_dyn), the ratio between arterial pulse pressure and stroke volume changes during respiration, has been postulated as an index of the coupling between the left ventricle (LV) and the arterial system. We aimed to confirm this hypothesis using the gold-standard for defining LV contractility, afterload, and evaluating ventricular-arterial (VA) coupling and LV efficiency during different loading and contractile experimental conditions. Twelve Yorkshire healthy female pigs submitted to three consecutive stages with two opposite interventions each: changes in afterload (phenylephrine/nitroprusside), preload (bleeding/fluid bolus), and contractility (esmolol/dobutamine). LV pressure-volume data was obtained with a conductance catheter, and arterial pressures were measured via a fluid-filled catheter in the proximal aorta and the radial artery. End-systolic elastance (Ees), a load-independent index of myocardial contractility, was calculated during an inferior vena cava occlusion. Effective arterial elastance (Ea, an index of LV afterload) was calculated as LV end-systolic pressure/stroke volume. VA coupling was defined as the ratio Ea/Ees. LV efficiency (LV_eff) was defined as the ratio between stroke work and the LV pressure-volume area. Ea_dyn was calculated as the ratio between the aortic pulse pressure variation (PPV) and conductance-derived stroke volume variation (SVV). A linear mixed model was used for evaluating the relationship between Ees, Ea, VA coupling, LV_eff with Ea_dyn. Ea_dyn was inversely related to VA coupling and directly to LV_eff. The higher the Ea_dyn, the higher the LV_eff and the lower the VA coupling. Thus, Ea_dyn, an easily measured parameter at the bedside, may be of clinical relevance for hemodynamic assessment of the unstable patient.

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.