Hypotension Prediction Index Research Articles

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.

Performance of the Hypotension Prediction Index in living donor liver transplant recipients.

The Hypotension Prediction Index (HPI) was recently introduced and clinically validated in different surgical conditions. This prospective observational study evaluated HPI's performance in living donor liver transplant recipients under the hypothesis that HPI would be inferior to the previously reported predictability in major surgery due to the surgical characteristics of liver transplantation. Twenty adult patients undergoing living donor liver transplantation were enrolled. HPI was monitored during the surgery with the attending anesthesiologist blinded to the HPI. The mean arterial pressure and HPI were recorded at 1-minute intervals. The area under the curve (AUC) of the receiver operating characteristic curve was calculated for the whole dataset and at each phase of liver transplantation at 5, 10, and 15 minutes to analyze HPI's performance. A total of 9173 data points were analyzed. The AUC for predicting hypotension at 5 minutes was 0.810 (95% confidence interval [CI]: 0.780-0.840). The AUCs for predicting hypotension at 10 and 15 minutes were 0.726 (95% CI: 0.681-0.772) and 0.689 (95% CI: 0.642-0.737), respectively. The AUCs for predicting hypotension at 5 minutes in the preanhepatic, anhepatic, and neohepatic phase were 0.795 (95% CI: 0.711-0.876), 0.728 (95% CI: 0.638-0.819), and 0.837 (95% CI: 0.802-0.873), respectively. The HPI's performance was inferior to that previously reported in major surgery. HPI in this observational study in living donor liver transplantation predicted hypotension with moderate-to-low accuracy, its predictive value being highest in the neohepatic phase and lowest in the anhepatic phase.

Selection Bias in the Hypotension Prediction Index: Comment.

Selection Bias in the Hypotension Prediction Index: Comment.

Selection Bias in the Hypotension Prediction Index: Reply.

We have read the interesting comment by Giustiniano and Nisi1 about our article.2 The authors highlight that the diastolic arterial blood pressure can decrease significantly when the Hypotension Prediction Index (hereafter the Index) reaches approximately 50 and argue for the possible need for treatment when diastolic arterial blood pressure is very low. We appreciate the comment and would like to highlight additional aspects that probably need to be considered in this context.First, Giustiniano and Nisi mention the “gray zone” as the reason for commenting on our publication. We want to stress that the exclusion of “gray-zone” outcomes (mean arterial pressure [MAP] between 65 and 75 mmHg) is not our primary concern with the method used to develop and validate the Index. Our primary concern is that the method also enforces that the prediction data for nonhypotension cannot have a MAP less than 75 mmHg, while the prediction data for hypotension can. The difference between simply implementing a “gray zone” and the biased selection of predictors actually used in development and validation of the Index3 is illustrated in figure 1, B and C. With the biased data selection (fig. 1C), the MAP value at the time of prediction unintendedly carries definitive information about the future outcome: a MAP less than 75 mmHg is always associated with upcoming hypotension. As a consequence, MAP becomes a highly specific predictor of hypotension—similar to what is reported for the Index. We recommend comparing the predictive value of the Index head-to-head with that of MAP.It is certainly possible that diastolic arterial blood pressure has additional value in predicting upcoming hypotension, not least because of the problematic method used to train the Index. Due to the selection bias, the Index algorithm has likely learned the artificially high predictive value of MAP at the cost of underutilizing other relevant predictors of hypotension.2However, we need to understand the selection bias’s effect on the Index’s actual predictive abilities before we can meaningfully discuss the Index’s interplay with other variables.In line with our theoretical discussion of the Index,2 Figure 1, A and B, in Giustiniano and Nisi’s letter and our own observations (fig. 2) demonstrate anecdotally three important aspects:We thank Giustiniano and Nisi for their relevant DAP comment and for sharing data that visualize the dissociation between clinical reality and reported classification results. Before we discuss the interplay of the Index and other physiologic variables, we should address the consequence of the selection bias.Dr. Vistisen is associate editor of the Journal of Clinical Monitoring and Computing. Dr. Vistisen has coauthored one of the published scientific papers aiming to validate the Hypotension Prediction Index. The other authors declare no competing interests.

EE450 The Burden of Intraoperative Hypotension in Patients Undergoing High-Risk Noncardiac Surgery and the Role of a Machine Learning-Derived Algorithm to Optimize Its Management: What Are We Missing Ignoring Supportive Innovations?

Intraoperative hypotension (IOH) in noncardiac surgery entails a humanistic and economic burden associated with a higher risk of mortality and severe adverse events (AEs). Machine learning-derived algorithms, such as the Hypotension Prediction Index (HPI), contribute to reducing IOH depth and duration, minimizing its impact on patients’ recovery. Objectives were twofold: to highlight the impact of AEs of IOH in Spain and estimate potential savings provided by HPI use.

Noninvasive hypotension Prediction Index versus continuous blood pressure monitoring and intraoperative hypotension.

Noninvasive hypotension Prediction Index versus continuous blood pressure monitoring and intraoperative hypotension.

The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report

BackgroundThe Hypotension Prediction Index (HPI) displays an innovative monitoring tool which predicts intraoperative hypotension before its onset.Case presentationWe report the case of an 84-year-old Caucasian woman undergoing major spinal surgery with no possibility for the transfer of blood products given her status as a Jehovah’s Witness. The hemodynamic treatment algorithm we employed was based on HPI and resulted in a high degree of hemodynamic stability during the surgical procedure. Further, the patient was not at risk for either hypo- or hypervolemia, conditions which might have caused dilution anemia. By using HPI as a tool for patient blood management, it was possible to reduce the incidence of intraoperative hypotension to a minimum.ConclusionsIn sum, this HPI-based treatment algorithm represents a useful application for the treatment of complex anesthesia and perioperative patient blood management. It is a simple but powerful extension of standard monitoring for the prevention of intraoperative hypotension.

Indicators of haemodynamic instability and left ventricular function in a porcine model of esmolol induced negative inotropy

To investigate if the Hypotension Prediction Index was an early indicator of haemodynamic instability in a negative inotropy porcine model, and to assess the correlation of commonly measured indicators of left ventricular systolic function. Eight anaesthetised pigs were volume resuscitated and then underwent an incremental infusion of esmolol hydrochloride (0-3000 mg/hr), following which it was then reduced in a stepwise manner. Full haemodynamic measurements were taken at each stage and measurements of left ventricular systolic function including left ventricular stroke work index, ejection fraction and peripheral dP/dT were obtained. At an infusion rate of 500 mg/hr of esmolol there were no significant changes in any measured variables. At 1000 mg/hr MAP was on average 11 mmHg lower (95% CI 1 to 11 mmHg, p = 0.027) with a mean of 78 mmHg, HPI increased by 33 units (95% CI 4 to 62, p = 0.026) with a mean value of 63. No other parameters showed significant change from baseline values. Subsequent increases in esmolol showed changes in all parameters except SVV, SVR and PA mean. Correlation between dP/dt and LVSWI was 0.85 (95% CI 0.77 to 0.90, p < 0.001), between LVEF and dP/dt 0.39 (95% CI 0.18 to 0.57, p < 0.001), and between LSWI and LVEF 0.41 (95% CI 0.20 to 0.59, p < 0.001). In this model haemodynamic instability induced by negative inotropy was detected by the HPI algorithm prior to any clinically significant change in commonly measured variables. In addition, the peripheral measure of left ventricular contractility dP/dt correlates well with more established measurements of LV systolic function.

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.

Journal-related Activities and Other Special Activities at the 2022 American Society of Anesthesiologists Meeting

Journal-related Activities and Other Special Activities at the 2022 American Society of Anesthesiologists Meeting

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.

P4.02: Performance of the Hypotension Prediction Index in Living Donor Liver Transplant Recipients: A Prospective Observational Study

P4.02: Performance of the Hypotension Prediction Index in Living Donor Liver Transplant Recipients: A Prospective Observational Study

Performance of the Hypotension Prediction Index With Noninvasive Arterial Pressure Waveforms in Awake Cesarean Delivery Patients Under Spinal Anesthesia

(Anesth Analg. 2022;134:633–643) Many consider spinal anesthesia (SA) the foremost method for cesarean delivery, but previous studies show this can lead to hypotension. A previous research team determined the effectiveness of the noninvasive hypotension prediction index (HPI) algorithm, an algorithm using high-fidelity arterial pressure waveform recordings and 22 additional features to measure the likelihood of a hypotensive event on a scale from 1 to 100, in predicting hypotension. This study assessed the ability of the HPI algorithm and the ClearSight system in comparison with noninvasive blood pressure monitoring (NIBP) during SA procedures.

This Month in Anesthesiology

This Month in Anesthesiology

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.

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.

Applicability of hypotension prediction index to surgical patients

Applicability of hypotension prediction index to surgical patients

Prediction of intraoperative hypotension from the linear extrapolation of mean arterial pressure.

Hypotension prediction index (HPI) software is a proprietary machine learning-based algorithm used to predict intraoperative hypotension (IOH). HPI has shown superiority in predicting IOH when compared to the predictive value of changes in mean arterial pressure (ΔMAP) alone. However, the predictive value of ΔMAP alone, with no reference to the absolute level of MAP, is counterintuitive and poor at predicting IOH. A simple linear extrapolation of mean arterial pressure (LepMAP) is closer to the clinical approach. Our primary objective was to investigate whether LepMAP better predicts IOH than ΔMAP alone. Retrospective diagnostic accuracy study. Two tertiary University Hospitals between May 2019 and December 2019. A total of 83 adult patients undergoing high risk non-cardiac surgery. Arterial pressure data were automatically extracted from the anaesthesia data collection software (one value per minute). IOH was defined as MAP < 65 mmHg. Correlations for repeated measurements and the area under the curve (AUC) from receiver operating characteristics (ROC) were determined for the ability of LepMAP and ΔMAP to predict IOH at 1, 2 and 5 min before its occurrence (A-analysis, using the whole dataset). Data were also analysed after exclusion of MAP values between 65 and 75 mmHg (B-analysis). A total of 24 318 segments of ten minutes duration were analysed. In the A-analysis, ROC AUCs to predict IOH at 1, 2 and 5 min before its occurrence by LepMAP were 0.87 (95% confidence interval, CI, 0.86 to 0.88), 0.81 (95% CI, 0.79 to 0.83) and 0.69 (95% CI, 0.66 to 0.71) and for ΔMAP alone 0.59 (95% CI, 0.57 to 0.62), 0.61 (95% CI, 0.59 to 0.64), 0.57 (95% CI, 0.54 to 0.69), respectively. In the B analysis for LepMAP these were 0.97 (95% CI, 0.9 to 0.98), 0.93 (95% CI, 0.92 to 0.95) and 0.86 (95% CI, 0.84 to 0.88), respectively, and for ΔMAP alone 0.59 (95% CI, 0.53 to 0.58), 0.56 (95% CI, 0.54 to 0.59), 0.54 (95% CI, 0.51 to 0.57), respectively. LepMAP ROC AUCs were significantly higher than ΔMAP ROC AUCs in all cases. LepMAP provides reliable real-time and continuous prediction of IOH 1 and 2 min before its occurrence. LepMAP offers better discrimination than ΔMAP at 1, 2 and 5 min before its occurrence. Future studies evaluating machine learning algorithms to predict IOH should be compared with LepMAP rather than ΔMAP.

Real-world outcomes of the hypotension prediction index in the management of intraoperative hypotension during non-cardiac surgery: a retrospective clinical study.

The Hypotension Prediction Index (HPI) is a validated algorithm developed by applying machine learning for predicting intraoperative arterial hypotension (IOH). We evaluated whether the HPI, combined with a personalized treatment protocol, helps to reduce IOH (depth and duration) and perioperative events in real practice. This was a single-center retrospective study including 104 consecutive adults undergoing urgent or elective non-cardiac surgery with moderate-to-high risk of bleeding, requiring invasive blood pressure and continuous cardiac output monitoring. Depending on the sensor, two comparable groups were identified: patients managed following the institutional protocol of personalized goal-directed fluid therapy (GDFT, n = 52), or this GDFT supported by the HPI (HPI, n = 52). The time-weighted average of hypotension for a mean arterial pressure < 65mmHg (TWAMAP<65), postoperative complications and length of hospital stay (LOS) were automatically downloaded from medical records and revised by clinicians blinded to the management received by patients. Differences in preoperative variables (i.e. physical status -ASA class-, acute kidney Injury-AKI- risk) and outcomes were analyzed using non-parametric tests with Hodges-Lehmann estimator for the median of differences. ASA class and AKI risk were similar (p = 0.749 and p = 0.837, respectively). Blood loss was also comparable (p = 0.279). HPI patients had a lower TWAMAP<65 [0.09mmHg (0-0.48mmHg)] vs [0.23mmHg (0.01 to 0.97mmHg)], p = 0.037. Postoperative complications were less prevalent in the HPI patients (0.46 ± 0.98 vs. 0.88 ± 1.20), p = 0.035. Finally, LOS was significantly shorter among HPI patients with a median difference of 2days (p = 0.019). The HPI combined with a GDFT protocol may help to minimize the severity of IOH during non-cardiac surgery.

Intraoperative haemodynamic optimisation using the Hypotension Prediction Index and its impact on tissular perfusion: a protocol for a randomised controlled trial

IntroductionIntraoperative arterial hypotension is associated with poor postoperative outcomes. The Hypotension Prediction Index (HPI) developed using machine learning techniques, allows the prediction of arterial hypotension analysing the arterial pressure waveform....