Prediction Of Hypotension Research Articles

Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial

PurposeIntraoperative hypotension is linked to increased incidence of perioperative adverse events such as myocardial and cerebrovascular infarction and acute kidney injury. Hypotension prediction index (HPI) is a novel machine learning guided algorithm which can predict hypotensive events using high fidelity analysis of pulse-wave contour. Goal of this trial is to determine whether use of HPI can reduce the number and duration of hypotensive events in patients undergoing major thoracic procedures.MethodsThirty four patients undergoing esophageal or lung resection were randomized into 2 groups -“machine learning algorithm” (AcumenIQ) and “conventional pulse contour analysis” (Flotrac). Analyzed variables were occurrence, severity and duration of hypotensive events (defined as a period of at least one minute of MAP below 65 mmHg), hemodynamic parameters at 9 different timepoints interesting from a hemodynamics viewpoint and laboratory (serum lactate levels, arterial blood gas) and clinical outcomes (duration of mechanical ventilation, ICU and hospital stay, occurrence of adverse events and in-hospital and 28-day mortality).ResultsPatients in the AcumenIQ group had significantly lower area below the hypotensive threshold (AUT, 2 vs 16.7 mmHg x minutes) and time-weighted AUT (TWA, 0.01 vs 0.08 mmHg). Also, there were less patients with hypotensive events and cumulative duration of hypotension in the AcumenIQ group. No significant difference between groups was found in terms of laboratory and clinical outcomes.ConclusionsHemodynamic optimization guided by machine learning algorithm leads to a significant decrease in number and duration of hypotensive events compared to traditional goal directed therapy using pulse-contour analysis hemodynamic monitoring in patients undergoing major thoracic procedures. Further, larger studies are needed to determine true clinical utility of HPI guided hemodynamic monitoring.Trial registrationDate of first registration: 14/11/2022Registration number: 04729481-3a96-4763-a9d5-23fc45fb722d

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.

Maternal heart rate variability patterns associated with maternal hypotension and non-reassuring fetal heart rate patterns following initiation of combined spinal-epidural labor analgesia: a prospective observational trial

BackgroundWe evaluated whether baseline maternal heart rate variability (HRV), including the Analgesia Nociception Index (ANI), is associated with maternal hypotension and fetal heart rate (FHR) abnormalities following combined spinal-epidural (CSE) labor analgesia. MethodsLaboring women were enrolled in this prospective observational study. The primary endpoint was maternal hypotension. The secondary endpoint was FHR abnormalities within 30 min following CSE analgesia initiated with intrathecal plain bupivacaine 1.0 mg and fentanyl 20 µg. The maternal ANI, electrocardiogram, blood pressure, heart rate, oxygen saturation, and FHR tracings were recorded 15 min before and 30 min after CSE. Parturients were grouped based on presence of hypotension and FHR abnormalities. Patient demographics and HRV metrics were compared. Receiver operating characteristics (ROC) curves were constructed for the prediction of hypotension and FHR abnormalities. ResultsNo significant intergroup differences were detected in patient characteristics. Several baseline HRV metrics and ANI differed significantly between the normotensive (n = 50) and hypotensive (n = 31) groups and between parturients showing FHR abnormalities (n = 19) and those showing reassuring FHR traces (n = 62). The area under the ROC curve (AUC) for predicting hypotension of the baseline low-frequency (LF)/high-frequency (HF) ratio was 0.677 (95% CI 0.55 to 0.80), and that of the ANI was 0.858 (95% CI 0.78 to 0.94). For predicting non-reassuring FHR patterns, the AUC of the LF/HF ratio was 0.77 (95% CI 0.65 to 0.89), and that of the ANI was 0.833 (95% CI 0.72 to 0.94). ConclusionsThe ANI can predict the propensity for maternal hypotension and non-reassuring FHR patterns following CSE.

PERFUSION INDEX FROM UPPER LIMB AS A TOOL IN THE PREDICTION OF SPINAL ANAESTHESIA INDUCED HYPOTENSION DURING CAESAREAN DELIVERY

Background: Hypotension following spinal anaesthesia during elective caesarean commonly seen either due to decreased in vascular tone or decreased cardiac output because of blood pooling. Peripheral perfusion index (PI) being a non-invasive measure of real time change in peripheral blood flow can be a dynamic measure in prediction of hypotension. This is a prospective study done in Assam Medical college and hospital recruiting 100 Methodology: parturient based on inclusion and exclusion criteria. They were divided into two groups with 50 parturient in each group with baseline PI&gt;3.5 and &lt;3.5 respectively. The statistical analysis was done using SPSS. Statistical significance was fixed at 5% level (p value &lt;0.05). Majority of parturient were less than 25years of age, less than 160cms Results: in height and weight less than 65kgs in both groups. No statistical significance observed between the study groups in terms of duration of surgery. The incidences of Hypotension in Group I (PI &gt; 3.5) was 54% compared to Group II (PI ≤ 3.5) which was 13% and was highly statistically significant (p=0.001). Parturient with a baseline PI &gt;3.5 were Conclusion: at higher risk of developing hypotension compared to those with a baseline PI ≤ 3.5 following spinal anaesthesia. Thus, preoperative baseline PI can be used as a predictive tool for hypotension.

Prediction of hypotension during the alveolar recruitment maneuver in spine surgery: a prospective observational study

BackgroundAtelectasis can occur in many clinical practices. One way to prevent this complication is through the alveolar recruitment maneuver (ARM). However, hemodynamic compromise can accompany ARM. This study aims to predict ARM-induced hypotension using a non-invasive method.Methods94 American Society of Anesthesiologists physical status I–II patients aged 19 to 75 with scheduled spinal surgery were enrolled. After anesthesia, we performed a stepwise ARM. Data on perfusion index, mean arterial pressure, heart rate, pleth variability index, cardiac index, and stroke volume variation was collected before induction of anesthesia (T0), just before ARM (T1), at the start of ARM (T2), 0.5 min (T3), 1 min (T4), 1.5 min (T5, end of ARM), and 2 min after the beginning of ARM (T6). Hypotension was defined as when the mean arterial pressure at T5 decreased by 20% or more compared to the baseline. The primary endpoint is that the perfusion index measuring before induction of anesthesia, which reflects the patients’ own vascular tone, was correlated with hypotension during ARM.ResultsSeventy-five patients (79.8%) patients developed hypotension during ARM. The pre-induction persufion index (Pi) (95% confidence interval) was 1.7(1.4–3.1) in the non-hypotension group and 3.4(2.4–3.9) in the hypotension group. (p < 0.004) The hypotension group showed considerably higher Pi than the non-hypotension group before induction. The decrease of Pi (%) [IQR] in the non-hypotensive group (52.8% [33.3–74.7]) was more significant than in the hypotensive group. (36% [17.6–53.7]) (p < 0.05) The area under the receiver operating characteristic curve of Pi for predicting hypotension during ARM was 0.718 (95% CI 0.615–0.806; p = 0.004), and the threshold value of the Pi was 2.4.ConclusionA higher perfusion index value measuring before induction of anesthesia can be used to predict the development of hypotension during ARM. Prophylactic management of the following hypotension during ARM could be considered in high baseline Pi patients.

Comparison of ultrasound-based measures of inferior vena cava and internal jugular vein for prediction of hypotension during induction of general anesthesia

ABSTRACT Background Hypotension after GA induction may induce organ injury. Ultrasonographic IVC and IJV studies for intravascular volume assessment and hypotension prediction were established. This study compared between them in prolonged hypotension during induction of GA prediction. Methods One-hundred sixty three adult patients, of ASA physical status I & II, for elective surgery under GA were screened. One hundred thirty three patients were included. Preoperative ultrasonographic IVC parameters (dIVC max, dIVC min & CI) and IJV parameters (IJV area in supine and Trendelenberg positions & IJV change rate) measurements were recorded with baseline MBP. After anesthetics administration, MBP was recorded every minute for 15 minutes. Intubation was established after 3 minutes. Study used hypotension definition as over 20% decrease from baseline MBP. Prolonged hypotension definition used was hypotension duration ≥ 2 minutes. Severe hypotension definition used was decreased MBP from baseline value over 40%. Results Six patients didn’t satisfy inclusion criteria; seventeen patients showed low IVC visualization and 133 patients were involved. The prolonged hypotension after GA induction incidence was 63.2% (84 patients), while severe hypotension incidence was 9% (12 patients). IJV change rate > 0.28 predicted prolonged post-induction hypotension with specificity 76% (95% confidence interval, 61–87%) and sensitivity 55% (95% confidence interval, 44–66%). IJV change rate > 0.35 predicted severe post-induction hypotension with specificity 72% (95% confidence interval, 63–80%) and sensitivity 75% (95% confidence interval, 43–95%). Alternatively, no IVC measure reliably estimated prolonged or severe post-induction hypotension. Conclusion IJV surpasses IVC in prolonged post-induction hypotension prediction with moderate predictive value.

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

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.

Pro: The Best Induction for the Physiologically Difficult Airway is Ketamine-Propofol Admixture (“Ketofol”)

THE INDUCTION AND INTUBATION of critically ill patients are high-risk interventions, with nearly half of patients experiencing a major adverse event, such as cardiovascular instability, severe hypoxia, or cardiac arrest. 1 Russotto V Myatra SN Laffey JG et al. Intubation practices and adverse peri-intubation events in critically ill patients from 29 countries. JAMA. 2021; 325: 1164-1172 Crossref PubMed Scopus (98) Google Scholar Preexisting physiologic derangements are a major contributor to decompensation, and the term “physiologically difficult airway” is used to describe patients with significant physiologic alterations prior to airway management. 2 Myatra SN Divatia JV Brewster DJ. The physiologically difficult airway: An emerging concept. Curr Opin Anaesthesiol. 2022; 35: 115-121 Crossref PubMed Scopus (6) Google Scholar Although many factors may contribute to a patient with a physiologically difficult airway, the derangements of most concern are hypotension, hypoxia, severe metabolic acidosis, and right ventricular dysfunction. 2 Myatra SN Divatia JV Brewster DJ. The physiologically difficult airway: An emerging concept. Curr Opin Anaesthesiol. 2022; 35: 115-121 Crossref PubMed Scopus (6) Google Scholar Inducing a patient who is already hypotensive can be dangerous, as both the induction agents, as well as positive-pressure ventilation, can worsen hypotension. Risk factors for periintubation hypotension have been explored recently, and one study derived and validated a scoring system for periintubation hypotension in critically ill patients (C-statistic 0.75 [95% CI 0.72-0.78]). 3 Smischney NJ Kashyap R Khanna AK et al. Risk factors for and prediction of post-intubation hypotension in critically ill adults: A multicenter prospective cohort study. PLoS One. 2020; 15e0233852 Crossref PubMed Scopus (15) Google Scholar Given the literature surrounding the negative downstream effects of hypotension on the critically ill, one could argue that preventing further decreases in blood pressure is the most important consideration when faced with intubation in a patient with preexisting hypotension. For example, a multicenter cohort study demonstrated that mean arterial pressures ≤65 mmHg in critically ill patients were associated with an increased risk of 30-day major adverse cardiac or cerebrovascular events and increased 30-day mortality. This held true even for hypotension of limited duration. 4 Smischney NJ Shaw AD Stapelfeldt WH et al. Postoperative hypotension in patients discharged to the intensive care unit after non-cardiac surgery is associated with adverse clinical outcomes. Crit Care. 2020; 24: 682 Crossref PubMed Scopus (12) Google Scholar Preexisting hypoxia is also a significant risk factor for decompensation because any apnea time is tolerated poorly by patients with limited arterial oxygen content reserve. Additionally, induction agents typically worsen the ventilation-perfusion mismatch that is already present to some degree in hypoxic patients. Hypoxia, in general, also places patients at greater risk for hemodynamic instability, arrhythmia, and cardiopulmonary arrest. 3 Smischney NJ Kashyap R Khanna AK et al. Risk factors for and prediction of post-intubation hypotension in critically ill adults: A multicenter prospective cohort study. PLoS One. 2020; 15e0233852 Crossref PubMed Scopus (15) Google Scholar Severe metabolic acidosis contributes to a physiologically difficult airway, as any apnea time will result in an increased PaCO2 and a worsening acidosis. Preexisting right ventricular dysfunction presents many challenges to intubation, as the rise in PaCO2 with induction will result in increases in pulmonary arterial pressures and right ventricular strain. Additionally, positive-pressure ventilation tends to overall reduce preload and may precipitate cardiovascular collapse due to insufficient preload to a poorly functioning right ventricle. Patients with a physiologically difficult airway may possess one or a combination of these physiologic derangements. Once a physiologically difficult airway is identified, the next challenge for the clinician is to design an induction regimen that would be best tolerated by a patient with preexisting physiologic challenges.

The Value of Short-term Physiological History and Contextual Data in Predicting Hypotension in the ICU Settings

Hypotension frequently occurs in intensive care units (ICUs) and is correlated to worsening patient outcomes. In this study, we propose a machine learning (ML) algorithm that predicts hypotensive events in ICUs by extracting the information from patients' contextual data and physiological signals. The algorithm uses patients’ history including demographics, pre-ICU medication, and pre-existing comorbidities, and only five minutes of prior physiological history to predict hypotension up to 30 min in advance. We show that adding demographic information to the physiological data does not improve the algorithm's predictive performance of 84% sensitivity, 89% positive predictive value (PPV), and 98% specificity. Furthermore, the results show that including features extracted from patients’ pre-ICU medications and comorbidities lowers the learning algorithm’ prediction performance and leads to 2% degradation in its F1-score. The feature importance analysis showed that the ratio of MAP to HR (MAP2HR) and the average of RR intervals on the ECG (RRI), both extracted from physiological signals, have the highest weights in the prediction of hypotension.

Evaluation of perfusion index as a screening tool for prediction of hypotension and shivering in cesarean section

Evaluation of perfusion index as a screening tool for prediction of hypotension and shivering in cesarean section

The use ofpreoperative inferior vena cava ultrasound to predict anaesthesia-induced hypotension: asystematic review.

Preoperative ultrasound assessment of inferior vena cava (IVC) diameter and the collapsi-bility index might identify patients with intravascular volume depletion. The purpose of this review was to gather the existing evidence to find out whether preoperative IVC ultrasound (IVCUS) derived parameters can reliably predict hypotension after spinal or general anaesthesia. PubMed was searched to identify research articles that addressed the role of IVC ultrasound in predicting hypotension after spinal and general anaesthesia in adult patients. We included 4 randomized control trials and 17 observational studies in our final review. Among these, 15 studies involved spinal anaesthesia and 6 studies involved general anaesthesia. Heterogeneity with respect to the patient populations under evaluation, definitions used for hypotension after anaesthesia, IVCUS assessment methods, and cut-off values for IVCUS-derived parameters to predict hypotension precluded pooled meta-analysis. The maximum and minimum reported sensitivity of the IVC collapsibility index (IVCCI) for predicting post-spinal hypotension was 84.6% and 58.8% respectively, while the maximum and minimum specificities were 93.1% and 23.5% respectively. For the prediction of hypotension after general anaesthesia induction, the reported ranges of sensitivity and specificity of IVCCI were 86.67% to 45.5% and 94.29% to 77.27%, respectively. Current literature on the predictive role of IVCUS for hypotension after anaesthesia is heterogeneous both in methodology and in results. Standardization of the definition of hypotension under anaesthesia, method of IVCUS assessment, and the cut-offs for IVC diameter and the collapsibility index for prediction of hypotension after anaesthesia are necessary for drawing clinically relevant conclusions.

The Usefulness of Perfusion Index Derived from a Pulse Oximeter in Predicting Hypotension following Spinal Anesthesia for Cesarean Section

Hypotension is a frequent complication of spinal anesthesia. Decrease in peripheral vascular tone that occurs during pregnancy is one of the factors causing hypotension in patients undergoing cesarean section under spinal anesthesia. Perfusion index derived from pulse oximeter can be an easy and non-invasive measure of peripheral perfusion. This study was conducted to test the usefulness of pulse oximeter in predicting hypotension following spinal anesthesia for cesarean section. In this study, 247 parturients undergoing elective cesarean section under spinal anesthesia were included. Parturients who had baseline PI&lt;3.5 were kept in Group I and parturients who had baseline PI&gt;3.5 were kept in Group II. All the patients were given 0.5% bupivacaine heavy 2.2 ml for spinal anesthesia. In group I, 30 patients (23.62%) had hypotension whereas in Group II, 119 patients (100%) had hypotension. The episodes of hypotension were significantly lower in Group I as compared to Group II (p&lt;0.001). The dose of mephentermine (p&lt;0.001) used was also significantly lower in Group I as compared to Group II. Therefore, we concluded that a baseline PI&gt;3.5 is a prediction of hypotension following spinal anesthesia in patients undergoing cesarean section.

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

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

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.

Carotid artery corrected flow time and respiratory variations of peak blood flow velocity for prediction of hypotension after induction of general anesthesia in elderly patients

BackgroundPostinduction hypotension is closely related to postoperative complications. Elderly patients with compromised cardiovascular compensatory reserve are more susceptible to hypotension after induction of general anesthesia. This study investigated whether the carotid artery corrected flow time (FTc) and respiratory variation of peak blood flow velocity in the common carotid artery (ΔVpeak) could predict postinduction hypotension in elderly patients.MethodsThis prospective observational study included elderly patients aged 65 to 75 who were scheduled for elective surgery under general anesthesia with ASA physical status class of I-II, without cardiovascular disease, hypertension, diabetes, or obesity. Anesthesia was induced by midazolam, sufentanil, and etomidate and was maintained by sevoflurane. The carotid artery FTc and ΔVpeak were measured by ultrasound before induction of anesthesia. Hemodynamic data were recorded before induction and then during the first 10 min after induction.ResultsNinety-nine patients were included in the final analysis, of whom 63 developed postinduction hypotension. The area under the receiver operating characteristic curves was 0.87 (0.78 to 0.93) for carotid artery FTc and 0.67 (0.56 to 0.76) for ΔVpeak, respectively. The optimal cutoff value for predicting postinduction hypotension was 379.1 ms for carotid artery FTc, with sensitivity and specificity of 72.2 and 93.7%, respectively. The best cutoff value was 7.5% for ΔVpeak, with sensitivity and specificity of 55.6 and 75.0%, respectively.ConclusionsThe carotid artery FTc is a reliable predictor of postinduction hypotension in elderly patients with ASA status of I or II, without cardiovascular disease, hypertension, diabetes, or obesity. Elderly patients with a carotid artery FTc less than 379.1 ms before anesthesia have a higher risk of postinduction hypotension.Trial registrationClinical Trial Registry on August 2nd, 2020 (www.chictr.org.cn; ChiCTR2000035190).

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.