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Purpose: Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension. Methods: A systematic search was performed in Pubmed and Scopus to retrieve articles published from January 2018 to February 2024 regarding the impact of the HPI software on reducing IOH in adult patients undergoing non-cardio/thoracic surgery. Excluded were case series, case reports, meta-analyses, systematic reviews, and studies using non-invasive arterial waveform analysis. The risk of bias was assessed by the Cochrane risk-of-bias tool (RoB 2) and the Risk Of Bias In Non-randomised Studies (ROBINS-I). A meta-analysis was undertaken solely for outcomes where sufficient data were available from the included studies. Results: 9 RCTs and 5 cohort studies were retrieved. The overall median differences between the HPI-guided and the control groups were − 0.21 (95% CI:-0.33, -0.09) – p < 0.001 for the Time-Weighted Average (TWA) of Mean Arterial Pressure (MAP) < 65mmHg, -3.71 (95% CI= -6.67, -0.74)-p = 0.014 for the incidence of hypotensive episodes per patient, and − 10.11 (95% CI= -15.82, -4.40)-p = 0.001 for the duration of hypotension. Notably a large amount of heterogeneity was detected among the studies. Conclusions: While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation. [ABSTRACT FROM AUTHOR]

Background and Aims: Reports on the utility of the hypotension prediction index (HPI) in reducing the occurrence of intraoperative hypotension are conflicting. Therefore, the aim of this systematic review and meta-analysis of randomised controlled trials (RCTs) was to evaluate the overall effect of using HPI on intraoperative hypotension outcomes of time-weighted average (TWA), area under the hypotension threshold (AUHT), incidence and duration of hypotension. Methods: We searched the electronic databases of PubMed, ProQuest and Scopus from inception till 30 October 2023. The search strategy was refined for each database. No time or language restrictions were applied. Only RCTs were included. The systematic review protocol is registered with PROSPERO (ID: CRD42023478150). Statistical analysis was performed using Review Manager Software. Results: Of 281 records, eight eligible RCTs (613 patients) were included. Significant differences were found between HPI and no HPI groups for the TWA of hypotension during surgery [mean difference (MD) = -0.19 mmHg, 95% confidence interval (95% CI): -0.31, -0.08, P = 0.001], AUHT [MD = -65.03 (mmHg × min), 95% CI: -105.47, -24.59, P = 0.002], incidence of hypotension (risk ratio = 0.83, 95% CI: 0.7, 0.99, P = 0.04), total hypotension duration (MD = -12.07 min, 95% CI: -17.49, -6.66, P < 0.001) and hypotension duration as a percentage of surgery time (MD = -6.30%, 95% CI: -10.23, -2.38, P = 0.002). Conclusions: Available evidence supports the role of HPI in minimising hypotension outcomes during surgery. The certainty of evidence is low to moderate for studied outcomes. [ABSTRACT FROM AUTHOR]

Intraoperative hypotension (IOH) is a common issue associated with acute kidney injury, myocardial injury, stroke, and death. IOH may be avoided with the incorporation of newer advanced hemodynamic monitoring technologies. This case study examines the use of advanced hemodynamic monitoring with an early warning system for the intraoperative hemodynamic management of a patient presenting for pancreaticoduodenectomy. Incorporating the hypotension prediction index and other hemodynamic parameters to anticipate impending hypotension and treat potential causative factors is an emerging technological advancement. Understanding and embracing the potential for new advanced hemodynamic technology to reduce intraoperative hypotension's severity, duration, and occurrence is key to reducing negative patient outcomes. [ABSTRACT FROM AUTHOR]

Acute respiratory distress syndrome (ARDS) is a critical illness in critically unwell patients, characterized by refractory hypoxemia and shock. This study evaluates an early detection tool and investigates the relationship between hypoxia and circulatory shock in ARDS, to improve diagnostic precision and therapy customization. We used a porcine model, inducing ARDS with mechanical ventilation and intratracheal plus intravenous lipopolysaccharide (LPS) injection. Hemodynamic changes were monitored using an Acumen IQ sensor and a ForeSight Elite sensor connected to the HemoSphere platform. We evaluated tissue damage, inflammatory response, and hypoxia-inducible factor (HIF) alterations using enzyme-linked immunosorbent assay and immunohistochemistry. The results showed severe hypotension and increased heart rates post-LPS exposure, with a notable rise in the hypotension prediction index (HPI) during acute lung injury (p = 0.024). Tissue oxygen saturation dropped considerably in the right brain region. Interestingly, post-injury HIF-2α levels were lower at the end of the experiment. Our findings imply that the HPI can effectively predict ARDS-related hypotension. HIF expression levels may serve as possible markers of rapid ARDS progression. Further research should be conducted on the clinical value of this novel approach in critical care, as well as the relationship between the HIF pathway and ARDS-associated hypotension. [ABSTRACT FROM AUTHOR]

Abstract Background Intraoperative hypotension is a common side effect of general anesthesia. Here we examined whether the Hypotension Prediction Index (HPI), a novel warning system, reduces the severity and duration of intraoperative hypotension during general anesthesia. Methods This randomized controlled trial was conducted in a tertiary referral hospital. We enrolled patients undergoing general anesthesia with invasive arterial monitoring. Patients were randomized 1:1 either to receive hemodynamic management with HPI guidance (intervention) or standard of care (control) treatment. Intraoperative hypotension treatment was initiated at HPI > 85 (intervention) or mean arterial pressure (MAP)

Objective: To analyze the intraoperative hemodynamic management by hypotension prediction index (HPI) in one patient underwent robot-assisted laparoscopic cystectomy, and to provide the reference for anesthesia monitoring and hemodynamic management in the similar major surgery. Methods: The clinical data, intraoperative hemodynamic data, usage and dosage of vasoactive drugs, and clinical outcomes of one patient underwent robot-assisted laparoscopic cystectomy with HPI-guided intraoperative hemodynamic management were retrospectively analyzed, and the relevant literatures were reviewed. Results: The patient, a 72-year-old female, was admitted due to macroscopic hematuria for 5 months accompanied by dysuria for 3 months. The cystoscope results showed a 7 cm×7 cm×5 cm mass on the right side of the bladder trigone and a 4 cm×3 cm×3 cm mass near the bladder neck. The positron emission tomography/computed tomography (PET/CT) results showed thickening of the right posterior bladder wall with high metabolism, and the preliminary diagnosis was bladder malignancy. After preoperative anesthesia evaluation, the robot-assisted laparoscopic cystectomy was planned. After entering the operating room, the routine monitoring was conducted, and the monitor equipped with HPI software was used to guide intraoperative hemodynamic management. After routine anesthesia induction, the tracheal intubation was performed by video laryngoscope. The patient experienced intraoperative hypotension (IOH) for six times, the cumulative time of mean arterial pressure (MAP)<65 mmHg was 13. 7 min, accounting for 4. 40% of the anesthesia duration, and the time-weighted average of MAP< 65 mmHg was 0. 28 mmHg. The time range with HPI≥85 roughly overlapped with and included the period of MAP<65 mmHg. At 146 time points with HPI≥85, the MAP remained greater than 65 mmHg at 68. 5% (100/146) of the points. At 47 time points with MAP<65 mmHg, HPI≥85 occurred at 97. 9% (46/47) of the points. On the first postoperative day, the patient’s hypersensitive cardiac troponin I was <0. 01 μg·L-1, and no perioperative adverse events occurred. The patient was discharged on the eighth day. Conclusion: HPI can promptly and accurately predict the occurrence of IOH in the patients undergoing robot-assisted laparoscopic cystectomy. The use of HPI-based hypotension correction strategies during surgery can maintain the time-weighted average of MAP<65 mmHg at a lower level. [ABSTRACT FROM AUTHOR]

Background: Intraoperative hypotension is a common side effect of general anesthesia. Here we examined whether the Hypotension Prediction Index (HPI), a novel warning system, reduces the severity and duration of intraoperative hypotension during general anesthesia. Methods: This randomized controlled trial was conducted in a tertiary referral hospital. We enrolled patients undergoing general anesthesia with invasive arterial monitoring. Patients were randomized 1:1 either to receive hemodynamic management with HPI guidance (intervention) or standard of care (control) treatment. Intraoperative hypotension treatment was initiated at HPI > 85 (intervention) or mean arterial pressure (MAP) < 65 mmHg (control). The primary outcome was hypotension severity, defined as a time-weighted average (TWA) MAP < 65 mmHg. Secondary outcomes were TWA MAP < 60 and < 55 mmHg. Results: Of the 60 patients who completed the study, 30 were in the intervention group and 30 in the control group. The patients' median age was 62 years, and 48 of them were male. The median duration of surgery was 490 min. The median MAP before surgery presented no significant difference between the two groups. The intervention group showed significantly lower median TWA MAP < 65 mmHg than the control group (0.02 [0.003, 0.08] vs. 0.37 [0.20, 0.58], P < 0.001). Findings were similar for TWA MAP < 60 mmHg and < 55 mmHg. The median MAP during surgery was significantly higher in the intervention group than that in the control group (87.54 mmHg vs. 77.92 mmHg, P < 0.001). Conclusions: HPI guidance appears to be effective in preventing intraoperative hypotension during general anesthesia. Further investigation is needed to assess the impact of HPI on patient outcomes. Trial registration: ClinicalTrials.gov (NCT04966364); 202105065RINA; Date of registration: July 19, 2021; The recruitment date of the first patient: July 22, 2021. [ABSTRACT FROM AUTHOR]

Abstract Background Intraoperative hypotension is common during noncardiac surgery and is associated with postoperative myocardial infarction, acute kidney injury, stroke, and severe infection. The Hypotension Prediction Index software is an algorithm based on arterial waveform analysis that alerts clinicians of the patient’s likelihood of experiencing a future hypotensive event, defined as mean arterial pressure < 65 mmHg for at least 1 min. Methods Two analyses included (1) a prospective, single-arm trial, with continuous blood pressure measurements from study monitors, compared to a historical comparison cohort. (2) A post hoc analysis of a subset of trial participants versus a propensity score-weighted contemporaneous comparison group, using external data from the Multicenter Perioperative Outcomes Group (MPOG). The trial included 485 subjects in 11 sites; 406 were in the final effectiveness analysis. The post hoc analysis included 457 trial participants and 15,796 comparison patients. Patients were eligible if aged 18 years or older, American Society of Anesthesiologists (ASA) physical status 3 or 4, and scheduled for moderate- to high-risk noncardiac surgery expected to last at least 3 h. Measurements: minutes of mean arterial pressure (MAP) below 65 mmHg and area under MAP < 65 mmHg. Results Analysis 1: Trial subjects (n = 406) experienced a mean of 9 ± 13 min of MAP below 65 mmHg, compared with the MPOG historical control mean of 25 ± 41 min, a 65% reduction (p < 0.001). Subjects with at least one episode of hypotension (n = 293) had a mean of 12 ± 14 min of MAP below 65 mmHg compared with the MPOG historical control mean of 28 ± 43 min, a 58% reduction (p< 0.001). Analysis 2: In the post hoc inverse probability treatment weighting model, patients in the trial demonstrated a 35% reduction in minutes of hypotension compared to a contemporaneous comparison group [exponentiated coefficient: − 0.35 (95%CI − 0.43, − 0.27); p < 0.001]. Conclusions The use of prediction software for blood pressure management was associated with a clinically meaningful reduction in the duration of intraoperative hypotension. Further studies must investigate whether predictive algorithms to prevent hypotension can reduce adverse outcomes. Trial registration Clinical trial number: NCT03805217. Registry URL: https://clinicaltrials.gov/ct2/show/NCT03805217 . Principal investigator: Xiaodong Bao, MD, PhD. Date of registration: January 15, 2019.

Purpose: Intraoperative hypotension (IOH) is associated with adverse outcomes. We therefore explored beliefs regarding IOH and barriers to its treatment. Secondarily, we assessed if an educational intervention and mandated mean arterial pressure (MAP), or the implementation of the Hypotension Prediction Index-software (HPI) were associated with a reduction in IOH. Methods: Structured interviews (n = 27) and questionnaires (n = 84) were conducted to explore clinicians' beliefs and barriers to IOH treatment, in addition to usefulness of HPI questionnaires (n = 14). 150 elective major surgical patients who required invasive blood pressure monitoring were included in three cohorts to assess incidence and time-weighted average (TWA) of hypotension (MAP < 65 mmHg). Cohort one received standard care (baseline), the clinicians of cohort two had a training on hypotension and a mandated MAP > 65 mmHg, and patients of the third cohort received protocolized care using the HPI. Results: Clinicians felt challenged to manage IOH in some patients, yet they reported sufficient knowledge and skills. HPI-software was considered useful and beneficial. No difference was found in incidence of IOH between cohorts. TWA was comparable between baseline and education cohort (0.15 mmHg [0.05–0.41] vs. 0.11 mmHg [0.02–0.37]), but was significantly lower in the HPI cohort (0.04 mmHg [0.00 to 0.11], p < 0.05 compared to both). Conclusions: Clinicians believed they had sufficient knowledge and skills, which could explain why no difference was found after the educational intervention. In the HPI cohort, IOH was significantly reduced compared to baseline, therefore HPI-software may help prevent IOH. Trial registration: ISRCTN 17,085,700 on May 9th, 2019. [ABSTRACT FROM AUTHOR]

Introduction: Abdominal oncologic surgeries pose significant risks due to the complexity of the surgery and patients' often weakened health, multiple comorbidities, and increased perioperative hazards. Hypotension is a major risk factor for perioperative cardiovascular complications, necessitating individualized management in modern anesthesiology. Aim: This study aimed to determine the dynamics of changes in troponin and NTproBNP levels during the first two postoperative days in patients undergoing major cancer abdominal surgery with advanced hemodynamic monitoring including The AcumenTM Hypotension Prediction Index software (HPI) (Edwards Lifesciences, Irvine, CA, USA) and their association with the occurrence of postoperative cardiovascular complications. Methods: A prospective study was conducted, including 50 patients scheduled for abdominal cancer surgery who, due to the overall risk of perioperative complications (ASA class 3 or 4), were monitored using the HPI software. Hypotension was qualified as at least one ≥ 1 min episode of a MAP < 65 mm Hg. Preoperatively and 24 and 48 h after the procedure, the levels of NTproBNP and troponin were measured, and an ECG was performed. Results: We analyzed data from 46 patients and found that 82% experienced at least one episode of low blood pressure (MAP < 65 mmHg). However, the quality indices of hypotension were low, with a median time-weighted average MAP < 65 mmHg of 0.085 (0.03–0.19) mmHg and a median of 2 (2–1.17) minutes spent below MAP < 65 mmHg. Although the incidence of perioperative myocardial injury was 10%, there was no evidence to suggest a relationship with hypotension. Acute kidney injury was seen in 23.9% of patients, and it was significantly associated with a number of episodes of MAP < 50 mmHg. Levels of NTproBNP were significantly higher on the first postoperative day compared to preoperative values (285.8 [IQR: 679.8] vs. 183.9 [IQR: 428.1] pg/mL, p < 0.001). However, they decreased on the second day (276.65 [IQR: 609.4] pg/mL, p = 0.154). The dynamics of NTproBNP were similar for patients with and without heart failure, although those with heart failure had significantly higher preoperative concentrations (435.9 [IQR: 711.15] vs. 87 [IQR: 232.2] pg/mL, p < 0.001). Patients undergoing laparoscopic surgery showed a statistically significant increase in NTproBNP. Conclusions: This study suggests that advanced HPI monitoring in abdominal cancer surgery effectively minimizes intraoperative hypotension with no significant NTproBNP or troponin perioperative dynamics, irrespective of preoperative heart failure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This narrative review explores the evolving role of artificial intelligence (AI) in haemodynamic monitoring, emphasising its potential to revolutionise patient care. The historical reliance on invasive procedures for haemodynamic assessments is contrasted with the emerging non-invasive AI-driven approaches that address limitations and risks associated with traditional methods. Developing the hypotension prediction index and introducing CircEWSTM and CircEWS-lite TM showcase AI's effectiveness in predicting and managing circulatory failure. The crucial aspects include the balance between AI and healthcare professionals, ethical considerations, and the need for regulatory frameworks. The use of AI in haemodynamic monitoring will keep growing with ongoing research, better technology, and teamwork. As we navigate these advancements, it is crucial to balance AI's power and healthcare professionals' essential role. Clinicians must continue to use their clinical acumen to ensure that patient outliers or system problems do not compromise the treatment of the condition and patient safety.

Objective: The study objective was to report early outcomes of integrating Hypotension Prediction Index–guided hemodynamic management within a cardiac enhanced recovery pathway on total initial ventilation hours and length of stay in the intensive care unit. Methods: A multicenter, historical control, observational analysis of implementation of a hemodynamic management tool within enhanced recovery pathways was conducted by identifying cardiac surgery cases from 3 sites during 2 time periods, August 1 to December 31, 2019 (preprogram), and April 1 to August 31, 2021 (program). Reoperations, emergency (salvage), or cases requiring mechanical assist were excluded. Data were extracted from electronic medical records and chart reviews. Two primary outcome variables were length of stay in the intensive care unit (using Society of Thoracic Surgeons definitions) and acute kidney injury (using modified Kidney Disease Improving Global Outcomes criteria). One secondary outcome variable, total initial ventilation hours, used Society of Thoracic Surgeons definitions. Differences in length of stay in the intensive care unit and total ventilation time were analyzed using Kruskal–Wallis and stepwise multiple linear regression. Acute kidney injury stage used chi-square and stepwise cumulative logistic regression. Results: A total of 1404 cases (795 preprogram; 609 program) were identified. Overall reductions of 6.8 and 4.4 hours in intensive care unit length of stay (P = .08) and ventilation time (P = .03) were found, respectively. No significant association between proportion of patients identified with acute kidney injury by stage and period was found. Conclusions: Adding artificial intelligence–guided hemodynamic management to cardiac enhanced recovery pathways resulted in associated reduced time in the intensive care unit for patients undergoing nonemergency cardiac surgery across institutions in a real-world setting.

Intraoperative hypotension happens in everyday clinical practice. It was suggested to have a strong association with adverse postoperative outcomes. Hypotension prediction index (HPI) was developed to predict intraoperative hypotension (mean arterial pressure

This narrative review explores the evolving role of artificial intelligence (AI) in haemodynamic monitoring, emphasising its potential to revolutionise patient care. The historical reliance on invasive procedures for haemodynamic assessments is contrasted with the emerging non-invasive AI-driven approaches that address limitations and risks associated with traditional methods. Developing the hypotension prediction index and introducing CircEWSTM and CircEWS-lite TM showcase AI's effectiveness in predicting and managing circulatory failure. The crucial aspects include the balance between AI and healthcare professionals, ethical considerations, and the need for regulatory frameworks. The use of AI in haemodynamic monitoring will keep growing with ongoing research, better technology, and teamwork. As we navigate these advancements, it is crucial to balance AI's power and healthcare professionals' essential role. Clinicians must continue to use their clinical acumen to ensure that patient outliers or system problems do not compromise the treatment of the condition and patient safety. [ABSTRACT FROM AUTHOR]

ObjectiveThe hypotension prediction index (HPI) is a novel parameter developed by Edwards Lifesciences (Irvine, CA) that is obtained through an algorithm based on arterial pressure waveform characteristics. Past studies have demonstrated its accuracy in predicting hypotensive events in noncardiac surgeries. The authors aimed to evaluate the use of the HPI in cardiac surgeries requiring cardiopulmonary bypass (CPB).DesignProspective cohort feasibility study.SettingSingle university medical center.ParticipantsSequential adult patients undergoing elective cardiac surgeries requiring CPB between October 1, 2018, and December 31, 2018.InterventionsHPI monitor was connected to the patient's arterial pressure transducer. Anesthesiologists and surgeons were blinded to the monitor output.Measurements and main resultsHPI values and hypotensive events were recorded before and after CPB. The primary outcomes were the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity of HPI predicting hypotension. The AUC, sensitivity, and specificity for HPI lead time to hypotension five minutes before the event were 0.90 (95% confidence interval [CI]: 0.853-0.949), 84% (95% CI: 77.7-90.5), and 84% (95% CI: 70.9-96.8), respectively. Ten minutes before the event AUC, sensitivity, and specificity for HPI lead time to hypotension were 0.83 (95% CI: 0.750-0.905), 79% (95% CI: 69.8-88.1), and 74% (95% CI: 58.8-89.6), respectively. Fifteen minutes before the hypotensive event AUC, sensitivity, and specificity for HPI lead time to hypotension were 0.83 (95% CI: 0.746-0.911), 79% (95% CI: 68.4-89.0), and 74% (95% CI: 58.8-89.6), respectively.ConclusionHPI predicted hypotensive episodes during cardiac surgeries with a high degree of sensitivity and specificity.

Acute respiratory distress syndrome (ARDS) is a critical illness in critically unwell patients, characterized by refractory hypoxemia and shock. This study evaluates an early detection tool and investigates the relationship between hypoxia and circulatory shock in ARDS, to improve diagnostic precision and therapy customization. We used a porcine model, inducing ARDS with mechanical ventilation and intratracheal plus intravenous lipopolysaccharide (LPS) injection. Hemodynamic changes were monitored using an Acumen IQ sensor and a ForeSight Elite sensor connected to the HemoSphere platform. We evaluated tissue damage, inflammatory response, and hypoxia-inducible factor (HIF) alterations using enzyme-linked immunosorbent assay and immunohistochemistry. The results showed severe hypotension and increased heart rates post-LPS exposure, with a notable rise in the hypotension prediction index (HPI) during acute lung injury (p = 0.024). Tissue oxygen saturation dropped considerably in the right brain region. Interestingly, post-injury HIF-2α levels were lower at the end of the experiment. Our findings imply that the HPI can effectively predict ARDS-related hypotension. HIF expression levels may serve as possible markers of rapid ARDS progression. Further research should be conducted on the clinical value of this novel approach in critical care, as well as the relationship between the HIF pathway and ARDS-associated hypotension.

A prospective, single-centre, non-randomised, case–control study aimed to evaluate the effectiveness of intraoperative haemodynamic monitoring, prediction of hypotension and goal-directed therapy on the outcomes of patients undergoing posterior fusion for adolescent idiopathic scoliosis (AIS). The control group (n = 35, mean age: 15 years) received standard blood pressure control during surgery, while the intervention group (n = 24, mean age: 14 years) underwent minimally invasive haemodynamic monitoring and goal-directed therapy. The intervention group showed significantly shorter durations of hypotension (mean arterial pressure < 60 mmHg), reduced hospital stays and smaller decreases in post-surgery haemoglobin levels. Additionally, the intervention group experienced shorter times from the end of surgery to extubation. These findings suggest that incorporating targeted interventions during intraoperative care for AIS patients undergoing posterior fusion can lead to improved outcomes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

Intraoperative hypotension happens in everyday clinical practice. It was suggested to have a strong association with adverse postoperative outcomes. Hypotension prediction index (HPI) was developed to predict intraoperative hypotension (mean arterial pressure <65 mmHg) in real time. However, pressure autoregulation also plays an important role in maintaining adequate organ perfusion/oxygenation during hypotension. A cerebral oxygenation monitor provides clinicians with the values of organ oxygenation. We reported a case that the cerebral oxygenation monitor was used together with HPI to guide intraoperative blood pressure management. We found that cerebral oxygenation was maintained in the event of hypotension during surgery. The patient had no intraoperative or postoperative adverse outcomes despite the hypotension. We believe this can provide an individualized intraoperative blood pressure management to avoid over- or under-treating hypotension. [ABSTRACT FROM AUTHOR]

It remains unclear whether it is the hypotension prediction index itself or goal-directed haemodynamic therapy that mitigates intraoperative hypotension. A single centre randomised controlled trial. Sapporo Medical University Hospital. A total of 64 adults patients undergoing major non-cardiac surgery under general anaesthesia. Patients were randomly assigned to either group receiving conventional goal-directed therapy (FloTrac group) or combination of the hypotension prediction index and conventional goal-directed therapy (HPI group). To investigate the independent utility of the index, the peak rates of arterial pressure and dynamic arterial elastance were not included in the treatment algorithm for the HPI group. The primary outcome was the time-weighted average of the areas under the threshold. Secondary outcomes were area under the threshold, the number of hypotension events, total duration of hypotension events, mean mean arterial pressure during the hypotension period, number of hypotension events with mean arterial pressure < 50 mmHg, amounts of fluids, blood products, blood loss, and urine output, frequency and amount of vasoactive agents, concentration of haemoglobin during the monitoring period, and 30-day mortality. The time-weighted average of the area below the threshold was lower in the HPI group than in the control group; 0.19 mmHg (interquartile range, 0.06–0.80 mmHg) vs. 0.66 mmHg (0.28–1.67 mmHg), with a median difference of −0.41 mmHg (95% confidence interval, −0.69 to −0.10 mmHg), p = 0.005. Norepinephrine was administered to 12 (40%) and 5 (17%) patients in the HPI and FloTrac groups, respectively (p = 0.045). No significant differences were observed in the volumes of fluid and blood products between the study groups. The current randomised controlled trial results suggest that using the hypotension prediction index independently lowered the cumulative amount of intraoperative hypotension during major non-cardiac surgery. [Display omitted] • It remains unclear whether it is HPI itself or goal-directed haemodynamic therapy that mitigates intraoperative hypotension. • The independent utility of HPI was evaluated during major non-cardiac surgery. • Using HPI independently lowered the cumulative amount of intraoperative hypotension during major non-cardiac surgery. • HPI monitoring facilitated prompt use of vasopressors to reduce intraoperative hypotension. [ABSTRACT FROM AUTHOR]

An algorithm derived from machine learning uses the arterial waveform to predict intraoperative hypotension some minutes before episodes, possibly giving clinician's time to intervene and prevent hypotension. Whether the Hypotension Prediction Index works well with noninvasive arterial pressure waveforms remains unknown. We therefore evaluated sensitivity, specificity, and positive predictive value of the Index based on non-invasive arterial waveform estimates. We used continuous hemodynamic data measured from ClearSight (formerly Nexfin) noninvasive finger blood pressure monitors in surgical patients. We re-evaluated data from a trial that included 320 adults ≥ 45 years old designated ASA physical status 3 or 4 who had moderate-to-high-risk non-cardiac surgery with general anesthesia. We calculated sensitivity and specificity for predicting hypotension, defined as mean arterial pressure ≤ 65 mmHg for at least 1 min, and characterized the relationship with receiver operating characteristics curves. We also evaluated the number of hypotensive events at various ranges of the Hypotension Prediction Index. And finally, we calculated the positive predictive value for hypotension episodes when the Prediction Index threshold was 85. The algorithm predicted hypotension 5 min in advance, with a sensitivity of 0.86 [95% confidence interval 0.82, 0.89] and specificity 0.86 [0.82, 0.89]. At 10 min, the sensitivity was 0.83 [0.79, 0.86] and the specificity was 0.83 [0.79, 0.86]. And at 15 min, the sensitivity was 0.75 [0.71, 0.80] and the specificity was 0.75 [0.71, 0.80]. The positive predictive value of the algorithm prediction at an Index threshold of 85 was 0.83 [0.79, 0.87]. A Hypotension Prediction Index of 80–89 provided a median of 6.0 [95% confidence interval 5.3, 6.7] minutes warning before mean arterial pressure decreased to < 65 mmHg. The Hypotension Prediction Index, which was developed and validated with invasive arterial waveforms, predicts intraoperative hypotension reasonably well from non-invasive estimates of the arterial waveform. Hypotension prediction, along with appropriate management, can potentially reduce intraoperative hypotension. Being able to use the non-invasive pressure waveform will widen the range of patients who might benefit. Clinical Trial Number: ClinicalTrials.gov NCT02872896. [ABSTRACT FROM AUTHOR]

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.

The "Hypotension Prediction Index (HPI)" represents a newly introduced monitoring-tool that aims to predict episodes of intraoperative hypotension (IOH) before their occurrence. In order to evaluate the feasibility of protocolized care according to HPI monitoring, we hypothesized that HPI predicts the incidence of IOH and reduces the incidence and duration of IOH. This single centre feasibility randomised blinded prospective interventional trial included at total of 99 patients. One group was managed by goal-directed therapy algorithm based on HPI (HPI, n = 25), which was compared to a routine anaesthetic care cohort (CTRL, n = 24) and a third historic control group (hCTRL, n = 50). Primary endpoints included frequency (n)/h, absolute and relative duration (t (min)/% of total anaesthesia time) of IOH. Significant reduction of intraoperative hypotension was recorded in the HPI group compared to the control groups (HPI 48%, CTRL 87.5%, hCTRL 80%; HPI vs. CTRL, respectively hCTRL p < 0.001). Perioperative quantity of IOH was significantly reduced in the interventional group compared to both other study groups (HPI: 0 (0-1), CTRL: 5 (2-6), hCTRL: 2 (1-3); p < 0.001). Same observations were identified for absolute (HPI: 0 (0-140) s, CTRL: 640 (195-1315) s, hCTRL 660 (180-1440) s; p < 0.001) and relative duration of hypotensive episodes (minutes MAP ≤ 65 mmHg in  % of total anaesthesia time; HPI: 0 (0-1), CTRL: 6 (2-12), hCTRL 7 (2-17); p < 0.001). The HPI algorithm combined with a protocolized treatment was able to reduce the incidence and duration of hypotensive events in patients undergoing primary hip arthroplasty.Trial registration: NCT03663270. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Blood pressure is the main determinant of organ perfusion. Hypotension is common in patients having surgery and in critically ill patients. The severity and duration of hypotension are associated with hypoperfusion and organ dysfunction. Hypotension is mostly treated reactively after low blood pressure values have already occurred. However, prediction of hypotension before it becomes clinically apparent would allow the clinician to treat hypotension pre-emptively, thereby reducing the severity and duration of hypotension. Hypotension can now be predicted minutes before it actually occurs from the blood pressure waveform using machine-learning algorithms that can be trained to detect subtle changes in cardiovascular dynamics preceding clinically apparent hypotension. However, analyzing the complex cardiovascular system is a challenge because cardiovascular physiology is highly interdependent, works within complicated networks, and is influenced by compensatory mechanisms. Improved hemodynamic data collection and integration will be a key to improve current models and develop new hypotension prediction models. [ABSTRACT FROM AUTHOR]

Objective: The study objective was to report early outcomes of integrating Hypotension Prediction Index-guided hemodynamic management within a cardiac enhanced recovery pathway on total initial ventilation hours and length of stay in the intensive care unit., Methods: A multicenter, historical control, observational analysis of implementation of a hemodynamic management tool within enhanced recovery pathways was conducted by identifying cardiac surgery cases from 3 sites during 2 time periods, August 1 to December 31, 2019 (preprogram), and April 1 to August 31, 2021 (program). Reoperations, emergency (salvage), or cases requiring mechanical assist were excluded. Data were extracted from electronic medical records and chart reviews. Two primary outcome variables were length of stay in the intensive care unit (using Society of Thoracic Surgeons definitions) and acute kidney injury (using modified Kidney Disease Improving Global Outcomes criteria). One secondary outcome variable, total initial ventilation hours, used Society of Thoracic Surgeons definitions. Differences in length of stay in the intensive care unit and total ventilation time were analyzed using Kruskal-Wallis and stepwise multiple linear regression. Acute kidney injury stage used chi-square and stepwise cumulative logistic regression., Results: A total of 1404 cases (795 preprogram; 609 program) were identified. Overall reductions of 6.8 and 4.4 hours in intensive care unit length of stay ( P  = .08) and ventilation time ( P  = .03) were found, respectively. No significant association between proportion of patients identified with acute kidney injury by stage and period was found., Conclusions: Adding artificial intelligence-guided hemodynamic management to cardiac enhanced recovery pathways resulted in associated reduced time in the intensive care unit for patients undergoing nonemergency cardiac surgery across institutions in a real-world setting., (© 2023 Published by Elsevier Inc. on behalf of The American Association for Thoracic Surgery.)

Intraoperative hypotension (IOH) is common in noncardiac surgery and is associated with serious postoperative complications. Hypotension Prediction Index (HPI) has shown high sensitivity and specificity for predicting hypotension and may reduce IOH in noncardiac surgery. We conducted a systematic review of randomized controlled trials (RCTs) to evaluate the applications and effects of HPI in reducing hypotension during noncardiac surgery. We comprehensively searched the PubMed, Embase, Cochrane Library, Google Scholar, and http://ClinicalTrials.gov databases to identify RCTs conducted before May 2022. The primary outcome measures were the time-weighted average (TWA) of hypotension and the area under the hypotensive threshold (65 mmHg). Secondary outcomes were the incidence and duration of hypotension and the percentage of hypotensive time during surgery. The Cochrane Risk of Bias (RoB) tool was used to assess the quality of selected studies. We conducted data synthesis for median differences and assessed the certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. We included five studies with a total of 461 patients. Limited evidence suggested that HPI-guided intraoperative hemodynamics management leads to lower a) TWA of hypotension (median of difference of medians [MDM], -0.27 mmHg; 95% confidence interval [CI], -0.38, -0.01), b) area under the hypotensive threshold (MDM, -60.28 mmHg*min; 95% CI, -74.00, -1.30), c) incidence of hypotension (MDM, -4.50; 95% CI, -5.00, -4.00), d) total duration of hypotension (MDM, -12.80 min; 95% CI, -16.11, -3.39), and e) percentage of hypotension (MDM, -5.80; 95% CI, -6.65, -4.82) than routine hemodynamic management during noncardiac surgery. However, only very low- to low-quality evidence on the benefit of intraoperative HPI-based hemodynamic management is available. Our review revealed that HPI has the potential to reduce the occurrence, duration, and severity of IOH during noncardiac surgery compared to standard intraoperative care with proper adherence to the protocol. Systematic review registration PROSPERO CRD42022333834. [ABSTRACT FROM AUTHOR]

(1) Background: The Hypotension Prediction Index (HPI) is an algorithm that predicts hypotension, defined as mean arterial pressure (MAP) less than 65 mmHg for at least 1 min, based on arterial waveform features. We tested the hypothesis that the use of this index reduces the duration and severity of hypotension during noncardiac surgery. (2) Methods: We enrolled adults having moderate- or high-risk noncardiac surgery with invasive arterial pressure monitoring. Participating patients were randomized 1:1 to standard of care or hemodynamic management with HPI guidance with a goal directed hemodynamic treatment protocol. The trigger to initiate treatment (with fluids, vasopressors, or inotropes) was a value of HPI of 85 (range, 0–100) or higher in the intervention group. Primary outcome was the amount of hypotension, defined as time-weighted average (TWA) MAP less than 65 mmHg. Secondary outcomes were time spent in hypertension defined as MAP more than 100 mmHg for at least 1 min; medication and fluids administered and postoperative complications. (3) Results: We obtained data from 99 patients. The median (IQR) TWA of hypotension was 0.16 mmHg (IQR, 0.01–0.32 mmHg) in the intervention group versus 0.50 mmHg (IQR, 0.11–0.97 mmHg) in the control group, for a median difference of −0.28 (95% CI, −0.48 to −0.09 mmHg; p = 0.0003). We also observed an increase in hypertension in the intervention group as well as a higher weight-adjusted administration of phenylephrine in the intervention group. (4) Conclusions: In this single-center prospective study of patients undergoing elective noncardiac surgery, the use of this prediction model resulted in less intraoperative hypotension compared with standard care. An increase in the time spent in hypertension in the treatment group was also observed, probably as a result of overtreatment. This should provide an insight for refining the use of this prediction index in future studies to avoid excessive correction of blood pressure. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases are the most common non-obstetric cause of maternal death. These cases became more common thanks to the improvement in cardiovascular therapies. A multidisciplinary team is necessary to manage these pregnancies., Case Report: A 32 years old women at the 25 th week of gestation for acute heart failure in pre-existing left ventricular dysfunction induced by radio-chemotherapy admitted to the Coronary Unit of IRCCS Policlinico Universitario Agostino Gemelli for worsening of dyspneic symptoms and anuria not responding to diuretic therapy. At the echocardiogram: ejection fraction 30%, enlarged left atrium, systolic pulmonary arterial pressure 38 mmHg, bilateral pleural effusion, bilateral diffused pulmonary B lines. A multidisciplinary team composed by cardiologists, gynecologists, anesthesiologists, cardiac surgeons, neonatologists and bioethicists decided for an elective cesarean delivery at the 27 th week of gestation in the hybrid cardio-thoracic operating theater. Anesthesia was provided by combined spinal-epidural technique under invasive continuous hemodynamic monitoring with the Edwards Lifesciences HemoSphere with Hypotension Prediction Index (HPI) and ForeSight technology (Edwards Lifesciences, Irvine, USA) through catheterization of the left radial artery. The femoral arteries were left available for extracorporeal circulation. Continuous norepinephrine infusion was started once liquor was collected in the spinal needle at a 0.1 mcg/kg/minute through a central line and was continued until the end of surgery. Fluid management consisted of a total of 200 ml of crystalloids. HPI values never reached alarm values (maximum value =10). The patient was discharged home on the 5 th day after delivery with good hemodynamic compensation. The baby was intubated at birth and then gradually weaned from mechanical ventilation, then discharged.