Intraoperative hypotension when using hypotension prediction index software during major noncardiac surgery: a European multicentre prospective observational registry (EU HYPROTECT)

Kouz, Karim; García, Manuel Ignacio Monge; Lisanti, Ivana; Draisci, Gaetano; Frassanito, Luciano; Sander, Michael; Akbari, Amir; Frey, Ulrich; Grundmann, Carla D; Davies, Simon; Donati, Abele; Ripollés‐Melchor, Javier; García-López, Daniel; Vojnar, Benjamin; Gayat, Étienne; Noll, Eric; Bramlage, Peter; Saugel, Bernd

doi:10.1016/j.bjao.2023.100140

Cited by 14 publications

(13 citation statements)

References 39 publications

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“…The EU HYPROTECT Registry monitored 749 patients undergoing major non-cardiac surgery by using the Acumen™ Hypotension Prediction Index software. [ 7 ] The analysis of 702 patients revealed a notably low median time-weighted average MAP of <65 mmHg (0.03 mmHg). A significant proportion (41%) experienced no extended hypotensive episodes, while 59% had at least one episode, with a median of 1 episode lasting ≥1 minute.…”

Section: Applications In Haemodynamic Monitoringmentioning

confidence: 99%

Role of artificial intelligence in haemodynamic monitoring

Myatra,

Jagiasi,

Singh

et al. 2024

Indian Journal of Anaesthesia

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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.

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Section: Applications In Haemodynamic Monitoringmentioning

confidence: 99%

Role of artificial intelligence in haemodynamic monitoring

Myatra,

Jagiasi,

Singh

et al. 2024

Indian Journal of Anaesthesia

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“…Patients in the registry were monitored using the Acumen IQ sensor (Edwards Lifesciences) and the HemoSphere monitoring platform (Edwards Lifesciences), which calculates and continuously displays HPI. 1 , 2 In registry patients, the degree and duration of intraoperative hypotension was low, as indicated by a low median (25% percentile–75% percentile) time-weighted average MAP less than 65 mm Hg of 0.03 (0.00–0.20) mm Hg. 1 During surgery with a median duration of 209 (153–290) min, patients spent a median of 2 (0–9) min with a MAP below 65 mm Hg.…”

mentioning

confidence: 97%

“… 1 , 2 In registry patients, the degree and duration of intraoperative hypotension was low, as indicated by a low median (25% percentile–75% percentile) time-weighted average MAP less than 65 mm Hg of 0.03 (0.00–0.20) mm Hg. 1 During surgery with a median duration of 209 (153–290) min, patients spent a median of 2 (0–9) min with a MAP below 65 mm Hg. 1 The observational nature of the registry makes it difficult to know to what extent the HPI-software monitoring contributed to limiting the degree and duration of hypotension.…”

mentioning

confidence: 97%

“…Editor—We recently reported the primary results 1 of the prospective observational EU-HYPROTECT registry 2 that includes 702 patients who were monitored using the Acumen Hypotension Prediction Index software (HPI-software) (Edwards Lifesciences, Irvine, CA, USA) during elective major noncardiac surgery in 12 medical centres in five European countries (France, Germany, Italy, Spain, and the United Kingdom). 1 The HPI-software was developed using machine learning to predict impending hypotension, defined as a MAP of less than 65 mm Hg for at least 1 min, by analysing features of the BP waveform.…”

mentioning

confidence: 99%

“… 1 During surgery with a median duration of 209 (153–290) min, patients spent a median of 2 (0–9) min with a MAP below 65 mm Hg. 1 The observational nature of the registry makes it difficult to know to what extent the HPI-software monitoring contributed to limiting the degree and duration of hypotension. Nevertheless, data on HPI alarms that occurred in registry patients may help to understand the role that HPI-software monitoring may have played.…”

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confidence: 99%

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Hypotension Prediction Index software alarms during major noncardiac surgery: a post hoc secondary analysis of the EU-HYPROTECT registry

Kouz,

Scheeren,

van den Boom

et al. 2023

BJA Open

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Impact of clinicians’ behavior, an educational intervention with mandated blood pressure and the hypotension prediction index software on intraoperative hypotension: a mixed methods study

de Keijzer,

Vos,

Yates

et al. 2023

J Clin Monit Comput

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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.

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Intraoperative hypotension when using hypotension prediction index software during major noncardiac surgery: a European multicentre prospective observational registry (EU HYPROTECT)

Cited by 14 publications

References 39 publications

Role of artificial intelligence in haemodynamic monitoring

Role of artificial intelligence in haemodynamic monitoring

Hypotension Prediction Index software alarms during major noncardiac surgery: a post hoc secondary analysis of the EU-HYPROTECT registry

Impact of clinicians’ behavior, an educational intervention with mandated blood pressure and the hypotension prediction index software on intraoperative hypotension: a mixed methods study

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