A comparison between invasive and noninvasive measurement of the Hypotension Prediction Index: A post hoc analysis of a prospective cohort study.
Journal
European journal of anaesthesiology
ISSN: 1365-2346
Titre abrégé: Eur J Anaesthesiol
Pays: England
ID NLM: 8411711
Informations de publication
Date de publication:
15 Oct 2024
15 Oct 2024
Historique:
medline:
16
10
2024
pubmed:
16
10
2024
entrez:
16
10
2024
Statut:
aheadofprint
Résumé
Clinical trials and validation studies demonstrate promising hypotension prediction capability by the Hypotension Prediction Index (HPI). Most studies that evaluate HPI derive it from invasive blood pressure readings, but a direct comparison with the noninvasive alternative remains undetermined. Such a comparison could provide valuable insights for clinicians in deciding between invasive and noninvasive monitoring strategies. Evaluating predictive differences between HPI when obtained through noninvasive versus invasive blood pressure monitoring. Post hoc analysis of a prospective observational study conducted between 2018 and 2020. Single-centre study conducted in an academic hospital in the Netherlands. Adult noncardiac surgery patients scheduled for over 2 h long elective procedures. After obtaining informed consent, 91 out of the 105 patients had sufficient data for analysis. The primary outcome was the difference in area under the receiver-operating characteristics (ROC) curve (AUC) obtained for HPI predictions between the two datasets. Additionally, difference in time-to-event estimations were calculated. AUC (95% confidence interval (CI)) results revealed a nonsignificant difference between invasive and noninvasive HPI, with areas of 94.2% (90.5 to 96.8) and 95.3% (90.4 to 98.2), respectively with an estimated difference of 1.1 (-3.9 to 6.1)%; P = 0.673. However, noninvasive HPI demonstrated significantly longer time-to-event estimations for higher HPI values. Noninvasive HPI is reliably accessible to clinicians during noncardiac surgery, showing comparable accuracy in HPI probabilities and the potential for additional response time. Clinicaltrials.gov (NCT03795831) on 10 January 2019. https://clinicaltrials.gov/study/NCT03795831.
Sections du résumé
BACKGROUND
BACKGROUND
Clinical trials and validation studies demonstrate promising hypotension prediction capability by the Hypotension Prediction Index (HPI). Most studies that evaluate HPI derive it from invasive blood pressure readings, but a direct comparison with the noninvasive alternative remains undetermined. Such a comparison could provide valuable insights for clinicians in deciding between invasive and noninvasive monitoring strategies.
OBJECTIVES
OBJECTIVE
Evaluating predictive differences between HPI when obtained through noninvasive versus invasive blood pressure monitoring.
DESIGN
METHODS
Post hoc analysis of a prospective observational study conducted between 2018 and 2020.
SETTING
METHODS
Single-centre study conducted in an academic hospital in the Netherlands.
PATIENTS
METHODS
Adult noncardiac surgery patients scheduled for over 2 h long elective procedures. After obtaining informed consent, 91 out of the 105 patients had sufficient data for analysis.
MAIN OUTCOME MEASURES
METHODS
The primary outcome was the difference in area under the receiver-operating characteristics (ROC) curve (AUC) obtained for HPI predictions between the two datasets. Additionally, difference in time-to-event estimations were calculated.
RESULTS
RESULTS
AUC (95% confidence interval (CI)) results revealed a nonsignificant difference between invasive and noninvasive HPI, with areas of 94.2% (90.5 to 96.8) and 95.3% (90.4 to 98.2), respectively with an estimated difference of 1.1 (-3.9 to 6.1)%; P = 0.673. However, noninvasive HPI demonstrated significantly longer time-to-event estimations for higher HPI values.
CONCLUSION
CONCLUSIONS
Noninvasive HPI is reliably accessible to clinicians during noncardiac surgery, showing comparable accuracy in HPI probabilities and the potential for additional response time.
TRIAL REGISTRATION
BACKGROUND
Clinicaltrials.gov (NCT03795831) on 10 January 2019. https://clinicaltrials.gov/study/NCT03795831.
Identifiants
pubmed: 39411994
doi: 10.1097/EJA.0000000000002082
pii: 00003643-990000000-00230
doi:
Banques de données
ClinicalTrials.gov
['NCT03795831']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Society of Anaesthesiology.
Références
Ciccone A, Celani MG, Chiaramonte R, et al. Continuous versus intermittent physiological monitoring for acute stroke. Cochrane Database Syst Rev 2013; (5):CD008444.
Hatib F, Jian Z, Buddi S, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology 2018; 129:663–674.
Frassanito L, Sonnino C, Piersanti A, et al. Performance of the hypotension prediction index with noninvasive arterial pressure waveforms in awake cesarean delivery patients under spinal anesthesia. Anesth Analg 2022; 134:633–643.
Wijnberge M, van der Ster BJP, Geerts BF, et al. Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: a cohort study. Eur J Anaesthesiol 2021; 38:609–615.
Frassanito L, Giuri PP, Vassalli F, et al. Hypotension Prediction Index with noninvasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery. J Clin Monit Comput 2022; 36:1325–1332.
Scheer B, Perel A, Pfeiffer UJ. Clinical review: complications and risk factors of peripheral arterial catheters used for haemodynamic monitoring in anaesthesia and intensive care medicine. Crit Care 2002; 6:199–204.
Nuttall G, Burckhardt J, Hadley A, et al. Surgical and patient risk factors for severe arterial line complications in adults. Anesthesiology 2016; 124:590–597.
Cousins TR, O’Donnell JM. Arterial cannulation: a critical review. Aana j 2004; 72:267–271.
Kho E, van der Ster BJP, van der Ven WH, et al. Clinical agreement of a novel algorithm to estimate radial artery blood pressure from the noninvasive finger blood pressure. J Clin Anesth 2022; 83:110976.
Enevoldsen J, Vistisen ST. Performance of the hypotension prediction index may be overestimated due to selection bias. Anesthesiology 2022; 137:283–289.
Michard F, Biais M, Futier E, et al. Mirror, mirror on the wall, who is going to become hypotensive? Eur J Anaesthesiol 2023; 40:72–74.
Mulder MP, Harmannij-Markusse M, Donker DW, et al. Is continuous intraoperative monitoring of mean arterial pressure as good as the Hypotension Prediction Index Algorithm?: research letter. Anesthesiology 2023; 138:657–658.
Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015; 350:g7594.
Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD Group STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ 2015; 351:h5527.
van der Ven WH, Terwindt LE, Risvanoglu N, et al. Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care unit: a cohort study. J Clin Monit Comput 2021; 36:1397–1405.
Davies SJ, Vistisen ST, Jian Z, et al. Ability of an arterial waveform analysis-derived Hypotension Prediction Index to predict future hypotensive events in surgical patients. Anesth Analg 2020; 130:352–359.
Maheshwari K, Buddi S, Jian Z, et al. Performance of the Hypotension Prediction Index with noninvasive arterial pressure waveforms in noncardiac surgical patients. J Clin Monit Comput 2021; 35:71–78.