Prediction of intraoperative hypotension from the linear extrapolation of mean arterial pressure.
Journal
European journal of anaesthesiology
ISSN: 1365-2346
Titre abrégé: Eur J Anaesthesiol
Pays: England
ID NLM: 8411711
Informations de publication
Date de publication:
01 07 2022
01 07 2022
Historique:
pubmed:
14
6
2022
medline:
30
6
2022
entrez:
13
6
2022
Statut:
ppublish
Résumé
Hypotension prediction index (HPI) software is a proprietary machine learning-based algorithm used to predict intraoperative hypotension (IOH). HPI has shown superiority in predicting IOH when compared to the predictive value of changes in mean arterial pressure (ΔMAP) alone. However, the predictive value of ΔMAP alone, with no reference to the absolute level of MAP, is counterintuitive and poor at predicting IOH. A simple linear extrapolation of mean arterial pressure (LepMAP) is closer to the clinical approach. Our primary objective was to investigate whether LepMAP better predicts IOH than ΔMAP alone. Retrospective diagnostic accuracy study. Two tertiary University Hospitals between May 2019 and December 2019. A total of 83 adult patients undergoing high risk non-cardiac surgery. Arterial pressure data were automatically extracted from the anaesthesia data collection software (one value per minute). IOH was defined as MAP < 65 mmHg. Correlations for repeated measurements and the area under the curve (AUC) from receiver operating characteristics (ROC) were determined for the ability of LepMAP and ΔMAP to predict IOH at 1, 2 and 5 min before its occurrence (A-analysis, using the whole dataset). Data were also analysed after exclusion of MAP values between 65 and 75 mmHg (B-analysis). A total of 24 318 segments of ten minutes duration were analysed. In the A-analysis, ROC AUCs to predict IOH at 1, 2 and 5 min before its occurrence by LepMAP were 0.87 (95% confidence interval, CI, 0.86 to 0.88), 0.81 (95% CI, 0.79 to 0.83) and 0.69 (95% CI, 0.66 to 0.71) and for ΔMAP alone 0.59 (95% CI, 0.57 to 0.62), 0.61 (95% CI, 0.59 to 0.64), 0.57 (95% CI, 0.54 to 0.69), respectively. In the B analysis for LepMAP these were 0.97 (95% CI, 0.9 to 0.98), 0.93 (95% CI, 0.92 to 0.95) and 0.86 (95% CI, 0.84 to 0.88), respectively, and for ΔMAP alone 0.59 (95% CI, 0.53 to 0.58), 0.56 (95% CI, 0.54 to 0.59), 0.54 (95% CI, 0.51 to 0.57), respectively. LepMAP ROC AUCs were significantly higher than ΔMAP ROC AUCs in all cases. LepMAP provides reliable real-time and continuous prediction of IOH 1 and 2 min before its occurrence. LepMAP offers better discrimination than ΔMAP at 1, 2 and 5 min before its occurrence. Future studies evaluating machine learning algorithms to predict IOH should be compared with LepMAP rather than ΔMAP.
Sections du résumé
BACKGROUND
Hypotension prediction index (HPI) software is a proprietary machine learning-based algorithm used to predict intraoperative hypotension (IOH). HPI has shown superiority in predicting IOH when compared to the predictive value of changes in mean arterial pressure (ΔMAP) alone. However, the predictive value of ΔMAP alone, with no reference to the absolute level of MAP, is counterintuitive and poor at predicting IOH. A simple linear extrapolation of mean arterial pressure (LepMAP) is closer to the clinical approach.
OBJECTIVES
Our primary objective was to investigate whether LepMAP better predicts IOH than ΔMAP alone.
DESIGN
Retrospective diagnostic accuracy study.
SETTING
Two tertiary University Hospitals between May 2019 and December 2019.
PATIENTS
A total of 83 adult patients undergoing high risk non-cardiac surgery.
DATA SOURCES
Arterial pressure data were automatically extracted from the anaesthesia data collection software (one value per minute). IOH was defined as MAP < 65 mmHg.
ANALYSIS
Correlations for repeated measurements and the area under the curve (AUC) from receiver operating characteristics (ROC) were determined for the ability of LepMAP and ΔMAP to predict IOH at 1, 2 and 5 min before its occurrence (A-analysis, using the whole dataset). Data were also analysed after exclusion of MAP values between 65 and 75 mmHg (B-analysis).
RESULTS
A total of 24 318 segments of ten minutes duration were analysed. In the A-analysis, ROC AUCs to predict IOH at 1, 2 and 5 min before its occurrence by LepMAP were 0.87 (95% confidence interval, CI, 0.86 to 0.88), 0.81 (95% CI, 0.79 to 0.83) and 0.69 (95% CI, 0.66 to 0.71) and for ΔMAP alone 0.59 (95% CI, 0.57 to 0.62), 0.61 (95% CI, 0.59 to 0.64), 0.57 (95% CI, 0.54 to 0.69), respectively. In the B analysis for LepMAP these were 0.97 (95% CI, 0.9 to 0.98), 0.93 (95% CI, 0.92 to 0.95) and 0.86 (95% CI, 0.84 to 0.88), respectively, and for ΔMAP alone 0.59 (95% CI, 0.53 to 0.58), 0.56 (95% CI, 0.54 to 0.59), 0.54 (95% CI, 0.51 to 0.57), respectively. LepMAP ROC AUCs were significantly higher than ΔMAP ROC AUCs in all cases.
CONCLUSIONS
LepMAP provides reliable real-time and continuous prediction of IOH 1 and 2 min before its occurrence. LepMAP offers better discrimination than ΔMAP at 1, 2 and 5 min before its occurrence. Future studies evaluating machine learning algorithms to predict IOH should be compared with LepMAP rather than ΔMAP.
Identifiants
pubmed: 35695749
doi: 10.1097/EJA.0000000000001693
pii: 00003643-202207000-00002
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
574-581Commentaires et corrections
Type : CommentIn
Type : AssociatedDataset
Informations de copyright
Copyright © 2022 European Society of Anaesthesiology and Intensive Care. Unauthorized reproduction of this article is prohibited.
Références
Futier E, Lefrant J-Y, Guinot P-G, et al. Effect of individualized vs standard blood pressure management strategies on postoperative organ dysfunction among high-risk patients undergoing major surgery: a randomized clinical trial. JAMA 2017; 318:1346–1357.
Bijker JB, Persoon S, Peelen LM, et al. Intraoperative hypotension and perioperative ischemic stroke after general surgery: a nested case-control study. Anesthesiology 2012; 116:658–664.
Walsh M, Devereaux PJ, Garg AX, et al. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology 2013; 119:507–515.
Hallqvist L, Mårtensson J, Granath F, et al. Intraoperative hypotension is associated with myocardial damage in noncardiac surgery: an observational study. Eur J Anaesthesiol 2016; 33:450–456.
van Waes Judith AR, Wijeysundera DN, vanWolfswinkel L, T.F. L, et al. Association between intraoperative hypotension and myocardial injury after vascular surgery. Anesthesiology 2016; 124:35–44.
Sun LY, Wijeysundera DN, Tait GA, et al. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology 2015; 123:515–523.
Salmasi V, Maheshwari K, Yang D, et al. Relationship between intraoperative hypotension, defined by either reduction from baseline or absolute thresholds, and acute kidney and myocardial injury after noncardiac surgery: a retrospective cohort analysis. Anesthesiology 2017; 126:47–65.
Maheshwari K, Khanna S, Bajracharya GR, et al. A randomized trial of continuous noninvasive blood pressure monitoring during noncardiac surgery. Anesth Analg 2018; 127:424–431.
Willingham MD, Karren E, Shanks AM, et al. Concurrence of intraoperative hypotension, low minimum alveolar concentration, and low bispectral index is associated with postoperative death. Anesthesiology 2015; 123:775–785.
Mascha EJ, Yang D, Weiss S, et al. Intraoperative mean arterial pressure variability and 30-day mortality in patients having noncardiac surgery. Anesthesiology 2015; 123:79–91.
Monk TG, Bronsert MR, Henderson WG, et al. Association between intraoperative hypotension and hypertension and 30-day postoperative mortality in noncardiac surgery. Anesthesiology 2015; 123:307–319.
Reich DL, Hossain S, Krol M, et al. Predictors of hypotension after induction of general anesthesia. Anesth Analg 2005; 101:622–628.
Bijker JB, van Klei WA, Kappen TH, et al. Incidence of intraoperative hypotension as a function of the chosen definition: literature definitions applied to a retrospective cohort using automated data collection. Anesthesiology 2007; 107:213–220.
Wesselink EM, Kappen TH, Torn HM, et al. Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review. Br J Anaesth 2018; 121:706–721.
Wijnberge M, Geerts BF, Hol L, et al. Effect of a machine learning–derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: The HYPE Randomized Clinical Trial. JAMA 2020; 323:1052–1060.
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.
Vos JJ, Scheeren TWL. Intraoperative hypotension and its prediction. Ind J Anaesth 2019; 63:877–885.
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.
Sessler DI, Bloomstone JA, Aronson S, et al. Perioperative quality initiative consensus statement on intraoperative blood pressure, risk and outcomes for elective surgery. Br J Anaesth 2019; 122:563–574.
Toulouse E, Lafont B, Granier S, et al. French legal approach to patient consent in clinical research. Anaesth Crit Care Pain Med 2020; 39:883–885.
Ngan Kee WD. A Random-allocation graded dose-response study of norepinephrine and phenylephrine for treating hypotension during spinal anesthesia for cesarean delivery. Anesthesiology 2017; 127:934–941.
Vistisen ST, Johnson AEW, Scheeren TWL. Predicting vital sign deterioration with artificial intelligence or machine learning. J Clin Monitoring Comput 2019; 33:949–951.