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Ability of an arterial waveform analysis-derived hypotension prediction index to predict future hypotensive events in surgical patients
Open accessAbstract
BACKGROUND: Intraoperative hypotension is associated with worse perioperative outcomes for patients undergoing major noncardiac surgery. The Hypotension Prediction Index is a unitless number that is derived from an arterial pressure waveform trace, and as the number increases, the risk of hypotension occurring in the near future increases. We investigated the diagnostic ability of the Hypotension Prediction Index in predicting impending intraoperative hypotension in comparison to other commonly collected perioperative hemodynamic variables. METHODS: This is a 2-center retrospective analysis of patients undergoing major surgery. Data were downloaded and analyzed from the Edwards Lifesciences EV1000 platform. Receiver operating characteristic curves were constructed for the Hypotension Prediction Index and other hemodynamic variables as well as event rates and time to event. RESULTS: Two hundred fifty-five patients undergoing major surgery were included in the analysis yielding 292,025 data points. The Hypotension Prediction Index predicted hypotension with a sensitivity and specificity of 85.8% (95% CI, 85.8%–85.9%) and 85.8% (95% CI, 85.8%–85.9%) 5 minutes before a hypotensive event (area under the curve, 0.926 [95% CI, 0.925–0.926]); 81.7% (95% CI, 81.6%–81.8%) and 81.7% (95% CI, 81.6%–81.8%) 10 minutes before a hypotensive event (area under the curve, 0.895 [95% CI, 0.894–0.895]); and 80.6% (95% CI, 80.5%–80.7%) and 80.6% (95% CI, 80.5%–80.7%) 15 minutes before a hypotensive event (area under the curve, 0.879 [95% CI, 0.879–0.880]). The Hypotension Prediction Index performed superior to all other measured hemodynamic variables including mean arterial pressure and change in mean arterial pressure over a 3-minute window. CONCLUSIONS: The Hypotension Prediction Index provides an accurate real time and continuous prediction of impending intraoperative hypotension before its occurrence and has superior predictive ability than the commonly measured perioperative hemodynamic variables.
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Wijnberge, M., Geerts, B. F., Hol, L., Lemmers, N., Mulder, M. P., Berge, P., Schenk, J., Terwindt, L. E., Hollmann, M. W., Vlaar, A. P., et al. (2020) · JAMA · doi:10.1001/jama.2020.0592 · Cited by 586
“We calculated (1) total number of silent alarms; (2) number of alarms per patient; (3) number of alarms that led to treatment; (4) number of treatments per alarm; (5) time from alarm to treatment in the intervention group compared with time from silent alarm to treatment in the control group (all alarms per patient we…”
MethodAccessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326
“Preventative measures may be taken earlier and with more accuracy if AI metrics are implemented as opposed to the traditional risk-evaluation scores (40, 42).”
BackgroundMachine learning in clinical decision making
Adlung, L., Cohen, Y., Mor, U., & Elinav, E. (2021) · Med · doi:10.1016/j.medj.2021.04.006 · Cited by 321
Perioperative Blood Pressure Management
Saugel, B., & Sessler, D. I. (2020) · Anesthesiology · doi:10.1097/aln.0000000000003610 · Cited by 194
“In another validation study in 255 patients having major surgery, the hypotension prediction index predicted intraoperative hypotension up to 15 min before the event with a sensitivity and specificity of 81% each.(51) Naturally, the hypotension prediction index algorithm cannot predict hypotension consequent to clinic…”
BackgroundEffective hemodynamic monitoring
Pinsky, M. R., Cecconi, M., Chew, M. S., De Backer, D., Douglas, I., Edwards, M., Hamzaoui, O., Hernandez, G., Martin, G., Monnet, X., et al. (2022) · Critical Care · doi:10.1186/s13054-022-04173-z · Cited by 167
Self-citation
Metadata
- Authors
- Davies, S. J., Vistisen, S. T., Jian, Z., Hatib, F., & Scheeren, T. W. L.
- Affiliations
- National Health Service (GB)
- Aarhus University (DK)
- Edwards Lifesciences (United States) (US)
- University Medical Center Groningen (NL)
- Corresponding author
- Davies, Simon James
- Venue
- Anesthesia & Analgesia
- Published
- 2020-02
- Volume
- 130
- Issue
- 2
- Pages
- 352-359
- ISSN
- 0003-2999 (ISSN-L)
- OpenAlex
- W2928352011
- Cited by
- 219
- References
- 23
- Citations by year
- 2026: 26 · 2025: 29 · 2024: 42 · 2023: 33 · 2022: 28 · 2021: 30 · 2020: 24
- FWCI
- 21.44
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-09-01)
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- Anesthesia & Analgesia (publishedVersion)
- PubMed (publishedVersion)
- Scopus (Elsevier) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Funders
- Edwards Lifesciences
- Topics
- Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Keywords
- Hypotension Prediction Index, intraoperative hypotension, arterial waveform analysis, non-cardiac surgery, hemodynamic monitoring, hypotension prediction, mean arterial pressure, perioperative outcomes, sensitivity and specificity, time-to-event prediction
- MeSH terms
- Aged, Aged, 80 and over, Female, Forecasting, Humans, Hypotension, Intraoperative Complications, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Retrospective Studies, Monitoring, Intraoperative, Arterial Pressure
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