Evidence map›Paper›PMID 31989416›Full record

Trial reportJournal of clinical monitoring and computing2021

Performance of the Hypotension Prediction Index with non-invasive arterial pressure waveforms in non-cardiac surgical patients.

Kamal Maheshwari, Sai Buddi, Zhongping Jian, Jos Settels, Tetsuya Shimada, Barak Cohen, Daniel I Sessler, Feras Hatib

4 registry-linked trialsOpen access · hybridAbstract readClinical Trial
In one paragraph

Trial report in Journal of clinical monitoring and computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Cited by 26 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 4 pooled it
6.7field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02872896 nacompletednot on this map

The Role of Noninvasive Beat-to-Beat ClearSight Blood Pressure Monitoring During Non-cardiac Surgery

TypeinterventionalSponsorThe Cleveland ClinicRan2016 to 2018Enrolled320ConditionsHypotensionArmsOrthopedic, urologic or general surgery
NCT05354661 completednot on this map

Non-Invasive Hemodynamic Monitoring and Incidence of Perioperative Hypotension in Gynaecologic Oncologic Surgery: Hypotension Prediction Index Working With ClearSight Versus Arterial Waveform Analysis Alone

TypeobservationalSponsorFondazione Policlinico Universitario Agostino Gemelli IRCCSRan2019 to 2022Enrolled68ConditionsHypotension, Hemodynamic Instability, Gynecologic CancerArmsHypotension Prediction Index
NCT05964374 nacompletednot on this mapstarted 2023, after this paper: background citation

Goal-Directed Therapy (GDT) Using Hypotension Prediction Index (HPI) Following Cardiac Surgery: a Pilot Randomized Control Trial

TypeinterventionalSponsorUniversity of CalgaryRan2023 to 2025Enrolled100ConditionsSurgeryArmsGoal-directed therapy
NCT06729827 nacompletednot on this mapstarted 2025, after this paper: background citation

Non-invasive PREDIctive Hemodynamic Continuous moniToring vs Standard Intermittent Blood Pressure and Post-spinal hypotensION During Elective Cesarean Section: a Randomized Clinical Trial

TypeinterventionalSponsorFondazione Policlinico Universitario Agostino Gemelli IRCCSRan2025 to 2025Enrolled102ConditionsPregnancy Related, Cesarean Section Complications, Hypotension on Induction, Anesthesia ComplicationArmsHypotension prediction Index - HPI
3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 71 citations in OpenAlex.

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  17. Physiowise: A Physics-aware Approach to Dicrotic Notch Identification.ACM transactions on computing for healthcare · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 3 countries.

Kamal MaheshwariDepartment of General Anesthesiology, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA. maheshk@ccf.org.ORCID http://orcid.org/0000-0003-1961-5577
Sai BuddiEdwards Lifesciences Critical Care, Irvine, CA, USA.
Zhongping JianEdwards Lifesciences Critical Care, Irvine, CA, USA.
Jos SettelsEdwards Lifesciences Critical Care, Irvine, CA, USA.
Tetsuya ShimadaDepartment of Outcomes Research, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA.
Barak CohenDepartment of Outcomes Research, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA.
Daniel I SesslerDepartment of Outcomes Research, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA.
Feras HatibEdwards Lifesciences Critical Care, Irvine, CA, USA.
Cleveland Clinic · USEdwards Lifesciences (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Arterial PressureHypotensionAdultHumansMachine LearningMiddle AgedPredictive Value of TestsSensitivity and SpecificityHypotension predictionHypotension Prediction IndexIntraoperative hypotensionMachine learningNon-invasive blood pressure

Identifiers

PMID31989416
PMCPMC7889685
OpenAlexW3003152554

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.