Evidence map›Paper›PMID 31772395›Full record

ReviewIndian journal of anaesthesia2019

Intraoperative hypotension and its prediction.

Jaap J Vos, Thomas W L Scheeren

Registry-linked trialOpen access · diamondAbstract readReview
In one paragraph

Review in Indian journal of anaesthesia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06202638 (An Observational, Prospective, Non-randomized Multi-centre Cohort Feasibility Study of the Hypotension Prediction Index), which is not on this map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 5% 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.

NCT06202638 recruitingnot on this mapstarted 2023, after this paper: background citation

An Observational, Prospective, Non-randomized Multi-centre Cohort Feasibility Study of the Hypotension Prediction Index (HPI) in Patients Undergoing Lung Resections With the Use of One-lung Ventilation.

Typeobservational_patient_registrySponsorJohn Paul II Hospital, KrakowRan2023 to 2025Enrolled60ConditionsIntraoperative HypotensionArmsHemoSphere monitor with Acumen Hypotension Prediction Index Software
3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Review
  5. Observational
  6. Article
  7. Bone cement implantation syndrome: a scoping review.British journal of anaesthesia · 2025
    Article
  8. Risk factors for intraoperative hypotension during cardiac surgery.Indian journal of thoracic and cardiovascular surgery · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. Observational
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

2 authors at 1 institution in 1 country.

Jaap J VosDepartment of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
Thomas W L ScheerenDepartment of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
University of Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intraoperative hypotension (IOH) very commonly accompanies general anaesthesia in patients undergoing major surgical procedures. The development of IOH is unwanted, since it is associated with adverse outcomes such as acute kidney injury and myocardial injury, stroke and mortality. Although the definition of IOH is variable, harm starts to occur below a mean arterial pressure (MAP) threshold of 65 mmHg. The odds of adverse outcome increase for increasing duration and/or magnitude of IOH below this threshold, and even short periods of IOH seem to be associated with adverse outcomes. Therefore, reducing the hypotensive burden by predicting and preventing IOH through proactive appropriate treatment may potentially improve patient outcome. In this review article, we summarise the current state of the prediction of IOH by the use of so-called machine-learning algorithms. Machine-learning algorithms that use high-fidelity data from the arterial pressure waveform, may be used to reveal 'traits' that are unseen by the human eye and are associated with the later development of IOH. These algorithms can use large datasets for 'training', and can subsequently be used by clinicians for haemodynamic monitoring and guiding therapy. A first clinically available application, the hypotension prediction index (HPI), is aimed to predict an impending hypotensive event, and additionally, to guide appropriate treatment by calculated secondary variables to asses preload (dynamic preload variables), contractility (dP/dt

Indexed as

Blood pressurehemodynamic monitoringhypotension prediction indexmachine learningpredictive analysis

Identifiers

PMID31772395
PMCPMC6868662
OpenAlexW2984101034

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.