Evidence map›Paper›PMID 28275978›Full record

Observational studyJournal of clinical monitoring and computing2018

Infrared pupillometry helps to detect and predict delirium in the post-anesthesia care unit.

Eric Yang, Matthias Kreuzer, September Hesse, Paran Davari, Simon C Lee, Paul S García

Registry-linked trialAbstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in Journal of clinical monitoring and computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06187792 (Constructing a Model of Pupillary Parameters in Predicting Delirium Among Critically Ill Patients in the Intensive Unit), which is not on this map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.1field-weighted citation impact, top 24% 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.

NCT06187792 unknown statusnot on this mapstarted 2023, after this paper: background citation

Constructing a Model of Pupillary Parameters in Predicting Delirium Among Critically Ill Patients in the Intensive Unit

TypeobservationalSponsorNational Taiwan University HospitalRan2023 to 2025Enrolled200ConditionsICU Delirium
3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  14. Reducing Opioid Dose Via Targeted Nociception Monitoring.Deutsches Arzteblatt international · 2020
    Article
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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

6 authors at 4 institutions in 1 country.

Eric YangChicago Medical School, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA.
Matthias KreuzerDepartment of Anesthesiology, Emory University, Atlanta, GA, USA.
September HesseDepartment of Anesthesiology, Emory University, Atlanta, GA, USA.
Paran DavariDepartment of Anesthesiology, Emory University, Atlanta, GA, USA.
Simon C LeeDepartment of Anesthesiology, Emory University, Atlanta, GA, USA.
Paul S GarcíaDepartment of Anesthesiology, Emory University, Atlanta, GA, USA. pgarcia@emory.edu.
Emory University · USAtlanta Medical Center · USAtlanta VA Medical Center · USRosalind Franklin University of Medicine and Science · US

Funding

TRAINING IN TRANSLATIONAL RESEARCH IN NEUROLOGYT32NS007480 · NINDS · EMORY UNIVERSITY · PI LEVEY, ALLAN I · 2000 to 2020
$4.5M
Effects of Antiretrovirals on Neuronal Health: Screening ARVs for NeurotoxicityIK2BX001677 · VA · VETERANS HEALTH ADMINISTRATION · PI GARCIA, PAUL S · 2014 to 2018
–
BLRD VA IK2 BX001677Foundation for Anesthesia Education and Research (US) Medical Student Anesthesia Research FellowshipJames S. McDonnell Foundation 220023046National Institute of Neurological Disorders and Stroke 5T32NS007480-15U.S. Department of Veterans Affairs BX00167
6 · The paper itself

Abstract

This study evaluates the capability of pupillary parameters to detect and predict delirium in the post-anesthesia care unit (PACU-D) following general anesthesia. PACU-D may complicate and prolong the patient's postoperative course, consequently increasing hospital costs. After institutional approval, 47 patients undergoing surgical interventions with general anesthesia were included in the study. We measured the pupillary reflexes at signing of informed consent, during surgery 20 min after intubation and when the primary inhaled anesthetic was turned off, and 15 and 45 min after PACU admittance and upon discharge from the PACU. We evaluated patients for delirium using the confusion assessment method for the intensive care unit (CAM-ICU) score after 15 and 60 min in the PACU. We chose receiver operating curve (ROC) and area under the curve (AUC) to compare the performance of non-pupillary parameters to pupillary parameters, such as pupil diameter, percent constriction, and dilation velocity, to detect and predict PACU-D. Percent constriction (AUC = 0.93, optimal threshold = 18.5%) and dilation velocity (AUC = 0.93, optimal threshold = 0.35 mm/s) showed excellent ability to detect and predict delirium persisting throughout the PACU stay. These pupillary measures showed superior performance compared to other pupillary measures and features commonly associated with delirium, e.g., age (AUC = 0.73), total opioids (AUC = 0.56), or length of surgery (AUC = 0.40). Our results suggest that pupillometry and the parameters derived from the recording may identify delirious patients in the PACU. This information can help to efficiently structure their care in a timely manner, and potentially avoid adverse complications for the patient and financial consequences for the hospital.

Indexed as

Anesthesia Recovery PeriodReflex, PupillaryAdultAgedAged, 80 and overAnesthesia, GeneralDeliriumFemaleHumansMaleMiddle AgedMonitoring, IntraoperativePatient DischargePostoperative ComplicationsPredictive Value of TestsProspective StudiesAnesthesiaAnesthesia recovery periodDelayed emergence from anesthesiaDeliriumGeneralPupillaryReflex

Identifiers

PMID28275978
OpenAlexW2593826512

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.