Evidence map›Paper›PMID 36862171›Full record

ArticleSurgical endoscopy2023

The physiology of intraoperative error: using electrokardiograms to understand operator performance during robot-assisted surgery simulations.

Christopher D'Ambrosia, Eliah Aronoff-Spencer, Estella Y Huang, Nicole H Goldhaber, Garth R Jacobsen, Bryan Sandler, Santiago Horgan, Lawrence G Appelbaum, Henrik Christensen, Ryan C Broderick

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Surgical endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 30% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Observational
  2. Article
  3. Review
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

10 authors at 1 institution in 1 country.

Christopher D'AmbrosiaDepartment of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. cdambros@eng.ucsd.edu.ORCID 0000-0002-3748-995X
Eliah Aronoff-SpencerDepartment of Medicine, University of California San Diego, La Jolla, CA, USA.
Estella Y HuangDivision of Minimally Invasive Surgery, Department of Surgery, University of California San Diego, La Jolla, CA, USA.
Nicole H GoldhaberDivision of Minimally Invasive Surgery, Department of Surgery, University of California San Diego, La Jolla, CA, USA.
Garth R JacobsenDivision of Minimally Invasive Surgery, Department of Surgery, University of California San Diego, La Jolla, CA, USA.
Bryan SandlerDivision of Minimally Invasive Surgery, Department of Surgery, University of California San Diego, La Jolla, CA, USA.
Santiago HorganDivision of Minimally Invasive Surgery, Department of Surgery, University of California San Diego, La Jolla, CA, USA.
Lawrence G AppelbaumDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Henrik ChristensenDepartment of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Ryan C BroderickDivision of Minimally Invasive Surgery, Department of Surgery, University of California San Diego, La Jolla, CA, USA.
University of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNo platform for objective, synchronous and on-line evaluation of both intraoperative error and surgeon physiology yet exists. Electrokardiogram (EKG) metrics have been associated with cognitive and affective features that are known to impact surgical performance but have not yet been analyzed in conjunction with real-time error signals using objective, real-time methods.

methodsEKGs and operating console point-of-views (POVs) for fifteen general surgery residents and five non-medically trained participants were captured during three simulated robotic-assisted surgery (RAS) procedures. Time and frequency-domain EKG statistics were extracted from recorded EKGs. Intraoperative errors were detected from operating console POV videos. EKG statistics were synchronized with intraoperative error signals.

resultsRelative to personalized baselines, IBI, SDNN and RMSSD decreased 0.15% (S.E. 3.603e-04; P = 3.25e-05), 3.08% (S.E. 1.603e-03; P < 2e-16) and 1.19% (S.E. 2.631e-03; P = 5.66e-06), respectively, during error. Relative LF RMS power decreased 1.44% (S.E. 2.337e-03; P = 8.38e-10), and relative HF RMS power increased 5.51% (S.E. 1.945e-03; P < 2e-16).

conclusionsUse of a novel, on-line biometric and operating room data capture and analysis platform enabled detection of distinct operator physiological changes during intraoperative errors. Monitoring operator EKG metrics during surgery may help improve patient outcomes through real-time assessments of intraoperative surgical proficiency and perceived difficulty as well as inform personalized surgical skills development.

Indexed as

LaparoscopyRobotic Surgical ProceduresSurgeonsHumansOperating RoomsLaparoscopyMinimally invasive surgeryRobotic surgerySurgical educationSurgical performance

Identifiers

PMID36862171
PMCPMC10234932
OpenAlexW4322758098

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.