Evidence map›Paper›PMID 37096572›Full record

ArticleClinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery2023

Artificial neural network prediction of post-thyroidectomy outcome.

Kotaro Tsutsumi, Khodayar Goshtasbi, Khwaja H Ahmed, Pooya Khosravi, Karen Tawk, Yarah M Haidar, Tjoson Tjoa, William B Armstrong, Mehdi Abouzari

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

Article in Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 88% 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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Article
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

9 authors at 1 institution in 1 country.

Kotaro TsutsumiDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
Khodayar GoshtasbiDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
Khwaja H AhmedDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
Pooya KhosraviDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.ORCID 0000-0002-3631-4760
Karen TawkDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
Yarah M HaidarDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
Tjoson TjoaDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
William B ArmstrongDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
Mehdi AbouzariDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.ORCID 0000-0002-3585-698X
University of California, Irvine · US

Funding

University of California Health Participation in the National COVID Cohort Collaborative (N3C)UL1TR001414 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M, VILAIN, ERIC J. · 2015 to 2023
$35.1M
NRSA Training CoreTL1TR001415 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI CAIOZZO, VINCENT JAMES, HEAD, ELIZABETH · 2015 to 2023
$4.2M
NCATS NIH HHS TL1 TR001415NCATS NIH HHS TL1TR001415NCATS NIH HHS UL1 TR001414NCRR NIH HHS TL1TR001415
6 · The paper itself

Abstract

objectivesThe goal of this study was to develop a deep neural network (DNN) for predicting surgical/medical complications and unplanned reoperations following thyroidectomy. DESIGN, SETTING, AND

participantsThe 2005-2017 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried to extract patients who underwent thyroidectomy. A DNN consisting of 10 layers was developed with an 80:20 breakdown for training and testing.

main outcome measuresThree primary outcomes of interest, including occurrence of surgical complications, medical complications, and unplanned reoperation were predicted.

resultsOf the 21 550 patients who underwent thyroidectomy, medical complications, surgical complications and reoperation occurred in 1723 (8.0%), 943 (4.38%) and 2448 (11.36%) patients, respectively. The DNN performed with an area under the curve of receiver operating characteristics of .783 (medical complications), .709 (surgical complications) and .703 (reoperations). Accuracy, specificity and negative predictive values of the model for all outcome variables ranged 78.2%-97.2%, while sensitivity and positive predictive values ranged 11.6%-62.5%. Variables with high permutation importance included sex, inpatient versus outpatient and American Society of Anesthesiologists class.

conclusionsWe predicted surgical/medical complications and unplanned reoperation following thyroidectomy via development of a well-performing ML algorithm. We have also developed a web-based application available on mobile devices to demonstrate the predictive capacity of our models in real time.

Indexed as

Postoperative ComplicationsThyroidectomyAlgorithmsHumansNeural Networks, ComputerRetrospective StudiesRisk FactorsROC Curveartificial neural networkcomplicationmachine learningreoperationthyroidectomy

Identifiers

PMID37096572
PMCPMC10330281
OpenAlexW4366991002

What OpenQuestion holds

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Registered trials

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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.