Evidence map›Paper›PMID 29664888›Full record

ArticleAnesthesiology2018

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Christine K Lee, Ira Hofer, Eilon Gabel, Pierre Baldi, Maxime Cannesson

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

Article in Anesthesiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
86citing papers in PubMed, 2 pooled it
13.5field-weighted citation impact, top 1% 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

86 citing papers in PubMed, 2 syntheses or guidelines pooled it, 190 citations in OpenAlex.

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  3. Hierarchical Poincaré analysis for anaesthesia monitoring.Journal of clinical monitoring and computing · 2020
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26 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 3 countries.

Christine K LeeFrom the Department of Anesthesiology and Perioperative Care (C.K.L., M.C.) Department of Computer Sciences (C.K.L., P.B.) Department of Bioengineering (M.C.), University of California Irvine, Irvine, California Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, California (I.H., E.G., M.C.).
Ira Hofer
Eilon Gabel
Pierre Baldi
Maxime Cannesson
School of the Art Institute of Chicago · US

Funding

Predicting Patient Instability Noninvasively for Nursing Care-Two (PPINNC-2)R01NR013912 · NINR · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI AL-ZAITI, SALAH S, CLERMONT, GILLES · 2012 to 2025
$7.0M
Machine learning of physiological variables to predict diagnose and treat cardiorespiratory instabilityR01GM117622 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI PINSKY, MICHAEL R · 2016 to 2019
$2.4M
NIGMS NIH HHS R01 GM117622NINR NIH HHS R01 NR013912
6 · The paper itself

Abstract

what we already know about this topicWHAT THIS ARTICLE TELLS US THAT IS NEW:

background: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality.

methodsThe data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index.

resultsIn-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99).

conclusionsDeep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.

Indexed as

Neural Networks, ComputerAdultAgedFemaleHospital MortalityHumansMachine LearningMaleMiddle AgedPostoperative ComplicationsPredictive Value of TestsROC Curve

Identifiers

PMID29664888
PMCPMC6148401
OpenAlexW2800777678

What OpenQuestion holds

Textmetadata
LicenceTDM
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.