Evidence map›Paper›PMID 34185798›Full record

ArticlePloS one2021

Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

Naomi George, Edward Moseley, Rene Eber, Jennifer Siu, Mathew Samuel, Jonathan Yam, Kexin Huang, Leo Anthony Celi, Charlotta Lindvall

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

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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

9 authors.

Naomi GeorgeDepartment of Emergency Medicine, Division of Critical Care, University of New Mexico Health Science Center, Albuquerque, New Mexico, United States of America.ORCID 0000-0003-4868-095X
Edward MoseleyDepartment of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
Rene EberMassachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-4347-0198
Jennifer SiuHarvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Mathew SamuelHarvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Jonathan YamHarvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Kexin HuangHarvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Leo Anthony CeliHarvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Charlotta LindvallDepartment of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAmong patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.

methodsRetrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.

resultsThere were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality. DISCUSSION: We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

Indexed as

Deep LearningHospital MortalityIntensive Care UnitsModels, BiologicalRespiration, ArtificialAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsTime Factors

Identifiers

PMID34185798
PMCPMC8241081

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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.