Evidence map›Paper›PMID 31035074›Full record

Trial reportComputers in biology and medicine2019

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

Christopher Barton, Uli Chettipally, Yifan Zhou, Zirui Jiang, Anna Lynn-Palevsky, Sidney Le, Jacob Calvert, Ritankar Das

Abstract readClinical TrialComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 8 pooled it
–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

64 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  6. Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024
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4 more citing papers are in PubMed but not listed here.

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

8 authors.

Christopher BartonDepartment of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA.
Uli ChettipallyDepartment of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA; Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, USA.
Yifan ZhouDascena Inc., Oakland, CA, USA; Department of Statistics, University of California Berkeley, Berkeley, CA, USA.
Zirui JiangDascena Inc., Oakland, CA, USA; Department of Nuclear Engineering, University of California Berkeley, Berkeley, CA, USA.
Anna Lynn-PalevskyDascena Inc., Oakland, CA, USA.
Sidney LeDascena Inc., Oakland, CA, USA.
Jacob CalvertDascena Inc., Oakland, CA, USA.
Ritankar DasDascena Inc., Oakland, CA, USA. Electronic address: ritankar@dascena.com.

Funding

Using clinical treatment data in a machine learning approach for sepsis detectionR43TR002309 · NCATS · DASCENA, INC. · PI DAS, RITANKAR · 2018 to 2018
$325k
A computational approach to early sepsis detectionR43TR002221 · NCATS · DASCENA, INC. · PI DAS, RITANKAR · 2018 to 2018
$311k
NCATS NIH HHS R43 TR002221NCATS NIH HHS R43 TR002309
6 · The paper itself

Abstract

objectiveSepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods. MATERIALS AND

methodsRetrospective data was drawn from databases at the University of California, San Francisco (UCSF) Medical Center and the Beth Israel Deaconess Medical Center (BIDMC). Adult patient encounters without sepsis on admission, and with at least one recording of each of six vital signs (SpO

resultsThe MLA achieved an AUROC of 0.88, 0.84, and 0.83 for sepsis onset and 24 and 48 h prior to onset, respectively. These values were superior to those of SIRS (0.66), MEWS (0.61), SOFA (0.72), and qSOFA (0.60) at time of onset. When trained on UCSF data and tested on BIDMC data, sepsis onset AUROC was 0.89. DISCUSSION AND

conclusionThe MLA predicts sepsis up to 48 h in advance and identifies sepsis onset more accurately than commonly used tools, maintaining high performance for sepsis detection when trained and tested on separate datasets.

Indexed as

Databases, FactualDiagnosis, Computer-AssistedElectronic Health RecordsMachine LearningVital SignsAdolescentAdultAgedFemaleHumansIntensive Care UnitsMaleMiddle AgedSepsisSeverity of Illness IndexElectronic health recordsMachine learningPredictionSepsis

Identifiers

PMID31035074
PMCPMC6556419

What OpenQuestion holds

Textmetadata
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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.