Evidence map›Paper›PMID 30771483›Full record

ArticleJournal of biomedical informatics2019

Statistical outbreak detection by joining medical records and pathogen similarity.

James K Miller, Jieshi Chen, Alexander Sundermann, Jane W Marsh, Melissa I Saul, Kathleen A Shutt, Marissa Pacey, Mustapha M Mustapha, Lee H Harrison, Artur Dubrawski

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  4. Whole-genome sequencing surveillance of vancomycin-resistantInfection control and hospital epidemiology · 2025
    Article
  5. The future of automated infection detection: Innovation to transform practice (Part III/III).Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023
    Review
  6. Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022
    Article
  7. Article
  8. Review
  9. Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
  10. Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
  11. Outbreak of Vancomycin-resistant Enterococcus faecium in Interventional Radiology: Detection Through Whole-genome Sequencing-based Surveillance.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020
    Article
  12. Article
  13. Article
  14. Article
  15. 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

10 authors.

James K MillerAuton Lab, Carnegie Mellon University, Pittsburgh, PA, United States. Electronic address: mille856@andrew.cmu.edu.
Jieshi ChenAuton Lab, Carnegie Mellon University, Pittsburgh, PA, United States.
Alexander SundermannInfectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, PA, United States; Department of Infection Control and Hospital Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States.
Jane W MarshInfectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, PA, United States.
Melissa I SaulDepartment of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.
Kathleen A ShuttInfectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, PA, United States.
Marissa PaceyInfectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, PA, United States.
Mustapha M MustaphaInfectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, PA, United States.
Lee H HarrisonInfectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, PA, United States.
Artur DubrawskiAuton Lab, Carnegie Mellon University, Pittsburgh, PA, United States.

Funding

Enhanced Detection System for Healthcare-Associated Transmission of InfectionR01AI127472 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HARRISON, LEE H · 2016 to 2025
$7.6M
Genomic Epidemiology for Hospital Outbreak DetectionR21AI109459 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HARRISON, LEE H · 2014 to 2015
$437k
NIAID NIH HHS R01 AI127472NIAID NIH HHS R21 AI109459
6 · The paper itself

Abstract

We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by whole-genome sequencing, to simultaneously identify probable outbreaks and their root-causes. We show how our model can be used to target isolates for whole-genome sequencing, improving outbreak detection and characterization even without comprehensive sequencing. Additionally, we demonstrate how to learn model parameters from reference data of known outbreaks. We demonstrate model performance using semi-synthetic experiments.

Indexed as

Disease OutbreaksMachine LearningMedical RecordsCross InfectionHumansModels, TheoreticalUnited StatesElectronic medical recordsEpidemiologyOutbreak detectionStatistical inferenceTransmission of pathogensWhole genome sequencing

Identifiers

PMID30771483
PMCPMC6424617

What OpenQuestion holds

Textmetadata
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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.