Evidence map›Paper›PMID 38781684›Full record

ReviewEmerging infectious diseases2024

Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Noelle M Cocoros, Karen Eberhardt, Vu-Thuy Nguyen, Catherine M Brown, Alfred DeMaria, Lawrence C Madoff, Liisa M Randall, Michael Klompas

Abstract readReview
In one paragraph

Review in Emerging infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

8 authors.

Noelle M Cocoros
Karen Eberhardt
Vu-Thuy Nguyen
Catherine M Brown
Alfred DeMaria
Lawrence C Madoff
Liisa M Randall
Michael Klompas

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Indexed as

AlgorithmsElectronic Health RecordsRespiratory Tract InfectionsAdolescentAdultAgedChildChild, PreschoolCOVID-19FemaleHumansInfluenza, HumanMaleMassachusettsMiddle AgedPopulation Surveillancealgorithmsdisease surveillanceelectronic health recordsinfluenza-like illnessMassachusettsRAVIOLIrespiratory infectionsrespiratory virus–like illnessUnited Statesviruses

Identifiers

PMID38781684
PMCPMC11138993

What OpenQuestion holds

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LicenceCC BY
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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.