Evidence map›Paper›PMID 38741373›Full record

ArticleESC heart failure2024

Applying natural language processing to identify emergency department and observation encounters for worsening heart failure.

Steven A Hamilton, Andrew P Ambrosy, Rishi V Parikh, Thida C Tan, Jesse K Fitzpatrick, Harshith R Avula, Alexander T Sandhu, Ivy A Ku, Alan S Go, Dana Sax and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ESC heart failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

11 authors.

Steven A HamiltonDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.ORCID 0000-0002-8539-4322
Andrew P AmbrosyDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.
Rishi V ParikhDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.
Thida C TanDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.
Jesse K FitzpatrickDepartment of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA, USA.
Harshith R AvulaDepartment of Cardiology, Kaiser Permanente Walnut Creek Medical Center, Walnut Creek, CA, USA.
Alexander T SandhuDivision of Cardiology and the Cardiovascular Institute, Department of Medicine, Stanford University, Stanford, CA, USA.
Ivy A KuDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.
Alan S GoDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA.
Dana SaxDepartment of Emergency Medicine, Kaiser Permanente Oakland Medical Center, Oakland, CA, USA.
Ankeet S BhattDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.

Funding

Kaiser Permanente Northern California Community Benefit ProgramNovartis AG
6 · The paper itself

Abstract

aimsWorsening heart failure (WHF) events occurring in non-inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)-based approach compared with traditional diagnostic coding for non-inpatient clinical encounters and left ventricular ejection fraction (LVEF). METHODS AND

resultsWe compared characteristics for encounters that did vs. did not meet WHF criteria, stratified by care setting [i.e. emergency department (ED) and observation stay]. Overall, 8407 (22%) encounters met NLP-based criteria for WHF (3909 ED visits and 4498 observation stays). The use of an NLP-derived definition adjudicated 3983 (12%) of non-primary HF diagnoses as meeting consensus definitions for WHF. The most common diagnosis indicated in these encounters was dyspnoea. Results were primarily driven by observation stays, in which 2205 (23%) encounters with a secondary HF diagnosis met the WHF definition by NLP.

conclusionsThe use of standard claims-based adjudication for primary diagnosis in the non-inpatient setting may lead to misclassification of WHF events in the ED and overestimate observation stays. Primary diagnoses alone may underestimate the burden of WHF in non-hospitalized settings.

Indexed as

Disease ProgressionEmergency Service, HospitalHeart FailureNatural Language ProcessingAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisRetrospective StudiesStroke VolumeVentricular Function, LeftEmergency departmentNatural language processing (NLP)Observation unitsOutpatientWorsening heart failure

Identifiers

PMID38741373
PMCPMC11424295

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LicenceCC BY-NC
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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.