Evidence map›Paper›PMID 28066594›Full record

ArticleJournal of thoracic disease2016

Cough frequency monitors: can they discriminate patient from environmental coughs?

Stefan T Kulnik, Natalie M Williams, Lalit Kalra, John Moxham, Surinder S Birring

Open access · hybridAbstract read
In one paragraph

Article in Journal of thoracic disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 20% of its field
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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Validation of a small cough detector.ERJ open research · 2023
    Article
  6. Review
  7. Article
  8. Article
  9. The present and future of cough counting tools.Journal of thoracic disease · 2020
    Review
  10. Article
  11. Review
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

5 authors at 2 institutions in 1 country.

Stefan T KulnikFaculty of Health, Social Care and Education, Kingston University and St George's, University of London, London, UK.
Natalie M WilliamsDivision of Asthma, Allergy & Lung Biology, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Lalit KalraDepartment of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
John MoxhamDivision of Asthma, Allergy & Lung Biology, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Surinder S BirringDivision of Asthma, Allergy & Lung Biology, Faculty of Life Sciences and Medicine, King's College London, London, UK.
King's College London · GBKingston University · GB

Funding

Department of Health PB-PG-0408-16096
6 · The paper itself

Abstract

backgroundObjective cough frequency measurements are increasingly applied in clinical research. Technological advances enable automated detection and counting of cough events from sound recordings of many hours' duration. A possible limitation of sound-based cough frequency measurement is the contamination of recordings by environmental coughs (coughs from persons other than the patient). This study aimed to investigate the accuracy of a sound-based cough monitor for detecting and discriminating patient cough from environmental cough.

methodsAs part of a stroke trial (ISRCTN40298220), patients on a hospital ward underwent 15-minute recordings using the Leicester Cough Monitor (LCM), a sound-based cough monitor ('semi-automated counts'). Participants and other persons in the environment were prompted to cough. An observer present in the room recorded the number of patient and environmental coughs ('live counts'). LCM counts were also compared against a manual cough count, the most commonly used gold standard to determine accuracy ('manual sound counts' from listening to recordings), by a blinded assessor who cross-referenced timed cough events from the respective methods. Data for automated, manual and live cough counts were analyzed using agreement statistics.

resultsOn sound recordings from five patients, there were 65 patient coughs and 78 environmental coughs (manual counts). Absolute agreement for patient cough count between all three measurement methods (LCM automated, live, and manual sound counts) was high, with intra-class correlation coefficient of 0.94 [95% confidence intervals (CI): 0.74, 0.99]. The proportion of exact agreements for patient cough between LCM and manual count was 0.92, and kappa was 0.84 (95% CI: 0.75, 0.93). The LCM showed sensitivity of 0.94 (95% CI: 0.84, 0.98), specificity of 0.91 (95% CI: 0.82, 0.96), positive predictive value of 0.90 (95% CI: 0.79, 0.95) and negative predictive value of 0.95 (95% CI: 0.86, 0.98) for detecting patient coughs.

conclusionsThis preliminary study supports the validity of the cough monitor for detecting and discriminating patient from environmental cough. Further validation is recommended, to describe the level of accuracy with greater precision.

Indexed as

accuracyCough frequencyLeicester Cough Monitor (LCM)measurementvalidity

Identifiers

PMID28066594
PMCPMC5179435
OpenAlexW2557413886

What OpenQuestion holds

Textmetadata
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.