Evidence map›Paper›PMID 34622017›Full record

ArticleHealth science reports2021

Prospectively predicting

Nikki Totton, Mike Bradburn, Zhe Hui Hoo, Jen Lewis, Daniel Hind, Carla Girling, Elizabeth Shepherd, Julia Nightingale, Thomas Daniels, Jane Dewar and 7 more

Open access · goldAbstract read
In one paragraph

Article in Health science reports, 2021. 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
0.4field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Prospectively predictingHealth science reports · 2021
    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

17 authors at 4 institutions in 1 country.

Nikki TottonClinical Trials Research Unit, School of Health and Related Research University of Sheffield Sheffield UK.ORCID https://orcid.org/0000-0002-1900-2773
Mike BradburnClinical Trials Research Unit, School of Health and Related Research University of Sheffield Sheffield UK.
Zhe Hui HooClinical Trials Research Unit, School of Health and Related Research University of Sheffield Sheffield UK.ORCID https://orcid.org/0000-0002-7067-3783
Jen LewisClinical Trials Research Unit, School of Health and Related Research University of Sheffield Sheffield UK.
Daniel HindClinical Trials Research Unit, School of Health and Related Research University of Sheffield Sheffield UK.
Carla GirlingClinical Trials Research Unit, School of Health and Related Research University of Sheffield Sheffield UK.
Elizabeth ShepherdDepartment of Adult Cystic Fibrosis University Hospital Southampton NHS Foundation Trust Southampton UK.
Julia NightingaleDepartment of Adult Cystic Fibrosis University Hospital Southampton NHS Foundation Trust Southampton UK.
Thomas DanielsDepartment of Adult Cystic Fibrosis University Hospital Southampton NHS Foundation Trust Southampton UK.
Jane DewarWolfson Cystic Fibrosis Centre Nottingham University Hospital NHS Trust Nottingham UK.
Sophie DawsonWolfson Cystic Fibrosis Centre Nottingham University Hospital NHS Trust Nottingham UK.
Mary CarrollDepartment of Adult Cystic Fibrosis University Hospital Southampton NHS Foundation Trust Southampton UK.
Mark AllenbyDepartment of Adult Cystic Fibrosis University Hospital Southampton NHS Foundation Trust Southampton UK.
Frank EdenboroughSheffield Adult Cystic Fibrosis Centre Sheffield Teaching Hospital NHS Foundation Trust Sheffield UK.
Rachael CurleySheffield Adult Cystic Fibrosis Centre Sheffield Teaching Hospital NHS Foundation Trust Sheffield UK.
Charlotte CarolanSheffield Adult Cystic Fibrosis Centre Sheffield Teaching Hospital NHS Foundation Trust Sheffield UK.
Martin WildmanSheffield Adult Cystic Fibrosis Centre Sheffield Teaching Hospital NHS Foundation Trust Sheffield UK.
Sheffield Teaching Hospitals NHS Foundation Trust · GBUniversity Hospital Southampton NHS Foundation Trust · GBUniversity of Sheffield · GBNottingham University Hospitals NHS Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RATIONALE AND

aimsLung health of people with cystic fibrosis (PwCF) can be preserved by daily use of inhaled therapy. Adherence to inhaled therapy, therefore, provides an important process measure to understand the success of care and can be used as a quality indicator. Defining adherence is problematic, however, since the number of prescribed treatments varies considerably between PwCF. The problem is less pronounced among those with

methodUK CF Registry data from 2011 to 2015 for PwCF aged ≥16 was used to determine a pragmatic prediction rule for identifying contemporary PA status using historic registry data. Accuracy of three different prediction rules was assessed using the positive predictive value (PPV). The number and proportion of adults predicted to have PA infection were determined overall and per center for the selected prediction rule. Known characteristics linked to PA status were explored to ensure the robustness of the prediction rule.

resultsHaving CF Registry defined chronic PA status in the two previous years is the selected definition to predict a patient will have PA infection within the current year (population-level PPV = 96%-97%, centre level PPV = 85%-100%). This approach provides a subset of data between 1852 and 1872 patients overall and a range of 8 to 279 patients per center.

conclusionHistoric registry data can be used to contemporaneously identify a subgroup of patients with chronic PA. Since this patient group has a narrower treatment schedule, this can facilitate a better benchmarking of adherence across centers.

Indexed as

adherencecystic fibrosisoutcomeprocessquality improvement

Identifiers

PMID34622017
PMCPMC8485591
OpenAlexW3201841563

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.