Evidence map›Paper›PMID 27920578›Full record

ArticleClinical epidemiology2016

Recording of hospitalizations for acute exacerbations of COPD in UK electronic health care records.

Kieran J Rothnie, Hana Müllerová, Sara L Thomas, Joht S Chandan, Liam Smeeth, John R Hurst, Kourtney Davis, Jennifer K Quint

Abstract read
In one paragraph

Article in Clinical epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 3 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

58 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. The economic burden of chronic obstructive pulmonary disease (COPD) in Europe: results from a systematic review of the literature.The European journal of health economics : HEPAC : health economics in prevention and care · 2020
    Pooled it
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  15. Prompt versus Delayed Triple Therapy in COPD: Solutions to Time-Related Biases in Observational Studies.International journal of chronic obstructive pulmonary disease · 2025
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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.

Kieran J RothnieRespiratory Epidemiology, Occupational Medicine and Public Health, National Heart and Lung Institute, Imperial College London, London, UK; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Hana MüllerováRespiratory Epidemiology, GlaxoSmithKline R&D, Uxbridge, London.
Sara L ThomasFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Joht S ChandanMedical School.
Liam SmeethFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
John R HurstUCL Respiratory, University College London, London, UK.
Kourtney DavisRespiratory Epidemiology, GlaxoSmithKline R&D, Uxbridge, London.
Jennifer K QuintRespiratory Epidemiology, Occupational Medicine and Public Health, National Heart and Lung Institute, Imperial College London, London, UK; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.

Funding

Department of Health CDF-2010-03-32Medical Research Council G0902135Medical Research Council MR/K006584/1
6 · The paper itself

Abstract

backgroundAccurate identification of hospitalizations for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) within electronic health care records is important for research, public health, and to inform health care utilization and service provision. We aimed to develop a strategy to identify hospitalizations for AECOPD in secondary care data and to investigate the validity of strategies to identify hospitalizations for AECOPD in primary care data.

methodsWe identified patients with chronic obstructive pulmonary disease (COPD) in the Clinical Practice Research Datalink (CPRD) with linked Hospital Episodes Statistics (HES) data. We used discharge summaries for recent hospitalizations for AECOPD to develop a strategy to identify the recording of hospitalizations for AECOPD in HES. We then used the HES strategy as a reference standard to investigate the positive predictive value (PPV) and sensitivity of strategies for identifying AECOPD using general practice CPRD data. We tested two strategies: 1) codes for hospitalization for AECOPD and 2) a code for AECOPD other than hospitalization on the same day as a code for hospitalization due to unspecified reason.

resultsIn total, 27,182 patients with COPD were included. Our strategy to identify hospitalizations for AECOPD in HES had a sensitivity of 87.5%. When compared with HES, using a code suggesting hospitalization for AECOPD in CPRD resulted in a PPV of 50.2% (95% confidence interval [CI] 48.5%-51.8%) and a sensitivity of 4.1% (95% CI 3.9%-4.3%). Using a code for AECOPD and a code for hospitalization due to unspecified reason resulted in a PPV of 43.3% (95% CI 42.3%-44.2%) and a sensitivity of 5.4% (95% CI 5.1%-5.7%).

conclusionHospitalization for AECOPD can be identified with high sensitivity in the HES database. The PPV and sensitivity of strategies to identify hospitalizations for AECOPD in primary care data alone are very poor. Primary care data alone should not be used to identify hospitalizations for AECOPD. Instead, researchers should use data that are linked to data from secondary care.

Indexed as

cause-specific hospitalizationCOPDhospitalizationlinked datavalidation

Identifiers

PMID27920578
PMCPMC5123723

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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.