| Title |
Recording of hospitalizations for acute exacerbations of COPD in UK electronic health care records
|
|---|---|
| Published in |
Clinical Epidemiology, November 2016
|
| DOI | 10.2147/clep.s117867 |
| Pubmed ID | |
| Authors |
Kieran J Rothnie, Hana Müllerová, Sara L Thomas, Joht S Chandan, Liam Smeeth, John R Hurst, Kourtney Davis, Jennifer K Quint |
| Abstract |
Accurate 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. We 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. In 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%). Hospitalization 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. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 57% |
| United States | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 86% |
| Scientists | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 56 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 27% |
| Student > Ph. D. Student | 8 | 14% |
| Student > Master | 6 | 11% |
| Other | 4 | 7% |
| Student > Bachelor | 2 | 4% |
| Other | 7 | 13% |
| Unknown | 14 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 22 | 39% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 9% |
| Biochemistry, Genetics and Molecular Biology | 4 | 7% |
| Nursing and Health Professions | 2 | 4% |
| Economics, Econometrics and Finance | 2 | 4% |
| Other | 4 | 7% |
| Unknown | 17 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 May 2020.
All research outputs
#4,064,300
of 22,903,988 outputs
Outputs from Clinical Epidemiology
#164
of 721 outputs
Outputs of similar age
#67,637
of 311,689 outputs
Outputs of similar age from Clinical Epidemiology
#4
of 21 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 721 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,689 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.