What Does Routine Healthcare Data Tell Us about the Diagnostic Pathway for Older People with Heart Failure in Wales: Under-Testing, Problems with Access, or Poor Coding?
EUROPEAN HEART JOURNAL(2024)
Swansea Univ | Swansea Bay Univ Hlth Board
Abstract
Abstract Background United Kingdom guidelines (NICE, NG106) for suspected chronic heart failure (HF) recommend that NT pro-BNP is performed as a first-line test. The result is also used to triage individuals; when NT pro-BNP is <400ng/L, a diagnosis of HF is less likely, echocardiography (echo) and specialist assessment should be performed within 6 weeks when the result is 400-2000ng/L, and within 2 weeks if >2000ng/L. How accurately this guidance is followed in clinical practice in Wales is not known. Purpose To examine how the current diagnostic pathway is recorded in routinely-collected electronic health record (EHR) data for older people in Wales with a diagnosis of heart failure. Methods We conducted a retrospective, population-level, observational study using linked anonymised EHR data in Wales (2015-22). We included patients aged 65y+ with a HF diagnosis in their primary or secondary care records and with a minimum of 12m pre and post-diagnosis data (for assessment of diagnostic pathway). We looked back from the point of HF diagnosis for codes associated with prior cardiovascular conditions (any time prior), symptoms (within 3 years), and key diagnostic tests (within 90 days). Results The final cohort comprised 51,020 individuals 65y and over with a diagnosis of heart failure between 2015-2022. Incidence and prevalence of heart failure increased across the study period (1.4-1.5% and 8.5-9.1% respectively). HF diagnosis was first evident in secondary care records in 54.9%, in primary care 36.3%, and in both data sources on the same day in 8.8%. Preceding cardiovascular diagnoses included hypertension (69.5%), atrial fibrillation (36.7%), myocardial infarction (30.5%), coronary artery disease (25.7%), and valve disease (17.7%). The most common recorded symptoms preceding diagnosis were breathlessness (41.4%), ankle oedema (9.8%), and fatigue (8.8%). Searched for symptoms were not recorded in 43.8%. In the 90 days preceding the diagnosis, 7-30% had recorded NT pro-BNP with the number of tests increasing throughout the study period (Figure 1). In the 90 days prior to HF diagnosis, 28-31% had an echo recorded. NT pro-BNP was >2000ng/L in 51.8%, 400-2000ng/L in 38.6% and <400ng/L in 9.6%. Conclusions In Wales, a greater number of individuals with heart failure appear to be diagnosed during hospital admission, compared with primary care. Many individuals with a code for HF in their records, do not have recorded objective evidence of cardiac dysfunction. The proportion of first-line and definitive tests appears low. It is unclear whether this is because of under-referral, difficulty accessing tests in primary care, or inaccurate coding of test referrals and results. These results are important in evaluating health service delivery and workforce planning.
MoreTranslated text
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话