Impact of an extended audit on identifying heart failure patients in general practice: baseline results of the OSCAR‐HF pilot study

Smeets, Miek; Vaes, Bert; Aertgeerts, Bert; Raat, W.K. de; Penders, Joris; Vercammen, Jan; Droogné, Walter; Mullens, Wilfried; Janssens, Stefan

doi:10.1002/ehf2.12990

Cited by 11 publications

(23 citation statements)

References 43 publications

(172 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of the 18 011 patients eligible for the audit intervention, 4678 (26.0%) were identified as possible HF patients by the combined basic and extended audits and submitted for GPs' assessment in the audit stage ( Figure 1 ). There were 310 patients with registered HF before GP assessment (basic audit), 10 of whom 146 (47.1%) were judged not to have HF by their GP (over‐registration). There were 538 patients with registered HF after GP assessment, 10 of whom 374 (69.5%) did not have registered HF before GP assessment (under‐registration).…”

Section: Resultsmentioning

confidence: 99%

“…There were 310 patients with registered HF before GP assessment (basic audit), 10 of whom 146 (47.1%) were judged not to have HF by their GP (over‐registration). There were 538 patients with registered HF after GP assessment, 10 of whom 374 (69.5%) did not have registered HF before GP assessment (under‐registration).…”

Section: Resultsmentioning

confidence: 99%

“…These two audits resulted in a list of possible HF patients, which participating GPs then classified as HF or non‐HF. Patients classified as having HF constituted the OSCAR‐HF study population 10 . In addition, an expert panel assessed the validity of each HF diagnosis, ruling diagnoses as either objectified or non‐objectified.…”

Section: Methodsmentioning

confidence: 99%

“…Patients classified as having HF constituted the OSCAR‐HF study population. 10 In addition, an expert panel assessed the validity of each HF diagnosis, ruling diagnoses as either objectified or non‐objectified. Patients could be included in the study if they were 40 years or older and were registered in the participating practices.…”

Section: Methodsmentioning

confidence: 99%

“…We previously designed and reported a multifaceted intervention in eight general practices ( n = 18 011 patients ≥ 40 years) aimed at improving case‐finding through an audit in GPs' EHR 10,11 . Although the audit resulted in a more than 74% increase in the number of registered HF diagnoses in GPs' EHR, the accuracy of registered HF pre‐audit remains unclear.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Machine learning optimization of an electronic health record audit for heart failure in primary care

et al. 2021

Self Cite

View full text Add to dashboard Cite

Aims The diagnosis of heart failure (HF) is an important problem in primary care. We previously demonstrated a 74% increase in registered HF diagnoses in primary care electronic health records (EHRs) following an extended audit procedure. What remains unclear is the accuracy of registered HF pre‐audit and which EHR variables are most important in the extended audit strategy. This study aims to describe the diagnostic HF classification sequence at different stages, assess general practitioner (GP) HF misclassification, and test the predictive performance of an optimized audit. Methods and results This is a secondary analysis of the OSCAR‐HF study, a prospective observational trial including 51 participating GPs. OSCAR used an extended audit based on typical HF risk factors, signs, symptoms, and medications in GPs' EHR. This resulted in a list of possible HF patients, which participating GPs had to classify as HF or non‐HF. We compared registered HF diagnoses before and after GPs' assessment. For our analysis of audit performance, we used GPs' assessment of HF as primary outcome and audit queries as dichotomous predictor variables for a gradient boosted machine (GBM) decision tree algorithm and logistic regression model. Of the 18 011 patients eligible for the audit intervention, 4678 (26.0%) were identified as possible HF patients and submitted for GPs' assessment in the audit stage. There were 310 patients with registered HF before GP assessment, of whom 146 (47.1%) were judged not to have HF by their GP (over‐registration). There were 538 patients with registered HF after GP assessment, of whom 374 (69.5%) did not have registered HF before GP assessment (under‐registration). The GBM and logistic regression model had a comparable predictive performance (area under the curve of 0.70 [95% confidence interval 0.65–0.77] and 0.69 [95% confidence interval 0.64–0.75], respectively). This was not significantly impacted by reducing the set of predictor variables to the 10 most important variables identified in the GBM model (free‐text and coded cardiomyopathy, ischaemic heart disease and atrial fibrillation, digoxin, mineralocorticoid receptor antagonists, and combinations of renin‐angiotensin system inhibitors and beta‐blockers with diuretics). This optimized query set was enough to identify 86% (n = 461/538) of GPs' self‐assessed HF population with a 33% reduction (n = 1537/4678) in screening caseload. Conclusions Diagnostic coding of HF in primary care health records is inaccurate with a high degree of under‐registration and over‐registration. An optimized query set enabled identification of more than 80% of GPs' self‐assessed HF population.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine learning optimization of an electronic health record audit for heart failure in primary care

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

Clinical implications of the universal definition for the prevention and treatment of heart failure

Chandramouli

Stewart

Almahmeed

et al. 2022

Clinical Cardiology

View full text Add to dashboard Cite

The diagnosis of heart failure (HF) primarily relies on signs and symptoms that are neither sensitive nor specific. This impedes timely diagnosis and delays effective therapies or interventions, despite the availability of several evidence‐based treatments for HF. Through monumental collaborative efforts from representatives of HF societies worldwide, the universal definition of HF was published in 2021, to provide the necessary standardized framework required for clinical management, clinical trials, and research. This review elaborates the key concepts of the new universal definition of HF, highlighting the key merits and potential avenues, which can be nuanced further in future iterations. We also discuss the key implications of the universal definition document from the perspectives of various stakeholders within the healthcare framework, including patients, care providers, system/payers and policymakers.

show abstract

Mixed‐methods evaluation of a multifaceted heart failure intervention in general practice: the OSCAR‐HF pilot study

et al. 2022

Self Cite

View full text Add to dashboard Cite

Aims Heart failure (HF) is an important health problem for which multidisciplinary care is recommended, yet few studies involve primary care practitioners in the multidisciplinary management of HF. We set up a multifaceted prospective observational trial, OSCAR-HF, piloting audit and feedback, natriuretic peptide testing at the point of care, and the assistance of a specialist HF nurse in primary care. The aim was to optimize HF care in general practice. Methods and resultsThis is an analysis at 6 month follow-up of the study interventions of the OSCAR-HF pilot study, a nonrandomized, noncontrolled prospective observational trial conducted in eight Belgian general practices [51 general practitioners (GPs)]. Patients who were assessed by their GP to have HF constituted the OSCAR-HF study population. We used descriptive statistics and mixed-effects modelling for the quantitative analysis and thematic analysis of the focus group interviews. There was a 10.2% increase in the registered HF population after 6 months of follow-up (n = 593) compared with baseline (n = 538) and a 27% increase in objectified HF diagnoses (baseline n = 359 to 456 at T6 M). Natriuretic peptide testing (with or without referral) accounted for 54% (n = 60/111) of the newly registered HF diagnoses. There was no difference in the proportion of patients with HF with reduced ejection fraction who received their target dosage of renin-angiotensinaldosterone system inhibitors or beta-blockers at 6 months compared with baseline (P = 0.9). Patients who received an HF nurse intervention (n = 53) had significantly worse quality of life at baseline [difference in Minnesota Living with Heart Failure Questionnaire (MLHFQ) score 9.2 points; 95% confidence interval (CI) 4.0, 14] and had a significantly greater improvement in quality-of-life scores at the 6 month follow-up [change in MLHFQ score À9.8 points; 95% CI À15, À4.5] than patients without an HF nurse intervention. GPs found audit and feedback valuable but time intensive. Natriuretic peptides were useful, but the point-of-care test was impractical, and the assistance of an HF nurse was a useful addition to routine HF care. Conclusions The use of audit and feedback combined with natriuretic peptide testing was a successful strategy to increase the number of registered and objectified HF diagnoses at 6 months. GPs and HF nurses selected patients with worse quality-oflife scores at baseline for the HF nurse intervention, which led to a significantly greater improvement in quality-of-life scores at the 6 month follow-up compared with patients without an HF nurse intervention. The interventions were deemed feasible and useful by the participating GPs with some specific remarks that can be used for optimization. Trial Registration: ClinicalTrials.gov (NCT02905786), registered on 14 September 2016 at https://clinicaltrials.gov/.

show abstract

Impact of an extended audit on identifying heart failure patients in general practice: baseline results of the OSCAR‐HF pilot study

Cited by 11 publications

References 43 publications

Machine learning optimization of an electronic health record audit for heart failure in primary care

Machine learning optimization of an electronic health record audit for heart failure in primary care

Clinical implications of the universal definition for the prevention and treatment of heart failure

Mixed‐methods evaluation of a multifaceted heart failure intervention in general practice: the OSCAR‐HF pilot study

Contact Info

Product

Resources

About