Dataset Information

Guideline-directed device therapies in heart failure: A clinical practice-based analysis using electronic health record data.

ABSTRACT:

Background

Guideline-directed device therapies (GDDT) improve outcomes for eligible patients with heart failure (HF) with reduced ejection fraction (HFrEF). Utilization rates of device therapies in HFrEF after the 2012 ACCF/AHA/HRS Focused Update for Device-based Therapies of Cardiac Rhythm Abnormalities have not been well studied.

Objective

Characterize the use of GDDT in newly indicated HFrEF patients from 2012 to 2019 using aggregated electronic health record (EHR) data.

Methods

Computable phenotyping algorithms for implantable cardioverter defibrillator/cardiac resynchronization therapy-defibrillator (ICD/CRT-D) indications from the GuideLine Indications Detected in EHR for Heart Failure program (GLIDE-HF) used diagnoses, procedures, measures, prescriptions, and the output of natural language processed provider notes from de-identified Optum® EHR data. Patients had a diagnosis of HF, dilated cardiomyopathy, or prior infarct, and were included if they had HFrEF with >1 year of records prior to a new Class 1 or Class 2a indication for an ICD or cardiac resynchronization therapy with defibrillator (CRT-D) from 2012 to 2019.

Results

Records showed 137,476 HFrEF patients were newly indicated for an ICD or CRT-D. GDDT was used in 14,892 of 36,358 (41.0%) CRT-D indicated patients and in 14,904 of 101,118 (14.7%) ICD-indicated patients. While GDDT use was low, 95.7% had echocardiography and 92.1% had prescriptions for beta-blockers or angiotensin-converting enzyme/angiotensin-receptor blockers medications.

Conclusions

In this modern cohort of HF patients, a large proportion of eligible patients did not receive ICDs or CRT-Ds, while frequently receiving other indicated cardiovascular interventions and treatments.

SUBMITTER: Curtis AB

PROVIDER: S-EPMC10976280 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Guideline-directed device therapies in heart failure: A clinical practice-based analysis using electronic health record data.

Curtis Anne B AB Manrodt Christopher C Jacobsen Luke D LD Soderlund Dana D Fonarow Gregg C GC

American heart journal plus : cardiology research and practice 20220401

<h4>Background</h4>Guideline-directed device therapies (GDDT) improve outcomes for eligible patients with heart failure (HF) with reduced ejection fraction (HFrEF). Utilization rates of device therapies in HFrEF after the 2012 ACCF/AHA/HRS Focused Update for Device-based Therapies of Cardiac Rhythm Abnormalities have not been well studied.<h4>Objective</h4>Characterize the use of GDDT in newly indicated HFrEF patients from 2012 to 2019 using aggregated electronic health record (EHR) data.<h4>Met ...[more]

PMID: 38559281

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Project description:AimsMultimorbidity is common among heart failure (HF) patients and may attenuate guideline-directed medical therapy (GDMT). Multimorbid patients are under-represented in clinical trials; therefore, the effect of multimorbidity clustering on the prognosis of HF patients remains unknown. We evaluated the prevalence of multimorbidity clusters among consecutively registered hospitalized HF patients and assessed whether GDMT attenuated outcomes.Methods and resultsWe examined 1924 hospitalized HF patients with reduced left ventricular ejection fraction (<50%) in a multicentre registry (West Tokyo HF Registry: WET-HF). Ten comorbid conditions in the WET-HF were abstracted: coronary artery disease, atrial fibrillation, stroke, anaemia, chronic obstructive pulmonary disease, renal dysfunction, obesity, hypertension, dyslipidaemia, and diabetes. Patients were divided into three groups (0-2: n = 451; 3-4: n = 787; and ≥5: n = 686) based on the number of comorbid conditions. The primary composite endpoint was all-cause mortality and HF rehospitalization. The most prevalent comorbidities were renal dysfunction (67.9%), hypertension (66.0%), and anaemia (53.8%). Increased comorbidity was associated with increased adverse outcomes [3-4: hazard ratio (HR) 1.42, 95% confidence interval (CI) 1.13-1.77, P = 0.003; ≥5: HR 2.12, 95%CI 1.69-2.65, P < 0.001; and reference: 0-2] and lower GDMT prescription rate (0-2: 69.2%; 3-4: 57.7%; and ≥5: 57.6%). GDMT was associated with decreased adverse outcomes; this association was maintained even as the comorbidity burden increased but tended to weaken (0-2: HR 0.53, 95%CI 0.35-0.78; P = 0.001; 3-4: HR 0.82, 95%CI 0.65-1.04, P = 0.095; and ≥5: HR 0.81, 95%CI 0.65-1.00, P = 0.053; P for interaction = 0.156).ConclusionsComorbidity clusters were prevalent and associated with poorer outcomes. GDMT remained beneficial regardless of the comorbidity burden but tended to weaken with increasing comorbidity burden. Further research is required to optimize medical care in these patients.

Project description:BackgroundStudies have demonstrated that heart failure (HF) patients who receive direct pharmacist input as part of multidisciplinary care have better clinical outcomes. This study evaluated/compared the difference in prescribing practices of guideline-directed medical therapy (GDMT) for chronic HF patients between two multidisciplinary clinics-with and without the direct involvement of a pharmacist.MethodsA retrospective audit of chronic HF patients, presenting to two multidisciplinary outpatient clinics between March 2005 and January 2017, was performed; a Multidisciplinary Ambulatory Consulting Service (MACS) with an integrated pharmacist model of care and a General Cardiology Heart Failure Service (GCHFS) clinic, without the active involvement of a pharmacist.ResultsMACS clinic patients were significantly older (80 vs. 73 years, p < .001), more likely to be female (p < .001), and had significantly higher systolic (123 vs. 112 mmHg, p < .001) and diastolic (67 vs. 60 mmHg, p < .05) blood pressures compared to the GCHF clinic patients. Moreover, the MACS clinic patients showed more polypharmacy and higher prevalence of multiple comorbidities. Both clinics had similar prescribing rates of GDMT and achieved maximal tolerated doses of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in HFrEF. However, HFpEF patients in the MACS clinic were significantly more likely to be prescribed ACEIs/ARBs (70.5% vs. 56.2%, p = 0.0314) than the GCHFS patients. Patients with both HFrEF and HFpEF (MACS clinic) were significantly less likely to be prescribed β-blockers and mineralocorticoid receptor antagonists. Use of digoxin in chronic atrial fibrillation (AF) in MACS clinic was significantly higher in HFrEF patients (82.5% vs. 58.5%, p = 0.004), but the number of people anticoagulated in presence of AF (27.1% vs. 48.0%, p = 0.002) and prescribed diuretics (84.0% vs. 94.5%, p = 0.022) were significantly lower in HFpEF patients attending the MACS clinic. Age, heart rate, systolic blood pressure (SBP), anemia, chronic renal failure, and other comorbidities were the main significant predictors of utilization of GDMT in a multivariate binary logistic regression.ConclusionsLower prescription rates of some medications in the pharmacist-involved multidisciplinary team were found. Careful consideration of demographic and clinical characteristics, contraindications for use of medications, polypharmacy, and underlying comorbidities is necessary to achieve best practice.

Project description:ImportanceAccurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation.ObjectiveTo develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital.Design, setting, and participantsWe performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data.Main outcomes and measuresHeart failure diagnosis based on discharge diagnosis and physician review of sampled medical records.ResultsA total of 47 119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively.Conclusions and relevanceThe problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

Project description:BackgroundAcute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.ResultsWhen trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG.ConclusionsA model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient's risk profiles.

Dataset Information

Guideline-directed device therapies in heart failure: A clinical practice-based analysis using electronic health record data.

Background

Objective

Methods

Results

Conclusions

Publications

Guideline-directed device therapies in heart failure: A clinical practice-based analysis using electronic health record data.

Similar Datasets

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