Treatment patterns and clinical outcomes among patients &lt;65 years with a worsening heart failure event

Butler, Javed; Yang, Mei; Sawhney, Baanie; Chakladar, Sreya; Yang, Lingfeng; Djatche, Laurence

doi:10.1002/ejhf.2252

Cited by 17 publications

(11 citation statements)

References 18 publications

(35 reference statements)

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“…28,29 A national-level analysis comparing GDMT regimens 3 months before with those 3 months after a worsening HF event in patients with HFrEF reported similar rates of patients on one class of GDMT (29-29%), two GDMT classes (39-42%) and three GDMT classes (14-17%) following the worsening HF event, with these low rates persisting 1 year later. 30 In addition to reinforcing previous reports, that study provided a unique insight into the potential barriers to GDMT implementation. By studying younger patients with commercial insurance, it was found that low GDMT implementation is unlikely to be predominantly driven by medication intolerance due to advanced age, comorbidities or financial barriers.…”

Section: In-hospital Gdmt Initiation and Titrationsupporting

confidence: 70%

“…By studying younger patients with commercial insurance, it was found that low GDMT implementation is unlikely to be predominantly driven by medication intolerance due to advanced age, comorbidities or financial barriers. 30 Similarly, alerting providers to a patient's prognosis also did not improve GDMT prescription rates in a recent randomised trial, diminishing the likelihood that provider misunderstanding of prognostic risk is responsible for poor GDMT implementation. 31,32 GDMT initiation and titration during ADHF hospitalisation is an emerging strategy to increase both acute GDMT implementation and chronic adherence.…”

Section: In-hospital Gdmt Initiation and Titrationmentioning

confidence: 98%

“…33 After hospital discharge, minimal GDMT dose titration occurs. 24,30 Clinical inertia is often driven by a fear of causing adverse effects in apparently clinically stable patients; however, failure to further initiate and titrate GDMT to goal doses may worsen the quality of life, increase HF hospitalisations and decrease survival. 28 Several mechanisms to promote outpatient optimisation of GDMT have been investigated, including multidisciplinary titration clinics, electronic health records (EHR) and artificial intelligence.…”

Section: Continuing Gdmt Optimisation In the Post-discharge Phasementioning

confidence: 99%

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In-hospital Initiation and Up-titration of Guideline-directed Medical Therapies for Heart Failure with Reduced Ejection Fraction

et al. 2022

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Implementation of guideline-directed medical therapy for patients with heart failure is suboptimal. The use of guideline-directed medical therapy improves minimally after heart failure hospitalisation, despite this event clearly indicating increased risk of further hospitalisation and death. In-hospital initiation and titration of guideline-directed medical therapies is one potential strategy to fill these gaps in care, both in the acute vulnerable period after hospital discharge and in the long term. The purpose of this article is to review the knowledge gaps in best practices of in-hospital initiation and up-titration of guideline-directed medical therapies, the benefits and risks of in-hospital initiation and post-discharge focused titration of guideline-directed medical therapies, the recent literature evaluating these practices, and propose strategies to apply these principles to the care of patients with heart failure with reduced ejection fraction.

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Section: In-hospital Gdmt Initiation and Titrationsupporting

confidence: 70%

Section: In-hospital Gdmt Initiation and Titrationmentioning

confidence: 98%

Section: Continuing Gdmt Optimisation In the Post-discharge Phasementioning

confidence: 99%

See 1 more Smart Citation

In-hospital Initiation and Up-titration of Guideline-directed Medical Therapies for Heart Failure with Reduced Ejection Fraction

et al. 2022

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“…The diagnosis of HFrEF was considered confirmed if at least one of the following criteria was met: a) one inpatient claim with an HFrEF diagnosis using the International Classification of Diseases, Tenth Revision (ICD-10) codes I50.2x, I50.4x, or I50.1; b) two outpatient claims for HFrEF; or c) one outpatient claim for HFrEF (I50.2x, I50.4x, or I50.1) plus one outpatient claim with any HF diagnosis (I50.1, I50.2x, I50.3x, I50.4x, I50.8x, I50.9, or I11.0) on two different dates within 12 months. 15,16 In addition, patients in our study population had to be continuously enrolled in a health plan for at least 12 months before and after the index date, with an allowed gap of fewer than 45 days between periods of continuous enrollment. Patients with evidence of heart transplant, a left ventricular assist device, adult congenital heart disease (eg, single ventricle disease), or amyloidosis prior to the index date were excluded.…”

Section: Study Populationmentioning

confidence: 99%

Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction

Wang

Chen

Tan

et al. 2021

JHEOR

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Background: Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). Objectives: This study aimed to use DL models to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart failure with reduced ejection fraction (HFrEF). Methods: We analyzed the data of adult HFrEF patients from the IBM® MarketScan® Commercial and Medicare Supplement databases between January 1, 2015 and December 31, 2017. A sequential model architecture based on bi-directional long short-term memory (Bi-LSTM) layers was utilized. For DL models to predict HF hospitalizations and worsening HF events, we utilized two study designs: with and without a buffer window. For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was assessed by area under the curve (AUC) values, precision, and recall on an independent testing dataset. Results: A total of 47 498 HFrEF patients were included; 9427 with at least one HF hospitalization. The best AUCs of DL models without a buffer window in predicting HF hospitalizations and worsening HF events in the total patient cohort were 0.977 and 0.972; with a 7-day buffer window the best AUCs were 0.573 and 0.608, respectively. The best AUCs in predicting 30- and 90-day readmissions in all adult patients were 0.597 and 0.614, respectively. An AUC of 0.861 was attained for prediction of 90-day readmission in patients aged 18-64. For all outcomes assessed, the DL approach outperformed traditional machine learning models. Discussion: The DL approach can automate feature engineering during the model learning, which can increase the clinical applicability and lead to comparable or better model performance. However, the lack of granular clinical data, and sample size and imbalance issues may have limited the model’s performance. Conclusions: A DL approach using Bi-LSTM was shown to be a feasible and useful tool to predict HF-related outcomes. This study can help inform the future development and deployment of predictive tools to identify high-risk HFrEF patients and ultimately facilitate targeted interventions in clinical practice.

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“…Pharmacotherapy is the cornerstone of treatment for HFrEF and is based on combined administration of drugs acting through different pathways 2,86–88 . However, implementation of the so‐called guidelines directed medical treatment (GDMT) remains suboptimal in a large proportion of patients 14,89–95 . In a multinational observational study, using healthcare databases in Sweden, the United Kingdom, and the United States, among new users of HFrEF drugs, over 12 month follow‐up, target doses of angiotensin‐converting enzyme inhibitors (ACEis), angiotensin receptor blockers (ARBs), beta‐blockers, and angiotensin receptor–neprilysin inhibitors (ARNIs) were achieved in 15%, 10%, 12%, and 30% of patients, respectively.…”

Section: Treatment Of Heart Failure With Reduced Ejection Fractionmentioning

confidence: 99%

Heart failure: an update from the last years and a look at the near future

et al. 2022

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In the last years, major progress occurred in heart failure (HF) management. Quadruple therapy is now mandatory for all the patients with HF with reduced ejection fraction. Whilst verciguat is becoming available across several countries, omecamtiv mecarbil is waiting to be released for clinical use. Concurrent use of potassium-lowering agents may counteract hyperkalaemia and facilitate renin-angiotensin-aldosterone system inhibitor implementations. The results of the EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved) trial were confirmed by the Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction (DELIVER) trial, and we now have, for the first time, evidence for treatment of also patients with HF with preserved ejection fraction. In a pre-specified meta-analysis of major randomized controlled trials, sodium-glucose co-transporter-2 inhibitors reduced all-cause mortality, cardiovascular (CV) mortality, and HF hospitalization in the patients with HF regardless of left ventricular ejection fraction. Other steps forward have occurred in the treatment of decompensated HF. Acetazolamide in Acute Decompensated Heart Failure with Volume Overload (ADVOR) trial showed that the addition of intravenous acetazolamide to loop diuretics leads to greater decongestion vs. placebo. The addition of hydrochlorothiazide to loop diuretics was evaluated in the CLOROTIC trial. Torasemide did not change outcomes, compared with furosemide, in TRANSFORM-HF. Ferric derisomaltose had an effect on the primary outcome of CV mortality or HF rehospitalizations in IRONMAN (rate ratio 0.82; 95% confidence interval 0.66-1.02; P = 0.070). Further options for the treatment of HF, including device therapies, cardiac contractility modulation, and percutaneous treatment of valvulopathies, are summarized in this article.

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Treatment patterns and clinical outcomes among patients <65 years with a worsening heart failure event

Cited by 17 publications

References 18 publications

In-hospital Initiation and Up-titration of Guideline-directed Medical Therapies for Heart Failure with Reduced Ejection Fraction

In-hospital Initiation and Up-titration of Guideline-directed Medical Therapies for Heart Failure with Reduced Ejection Fraction

Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction

Heart failure: an update from the last years and a look at the near future

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