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Aims: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD‐code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. Methods and results: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP‐HF) and non‐HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF‐related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP‐HF as cases (PPV, 0.86; sensitivity, 0.86). From the 'HF ICD‐code universe', we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). Conclusions: These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation. [ABSTRACT FROM AUTHOR]

Aims: According to the Kidney Disease: Improving Global Outcomes (KDIGO) guideline, the definition of chronic kidney disease (CKD) requires the presence of abnormal kidney structure or function for >3 months with implications for health. CKD in patients with heart failure (HF) has not been defined using this definition, and less is known about the true health implications of CKD in these patients. The objective of the current study was to identify patients with HF who met KDIGO criteria for CKD and examine their outcomes. Methods and results: Of the 1 419 729 Veterans with HF not receiving kidney replacement therapy, 828 744 had data on ≥2 ambulatory serum creatinine >90 days apart. CKD was defined as estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 (n = 185 821) or urinary albumin‐to‐creatinine ratio (uACR) >30 mg/g (n = 32 730) present twice >3 months apart. Normal kidney function (NKF) was defined as eGFR ≥60 ml/min/1.73 m2, present for >3 months, without any uACR >30 mg/g (n = 365 963). Patients with eGFR <60 ml/min/1.73 m2 were categorized into four stages: 45–59 (n = 72 606), 30–44 (n = 74 812), 15–29 (n = 32 077), and <15 (n = 6326) ml/min/1.73 m2. Five‐year all‐cause mortality occurred in 40.4%, 57.8%, 65.6%, 73.3%, 69.7%, and 47.5% of patients with NKF, four eGFR stages, and uACR >30mg/g (albuminuria), respectively. Compared with NKF, hazard ratios (HR) (95% confidence intervals [CI]) for all‐cause mortality associated with the four eGFR stages and albuminuria were 1.63 (1.62–1.65), 2.00 (1.98–2.02), 2.49 (2.45–2.52), 2.28 (2.21–2.35), and 1.22 (1.20–1.24), respectively. Respective age‐adjusted HRs (95% CIs) were 1.13 (1.12–1.14), 1.36 (1.34–1.37), 1.87 (1.84–1.89), 2.24 (2.18–2.31) and 1.19 (1.17–1.21), and multivariable‐adjusted HRs (95% CIs) were 1.11 (1.10–1.12), 1.24 (1.22–1.25), 1.46 (1.43–1.48), 1.42 (1.38–1.47), and 1.13 (1.11–1.16). Similar patterns were observed for associations with hospitalizations. Conclusion: Data needed to define CKD using KDIGO criteria were available in six out of ten patients, and CKD could be defined in seven out of ten patients with data. HF patients with KDIGO‐defined CKD had higher risks for poor outcomes, most of which was not explained by abnormal kidney structure or function. Future studies need to examine whether CKD defined using a single eGFR is characteristically and prognostically different from CKD defined using KDIGO criteria. [ABSTRACT FROM AUTHOR]

Background: Due to anatomic and physiologic concerns, prior generations of the left ventricular assist devices (LVAD) have frequently been denied to patients with small body size. However, outcomes in patients with small body surface area (BSA) following HeartMate 3 (HM3) LVAD implantation remain relatively unknown. Methods: A cohort of 220 patients implanted at a single center was divided into two groups: BSA ≤1.8 m2 (small BSA, n = 37) and BSA >1.8 m2 (large BSA, n = 183). We investigated baseline characteristics and clinical outcomes including survival and incidence of adverse events. Results: Small BSA patients were older (60 vs. 57 years), more likely female (60% vs. 20%), had a lower body mass index (24 vs. 32 kg/m2), lower incidence of diabetes (32% vs. 51%), history of stroke (5% vs. 19%), and left ventricular thrombus (0% vs. 11%). They had smaller left ventricular end diastolic diameter (64.8 vs. 69.3 mm). Pump speed and pump flows at discharge were lower in the small BSA group. Survival at 1 year and 2 years was 86% versus 87% and 86% versus 79% for small versus large BSA groups (p = 0.408), respectively. The rates of adverse events were similar between groups and there were no cases of confirmed pump thrombosis. The incidence of readmissions for low flow alarms was higher in the small BSA group (0.55 vs. 0.24 EPPY). Conclusions: These findings demonstrate comparable outcomes in patients with small body size and suggest that this parameter should not be an exclusion criterion on patients who are otherwise candidates for HM3 LVAD implantation. [ABSTRACT FROM AUTHOR]

Background: National heart failure guidelines recommend quadruple therapy with renin-angiotensin system inhibitors, beta-blockers, mineralocorticoid receptor antagonists, and sodium-glucose cotransporter 2 inhibitors for patients with heart failure with reduced ejection fraction (HFrEF), most of whom also receive loop diuretics. However, the guidelines are less clear about the safe approaches to discontinuing older drugs whose decreasing or residual benefit is less well understood. The objective of this study was to examine whether digoxin can be safely discontinued in patients with HFrEF receiving beta-blockers., Methods: In OPTIMIZE-HF, of 2,477 patients with HFrEF (EF ≤45%) receiving beta-blockers and digoxin, digoxin was discontinued in 450 patients. We assembled a propensity score-matched cohort of 433 pairs of patients in which digoxin continuation vs. discontinuation groups were balanced on 51 baseline characteristics. Using the same approach, from 992 patients not on beta-blockers, we assembled a matched cohort of 198 pairs of patients also balanced on 51 baseline characteristics. Hazard ratios (HRs) and 95% CIs for one-year outcomes were estimated., Results: Among patients receiving beta-blockers, digoxin discontinuation had no association with the combined endpoint of heart failure readmission or death (HR, 1.01; 95% CI, 0.85-1.19), heart failure readmission (HR, 1.03; 95% CI, 0.85-1.25) or death (HR, 0.91; 95% CI, 0.72-1.14). Respective HRs (95% CIs) among patients not receiving beta-blockers were 1.60 (1.25-2.04), 1.62 (1.18-2.22) and 1.43 (1.08-1.89)., Conclusions: Digoxin can be discontinued without increasing the risk of adverse outcomes in patients with HFrEF receiving beta-blockers. Future studies need to examine the residual benefit of older heart failure drugs to ensure their safe discontinuation in patients with HFrEF receiving newer guideline-directed medical therapy., (Copyright © 2024. Published by Elsevier Inc.)

Heart transplant (HT) recipients are more frequently reaching childbearing age given improvement in median survival and outcomes after HT. Although most pregnancies in HT recipients have favorable outcomes, poor fetal outcomes and maternal complications such as hypertensive disorders of pregnancy are more common in HT recipients than in the general population. In this review, we summarize the current evidence to guide the management of pregnancy in HT recipients. Preconception counseling, focused on risk stratification and optimal timing of conception, is the first important step to optimize pregnancy outcomes. During pregnancy and in the postpartum period, frequent monitoring of graft function and immunosuppressive levels is recommended. Calcineurin inhibitors and corticosteroids should be the mainstay of treatment for both prevention and treatment of graft rejection. Delivery planning should follow usual obstetric indications, preferably with vaginal delivery at term using regional anesthesia. A multidisciplinary care team should be involved in management through all stages of pregnancy to ensure success., Competing Interests: Disclosure: R.C.: CVRx honorarium for educational presentations. The other authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

The impact of cardiac resynchronization therapy (CRT) in patients receiving durable left ventricular assist device (LVAD) implantation remains unclear and there is no consensus regarding postoperative management. We sought to determine the impact of postoperative management of CRT on clinical outcomes following LVAD implantation. A total of 789 patients underwent LVAD implantation at our institution from 2007 to 2022 including 195 patients (24.7%) with preoperative CRT. Patients with preoperative CRT were significantly older and more frequently received an LVAD as destination therapy compared to patients without preoperative CRT. After LVAD implantation, 85 patients had CRT programmed "off" and 74 patients had CRT programmed "on." The risk of mortality was significantly increased amongst patients with preoperative CRT that was turned "on" following LVAD implantation compared to patients with preoperative CRT turned "off" following implant (subdistribution hazard ratio [sdHR] = 1.54; 1.06-2.37 95% confidence interval [CI]; p = 0.036). There was no significant difference between incidence of ventricular arrhythmias in patients with and without postoperative CRT "on" (35.1% vs. 48.2%; p = 0.095). Additional clinical trials are warranted to determine the best CRT programming strategy following LVAD implantation., Competing Interests: Disclosure: F.H.S. has received institutional research support and honorarium for educational conferences from Abbott. The other authors have no conflicts of interest to report., (Copyright © ASAIO 2024.)