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BACKGROUND: Although sustained ventricular arrhythmias (VAs) are a common complication after durable left ventricular assist device (LVAD) implantation, the incidence, risk factors, and prognostic implications of postoperative early VAs (EVAs) in contemporary patients with LVAD are poorly understood. METHODS AND RESULTS: A single-center retrospective analysis was performed of patients who underwent LVAD implantation from October 1, 2006, to October 1, 2022. EVA was defined as an episode of sustained VA identified ≤30 days after LVAD implantation. A total of 789 patients underwent LVAD implantation (mean age 62.9 ± 0. years 5, HeartMate 3 41.4%, destination therapy 43.3%). EVAs occurred in 100 patients (12.7%). A history of end-stage renal disease (odds ratio [OR] 5.6, 95% confidence interval [CI] 1.45-21.70), preoperative electrical storm (OR 2.82, 95% CI 1.11-7.16), and appropriate implantable cardiac defibrillator therapy before implantation (OR 2.8, 95% CI 1.26-6.19) are independently associated with EVAs. EVA was associated with decreased 30-day survival (hazard ratio 3.02, 95% CI 1.1-8.3, Pâ¯=â¯.032). There was no difference in transplant-free survival time between patients with and without EVAs (hazard ratio 0.82, 95% CI 0.5-1.4, Pâ¯=â¯.454). CONCLUSIONS: EVAs are common after durable LVAD implantation and are associated with an increased risk of 30-day mortality.
Asunto(s)
Insuficiencia Cardíaca , Corazón Auxiliar , Humanos , Corazón Auxiliar/efectos adversos , Masculino , Femenino , Persona de Mediana Edad , Estudios Retrospectivos , Insuficiencia Cardíaca/terapia , Insuficiencia Cardíaca/cirugía , Insuficiencia Cardíaca/epidemiología , Anciano , Factores de Riesgo , Complicaciones Posoperatorias/epidemiología , Complicaciones Posoperatorias/etiología , Incidencia , Arritmias Cardíacas/epidemiología , Arritmias Cardíacas/terapia , Arritmias Cardíacas/etiología , Factores de Tiempo , Taquicardia Ventricular/epidemiología , Taquicardia Ventricular/etiología , Taquicardia Ventricular/terapia , Estudios de SeguimientoRESUMEN
Understanding noncardiovascular comorbidities and geriatric syndromes in elderly patients with heart failure (HF) is important as the average age of the population increases. Healthcare professionals need to consider these complex dynamics when managing older adults with HF, especially those older than 80. A number of small studies have described associations between HF and major geriatric domains. With information on patients' cognitive, functional decline, and ability to adhere to therapy, physicians can plan for individualized treatment goals and recommendations for these patients.
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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.
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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.
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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.
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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.