Recovery of left ventricular function is associated with improved outcomes in LVAD recipients.

BACKGROUND: The significance of recovered left ventricular ejection fraction (LVEF) in LVAD recipients, outside of pump explantation, is unclear. METHODS: Patients undergoing first LVAD implantation at Duke University Hospital between 2006 and 2017 were evaluated for LVEF recovery up to 2 years following implant. Occurrence of gastrointestinal bleeding (GIB), hospitalization for heart failure (HF), pump thrombosis and death were assessed before and after LVEF recovery. RESULTS: Of 286 patients who met inclusion criteria, 9.8% reached a "threshold" of recovery with an LVEF ≥ 40%. 17.4% achieved "relative" recovery with an increase in LVEF ≥ 10% since LVAD implantation. For either definition, recovered patients had a lower incidence of a composite endpoint of GIB, HF hospitalization, pump thrombosis, or death compared to patients without recovery. Patients with "threshold" recovery had 4.7 events per 100 patient-years (95% CI, 0.7-33.6) compared to 48.8 events per 100 patient-years (95% CI, 39.5-60.3) without "threshold" recovery [p = .020]. Those with "relative" recovery had 14.1 events per 100 patient-years [95% CI, 5.9-33.8] versus 50.7 events per 100 patient-years (95% CI, 40.7-63.0) without "relative" recovery [p = 0.005]. However, improved outcomes in the "relative" recovery group were limited to those who also met the "threshold" definition. Importantly, among patients who achieved "threshold" recovery, the incidence of the composite endpoint declines in the postrecovery period, suggesting that LVEF recovery mechanistically results in improved outcomes. CONCLUSIONS: An LVEF ≥ 40% associates with better outcomes in LVAD recipients. Methods to promote recovery could reduce morbidity and mortality related to LVAD support.

Novel Acoustic Biomarker of Quality of Life in Left Ventricular Assist Device Recipients.

Background Although technological advances to pump design have improved survival, left ventricular assist device (LVAD) recipients experience variable improvements in quality of life. Methods for optimizing LVAD support to improve quality of life are needed. We investigated whether acoustic signatures obtained from digital stethoscopes can predict patient-centered outcomes in LVAD recipients. Methods and Results We followed precordial sounds over 6 months in 24 LVAD recipients (8 HeartWare HVAD™, 16 HeartMate 3 [HM3]). Subjects recorded their precordial sounds with a digital stethoscope and completed a Kansas City Cardiomyopathy Questionnaire weekly. We developed a novel algorithm to filter LVAD sounds from recordings. Unsupervised clustering of LVAD-mitigated sounds revealed distinct groups of acoustic features. Of 16 HM3 recipients, 6 (38%) had a unique acoustic feature that we have termed the pulse synchronized sound based on its temporal association with the artificial pulse of the HM3. HM3 recipients with the pulse synchronized sound had significantly better Kansas City Cardiomyopathy Questionnaire scores at baseline (median, 89.1 [interquartile range, 86.2-90.4] versus 66.1 [interquartile range, 31.1-73.7]; P=0.03) and over the 6-month study period (marginal mean, 77.6 [95% CI, 66.3-88.9] versus 59.9 [95% CI, 47.9-70.0]; P<0.001). Mechanistically, the pulse synchronized sound shares acoustic features with patient-derived intrinsic sounds. Finally, we developed a machine learning algorithm to automatically detect the pulse synchronized sound within precordial sounds (area under the curve, 0.95, leave-one-subject-out cross-validation). Conclusions We have identified a novel acoustic biomarker associated with better quality of life in HM3 LVAD recipients, which may provide a method for assaying optimized LVAD support.

Heart Sound Analysis in Individuals Supported With Left Ventricular Assist Devices.

OBJECTIVE: LVADs are surgically implanted mechanical pumps that improve survival rates of individuals with advanced heart failure. LVAD therapy is associated with high morbidity, which can be partially attributed to challenges with detecting LVAD complications before adverse events occur. Current methods used to monitor for complications with LVAD support require frequent clinical assessments at specialized LVAD centers. Analysis of recorded precordial sounds may enable real-time, remote monitoring of device and cardiac function for early detection of LVAD complications. The dominance of LVAD sounds in the precordium limits the utility of routine cardiac auscultation of LVAD recipients. In this work, we develop a signal processing pipeline to mitigate sounds generated by the LVAD. METHODS: We collected in vivo precordial sounds from 17 LVAD recipients, and contemporaneous echocardiograms from 12 of these individuals, to validate heart valve closure timings. RESULTS: We characterized various acoustic signatures of heart sounds extracted from in vivo recordings, and report preliminary findings linking fundamental heart sound characteristics and level of LVAD support. CONCLUSION: Mitigation of LVAD sounds from precordial sound recordings of LVAD recipients enables analysis of intrinsic heart sounds. SIGNIFICANCE: These findings provide proof-of-concept evidence of the clinical utility of heart sound analysis for bedside and remote monitoring of LVAD recipients.

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure.

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

Permanganate-based synthesis of manganese oxide nanoparticles in ferritin.

This paper investigates the comproportionation reaction of MnII with [Formula: see text] as a route for manganese oxide nanoparticle synthesis in the protein ferritin. We report that [Formula: see text] serves as the electron acceptor and reacts with MnII in the presence of apoferritin to form manganese oxide cores inside the protein shell. Manganese loading into ferritin was studied under acidic, neutral, and basic conditions and the ratios of MnII and permanganate were varied at each pH. The manganese-containing ferritin samples were characterized by transmission electron microscopy, UV/Vis absorption, and by measuring the band gap energies for each sample. Manganese cores were deposited inside ferritin under both the acidic and basic conditions. All resulting manganese ferritin samples were found to be indirect band gap materials with band gap energies ranging from 1.01 to 1.34 eV. An increased UV/Vis absorption around 370 nm was observed for samples formed under acidic conditions, suggestive of MnO2 formation inside ferritin.

Tuning Ferritin's band gap through mixed metal oxide nanoparticle formation.

This study uses the formation of a mixed metal oxide inside ferritin to tune the band gap energy of the ferritin mineral. The mixed metal oxide is composed of both Co and Mn, and is formed by reacting aqueous Co2+ with [Formula: see text] in the presence of apoferritin. Altering the ratio between the two reactants allowed for controlled tuning of the band gap energies. All minerals formed were indirect band gap materials, with indirect band gap energies ranging from 0.52 to 1.30 eV. The direct transitions were also measured, with energy values ranging from 2.71 to 3.11 eV. Tuning the band gap energies of these samples changes the wavelengths absorbed by each mineral, increasing ferritin's potential in solar-energy harvesting. Additionally, the success of using [Formula: see text] in ferritin mineral formation opens the possibility for new mixed metal oxide cores inside ferritin.