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ImportanceTransthyretin amyloid cardiomyopathy (ATTR-CM) is a form of heart failure (HF) with preserved ejection fraction (HFpEF). Technetium Tc 99m pyrophosphate scintigraphy (PYP) enables ATTR-CM diagnosis. It is unclear which patients with HFpEF have sufficient risk of ATTR-CM to warrant PYP.ObjectiveTo derive and validate a simple ATTR-CM score to predict increased risk of ATTR-CM in patients with HFpEF.Design, Setting, and ParticipantsRetrospective cohort study of 666 patients with HF (ejection fraction ≥ 40%) and suspected ATTR-CM referred for PYP at Mayo Clinic, Rochester, Minnesota, from May 10, 2013, through August 31, 2020. These data were analyzed September 2020 through December 2020. A logistic regression model predictive of ATTR-CM was derived and converted to a point-based ATTR-CM risk score. The score was further validated in a community ATTR-CM epidemiology study of older patients with HFpEF with increased left ventricular wall thickness ([WT] ≥ 12 mm) and in an external (Northwestern University, Chicago, Illinois) HFpEF cohort referred for PYP. Race was self-reported by the participants. In all cohorts, both case patients and control patients were definitively ascertained by PYP scanning and specialist evaluation.Main Outcomes and MeasuresPerformance of the derived ATTR-CM score in all cohorts (referral validation, community validation, and external validation) and prevalence of a high-risk ATTR-CM score in 4 multinational HFpEF clinical trials.ResultsParticipant cohorts included were referral derivation (n = 416; 13 participants [3%] were Black and 380 participants [94%] were White; ATTR-CM prevalence = 45%), referral validation (n = 250; 12 participants [5%]were Black and 228 participants [93%] were White; ATTR-CM prevalence = 48% ), community validation (n = 286; 5 participants [2%] were Black and 275 participants [96%] were White; ATTR-CM prevalence = 6% ), and external validation (n = 66; 23 participants [37%] were Black and 36 participants [58%] were White; ATTR-CM prevalence = 39%). Score variables included age, male sex, hypertension diagnosis, relative WT more than 0.57, posterior WT of 12 mm or more, and ejection fraction less than 60% (score range −1 to 10). Discrimination (area under the receiver operating characteristic curve [AUC] 0.89; 95% CI, 0.86-0.92; P 2 = 4.6; P = .46) were strong. Discrimination (AUC ≥ 0.84; P 2 = 2.8; P = .84; Hosmer-Lemeshow χ2 = 4.4; P = .35; Hosmer-Lemeshow χ2 = 2.5; P = .78 in referral, community, and external validation cohorts, respectively) were maintained in all validation cohorts. Precision-recall curves and predictive value vs prevalence plots indicated clinically useful classification performance for a score of 6 or more (positive predictive value ≥25%) in clinically relevant ATTR-CM prevalence (≥10% of patients with HFpEF) scenarios. In the HFpEF clinical trials, 11% to 35% of male and 0% to 6% of female patients had a high-risk (≥6) ATTR-CM score.Conclusions and RelevanceA simple 6 variable clinical score may be used to guide use of PYP and increase recognition of ATTR-CM among patients with HFpEF in the community. Further validation in larger and more diverse populations is needed.

Serum sodium is an established prognostic marker in heart failure (HF) patients and is associated with an increased risk of morbidity and mortality. We sought to study the prognostic value of serum sodium in left ventricular assist device (LVAD) patients and whether hyponatremia reflects worsening HF or an alternative mechanism. We identified HF patients that underwent LVAD implantation between 2008 and 2019. Hyponatremia was defined as Na ≤134 mEq/L at 3 months after implantation. We assessed for differences in hyponatremia before and after LVAD implantation. We also evaluated the association of hyponatremia with all-cause mortality and recurrent HF hospitalizations. There were 342 eligible LVAD patients with a sodium value at 3 months. Among them, there was a significant improvement in serum sodium after LVAD implantation compared to preoperatively (137.2 vs. 134.7 mEq/L, P0.0001). Patients with and without hyponatremia had no significant differences in echocardiographic and hemodynamic measurements. In a multivariate analysis, hyponatremia was associated with a markedly increased risk of all-cause mortality (HR 3.69, 95% CI, 1.93-7.05, P0.001) when accounting for age, gender, co-morbidities, use of loop diuretics, and B-type natriuretic peptide levels. Hyponatremia was also significantly associated with recurrent HF hospitalizations (HR 2.11, 95% CI, 1.02-4.37, P = 0.04). Hyponatremia in LVAD patients is associated with significantly higher risk of all-cause mortality and recurrent HF hospitalizations. Hyponatremia may be a marker of ongoing neurohormonal activation that is more sensitive than other lab values, echocardiography parameters, and hemodynamic measurements.

Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately. Although machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls, which are important factors to consider when interpreting machine learning studies.

Background There are characteristic findings of coronavirus disease 2019 (COVID-19) on chest images. An artificial intelligence (AI) algorithm to detect COVID-19 on chest radiographs might be useful for triage or infection control within a hospital setting, but prior reports have been limited by small data sets, poor data quality, or both. Purpose To present DeepCOVID-XR, a deep learning AI algorithm to detect COVID-19 on chest radiographs, that was trained and tested on a large clinical data set. Materials and Methods DeepCOVID-XR is an ensemble of convolutional neural networks developed to detect COVID-19 on frontal chest radiographs, with reverse-transcription polymerase chain reaction test results as the reference standard. The algorithm was trained and validated on 14 788 images (4253 positive for COVID-19) from sites across the Northwestern Memorial Health Care System from February 2020 to April 2020 and was then tested on 2214 images (1192 positive for COVID-19) from a single hold-out institution. Performance of the algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar test for sensitivity and specificity and the DeLong test for the area under the receiver operating characteristic curve (AUC). Results A total of 5853 patients (mean age, 58 years ± 19 [standard deviation]; 3101 women) were evaluated across data sets. For the entire test set, accuracy of DeepCOVID-XR was 83%, with an AUC of 0.90. For 300 random test images (134 positive for COVID-19), accuracy of DeepCOVID-XR was 82%, compared with that of individual radiologists (range, 76%–81%) and the consensus of all five radiologists (81%). DeepCOVID-XR had a significantly higher sensitivity (71%) than one radiologist (60%, P < .001) and significantly higher specificity (92%) than two radiologists (75%, P < .001; 84%, P = .009). AUC of DeepCOVID-XR was 0.88 compared with the consensus AUC of 0.85 (P = .13 for comparison). With consensus interpretation as the reference standard, the AUC of DeepCOVID-XR was 0.95 (95% CI: 0.92, 0.98). Conclusion DeepCOVID-XR, an artificial intelligence algorithm, detected coronavirus disease 2019 on chest radiographs with a performance similar to that of experienced thoracic radiologists in consensus. © RSNA, 2020 Supplemental material is available for this article. See also the editorial by van Ginneken in this issue.

Over 6.5 million Americans are living with heart failure (HF) today and the prevalence continues to increase.1 Despite substantial advances in medical therapy, many patients progress to end-stage disease with a 5-year absolute mortality rate of approximately 50%.2 Moreover, HF is a significant burden on the United States health care system, accounting for ~800,000 hospitalizations in 2016 and projected to cost $69.7 billion annually by the year 2030.1,3 Identifying high risk HF patients is therefore an important, yet challenging pursuit for clinicians and health care systems alike. Prognosis of individual patients with HF is highly variable in contemporary cohorts, and the risk of serious clinical outcomes such as mortality and hospitalization for HF can differ more than 20-fold.4,5 Predicting adverse outcomes in patients with HF could theoretically help direct resources to patients at the highest levels of risk who might benefit the most from earlier and more intensive monitoring and treatment (e.g. targeted medications, cardiac devices, home monitoring systems, and social services), while avoiding unnecessary interventions and costs for patients at low risk.6–9 Ideally, this would translate into improved outcomes and cost-efficiency in providing care for patients with HF. Given the importance of risk prediction for process improvement in HF, we aim to review the available evidence on prognostic variables and the current state of risk prediction for patients with HF. Additionally, we will discuss limitations of traditional risk modeling and provide a glimpse into the future of risk prediction in HF.

Graphical abstract, Highlights • Fat in the transverse pericardial sinus can mimic thrombus on TEE. • Epicardial fat is more common in patients with AF. • UEAs can differentiate extracardiac from intracardiac structures.

Cardiac scintigraphy has emerged as a key diagnostic test for transthyretin cardiac amyloidosis (ATTR-CA). However, there are potential limitations and pitfalls in the interpretation of cardiac scintigraphy for ATTR-CA that are worth noting. We present here a series of three cases which illustrate some of these important principles.

BACKGROUND: Prospective studies demonstrate that aggressive pharmacological therapy combined with pump speed optimization may result in myocardial recovery in larger numbers of patients supported with left ventricular assist device (LVAD). This study sought to determine whether use of machine learning based models predict LVAD patients with myocardial recovery resulting in pump explant METHODS: 20,270 adult patients with a durable continuous-flow LVAD in the INTERMACS registry were included in the study. 98 raw clinical variables were screened using least absolute shrinkage and selection operator (LASSO) for selection of features associated with LVAD-induced myocardial recovery. Machine learning models were developed in the training dataset (70%) and were assessed in the validation dataset (30%) by receiver operating curve (ROC) and Kaplan-Meier analysis. RESULTS: LASSO identified 28 unique clinical features associated with LVAD-induced myocardial recovery including, age, etiology of HF, psychosocial risk factors, laboratory values, cardiac rate and rhythm, and echocardiographic indices. Machine learning models achieved areas under the ROC curve (AUCs) of 0.813 – 0.824 in the validation dataset outperforming logistic regression-based new INTERMACS recovery risk score (AUC of 0.796) and previously established LVAD recovery risk scores (I-CARS and I-TOPS) with AUCs of 0.744 and 0.748 (p< 0.05). Patients who were predicted to recover by machine learning model demonstrated significantly higher incidence of myocardial recovery resulting in LVAD explant in the validation cohort compared to those who were not predicted to recover (18.8% vs 2.6% at 4 years of pump support). CONCLUSION: Machine learning can be a valuable tool to identify subsets of LVAD patients who may be more likely to respond to myocardial recovery protocols.

Introduction: Myocardial strain imaging provides a complex dataset that is often reduced to a single parameter, global longitudinal peak strain (GLPS). Raw temporal strain data is largely unexplored and restricted as studies often only save images of graphs lacking numerical data. While certain strain imaging packages do allow for the prospective export of raw data, the task is slow, rarely done, and cannot be done retrospectively. The troves of retrospective strain studies saved as images can be unlocked by reconstructing the numerical values contained in strain graphs. Such a dataset would be critical to future artificial intelligence strain analysis and morphological detection of disease modalities. This study aims to investigate the methods of extracting and reconstructing strain curve graphs to raw numerical outputs. Methods: By using vendor-specific color recognition algorithms, individual curves can be isolated as scattergram type plots of pixels. These pixel locations are converted to temporal strain values by defining scale factors for the graph. Intelligence-guided filtering, interpolation, and curve overlap filling can convert the pixel scattergrams into clean and complete temporal strain data. Using a set of 21 studies each containing 3 views (2Ch, 4Ch, APLAx) with each view containing 6 regional curves, 378 unique curves were reconstructed and compared to prospectively exported temporal strain data. Results: Results demonstrate that this method reconstructs the numerical data with high accuracy and precision. The reconstructed data was compared with the exported numerical data, yielding an average R 2 of .996 with an average runtime per study of 7.32s. The minute deviation from fidelity can be attributed to the finite resolution of the pixels in the analyzed graph. Discussion: Highly accurate temporal data can be extracted from routine strain graphs in clinical studies, which may allow for more nuanced assessment and deep learning of strain.

Introduction Right ventricular failure (RVF) is a major cause of morbidity and mortality after left ventricular assist device (LVAD), however predicting RVF remains challenging. Hypothesis We hypothesized that an analysis of late gadolinium enhancement (LGE) patterns on pre-operative cardiac MRI (cMRI) could identify patients at risk for RVF after LVAD. Methods We analyzed reports for cMRIs performed on patients within one year prior to LVAD at our institution and abstracted LGE patterns using the 17-segment model. Patients were then grouped into clusters by similarities in LGE patterns using an unsupervised machine learning (ML) algorithm of hierarchical agglomerative clustering. Statistical comparison of the resulting clusters was then performed. Results Patients (N=31) were grouped into 3 clusters (Figure) with varying patterns of LGE. Cluster 1 patients (n=16) had no LGE or atypical LGE patterns and were significantly younger (age 42 ± 18) than other clusters (p=0.029). Cluster 2 patients (n=11) had extensive transmural LGE patterns and were more likely to have hypertension (p=0.006) and dyslipidemia (p=0.002) than other groups. Cluster 3 patients (n=4) had some degree of subendocardial LGE but no extensive transmural LGE patterns. No patients in cluster 2 developed RVF after LVAD, while 4 patients (25%) in cluster 1 and 2 patients in cluster 3 (50%) had RVF after LVAD, though the difference between groups did not reach statistical significance due to small number of patients in the cohort overall (p=0.058). Importantly, traditional factors associated with RVF including hemodynamics and echocardiographic/MRI parameters of LV and RV function were not significantly different between clusters. Further, LGE enhancement of the RV myocardium or RV insertion points were not associated with RVF after LVAD. Conclusions Unsupervised ML of LGE patterns on cMRI can identify clusters of patients at risk for RVF. LGE patterns on cMRI may identify patients with non-ischemic (cluster 1) and mixed (cluster 3) etiologies of their heart failure who are at higher risk for developing RVF due to global biventricular myocardial involvement than patients with a truly ischemic etiology of their heart failure (cluster 2). Future research in a larger cohort is needed to confirm this hypothesis. Figure: Dendrogram produced from agglomerative hierarchal clustering of LGE analysis of cardiac MRIs using standardized myocardial segmentation

Background Right ventricular failure (RVF) following left ventricular assist device (LVAD) implantation is an important source of morbidity and mortality but is difficult to predict. Right atrial (RA) strain by echocardiography is a novel hemodynamic parameter likely indicative of atrial functional capacity and RV diastolic function. Previous studies have suggested that pre-LVAD RV strain may be a predictor of RVF, but no studies have systematically evaluated the association of RA strain with outcomes after LVAD. We hypothesized that pre-LVAD RA strain may improve the ability to predict RVF after LVAD. Methods We evaluated 31 adults who received continuous flow LVAD between 2008-2018. Peak RA and RV strain values were measured by two-dimensional speckle-tracking echocardiography. RA strain was measured during three phases: reservoir phase (end-diastole to onset of ventricular filling), conduit phase (onset of ventricular filling to onset of atrial contraction), and contractile phase (onset of atrial contraction to end-diastole). RVF was defined as need for inotropic support longer than 14 days, inhaled nitric oxide for longer than 48 hours, or unplanned RVAD. Pearson correlation was used to compare RA and RV strain parameters. Logistic regression was performed for the outcome of RVF. Cox-proportional hazards modelling was performed for the outcome of survival free from transplant or device explant/exchange and hazard ratios were calculated per standard deviation change in regressors. Results Four patients (13%) developed RVF. Median time from echo to LVAD was 31 days. Correlation between RV free wall longitudinal strain (FWLS) and RA reservoir (R=-0.45, p=0.01), conduit (R=-0.30, p=0.10), and contractile strain (R=-0.27, p=0.15) was modest at best. None of the RA strain parameters were associated with RVF. Lower RA reservoir strain (HR 2.26, p=0.003) and RA conduit strain (HR 3.63, p=0.004) were associated with decreased survival free from transplant or device explant/exchange, while RA contractile strain was not. Conclusions In this small cohort, RA strain parameters were not associated with early RVF following LVAD implant. However, RA reservoir strain and RA conduit strain were strongly associated with post LVAD long term survival. RA strain parameters may represent a form of RV diastolic dysfunction that impacts long term outcomes more so than early post operative events. The utility of RA strain parameters for predicting outcomes following LVAD implant deserves further investigation and validation in a larger cohort.

Background Few studies have evaluated sex differences in LVAD morbidity and mortality, and they have yielded mixed results. A likely explanation is these studies have relied on ‘time to first event’ analysis, which ignores recurrent events that are clinically meaningful and add statistical power. We sought to compare outcomes after LVAD in women vs. men including the burden of hospital readmissions. Methods We included consecutive adult patients implanted with LVAD at our institution from 2008-2018. We used propensity score matching to match men and women on clinically relevant preoperative characteristics. We used a competing risks regression with cause-specific hazards to compare the risk of death and transplant between men and women. We used a joint frailty model to simultaneously model recurrent hospitalizations and competing terminal events of death, cardiac transplant, and device explant. Results The overall cohort included 338 patients (77 women, 261 men). After propensity score matching, there were 77 women and 77 men with balanced pre-operative characteristics. There was a non-significant trend towards increased risk of death (HR 1.49, p=0.12) and no significant difference in transplant (HR=0.87, p=.58) in women compared to men. We found a non-significant trend towards increased all-cause hospitalization in women (RR=1.30, p=0.12). This was driven by a significantly increased rate of “late” readmissions in women occurring more than 1 year post-implant (RR=2.38, p=0.01; figure 1). When evaluating reasons for hospital admission, women had a non-significant increase in rates of all causes with the exception of uncontrolled hypertension and subtherapeutic INR (figure 2). Women were at significantly higher risk of being admitted for anemia evaluation (RR=3.12, p=0.04). Conclusions We found a strong trend towards an increase in recurrent all-cause hospitalizations in women vs. men, driven primarily by a significantly higher rate of late readmissions after the first year post implant in women. These findings require further study in a larger cohort and if confirmed, deserve further investigation to better understand the underlying reasons for such disparities in outcomes in women supported by LVADs.

This study uses data from a large academic medical center to estimate the accuracy of the criteria used by Medicare to represent the burden of hospitalizations for heart failure.

Introduction Serum sodium is an important prognostic marker in heart failure patients, with lower values associated with increased risk of mortality. However, prognostic value of serum sodium has not been assessed in left ventricular assist device (LVAD) patients. Hypothesis We hypothesized lower serum sodium is associated with increased risk of mortality in LVAD patients. Methods We retrospectively identified 253 consecutive patients that had LVAD implantation at a single center between 2008-2016. To minimize confounding by peri-operative factors, we evaluated serum sodium at 3 months after implantation and time to all-cause mortality with a univariate Cox proportional hazards analysis. A secondary outcome was time to hospital readmission. Groups were defined as having lower and higher sodium levels relative to the median value. Censoring occurred at death, transplant, or pump exchange. A multivariate Cox proportional hazards analysis included estimated GFR and bicarbonate levels to account for effects of renal dysfunction and acid-base disturbance on sodium levels. Results There were 195 eligible LVAD patients with a sodium value at 3 months. Average age was 53 ± 14 yrs, 77% were male, 26% black. Half (50%) received a Heartmate II device and 50% received a Heartware HVAD. Median serum sodium was 137 mmol/L. Median follow-up was 346 days with 26 deaths (13%). In a univariate analysis, increasing serum sodium at 3 months was associated with a lower risk of all-cause mortality (HR 0.826, p=0.006). Pre-operative sodium levels were not associated with mortality (HR 1.004, p=0.941). Serum sodium at 3 months remained predictive of mortality in a multivariate model with estimated GFR and HCO3 (Table). When stratified by the median, the lower sodium group had a significantly shorter time to all-cause mortality (Figure). Sodium levels at 3 months were not associated with time to readmission. Conclusions Hyponatremia in LVAD patients (sodium

Purpose of Study The evaluation of patients for LVAD candidacy routinely includes an assessment of patient characteristics referred to as “psychosocial” variables. While psychosocial risk may impact long term survival in heart transplantation, its impact on LVAD outcomes is less clear. We thus sought to determine whether psychosocial characteristics are predictive of worse survival following LVAD implantation. Methods We included all patients implanted with an LVAD at our institution from 2008-2018, each of whom underwent a highly detailed psychosocial assessment by our clinical psychology team as part of each patient's comprehensive pre-implant evaluation. Survival analyses were performed using the Fine-Gray model for competing-risks to determine cumulative incidence functions. For statistically significant variables, cause-specific subdistribution hazard ratio estimates were obtained. Results A total of 365 patients received an LVAD, 207 as bridge to transplant (BTT) and 158 as destination therapy (DT). The psychosocial characteristics of the entire LVAD cohort, including stratification by LVAD indication of BTT or DT, are shown in the table. Many patients receiving LVADs had features often perceived to be of higher psychosocial risk including absence of a reliable caregiver (26%), gaps in health literacy (30%), and poor medication (18%), diet (37%), and appointment (17%) adherence, respectively. Yet, none of these variables or others were associated with worse survival (Table). The only variable found to be associated with worse survival was living alone (HR 1.76, p=0.02) but this was no longer significant when adjusted for gender and age (p=0.39). Interestingly, a conclusion of “significant or serious” overall psychosocial concerns by a clinical psychologist was also not shown to be associated with worse survival (p=0.47). Conclusions In this study, psychosocial variables were not predictive of worse survival in patients undergoing LVAD implantation. However, whether psychosocial risk is associated with an increase in adverse events and/or hospital readmissions in LVAD patients warrants further exploration.

DeFilippis et al. [(1)][1] recently published their findings on sex-related differences in a population bridged to cardiac transplantation with a left ventricular assist device (LVAD). Using the United Network for Organ Sharing database, they found in propensity-matched cohorts that women had lower

Nonvalvular atrial fibrillation (AF) is the most widespread cardiac arrhythmia of clinical significance worldwide, and its prevalence is increasing.1,2 Atrial fibrillation is a substantial health problem because it significantly increases the risk of thromboembolic events, particularly that of stroke.3 Moreover, strokes associated with AF are more severe, involve larger vascular territories, and cause more morbidity and death than do strokes from other causes.4

Pulmonary vasoreactivity testing (PVT) with low dose adenosine predicts the clinical response to calcium channel blocker therapy in patients with pulmonary arterial hypertension (PAH). While higher dose adenosine PVT predicts the hemodynamic response to prostacyclin therapy (PGI2) after 1 year, it

We report here on our findings from adolescent and young adult females (ages 14–25) with a family history of fragile X syndrome regarding their perceptions of the optimal ages for (1) learning fragile X is inherited, (2) learning one could be a carrier for fragile X, and (3) offering carrier testing for fragile X. Three groups were enrolled: those who knew they were carriers or noncarriers and those who knew only they were at-risk to be a carrier. Only 2 of the 53 participants felt that offering carrier testing should be delayed until the age of 18 years. Participants who knew only that they were at-risk to be a carrier provided older optimal ages for offering carrier testing than those who knew their actual carrier status. Participants did not express regret or negative emotions about the timing of the disclosure of genetic risk information regarding their own experiences. Participants’ reasoning behind reported ages for informing about genetic risk and offering carrier testing varied depending on what type of information was being disclosed, which carrier status group the participant belonged to, and the preferred age for learning the information. Study findings suggest that decisions regarding the timing to inform about genetic risk and offer testing should be tailored to the individual needs of the child and his/her family. 2009 Wiley-Liss, Inc.

Background: Unlike in classic heart failure populations where natriuretic peptides (NPs) are well-validated biomarkers for diagnosis and prognosis, the utility of NPs in patients with mechanical circulatory support is unknown. No prior study has systematically evaluated the predictive value of changes in NP levels on outcomes among contemporary patients after LVAD implantation. Methods: We reviewed records for all consecutive patients implanted with a continuous-flow LVAD at our institution from 2008-2012. Trends in NT-proBNP during LVAD support were measured, and the association of pre-implant NT-proBNP, as well as relative change in NT-proBNP, with mortality and hospitalization were analyzed. Results: Ninety-eight patients were included - 79 as bridge to transplantation (BTT), 19 as destination therapy (DT). Median follow-up was 256d, during which 24 patients died, 31 received a heart transplant, and 43 remained on LVAD support. Median NT-proBNP (pre-implant = 5540 pg/mL) decreased exponentially following device implant and stabilized at 60d (1950 pg/mL; Fig 1). In a Cox-proportional hazards model, neither pre-implant nor 60d NT-proBNP levels were associated with survival or readmission. However, a greater relative decrease in NT-proBNP from pre-implant to 60d was significantly associated with freedom from hospitalization (HR 1.77, p=0.04; Fig 2). Conclusions: NT-proBNP decreased exponentially and reliably, before reaching a steady state at 60d following device implantation. Our study is the first to show that serial changes in NPs may be useful in predicting LVAD outcomes. Future study should focus on whether NT-proBNP changes are influenced by baseline variables, and if NPs can be used to guide clinical management.

Abstract 4258 Mutations in TMPRSS6 (matriptase-2), a transmembrane serine protease expressed by the liver, result in the clinical phenotype of iron refractory iron deficiency anemia (IRIDA). Additionally, common polymorphisms in TMPRSS6 have been associated with variation in laboratory parameters of iron homeostasis in healthy populations. TMPRSS6 increases iron absorption by reducing expression of the hepatic hormone, hepcidin, via down-regulation of a BMP/SMAD signaling cascade. Hepcidin promotes the internalization and degradation of the duodenal iron transporter, ferroportin, thereby inhibiting iron absorption. Previous studies have demonstrated that adult mice with Tmprss6 deficiency exhibit elevated hepatic hepcidin mRNA levels that are associated with decreased hepatic iron stores. In one study, genetic loss of Tmprss6 was shown to result in significant elevation of hepatic hepcidin expression in mice at birth; however, whether this hepcidin elevation was associated with abnormalities in iron homeostasis was not reported. We therefore asked if the elevated hepcidin levels present in newborn Tmprss6-/- pups correlate with abnormal parameters of iron homeostasis in the fetal or neonatal periods. To answer this question, we intercrossed Tmprss6+/− mice to generate Tmprss6+/+, Tmprss6+/−, and Tmprss6-/- progeny for phenotypic characterization at either gestational day 17.5 (E17.5) or postnatal day 0 (P0). Consistent with prior observations, Tmprss6-/- pups at P0 showed a 4.6-fold increase in hepatic hepcidin mRNA compared to Tmprss6+/+ littermates (p=.006). However, despite this elevation in hepcidin expression, Tmprss6-/- pups were not pale, and they showed no significant differences in body mass or hepatic non-heme iron concentration compared to Tmprss6+/+ and Tmprss6+/− littermates. At E17.5, Tmprss6-/- fetuses showed a 50-fold increase in hepatic hepcidin mRNA compared to Tmprss6+/+ littermates (p=.005). However, Tmprss6-/- fetuses also were not pale, and they showed no significant difference in body mass compared to Tmprss6+/+ and Tmprss6+/− littermates. Surprisingly, hepatic non-heme iron concentration at E17.5 was significantly higher in Tmprss6-/- fetuses than in Tmprss6+/+ fetuses (p=.003). To determine if the increased hepcidin expression of Tmprss6-/- fetuses might affect iron homeostasis in their pregnant mothers, we measured iron parameters in Tmprss6+/− females gestating E17.5 litters that were enriched for either Tmprss6+/+ or Tmprss6-/- fetuses. No significant effects of fetal genotype on maternal iron parameters were observed. In summary, our results demonstrate that Tmprss6 regulates hepcidin expression in the fetal and neonatal periods in mice. However, Tmprss6 deficiency does not appear to be associated with systemic iron deficiency at these stages of development, and fetal Tmprss6 expression does not have a significant effect on maternal iron homeostasis in late gestation. These results may have implications for understanding the maintenance of iron homeostasis in early development, and may provide insight into the evolution of IRIDA as well as other disorders of iron homeostasis. Disclosures: No relevant conflicts of interest to declare.