Score: 2.0, Published: 2024-02-06
DOI: 10.1101/2024.02.05.24302236
BackgroundCardiovascular responses to psychological stressors have been separately associated with preclinical atherosclerosis and hemodynamic brain activity patterns across different studies and cohorts; however, what has not been established is whether cardiovascular stress responses reliably link indicators of stressor-evoked brain activity and preclinical atherosclerosis that have been measured in the same individuals. Accordingly, the present study used cross-validation and predictive modeling to test for the first time whether stressor-evoked systolic blood pressure (SBP) responses statistically mediated the association between concurrently measured brain activity and a vascular marker of preclinical atherosclerosis in the carotid arteries. Methods624 midlife adults (aged 28-56 years, 54.97% female) from two different cohorts underwent two information-conflict fMRI tasks, with concurrent SBP measures collected. Carotid artery intima-media thickness (CA-IMT) was measured by ultrasonography. A mediation framework that included harmonization, cross-validation, and penalized principal component regression was then employed, while significant areas in possible direct and indirect effects were identified through bootstrapping. Sensitivity analysis further tested the robustness of findings after accounting for prevailing levels of cardiovascular disease risk and brain imaging data quality control. ResultsTask-averaged patterns of hemodynamic brain responses exhibited a generalizable association with CA-IMT, which was mediated by an area-under-the-curve measure of aggregate SBP reactivity. Importantly, this effect held in sensitivity analyses. Implicated brain areas in this mediation included the ventromedial prefrontal cortex, anterior cingulate cortex, insula and amygdala. ConclusionsThese novel findings support a link between stressor-evoked brain activity and preclinical atherosclerosis accounted for by individual differences in corresponding levels of stressor-evoked cardiovascular reactivity.
Score: 1.0, Published: 2024-02-07
DOI: 10.1101/2024.02.06.24302412
ImportanceDeep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram waveforms. Despite their impressive accuracy, they are difficult to interpret and deploy broadly in the clinical setting. ObjectiveTo determine whether simpler models based on standard electrocardiogram measurements could detect LVSD with similar accuracy to deep learning models. DesignUsing an observational dataset of 40,994 matched 12-lead electrocardiograms (ECGs) and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. We additionally evaluated models in two independent cohorts from different medical centers, vendors, and countries. SettingThe training data was acquired from Stanford University Medical Center. External validation data was acquired from Cedars-Sinai Medical Center and the UK Biobank. ExposuresThe performance of models based on ECG waveforms in their detection of LVSD, as defined by ejection fraction below 35%. Main outcomesThe performance of the models as measured by area under the receiver operator characteristic curve (AUC) and other measures of classification accuracy. ResultsThe Stanford dataset consisted of 40,994 matched ECGs and echocardiograms, the test set having an average age of 62.13 (17.61) and 55.20% Male patients, of which 9.72% had LVSD. We found that a random forest model using 555 discrete, automated measurements achieves an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). Furthermore, a linear model based on 5 measurements achieves high performance (AUC of 0.86 (0.85-0.87)), close to a deep learning model and better than NT-proBNP (0.77 (0.74-0.79)). Finally, we find that simpler models generalize better to other sites, with experiments at two independent, external sites. ConclusionOur study demonstrates the value of simple electrocardiographic models which perform nearly as well as deep learning models while being much easier to implement and interpret.
Score: 1.0, Published: 2024-02-12
DOI: 10.1101/2024.02.08.24302547
BackgroundDiagnosis of mitral regurgitation (MR) requires careful evaluation of echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical-4-chamber view videos with color Doppler and detection of clinically significant (moderate or severe) mitral regurgitation from transthoracic echocardiography studies. MethodsA total of 58,614 studies (2,587,538 videos) from Cedars-Sinai Medical Center (CSMC) were used to develop and test an automated pipeline to identify apical-4-chamber view videos with color Doppler across the mitral valve and then assess mitral valve regurgitation severity. The model was tested on an internal test set of 1,800 studies (80,833 videos) from CSMC and externally evaluated in a geographically distinct cohort of 915 studies (46,890 videos) from Stanford Healthcare (SHC). ResultsIn the held-out CSMC test set, the view classifier demonstrated an AUC of 0.998 (0.998 - 0.999) and correctly identified 3,452 of 3,539 MR color Doppler videos (sensitivity of 0.975 (0.968-0.982) and specificity of 0.999 (0.999-0.999) compared with manually curated videos). In the external test cohort from SHC, the view classifier correctly identified 1,051 of 1,055 MR color Doppler videos (sensitivity of 0.996 (0.990 - 1.000) and specificity of 0.999 (0.999 - 0.999) compared with manually curated videos). For evaluating clinically significant MR, in the CSMC test cohort, moderate-or-severe MR was detected with AUC of 0.916 (0.899 - 0.932) and severe MR was detected with an AUC of 0.934 (0.913 - 0.953). In the SHC test cohort, the model detected moderate-or-severe MR with an AUC of 0.951 (0.924 - 0.973) and severe MR with an AUC of 0.969 (0.946 - 0.987). ConclusionsIn this study, we developed and validated an automated pipeline for identifying clinically significant MR from transthoracic echocardiography studies. Such an approach has potential for automated screening of MR and precision evaluation for surveillance.
Score: 4.8, Published: 2024-02-07
DOI: 10.1101/2024.02.07.24302443
Background and AimsPhase 3 trials testing whether pharmacologic interventions targeting myocardial fibrosis (MF) improve outcomes require MF measurement that does not rely on tomographic imaging with intravenous contrast. MethodsWe developed and externally validated extracellular volume (ECV) prediction models incorporating readily available data (comorbidity and natriuretic peptide variables), excluding tomographic imaging variables. Survival analysis tested associations between predicted ECV and incident outcomes (death or hospitalization for heart failure). We created various sample size estimates for a hypothetical therapeutic clinical trial testing an anti-fibrotic therapy using: a) predicted ECV, b) measured ECV, or c) no ECV. ResultsMultivariable models predicting ECV had reasonable discrimination (optimism corrected C-statistic for predicted ECV [≥]27% 0.78 (95%CI 90.75-0.80) in the derivation cohort (n=1663) and 0.74 (95%CI 0.71-0.76) in the validation cohort (n=1578)) and reasonable calibration. Predicted ECV associated with adverse outcomes in Cox regression models: ECV [≥]27% (binary variable) HR 2.21 (1.84-2.66). For a hypothetical clinical trial with an inclusion criterion of ECV [≥]27%, use of predicted ECV (with probability threshold of 0.69 and 80% specificity) compared to measured ECV would obviate the need to perform 3940 CMR scans, at the cost of an additional 3052 participants screened and 705 participants enrolled. ConclusionsPredicted ECV (derived without tomographic imaging) associates with outcomes and efficiently identifies vulnerable patients who might benefit from treatment. Predicted ECV may foster the design of phase 3 trials targeting MF with higher numbers of screened and enrolled participants, but with simplified eligibility criteria, avoiding the complexity of tomographic imaging. Structured Graphical AbstractO_ST_ABSKey QuestionC_ST_ABSPhase 3 trials targeting myocardial fibrosis (MF) to improve outcomes require MF measurement that does not rely on tomographic imaging with intravenous contrast. So, we developed and validated extracellular volume (ECV) prediction models incorporating clinical data, excluding tomographic imaging. Key FindingPredicted ECV had reasonable discrimination and associated with outcomes. For a hypothetical trial with an ECV [≥]27% inclusion criterion, using predicted ECV versus measured ECV would avoid 3940 cardiovascular magnetic resonance (CMR) scans, but require an additional 3052 participants screened and 705 enrolled. Take-home MessagePredicted ECV (derived without imaging) associates with outcomes and efficiently identifies vulnerable patients. Predicted ECV may foster phase 3 trials targeting MF with higher numbers of screened and enrolled participants, but simplified eligibility criteria, avoiding the complexity of tomographic imaging. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/24302443v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@c7df32org.highwire.dtl.DTLVardef@e72fforg.highwire.dtl.DTLVardef@9fef7corg.highwire.dtl.DTLVardef@23de37_HPS_FORMAT_FIGEXP M_FIG For a hypothetical trial requiring 1812 participants with measured ECV [≥]27%, 3940 patients would need to undergo screening with CMR. If predicted ECV is used, an additional 3052 patients would need to be screened and an additional 705 patients enrolled, but no patients would require CMR. If no screening is used, an additional 2128 patients would need to be enrolled. C_FIG
Score: 1.2, Published: 2024-01-20
DOI: 10.1101/2024.01.18.24301496
Background and AimsIntravenous iron therapies contain iron-carbohydrate complexes, designed to ensure iron becomes bioavailable via the intermediary of spleen and liver reticuloendothelial macrophages. How other tissues obtain and handle this iron remains unknown. This study addresses this question in the context of the heart. MethodsA prospective observational study was conducted in 12 patients receiving ferric carboxymaltose (FCM) for iron deficiency. Myocardial, spleen and liver magnetic resonance relaxation times, and plasma iron markers were collected longitudinally. To examine the handling of iron taken up by the myocardium, intracellular labile iron pool (LIP) was imaged in FCM-treated mice and cells. ResultsIn patients, myocardial relaxation time T1 dropped maximally 3hrs post FCM, remaining low 42 days later, while splenic T1 dropped maximally at 14 days, recovering by 42 days. In plasma, non-transferrin bound iron (NTBI) peaked at 3hrs, while ferritin peaked at 14 days. Changes in liver T1 diverged amongst patients. In mice, myocardial LIP rose 1h and remained elevated 42 days after FCM. In cardiomyocytes, FCM exposure raised LIP rapidly. This was prevented by inhibitors of NTBI transporters T-type and L-Type calcium channels and divalent metal transporter 1. Conclusions Intravenous iron therapy with FCM delivers iron to the myocardium rapidly through NTBI transporters, independently of reticuloendothelial macrophages. This iron remains labile for weeks, reflecting the myocardiums limited iron storage capacity. These findings challenge current notions of how the heart obtains iron from these therapies and highlight the potential for long-term dosing to cause cumulative iron build-up in the heart. TRANSLATIONAL PERSPECTIVEO_LIMany patients now receive long-term IV iron therapy. The finding that a single standard dose of IV iron causes a sustained rise in myocardial iron underscores the risk for cumulative build-up to occur with multiple doses. Magnetic resonance monitoring of myocardial iron may be required to safeguard against progression towards pathological myocardial iron overload in these patients. C_LIO_LIPlasma ferritin levels reflect the iron content of reticuloendothelial macrophages. The finding that myocardial iron elevation following IV iron therapy is independent from reticuloendothelial macrophages highlights the limitations of using plasma ferritin cut-offs to safeguard against the risk of tissue iron overload. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=196 HEIGHT=200 SRC="FIGDIR/small/24301496v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1b676fcorg.highwire.dtl.DTLVardef@3da794org.highwire.dtl.DTLVardef@1c4f301org.highwire.dtl.DTLVardef@12dea70_HPS_FORMAT_FIGEXP M_FIG C_FIG