Using Deep learning to Predict Cardiovascular Magnetic Resonance Findings from Echocardiography Videos.

Background: Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning. We hypothesized that deep learning applied to echocardiography could predict CMR-based measurements. Methods: In a retrospective single-center study, adult patients with CMRs and echocardiography studies within 30 days were included. A video-based convolutional neural network was trained on echocardiography videos to predict CMR-derived labels including wall motion abnormality (WMA) presence, LGE presence, and abnormal T1, T2 or ECV across echocardiography views. The model performance was evaluated in a held-out test dataset not used for training. Results: The study population included 1,453 adult patients (mean age 56±18 years, 42% female) with 2,556 paired echocardiography studies occurring on average 2 days after CMR (interquartile range 2 days prior to 6 days after). The model had high predictive capability for presence of WMA (AUC 0.873 [95%CI 0.816-0.922]), however, the model was unable to reliably detect the presence of LGE (AUC 0.699 [0.613-0.780]), native T1 (AUC 0.614 [0.500-0.715]), T2 0.553 [0.420-0.692], or ECV 0.564 [0.455-0.691]). Conclusions: Deep learning applied to echocardiography accurately identified CMR-based WMA, but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos, and that the use of CMR for tissue characterization remains essential within cardiology. Clinical Perspective: Tissue characterization of the heart muscle is useful for clinical diagnosis and prognosis by identifying myocardial fibrosis, inflammation, and infiltration, and can be measured using cardiac MRI. While echocardiography is highly accessible and provides excellent functional information, its ability to provide tissue characterization information is limited at this time. Our study using a deep learning approach to predict cardiac MRI-based tissue characteristics from echocardiography showed limited ability to do so, suggesting that alternative approaches, including non-deep learning methods should be considered in future research.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

PURPOSE: To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. METHODS: A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. RESULTS: In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. CONCLUSION: The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Early-Onset Hypertension and Sex-Specific Residual Risk for Cardiovascular Disease in Type 2 Diabetes.

OBJECTIVE: To investigate whether the sex disparities in type 2 diabetes-associated cardiovascular disease (CVD) risks may be related to early-onset hypertension that could benefit from intensive blood pressure (BP) control. RESEARCH DESIGN AND METHODS: We analyzed intensive versus standard BP control in relation to incident CVD events in women and men with type 2 diabetes, based on their age of hypertension diagnosis. RESULTS: Among 3,792 adults with type 2 diabetes (49% women), multivariable-adjusted CVD risk was increased per decade earlier age at hypertension diagnosis (hazard ratio 1.11 [1.03-1.21], P = 0.006). Excess risk associated with early-diagnosed hypertension was attenuated in the presence of intensive versus standard antihypertensive therapy in women (P = 0.036) but not men (P = 0.76). CONCLUSIONS: Women with type 2 diabetes and early-onset hypertension may represent a higher-risk subpopulation that not only contributes to the female excess in diabetes-related CVD risk but may benefit from intensive BP control.

Point of Care Ultrasound for Diagnosis and Management in Heart Failure: A Targeted Literature Review.

BACKGROUND: Cardiac point of care ultrasound (POCUS) has shown increasing utility as a tool for diagnosing and managing heart failure (HF). Within cardiology, intravascular volume assessment leveraging visualization of the inferior vena cava (IVC) is a central aspect of care, as IVC size correlates with central venous pressure. This targeted literature review aimed to examine the existing literature assessing the use of POCUS in diagnosis and management of HF patients utilizing POCUS-based IVC measurement either alone or in combination with secondary methods. METHODS: A targeted PubMed and Ovid database search up until August 28, 2023 using a keyword search was completed. Studies that did not include IVC assessment with POCUS in HF were excluded. RESULTS: The initial search using both PubMed and Ovid resulted in 370 journal publications. After exclusion criteria were used 15 studies were included in the review. Studies were grouped into three categories: 1) how well POCUS was able to identify HF, 2) whether POCUS-based findings correlated with other measures evaluating HF and was able to predict the effect of diuretic administration, and 3) whether POCUS-based findings served as a good prognostic indicator. The 5 studies that evaluated HF identification with POCUS found that both diagnostic sensitivity and specificity may reach 90%-100% when IVC measurement was coupled with a lung ultrasound assessing the presence of B-lines or pleural effusion. Five studies assessing POCUS findings correlating with other HF measures and diuretic effect found that IVC diameter changed significantly with diuretic administration (p<0.05). All 6 studies assessing POCUS as a predictor of long-term mortality or hospital readmission found measures that achieved statistical significance with p<0.05. CONCLUSIONS: Including POCUS as standard-of-care - both as a diagnostic tool in the emergency department and a management tool in in-patient and out-patient facilities - may improve the treatment of HF.

Deep Learning-Derived Myocardial Strain.

BACKGROUND: Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses. OBJECTIVES: This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations. METHODS: Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy. RESULTS: In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI: -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI: 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC]: 0.56, P = 0.002) with a bias of -3.31% (limits of agreement: -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis: ICC was 0.64 (95% CI: 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS. CONCLUSIONS: The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges.

Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.

Sex Differences in Association of Physical Activity With All-Cause and Cardiovascular Mortality.

BACKGROUND: Although physical activity is widely recommended for reducing cardiovascular and all-cause mortality risks, female individuals consistently lag behind male individuals in exercise engagement. OBJECTIVES: The goal of this study was to evaluate whether physical activity derived health benefits may differ by sex. METHODS: In a prospective study of 412,413 U.S. adults (55% female, age 44 ± 17 years) who provided survey data on leisure-time physical activity, we examined sex-specific multivariable-adjusted associations of physical activity measures (frequency, duration, intensity, type) with all-cause and cardiovascular mortality from 1997 through 2019. RESULTS: During 4,911,178 person-years of follow-up, there were 39,935 all-cause deaths including 11,670 cardiovascular deaths. Regular leisure-time physical activity compared with inactivity was associated with 24% (HR: 0.76; 95% CI: 0.73-0.80) and 15% (HR: 0.85; 95% CI: 0.82-0.89) lower risk of all-cause mortality in women and men, respectively (Wald F = 12.0, sex interaction P < 0.001). Men reached their maximal survival benefit of HR 0.81 from 300 min/wk of moderate-to-vigorous physical activity, whereas women achieved similar benefit at 140 min/wk and then continued to reach a maximum survival benefit of HR 0.76 also at ∼300 min/wk. Sex-specific findings were similar for cardiovascular death (Wald F = 20.1, sex interaction P < 0.001) and consistent across all measures of aerobic activity as well as muscle strengthening activity (Wald F = 6.7, sex interaction P = 0.009). CONCLUSIONS: Women compared with men derived greater gains in all-cause and cardiovascular mortality risk reduction from equivalent doses of leisure-time physical activity. These findings could enhance efforts to close the "gender gap" by motivating especially women to engage in any regular leisure-time physical activity.

Postural orthostatic tachycardia syndrome after COVID-19 vaccination.

BACKGROUND: There is an association between coronavirus disease 2019 (COVID-19) mRNA vaccination and the incidence or exacerbation of postural orthostatic tachycardia syndrome (POTS). OBJECTIVE: The purpose of this study was to characterize patients reporting new or exacerbated POTS after receiving the mRNA COVID-19 vaccine. METHODS: We prospectively collected data from sequential patients in a POTS clinic between July 2021 and June 2022 reporting new or exacerbated POTS symptoms after COVID-19 vaccination. Heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) were compared against those of 24 healthy controls. RESULTS: Ten patients (6 women and 4 men; age 41.5 ± 7.9 years) met inclusion criteria. Four patients had standing norepinephrine levels > 600 pg/mL. All patients had conditions that could raise POTS risk, including previous COVID-19 infection (N = 4), hypermobile Ehlers-Danlos syndrome (N = 6), mast cell activation syndrome (N = 6), and autoimmune (N = 7), cardiac (N = 7), neurological (N = 6), or gastrointestinal conditions (N = 4). HRV analysis indicated a lower ambulatory root mean square of successive differences (46.19 ±24 ms; P = .042) vs control (72.49 ± 40.8 ms). SKNA showed a reduced mean amplitude (0.97 ± 0.052 µV; P = .011) vs control (1.2 ± 0.31 µV) and burst amplitude (1.67 ± 0.16 µV; P = .018) vs control (4. 3 ± 4.3 µV). After 417.2 ± 131.4 days of follow-up, all patients reported improvement with the usual POTS care, although 2 with COVID-19 reinfection and 1 with small fiber neuropathy did have relapses of POTS symptoms. CONCLUSION: All patients with postvaccination POTS had pre-existing conditions. There was no evidence of myocardial injuries or echocardiographic abnormalities. The decreased HRV suggests a sympathetic dominant state. Although all patients improved with guideline-directed care, there is a risk of relapse.

Impact of Measurement Noise on Genetic Association Studies of Cardiac Function.

Recent research has effectively used quantitative traits from imaging to boost the capabilities of genome-wide association studies (GWAS), providing further understanding of disease biology and various traits. However, it's important to note that phenotyping inherently carries measurement error and noise that could influence subsequent genetic analyses. The study focused on left ventricular ejection fraction (LVEF), a vital yet potentially inaccurate quantitative measurement, to investigate how imprecision in phenotype measurement affects genetic studies. Several methods of acquiring LVEF, along with simulating measurement noise, were assessed for their effects on ensuing genetic analyses. The results showed that by introducing just 7.9% of measurement noise, all genetic associations in an LVEF GWAS with almost forty thousand individuals could be eliminated. Moreover, a 1% increase in mean absolute error (MAE) in LVEF had an effect equivalent to a 10% reduction in the sample size of the cohort on the power of GWAS. Therefore, enhancing the accuracy of phenotyping is crucial to maximize the effectiveness of genome-wide association studies.

Deriving Convergent and Divergent Metabolomic Correlates of Pulmonary Arterial Hypertension.

High-dimensional metabolomics analyses may identify convergent and divergent markers, potentially representing aligned or orthogonal disease pathways that underly conditions such as pulmonary arterial hypertension (PAH). Using a comprehensive PAH metabolomics dataset, we applied six different conventional and statistical learning techniques to identify analytes associated with key outcomes and compared the results. We found that certain conventional techniques, such as Bonferroni/FDR correction, prioritized metabolites that tended to be highly intercorrelated. Statistical learning techniques generally agreed with conventional techniques on the top-ranked metabolites, but were also more inclusive of different metabolite groups. In particular, conventional methods prioritized sterol and oxylipin metabolites in relation to idiopathic versus non-idiopathic PAH, whereas statistical learning methods tended to prioritize eicosanoid, bile acid, fatty acid, and fatty acyl ester metabolites. Our findings demonstrate how conventional and statistical learning techniques can offer both concordant or discordant results. In the case of a rare yet morbid condition, such as PAH, convergent metabolites may reflect common pathways to shared disease outcomes whereas divergent metabolites could signal either distinct etiologic mechanisms, different sub-phenotypes, or varying stages of disease progression. Notwithstanding the need to investigate the mechanisms underlying the observed results, our main findings suggest that a multi-method approach to statistical analyses of high-dimensional human metabolomics datasets could effectively broaden the scientific yield from a given study design.

Deep learning-based electrocardiographic screening for chronic kidney disease.

BACKGROUND: Undiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs. METHODS: We collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs between 2005 and 2018. RESULTS: Using 12-lead ECG waveforms, our deep learning algorithm achieves discrimination for CKD of any stage with an AUC of 0.767 (95% CI 0.760-0.773) in a held-out test set and an AUC of 0.709 (0.708-0.710) in the external cohort. Our 12-lead ECG-based model performance is consistent across the severity of CKD, with an AUC of 0.753 (0.735-0.770) for mild CKD, AUC of 0.759 (0.750-0.767) for moderate-severe CKD, and an AUC of 0.783 (0.773-0.793) for ESRD. In patients under 60 years old, our model achieves high performance in detecting any stage CKD with both 12-lead (AUC 0.843 [0.836-0.852]) and 1-lead ECG waveform (0.824 [0.815-0.832]). CONCLUSIONS: Our deep learning algorithm is able to detect CKD using ECG waveforms, with stronger performance in younger patients and more severe CKD stages. This ECG algorithm has the potential to augment screening for CKD.

Chronic kidney disease (CKD) is a common condition involving loss of kidney function over time and results in a substantial number of deaths. However, CKD often has no symptoms during its early stages. To detect CKD earlier, we developed a computational approach for CKD screening using routinely acquired electrocardiograms (ECGs), a cheap, rapid, non-invasive, and commonly obtained test of the heart's electrical activity. Our model achieved good accuracy in identifying any stage of CKD, with especially high accuracy in younger patients and more severe stages of CKD. Given the high global burden of undiagnosed CKD, novel and accessible CKD screening strategies have the potential to help prevent disease progression and reduce premature deaths related to CKD.

Sex-specific differences in the genetic and environmental effects on cardiac phenotypic variation assessed by echocardiography.

The drivers of sexual dimorphism in heart failure phenotypes are currently poorly understood. Divergent phenotypes may result from differences in heritability and genetic versus environmental influences on the interplay of cardiac structure and function. To assess sex-specific heritability and genetic versus environmental contributions to variation and inter-relations between echocardiography traits in a large community-based cohort. We studied Framingham Heart Study participants of Offspring Cohort examination 8 (2005-2008) and Third Generation Cohort examination 1 (2002-2005). Five cardiac traits and six functional traits were measured using standardized echocardiography. Sequential Oligogenic Linkage Analysis Routines (SOLAR) software was used to perform singular and bivariate quantitative trait linkage analysis. In our study of 5674 participants (age 49 ± 15 years; 54% women), heritability for all traits was significant for both men and women. There were no significant differences in traits between men and women. Within inter-trait correlations, there were two genetic, and four environmental trait pairs with sex-based differences. Within both significant genetic trait pairs, men had a positive relation, and women had no significant relation. We observed significant sex-based differences in inter-trait genetic and environmental correlations between cardiac structure and function. These findings highlight potential pathways of sex-based divergent heart failure phenotypes.

Blinded, randomized trial of sonographer versus AI cardiac function assessment.

Artificial intelligence (AI) has been developed for echocardiography1-3, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of -10.4%, 95% confidence interval: -13.2% to -7.7%, P < 0.001 for non-inferiority, P < 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of -0.96%, 95% confidence interval: -1.34% to -0.54%, P < 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.

BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology. METHODS: We measured the left ventricle (LV) sphericity index (short axis length/long axis length) using deep learning-enabled image segmentation of cardiac magnetic resonance imaging data from the UK Biobank. Subjects with abnormal LV size or systolic function were excluded. The relationship between LV sphericity and cardiomyopathy was assessed using Cox analyses, genome-wide association studies, and two-sample Mendelian randomization. FINDINGS: In a cohort of 38,897 subjects, we show that a one standard deviation increase in sphericity index is associated with a 47% increased incidence of cardiomyopathy (hazard ratio [HR]: 1.47, 95% confidence interval [CI]: 1.10-1.98, p = 0.01) and a 20% increased incidence of atrial fibrillation (HR: 1.20, 95% CI: 1.11-1.28, p < 0.001), independent of clinical factors and traditional magnetic resonance imaging (MRI) measurements. We identify four loci associated with sphericity at genome-wide significance, and Mendelian randomization supports non-ischemic cardiomyopathy as causal for LV sphericity. CONCLUSIONS: Variation in LV sphericity in otherwise normal hearts predicts risk for cardiomyopathy and related outcomes and is caused by non-ischemic cardiomyopathy. FUNDING: This study was supported by grants K99-HL157421 (D.O.) and KL2TR003143 (S.L.C.) from the National Institutes of Health.

Association of COVID-19 Vaccination With Risk for Incident Diabetes After COVID-19 Infection.

This cohort study compares the risk of new-onset hypertension, hyperlipidemia, and diabetes before and after COVID-19 infection among patients who were vaccinated vs unvaccinated before infection.

Sex differences in contributors to coronary microvascular dysfunction.

Background: Coronary microvascular dysfunction (CMD) has differences in prevalence and presentation between women and men; however, we have limited understanding about underlying contributors to sex differences in CMD. Myocardial perfusion reserve index (MPRI), as semi-quantitative measure of myocardial perfusion derived from cardiac magnetic resonance (CMR) imaging has been validated as a measure of CMD. We sought to understand the sex differences in the relations between the MPRI and traditional measures of cardiovascular disease by CMR. Methods: A retrospective analysis of a single-center cohort of patients receiving clinical stress CMR from 2015 to 2022 was performed. Patients with calculated MPRI and no visible perfusion defects consistent with obstructive epicardial coronary disease were included. We compared associations between MPRI versus traditional cardiovascular risk factors and markers of cardiac structure/function in sex-stratified populations using univariable and multivariable regression models. Results: A total of 229 patients [193 female, 36 male, median age 57 (47-67) years] were included in the analysis. In the female population, no traditional cardiovascular risk factors were associated with MPRI, whereas in the male population, diabetes (ß: -0.80, p = 0.03) and hyperlipidemia (ß: -0.76, p = 0.006) were both associated with reduced MPRI in multivariable models. Multivariable models revealed significant associations between reduced MPRI and increased ascending aortic diameter (ß: -0.42, p = 0.005) and T1 times (ß: -0.0056, p = 0.03) in the male population, and increased T1 times (ß: -0.0037, p = 0.006) and LVMI (ß: -0.022, p = 0.0003) in the female population. Conclusion: The findings suggest different underlying pathophysiology of CMD in men versus women, with lower MPRI in male patients fitting a more "traditional" atherosclerotic profile.

Impact of Measurement Imprecision on Genetic Association Studies of Cardiac Function.

Background: Recent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses. Methods: Left ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses. Results: Even within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies. Conclusions: Improving the precision of phenotyping is important for maximizing the yield of genome-wide association studies.