Blood pressure variability supersedes heart rate variability as a real-world measure of dementia risk.

Blood pressure variability (BPV) and heart rate variability (HRV) have been associated with Alzheimer's Disease and Related Dementias (ADRD) in rigorously controlled studies. However, the extent to which BPV and HRV may offer predictive information in real-world, routine clinical care is unclear. In a retrospective cohort study of 48,204 adults (age 54.9 ± 17.5 years, 60% female) receiving continuous care at a single center, we derived BPV and HRV from routinely collected clinical data. We use multivariable Cox models to evaluate the association of BPV and HRV, separately and in combination, with incident ADRD. Over a median 3 [2.4, 3.0] years, there were 443 cases of new-onset ADRD. We found that clinically derived measures of BPV, but not HRV, were consistently associated with incident ADRD. In combined analyses, only patients in both the highest quartile of BPV and lowest quartile of HRV had increased ADRD risk (HR 2.34, 95% CI 1.44-3.81). These results indicate that clinically derived BPV, rather than HRV, offers a consistent and readily available metric for ADRD risk assessment in a real-world patient care setting. Thus, implementation of BPV as a widely accessible tool could allow clinical providers to efficiently identify patients most likely to benefit from comprehensive ADRD screening.

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.

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.

Association of blood pressure variability during acute care hospitalization and incident dementia.

Background and objectives: Recognized as a potential risk factor for Alzheimer's disease and related dementias (ADRD), blood pressure variability (BPV) could be leveraged to facilitate identification of at-risk individuals at a population level. Granular BPV data are available during acute care hospitalization periods for potentially high-risk patients, but the incident ADRD risk association with BPV measured in this setting is unknown. Our objective was to evaluate the relation of BPV, measured during acute care hospitalization, and incidence of ADRD. Methods: We retrospectively studied adults, without a prior ADRD diagnosis, who were admitted to a large quaternary care medical center in Southern California between January 1, 2013 and December 31, 2019. For all patients, determined BPV, calculated as variability independent of the mean (VIM), using blood pressure readings obtained as part of routine clinical care. We used multivariable Cox proportional hazards regression to examine the association between BP VIM during hospitalization and the development of incident dementia, determined by new ICD-9/10 coding or the new prescription of dementia medication, occurring at least 2 years after the index hospitalization. Results: Of 81,892 adults hospitalized without a prior ADRD diagnosis, 2,442 (2.98%) went on to develop ADRD (2.6 to 5.2 years after hospitalization). In multivariable-adjusted Cox models, both systolic (HR 1.05, 95% CI 1.00-1.09) and diastolic (1.06, 1.02-1.10) VIM were associated with incident ADRD. In pre-specified stratified analyses, the VIM associations with incident ADRD were most pronounced in individuals over age 60 years and among those with renal disease or hypertension. Results were similar when repeated to include incident ADRD diagnoses made at least 1 or 3 years after index hospitalization. Discussion: We found that measurements of BPV from acute care hospitalizations can be used to identify individuals at risk for developing a diagnosis of ADRD within approximately 5 years. Use of the readily accessible BPV measure may allow healthcare systems to risk stratify patients during periods of intense patient-provider interaction and, in turn, facilitate engagement in ADRD screening programs.

Serological response to vaccination in post-acute sequelae of COVID.

BACKGROUND: Individuals with post-acute sequelae of COVID (PASC) may have a persistence in immune activation that differentiates them from individuals who have recovered from COVID without clinical sequelae. To investigate how humoral immune activation may vary in this regard, we compared patterns of vaccine-provoked serological response in patients with PASC compared to individuals recovered from prior COVID without PASC. METHODS: We prospectively studied 245 adults clinically diagnosed with PASC and 86 adults successfully recovered from prior COVID. All participants had measures of humoral immunity to SARS-CoV-2 assayed before or after receiving their first-ever administration of COVID vaccination (either single-dose or two-dose regimen), including anti-spike (IgG-S and IgM-S) and anti-nucleocapsid (IgG-N) antibodies as well as IgG-S angiotensin-converting enzyme 2 (ACE2) binding levels. We used unadjusted and multivariable-adjusted regression analyses to examine the association of PASC compared to COVID-recovered status with post-vaccination measures of humoral immunity. RESULTS: Individuals with PASC mounted consistently higher post-vaccination IgG-S antibody levels when compared to COVID-recovered (median log IgG-S 3.98 versus 3.74, P < 0.001), with similar results seen for ACE2 binding levels (median 99.1 versus 98.2, P = 0.044). The post-vaccination IgM-S response in PASC was attenuated but persistently unchanged over time (P = 0.33), compared to in COVID recovery wherein the IgM-S response expectedly decreased over time (P = 0.002). Findings remained consistent when accounting for demographic and clinical variables including indices of index infection severity and comorbidity burden. CONCLUSION: We found evidence of aberrant immune response distinguishing PASC from recovered COVID. This aberrancy is marked by excess IgG-S activation and ACE2 binding along with findings consistent with a delayed or dysfunctional immunoglobulin class switching, all of which is unmasked by vaccine provocation. These results suggest that measures of aberrant immune response may offer promise as tools for diagnosing and distinguishing PASC from non-PASC phenotypes, in addition to serving as potential targets for intervention.

High-Throughput Assessment of Real-World Medication Effects on QT Interval Prolongation: Observational Study.

BACKGROUND: Drug-induced prolongation of the corrected QT interval (QTc) increases the risk for Torsades de Pointes (TdP) and sudden cardiac death. Medication effects on the QTc have been studied in controlled settings but may not be well evaluated in real-world settings where medication effects may be modulated by patient demographics and comorbidities as well as the usage of other concomitant medications. OBJECTIVE: We demonstrate a new, high-throughput method leveraging electronic health records (EHRs) and the Surescripts pharmacy database to monitor real-world QTc-prolonging medication and potential interacting effects from demographics and comorbidities. METHODS: We included all outpatient electrocardiograms (ECGs) from September 2008 to December 2019 at a large academic medical system, which were in sinus rhythm with a heart rate of 40-100 beats per minute, QRS duration of <120 milliseconds, and QTc of 300-700 milliseconds, determined using the Bazett formula. We used prescription information from the Surescripts pharmacy database and EHR medication lists to classify whether a patient was on a medication during an ECG. Negative control ECGs were obtained from patients not currently on the medication but who had been or would be on that medication within 1 year. We calculated the difference in mean QTc between ECGs of patients who are on and those who are off a medication and made comparisons to known medication TdP risks per the CredibleMeds.org database. Using linear regression analysis, we studied the interaction of patient-level demographics or comorbidities on medication-related QTc prolongation. RESULTS: We analyzed the effects of 272 medications on 310,335 ECGs from 159,397 individuals. Medications associated with the greatest QTc prolongation were dofetilide (mean QTc difference 21.52, 95% CI 10.58-32.70 milliseconds), mexiletine (mean QTc difference 18.56, 95% CI 7.70-29.27 milliseconds), amiodarone (mean QTc difference 14.96, 95% CI 13.52-16.33 milliseconds), rifaximin (mean QTc difference 14.50, 95% CI 12.12-17.13 milliseconds), and sotalol (mean QTc difference 10.73, 95% CI 7.09-14.37 milliseconds). Several top QT prolonging medications such as rifaximin, lactulose, cinacalcet, and lenalidomide were not previously known but have plausible mechanistic explanations. Significant interactions were observed between demographics or comorbidities and QTc prolongation with many medications, such as coronary disease and amiodarone. CONCLUSIONS: We demonstrate a new, high-throughput technique for monitoring real-world effects of QTc-prolonging medications from readily accessible clinical data. Using this approach, we confirmed known medications for QTc prolongation and identified potential new associations and demographic or comorbidity interactions that could supplement findings in curated databases. Our single-center results would benefit from additional verification in future multisite studies that incorporate larger numbers of patients and ECGs along with more precise medication adherence and comorbidity data.

Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms.

BACKGROUND: Coronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially obtainable. It remains unclear whether other widespread tests such as transthoracic echocardiograms (TTEs) can be used to predict CAC. METHODS: Using a data set of 2,881 TTE videos paired with coronary calcium CTs, we trained a video-based artificial intelligence convolutional neural network to predict CAC scores from parasternal long-axis views. We evaluated the model's ability to classify patients from a held-out sample as well as an external site sample into zero CAC and high CAC (CAC ≥ 400 Agatston units) groups by receiver operating characteristic and precision-recall curves. We also investigated whether such classifications prognosticated significant differences in 1-year mortality rates by the log-rank test of Kaplan-Meier curves. RESULTS: Transthoracic echocardiogram artificial intelligence models had high discriminatory abilities in predicting zero CAC (receiver operating characteristic area under the curve [AUC] = 0.81 [95% CI, 0.74-0.88], F1 score = 0.95) and high CAC (AUC = 0.74 [0.68-0.8], F1 score = 0.74). This performance was confirmed in an external test data set of 92 TTEs (AUC = 0.75 [0.65-0.85], F1 score = 0.77; and AUC = 0.85 [0.76-0.93], F1 score = 0.59, respectively). Risk stratification by TTE-predicted CAC performed similarly to CT CAC scores in prognosticating significant differences in 1-year survival in high-CAC patients (CT CAC ≥ 400 vs CT CAC < 400, P = .03; TTE-predicted CAC ≥ 400 vs TTE-predicted CAC < 400, P = .02). CONCLUSIONS: A video-based deep learning model successfully used TTE videos to predict zero CAC and high CAC with high accuracy. Transthoracic echocardiography-predicted CAC prognosticated differences in 1-year survival similar to CT CAC. Deep learning of TTEs holds promise for future adjunctive coronary artery disease risk stratification to guide preventive therapies.

Cardiovascular and hepatic disease associations by magnetic resonance imaging: A retrospective cohort study.

Background: Hepatic disease is linked to cardiovascular events but the independent association between hepatic and cardiovascular disease remains unclear, given shared risk factors. Methods: This was a retrospective study of consecutive patients with a clinical cardiac MRI (CMR) and a serological marker of hepatic fibrosis, the FIB-4 score, within one year of clinical imaging. We assessed the relations between FIB-4 scores grouped based on prior literature: low (< 1.3), moderate (1.3-3.25), and high (>3.25), and abnormalities detected by comprehensive CMR grouped into 4 domains: cardiac structure (end diastolic volumes, atrial dimensions, wall thickness); cardiac function (ejection fractions, wall motion abnormalities, cardiac output); vascular structure (ascending aortic and pulmonary arterial sizes); and cardiac composition (late gadolinium enhancement, T1 and T2 times). We used Poisson regression to examine the association between the conventionally defined FIB-4 category (low <1.3, moderate 1.3-3.25, and high >3.25) and any CMR abnormality while adjusting for demographics and traditional cardiovascular risk factors. Results: Of the 1668 patients studied (mean age: 55.971 ± 7.28, 901 [54%] male), 85.9% had ≥1 cardiac abnormality with increasing prevalence seen within the low (82.0%) to moderate (88.8%) to high (92.3%) FIB-4 categories. Multivariable analyses demonstrated the presence of any cardiac abnormality was significantly associated with having a high-range FIB-4 (prevalence ratio 1.07, 95% CI: 1.01-1.13); notably, the presence of functional cardiac abnormalities were associated with being in the high FIB-4 range (1.41, 1.21-1.65) and any vascular abnormalities with being in the moderate FIB-4 range (1.22, 1.01-1.47). Conclusions: Elevated FIB-4 was associated with cardiac functional and vascular abnormalities even after adjustment for shared risk factors in a cohort of patients with clinically referred CMR. These CMR findings indicate that cardiovascular abnormalities exist in the presence of subclinical hepatic fibrosis, irrespective of shared risk factors, underscoring the need for further studies of the heart-liver axis.

Variability independent of mean blood pressure as a real-world measure of cardiovascular risk.

Background: Individual-level blood pressure (BP) variability, independent of mean BP levels, has been associated with increased risk for cardiovascular events in cohort studies and clinical trials using standardized BP measurements. The extent to which BP variability relates to cardiovascular risk in the real-world clinical practice setting is unclear. We sought to determine if BP variability in clinical practice is associated with adverse cardiovascular outcomes using clinically generated data from the electronic health record (EHR). Methods: We identified 42,482 patients followed continuously at a single academic medical center in Southern California between 2013 and 2019 and calculated their systolic and diastolic BP variability independent of the mean (VIM) over the first 3 years of the study period. We then performed multivariable Cox proportional hazards regression to examine the association between VIM and both composite and individual outcomes of interest (incident myocardial infarction, heart failure, stroke, and death). Findings: Both systolic (HR, 95% CI 1.22, 1.17-1.28) and diastolic VIM (1.24, 1.19-1.30) were positively associated with the composite outcome, as well as all individual outcome measures. These findings were robust to stratification by age, sex and clinical comorbidities. In sensitivity analyses using a time-shifted follow-up period, VIM remained significantly associated with the composite outcome for both systolic (1.15, 1.11-1.20) and diastolic (1.18, 1.13-1.22) values. Interpretation: VIM derived from clinically generated data remains associated with adverse cardiovascular outcomes and represents a risk marker beyond mean BP, including in important demographic and clinical subgroups. The demonstrated prognostic ability of VIM derived from non-standardized BP readings indicates the utility of this measure for risk stratification in a real-world practice setting, although residual confounding from unmeasured variables cannot be excluded. Funding: This study was funded in part by National Institutes of Health grants R01-HL134168, R01-HL131532, R01-HL143227, R01-HL142983, U54-AG065141; R01-HL153382, K23-HL136853, K23-HL153888, and K99-HL157421; China Scholarship Council grant 201806260086; Academy of Finland (Grant no: 321351); Emil Aaltonen Foundation; Finnish Foundation for Cardiovascular Research.

Demographic and clinical characteristics associated with variations in antibody response to BNT162b2 COVID-19 vaccination among healthcare workers at an academic medical centre: a longitudinal cohort analysis.

OBJECTIVES: We sought to understand the demographic and clinical factors associated with variations in longitudinal antibody response following completion of two-dose regiment of BNT162b2 vaccination. DESIGN: This study is a 10-month longitudinal cohort study of healthcare workers and serially measured anti-spike protein IgG (IgG-S) antibody levels using mixed linear models to examine their associations with participant characteristics. SETTING: A large, multisite academic medical centre in Southern California, USA. PARTICIPANTS: A total of 843 healthcare workers met inclusion criteria including completion of an initial two-dose course of BNT162b2 vaccination, complete clinical history and at least two blood samples for analysis. Patients had an average age of 45±13 years, were 70% female and 7% with prior SARS-CoV-2 infection. RESULTS: Vaccine-induced IgG-S levels remained in the positive range for 99.6% of individuals up to 10 months after initial two-dose vaccination. Prior SARS-CoV-2 infection was the primary correlate of sustained higher postvaccination IgG-S levels (partial R2=0.133), with a 1.74±0.11 SD higher IgG-S response (p<0.001). Female sex (beta 0.27±0.06, p<0.001), younger age (0.01±0.00, p<0.001) and absence of hypertension (0.17±0.08, p=0.003) were also associated with persistently higher IgG-S responses. Notably, prior SARS-CoV-2 infection augmented the associations of sex (-0.42 for male sex, p=0.08) and modified the associations of hypertension (1.17, p=0.001), such that infection-naïve individuals with hypertension had persistently lower IgG-S levels whereas prior infected individuals with hypertension exhibited higher IgG-S levels that remained augmented over time. CONCLUSIONS: While the IgG-S antibody response remains in the positive range for up to 10 months following initial mRNA vaccination in most adults, determinants of sustained higher antibody levels include prior SARS-CoV-2 infection, female sex, younger age and absence of hypertension. Certain determinants of the longitudinal antibody response appear significantly modified by prior infection status. These findings offer insights regarding factors that may influence the 'hybrid' immunity conferred by natural infection combined with vaccination.

Seasonal COVID-19 surge related hospital volumes and case fatality rates.

BACKGROUND: Seasonal and regional surges in COVID-19 have imposed substantial strain on healthcare systems. Whereas sharp inclines in hospital volume were accompanied by overt increases in case fatality rates during the very early phases of the pandemic, the relative impact during later phases of the pandemic are less clear. We sought to characterize how the 2020 winter surge in COVID-19 volumes impacted case fatality in an adequately-resourced health system. METHODS: We performed a retrospective cohort study of all adult diagnosed with COVID-19 in a large academic healthcare system between August 25, 2020 to May 8, 2021, using multivariable logistic regression to examine case fatality rates across 3 sequential time periods around the 2020 winter surge: pre-surge, surge, and post-surge. Subgroup analyses of patients admitted to the hospital and those receiving ICU-level care were also performed. Additionally, we used multivariable logistic regression to examine risk factors for mortality during the surge period. RESULTS: We studied 7388 patients (aged 52.8 ± 19.6 years, 48% male) who received outpatient or inpatient care for COVID-19 during the study period. Patients treated during surge (N = 6372) compared to the pre-surge (N = 536) period had 2.64 greater odds (95% CI 1.46-5.27) of mortality after adjusting for sociodemographic and clinical factors. Adjusted mortality risk returned to pre-surge levels during the post-surge period. Notably, first-encounter patient-level measures of illness severity appeared higher during surge compared to non-surge periods. CONCLUSIONS: We observed excess mortality risk during a recent winter COVID-19 surge that was not explained by conventional risk factors or easily measurable variables, although recovered rapidly in the setting of targeted facility resources. These findings point to how complex interrelations of population- and patient-level pandemic factors can profoundly augment health system strain and drive dynamic, if short-lived, changes in outcomes.

Subclinical hepatic fibrosis is associated with coronary microvascular dysfunction by myocardial perfusion reserve index: a retrospective cohort study.

The heart-liver axis is of growing importance. Previous studies have identified independent association of liver dysfunction and fibrosis with adverse cardiac outcomes, but mechanistic pathways remain uncertain. We sought to understand the relations between the degree of hepatic fibrosis identified by the Fibrosis-4 (Fib-4) risk score and comprehensive cardiac MRI (CMR) measures of subclinical cardiac disease. We conducted a retrospective single-center cohort study of patients between 2011 and 2021. We identified consecutive patients who underwent a comprehensive CMR imaging protocol including contrast enhanced with stress/rest perfusion, and lacked pre-existing cardiovascular disease or perfusion abnormalities on CMR. We examined the association of hepatic fibrosis, using the Fib-4 score, with subclinical cardiac disease on CMR while adjusting for cardiometabolic traits. Given known associations of hepatic disease and coronary microvascular dysfunction, we prioritized analyses with the myocardial perfusion reserve index (MPRI), a marker of coronary microvascular function. Of the 66 patients in our study cohort, 54 were female (81%) and the mean age was 53.7 ± 15.3 years. We found that higher Fib-4 was associated with reduction in the MPRI (ß [SE] - 1.12 [0.46], P = 0.02), after adjusting for cardiometabolic risk factors. Importantly, Fib-4 was not significantly associated with any other CMR phenotypes including measures of cardiac remodeling, inflammation, fibrosis, or dysfunction. We found evidence that hepatic fibrosis associated with coronary microvascular dysfunction, in the absence of overt associations with any other subclinical cardiac disease measures. These findings highlight a potentially important precursor pathway leading to development of subsequent heart-liver disease.

Apparent Risks of Postural Orthostatic Tachycardia Syndrome Diagnoses After COVID-19 Vaccination and SARS-Cov-2 Infection.

Postural orthostatic tachycardia syndrome (POTS) has been previously described after SARS-CoV-2 infection; however, limited data is available on the relation of POTS with COVID-19 vaccination. Here we show in a cohort of 284,592 COVID-19 vaccinated individuals using a sequence-symmetry analysis, that the odds of POTS are higher 90 days after vaccine exposure than 90 days prior to exposure, and that the odds for POTS are higher than referent conventional primary care diagnoses, but lower than the odds of new POTS diagnosis after SARS-CoV-2 infection. Our results identify a possible association between COVID-19 vaccination and incidence of POTS. Notwithstanding the probable low incidence of POTS after COVID-19 vaccination, particularly when compared to SARS-Cov-2 post-infection odds which were five times higher, our results suggest that further studies, are needed to investigate the incidence and etiology of POTS occurring after COVID-19 vaccination.

Deep learning evaluation of biomarkers from echocardiogram videos.

BACKGROUND: Laboratory testing is routinely used to assay blood biomarkers to provide information on physiologic state beyond what clinicians can evaluate from interpreting medical imaging. We hypothesized that deep learning interpretation of echocardiogram videos can provide additional value in understanding disease states and can evaluate common biomarkers results. METHODS: We developed EchoNet-Labs, a video-based deep learning algorithm to detect evidence of anemia, elevated B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN), as well as values of ten additional lab tests directly from echocardiograms. We included patients (n = 39,460) aged 18 years or older with one or more apical-4-chamber echocardiogram videos (n = 70,066) from Stanford Healthcare for training and internal testing of EchoNet-Lab's performance in estimating the most proximal biomarker result. Without fine-tuning, the performance of EchoNet-Labs was further evaluated on an additional external test dataset (n = 1,301) from Cedars-Sinai Medical Center. We calculated the area under the curve (AUC) of the receiver operating characteristic curve for the internal and external test datasets. FINDINGS: On the held-out test set of Stanford patients not previously seen during model training, EchoNet-Labs achieved an AUC of 0.80 (0.79-0.81) in detecting anemia (low hemoglobin), 0.86 (0.85-0.88) in detecting elevated BNP, 0.75 (0.73-0.78) in detecting elevated troponin I, and 0.74 (0.72-0.76) in detecting elevated BUN. On the external test dataset from Cedars-Sinai, EchoNet-Labs achieved an AUC of 0.80 (0.77-0.82) in detecting anemia, of 0.82 (0.79-0.84) in detecting elevated BNP, of 0.75 (0.72-0.78) in detecting elevated troponin I, and of 0.69 (0.66-0.71) in detecting elevated BUN. We further demonstrate the utility of the model in detecting abnormalities in 10 additional lab tests. We investigate the features necessary for EchoNet-Labs to make successful detection and identify potential mechanisms for each biomarker using well-known and novel explainability techniques. INTERPRETATION: These results show that deep learning applied to diagnostic imaging can provide additional clinical value and identify phenotypic information beyond current imaging interpretation methods. FUNDING: J.W.H. and B.H. are supported by the NSF Graduate Research Fellowship. D.O. is supported by NIH K99 HL157421-01. J.Y.Z. is supported by NSF CAREER 1942926, NIH R21 MD012867-01, NIH P30AG059307 and by a Chan-Zuckerberg Biohub Fellowship.

Symptomology following mRNA vaccination against SARS-CoV-2.

Despite demonstrated efficacy of vaccines against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of coronavirus disease-2019 (COVID-19), widespread hesitancy to vaccination persists. Improved knowledge regarding frequency, severity, and duration of vaccine-associated symptoms may help reduce hesitancy. In this prospective observational study, we studied 1032 healthcare workers who received both doses of the Pfizer-BioNTech SARS-CoV-2 mRNA vaccine and completed post-vaccine symptom surveys both after dose 1 and after dose 2. We defined appreciable post-vaccine symptoms as those of at least moderate severity and lasting at least 2 days. We found that symptoms were more frequent following the second vaccine dose than the first (74% vs. 60%, P < 0.001), with >80% of all symptoms resolving within 2 days. The most common symptom was injection site pain, followed by fatigue and malaise. Overall, 20% of participants experienced appreciable symptoms after dose 1 and 30% after dose 2. In multivariable analyses, female sex was associated with greater odds of appreciable symptoms after both dose 1 (OR, 95% CI 1.73, 1.19-2.51) and dose 2 (1.76, 1.28-2.42). Prior COVID-19 was also associated with appreciable symptoms following dose 1, while younger age and history of hypertension were associated with appreciable symptoms after dose 2. We conclude that most post-vaccine symptoms are reportedly mild and last <2 days. Appreciable post-vaccine symptoms are associated with female sex, prior COVID-19, younger age, and hypertension. This information can aid clinicians in advising patients on the safety and expected symptomatology associated with vaccination.

Practice Patterns and Patient Outcomes After Widespread Adoption of Remote Heart Failure Care.

BACKGROUND: An unprecedented shift to remote heart failure outpatient care occurred during the coronavirus disease 2019 (COVID-19) pandemic. Given challenges inherent to remote care, we studied whether remote visits (video or telephone) were associated with different patient usage, clinician practice patterns, and outcomes. METHODS: We included all ambulatory cardiology visits for heart failure at a multisite health system from April 1, 2019, to December 31, 2019 (pre-COVID) or April 1, 2020, to December 31, 2020 (COVID era), resulting in 10 591 pre-COVID in-person, 7775 COVID-era in-person, 1009 COVID-era video, and 2322 COVID-era telephone visits. We used multivariable logistic and Cox proportional hazards regressions with propensity weighting and patient clustering to study ordering practices and outcomes. RESULTS: Compared with in-person visits, video visits were used more often by younger (mean 64.7 years [SD 14.5] versus 74.2 [14.1]), male (68.3% versus 61.4%), and privately insured (45.9% versus 28.9%) individuals (P<0.05 for all). Remote visits were more frequently used by non-White patients (35.8% video, 37.0% telephone versus 33.2% in-person). During remote visits, clinicians were less likely to order diagnostic testing (odds ratio, 0.20 [0.18-0.22] video versus in-person, 0.18 [0.17-0.19] telephone versus in-person) or prescribe ß-blockers (0.82 [0.68-0.99], 0.35 [0.26-0.47]), mineralocorticoid receptor antagonists (0.69 [0.50-0.96], 0.48 [0.35-0.66]), or loop diuretics (0.67 [0.53-0.85], 0.45 [0.37-0.55]). During telephone visits, clinicians were less likely to prescribe ACE (angiotensin-converting enzyme) inhibitor/ARB (angiotensin receptor blockers)/ARNIs (angiotensin receptor-neprilysin inhibitors; 0.54 [0.40-0.72]). Telephone visits but not video visits were associated with higher rates of 90-day mortality (1.82 [1.14-2.90]) and nonsignificant trends towards higher rates of 90-day heart failure emergency department visits (1.34 [0.97-1.86]) and hospitalizations (1.36 [0.98-1.89]). CONCLUSIONS: Remote visits for heart failure care were associated with reduced diagnostic testing and guideline-directed medical therapy prescription. Telephone but not video visits were associated with increased 90-day mortality.

Temporal variations in the severity of COVID-19 illness by race and ethnicity.

INTRODUCTION: Early reports highlighted racial/ethnic disparities in the severity of COVID-19 seen across the USA; the extent to which these disparities have persisted over time remains unclear. Our research objective was to understand temporal trends in racial/ethnic variation in severity of COVID-19 illness presenting over time. METHODS: We conducted a retrospective cohort analysis using longitudinal data from Cedars-Sinai Medical Center, a high-volume health system in Southern California. We studied patients admitted to the hospital with COVID-19 illness from 4 March 2020 through 5 December 2020. Our primary outcome was COVID-19 severity of illness among hospitalised patients, assessed by racial/ethnic group status. We defined overall illness severity as an ordinal outcome: hospitalisation but no intensive care unit (ICU) admission; admission to the ICU but no intubation; and intubation or death. RESULTS: A total of 1584 patients with COVID-19 with available demographic and clinical data were included. Hispanic/Latinx compared with non-Hispanic white patients had higher odds of experiencing more severe illness among hospitalised patients (OR 2.28, 95% CI 1.62 to 3.22) and this disparity persisted over time. During the initial 2 months of the pandemic, non-Hispanic blacks were more likely to suffer severe illness than non-Hispanic whites (OR 2.02, 95% CI 1.07 to 3.78); this disparity improved by May, only to return later in the pandemic. CONCLUSION: In our patient sample, the severity of observed COVID-19 illness declined steadily over time, but these clinical improvements were not seen evenly across racial/ethnic groups; greater illness severity continues to be experienced among Hispanic/Latinx patients.

Patient Use and Clinical Practice Patterns of Remote Cardiology Clinic Visits in the Era of COVID-19.

Importance: The COVID-19 pandemic has led to an unprecedented shift in ambulatory cardiovascular care from in-person to remote visits. Objective: To understand whether the transition to remote visits is associated with disparities in patient use of care, diagnostic test ordering, and medication prescribing. Design, Setting, and Participants: This cross-sectional study used electronic health records data for all ambulatory cardiology visits at an urban, multisite health system in Los Angeles County, California, during 2 periods: April 1, 2019, to December 31, 2019 (pre-COVID) and April 1 to December 31, 2020 (COVID-era). Statistical analysis was performed from January to February 2021. Exposure: In-person or remote ambulatory cardiology clinic visit at one of 31 during the pre-COVID period or COVID-era period. Main Outcomes and Measures: Comparison of patient characteristics and frequencies of medication ordering and cardiology-specific testing across 4 visit types (pre-COVID in-person (reference), COVID-era in-person, COVID-era video, COVID-era telephone). Results: This study analyzed data from 87 182 pre-COVID in-person, 74 498 COVID-era in-person, 4720 COVID-era video, and 10 381 COVID-era telephone visits. Across visits, 79 572 patients were female (45.0%), 127 080 patients were non-Hispanic White (71.9%), and the mean (SD) age was 68.1 (17.0) years. Patients accessing COVID-era remote visits were more likely to be Asian, Black, or Hispanic individuals (24 934 pre-COVID in-person visits [28.6%] vs 19 742 COVID-era in-person visits [26.5%] vs 3633 COVID-era video visits [30.4%] vs 1435 COVID-era telephone visits [35.0%]; P < .001 for all comparisons), have private insurance (34 063 pre-COVID in-person visits [39.1%] vs 25 474 COVID-era in-person visits [34.2%] vs 2562 COVID-era video visits [54.3%] vs 4264 COVID-era telephone visits [41.1%]; P < .001 for COVID-era in-person vs video and COVID-era in-person vs telephone), and have cardiovascular comorbidities (eg, hypertension: 37 166 pre-COVID in-person visits [42.6%] vs 31 359 COVID-era in-person visits [42.1%] vs 2006 COVID-era video visits [42.5%] vs 5181 COVID-era telephone visits [49.9%]; P < .001 for COVID-era in-person vs telephone; and heart failure: 14 319 pre-COVID in-person visits [16.4%] vs 10 488 COVID-era in-person visits [14.1%] vs 1172 COVID-era video visits [24.8%] vs 2674 COVID-era telephone visits [25.8%]; P < .001 for COVID-era in-person vs video and COVID-era in-person vs telephone). After adjusting for patient and visit characteristics and in comparison with pre-COVID in-person visits, during video and telephone visits, clinicians had lower odds of ordering any medication (COVID-era in-person: odds ratio [OR], 0.62 [95% CI, 0.60-0.64], COVID-era video: OR, 0.22 [95% CI, 0.20-0.24]; COVID-era telephone: OR, 0.14 [95% CI, 0.13-0.15]) or tests, such as electrocardiograms (COVID-era in-person: OR, 0.60 [95% CI, 0.58-0.62]; COVID-era video: OR, 0.03 [95% CI, 0.02-0.04]; COVID-era telephone: OR, 0.02 [95% CI, 0.01-0.03]) or echocardiograms (COVID-era in-person: OR, 1.21 [95% CI, 1.18-1.24]; COVID-era video: OR, 0.47 [95% CI, 0.42-0.52]; COVID-era telephone: OR, 0.28 [95% CI, 0.25-0.31]). Conclusions and Relevance: Patients who were Asian, Black, or Hispanic, had private insurance, and had at least one of several cardiovascular comorbidities used remote cardiovascular care more frequently in the COVID-era period. Clinician ordering of diagnostic testing and medications consistently decreased when comparing pre-COVID vs COVID-era and in-person vs remote visits. Further studies are needed to clarify whether these decreases represent a reduction in the overuse of tests and medications vs an underuse of indicated testing and prescribing.