Artificial Intelligence in Cardiovascular Care - Part 2: Applications: JACC Review Topic of the Week

Recent Artificial Intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. Over 600 Food and Drug Administration (FDA)-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care - Part 1: Advances: JACC Review Topic of the Week

Recent AI advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitates rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Deep learning to detect left ventricular structural abnormalities in chest X-rays

BACKGROUND AND AIMS

Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs.

METHODS

A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists.

RESULTS

The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%.

CONCLUSIONS

Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.

Impact of cardiac size on SPECT myocardial perfusion imaging performance: Insights from the REFINE-SPECT registry

AIMS

Variation in diagnostic performance of SPECT myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD), and its interaction with age and sex.

METHODS AND RESULTS

A total of 2,066 patients without known CAD (67% male, 64.7 ± 11.2 years) across 9 institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated performance of quantitative and visual assessments according to cardiac size (end- diastolic volume [EDV];  < 20th vs. ≥ 20th population or sex-specific percentiles), age (<75 vs. ≥ 75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared to those without (AUC: population 0.72 vs. 0.78, p = 0.03; sex-specific 0.72 vs. 0.79, p = 0.01) and elderly patients compared to younger patients (AUC 0.72 vs. 0.78, p = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, p = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, p = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex.

CONCLUSIONS

Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

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.

Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions

Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.

Left Ventricular Function and Dimensions Are Altered Early in Infants Developing Brain Injury in the Setting of Neonatal Encephalopathy

We evaluated the association between left cardiac 3-dimensional echocardiographic parameters and brain injury in a single-center prospective study of neonates with neonatal encephalopathy. On day 2 of life, neonates with brain injury had greater left ventricle end-diastolic and stroke volume but also greater peak global circumferential strain detected by 3-dimensional echocardiogram.

A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease

The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease

BACKGROUND

Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR).

OBJECTIVES

This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination.

METHODS

A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision-recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model.

RESULTS

The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU-ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively.

CONCLUSIONS

Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program.

Machine Learning-Based Prediction of Myocardial Recovery in Patients With Left Ventricular Assist Device Support

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 the use of machine learning (ML) based models predict LVAD patients with myocardial recovery resulting in pump explant.

METHODS

A total of 20 270 adult patients with a durable continuous-flow LVAD in the INTERMACS registry (Interagency Registry for Mechanically Assisted Circulatory Support) were included in the study. Ninety-eight raw clinical variables were screened using the least absolute shrinkage and selection operator for selection of features associated with LVAD-induced myocardial recovery. ML models were developed in the training data set (70%) and were assessed in the validation data set (30%) by receiver operating curve and Kaplan-Meier analysis.

RESULTS

Least absolute shrinkage and selection operator identified 28 unique clinical features associated with LVAD-induced myocardial recovery, including age, cause of heart failure, psychosocial risk factors, laboratory values, cardiac rate and rhythm, and echocardiographic indices. ML models achieved area under the receiver operating curve of 0.813 to 0.824 in the validation data set outperforming logistic regression-based new INTERMACS recovery risk score (area under the receiver operating curve of 0.796) and previously established LVAD recovery risk scores (INTERMACS Cardiac Recovery Score and INTERMACS Recovery Score by Topkara et al) with area under the receiver operating curve of 0.744 and 0.748 (P<0.05). Patients who were predicted to recover by ML models demonstrated a significantly higher incidence of myocardial recovery resulting in LVAD explant in the validation cohort compared with those who were not predicted to recover (18.8% versus 2.6% at 4 years of pump support).

CONCLUSIONS

ML can be a valuable tool to identify subsets of LVAD patients who may be more likely to respond to myocardial recovery protocols.

Impact of age, sex, and cardiac size on the diagnostic performance of myocardial perfusion single-photon emission computed tomography: insights from the REFINE SPECT registry

Background

Single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) is a well-validated non-invasive method for detecting coronary artery disease (CAD). Variations in diagnostic performance due to age and sex have been thoroughly investigated in the literature yet have demonstrated conflicting results. Several studies have associated female sex with reduced accuracy, although others have discovered no significant difference (1). Similarly, while SPECT MPI in the elderly has shown prognostic utility, cardiac event rates are elevated compared to younger patients despite a normal study (2). Additional analyses have suggested that cardiac chamber size may contribute to these observed differences due to its relationship with spatial resolution; however, the interaction of age, sex, and cardiac size remains unknown.

Purpose

We aimed to leverage a large, multicenter, international registry to assess the impact of age, sex, and left ventricular size on the diagnostic accuracy of contemporary SPECT MPI.

Methods

In 9 centers, 2067 patients (67% male, 64.7±11.2 years) in the REFINE SPECT database (REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT) underwent MPI with new generation solid-state scanners followed by invasive coronary angiography within 6 months (3). Stress total perfusion deficit was quantified automatically, and obstructive CAD was defined as &gt;70% stenosis or &gt;50% for left main. Receiver-operating characteristic curves and corresponding areas under the curve (AUC) were computed to compare diagnostic performance between cohorts created based on age (&lt;75 vs. ≥75 years), sex, and end-diastolic volume (EDV; ≥20th vs. &lt;20th sex-specific percentile).

Results

Female and elderly patients had a significantly lower EDV than male and younger patients respectively (p&lt;0.001, Figure 1). Diagnostic accuracy of SPECT was similar by sex (p=0.63). Elderly patients (AUC 0.72 vs. 0.78, p=0.025) and patients with reduced volumes (AUC 0.72 vs. 0.79, p=0.009) exhibited significantly worse performance. When isolating male patients with reduced volumes, a significant difference in accuracy was observed (AUC 0.69 vs. 0.79, p=0.001; Figure 2A), while female patients trended towards significance (p=0.32). Likewise, SPECT performed poorly for elderly patients with reduced volumes (AUC 0.64 vs. 0.78, p=0.01; Figure 2B). If patients possessed any two characteristics of male sex, age ≥75, or low EDV, prediction of CAD with SPECT was significantly decreased (p=0.002; Figure 2C).

Conclusions

Our findings suggest that men with reduced cardiac volumes display worse diagnostic SPECT performance, although it is uncertain whether a pathophysiologic reason exists or further investigation is required for female patients. Patients age ≥75 tended to have lower cardiac volumes as well as lower diagnostic performance. Given these results, alternative diagnostic modalities may better diagnose CAD in patients with these characteristics.

Funding Acknowledgement

Type of funding sources: None. Figure 1Figure 2

Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients

OBJECTIVE

Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.

MATERIALS AND METHODS

For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output.

RESULTS

The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.

DISCUSSION

Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.

CONCLUSIONS

We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

Cardiac Corrected QT Interval Changes Among Patients Treated for COVID-19 Infection During the Early Phase of the Pandemic

IMPORTANCE

Critical illness, a marked inflammatory response, and viruses such as SARS-CoV-2 may prolong corrected QT interval (QTc).

OBJECTIVE

To evaluate baseline QTc interval on 12-lead electrocardiograms (ECGs) and ensuing changes among patients with and without COVID-19.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 3050 patients aged 18 years and older who underwent SARS-CoV-2 testing and had ECGs at Columbia University Irving Medical Center from March 1 through May 1, 2020. Patients were analyzed by treatment group over 5 days, as follows: hydroxychloroquine with azithromycin, hydroxychloroquine alone, azithromycin alone, and neither hydroxychloroquine nor azithromycin. ECGs were manually analyzed by electrophysiologists masked to COVID-19 status. Multivariable modeling evaluated clinical associations with QTc prolongation from baseline.

EXPOSURES

COVID-19, hydroxychloroquine, azithromycin.

MAIN OUTCOMES AND MEASURES

Mean QTc prolongation, percentage of patients with QTc of 500 milliseconds or greater.

RESULTS

A total of 965 patients had more than 2 ECGs and were included in the study, with 561 (58.1%) men, 198 (26.2%) Black patients, and 191 (19.8%) aged 80 years and older. There were 733 patients (76.0%) with COVID-19 and 232 patients (24.0%) without COVID-19. COVID-19 infection was associated with significant mean QTc prolongation from baseline by both 5-day and 2-day multivariable models (5-day, patients with COVID-19: 20.81 [95% CI, 15.29 to 26.33] milliseconds; P < .001; patients without COVID-19: -2.01 [95% CI, -17.31 to 21.32] milliseconds; P = .93; 2-day, patients with COVID-19: 17.40 [95% CI, 12.65 to 22.16] milliseconds; P < .001; patients without COVID-19: 0.11 [95% CI, -12.60 to 12.81] milliseconds; P = .99). COVID-19 infection was independently associated with a modeled mean 27.32 (95% CI, 4.63-43.21) millisecond increase in QTc at 5 days compared with COVID-19-negative status (mean QTc, with COVID-19: 450.45 [95% CI, 441.6 to 459.3] milliseconds; without COVID-19: 423.13 [95% CI, 403.25 to 443.01] milliseconds; P = .01). More patients with COVID-19 not receiving hydroxychloroquine and azithromycin had QTc of 500 milliseconds or greater compared with patients without COVID-19 (34 of 136 [25.0%] vs 17 of 158 [10.8%], P = .002). Multivariable analysis revealed that age 80 years and older compared with those younger than 50 years (mean difference in QTc, 11.91 [SE, 4.69; 95% CI, 2.73 to 21.09]; P = .01), severe chronic kidney disease compared with no chronic kidney disease (mean difference in QTc, 12.20 [SE, 5.26; 95% CI, 1.89 to 22.51; P = .02]), elevated high-sensitivity troponin levels (mean difference in QTc, 5.05 [SE, 1.19; 95% CI, 2.72 to 7.38]; P < .001), and elevated lactate dehydrogenase levels (mean difference in QTc, 5.31 [SE, 2.68; 95% CI, 0.06 to 10.57]; P = .04) were associated with QTc prolongation. Torsades de pointes occurred in 1 patient (0.1%) with COVID-19.

CONCLUSIONS AND RELEVANCE

In this cohort study, COVID-19 infection was independently associated with significant mean QTc prolongation at days 5 and 2 of hospitalization compared with day 0. More patients with COVID-19 had QTc of 500 milliseconds or greater compared with patients without COVID-19.

Admission Cardiac Diagnostic Testing with Electrocardiography and Troponin Measurement Prognosticates Increased 30-Day Mortality in COVID-19

Background

Cardiovascular involvement in coronavirus disease 2019 (COVID‐19) is common and leads to worsened mortality. Diagnostic cardiovascular studies may be helpful for resource appropriation and identifying patients at increased risk for death.

Methods and Results

We analyzed 887 patients (aged 64±17 years) admitted with COVID‐19 from March 1 to April 3, 2020 in New York City with 12 lead electrocardiography within 2 days of diagnosis. Demographics, comorbidities, and laboratory testing, including high sensitivity cardiac troponin T (hs‐cTnT), were abstracted. At 30 days follow‐up, 556 patients (63%) were living without requiring mechanical ventilation, 123 (14%) were living and required mechanical ventilation, and 203 (23%) had expired. Electrocardiography findings included atrial fibrillation or atrial flutter (AF/AFL) in 46 (5%) and ST‐T wave changes in 306 (38%). 27 (59%) patients with AF/AFL expired as compared to 181 (21%) of 841 with other non‐life‐threatening rhythms (P<0.001). Multivariable analysis incorporating age, comorbidities, AF/AFL, QRS abnormalities, and ST‐T wave changes, and initial hs‐cTnT ≥20 ng/L showed that increased age (HR 1.04/year), elevated hs‐cTnT (HR 4.57), AF/AFL (HR 2.07), and a history of coronary artery disease (HR 1.56) and active cancer (HR 1.87) were associated with increased mortality.

Conclusions

Myocardial injury with hs‐cTnT ≥20 ng/L, in addition to cardiac conduction perturbations, especially AF/AFL, upon hospital admission for COVID‐19 infection is associated with markedly increased risk for mortality than either diagnostic abnormality alone.

The Prognostic Value of Electrocardiogram at Presentation to Emergency Department in Patients With COVID-19

OBJECTIVE

To study whether combining vital signs and electrocardiogram (ECG) analysis can improve early prognostication.

METHODS

This study analyzed 1258 adults with coronavirus disease 2019 who were seen at three hospitals in New York in March and April 2020. Electrocardiograms at presentation to the emergency department were systematically read by electrophysiologists. The primary outcome was a composite of mechanical ventilation or death 48 hours from diagnosis. The prognostic value of ECG abnormalities was assessed in a model adjusted for demographics, comorbidities, and vital signs.

RESULTS

At 48 hours, 73 of 1258 patients (5.8%) had died and 174 of 1258 (13.8%) were alive but receiving mechanical ventilation with 277 of 1258 (22.0%) patients dying by 30 days. Early development of respiratory failure was common, with 53% of all intubations occurring within 48 hours of presentation. In a multivariable logistic regression, atrial fibrillation/flutter (odds ratio [OR], 2.5; 95% CI, 1.1 to 6.2), right ventricular strain (OR, 2.7; 95% CI, 1.3 to 6.1), and ST segment abnormalities (OR, 2.4; 95% CI, 1.5 to 3.8) were associated with death or mechanical ventilation at 48 hours. In 108 patients without these ECG abnormalities and with normal respiratory vitals (rate <20 breaths/min and saturation >95%), only 5 (4.6%) died or required mechanical ventilation by 48 hours versus 68 of 216 patients (31.5%) having both ECG and respiratory vital sign abnormalities.

CONCLUSION

The combination of abnormal respiratory vital signs and ECG findings of atrial fibrillation/flutter, right ventricular strain, or ST segment abnormalities accurately prognosticates early deterioration in patients with coronavirus disease 2019 and may assist with patient triage.

Association Between Antecedent Statin Use and Decreased Mortality in Hospitalized Patients with COVID-19

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can result in a hyperinflammatory state, leading to acute respiratory distress syndrome (ARDS), myocardial injury, and thrombotic complications, among other sequelae. Statins, which are known to have anti-inflammatory and antithrombotic properties, have been studied in the setting of other viral infections and ARDS, but their benefit has not been assessed in COVID-19. Thus, we sought to determine whether antecedent statin use is associated with lower in-hospital mortality in patients hospitalized for COVID-19. This is a retrospective analysis of patients admitted with COVID-19 from February 1^st through May 12^th, 2020 with study period ending on June 11^th, 2020. Antecedent statin use was assessed using medication information available in the electronic medical record. We constructed a multivariable logistic regression model to predict the propensity of receiving statins, adjusting for baseline socio-demographic and clinical characteristics, and outpatient medications. The primary endpoint included in-hospital mortality within 30 days. A total of 2626 patients were admitted during the study period, of whom 951 (36.2%) were antecedent statin users. Among 1296 patients (648 statin users, 648 non-statin users) identified with 1:1 propensity-score matching, demographic, baseline, and outpatient medication information were well balanced. Statin use was significantly associated with lower odds of the primary endpoint in the propensity-matched cohort (OR 0.48, 95% CI 0.36 – 0.64, p<0.001). We conclude that antecedent statin use in patients hospitalized with COVID-19 was associated with lower inpatient mortality. Randomized clinical trials evaluating the utility of statin therapy in patients with COVID-19 are needed.

Indications for and Findings on Transthoracic Echocardiography in COVID-19

Background

Despite growing evidence of cardiovascular complications associated with coronavirus disease 2019 (COVID-19), there are few data regarding the performance of transthoracic echocardiography (TTE) and the spectrum of echocardiographic findings in this disease.

Methods

A retrospective analysis was performed among adult patients admitted to a quaternary care center in New York City between March 1 and April 3, 2020. Patients were included if they underwent TTE during the hospitalization after a known positive diagnosis for COVID-19. Demographic and clinical data were obtained using chart abstraction from the electronic medical record.

Results

Of 749 patients, 72 (9.6%) underwent TTE following positive results on severe acute respiratory syndrome coronavirus-2 polymerase chain reaction testing. The most common clinical indications for TTE were concern for a major acute cardiovascular event (45.8%) and hemodynamic instability (29.2%). Although most patients had preserved biventricular function, 34.7% were found to have left ventricular ejection fractions ≤ 50%, and 13.9% had at least moderately reduced right ventricular function. Four patients had wall motion abnormalities suggestive of stress-induced cardiomyopathy. Using Spearman rank correlation, there was an inverse relationship between high-sensitivity troponin T and left ventricular ejection fraction (ρ = −0.34, P = .006). Among 20 patients with prior echocardiograms, only two (10%) had new reductions in LVEF of >10%. Clinical management was changed in eight individuals (24.2%) in whom TTE was ordered for concern for acute major cardiovascular events and three (14.3%) in whom TTE was ordered for hemodynamic evaluation.

Conclusions

This study describes the clinical indications for use and diagnostic performance of TTE, as well as findings seen on TTE, in hospitalized patients with COVID-19. In appropriately selected patients, TTE can be an invaluable tool for guiding COVID-19 clinical management.

Nintedanib in patients with progressive fibrosing interstitial lung diseases-subgroup analyses by interstitial lung disease diagnosis in the INBUILD trial: a randomised, double-blind, placebo-controlled, parallel-group trial

BACKGROUND

The INBUILD trial investigated the efficacy and safety of nintedanib versus placebo in patients with progressive fibrosing interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF). We aimed to establish the effects of nintedanib in subgroups based on ILD diagnosis.

METHODS

The INBUILD trial was a randomised, double-blind, placebo-controlled, parallel group trial done at 153 sites in 15 countries. Participants had an investigator-diagnosed fibrosing ILD other than IPF, with chest imaging features of fibrosis of more than 10% extent on high resolution CT (HRCT), forced vital capacity (FVC) of 45% or more predicted, and diffusing capacity of the lung for carbon monoxide (DLco) of at least 30% and less than 80% predicted. Participants fulfilled protocol-defined criteria for ILD progression in the 24 months before screening, despite management considered appropriate in clinical practice for the individual ILD. Participants were randomly assigned 1:1 by means of a pseudo-random number generator to receive nintedanib 150 mg twice daily or placebo for at least 52 weeks. Participants, investigators, and other personnel involved in the trial and analysis were masked to treatment assignment until after database lock. In this subgroup analysis, we assessed the rate of decline in FVC (mL/year) over 52 weeks in patients who received at least one dose of nintedanib or placebo in five prespecified subgroups based on the ILD diagnoses documented by the investigators: hypersensitivity pneumonitis, autoimmune ILDs, idiopathic non-specific interstitial pneumonia, unclassifiable idiopathic interstitial pneumonia, and other ILDs. The trial has been completed and is registered with ClinicalTrials.gov, number NCT02999178.

FINDINGS

Participants were recruited between Feb 23, 2017, and April 27, 2018. Of 663 participants who received at least one dose of nintedanib or placebo, 173 (26%) had chronic hypersensitivity pneumonitis, 170 (26%) an autoimmune ILD, 125 (19%) idiopathic non-specific interstitial pneumonia, 114 (17%) unclassifiable idiopathic interstitial pneumonia, and 81 (12%) other ILDs. The effect of nintedanib versus placebo on reducing the rate of FVC decline (mL/year) was consistent across the five subgroups by ILD diagnosis in the overall population (hypersensitivity pneumonitis 73·1 [95% CI -8·6 to 154·8]; autoimmune ILDs 104·0 [21·1 to 186·9]; idiopathic non-specific interstitial pneumonia 141·6 [46·0 to 237·2]; unclassifiable idiopathic interstitial pneumonia 68·3 [-31·4 to 168·1]; and other ILDs 197·1 [77·6 to 316·7]; p=0·41 for treatment by subgroup by time interaction). Adverse events reported in the subgroups were consistent with those reported in the overall population.

INTERPRETATION

The INBUILD trial was not designed or powered to provide evidence for a benefit of nintedanib in specific diagnostic subgroups. However, its results suggest that nintedanib reduces the rate of ILD progression, as measured by FVC decline, in patients who have a chronic fibrosing ILD and progressive phenotype, irrespective of the underlying ILD diagnosis.

FUNDING

Boehringer Ingelheim.

Evaluating the Impact of Interruptive Alerts within a Health System: Use, Response Time, and Cumulative Time Burden

BACKGROUND

Health systems often employ interruptive alerts through the electronic health record to improve patient care. However, concerns of "alert fatigue" have been raised, highlighting the importance of understanding the time burden and impact of these alerts on providers.

OBJECTIVES

Our main objective was to determine the total time providers spent on interruptive alerts in both inpatient and outpatient settings. Our secondary objectives were to analyze dwell time for individual alerts and examine both provider and alert-related factors associated with dwell time variance.

METHODS

We retrospectively evaluated use and response to the 75 most common interruptive ("popup") alerts between June 1st, 2015 and November 1st, 2016 in a large academic health care system. Alert "dwell time" was calculated as the time between the alert appearing on a provider's screen until it was closed. The total number of alerts and dwell times per provider per month was calculated for inpatient and outpatient alerts and compared across alert type.

RESULTS

The median number of alerts seen by a provider was 12 per month (IQR 4-34). Overall, 67% of inpatient and 39% of outpatient alerts were closed in under 3 seconds. Alerts related to patient safety and those requiring more than a single click to proceed had significantly longer median dwell times of 5.2 and 6.7 seconds, respectively. The median total monthly time spent by providers viewing alerts was 49 seconds on inpatient alerts and 28 seconds on outpatient alerts.

CONCLUSION

Most alerts were closed in under 3 seconds and a provider's total time spent on alerts was less than 1 min/mo. Alert fatigue may lie in their interruptive and noncritical nature rather than time burden. Monitoring alert interaction time can function as a valuable metric to assess the impact of alerts on workflow and potentially identify routinely ignored alerts.

Improving patient risk communication: Translating cardiovascular risk into standardized risk percentiles

BACKGROUND

Current cholesterol guidelines recommend using 10-year risk of atherosclerotic cardiovascular disease (ASCVD) to guide informed decisions regarding statin therapy, yet patients may have difficulty conceptualizing absolute risk estimates. Peer comparisons may provide an improved tool for patient risk comprehension.

METHODS

Using data from the 2009-2014 National Health and Nutrition Examination Survey (NHANES), we estimated standardized risk percentiles for various age-, sex-, and race-specific subgroups based on their 10-year ASCVD risks using the Pooled Cohort Equations.

RESULTS

We examined 9160 adults in NHANES who were free of cardiovascular disease and had complete clinical data. Among specific age, sex, and race groups, we estimated the distribution of 10-year risk, calculating the 10-year risk corresponding to each percentile in order to generate standardized cardiovascular risk percentiles. Estimated 10-year ASCVD absolute risk varied markedly by age, sex, and race subgroups. A 10-year risk of 7.0% would put a 55 year-old black male in the 20th percentile relative to his peers (ie, at lower risk than 80% of his peers), whereas a 10-year risk of 7.0% would put a 55 year-old white female in the 95th percentile (i.e., only 5% of her peers would have higher risk). Standardized cardiovascular risk percentiles by age, race, and sex are available online at populationrelativerisk.dcri.org.

CONCLUSION

Cardiovascular risk varies substantially by age, sex, and race. These data allow for 10-year absolute risks of ASCVD to be translated into a standardized cardiovascular risk percentile, providing patients with information that is easy to understanding regarding how their personal risk of cardiovascular disease compares with their age-, sex-, and race-matched peers.

Automating Venous Thromboembolism Risk Calculation Using Electronic Health Record Data upon Hospital Admission: The Automated Padua Prediction Score

BACKGROUND

Venous thromboembolism (VTE) risk scores assist providers in determining the relative benefit of prophylaxis for individual patients. While automated risk calculation using simpler electronic health record (EHR) data is feasible, it lacks clinical nuance and may be less predictive. Automated calculation of the Padua Prediction Score (PPS), requiring more complex input such as recent medical events and clinical status, may save providers time and increase risk score use.

OBJECTIVE

We developed the Automated Padua Prediction Score (APPS) to auto-calculate a VTE risk score using EHR data drawn from prior encounters and the first 4 hours of admission. We compared APPS to standard practice of clinicians manually calculating the PPS to assess VTE risk.

DESIGN

Cohort study of 30,726 hospitalized patients. APPS was compared to manual calculation of PPS by chart review from 300 randomly selected patients.

MEASUREMENTS

Prediction of hospital-acquired VTE not present on admission.

RESULTS

Compared to manual PPS calculation, no significant difference in average score was found (5.5 vs. 5.1, P = 0.073), and area under curve (AUC) was similar (0.79 vs. 0.76). Hospital- acquired VTE occurred in 260 (0.8%) of 30,726 patients. Those without VTE averaged APPS of 4.9 (standard deviation [SD], 2.6) and those with VTE averaged 7.7 (SD, 2.6). APPS had AUC = 0.81 (confidence interval [CI], 0.79-0.83) in patients receiving no pharmacologic prophylaxis and AUC = 0.78 (CI, 0.76- 0.82) in patients receiving pharmacologic prophylaxis.

CONCLUSIONS

Automated calculation of VTE risk had similar ability to predict hospital-acquired VTE as manual calculation despite differences in how often specific scoring criteria were considered present by the 2 methods. Journal of Hospital Medicine 2017;12: 231- 237.

[Research with big data: the European perspective]

The article examines the impact that legislative developments in the European Union have had, still have and are continuing to have on cross-border access to microdata for research purposes. Therefore, we describe two competing aims: the tension between the ambitions of the EU to create a European Research Area within which research communities gain access to and share data across national boundaries; and the desire within the EU to establish a harmonious legislative framework that provides protection from the misuse of personal information. We attempt to examine which new developments at the EU level will have an impact upon research plans and the challenges researchers face when analysing big data.

A Web-Based Tool for Patient Triage in Emergency Department Settings: Validation Using the Emergency Severity Index

Background

We evaluated the concordance between triage scores generated by a novel Internet clinical decision support tool, Clinical GPS (cGPS) (Lumiata Inc, San Mateo, CA), and the Emergency Severity Index (ESI), a well-established and clinically validated patient severity scale in use today. Although the ESI and cGPS use different underlying algorithms to calculate patient severity, both utilize a five-point integer scale with level 1 representing the highest severity.

Objective

The objective of this study was to compare cGPS results with an established gold standard in emergency triage.

Methods

We conducted a blinded trial comparing triage scores from the ESI: A Triage Tool for Emergency Department Care, Version 4, Implementation Handbook to those generated by cGPS from the text of 73 sample case vignettes. A weighted, quadratic kappa statistic was used to assess agreement between cGPS derived severity scores and those published in the ESI handbook for all 73 cases. Weighted kappa concordance was defined a priori as almost perfect (kappa > 0.8), substantial (0.6 < kappa < 0.8), moderate (0.4 < kappa < 0.6), fair (0.2 < kappa< 0.4), or slight (kappa < 0.2).

Results

Of the 73 case vignettes, the cGPS severity score matched the ESI handbook score in 95% of cases (69/73 cases), in addition, the weighted, quadratic kappa statistic showed almost perfect agreement (kappa = 0.93, 95% CI 0.854-0.996). In the subanalysis of 41 case vignettes assigned ESI scores of level 1 or 2, the cGPS and ESI severity scores matched in 95% of cases (39/41 cases).

Conclusions

These results indicate that the cGPS is a reliable indicator of triage severity, based on its comparison to a standardized index, the ESI. Future studies are needed to determine whether the cGPS can accurately assess the triage of patients in real clinical environments.

InSpire to Promote Lung Assessment in Youth: Evolving the Self-Management Paradigms of Young People With Asthma

Background

Asthma is the most common chronic disease in childhood, disproportionately affecting urban, minority, and disadvantaged children. Individualized care plans supported by daily lung-function monitoring can reduce morbidity and mortality. However, despite 20 years of interventions to increase adherence, only 50% of US youth accurately follow their care plans, which leads to millions of preventable hospitalizations, emergency room visits, and sick days every year. We present a feasibility study of a novel, user-centered approach to increasing young people’s lung-function monitoring and asthma self-care. Promoting Lung Assessment in Youth (PLAY) helps young people become active managers of their asthma through the Web 2.0 principles of participation, cocreation, and information sharing. Specifically, PLAY combines an inexpensive, portable spirometer with the motivational power and convenience of mobile phones and virtual-community gaming.

Objective

The objective of this study was to develop and pilot test InSpire, a fully functional interface between a handheld spirometer and an interactive game and individualized asthma-care instant-messaging system housed on a mobile phone.

Methods

InSpire is an application for mobile smartphones that creates a compelling world in which youth collaborate with their physicians on managing their asthma. Drawing from design-theory on global timer mechanics and role playing, we incentivized completing spirometry maneuvers by making them an engaging part of a game young people would want to play. The data can be sent wirelessly to health specialists and return care recommendations to patients in real-time. By making it portable and similar to applications normally desired by the target demographic, InSpire is able to seamlessly incorporate asthma management into their lifestyle.

Results

We describe the development process of building and testing the InSpire prototype. To our knowledge, the prototype is a first-of-its kind mobile one-stop shop for asthma management. Feasibility testing in children aged 7 to 14 with asthma assessed likability of the graphical user interface as well as young people’s interest in our incentivizing system. Nearly 100% of children surveyed said they would play games like those in PLAY if they involved breathing into a spirometer. Two-thirds said they would prefer PLAY over the spirometer alone, whereas 1/3 would prefer having both. No children said they would prefer the spirometer over PLAY.

Conclusions

Previous efforts at home-monitoring of asthma in children have experienced rapid decline in adherence. An inexpensive monitoring technology combined with the computation, interactive communication, and display ability of a mobile phone is a promising approach to sustainable adherence to lung-function monitoring and care plans. An exciting game that redefines the way youth conduct health management by inviting them to collaborate in their health better can be an incentive and a catalyst for more far-reaching goals.

Parental occupation is a risk factor for childhood wheeze and asthma

The present birth cohort study investigated whether or not childhood wheeze and asthma are associated with parental exposure to occupational sensitisers that cause asthma. Parental occupation, from the Avon Longitudinal Study of Parents and Children (ALSPAC), was related to wheeze, asthma, ventilatory function, airway responsiveness and atopic sensitisation in children aged 0-102 months. Occupation was recorded for 11,193 mothers and 9,473 fathers antenatally, and for 4,631 mothers and 5,315 fathers post-natally. Childhood respiratory outcomes were not associated with parental occupational exposure to diisocyanates, glues/resins, dyes, animal dust, solder, enzymes and wood dust. Maternal post-natal occupational exposure to latex and/or biocides/fungicides increased the likelihood of childhood wheeze and asthma. High levels of latex or biocide/fungicide exposure were associated with an OR (95% CI) of 1.26 (1.07-1.50) and 1.22 (1.02-2.05), respectively, for wheezing up to 81 months. Combined maternal latex and biocide/fungicide exposure increased the likelihood of childhood wheeze (1.22 (1.03-1.43)) and asthma. High paternal occupational flour dust exposure was associated with an increased likelihood of wheeze after 30 months (2.31 (1.05-5.10)) and asthma by 91 months (3.23 (1.34-7.79)). Maternal occupational exposure to latex and/or biocides and paternal exposure to flour dust increases the risk of childhood asthma. Further studies in this area are justified.

A New Probiotic Cheese with Antioxidative and Antimicrobial Activity

The aim of our study was to develop an original probiotic cheese based on the Estonian open-texture, smear-ripened, semisoft cheese "Pikantne." Cheese was produced by two methods using cheese starter cultures (Probat 505) in combination with 0.04% of probiotic Lactobacillus fermentum strain ME-3 (10(9) cfu/mL) with high antimicrobial activity and antioxidative properties. The probiotic Lactobacillus was added into milk simultaneously with starter cultures (cheese A) and into drained curd (cheese B). After addition of probiotic L. fermentum ME-3, the cheese composition, flavor, and aroma were comparable to the control cheese (score values = 4.5, 4.2, and 3.7 for control cheese, cheese A, and cheese B, respectively). Cheese A, which had good sensory properties, was chosen for further testing of viability and probiotic properties. The probiotic strain was found to withstand the technological processing of cheese, surviving and sustaining moderate antimicrobial and high antioxidative activity throughout ripening and storage (the ripened cheese contained approximately 5 x 10(7) cfu/g viable ME-3 cells), although the viability of the ME-3 strain incorporated into the cheese showed a slight decrease between d 24 and 54 after cheese preparation. Semisoft cheese "Pikantne" serves as a suitable carrier of antimicrobial and antioxidative L. fermentum ME-3.

Rep-dependent initiation of adeno-associated virus type 2 DNA replication by a herpes simplex virus type 1 replication complex in a reconstituted system

Productive infection by adeno-associated virus type 2 (AAV) requires coinfection with a helper virus, e.g., adenovirus or herpesviruses. In the case of adenovirus coinfection, the replication machinery of the host cell performs AAV DNA replication. In contrast, it has been proposed that the herpesvirus replication machinery might replicate AAV DNA. To investigate this question, we have attempted to reconstitute AAV DNA replication in vitro using purified herpes simplex virus type 1 (HSV-1) replication proteins. We show that the HSV-1 UL5, UL8, UL29, UL30, UL42, and UL52 gene products along with the AAV Rep68 protein are sufficient to initiate replication on duplex DNA containing the AAV origins of replication, resulting in products several hundred nucleotides in length. Initiation can occur also on templates containing only a Rep binding site and a terminal resolution site. We further demonstrate that initiation of DNA synthesis can take place with a subset of these factors: Rep68 and the UL29, UL30, and UL42 gene products. Since the HSV polymerase and its accessory factor (the products of the UL30 and UL42 genes) are unable to efficiently perform synthesis by strand displacement, it is likely that in addition to creating a hairpin primer, the AAV Rep protein also acts as a helicase for DNA synthesis. The single-strand DNA binding protein (the UL29 gene product) presumably prevents reannealing of complementary strands. These results suggest that AAV can use the HSV replication apparatus to replicate its DNA. In addition, they may provide a first step for the development of a fully reconstituted AAV replication assay.

Complementary intrastrand base pairing during initiation of Herpes simplex virus type 1 DNA replication

The herpes simplex virus type 1 origin of DNA replication, oriS, contains three copies of the recognition sequence for the viral initiator protein, origin binding protein (OBP), arranged in two palindromes. The central box I forms a short palindrome with box III and a long palindrome with box II. Single-stranded oriS adopts a conformation, oriS*, that is tightly bound by OBP. Here we demonstrate that OBP binds to a box III-box I hairpin with a 3' single-stranded tail in oriS*. Mutations designed to destabilize the hairpin abolish the binding of OBP to oriS*. The same mutations also inhibit DNA replication. Second site complementary mutations restore binding of OBP to oriS* as well as the ability of mutated oriS to support DNA replication. OriS* is also an efficient activator of the hydrolysis of ATP by OBP. Sequence analyses show that a box III-box I palindrome is an evolutionarily conserved feature of origins of DNA replication from human, equine, bovine, and gallid alpha herpes viruses. We propose that oriS facilitates initiation of DNA synthesis in two steps and that OBP exhibits exquisite specificity for the different conformations oriS adopts at these stages. Our model suggests that distance-dependent cooperative binding of OBP to boxes I and II in duplex DNA is succeeded by specific recognition of a box III-box I hairpin in partially unwound DNA.