Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms

AbstractPatients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85–0.91 for ECG and 0.89–1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3–4% at 52–71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74–77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.

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Artificial Intelligence-Enabled, Fully Automated Detection of Cardiac Amyloidosis Using Electrocardiograms and Echocardiograms.

10.1101/2020.07.02.20141028 ◽

2020 ◽

Author(s):

Shinichi Goto ◽

Keitaro Mahara ◽

Lauren Beussink-Nelson ◽

Hidehiko Ikura ◽

Yoshinori Katsumata ◽

...

Keyword(s):

Artificial Intelligence ◽

Heart Failure ◽

Rare Diseases ◽

Cardiac Amyloidosis ◽

Model Performance ◽

Intermediate Frequency ◽

The United States ◽

Automated Detection ◽

Paired Data ◽

Neural Network Models

Although individually uncommon, rare diseases collectively affect over 350 million patients worldwide and are increasingly the target of therapeutic development efforts. Unfortunately, the pursuit and use of such therapies have been hindered by a common challenge: patients with specific rare diseases are difficult to identify, especially if the conditions resemble more prevalent disorders. Cardiac amyloidosis is one such rare disease, which is characterized by deposition of misfolded proteins within the heart muscle resulting in heart failure and death. In recent years, specific therapies have emerged for cardiac amyloidosis and several more are under investigation, but because cardiac amyloidosis is mistaken for common forms of heart failure, it is typically diagnosed late in its course. As a possible solution, artificial intelligence methods could enable automated detection of rare diseases, but model performance must address low disease prevalence. Here we present an automated multi-modality pipeline for cardiac amyloidosis detection using two neural-network models; one using electrocardiograms (ECG) and the second using echocardiographic videos as input. These models were trained and validated on 3 and 5 academic medical centers (AMC), respectively, in the United States and Japan. Both models had excellent accuracy for detecting cardiac amyloidosis with C-statistics of 0.85-0.92 and 0.91-1.00 for the ECG and echocardiography models, respectively, with the latter outperforming expert diagnosis. Simulating deployment on 13,906 and 7775 patients with ECG-echocardiography paired data for AMC2 and AMC3 indicated a positive predictive value (PPV) for the ECG model of 4% and 3% at 61% and 54% recall, respectively. Pre-screening with ECG enhanced the echocardiography model performance from PPV 23% and 20% to PPV 58% and 53% at 64% recall, respectively for AMC2 and AMC3. In conclusion, we have developed a robust pipeline to augment detection of cardiac amyloidosis, which should serve as a generalizable strategy for other rare and intermediate frequency cardiac diseases with established or emerging therapies.

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Health Industry Practices That Create Conflicts of Interest: A Policy Proposal for Academic Medical Centers

Yearbook of Dermatology and Dermatologic Surgery ◽

10.1016/s0093-3619(08)70170-9 ◽

2006 ◽

Vol 2006 ◽

pp. 221-222

Author(s):

B.H. Thiers

Keyword(s):

Conflicts Of Interest ◽

Academic Medical Centers ◽

Policy Proposal ◽

Medical Centers ◽

Academic Medical

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Discharge Practices for COVID-19 Patients: Rapid Review of Published Guidance and Synthesis of Documents and Practices at 22 US Academic Medical Centers

Journal of General Internal Medicine ◽

10.1007/s11606-021-06711-x ◽

2021 ◽

Author(s):

S. Ryan Greysen ◽

◽

Andrew D. Auerbach ◽

Matthew D. Mitchell ◽

Jennifer N Goldstein ◽

...

Keyword(s):

Academic Medical Centers ◽

Rapid Review ◽

Medical Centers ◽

Academic Medical

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Unplanned Reoperation and Implant Revision After Total Wrist Arthroplasty

Hand ◽

10.1177/1558944719898817 ◽

2020 ◽

pp. 155894471989881 ◽

Cited By ~ 1

Author(s):

Taylor M. Pong ◽

Wouter F. van Leeuwen ◽

Kamil Oflazoglu ◽

Philip E. Blazar ◽

Neal Chen

Keyword(s):

Risk Factors ◽

Implant Removal ◽

Academic Medical Centers ◽

Implant Design ◽

Factors Associated ◽

Medical Centers ◽

Total Wrist Arthroplasty ◽

Academic Medical ◽

Related Risk ◽

Wrist Arthroplasty

Background: Total wrist arthroplasty (TWA) is a treatment option for many debilitating wrist conditions. With recent improvements in implant design, indications for TWA have broadened. However, despite these improvements, there are still complications associated with TWA, such as unplanned reoperation and eventual implant removal. The goal of this study was to identify risk factors for an unplanned reoperation or implant revision after a TWA at 2 academic medical centers between 2002 and 2015. Methods: In this retrospective study, 24 consecutive TWAs were identified using CPT codes. Medical records were manually reviewed to identify demographic, patient- or disease-related, and surgery-related risk factors for reoperation and implant removal after a primary TWA. Results: Forty-six percent of wrists (11 of 24 TWAs performed) had a reoperation after a median of 3.4 years, while 29% (7 of 24) underwent implant revision after a median of 5 years. Two patients had wrist surgery prior to their TWA, both eventually had their implant removed ( P = .08). There were no risk factors associated with reoperation or implant removal. Conclusion: Unplanned reoperation and implant removal after a primary TWA are common. Approximately 1 in 3 wrists are likely to undergo revision surgery. We found no factors associated with reoperation or implant removal; however, prior wrist surgery showed a trend toward risk of implant removal after TWA.

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