Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction

Importance

Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.

Objectives

To assess the utility of machine learning systems for automatic discrimination of TTS and AMI.

Design, Settings, and Participants

This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021.

Exposure

Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed.

Main Outcomes and Measures

Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis.

Results

In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%).

Conclusions and Relevance

In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.

The nature, frequency and course of psychiatric disorders in the first year after traumatic brain injury: a prospective study

BACKGROUND

Psychiatric disorders are common following traumatic brain injury (TBI). However, few studies have examined the course of disorder development and the influence of pre-injury psychiatric history. The present study aimed to examine the frequency of, and association between, psychiatric disorders occurring pre- and post-injury, and to examine the post-injury course of disorders.

METHOD

Participants were 102 adults (75.5% male) with predominantly moderate-severe TBI. Participants were initially assessed for pre-injury and current disorders, and reassessed at 3, 6 and 12 months post-injury using the Structured Clinical Interview for DSM-IV Disorders (SCID).

RESULTS

Over half of the participants had a pre-injury psychiatric disorder; predominantly substance use, mood, and anxiety disorders. In the first year post-injury, 60.8% of participants had a psychiatric disorder, commonly anxiety and mood disorders. Post-injury disorders were associated with the presence of a pre-injury history (p<0.01), with 74.5% of participants with a pre-injury psychiatric history experiencing a post-injury disorder, which commonly presented at initial assessment or in the first 6 months. However, 45.8% of participants without a pre-injury history developed a novel post-injury disorder, which was less likely to emerge at the initial assessment and generally developed later in the year.

CONCLUSIONS

Despite evidence that most post-injury psychiatric disorders represent the continuation of pre-existing disorders, a significant number of participants developed novel psychiatric disorders. This study demonstrates that the timing of onset may differ according to pre-injury history. There seem to be different trajectories for anxiety and depressive disorders. This research has important implications for identifying the time individuals are most at risk of psychiatric disorders post-injury.