Key result
Scalable and automated computational prognostic models have the potential to provide clinicians with new tools for accurate risk stratification of posttraumatic sequelae directly in the ED.
Why the study?
CVD-induced posttraumatic stress disorder after acute medical emergencies increases morbidity and mortality, requiring accurate risk-based computational prognostic models for early targeted prevention in the ED.
Scalable and automated computational prognostic models have the potential to provide clinicians with new tools for accurate risk stratification of posttraumatic sequelae directly in the ED for patients with CVD.
Should not yet guide ED risk stratification in CVD; leaves open whether automated models improve posttraumatic sequelae prediction.
The risk of developing adverse mental health effects after acute medical emergencies such as those caused by cardiovascular disease (CVD) and leading to emergency department (ED) admission is an enduring health care challenge with significant public health implications and patient burden. CVD-induced posttraumatic stress disorder is not uncommon and has been found to increase both psychological and cardiac morbidity and mortality. To implement early targeted prevention, a risk-based approach is needed, which requires an accurate clinical prediction model. Risk prediction is best if it relies on a multifactorial model accounting for nonlinear dependencies, which requires advanced computational models. The development of scalable and automated computational prognostic models has the potential to provide clinicians with new tools for accurate and feasible risk stratification directly in the ED. We present recent advancements and discuss the benefits and challenges for the clinical use of algorithm-based risk stratification for posttraumatic aftercare and digital health applications in patients with CVD. [ Psychiatr Ann . 2021;51(1):27–32.]
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Schultebraucks et al. (2021) conducted a review in Cardiovascular disease-induced posttraumatic stress disorder. Algorithm-based risk stratification and computational prognostic models was evaluated. Scalable and automated computational prognostic models have the potential to provide clinicians with new tools for accurate risk stratification of posttraumatic sequelae directly in the ED.
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