BACKGROUND
A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and impairing quality of life. A prediction model for PAs could help clinicians and patients monitor, control, and carry out early intervention for recurrent panic attacks, enabling more personalized treatment for panic disorder.
OBJECTIVE
This study aimed to provide a seven-day PA prediction model and determine the relationship between a future PA and various features, including physiological factors, anxiety and depressive factors, and air quality index.
METHODS
We enrolled 59 participants with PD (DSM-5 and MINI interview). Participants used smartwatches (Garmin Vívosmart 4) and mobile applications to collect their sleep, heart rate, activity level, anxiety, and depression scores (BDI, BAI, STAI-S, STAI-T, and PDSS-SR) in their real life for a duration of one year. We also included air quality indexes from open data. To analyze these data, our team used six machine learning methods: random forests, decision trees, LDA, AdaBoost, XGBoost, and regularized greedy forests.
RESULTS
For seven-day PA prediction, the random forest produced the best prediction rate. Overall, the accuracy of the testing set was 67.4–81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features such as BAI, BDI, STAI, MINI, average heart rate, resting heart rate, and deep sleep duration.
CONCLUSIONS
It is possible to predict panic attacks using a combination of data from questionnaires and physiological and environmental data. Prediction accuracy was 67.4–81.3% on the testing data.
CLINICALTRIAL