Insights from deep learning models on new-onset anxiety in patients following bariatric metabolic surgery.

BACKGROUND

Due to its long-term effectiveness in weight control and cost-efficiency, bariatric metabolic surgery (BMS) has emerged as a promising treatment option for patients with severe obesity. However, its impact on certain mental health disorders remains unclear.

OBJECTIVE

This study aimed to utilize a deep learning (DL) model, DeepBiomarker2, which integrates social determinants of health (SDoH) and electronic health records (EHR), to identify clinical features associated with new-onset anxiety disorder following BMS.

METHODS

We conducted a case-control study using longitudinal EHR data from the University of Pittsburgh Medical Center (Jan 2004-Oct 2019) on patients who underwent bariatric surgery. DeepBiomarker2, a DL model integrating diagnoses, medications, lab tests, and neighborhood socioeconomic status, predicted new-onset anxiety. Perturbation-based contribution analysis identified key predictive features.

RESULTS

A total of 14,856 eligible patients who underwent BMS without a prior history of anxiety disorder were identified. DL models outperformed traditional logistic regression in predicting post-BMS anxiety, yielding area under the curve (AUC) values exceeding 0.89. Key features associated with post-BMS anxiety included abnormal urine and blood lab results, opioid and psychiatric medication use, frequent emergency department (ED) visits, and pre-existing mental health conditions. Potential protective indicators included omega-3 fatty acids, vitamin B12, calcium citrate, and pravastatin. Inclusion of nSES data led to marginal improvements in model performance.

CONCLUSION

Our DL models successfully identified clinical features potentially associated with new-onset anxiety following BMS, offering valuable insights to support early intervention and personalized mental health strategies for postoperative care.