Machine Learning-Based Identification of Preoperative Psychological Distress and Its Association With Adverse Surgery-Related Outcomes: Evidence From the China Surgery and Anesthesia Cohort (CSAC).

BACKGROUND

Many patients experience psychological distress in the preoperative phase, whilst screening based on cut-off points of assessment scales showed limited value in predicting clinical postoperative adverse outcomes.

METHODS

To identify preoperative psychological distress and investigate their associations with adverse surgery-related outcomes, we included 16,662 patients from the China Surgery and Anesthesia Cohort (CSAC). We applied dimensionality reduction and unsupervised machine learning algorithms to classify participants into distinct psychological patterns. We then assessed the associations of machine learning-identified psychological patterns and traditional cut-off based psychological symptoms, with various adverse surgery-related outcomes, using logistic and linear regression models while adjusting for other relevant covariates.

RESULTS

We successfully established clustering algorithms for 16,298 participants, demonstrating strong consistency in pattern features. Six distinct psychological patterns among participants were identified, including one group with normal psychological functioning and five groups with varying levels of psychological distress. All identified psychological distress patterns were significantly associated with most surgery-related adverse outcomes, both in short-term (e.g., any within-hospital postoperative complication, odds ratios [ORs] = 1.24-1.30) and long-term (e.g., cognitive impairment at 12 months postsurgery, 1.29-2.35). In contrast, traditional cut-off-based methods identified only 266 patients with significant psychological symptoms, which showed no association with some key short-term outcomes (e.g., length of hospital stay and postoperative complication), though they remained linked to most long-term outcomes.

CONCLUSIONS

Our findings demonstrate the effectiveness of machine learning in accurately identifying patients with preoperative psychological distress who may require clinical attention, highlighting the potential of these techniques to guide targeted preoperative interventions and ultimately improve surgical outcomes.