Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic...

Optimal treatment for traumatic finger amputation is unknown to date.

To use statistical learning methods to estimate evidence-based treatment assignment rules to enhance long-term functional and patient-reported outcomes in patients after traumatic amputation of fingers distal to the metacarpophalangeal joint.

This decision analytical model used data from a retrospective cohort study of 338 consenting adult patients who underwent revision amputation or replantation at 19 centers in the United States and Asia from August 1, 2016, to April 12, 2018. Of those, data on 185 patients were included in the primary analysis.

Treatment with revision amputation or replantation.

Outcome measures were hand strength, dexterity, hand-related quality of life, and pain. A tree-based statistical learning method was used to derive clinical decision rules for treatment of traumatic finger amputation.

Among 185 study participants (mean [SD] age, 45 [16] years; 156 [84%] male), the median number of fingers amputated per patient was 1 (range, 1-5); 115 amputations (62%) were distal to the proximal interphalangeal joint, and 110 (60%) affected the nondominant hand. On the basis of the tree-based statistical learning estimates, to maximize hand dexterity or to minimize patient-reported pain, replantation was found to be the best strategy. To maximize hand strength, revision amputation was the best strategy for patients with a single-finger amputation but replantation was preferred for all other injury patterns. To maximize patient-reported quality of life, revision amputation was the best approach for patients with dominant hand injuries, and replantation was the best strategy for patients with nondominant hand injuries.

The findings suggest that the approach to treating traumatic finger amputations varies based on the patient's injury characteristics and functional needs.