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

ABSTRACT:

Importance

Optimal treatment for traumatic finger amputation is unknown to date.

Objective

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.

Design, setting, and participants

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.

Exposures

Treatment with revision amputation or replantation.

Main outcomes and measures

SUBMITTER: Speth KA 

PROVIDER: S-EPMC7043191 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature