Cargando…

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

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 dista...

Descripción completa

Detalles Bibliográficos
Autores principales: Speth, Kelly A., Yoon, Alfred P., Wang, Lu, Chung, Kevin C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043191/
https://www.ncbi.nlm.nih.gov/pubmed/32083690
http://dx.doi.org/10.1001/jamanetworkopen.2019.21626
_version_ 1783501413997871104
author Speth, Kelly A.
Yoon, Alfred P.
Wang, Lu
Chung, Kevin C.
author_facet Speth, Kelly A.
Yoon, Alfred P.
Wang, Lu
Chung, Kevin C.
author_sort Speth, Kelly A.
collection PubMed
description 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: 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. RESULTS: 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. CONCLUSIONS AND RELEVANCE: The findings suggest that the approach to treating traumatic finger amputations varies based on the patient’s injury characteristics and functional needs.
format Online
Article
Text
id pubmed-7043191
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher American Medical Association
record_format MEDLINE/PubMed
spelling pubmed-70431912020-03-10 Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic Finger Amputations Speth, Kelly A. Yoon, Alfred P. Wang, Lu Chung, Kevin C. JAMA Netw Open Original Investigation 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: 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. RESULTS: 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. CONCLUSIONS AND RELEVANCE: The findings suggest that the approach to treating traumatic finger amputations varies based on the patient’s injury characteristics and functional needs. American Medical Association 2020-02-21 /pmc/articles/PMC7043191/ /pubmed/32083690 http://dx.doi.org/10.1001/jamanetworkopen.2019.21626 Text en Copyright 2020 Speth KA et al. JAMA Network Open. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Speth, Kelly A.
Yoon, Alfred P.
Wang, Lu
Chung, Kevin C.
Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic Finger Amputations
title Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic Finger Amputations
title_full Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic Finger Amputations
title_fullStr Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic Finger Amputations
title_full_unstemmed Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic Finger Amputations
title_short Assessment of Tree-Based Statistical Learning to Estimate Optimal Personalized Treatment Decision Rules for Traumatic Finger Amputations
title_sort assessment of tree-based statistical learning to estimate optimal personalized treatment decision rules for traumatic finger amputations
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043191/
https://www.ncbi.nlm.nih.gov/pubmed/32083690
http://dx.doi.org/10.1001/jamanetworkopen.2019.21626
work_keys_str_mv AT spethkellya assessmentoftreebasedstatisticallearningtoestimateoptimalpersonalizedtreatmentdecisionrulesfortraumaticfingeramputations
AT yoonalfredp assessmentoftreebasedstatisticallearningtoestimateoptimalpersonalizedtreatmentdecisionrulesfortraumaticfingeramputations
AT wanglu assessmentoftreebasedstatisticallearningtoestimateoptimalpersonalizedtreatmentdecisionrulesfortraumaticfingeramputations
AT chungkevinc assessmentoftreebasedstatisticallearningtoestimateoptimalpersonalizedtreatmentdecisionrulesfortraumaticfingeramputations