Comparative Efficacy and Safety of Robot-Assisted vs. Freehand Screw Placement in Femoral Neck Fractures: An Updated Systematic Review and Meta-Analysis

医学 荟萃分析 概化理论 透视 学习曲线 股骨颈 元回归 外科 系统回顾 梅德林 内科学 骨质疏松症 统计 数学 管理 政治学 法学 经济
作者
Ümit Mert,Mohamad Agha Mahmoud,Maher Ghandour,Ahmad Al Zuabi,Marco Speicher,Philipp Kobbe,Klemens Horst,Frank Hildebrand,Koroush Kabir
出处
期刊:Journal of Clinical Medicine [Multidisciplinary Digital Publishing Institute]
卷期号:13 (17): 5072-5072
标识
DOI:10.3390/jcm13175072
摘要

Background: Femoral neck fractures pose significant surgical challenges with high morbidity and mortality. Traditional freehand screw placement often yields variable outcomes. Recent robotic advancements offer a promising alternative with enhanced precision. Methods: This systematic review compares the efficacy and safety of robot-assisted versus freehand techniques. A comprehensive literature search across multiple databases up to July 2024 included studies comparing both techniques. Primary outcomes were the union rate and time, functional outcomes, operative time, intraoperative parameters, and complication rates. Meta-regression analyses identified treatment response determinants. Results: Twenty-four studies (1437 patients) were included. Robot-assisted screw placement significantly improved the union rate, reduced the union time, and showed superior functional outcomes. Additionally, it resulted in shorter operative times, less intraoperative blood loss, and fewer instances of fluoroscopy and guide pin insertion. The risk of femoral neck necrosis was notably lower with robotic assistance. Meta-regression highlighted the robot type, patient age, and sample size as significant factors. Conclusions: Despite the promise of robot-assisted screw placement, limitations exist. The evidence being mainly from China raises concerns about generalizability. The lack of long-term follow-up data hinders assessment of technique durability. Unreported surgeon expertise levels and learning curves affect result validity. High initial costs and steep learning curves of robotic systems also present barriers to widespread adoption.
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