postoperative-length-stay-after-minimally-invasive-transforaminal

Postoperative Length of Stay After Minimally Invasive Transforaminal Lumbar Interbody Fusion: Analysis of Perioperative Predictors

Length of stay is a key quality and cost metric after minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF). Published benchmarks vary ...

People's Hospital of Chongqing Hechuan

The First Affiliated Hospital of Chongqing Medical University Hechuan District Hospital

The aims of this study were to develop and validate interpretable ML models for extended length of stay (eLOS) prediction following endoscopic lumbar spinal stenosis (LSS) decompression, and identify modifiable risk factors influencing healthcare costs and recovery.

Machine learning prediction of extended length of stay following endoscopic decompression for lumbar spinal stenosis: A retrospective cohort study.

Artificial intelligence (AI) has significantly advanced the field of joint arthroplasty by transforming key aspects such as surgical planning, implant design, and postoperative management. Despite their growing importance, research trends and priorities in AI applications for joint arthroplasty remain underexplored. This study employed bibliometric analysis to elucidate the main research focus areas and global trends in AI and arthroplasty from 2001 to 2025.

Artificial intelligence in joint arthroplasty: A bibliometric analysis of global research trends (2001-2025).

Extended length of stay (eLOS) after hip fracture surgery in elderly patients poses significant clinical and economic challenges. While traditional statistical models identify key predictors, they may miss complex variable interactions. This study compared logistic regression with machine learning (ML) algorithms to predict eLOS, emphasizing actionable factors within Enhanced Recovery After Surgery (ERAS) protocols.

Machine learning for predicting extended length of stay in elderly patients with hip fractures: An enhanced recovery after surgery perspective.

Total hip arthroplasty (THA) critically influences outcomes; however, a comprehensive mapping of the research landscape remains absent. We systematically characterized the knowledge structure, collaborative networks, and research priorities within the THA approach literature from 2000 to 2026. Multi-database bibliometric analysis integrated data from the Web of Science, PubMed, Embase, and Cochrane Library. Following deduplication, 4023 publications were analyzed using Bibliometrix, VOSviewer, and CiteSpace to map publication trends, collaboration networks, thematic evolution, and citation dynamics. Annual publications demonstrated sustained expansion (annual growth rate: 9.68% over the complete-year window 2000-2024), with marked acceleration after 2020 and peak production exceeding 300 publications during 2023 to 2024. The United States emerged as the primary hub (30.7% of publications), with European and East Asian centers forming secondary nodes. The Journal of Arthroplasty anchored volume (637 publications), while high-impact general journals (JBJS-Am, CORR) achieved the greatest citation efficiency (>60 citations per publication). Keyword clustering revealed well-differentiated themes ( = 0.351,  = 0.706) centered on outcome surveillance, direct anterior approach evaluation, and positioning accuracy. Citation bursts documented a 3-phase evolution: minimally invasive descriptions (2003-2012), comparative benchmarking (2012-2020), and registry-based evidence (2018-2026). The field has matured from seeking universal approach superiority toward patient-, surgeon-, and system-specific selection paradigms. Priority directions include implementation science for learning curves, standardized patient-centered outcomes, health economic assessment, and approach-technology-pathway interaction studies. Geographic expansion beyond North American and European centers is essential for evaluating context-dependent effectiveness across diverse healthcare systems worldwide.

Article	Year
Machine learning prediction of extended length of stay following endoscopic decompression for lumbar spinal stenosis: A retrospective cohort study. Digital health\| PubMed ID: 40718407	2025
Artificial intelligence in joint arthroplasty: A bibliometric analysis of global research trends (2001-2025). Medicine\| PubMed ID: 40898573	2025
Machine learning for predicting extended length of stay in elderly patients with hip fractures: An enhanced recovery after surgery perspective. Digital health\| PubMed ID: 41393839	2025
Research Trends and Knowledge Evolution of Surgical Approaches in Primary Total Hip Arthroplasty: A Bibliometric Analysis. Inquiry : a journal of medical care organization, provision and financing\| PubMed ID: 42033323	2026

Xu Peng

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