ArticleInternational orthopaedics2017
Predicting the post-operative length of stay for the orthopaedic trauma patient.
Article in International orthopaedics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Impact of Bariatric Surgery on Postoperative Outcomes, Complications, and Revision Rates in Total Knee Arthroplasty: A Big Data Analysis.Journal of clinical medicine · 2025Article
- Factors influencing length of stay in orthopedic Class I incision surgery: development and validation of a nomogram using 31,248 patient records.Frontiers in medicine · 2025Article
- Article
- Predictive Factors for Onset of Moderate-to-Severe Disability Following Hospital Discharge Due to Rib Fractures.Healthcare (Basel, Switzerland) · 2024Article
- Article
- Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi.Scientific reports · 2022Article
- Predicting Length of Stay After Thoracolumbar Trauma: A Single-Center, Retrospective Analysis.International journal of spine surgery · 2022Article
- Article
- Is there a 'weekend effect' in elective lower limb arthroplasty?Annals of the Royal College of Surgeons of England · 2021Article
- Who Is the Geriatric Trauma Patient? An Analysis of Patient Characteristics, Hospital Quality Measures, and Inpatient Cost.Geriatric orthopaedic surgery & rehabilitation · 2020Article
- Validating a transnational fracture treatment registry using a standardized method.BMC medical research methodology · 2019Article
- Might Doctors Really "Know Best"?: Utilizing Surgeon Intuition to Strengthen Preoperative Surgical Risk Assessment.The Iowa orthopaedic journal · 2018Article
- Investigating Length of Stay Patterns and Its Predictors in the South Wales Trauma Network.Advances in rehabilitation science and practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLength of stay (LOS) is a major driver of cost and quality of care. A bundled payment system makes it essential for orthopaedic surgeons to understand factors that increase a patient's LOS. Yet, minimal data regarding predictors of LOS currently exist. Using the ACS-NSQIP database, this is the first study to identify risk factors for increased LOS for orthopaedic trauma patients and create a personalized LOS calculator.
methodsAll orthopaedic trauma surgery between 2006 and 2013 were identified from the ACS-NSQIP database using CPT codes. Patient demographics, pre-operative comorbidities, anatomic location of injury, and post-operative in-hospital complications were collected. To control for individual patient comorbidities, a negative binomial regression model evaluated hospital LOS after surgery. Betas (β), were determined for each pre-operative patient characteristic. We selected significant predictors of LOS (p < 0.05) using backwards stepwise elimination.
results49,778 orthopaedic trauma patients were included in the analysis. Deep incisional surgical site infections and superficial surgical site infections were associated with the greatest percent change in predicted LOS (β = 1.2760 and 1.2473, respectively; p < 0.0001 for both). A post-operative LOS risk calculator was developed based on the formula: [Formula: see text].
conclusionsUtilizing a large prospective cohort of orthopaedic trauma patients, we created the first personalized LOS calculator based on pre-operative comorbidities, post-operative complications and location of surgery. Future work may assess the use of this calculator and attempt to validate its utility as an accurate model. To improve the quality measures of hospitals, orthopaedists must employ such predictive tools to optimize care and better manage resources.
Indexed as
Identifiers
28224191What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.