Deepak Chona1, Nikita Lakomkin1, Catherine Bulka2, Idine Mousavi1, Parth Kothari1, Ashley C Dodd1, Michelle S Shen1, William T Obremskey1, Manish K Sethi3. 1. Department of Orthopaedics, Vanderbilt Orthopaedic Institute Center for Health Policy, Vanderbilt University Medical Center, 1215 21st Avenue South, Suite 4200, Medical Center East, South Tower, Nashville, TN, 37232, USA. 2. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA. 3. Department of Orthopaedics, Vanderbilt Orthopaedic Institute Center for Health Policy, Vanderbilt University Medical Center, 1215 21st Avenue South, Suite 4200, Medical Center East, South Tower, Nashville, TN, 37232, USA. manish.sethi@vanderbilt.edu.
Abstract
PURPOSE: Length 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. METHODS: All 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. RESULTS: 49,778 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]. CONCLUSIONS: Utilizing 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.
PURPOSE: Length 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 traumapatients and create a personalized LOS calculator. METHODS: All 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. RESULTS: 49,778 orthopaedic traumapatients 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]. CONCLUSIONS: Utilizing a large prospective cohort of orthopaedic traumapatients, 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.
Authors: P S Whiting; G A White-Dzuro; F R Avilucea; A C Dodd; N Lakomkin; W T Obremskey; C A Collinge; M K Sethi Journal: Eur J Trauma Emerg Surg Date: 2016-02-15 Impact factor: 3.693
Authors: Vasanth Sathiyakumar; Rachel V Thakore; Sarah E Greenberg; Paul S Whiting; Cesar S Molina; William T Obremskey; Manish K Sethi Journal: J Orthop Trauma Date: 2015-07 Impact factor: 2.512
Authors: Daniel D Bohl; Benjamin C Mayo; Dustin H Massel; Stephanie E Iantorno; Junyoung Ahn; Bryce A Basques; Jonathan N Grauer; Kern Singh Journal: Spine (Phila Pa 1976) Date: 2016-06 Impact factor: 3.468
Authors: Cesar S Molina; Rachel V Thakore; Alexandra Blumer; William T Obremskey; Manish K Sethi Journal: Clin Orthop Relat Res Date: 2015-05 Impact factor: 4.176
Authors: Manouk Backes; Niels W L Schep; Jan S K Luitse; J Carel Goslings; Tim Schepers Journal: Arch Orthop Trauma Surg Date: 2015-04-26 Impact factor: 3.067
Authors: Sanjit R Konda; Ariana Lott; Jessica Mandel; Thomas R Lyon; Jonathan Robitsek; Abhishek Ganta; Kenneth A Egol Journal: Geriatr Orthop Surg Rehabil Date: 2020-09-15