Literature DB >> 31327649

Arthroplasty Care Redesign Impacts the Predictive Accuracy of the Risk Assessment and Prediction Tool.

Florian F Dibra1, Arnold J Silverberg1, Terri Vasilopoulos1, Chancellor F Gray1, Hari K Parvataneni1, Hernan A Prieto1.   

Abstract

BACKGROUND: The Risk Assessment and Prediction Tool (RAPT) is used to predict patient discharge disposition after total joint arthroplasty. Following a comprehensive, multidisciplinary redesign, our institution noticed a trend toward home discharge in patients with RAPT scores that historically predicted discharge to acute care facilities, presenting an opportunity to redefine the predictive ranges for RAPT.
METHODS: Retrospectively collected data were analyzed from a single institution in patients undergoing elective primary total joint arthroplasty from January 2016 to April 2017. Predictive accuracy (PA) was calculated for each RAPT score (1-12), RAPT score risk ranges (low, intermediate, and high), as well as overall. Other factors evaluated included patient-reported discharge expectation, body mass index, and American Society of Anesthesiologists scores as related to discharge disposition and the PA of RAPT.
RESULTS: Overall PA of RAPT was 88% (n = 1024 patients). Patients were high risk for acute care facility with a RAPT score of 1 to 3 (PA ≥ 83%), intermediate risk 4 to 7 (PA, 52%-79%), and low risk 8 to 12 (PA ≥ 89%). In multivariable analysis, RAPT score and patient-reported discharge expectation had the strongest correlation with actual discharge disposition.
CONCLUSION: Our multidisciplinary redesign has impacted the PA of RAPT. The original predictive ranges should be modified to reflect the increasing proportion of patients being discharged home following elective arthroplasty procedures. We have identified patient-expected discharge destination as a powerful modulator of the RAPT score and suggest that it be taken into consideration for discharge planning.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Comprehensive Care for Joint Replacement (CJR); RAPT; discharge planning; total hip arthroplasty; total joint arthroplasty; total knee arthroplasty

Year:  2019        PMID: 31327649     DOI: 10.1016/j.arth.2019.06.035

Source DB:  PubMed          Journal:  J Arthroplasty        ISSN: 0883-5403            Impact factor:   4.757


  8 in total

1.  An Electronic Medical Record-Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning.

Authors:  Alexander S Greenstein; Jack Teitel; David J Mitten; Benjamin F Ricciardi; Thomas G Myers
Journal:  Arthroplast Today       Date:  2020-10-14

2.  Readmission, Complication, and Disposition Calculators in Total Joint Arthroplasty: A Systemic Review.

Authors:  Cole M Howie; Simon C Mears; C Lowry Barnes; Jeffrey B Stambough
Journal:  J Arthroplasty       Date:  2020-11-03       Impact factor: 4.757

3.  Modifying the RAPT Score to Reflect Discharge Destination in Current Practice.

Authors:  Eric Cohen; Daniel B C Reid; Matthew Quinn; Devin Walsh; Jeremy Raducha; Leigh Hubbard; John Froehlich
Journal:  Arthroplast Today       Date:  2020-12-21

4.  Predictive Value of the Risk Assessment and Prediction Tool (RAPT) Score for Primary Hip and Knee Arthroplasty Patients: A Single-Center Study.

Authors:  Awf A Alshahwani; Maurice Dungey; Christopher Lillie; Steve Krikler; Christos Plakogiannis
Journal:  Cureus       Date:  2021-03-25

5.  The Predictive Accuracy of the CareMOSAIC Risk Assessment for Discharge Disposition in Medicare Bundle Patients After Total Joint Arthroplasty.

Authors:  Corey Anderson; William Schweinle
Journal:  Arthroplast Today       Date:  2022-01-20

6.  Effect of Algoplaque Hydrocolloid Dressing Combined with Nanosilver Antibacterial Gel under Predictive Nursing in the Treatment of Medical Device-Related Pressure Injury.

Authors:  Chunxiu Li; Hongmei Chen; Guanghui You
Journal:  Comput Math Methods Med       Date:  2022-07-11       Impact factor: 2.809

7.  Performance of Artificial Intelligence-Based Algorithms to Predict Prolonged Length of Stay after Lumbar Decompression Surgery.

Authors:  Babak Saravi; Alisia Zink; Sara Ülkümen; Sebastien Couillard-Despres; Frank Hassel; Gernot Lang
Journal:  J Clin Med       Date:  2022-07-13       Impact factor: 4.964

Review 8.  Compilation and Analysis of Web-Based Orthopedic Personalized Predictive Tools: A Scoping Review.

Authors:  Patrick Curtin; Alexandra Conway; Liu Martin; Eugenia Lin; Prakash Jayakumar; Eric Swart
Journal:  J Pers Med       Date:  2020-11-12
  8 in total

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