Initial Assessment of the Risk Assessment and Prediction Tool in a Heterogeneous Neurosurgical Patient Population.


Journal

Neurosurgery
ISSN: 1524-4040
Titre abrégé: Neurosurgery
Pays: United States
ID NLM: 7802914

Informations de publication

Date de publication:
01 07 2019
Historique:
received: 09 10 2017
accepted: 13 04 2018
pubmed: 23 5 2018
medline: 25 3 2020
entrez: 23 5 2018
Statut: ppublish

Résumé

Bundled care payments are increasingly being explored for neurosurgical interventions. In this setting, skilled nursing facility (SNF) is less desirable from a cost perspective than discharge to home, underscoring the need for better preoperative prediction of postoperative disposition. To assess the capability of the Risk Assessment and Prediction Tool (RAPT) and other preoperative variables to determine expected disposition prior to surgery in a heterogeneous neurosurgical cohort, through observational study. Patients aged 50 yr or more undergoing elective neurosurgery were enrolled from June 2016 to February 2017 (n = 623). Logistic regression was used to identify preoperative characteristics predictive of discharge disposition. Results from multivariate analysis were used to create novel grading scales for the prediction of discharge disposition that were subsequently compared to the RAPT Score using Receiver Operating Characteristic analysis. Higher RAPT Score significantly predicted home disposition (P < .001). Age 65 and greater, dichotomized RAPT walk score, and spinal surgery below L2 were independent predictors of SNF discharge in multivariate analysis. A grading scale utilizing these variables had superior discriminatory power between SNF and home/rehab discharge when compared with RAPT score alone (P = .004). Our analysis identified age, lower lumbar/lumbosacral surgery, and RAPT walk score as independent predictors of discharge to SNF, and demonstrated superior predictive power compared with the total RAPT Score when combined in a novel grading scale. These tools may identify patients who may benefit from expedited discharge to subacute care facilities and decrease inpatient hospital resource utilization following surgery.

Sections du résumé

BACKGROUND
Bundled care payments are increasingly being explored for neurosurgical interventions. In this setting, skilled nursing facility (SNF) is less desirable from a cost perspective than discharge to home, underscoring the need for better preoperative prediction of postoperative disposition.
OBJECTIVE
To assess the capability of the Risk Assessment and Prediction Tool (RAPT) and other preoperative variables to determine expected disposition prior to surgery in a heterogeneous neurosurgical cohort, through observational study.
METHODS
Patients aged 50 yr or more undergoing elective neurosurgery were enrolled from June 2016 to February 2017 (n = 623). Logistic regression was used to identify preoperative characteristics predictive of discharge disposition. Results from multivariate analysis were used to create novel grading scales for the prediction of discharge disposition that were subsequently compared to the RAPT Score using Receiver Operating Characteristic analysis.
RESULTS
Higher RAPT Score significantly predicted home disposition (P < .001). Age 65 and greater, dichotomized RAPT walk score, and spinal surgery below L2 were independent predictors of SNF discharge in multivariate analysis. A grading scale utilizing these variables had superior discriminatory power between SNF and home/rehab discharge when compared with RAPT score alone (P = .004).
CONCLUSION
Our analysis identified age, lower lumbar/lumbosacral surgery, and RAPT walk score as independent predictors of discharge to SNF, and demonstrated superior predictive power compared with the total RAPT Score when combined in a novel grading scale. These tools may identify patients who may benefit from expedited discharge to subacute care facilities and decrease inpatient hospital resource utilization following surgery.

Identifiants

pubmed: 29788192
pii: 5000047
doi: 10.1093/neuros/nyy197
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

50-57

Informations de copyright

Copyright © 2018 by the Congress of Neurological Surgeons.

Auteurs

Matthew Piazza (M)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

Nikhil Sharma (N)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

Benjamin Osiemo (B)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.
Department of Mathematics, Westchester University, Westchester, Pennsylvania.

Scott McClintock (S)

Department of Mathematics, Westchester University, Westchester, Pennsylvania.

Emily Missimer (E)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

Diana Gardiner (D)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

Eileen Maloney (E)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

Danielle Callahan (D)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

J Lachlan Smith (JL)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

William Welch (W)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

James Schuster (J)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

M Sean Grady (MS)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

Neil R Malhotra (NR)

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsy-lvania.

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