Identification of Factors Associated With 30-day Readmissions After Posterior Lumbar Fusion Using Machine Learning and Traditional Models: A National Longitudinal Database Study.

Autor:	Rezaii PG; Department of Neurosurgery, Stanford University, Stanford, CA., Herrick D; Department of Neurosurgery, Stanford University, Stanford, CA., Ratliff JK; Department of Neurosurgery, Stanford University, Stanford, CA., Rusu M; Department of Radiology, Stanford University, Stanford, CA., Scheinker D; Department of Neurosurgery, Stanford University, Stanford, CA., Desai AM; Department of Neurosurgery, Stanford University, Stanford, CA.
Jazyk:	angličtina
Zdroj:	Spine [Spine (Phila Pa 1976)] 2023 Sep 01; Vol. 48 (17), pp. 1224-1233. Date of Electronic Publication: 2023 Apr 07.
DOI:	10.1097/BRS.0000000000004664
Abstrakt:	Study Design: A retrospective cohort study. Objective: To identify the factors associated with readmissions after PLF using machine learning and logistic regression (LR) models. Summary of Background Data: Readmissions after posterior lumbar fusion (PLF) place significant health and financial burden on the patient and overall health care system. Materials and Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent posterior lumbar laminectomy, fusion, and instrumentation between 2004 and 2017. Four machine learning models and a multivariable LR model were used to assess factors most closely associated with 30-day readmission. These models were also evaluated in terms of ability to predict unplanned 30-day readmissions. The top-performing model (Gradient Boosting Machine; GBM) was then compared with the validated LACE index in terms of potential cost savings associated with the implementation of the model. Results: A total of 18,981 patients were included, of which 3080 (16.2%) were readmitted within 30 days of initial admission. Discharge status, prior admission, and geographic division were most influential for the LR model, whereas discharge status, length of stay, and prior admissions had the greatest relevance for the GBM model. GBM outperformed LR in predicting unplanned 30-day readmission (mean area under the receiver operating characteristic curve 0.865 vs. 0.850, P <0.0001). The use of GBM also achieved a projected 80% decrease in readmission-associated costs relative to those achieved by the LACE index model. Conclusions: The factors associated with readmission vary in terms of predictive influence based on standard LR and machine learning models used, highlighting the complementary roles these models have in identifying relevant factors for the prediction of 30-day readmissions. For PLF procedures, GBM yielded the greatest predictive ability and associated cost savings for readmission. Level of Evidence: 3. Competing Interests: The authors report no conflicts of interest. (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)
Databáze:	MEDLINE
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