Currently submitted to: Journal of Medical Internet Research
Date Submitted: Feb 17, 2025
Open Peer Review Period: Feb 18, 2025 - Apr 15, 2025
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
An Optimization Method for Perioperative ERAS Decision-making in Total Hip Arthroplasty through Bayesian network model: a multicenter study
ABSTRACT
Background:
Background:
With the growing demand for Total Hip Arthroplasty (THA), there is an urgent need to devise individualized perioperative management strategies to expedite patient recovery, particularly in scenarios involving multiple disciplines and concurrent decision-making.
Objective:
Objective:
This study aims to construct a decision network and provide decision recommendations based on Bayesian Networks (BNs) for stratified risk groups.
Methods:
Methods:
The MIMIC-IV database was employed to establish a developing dataset by extracting patients who underwent THA, followed by K-means clustering for patient phenotyping. A BN model for THA decisions were developed, and support degree was calculated according to conditional probability for decision recommendation. An external validation dataset from the multiple center big data platform was then utilized to validate the BN model's recommendations by calculating the conformity of risk-recommended protocols after phenotype classification. An online tool was developed for convenient application in clinical practice.
Results:
Results:
The developing dataset comprised 1701 admissions, while the validation dataset included 418 admissions. Patients in the developing dataset were divided into three phenotypes: a low-risk group (n=722), a moderate-risk group (n=673), and a high-risk group (n=306). Tailored recommendation protocols were formulated for each phenotype, covering preoperative and postoperative periods, based on support degree. Consistency in the distribution of differences among clustered indicators was observed in both datasets. Adherence to the recommended decisions was associated with reduced postoperative length of stay (PLOS) across all three decision protocols.
Conclusions:
Conclusions:
Decisions recommended by Bayesian Networks for patients of different risk levels can decrease the PLOS and hasten recovery. This systematic approach, integrating risk stratification through clustering with an indicator network constructed by the BN model for decision recommendations, is adept at handling multi-decision tasks and supports clinical physicians in making informed choices regarding perioperative risk assessment and treatment plan selection. Clinical Trial: The registration has been completed on the website of the China Clinical Trial Registry Center (ChiCTR1900023927).
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