ArticleBMC anesthesiology2022
Implementation of a machine learning application in preoperative risk assessment for hip repair surgery.
Article in BMC anesthesiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- [Application and prospect of machine learning in orthopaedic trauma].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023Pooled it
- Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.BMC endocrine disorders · 2023Trial
- Artificial intelligence for predicting perioperative anaesthetic complications and supporting clinical decision-making: a scoping review.Journal of clinical monitoring and computing · 2026Review
- Regulating digital surgery to balance safety and innovation: a SAGES white paper.Surgical endoscopy · 2026Article
- Unveiling the Algorithm: The Role of Explainable Artificial Intelligence in Modern Surgery.Healthcare (Basel, Switzerland) · 2025Review
- Implementation and Updating of Clinical Prediction Models: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- Artificial Intelligence in Trauma and Orthopaedic Surgery: A Comprehensive Review From Diagnosis to Rehabilitation.Cureus · 2025Review
- Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning.Scientific reports · 2025Article
- Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms.PLOS digital health · 2025Article
- The anesthesiologist's guide to critically assessing machine learning research: a narrative review.BMC anesthesiology · 2024Review
- Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control.Bioengineering (Basel, Switzerland) · 2023Article
- Review
- Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings.Yearbook of medical informatics · 2023Article
- Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to develop a machine learning-based application in a real-world medical domain to assist anesthesiologists in assessing the risk of complications in patients after a hip surgery.
methodsData from adult patients who underwent hip repair surgery at Chi-Mei Medical Center and its 2 branch hospitals from January 1, 2013, to March 31, 2020, were analyzed. Patients with incomplete data were excluded. A total of 22 features were included in the algorithms, including demographics, comorbidities, and major preoperative laboratory data from the database. The primary outcome was a composite of adverse events (in-hospital mortality, acute myocardial infarction, stroke, respiratory, hepatic and renal failure, and sepsis). Secondary outcomes were intensive care unit (ICU) admission and prolonged length of stay (PLOS). The data obtained were imported into 7 machine learning algorithms to predict the risk of adverse outcomes. Seventy percent of the data were randomly selected for training, leaving 30% for testing. The performances of the models were evaluated by the area under the receiver operating characteristic curve (AUROC). The optimal algorithm with the highest AUROC was used to build a web-based application, then integrated into the hospital information system (HIS) for clinical use.
resultsData from 4,448 patients were analyzed; 102 (2.3%), 160 (3.6%), and 401 (9.0%) patients had primary composite adverse outcomes, ICU admission, and PLOS, respectively. Our optimal model had a superior performance (AUROC by DeLong test) than that of ASA-PS in predicting the primary composite outcomes (0.810 vs. 0.629, p < 0.01), ICU admission (0.835 vs. 0.692, p < 0.01), and PLOS (0.832 vs. 0.618, p < 0.01).
conclusionsThe hospital-specific machine learning model outperformed the ASA-PS in risk assessment. This web-based application gained high satisfaction from anesthesiologists after online use.
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