Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database.
Tony Lin-Wei ChenMohammadAmin RezazadehSaatlouAnirudh BuddhirajuHenry Hojoon SeoMichelle Riyo ShimizuYoung-Min KwonPublished in: Archives of orthopaedic and trauma surgery (2024)
Our study demonstrated that the ML model accurately predicted prolonged LOS after revision THA. The results highlighted the importance of the indications for revision surgery in determining the risk of prolonged LOS. With the model's aid, clinicians can stratify individual patients based on key factors, improve care coordination and discharge planning for those at risk of prolonged LOS, and increase cost efficiency.
Keyphrases
- total hip arthroplasty
- total knee arthroplasty
- machine learning
- end stage renal disease
- healthcare
- palliative care
- quality improvement
- newly diagnosed
- ejection fraction
- minimally invasive
- acute coronary syndrome
- chronic kidney disease
- peritoneal dialysis
- coronary artery bypass
- prognostic factors
- emergency department
- adverse drug
- artificial intelligence
- deep learning
- data analysis