Prediction of Early Adverse Events After THA: A Comparison of Different Machine‐Learning Strategies Based on 262,356 Observations From the Nordic Arthroplasty Register Association (NARA) Dataset.

Autor:	Venäläinen, Mikko S., Panula, Valtteri J., Eskelinen, Antti P., Fenstad, Anne Marie, Furnes, Ove, Hallan, Geir, Rolfson, Ola, Kärrholm, Johan, Hailer, Nils P., Pedersen, Alma B., Overgaard, Søren, Mäkelä, Keijo T., Elo, Laura L.
Předmět:	RISK assessment MORTALITY TOTAL hip replacement PREDICTION models DATABASE management PROSTHESIS-related infections RESEARCH funding SURGERY PATIENTS STATISTICAL sampling LOGISTIC regression analysis PREOPERATIVE care DECISION making MANN Whitney U Test CHI-squared test SURGICAL complications ARTIFICIAL neural networks REOPERATION ADVERSE health care events MACHINE learning COMPARATIVE studies DATA analysis software CONFIDENCE intervals PERIPROSTHETIC fractures DISEASE risk factors
Zdroj:	ACR Open Rheumatology; Oct2024, Vol. 6 Issue 10, p669-677, 9p
Abstrakt:	Objective: Preoperative risk prediction models can support shared decision‐making before total hip arthroplasties (THAs). Here, we compare different machine‐learning (ML) approaches to predict the six‐month risk of adverse events following primary THA to obtain accurate yet simple‐to‐use risk prediction models. Methods: We extracted data on primary THAs (N = 262,356) between 2010 and 2018 from the Nordic Arthroplasty Register Association dataset. We benchmarked a variety of ML algorithms in terms of the area under the receiver operating characteristic curve (AUROC) for predicting the risk of revision caused by periprosthetic joint infection (PJI), dislocation or periprosthetic fracture (PPF), and death. All models were internally validated against a randomly selected test cohort (one‐third of the data) that was not used for training the models. Results: The incidences of revisions because of PJI, dislocation, and PPF were 0.8%, 0.4%, and 0.3%, respectively, and the incidence of death was 1.2%. Overall, Lasso regression with stable iterative variable selection (SIVS) produced models using only four to five input variables but with AUROC comparable to more complex models using all 32 variables available. The SIVS‐based Lasso models based on age, sex, preoperative diagnosis, bearing couple, fixation, and surgical approach predicted the risk of revisions caused by PJI, dislocations, and PPF, as well as death, with AUROCs of 0.61, 0.67, 0.76, and 0.86, respectively. Conclusion: Our study demonstrates that satisfactory predictive potential for adverse events following THA can be reached with parsimonious modeling strategies. The SIVS‐based Lasso models may serve as simple‐to‐use tools for clinical risk assessment in the future. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Plný text