Prediction of 90-day mortality after total hip arthroplasty.

Autor:	Garland A; Department of Surgical Sciences/Orthopaedics, Institute of Surgical Sciences, Uppsala University Hospital, Uppsala, Sweden.; The Swedish Hip Arthroplasty Register, Gothenburg, Sweden.; Department of Orthopaedics, Visby Hospital, Visby, Sweden., Bülow E; The Swedish Hip Arthroplasty Register, Gothenburg, Sweden.; Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden., Lenguerrand E; Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK., Blom A; Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.; The National Institute of Health Research Biomedical Research Centre, Bristol, UK., Wilkinson M; Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK., Sayers A; Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK., Rolfson O; The Swedish Hip Arthroplasty Register, Gothenburg, Sweden.; Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden., Hailer NP; Department of Surgical Sciences/Orthopaedics, Institute of Surgical Sciences, Uppsala University Hospital, Uppsala, Sweden.; The Swedish Hip Arthroplasty Register, Gothenburg, Sweden.
Jazyk:	angličtina
Zdroj:	The bone & joint journal [Bone Joint J] 2021 Mar; Vol. 103-B (3), pp. 469-478.
DOI:	10.1302/0301-620X.103B3.BJJ-2020-1249.R1
Abstrakt:	Aims: To develop and externally validate a parsimonious statistical prediction model of 90-day mortality after elective total hip arthroplasty (THA), and to provide a web calculator for clinical usage. Methods: We included 53,099 patients with cemented THA due to osteoarthritis from the Swedish Hip Arthroplasty Registry for model derivation and internal validation, as well as 125,428 patients from England and Wales recorded in the National Joint Register for England, Wales, Northern Ireland, the Isle of Man, and the States of Guernsey (NJR) for external model validation. A model was developed using a bootstrap ranking procedure with a least absolute shrinkage and selection operator (LASSO) logistic regression model combined with piecewise linear regression. Discriminative ability was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration belt plots were used to assess model calibration. Results: A main effects model combining age, sex, American Society for Anesthesiologists (ASA) class, the presence of cancer, diseases of the central nervous system, kidney disease, and diagnosed obesity had good discrimination, both internally (AUC = 0.78, 95% confidence interval (CI) 0.75 to 0.81) and externally (AUC = 0.75, 95% CI 0.73 to 0.76). This model was superior to traditional models based on the Charlson (AUC = 0.66, 95% CI 0.62 to 0.70) and Elixhauser (AUC = 0.64, 95% CI 0.59 to 0.68) comorbidity indices. The model was well calibrated for predicted probabilities up to 5%. Conclusion: We developed a parsimonious model that may facilitate individualized risk assessment prior to one of the most common surgical interventions. We have published a web calculator to aid clinical decision-making. Cite this article: Bone Joint J  2021;103-B(3):469-478.
Databáze:	MEDLINE
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