In:
BioMed Research International, Hindawi Limited, Vol. 2021 ( 2021-12-6), p. 1-10
Abstract:
Background. The main aim of this study was to develop a nomogram prediction model for poor functional prognosis after patellar fracture surgery in the elderly based on the hospital for special surgery (HSS) knee score. Methods. A retrospective analysis of 168 elderly patients with patellar fractures was performed to collect demographic data, knee imaging, and functional prognosis preoperatively and during the 6-month postoperative follow-up period. Good functional prognosis of knee joint was defined as the percentage of HSS knee scores on the injured side relative to the uninjured side ≥ 80 % at six-month postoperative review. Multifactorial linear regression analysis and logistic regression analysis were then used to identify risk factors of functional prognosis and develop the nomogram prediction model. Furthermore, the validity and accuracy of the prediction model were evaluated using C-index, area under the curve (AUC), and decision curve analyses. Results. The final screening from the 12 potential risk factors yielded three high-risk factors which were included in the nomogram prediction model: advanced age (OR 0.28 (95% CI 0.11-0.67), P = 0.005 ), sarcopenia (OR 0.11 (95% CI 0.05-0.26), P 〈 0.001 ), and low albumin level (OR 1.14 (95% CI 1.02-1.29), P = 0.025 ). The model had a good predictive ability with an AUC of 0.857 (95% CI (0.783-0.929)) for the training group and a C-index of 0.836 for the overall sample. In addition, the decision analysis curve indicated that the model had good clinical applicability. Conclusion. Our predictive model is effective in predicting the risk of poor functional prognosis after patellar fracture surgery in the elderly by assessing high-risk factors such as advanced age, sarcopenia, and serum albumin levels. This prediction model can help clinicians to make individualized risk prediction, early identification of patients at high risk for poor functional outcome, and appropriate interventions.
Type of Medium:
Online Resource
ISSN:
2314-6141
,
2314-6133
DOI:
10.1155/2021/6620504
Language:
English
Publisher:
Hindawi Limited
Publication Date:
2021
detail.hit.zdb_id:
2698540-8
