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Development and Validation of a Postoperative Delirium Prediction Model for Elderly Orthopedic Patients in the Intensive Care Unit
We developed a prediction model for delirium in elderly patients in the intensive care unit who underwent orthopedic surgery and then temporally validated its predictive power in the same hospital. In the development stage, we designed a prospective cohort study, and 319 consecutive patients aged ov...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205566/ https://www.ncbi.nlm.nih.gov/pubmed/34211683 http://dx.doi.org/10.1155/2021/9959077 |
Sumario: | We developed a prediction model for delirium in elderly patients in the intensive care unit who underwent orthopedic surgery and then temporally validated its predictive power in the same hospital. In the development stage, we designed a prospective cohort study, and 319 consecutive patients aged over 65 years from January 2018 to December 2019 were screened. Demographic characteristics and clinical variables were evaluated, and a final prediction model was developed using the multivariate logistic regression analysis. In the validation stage, 108 patients were included for temporal validation between January 2020 and June 2020. The effectiveness of the model was evaluated through discrimination and calibration. As a result, the prediction model contains seven risk factors (age, anesthesia method, score of mini-mental state examination, hypoxia, major hemorrhage, level of interleukin-6, and company of family members), which had an area under the receiver operating characteristics curve of 0.82 (95% confidence interval 0.76–0.88) and was stable after bootstrapping. The temporal validation resulted in an area under the curve of 0.80 (95% confidence interval 0.67–0.93). Our prediction model had excellent discrimination power in predicting postoperative delirium in elderly patients and could assist intensive care physicians with early prevention. |
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