Background Because of their poor physical state, elderly hip fracture patients commonly require prolonged hospitalization, resulting in a drop in bed circulation rate and an increased financial burden. There are currently few predictive models for delayed hospital discharge for hip fractures. This research aimed to develop the optimal model for delayed hospital discharge for hip fractures in order to support clinical decision-making. Methods This case-control research consisted of 1259 patients who were continuously hospitalized in the orthopedic unit of an acute hospital in Tianjin due to a fragility hip fracture between January and December 2021. Delayed discharge was defined as a hospital stay of more than 11 days. The prediction model was constructed through the use of a Cox proportional hazards regression model. Furthermore, the constructed prediction model was transformed into a nomogram. The model’s performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA). the STROBE checklist was used as the reporting guideline. Results The risk prediction model developed contained the Charlson Comorbidity Index (CCI), preoperative waiting time, anemia, hypoalbuminemia, and lower limbs arteriosclerosis. The AUC for the risk of delayed discharge was in the training set was 0.820 (95% CI,0.79 ~ 0.85) and 0.817 in the testing sets. The calibration revealed that the forecasted cumulative risk and observed probability of delayed discharge were quite similar. Using the risk prediction model, a higher net benefit was observed than when considered all patients were at high risk, demonstrating good clinical usefulness. Conclusion Our prediction models could support policymakers in developing strategies for the optimal management of hip fracture patients, with a particular emphasis on individuals at high risk of prolonged LOS.
Background: The length of hospital stay in hip fracture patients is closely associated with medical costs, the burden of which is increasing in aging societies. Herein, we developed and validated models for predicting prolonged length of stay in hip fracture patients to support efficient care in these patients. Methods: This was a retrospective analysis of all patients undergoing hip fracture from January 2019 to December 2021. Univariate and multivariate logistic regression analyses were used to evaluate the association between risk factors and delayed discharge after hip fracture . Finally, the risk factors obtained from the multivariate regression analysis were used to establish the nomogram model. The validation of the nomogram was assessed by the concordance index (C-index), the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curves. Results: A total of 684 patients were included in the present study for evaluation. Multivariate logistic regression analysis demonstrated that bone traction, pneumonia, second fracture, multiple trauma, venous thrombosis, pulmonary infection and ACCI were independent risk factors for delayed discharge after hip fracture. The C-index of this model was 0.794 (95% CI, 0.744-0.844). Internal validation proved the nomogram model’s adequacy and accuracy, and the results showed that the predicted value agreed well with the actual values. Conclusions: Our prediction models may help policymakers in developing strategies for the optimal management of hip fracture patients with a focus on patients at a high risk of prolonged length of stay.
Background The length of hospital stay in hip fracture patients is closely associated with medical costs, the burden of which is increasing in aging societies. Herein, we developed and validated models for predicting prolonged length of stay in hip fracture patients to support efficient care in these patients. Methods This was a retrospective analysis of all patients undergoing hip fracture from January 2021 to December 2021. Univariate and multivariate logistic regression analyses were used to evaluate the association between risk factors and delayed discharge after hip fracture. Finally, the risk factors obtained from the multivariate regression analysis were used to establish the nomogram model. The validation of the nomogram was assessed by the concordance index (C-index), the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curves. the STROBE checklist was used as the reporting guideline. Results A total of 1259 patients were included in the present study for evaluation. Multivariate logistic regression analysis demonstrated that CCI, Preoperative waiting time, Anemia, Hypoalbuminemia and Arteriosclerosis of lower limbs were independent risk factors for delayed discharge after hip fracture. The C-index of this model was 0.82 (95% CI, 0.793–0.847). Internal validation proved the nomogram model’s adequacy and accuracy, and the results showed that the predicted value agreed well with the actual values. Conclusions Our prediction models may help policymakers in developing strategies for the optimal management of hip fracture patients with a focus on patients at a high risk of prolonged length of stay.
Background The length of hospital stay in hip fracture patients is closely associated with medical costs, the burden of which is increasing in aging societies. Herein, we developed and validated models for predicting prolonged length of stay in hip fracture patients to support efficient care in these patients. Methods This was a retrospective analysis of all patients undergoing hip fracture from January 2021 to December 2021. Univariate and multivariate logistic regression analyses were used to evaluate the association between risk factors and delayed discharge after hip fracture. Finally, the risk factors obtained from the multivariate regression analysis were used to establish the nomogram model. The validation of the nomogram was assessed by the concordance index (C-index), the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curves. the STROBE checklist was used as the reporting guideline. Results A total of 1259 patients were included in the present study for evaluation. Multivariate logistic regression analysis demonstrated that CCI, Preoperative waiting time, Anemia, Hypoalbuminemia and Arteriosclerosis of lower limbs were independent risk factors for delayed discharge after hip fracture. The C-index of this model was 0.82 (95% CI, 0.793–0.847). Internal validation proved the nomogram model’s adequacy and accuracy, and the results showed that the predicted value agreed well with the actual values. Conclusions Our prediction models may help policymakers in developing strategies for the optimal management of hip fracture patients with a focus on patients at a high risk of prolonged length of stay.
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