Discharge destination following lower limb fracture: Development of a prediction model to assist with decision making

详细信息    查看全文

• 作者：Lara A. Kimmel^a ; ^b ; ^{l.kimmel@alfred.org.au} ; Anne E. Holland^b ; ^c ; Elton R. Edwards^a ; ^d ; Peter A. Cameron^a ; ^e ; ^f ; Richard De Steiger^g ; Richard S. Page^h ; Belinda Gabbe^a ; ^e
• 关键词：Discharge destination ; Trauma ; Fracture ; Predictors ; Rehabilitation ; Resource allocation
• 刊名：Injury
• 出版年：2012
• 出版时间：June, 2012
• 年：2012
• 卷：43
• 期：6
• 页码：829-834
• 全文大小：310 K

文摘

Background

Accurate prediction of the likelihood of discharge to inpatient rehabilitation following lower limb fracture made on admission to hospital may assist patient discharge planning and decrease the burden on the hospital system caused by delays in decision making.

Aims

To develop a prognostic model for discharge to inpatient rehabilitation.

Method

Isolated lower extremity fracture cases (excluding fractured neck of femur), captured by the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), were extracted for analysis. A training data set was created for model development and validation data set for evaluation. A multivariable logistic regression model was developed based on patient and injury characteristics. Models were assessed using measures of discrimination (C-statistic) and calibration (Hosmer-Lemeshow (H-L) statistic).

Results

A total of 1429 patients met the inclusion criteria and were randomly split into training and test data sets. Increasing age, more proximal fracture type, compensation or private fund source for the admission, metropolitan location of residence, not working prior to injury and having a self-reported pre-injury disability were included in the final prediction model. The C-statistic for the model was 0.92 (95 % confidence interval (CI) 0.88, 0.95) with an H-L statistic of ¦Ö² = 11.62, p = 0.17. For the test data set, the C-statistic was 0.86 (95 % CI 0.83, 0.90) with an H-L statistic of ¦Ö² = 37.98, p < 0.001.

Conclusion

A model to predict discharge to inpatient rehabilitation following lower limb fracture was developed with excellent discrimination although the calibration was reduced in the test data set. This model requires prospective testing but could form an integral part of decision making in regards to discharge disposition to facilitate timely and accurate referral to rehabilitation and optimise resource allocation.