Development of a Risk Stratification Model for Delayed Inpatient Recovery of Physical Activities in Patients Undergoing Total Hip Replacement

Study Design Prospective cohort design using data derived from usual care. Background It is important that patients are able to function independently as soon as possible after total hip replacement. However, the speed of regaining activities differs significantly. Objectives To develop a risk stratification model (RSM) to predict delayed inpatient recovery of physical activities in people who underwent total hip replacement surgery. Methods This study was performed in 2 routine orthopaedic settings: Diakonessenhuis Hospital (setting A) and Nij Smellinghe Hospital (setting B). Preoperative screening was performed for all consecutive patients. In-hospital recovery of activities was assessed with the Modified Iowa Level of Assistance Scale. Delayed inpatient recovery of activities was defined as greater than 5 days. The RSM, developed using logistic regression analysis and bootstrapping, was based on data from setting A (n = 154). External validation was performed on the data set from setting B (n = 271). Results Twenty-one percent of the patients in setting A had a delayed recovery of activities during their hospital stay. Multivariable logistic regression modeling yielded a preliminary RSM that included the following factors: male sex (odds ratio [OR] = 0.8; 95% confidence interval [CI]: 0.2, 2.6), 70 or more years of age (OR = 1.2; 95% CI: 0.4, 3.4), body mass index of 25 kg/m2 or greater (OR = 2.2; 95% CI: 0.7, 7.4), an American Society of Anesthesiologists score of 3 (OR = 1.2; 95% CI: 0.3, 4.4), a Charnley score of B or C (OR = 6.1; 95% CI: 2.2, 17.4), and a timed up-and-go score of 12.5 seconds or greater (OR = 3.1; 95% CI: 1.1, 9.0). The area under the receiver operating characteristic (ROC) curve was 0.82 (95% CI: 0.74, 0.90) and the Hosmer-Lemeshow test score was 3.57 (P>.05). External validation yielded an area under the ROC curve of 0.71 (95% CI: 0.61, 0.81). Conclusion We demonstrated that the risk for delayed recovery of activities during the hospital stay can be predicted by using preoperative data.