Abstract
Technology within medicine has great potential to bring about more accessible, efficient, and a higher quality delivery of care. Paediatric supracondylar fractures are the most common elbow fracture in children and at our institution often have high rates of unnecessary long term clinical follow-up, leading to an inefficient use of healthcare and patient resources. This study aims to evaluate patient and clinical factors that significantly predict necessity for further clinical visits following closed reduction and percutaneous pinning.
A total of 246 children who underwent closed reduction and percutaneous pinning following supracondylar humerus fractures were prospectively enrolled over a two year period. Patient demographics, perioperative course, goniometric measurements, functional outcome measures, clinical assessment and decision making for further follow up were assessed. Categorical and continuous variables were analyzed and screened for significance via bivariate regression. Significant covariates were used to develop a predictive model through multivariate logistical regression. A probability cut-off was determined on the Receiver Operator Characteristic (ROC) curve using the Youden index to maximize sensitivity and specificity. The regression model performance was then prospectively tested against 22 patients in a blind comparison to evaluate accuracy.
246 paediatrics patients were collected, with 29 cases requiring further follow up past the three month visit. Significant predictive factors for follow up were residual nerve palsy (p < 0 .001) and maximum active flexion angle of injured elbow (p < 0 .001). Insignificant factors included other goniometric measures, subjective evaluations, and functional outcomes scores. The probability of requiring further clinical follow up at the 3 month post-op point can be estimated with the equation: logit(follow-up) = 11.319 + 5.518(nerve palsy) − 0.108(maximum active flexion). Goodness of fit of the model was verified with Nagelkerke R2 = 0.574 and Hosmer & Lemeshow chi-square (p = 0.739). Area Under Curve of the ROC curve was C = 0.919 (SE = 0.035, 95% CI 0.850 – 0.988). Using Youden's Index, a cut-off for probability of follow up was set at 0.094 with the overall sensitivity and specificity maximized to 86.2% and 88% respectively. Using this model and cohort, 194 three month clinic visits would have been deemed medically unnecessary. Preliminary blind prospective testing against the 22 patient cohort demonstrates a model sensitivity and specificity at 100% and 75% respectively, correctly deeming 15 visits unnecessary.
Virtual clinics and automated clinical decision making can improve healthcare inefficiencies, unclog clinic wait times, and ultimately enhance quality of care delivery. Our regression model is highly accurate in determining medical necessity for physician examination at the three month visit following supracondylar fracture closed reduction and percutaneous pinning. When applied correctly, there is potential for significant reductions in health care expenditures and in the economic burden on patient families by removing unnecessary visits. In light of positive patient and family receptiveness toward technology, our promising findings and predictive model may pave the way for remote health care delivery, virtual clinics, and automated clinical decision making.
