Background: Risk stratification of individual patients who are prone to infection would allow surgeons to monitor high-risk patients more closely and intervene early when needed. This could reduce infection-related consequences such as increased health-care costs. The purpose of this study was to develop a machine learning (ML)-derived risk-stratification tool using the SPRINT (Study to Prospectively Evaluate Reamed Intramedullary Nails in Patients with Tibial Fractures) and FLOW (Fluid Lavage of Open Wounds) trial databases to estimate the probability of infection in patients with operatively treated tibial shaft fractures (TSFs). Methods: Patients with unilateral TSFs from the SPRINT and FLOW trials were randomly split into derivation (80%) and validation (20%) cohorts. Random forest algorithms were used to select features that are relevant to predicting infection. These features were included for algorithm training. Five ML algorithms were trained in recognizing patterns associated with infection. The performance of each ML algorithm was evaluated and compared based on (1) the area under the ROC (receiver operating characteristic) curve (AUC), (2) the calibration slope and the intercept, and (3) the Brier score. Results: There were 1,822 patients included in this study: 170 patients (9%) developed an infection that required treatment, 62 patients (3%) received nonoperative treatment with oral or intravenous antibiotics, and 108 patients (6%) underwent subsequent surgery in addition to antibiotic therapy. Random forest algorithms identified 7 variables that were relevant for predicting infection: (1) Gustilo-Anderson or Tscherne classification, (2) bone loss, (3) mechanism of injury, (4) multitrauma, (5) AO/OTA fracture classification, (6) age, and (7) fracture location. Training of the penalized logistic regression algorithm resulted in the best-performing prediction model, with AUC, calibration slope, calibration intercept, and Brier scores of 0.75, 0.94, 0.00, and 0.076, respectively, in the derivation cohort and 0.81, 1.07, 0.09, and 0.079, respectively, in the validation cohort. Conclusions: We developed an ML prediction model that can estimate the probability of infection for individual patients with TSFs based on patient and fracture characteristics that are readily available at hospital admission.

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.

There is little known about the impact of nonalcoholic fatty liver disease (NAFLD) on drug metabolism and transport. We examined the pharmacokinetics of oral apixaban (2.5 mg) and rosuvastatin (5 mg) when administered simultaneously in subjects with magnetic resonance imaging-confirmed NAFLD (N = 22) and healthy control subjects (N = 12). The area under the concentration-time curve to the last sampling time (AUC0-12) values for apixaban were not different between control and NAFLD subjects (671 and 545 ng/ml × hour, respectively; P = 0.15). Similarly, the AUC0-12 values for rosuvastatin did not differ between the control and NAFLD groups (25.4 and 20.1 ng/ml × hour, respectively; P = 0.28). Furthermore, hepatic fibrosis in NAFLD subjects was not associated with differences in apixaban or rosuvastatin pharmacokinetics. Decreased systemic exposures for both apixaban and rosuvastatin were associated with increased body weight (P < 0.001 and P < 0.05, respectively). In multivariable linear regression analyses, only participant weight but not NAFLD, age, or SLCO1B1/ABCG2/CYP3A5 genotypes, was associated with apixaban and rosuvastatin AUC0-12 (P < 0.001 and P = 0.06, respectively). NAFLD does not appear to affect the pharmacokinetics of apixaban or rosuvastatin.

INTRODUCTION:Inadequate sample size and power in randomized trials can result in misleading findings. This study demonstrates the effect of sample size in a large clinical trial by evaluating the results of the Study to Prospectively evaluate Reamed Intramedullary Nails in Patients with Tibial fractures (SPRINT) trial as it progressed.METHODS:The SPRINT trial evaluated reamed versus unreamed nailing of the tibia in 1226 patients, and in open and closed fracture subgroups (N = 400 and N = 826, respectively). We analyzed the reoperation rates and relative risk comparing treatment groups at 50, 100, and then increments of 100 patients up to the final sample size. Results at various enrollments were compared with the final SPRINT findings.RESULTS:In the final analysis, there was a statistically significant decreased risk of reoperation with reamed nails for closed fractures (relative risk reduction 35%). Results for the first 35 patients enrolled suggested that reamed nails increased the risk of reoperation in closed fractures by 165%. Only after 543 patients with closed fractures were enrolled did the results reflect the final advantage for reamed nails in this subgroup. Similarly, the trend toward an increased risk of reoperation for open fractures (23%) was not seen until 62 patients with open fractures were enrolled.CONCLUSIONS:Our findings highlight the risk of conducting a trial with insufficient sample size and power. Such studies are not only at risk of missing true effects but also of giving misleading results.