Aim

Ankle fracture surgery comes with a risk of fracture-related infection (FRI). Identifying risk factors are important in preoperative planning, in management of patients, and for information to the individual patient about their risk of complications. In addition, modifiable factors can be addressed prior to surgery. The aim of the current paper was to identify risk factors for FRI in patients operated for ankle fractures.

Aim

Surgical treatment of ankle fractures comes with a substantial risk of complications, including infection. An unambiguously definition of fracture-related infections (FRI) has been missing. Recently, FRI has been defined by a consensus group with a diagnostic algorithm containing suggestive and confirmatory criteria. The aim of the current study was to report the prevalence of FRI in patients operated for ankle fractures and to assess the applicability of the diagnostic algorithm from the consensus group.

The Norwegian Arthroplasty Register (NAR) started collecting data on total hip arthroplasty (THA) in 1987. Very long-term results of implants for THA are scarce. We aimed to show long-term results for the three most used femoral stems, operated from 1987.

We included the uncemented Corail femoral stem (n=66,309) and the cemented Exeter stem (n=35,050) both of which are currently in frequent use. In addition, we included the Charnley stem (n=32,578, in use until 2014). To ensure comparable conditions, stems fixated with low viscosity cement and stems revised due to infections were excluded. Differences in risk of revision (all reasons and stem revisions) were assessed with Kaplan-Meier and Cox regression analyses with adjustment for possible confounding from age, sex and diagnosis (OA, other). Stem revision was defined as a revision caused by loosening of the stem, dislocation, osteolysis in the femur, or periprosthetic femur fracture, and in which the femoral component was removed or exchanged.

The median and max follow-up for Corail, Exeter and Charnley were 6.3 (33.1), 8.0 (34.2) and 13.1 (34.3) respectively. Thirty years survival estimates for Corail, Exeter and Charnley stems were 88.6% (CI:85.8–90.9%), 86.7% (83.7–89.2%) and 87.1% (85.4–88.5%) respectively with stem revision as endpoint, and 56.1% (CI:53.1–59.1%), 73.3% (70.5–76.1%) and 80.2% (78.4–82.0%) with all THA revisions as endpoint. Compared to the Corail, the Exeter (HRR=1.3, CI:1.2–1.4) and the Charnley (HRR=1.9, CI:1.7–2.1) had a significant higher risk of stem revision. Women 75 years and older had better results with the cemented stems. Analyses accounting for competing risk from other causes of revision did not alter the findings.

The uncemented Corail stem performed well in terms of stem revisions for stem-related revision causes compared to two frequently used cemented stems with very long follow-up. The differences between the three stems were small.

External validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Recently, machine learning was used to develop a tool that can quantify revision risk for a patient undergoing primary anterior cruciate ligament (ACL) reconstruction (https://swastvedt.shinyapps.io/calculator_rev/). The source of data included nearly 25,000 patients with primary ACL reconstruction recorded in the Norwegian Knee Ligament Register (NKLR). The result was a well-calibrated tool capable of predicting revision risk one, two, and five years after primary ACL reconstruction with moderate accuracy. The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR).

The primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For the index study, 24 total predictor variables in the NKLR were included and the models eliminated variables which did not significantly improve prediction ability - without sacrificing accuracy. The result was a well calibrated algorithm developed using the Cox Lasso model that only required five variables (out of the original 24) for outcome prediction. For this external validation study, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables were: graft choice, femur fixation device, Knee Injury and Osteoarthritis Outcome Score (KOOS) Quality of Life subscale score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration.

In total, 10,922 DKLR patients were included for analysis. Average follow-up time or time-to-revision was 8.4 (±4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68-0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years.

The NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in-clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre-operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown.

Aim

Previous publications have suggested that the incidence of revisions due to infection after THA is increasing. We performed updated time-trend analyses of risk and timing of revision due to infection after primary THAs in the Nordic countries during the period 2004–2018.

Aim

In recent years, many studies on revision for infection after arthroplasty have been published. In national arthroplasty registers, revision for infection is defined as surgical debridement, with or without removal or exchange of the entire or parts of the prosthesis due to deep infection, and should be reported to the register immediately after surgery. The diagnosis of infection is made at the surgeon's discretion, based on pre- and perioperative assessment and evaluation, and is not to be corrected to the register based on peroperative bacterial cultures. Due to this lack of validation, the rate of revision for infection will only be an approximation of the true rate of periprosthetic joint infection (PJI). Our aim was to validate the reporting of infection after total hip arthroplasty, and to assess if revisions for infection actually represented true PJI.