A critical part in preoperative planning for revision arthroplasty surgery involves the identification of the failed implant. Using a predictive artificial neural network (ANN) model, the objectives of this study were: (1) to develop a machine-learning algorithm using operative big data to identify an implant from a radiograph; and (2) to compare algorithms that optimise accuracy in a timely fashion. Using 2116 postoperative anteroposterior (AP) hip radiographs of total hip arthroplasties from 2002 to 2019, 10 artificial neural networks were modeled and trained to classify the radiograph according to the femoral stem implanted. Stem brand and model was confirmed with 1594 operative reports. Model performance was determined by classification accuracy toward a random 706 AP hip radiographs, and again on a consecutive series of 324 radiographs prospectively collected over 2019. The Dense-Net 201 architecture outperformed all others with 100.00% accuracy in training data, 95.15% accuracy on validation data, and 91.16% accuracy in the unique prospective series of patients. This outperformed all other models on the validation (p < 0.0001) and novel series (p < 0.0001). The convolutional neural network also displayed the probability (confidence) of the femoral stem classification for any input radiograph. This neural network averaged a runtime of 0.96 (SD 0.02) seconds for an iPhone 6 to calculate from a given radiograph when converted to an application. Neural networks offer a useful adjunct to the surgeon in preoperative identification of the prior implant.

Background: Preoperative radiographic templating for total knee arthroplasty (TKA) has been shown to be inaccurate. Patient demographic data, such as gender, height, weight, age, and race, may be more predictive of implanted component size in TKA.Materials and methods: A multivariate linear regression model was designed to predict implanted femoral and tibial component size using demographic data along a consecutive series of 201 patients undergoing index TKA. Traditional, two-dimensional, radiographic templating was compared to demographic-based regression predictions on a prospective 181 consecutive patients undergoing index TKA in their ability to accurately predict intraoperative implanted sizes. Surgeons were blinded of any predictions.Results: Patient gender, height, weight, age, and ethnicity/race were predictive of implanted TKA component size. The regression model more accurately predicted implanted component size compared to radiographically templated sizes for both the femoral (P = 0.04) and tibial (P < 0.01) components. The regression model exactly predicted femoral and tibial component sizes in 43.7 and 43.7% of cases, was within one size 90.1 and 95.6% of the time, and was within two sizes in every case. Radiographic templating exactly predicted 35.4 and 36.5% of cases, was within one size 86.2 and 85.1% of the time, and varied up to four sizes for both the femoral and tibial components. The regression model averaged within 0.66 and 0.61 sizes, versus 0.81 and 0.81 sizes for radiographic templating for femoral and tibial components.Conclusions: A demographic-based regression model was created based on patient-specific demographic data to predict femoral and tibial TKA component sizes. In a prospective patient series, the regression model more accurately and precisely predicted implanted component sizes compared to radiographic templating.Level of evidence: Prospective cohort, level II.

Diagnosing periprosthetic joint infection

Brown NM, et al. Hip Pelvis. 2021 Sep;33(3):128-139. https://doi.org/10.5371/hp.2021.33.3.128

Background: Thrombocytopenia is an abnormally low level of blood platelets (less than 150,000/mL) resulting in an increased risk for bleeding. Typically, patients with platelet levels below 50,000/mL should delay arthroplasty or be transfused with platelets before surgery. However, existing studies are mixed regarding the effects of more moderate thrombocytopenia in terms of total knee and hip arthroplasty outcomes. Methods: This level III retrospective chart review examined the effects of different severities of preoperative thrombocytopenia on length of hospitalization, readmission, and transfusion rates in 5,617 primary total knee and hip arthroplasties at one tertiary academic medical center. Preoperative platelet levels were sectioned into clinically relevant groups and compared with clinical outcomes using univariable and multivariable models. Results: On univariate analysis, having platelet levels of <100,000/mL and 100 to 149,000/mL was associated with a longer length of stay. However, after controlling individual demographics, there was no association between platelet levels and length of stay, nor with 30-day readmission. Finally, on univariate analysis, patients with platelet levels of <100,000/mL and 100 to 149,000/mL were more likely to have a blood transfusion, which remained true for those with <100,000/mL after controlling for individual demographics. Conclusions: Total hip and total knee arthroplasty are safe in patients with varying platelet levels and not associated with increased length of stay or 30-day readmission. However, patients with more severe thrombocytopenia are more likely to receive red blood cell transfusions than patients with milder thrombocytopenia.