Validation of a Classification System for Optimal Application of Debridement, Antibiotics, and Implant Retention in Prosthetic Joint Infections following Total Knee Arthroplasty: A Retrospective Review.

INTRODUCTION: Periprosthetic joint infection (PJI) remains a serious complication after total knee arthroplasty (TKA). While debridement, antibiotics, and implant retention (DAIR) are considered for acute PJI, success rates vary. This study aims to assess a new scoring system's accuracy in predicting DAIR success. METHODS: 119 TKA patients (2008-2019) diagnosed with PJI who underwent DAIR were included for analysis. Data were collected on demographics, laboratory values, and clinical outcomes. This was used for validation of the novel classification system consisting of PJI acuteness, microorganism classification, and host health for DAIR indication. Statistical analysis was carried out using SPSS programming. RESULTS: Mean follow-up was 2.5 years with an average age of 65.5 ± 9.1 years, BMI of 31.9 ± 6.2 kg/m2, and CCI of 3.04 ± 1.8. Successful infection eradication occurred in 75.6% of patients. The classification system demonstrated 61.1% sensitivity, 72.4% specificity, and 87.3% positive predictive value (PPV) when the DAIR cutoff was a score less than 6. For a cutoff of less than 8, sensitivity was 100%, specificity was 37.9%, and PPV was 83.3%. CONCLUSIONS: To date, no consensus exists on a classification system predicting DAIR success. This novel scoring system, with high PPV, shows promise. Further refinement is essential for enhanced predictive accuracy.

Diversity in Renal Mass Data Cohorts: Implications for Urology AI Researchers.

INTRODUCTION: We examine the heterogeneity and distribution of the cohort populations in two publicly used radiological image cohorts, the Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCIA TCGA KIRC) collection and 2019 MICCAI Kidney Tumor Segmentation Challenge (KiTS19), and deviations in real-world population renal cancer data from the National Cancer Database (NCDB) Participant User Data File (PUF) and tertiary center data. PUF data are used as an anchor for prevalence rate bias assessment. Specific gene expression and, therefore, biology of RCC differ by self-reported race, especially between the African American and Caucasian populations. AI algorithms learn from datasets, but if the dataset misrepresents the population, reinforcing bias may occur. Ignoring these demographic features may lead to inaccurate downstream effects, thereby limiting the translation of these analyses to clinical practice. Consciousness of model training biases is vital to patient care decisions when using models in clinical settings. METHODS: Data elements evaluated included gender, demographics, reported pathologic grading, and cancer staging. American Urological Association risk levels were used. Poisson regression was performed to estimate the population-based and sample-specific estimation for prevalence rate and corresponding 95% confidence interval. SAS 9.4 was used for data analysis. RESULTS: Compared to PUF, KiTS19 and TCGA KIRC oversampled Caucasian by 9.5% (95% CI, -3.7 to 22.7%) and 15.1% (95% CI, 1.5 to 28.8%), undersampled African American by -6.7% (95% CI, -10% to -3.3%), and -5.5% (95% CI, -9.3% to -1.8%). Tertiary also undersampled African American by -6.6% (95% CI, -8.7% to -4.6%). The tertiary cohort largely undersampled aggressive cancers by -14.7% (95% CI, -20.9% to -8.4%). No statistically significant difference was found among PUF, TCGA, and KiTS19 in aggressive rate; however, heterogeneities in risk are notable. CONCLUSION: Heterogeneities between cohorts need to be considered in future AI training and cross-validation for renal masses.

Expecting the unexpected: incidental findings at a level 1 trauma center.

INTRODUCTION: Incidental findings on comprehensive imaging in the adult trauma population occur at rates as high as 54.8%. We sought to determine the incidence of potentially malignant or pre-malignant incidental findings in a high-volume level 1 trauma center and to evaluate follow-up recommendations. METHODS: This was a retrospective review of all patients with incidental findings on imaging who were admitted to the trauma service at our level 1 trauma center between January 1st, 2014, and October 1st, 2019. A multi-disciplinary team characterized findings as potentially malignant or pre-malignant. RESULTS: The study included 495 patients who had incidental findings, 410 of whom had potentially malignant or pre-malignant findings on imaging, resulting in a cumulative incidence of 6.6%. The mean age was 65 and 217 (52.9%) patients were male. The majority of "incidentalomas" were discovered on CT imaging (n=665, 98.1%); over half were solid (n=349, 51.5%), while 27.4% were cystic (n=186) in nature. The lungs (n=199, 29.4%), kidneys (n=154, 22.8%), liver (n=74, 10.9%), thyroid gland (n=58, 8.6%), and adrenal glands (n=53, 7.8%) harbored the most incidentalomas. Less than half of patients with incidental findings received specific follow-up recommendations on the radiologist's report (n=150, 39%). Sixty-one percent of patients (n=250) had their incidentalomas detailed in the discharge paperwork. CONCLUSION: The results of our study suggest that potentially malignant or pre-malignant incidental findings are common among trauma patients. Specific follow-up recommendations were not presented in 61% of the radiology reports, highlighting the need to standardize medical record capture of an incidentaloma to ensure adequate and appropriate follow-up.