Citations

AbstractGain of 1q22 at diagnosis portends poorer outcomes in multiple myeloma (MM), but the prognostic significance of acquired 1q22 gain is unknown. We identified 63 MM patients seen at Mayo Clinic from 1/2004 to 12/2019 without 1q22 gain at diagnosis who acquired it during follow up and compared them to 63 control patients who did not acquire 1q22 gain with similar follow up. We also compared outcomes in the acquired 1q22 gain group with outcomes in 126 patients with 1q22 gain present at diagnosis. The incidence of acquired 1q22 gain was 6.1% (median follow‐up 6.8 years); median time to acquisition was 5.0 years (range: 0.7–11.5 years). Abnormalities on baseline fluorescence in situ hybridization (FISH) included trisomies (54%) and monosomy 13 (39%); 16 (25%) had high‐risk (HR) translocations or del(17p). Median progression‐free survival with front line therapy was 29.5 months in patients with acquired 1q22 gain, versus 31.4 months in control patients (p = .34) and 31.2 months in patients with de novo 1q22 gain (p = .04). Median overall survival (OS) from diagnosis was 10.9 years in patients with acquired 1q22 gain, versus 13.0 years in control patients (p = .03) and 6.3 years in patients with de novo 1q22 gain (p = .01). Presence of HR FISH at baseline increased risk of 1q22 gain acquisition. We demonstrate that acquisition of 1q22 gain is a significant molecular event in MM, associated with reduced OS. Among HR patients for whom this clonal evolution is determined, a risk‐adapted approach and/or clinical trial should be considered.

Background: Several factors are associated with an increased risk of anterior cruciate ligament (ACL) reconstruction revision. However, the ability to accurately translate these factors into a quantifiable risk of revision at a patient-specific level has remained elusive. We sought to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can identify the most important risk factors associated with subsequent revision of primary ACL reconstruction and develop a clinically meaningful calculator for predicting revision of primary ACL reconstruction. Methods: Machine learning analysis was performed on the NKLR data set. The primary outcome was the probability of revision ACL reconstruction within 1, 2, and/or 5 years. Data were split randomly into training sets (75%) and test sets (25%). Four machine learning models were tested: Cox Lasso, survival random forest, generalized additive model, and gradient boosted regression. Concordance and calibration were calculated for all 4 models. Results: The data set included 24,935 patients, and 4.9% underwent a revision surgical procedure during a mean follow-up (and standard deviation) of 8.1 ± 4.1 years. All 4 models were well-calibrated, with moderate concordance (0.67 to 0.69). The Cox Lasso model required only 5 variables for outcome prediction. The other models either used more variables without an appreciable improvement in accuracy or had slightly lower accuracy overall. An in-clinic calculator was developed that can estimate the risk of ACL revision (Revision Risk Calculator). This calculator can quantify risk at a patient-specific level, with a plausible range from near 0% for low-risk patients to 20% for high-risk patients at 5 years. Conclusions: Machine learning analysis of a national knee ligament registry can predict the risk of ACL reconstruction revision with moderate accuracy. This algorithm supports the creation of an in-clinic calculator for point-of-care risk stratification based on the input of only 5 variables. Similar analysis using a larger or more comprehensive data set may improve the accuracy of risk prediction, and future studies incorporating patients who have experienced failure of ACL reconstruction but have not undergone subsequent revision may better predict the true risk of ACL reconstruction failure. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Abstract Background Tick populations have expanded in many parts of the globe, bringing with them an enhanced appreciation and discovery of novel tickborne pathogens, as well an increased in reported human cases of tickborne disease. Targeted and unbiased (shotgun) clinical metagenomic sequencing tests are increasingly used for detection of known and emerging infectious agents and have recently been employed for detection of tickborne pathogens. Content This review describes the types of metagenomic sequencing assays used for detection of emerging tickborne pathogens and reviews the recent literature on this topic. Important diagnostic and interpretative challenges are also covered. Summary Metagenomic analysis has emerged as a powerful tool for detection, discovery, characterization, and classification of tickborne pathogens. Shotgun metagenomics is especially promising because it allows for detection of all tickborne bacteria, viruses, and parasites in a single specimen. Despite the potential advantages, there are several important challenges, including high cost, complexity of testing and interpretation, and slow turnaround time. No doubt, these challenges will diminish with increased use and advances in the field.

Background: Patient-reported outcomes (PROs) measure progression and quality of care. While legacy PROs such as the International Knee Documentation Committee (IKDC) survey are well-validated, a lengthy PRO creates a time burden on patients, decreasing adherence. In recent years, PROs such as the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function and Pain Interference surveys were developed as computer adaptive tests, reducing time to completion. Previous studies have examined correlation between legacy PROs and PROMIS; however, no studies have developed effective prediction models utilizing PROMIS to create an IKDC index. While the IKDC is the standard knee PRO, computer adaptive PROs offer numerous practical advantages. Purpose: To develop a nonlinear predictive model utilizing PROMIS Physical Function and Pain Interference to estimate IKDC survey scores and examine algorithm sensitivity and validity. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: The MOTION (Military Orthopaedics Tracking Injuries and Outcomes Network) database is a prospectively collected repository of PROs and intraoperative variables. Patients undergoing knee surgery completed the IKDC and PROMIS surveys at varying time points. Nonlinear multivariable predictive models using Gaussian and beta distributions were created to establish an IKDC index score, which was then validated using leave-one-out techniques and minimal clinically important difference analysis. Results: A total of 1011 patients completed the IKDC and PROMIS Physical Function and Pain Interference, providing 1618 complete observations. The algorithms for the Gaussian and beta distribution were validated to predict the IKDC (Pearson = 0.84-0.86; R 2 = 0.71-0.74; root mean square error = 9.3-10.0). Conclusion: The publicly available predictive models can approximate the IKDC score. The results can be used to compare PROMIS Physical Function and Pain Interference against historical IKDC scores by creating an IKDC index score. Serial use of the IKDC index allows for a lower minimal clinically important difference than the conventional IKDC. PROMIS can be substituted to reduce patient burden, increase completion rates, and produce orthopaedic-specific survey analogs.

Cardiac complications frequently observed in COVID-19 patients are tentatively attributed to systemic inflammation and thrombosis, but viral replication has occasionally been confirmed in cardiac tissue autopsy materials. We developed an in vitro model of SARS-CoV-2 spread in myocardium using induced pluripotent stem cell-derived cardiomyocytes.

Abstract The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care. Level of evidence VI.

AbstractObjectivesThe aim of this study was to determine the safety of eliminating the pre‐discharge transthoracic echocardiogram (TTE) on 30‐day outcomes in patients undergoing transcatheter aortic valve replacement (TAVR).BackgroundTTE is utilized before, during, and after TAVR. Post‐procedural, pre‐discharge TTE assists in assessment of prosthesis function and detection of clinically significant paravalvular leak (PVL) after TAVR.MethodsPatients who underwent TAVR at Mayo Clinic from July 2018 to July 2019 were included in a prospective institutional registry. Patients undergoing TAVR prior to February 2019 received a pre‐discharge TTE, while those undergoing TAVR after February 2019 did not. Both cohorts were evaluated with TTE at 30 days post‐TAVR.ResultsA total of 330 consecutive patients were included. Of these, 160 patients (age 81.1 ± 7.6) had routine pre‐discharge TTE, while 170 patients (age 78.9 ± 7.5) were dismissed without routine pre‐discharge TTE. Mortality at 30 days was similar between the two groups (0% and 1.2%, respectively). One episode of PVL requiring intervention (0.6%) occurred in the pre‐discharge TTE group and none in the group without pre‐discharge TTE at 30‐day follow‐up. There was a similar incidence of total composite primary and secondary adverse events between the cohort receiving a pre‐discharge TTE and those without (28.1% vs. 25.3%, P = 0.56) at 30 days. The most common event was need for permanent pacemaker or ICD implantation in both groups (13.1% vs. 11.8%, P = 0.71).ConclusionsElimination of the pre‐discharge TTE is safe and associated with comparable 30‐day outcomes to routine pre‐discharge TTE. These findings have implication for TAVR practice cost‐efficiency and health care utilization.