Alpha Angle and Anterior Femoral Neck Offset Identify Different Cohorts of Cam Morphology: An Osteologic Study.

PURPOSE: To explore differences in cam morphology defined by alpha angle (AA) and anterior femoral neck offset (AFNO) in the context of other anthropometric parameters in an osteologic collection to further elucidate whether each measurement tool is identifying the same underlying pathology. METHODS: Anthropometric measurements of 992 cadaveric hips from the Hamann-Todd Osteological Collection were analyzed. Femurs with cam morphology were identified by AA >55° or AFNO <7 mm. Anthropometric parameters stratified by cam morphology were assessed with Wilcoxon rank-sum and Pearson χ2 tests. Multivariate logistic regressions were performed on significant variables in univariate analysis to examine the predictive ability of anthropometric variables to cam morphology. RESULTS: Cam morphology was identified in 242 hips via AA, 344 hips via AFNO, and 123 hips via both measures. Multivariate logarithmic regression analysis demonstrated that sex negatively predicted AA (females with less pathology, ß = -0.14, P = .04), race negatively predicted AA (Blacks with less pathology, ß = -0.21, P < .01), and proximal femoral osteoarthritis was positively associated with AA (ß = 0.16, P = .02), while none of these were associated with AFNO. On the other hand, right-side specimens were associated with AA (ß = 0.15, P = .02) and AFNO (ß = 0.25, P < .01), whereas the combined version was unassociated with both measures. CONCLUSIONS: In conclusion, cam morphology was identified in a modest percentage of osteologic specimens by both AA and AFNO in our study. Further, associations of multiple demographic, anthropometric, and anatomical parameters to AA and AFNO suggest they may identify different subsets of cam morphology. CLINICAL RELEVANCE: Cam morphology identified by AA versus AFNO may represent 2 different pathologic entities. Future studies should assess differences between these measures in a clinical cohort and determine whether these 2 definitions of cam morphology identify different clinical populations.

Is Minimally Invasive Resection of Large Thymoma Appropriate?

BACKGROUND: Current practice patterns suggest open rather than minimally invasive (MIS) approaches for thymomas >4 cm. We hypothesized there would be similar perioperative outcomes and overall survival between open and MIS approaches for large (>4 cm) thymoma resection. METHODS: The National Cancer Database was queried for patients who underwent thymectomy from 2010 to 2020. Surgical approach was characterized as either open or MIS. The primary outcome was overall survival and secondary outcomes were margin status, and length of stay (LOS). Differences between approach cohorts were compared after a 1:1 propensity match. RESULTS: Among 4121 thymectomies, 2474 (60%) were open and 1647 (40%) were MIS. Patients undergoing MIS were older, had fewer comorbidities, and had smaller tumors (median; 4.6 vs 6 cm, P < .001). In the unmatched cohort, MIS and open had similar 90-day mortality (1.1% vs 1.8%, P = .158) and rate of positive margin (25.1% vs 27.9%, P = .109). MIS thymectomy was associated with shorter LOS (2 (1-4) vs 4 (3-6) days, P < .001). Propensity matching reduced the bias between the groups. In this cohort, overall survival was similar between the groups by log-rank test (P = .462) and multivariate cox hazard analysis (HR .882, P = .472). Multivariable regression showed shorter LOS with MIS approach (Coef -1.139, P < .001), and similar odds of positive margin (OR 1.130, P = .150). DISCUSSION: MIS has equivalent oncologic benefit to open resection for large thymomas, but is associated with shorter LOS. When clinically appropriate, MIS thymectomy may be considered a safe alternative to open resection for large thymomas.

Computational risk model for predicting 2-year malignancy of pulmonary nodules using demographic and radiographic characteristics.

OBJECTIVES: To determine whether discriminatory performance of a computational risk model in classifying pulmonary lesion malignancy using demographic, radiographic, and clinical characteristics is superior to the opinion of experienced providers. We hypothesized that computational risk models would outperform providers. METHODS: Outcome of malignancy was obtained from selected patients enrolled in the NAVIGATE trial (NCT02410837). Five predictive risk models were developed using an 80:20 train-test split: univariable logistic regression model based solely on provider opinion, multivariable logistic regression model, random forest classifier, extreme gradient boosting model, and artificial neural network. Area under the receiver operating characteristic curve achieved during testing of the predictive models was compared to that of prebiopsy provider opinion baseline using the DeLong test with 10,000 bootstrapped iterations. RESULTS: The cohort included 984 patients, 735 (74.7%) of which were diagnosed with malignancy. Factors associated with malignancy from multivariable logistic regression included age, history of cancer, largest lesion size, lung zone, and positron-emission tomography positivity. Testing area under the receiver operating characteristic curve were 0.830 for provider opinion baseline, 0.770 for provider opinion univariable logistic regression, 0.659 for multivariable logistic regression model, 0.743 for random forest classifier, 0.740 for extreme gradient boosting, and 0.679 for artificial neural network. Provider opinion baseline was determined to be the best predictive classification system. CONCLUSIONS: Computational models predicting malignancy of pulmonary lesions using clinical, demographic, and radiographic characteristics are inferior to provider opinion. This study questions the ability of these models to provide additional insight into patient care. Expert clinician evaluation of pulmonary lesion malignancy is paramount.

Inflammation, coronary plaque progression, and statin use: A secondary analysis of the Risk Stratification with Image Guidance of HMG CoA Reductase Inhibitor Therapy (RIGHT) study.

BACKGROUND: Statin treatment is a potent lipid-lowering therapy associated with decreased cardiovascular risk and mortality. Recent studies including the PARADIGM trial have demonstrated the impact of statins on promoting calcified coronary plaque. HYPOTHESIS: The degree of systemic inflammation impacts the amount of increase in coronary plaque calcification over 2 years of statin treatment. METHODS: A subgroup of 142 participants was analyzed from the Risk Stratification with Image Guidance of HMG CoA Reductase Inhibitor Therapy (RIGHT) study (NCT01212900), who were on statin treatment and underwent cardiac computed tomography angiography (CCTA) at baseline and 2-year follow-up. This cohort was stratified by baseline median levels of high-sensitivity hs-CRP and analyzed with linear regressions using Stata-17 (StataCorp). RESULTS: In the high versus low hs-CRP group, patients with higher baseline median hs-CRP had increased BMI (median [IQR]; 29 [27-31] vs. 27 [24-28]; p < .001), hypertension (59% vs. 41%; p = .03), and LDL-C levels (97 [77-113] vs. 87 [75-97] mg/dl; p = .01). After 2 years of statin treatment, the high hs-CRP group had significant increase in dense-calcified coronary burden versus the low hs-CRP group (1.27 vs. 0.32 mm2 [100×]; p = .02), beyond adjustment (ß = .2; p = .03). CONCLUSIONS: Statin treatment over 2 years associated with a significant increase in coronary calcification in patients with higher systemic inflammation, as measured by hs-CRP. These findings suggest that systemic inflammation plays a role in coronary calcification and further studies should be performed to better elucidate these findings.

Is Our Science Representative? A Systematic Review of Racial and Ethnic Diversity in Orthopaedic Clinical Trials from 2000 to 2020.

BACKGROUND: A lack of racial and ethnic representation in clinical trials may limit the generalizability of the orthopaedic evidence base as it applies to patients in underrepresented minority populations and perpetuate existing disparities in use, complications, or functional outcomes. Although some commentators have implied the need for mandatory race or ethnicity reporting across all orthopaedic trials, the usefulness of race or ethnic reporting likely depends on the specific topic, prior evidence of disparities, and individualized study hypotheses. QUESTIONS/PURPOSES: In a systematic review, we asked: (1) What proportion of orthopaedic clinical trials report race or ethnicity data, and of studies that do, how many report data regarding social covariates or genomic testing? (2) What trends and associations exist for racial and ethnic reporting among these trials between 2000 and 2020? (3) What is the racial or ethnic representation of United States trial participants compared with that reported in the United States Census? METHODS: We performed a systematic review of randomized controlled trials with human participants published in three leading general-interest orthopaedic journals that focus on clinical research: The Journal of Bone and Joint Surgery, American Volume; Clinical Orthopaedics and Related Research; and Osteoarthritis and Cartilage. We searched the PubMed and Embase databases using the following inclusion criteria: English-language studies, human studies, randomized controlled trials, publication date from 2000 to 2020, and published in Clinical Orthopaedics and Related Research; The Journal of Bone and Joint Surgery, American Volume; or Osteoarthritis and Cartilage. Primary outcome measures included whether studies reported participant race or ethnicity, other social covariates (insurance status, housing or homelessness, education and literacy, transportation, income and employment, and food security and nutrition), and genomic testing. The secondary outcome measure was the racial and ethnic categorical distribution of the trial participants included in the studies reporting race or ethnicity. From our search, 1043 randomized controlled trials with 184,643 enrolled patients met the inclusion criteria. Among these studies, 21% (223 of 1043) had a small (< 50) sample size, 56% (581 of 1043) had a medium (50 to 200) sample size, and 23% (239 of 1043) had a large (> 200) sample size. Fourteen percent (141 of 1043) were based in the Northeast United States, 9.2% (96 of 1043) were in the Midwest, 4.7% (49 of 1043) were in the West, 7.2% (75 of 1043) were in the South, and 65% (682 of 1043) were outside the United States. We calculated the overall proportion of studies meeting the inclusion criteria that reported race or ethnicity. Then among the subset of studies reporting race or ethnicity, we determined the overall rate and distribution of social covariates and genomic testing reporting. We calculated the proportion of studies reporting race or ethnicity that also reported a difference in outcome by race or ethnicity. We calculated the proportion of studies reporting race or ethnicity by each year in the study period. We also calculated the proportions and 95% CIs of individual patients in each racial or ethnic category of the studies meeting the inclusion criteria. RESULTS: During the study period (2000 to 2020), 8.5% (89 of 1043) of studies reported race or ethnicity. Of the trials reporting this factor, 4.5% (four of 89) reported insurance status, 15% (13 of 89) reported income, 4.5% (four of 89) reported housing or homelessness, 18% (16 of 89) reported education and literacy, 0% (0 of 89) reported transportation, and 2.2% (two of 89) reported food security or nutrition of trial participants. Seventy-eight percent (69 of 89) of trials reported no social covariates, while 22% (20 of 89) reported at least one. However, 0% (0 of 89) of trials reported genomic testing. Additionally, 5.6% (five of 89) of these trials reported a difference in outcomes by race or ethnicity. The proportion of studies reporting race or ethnicity increased, on average, by 0.6% annually (95% CI 0.2% to 1.0%; p = 0.02). After controlling for potentially confounding variables such as funding source, we found that studies with an increased sample size were more likely to report data by race or ethnicity; location in North America overall, Europe, Asia, and Australia or New Zealand (compared with the Northeast United States) were less likely to; and specialty-topic studies (compared with general orthopaedics research) were less likely to. Our sample of United States trials contained 18.9% more white participants than that reported in the United States Census (95% CI 18.4% to 19.4%; p < 0.001), 5.0% fewer Black participants (95% CI 4.6% to 5.3%; p < 0.001), 17.0% fewer Hispanic participants (95% CI 16.8% to 17.1%; p < 0.001), 5.3% fewer Asian participants (95% CI 5.2% to 5.4%; p < 0.001), and 7.5% more participants from other groups (95% CI 7.2% to 7.9%; p < 0.001). CONCLUSION: Reporting of race or ethnicity data in orthopaedic clinical trials is low compared with other medical fields, although the proportion of diseases warranting this reporting might be lower in orthopaedics. CLINICAL RELEVANCE: Investigators should initiate discussions about race and ethnicity reporting in the early stages of clinical trial development by surveying available published evidence for relevant health disparities, social determinants, and, when warranted, genomic risk factors. The decision to include or exclude race and ethnicity data in study protocols should be based on specific hypotheses, necessary statistical power, and an appreciation for unmeasured confounding. Future studies should evaluate cost-efficient mechanisms for obtaining baseline social covariate data and investigate researcher perspectives on current administrative workflows and decision-making algorithms for race and ethnicity reporting.