Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics.

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

Pain control is comparable between opioid versus non-opioid management after otolaryngology procedures.

Objective: The current study aims to measure patient-reported satisfaction with pain control using opioid and non-opioid medications after undergoing the following otolaryngology procedures: parathyroidectomy, thyroid lobectomy, total thyroidectomy, and bilateral tonsillectomy. Materials and Methods: A prospective cohort study was performed at an academic medical center that included a telephone questionnaire and chart review. Opioid prescriptions, usage, and patient-reported pain outcomes were recorded. Bivariate analyses were used to compare opioid and non-opioid users. Results: Of the 107 total patients undergoing otolaryngology procedures included in the study, 49 (45.8%) used an opioid for pain management postoperatively and 58 (54.2%) did not. Among the 81 patients who underwent endocrine procedures (parathyroidectomy, total thyroidectomy/lobectomy), most patients reported being "very satisfied" or "satisfied" with pain control whether they used opioids (n = 27/30, 90%) or not (n = 50/51, 98%). Of the 26 patients who underwent bilateral tonsillectomy, 19 (73%) were prescribed opioids and among these, most (n = 17/19, 89%) reported they were "very satisfied" or "satisfied" with pain control. In the non-opioid usage group, all patients (n = 7/7, 100%) reported they were "satisfied" with pain control. There was no statistically significant difference in patient-reported satisfaction with pain control between opioid and non-opioid users for any of the procedures listed. Conclusion: The results of our study suggest that patients who did not use opioids have a similar level of satisfaction with pain control compared to those using opioids after thyroid, parathyroid and tonsillectomy surgeries. Considering the magnitude of the opioid crisis, providers should reassess the need for opioid prescriptions following certain ENT procedures. Level of Evidence: IV.

Effectiveness of Closed Incision Negative Pressure Wound Therapy for Infrainguinal Bypass in the Vascular Quality Initiative.

BACKGROUND: To analyze surgical site infections (SSIs) after infrainguinal bypass for standard dressings versus closed incision negative pressure wound therapy (ciNPWT) in the Society for Vascular Surgery's Vascular Quality Initiative (VQI). METHODS: We retrospectively analyzed SSI after infrainguinal bypass procedures in the VQI from December 2019 to December 2021 comparing ciNPWT and standard dressings. The primary outcome of any superficial or deep wound infection at 30 days was analyzed in a subset of procedures with 30-day follow-up data (cohort A, n = 1,575). Secondary outcomes including in-hospital SSI, return to the operating room (OR) for infection, and length of stay (LOS) were analyzed for all procedures (cohort B, n = 9,288). Outcomes were analyzed in propensity-matched cohorts. RESULTS: Patients who received ciNPWT (n = 1,389) were more likely to be female (34% vs. 32%, P = 0.04) with a higher rate of smoking history (90% vs. 86%, P = 0.003), diabetes (54% vs. 50%, P = 0.007), obesity (34% vs. 26%, P < 0.001), prior peripheral vascular intervention (57% vs. 51%, P < 0.001), and to prosthetic conduit (55% vs. 48%, P < 0.001) compared to patients with standard dressings (n = 7,899). After propensity matching of cohort A (n = 1,256), the 30-day SSI rate was 4% (12/341) in the ciNPWT and 6% (54/896) in the standard dressing group (P = 0.07, 95% CI 0.03-1.06). In the propensity-matched in-hospital cohort B (n = 5,435), SSI was 3% (35/1,371) in the ciNPWT group and 2% (95/4,064) in the standard dressing group (P = 0.66). There was no difference in the rate of return to the OR for infection, 1% (36/4,064) vs. 1% (19/1,371) (P = 0.13) or LOS, 9.0 vs. 9.0 days (P = 0.86) for the standard versus ciNPWT groups. CONCLUSIONS: In this analysis of the VQI registry, the use of ciNPWT after infrainguinal bypass did not result in a statistically significant decrease in 30-day SSI. We recommend that surgeons consider the use of ciNPWT as part of a bundled process of care for high risk rather than all patients, as it may reduce SSI after infrainguinal bypass.

Recalibrating single-study effect sizes using hierarchical Bayesian models.

Introduction: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. Methods: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. Results: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. Discussion: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.