Although current NCCN guidelines do not recommend surgical resection for T4b sinonasal tumors, recent studies report that up to 80% of patients undergo surgery as part of their treatment. This discrepancy between guideline-based recommendations and real-world clinical practice led us to investigate the clinical, demographic, and institutional factors associated with undergoing surgery among patients with clinically staged T4b sinonasal cancer. We analyzed data from the National Cancer Database (2004-2022) to identify patients diagnosed with clinical T4b sinonasal malignancies. Patients were grouped by treatment approach: surgery with adjuvant therapy vs. definitive chemoradiation. Multivariable logistic regression assessed clinical, demographic, and institutional predictors of surgery. Of 2244 patients, 57.5% received surgery as part of treatment. Surgical management was more likely among patients with private insurance (aOR 1.56, 95% CI 1.17-2.08), those treated in the Northeast (aOR 1.55, 95% CI 1.12-2.16), and patients traveling more than 100 miles for care (aOR 1.89, 95% CI 1.35-2.63). Histologic subtypes such as melanoma (aOR 13.37), neuroectodermal tumors (aOR 11.79), adenoid cystic carcinoma (aOR 2.98), and adenocarcinoma (aOR 2.99) were more likely to receive surgery. Node positivity was negatively associated with surgery (aOR 0.49). Despite guideline recommendations, surgery remains common for T4b sinonasal cancer. Surgical selection appears driven by tumor biology as well as access- and system-level factors. These findings highlight the evolving and context-dependent definition of unresectability in clinical practice.

Claims-based analyses can suffer from residual and unmeasured confounding due to factors that are poorly captured in claims. Some of these factors may be measured in other data sources, such as in structured fields of electronic health records (EHR), for example, laboratory test results, or in free-text physician notes. We conducted a proof-of-principle study to demonstrate a process for evaluating the potential risk of confounding-factors poorly captured in claims data but measurable in the EHR as part of drug safety surveillance activities. In future practical applications, this approach could be used along with other sensitivity analyses to evaluate potential residual confounding (e.g., E-values, negative controls). We used claims-EHR linked data from the Mass General Brigham site of the US Food and Drug Administration's (FDA) Sentinel Real World Evidence Data Enterprise. We extracted a cohort that was previously used in a prototypical Sentinel claims-based query that compared initiators of sacubitril-valsartan vs. angiotensin-converting enzyme inhibitors or angiotensin receptor blockers on the risk of angioedema. In this cohort, we used EHR data to characterize angioedema risk factors poorly captured in claims and observed that claims-based proxies balanced most risk factors that were measurable only in EHR data. While quantitative bias analysis methods can be used to adjust for residual confounding using external information on magnitude and direction of bias, this was deemed unnecessary for this example due to the observed balance achieved on risk factors for angioedema measured in the EHR. A robust linked EHR-claims data infrastructure is crucial for routine application of these methods to evaluate and mitigate residual confounding in drug safety surveillance studies.

Childhood maltreatment and FKBP5 CATT haplotypes increase identity dissociation risk. While maltreatment often fosters disorganized attachment, the specific role of the FKBP5 CATT haplotype within this developmental pathway remains unknown. In 310 trauma-exposed adults, we assessed childhood maltreatment, disorganized attachment, and identity dissociation. FKBP5 CATT haplotype status was dichotomized (present/absent). Regression models were estimated to examine the pathway Childhood Maltreatment → Disorganized Attachment → Identity Dissociation, comparing two competing hypotheses: 1) The Formation Hypothesis (Maltreatment × CATT → Disorganization), and 2) The Translation Hypothesis (Disorganization × CATT → Identity Dissociation). Model fit was compared using BIC, and simple slopes were examined. Disorganized attachment predicted identity dissociation (β = 0.143, z = 3.83, p < .001), and childhood maltreatment predicted disorganization (β = 0.007, z = 4.46, p < .001). The Formation hypothesis was not supported, as the Maltreatment × CATT interaction did not predict disorganization (β = -0.004, z = -1.18, p = .24). However, the Translation hypothesis was supported by three pieces of evidence: a significant Disorganization × CATT interaction predicting identity dissociation (β = 0.133, z = 2.22, p = .027), model fit (∆BIC = 265.3), and simple slopes showing a strong effect of disorganization on identity dissociation for CATT carriers (β = 0.15, p < .001; +16.5 % per unit) but not for non-carriers (β = 0.02, p = .65). The FKBP5 CATT haplotype does not promote disorganized attachment but amplifies the translation of disorganized attachment into identity dissociation. This result highlights a genetically influenced failure of identity development following disorganized attachment.