Publications

IntroductionPersonality assessments have been widely used in business to identify leadership potential and enhance team performance; however, their integration into surgical education remains limited. This study evaluated surgeons' personality profiles using the PRISM assessment and compared them with those of other health care professionals to guide the design of leadership development curricula for surgical training.MethodsA total of 199 surgeons (residents, fellows, and faculty) across multiple academic medical centers completed the PRISM personality assessment as part of structured leadership development programs. Data were compared with a normative cohort of 5887 health care professionals from the SurePeople database. Primary and under-pressure personality profiles were analyzed using chi-square and Fisher's exact tests, with significance set at P < .05.ResultsNo significant differences were found between surgeons and other health care professionals in primary personality distributions. Under-pressure profiles, however, revealed a greater proportion of surgeons classified as the Navigator type (22% vs 14%; P = .05), reflecting tendency towards higher decisiveness and structure in stressful conditions. Personality distributions were similar across residents, fellows, and attending surgeons.DiscussionAlthough surgeons' baseline personalities mirror those of other health care professionals, their stress-related behavioral tendencies demonstrate a unique shift toward structured, action-oriented responses. Incorporating personality assessments such as PRISM into leadership curricula may enhance self-awareness, emotional intelligence, and team performance.ConclusionIntegrating structured personality assessment into surgical leadership education provides valuable insight into behavioral responses under stress and supports the development of adaptive, emotionally intelligent leaders who foster effective and collaborative surgical teams.

BACKGROUND: Home blood pressure (BP) monitoring (HBPM) is increasingly used as an alternative to office BP. However, factors influencing agreement between office and home BP among very old adults remain unclear.

METHODS: During ARIC (Atherosclerosis Risk in Communities) visit 10, participants underwent 3 automated office BP (AOBP) measurements using an Omron HEM-907XL and performed HBPM twice daily for 8 days using an Omron BP7450. Discordance was defined as a systolic BP difference of ±10 mm Hg between mean AOBP and HBPM. Multivariable regression models evaluated demographic, anthropometric, and clinical factors associated with discordance.

RESULTS: Among 792 participants (58% female; mean age, 84±3.7 years), mean systolic BP was 130.6 mm Hg (AOBP) and 129.6 mm Hg (HBPM). Despite a minimal average difference (1.0±15.7 mm Hg), 49% had ≥10 mm Hg systolic BP discordance. Higher AOBP was associated with greater discordance. Compared with females, males had lower AOBP relative to HBPM (-4.69 mm Hg [95% CI, -6.86 to -2.51]). Smaller arm circumference was associated with higher discordance (β=14.4 mm Hg [95% CI, 4.78-24.04]). Frail adults had lower AOBP relative to HBPM (β, -5.1 mm Hg [95% CI, -11.0 to 0.9]). Baseline AOBP systolic BP ≥140 mm Hg strongly predicted discordance ≥+10 mm Hg (odds ratio, 8.27 [95% CI, 5.52-12.40]). Participants aged 91 to 100 years had lower AOBP than those aged 78 to 80 years (β, -5.0 mm Hg [95% CI, -10.06 to 0.001]).

CONCLUSIONS: Among very old adults, substantial BP discordance between AOBP and HBPM was common and influenced by higher BP, age, male sex, arm circumference, and frailty.

STUDY OBJECTIVE: To evaluate differences in the likelihood of endometriosis or adenomyosis diagnosis across genetically-defined ancestry groups (African, Admixed American, East Asian, European, and South Asian), and to determine whether social vulnerability and access to surgery influence diagnostic practices in a diverse urban population.

SETTING: Penn Medicine BioBank, a clinical and genomic biorepository.

PATIENTS: Female patients aged 18 to 51 enrolled from 2008 to 2020 and followed through March 2024.

INTERVENTIONS: Population diversity was characterized using genotyping data from peripheral blood samples. Genetic ancestry was inferred through principal component analysis and clustering with reference populations. Sociocultural factors were assessed as distinct covariates rather than conflated with racial/ethnic identity. Social vulnerability indices (socioeconomic status, family/household factors, and housing/transportation access) were defined by Census tract of the patient's residence. Endometriosis and adenomyosis were identified using International Classification of Diseases-9/10 codes, validated by chart review. Logistic regressions were adjusted for age, body mass index, social vulnerability, and interaction terms.

MEASUREMENTS AND MAIN RESULTS: Among 9258 patients, 357 (3.9%) were diagnosed with endometriosis and 464 (5.0%) with adenomyosis. When all diagnostic routes were considered (surgical, imaging, and clinical), the likelihood of endometriosis did not differ by ancestry group. However, the odds of a surgical diagnosis by laparoscopy was significantly lower for patients of African ancestry, compared with European (adjusted OR: 0.57, 95% CI: 0.38-0.85). Socioeconomic vulnerability strongly modified this association: African-ancestry patients with the highest socioeconomic vulnerability had the lowest odds of receiving an endometriosis diagnosis (aOR: 0.29, 0.14-0.61), including surgically confirmed diagnosis (aOR: 0.31, 0.12-0.80), relative to those of European ancestry with lowest vulnerability. By contrast, patients of African ancestry had higher odds of adenomyosis (aOR: 2.88, 2.16-3.85), including cases confirmed by hysterectomy (aOR: 3.44, 2.21-5.36).

CONCLUSION: Diagnostic disparities in endometriosis and adenomyosis were most pronounced at the intersection of ancestry and socioeconomic vulnerability, highlighting the need to address social determinants and barriers to equitable care.

BACKGROUND: This pilot study evaluated the feasibility of integrating an immunosuppressant area under the concentration-time curve (AUC) monitoring for tacrolimus and mycophenolic acid (MPA) into pediatric kidney transplantation care.

METHODS: Dedicated test codes, an AUC requisition form, and a coordinated laboratory sampling process were established for tacrolimus and MPA AUC. AUC was calculated using the ISBA 3.0 Bayesian pharmacokinetic platform. AUC results were correlated with doses, trough concentrations (C0), 3 h post-dose concentrations (C3h), and clinical outcomes.

RESULTS: The AUC protocol was successfully integrated into the routine clinical workflow. Tacrolimus AUC showed correlations with dose (r = 0.85) and C0 (r = 0.82); similarly, MPA AUC showed correlation with dose (r = 0.61) and C3h (r = 0.65). Of the 21 Tacrolimus AUC measurements, 76% were within the target range, and 24% were below the range. For MPA AUC measurements, 65% (13/20) were within the target range, 5% (1/20) were below the range, and 30% (6/20) were above the range. Following individual AUC measurements, the tacrolimus dose was adjusted after 43% (9/21) of measurements, and the mycophenolate mofetil (MMF) dose was adjusted after 50% (10/20) of measurements.

CONCLUSION: This AUC pilot study demonstrated the feasibility of integrating AUC-guided monitoring into the routine management of pediatric kidney transplant recipients.

Thrombosis drives substantial global mortality across atrial fibrillation, venous thromboembolism, and atherosclerosis. However, clinical scores treat risk as a static variable and omit evolving comorbidities, functional biomarkers, anatomy, and treatment exposure, leading to misclassification and preventable events. This statement advances a unified scientific agenda for patient-specific digital twins that dynamically integrate multimodal longitudinal data with mechanistic insight to predict thrombogenesis risks. We position these digital twins as hybrid models anchored in physics and data-driven algorithms that can simulate disease progression and therapy. The goal of this approach is to refine stroke and bleeding estimation beyond current clinical rules. Continuous updating from imaging data, laboratory test results, wearables, and electronic health records supports dynamic risk trajectories and adaptive care pathways, facilitating continuous risk reassessment. This statement analyzes gaps in data quality, calibration, validation, and uncertainty quantification that presently limit the clinical translation of this technology. Research priorities are then proposed for multiscale thrombosis modelling, physics-informed learning, probabilistic forecasting, and regulatory-compliant data stewardship. Finally, we outline translation to in silico trials, regulatory alignment, and hospital workflows that link predictions to decisions. By articulating shared challenges across thrombosis-driven diseases and reframing risk as a time-varying measurable quantity, this statement lays a foundation for developing digital twin approaches that support a shift from population heuristics towards precise, timely thrombosis care. These advances are essential for translating digital twin technology from research to clinical practice, enabling dynamic risk prediction and personalized anticoagulation therapy.

Ultrasound-guided nerve blocks (USGNBs) are increasingly used in the emergency department (ED) as a safe and effective part of multimodal pain management. Their use has been shown to reduce reliance on opioids and procedural sedation, improve pain scores, and enhance functional outcomes for patients. Additionally, USGNBs in the ED have a complication rate of 0.4%, markedly lower than procedural sedation (4-11%), and they significantly reduce opioid requirements, which is critical considering the current opioid epidemic and the risks of persistent opioid use and overdose. Despite these benefits, relevant concerns about medicolegal liability, informed consent, evolving standards of care, may still influence the adoption of USGNBs in clinical practice. To address these issues, this review examines the legal risks associated with USGNBs by drawing on current clinical literature, closed claims data, and case law. We highlight common adverse events such as peripheral nerve injury and local anesthetic systemic toxicity and assess their legal implications. Potential legal risk including liability related to alternatives like opioid use and procedural sedation, are discussed. While the risk of litigation remains low when best practices are followed, failing to offer a USGNB when clearly indicated may increasingly be viewed as a liability if preventable complications occur. This article aims to provide a practical, interdisciplinary framework, including legal risk assessment, training, credentialing, and risk mitigation, to help clinicians, educators, and hospital administrators safely and confidently integrate USGNBs into ED practice.

Dissecting cellulitis of the scalp (DCS) is a chronic inflammatory condition characterized by painful, draining nodules and progressive, scarring alopecia. It is often refractory to treatment, and there are currently no FDA-approved therapies. We present a case of a 15-year-old boy with keratitis-ichthyosis-deafness (KID) syndrome and DCS who experienced significant clinical response to treatment with adalimumab after failing multiple courses of oral antimicrobials, surgical interventions, intralesional corticosteroids, and topical therapies. Treatment with adalimumab led to reduction in drainage and pain and visible hair regrowth, highlighting its therapeutic potential in refractory, pediatric DCS including in the setting of syndromic skin disease.

Diagnosing sleep disordered breathing requires manual annotation of events from sleep studies, such as nocturnal polysomnography, a process that is time-intensive, costly, and prone to inter-rater variability. Automatic approaches exist but lack generalizability due to signal variability across centers. We develop an automatic apneic breathing event detector to localize and classify obstructive apneas, central apneas, hypopneas, and isolated respiratory events without arousals or desaturations. The model is trained on 5456 polysomnographies and tested on 1099 polysomnographies from six cohorts uses an end-to-end deep learning architecture. The model's predictions show a strong correlation with expert annotations for apnea-hypopnea index (r² = 0.84) and achieve an F1 score of 0.78 across apnea event types, with specific F1 scores of 0.71, 0.51, and 0.65 for obstructive apnea, central apnea, and hypopnea events, respectively. In two independent, multi-scored datasets, The model performs comparably or better than individual expert raters. The model's probabilistic output, termed "apnotyping," provides insights into sleep disordered breathing etiology, with event probabilities correlating more strongly with key sleep apnea traits-such as loop gain and pharyngeal muscle compensation-than traditional apnea indexes. This probabilistic approach may enhance diagnostic accuracy and support personalized treatment strategies, leading to improved patient outcomes.