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Can decreased femoral head enhancement differentiate between septic hip arthritis and transient synovitis?
No full textObjective: To determine whether decreased femoral head enhancement on MRI differentiates septic arthritis from transient synovitis.
Materials and methods: This retrospective study included children < 10 years old with hip effusion on post-contrast MRI for suspected musculoskeletal infection. Two pediatric radiologists independently assessed femoral head enhancement. Kocher and modified Kocher scores were calculated from clinical and lab data. Differences between septic arthritis and transient synovitis were analyzed using Student's t-tests and Fisher's exact tests. Sensitivity and specificity for diagnosing septic arthritis were calculated for Kocher scores, their individual components, decreased femoral head enhancement, and muscle edema. Interobserver agreement was assessed.
Results: Thirty-four children were included (20 transient synovitis, 14 septic arthritis). Kocher and modified Kocher scores were significantly higher in septic arthritis (p = 0.003, 0.008). Interobserver agreement for femoral head enhancement was substantial (kappa = 0.70). On consensus read, decreased femoral head enhancement was seen in 71.4% of septic arthritis and 50.0% of transient synovitis cases (p = 0.296). Bone marrow edema was present in two septic arthritis cases. Muscle edema had moderate to high sensitivity (71.4%, 92.9%) but moderate to low specificity (75.0%, 50.0%) for septic arthritis.
Conclusion: Decreased femoral head enhancement does not reliably distinguish septic arthritis from transient synovitis. Relying on this finding alone may lead to unnecessary interventions in children with transient synovitis. Muscle edema and bone marrow edema may support the diagnosis of septic arthritis. Clinical evaluation and inflammatory markers remain critical in guiding decisions for hip aspiration
Genotype–Specific Safety and Pharmacokinetics of Cannabidiol in Healthy Volunteers
No full textCannabidiol (CBD) use has increased in America due to its widespread availability. Cannabidiol is metabolized by multiple polymorphic enzymes including CYP3A, CYP2C9, and CYP2C19. We sought to evaluate the genotype-specific adverse events and pharmacokinetic profiles of cannabidiol, 7-OH cannabidiol (an active metabolite), and 7-COOH cannabidiol. We completed a secondary analysis of an open-label, fixed-sequence, single-center study of cannabidiol in 33 healthy subjects. Patients first received a single dose of cannabidiol 5 mg/kg orally with serial plasma concentrations measured. Later, patients were titrated to 5 mg/kg twice daily for 14 days to reach steady state with serial plasma concentrations measured. CYP3A, CYP2C9, and CYP2C19 genotypes were assessed. Pharmacokinetic parameters were calculated by noncompartmental analysis. Diarrhea was observed more frequently in individuals with both CYP3A5 poor metabolism and CYP2C19 intermediate/normal metabolism (39%) compared to individuals with other genotypes (7%, p = 0.0463). Individuals with both CYP3A5 poor metabolism and CYP2C19 intermediate/normal metabolism had increased 7-OH cannabidiol and 7-COOH cannabidiol exposure at steady state. Cannabidiol parent drug exposure varied by CYP2C19 metabolizer status, with lower cannabidiol exposure and parent to metabolite ratios in intermediate metabolizers after single dose (p = 0.014) and at steady state (p = 0.0033). Similar CYP2C19 genotype-specific exposure was observed in an external validation cohort. Minor differences in exposure of cannabidiol and its metabolites were observed between CYP3A5 and CYP2C9 genotype groups. Significant changes in pharmacokinetics were observed between CYP2C9, CYP2C19, and CYP3A5 genotype groups. Future studies should assess whether pharmacogenomics can predict intestinal concentrations of CBD, its metabolites, and diarrhea
Dynamic navigation method for rapid confirmation of multi-unit abutment position and angulation
No full textDetecting Precise Adverse Drug Event (ADE) Signals from Real-World Data
No full textIUIAdverse drug events (ADEs) are a major public health burden, yet many risks remain undetected before drugs reach the market. While large-scale real-world data (RWD) offers a powerful resource for post-market surveillance, its use is hindered by methodological challenges like confounding, model misspecification, and high false positive rates. This dissertation develops and applies novel statistical methods to overcome these challenges. The research focuses on detecting nuanced drug safety signals, specifically identifying subpopulation-specific ADEs, timing-dependent drug-drug interactions (DDIs), and complex drug-drug-host interactions (DDHIs).
Three models were developed and applied to a large U.S. administrative claims database. The Precision Mixture Risk Model (PMRM) uses a case-crossover design to find ADEs in specific patient subgroups while controlling confounding and false discovery rates (FDR). The Sensitive and Timing-awarE Model (STEM) identifies DDIs by accounting for the sequence of drug administration. Finally, the Trajectory-Informed Model (TIM), coupled with an optimal control selection strategy, detects DDHIs where risk is amplified in patients with specific characteristics.
The models successfully identified numerous signals missed by traditional methods. PMRM revealed drugs posing risks only in distinct demographic and clinical subgroups. STEM detected substantially more DDI signals than conventional approaches, including interactions with timing-dependent risks. TIM identified thousands of potential DDIs and DDHIs, demonstrating that many adverse interactions manifest exclusively within patient subgroups defined by composite risk factors (e.g., age, sex). The proposed methods consistently showed superior detection power while maintaining rigorous FDR control.
This dissertation delivers a robust statistical framework for precision pharmacovigilance. By effectively identifying complex drug-host, drug-drug, and drug-drug-host interactions from RWD, these models support a more personalized approach to prescribing. This work enables the anticipation and mitigation of ADE risks based on individual patient profiles, ultimately advancing drug safety
Inferring high-fat dietary patterns from electronic health record data using machine learning
No full textObjectives: Electronic health records (EHRs) rarely capture dietary detail, limiting diet-disease research. We aimed to develop machine learning (ML) computable phenotypes to identify high-fat diet (HFD) using variables typically available in EHRs.
Materials and methods: We used National Health and Nutrition Examination Survey (NHANES) 1999-2020 data, where 24-h dietary recall served as ground truth. Dietary fat intake was summarized into a score (0-30) based on percent energy from fat, carbohydrate, and protein; lower scores indicated HFD. We defined HFD at cutoffs of 10, 15, and 20, and trained ML models (Extreme Gradient Boosting, logistic regression, random forest) using EHR-compatible variables (demographics, comorbidities, labs, anthropometrics). Model interpretability was assessed using Shapley Additive Explanations. To evaluate clinical relevance, we compared cancer associations using ML-predicted vs true diet labels.
Results: Machine learning models classified HFD with good performance, strongest at broader definitions. Random forest achieved an F1-score of 0.79 (recall 0.74, precision 0.84) at cutoff 20. Key predictors included race/ethnicity, triglycerides, obesity metrics (body mass index and derived indices), and metabolic panel results.
Discussion: These findings indicate that dietary patterns, though seldom recorded in EHRs, can be inferred from routinely available variables. The ability of ML-derived phenotypes to reproduce known diet-disease relationships underscore their epidemiologic validity. Top predictors also align with established biological pathways linking obesity, lipid metabolism, and cancer risk, supporting plausibility.
Conclusion: A high-fat dietary pattern can be inferred from EHR-compatible variables using ML-based phenotyping. This approach offers a scalable tool to integrate diet into EHR-based research and precision medicine
Early-Life Parental Affection, Social Relationships in Adulthood, and Later-Life Cognitive Function
No full textObjective:
Although research has demonstrated the long-term health consequences of childhood adversities, less is known about the long-term impact of positive childhood experiences, such as parental affection.
Method:
Using longitudinal data (1995–2014) from the Midlife in the United States (MIDUS) study, we analyze structural equation models estimating direct and indirect pathways from early-life parental affection to changes in later-life cognitive function through relationship quality in adulthood among Black and White older adults (N = 1983).
Results:
Analyses revealed significant indirect effects of parental affection on better cognitive function through higher levels of social support (both average social support and family social support) in adulthood in the full sample and among Black respondents. Indirect pathways through relationship strain and through friend support were not significant.
Discussion:
This work elevates the importance of promoting positive parental relationships during childhood, with implications for better social relationships in adulthood and cognitive function in later life
Current landscape and clinical progress of targeted alpha radioimmunotherapy
No full textTheranostics is an interesting area of cancer research that describes the use of radiotracers to first diagnose and then treat
cancer. By coupling a radioisotope to an agent that selectively targets malignant cells, one can distribute focused radiation to disease
sites. There are a variety of different radiopharmaceutical vectors that have been utilized in this way, such as peptides, small molecules
and antibodies. Because antibodies bind to highly specific antigens, radioimmunotherapy (RIT) offers a promising route to precisely
targeted treatments with reduced systemic toxicity compared to conventional radiotherapy. Beta (β)-emitting isotopes (e.g., 131I, 90Y)
have been more commonly coupled in RIT, but the use of alpha (α)-emitters (e.g., 225Ac, 212Pb), for RIT (α-RIT) has rising popularity due to
their shorter tissue range and higher linear energy transfer. These characteristics decrease off-target effects in neighboring tissues and
increase tumor cell destruction, respectively. However, there are several challenges to RIT. The production of daughter isotopes from α
decay makes dosimetric assessments difficult and could potentially cause off target toxicities. Additionally, whole antibodies tend to ac-
cumulate in liver tissue and have long biological clearance times, which may cause excess radiation to the blood, marrow and liver. Yet,
there are a variety of α-RIT agents currently in development to treat prostate cancer, hematologic malignancies, and other solid tumors.
Many agents show promise, like 227Th-epratuzumab, a CD22-targeting antibody used in the treatment of relapsed or refractory acute
myeloid leukemia (R/R AML). While notoriously deadly and difficult to treat, the disease control rate in patients with R/R AML taking 227Th-
epratuzumab was 38%. Like many α-RIT therapies, follow-up studies are needed to continue to improve efficacy. Strategies to widen the
therapeutic indices of these agents have been investigated such as pretargeting, use of antibody fragments, chelator optimization and
combination therapies. This review describes the current landscape and clinical progress of targeted α radioimmunotherapy.Indiana Clinical and Translational Sciences Institute, UL1TR002529 from the National Institutes of Healt
Increasing Primary Care and Preventive Care Utilization in Indiana: A State-Level Approach
No full textStrengthening primary care and preventive care is a key priority for the state of Indiana, as codified in recent legislation and backed by employers and health leaders. Policymakers, business and provider coalitions, insurers, and residents broadly agree on the importance of primary care and preventive care as pillars of Hoosier health and key requirements for continued state-level economic growth. However, stakeholders are not always aligned on the strategies and investments needed to build a stronger primary care and preventive care system in the state
Comparative risk of postoperative complications after total shoulder arthroplasty in patients with non-alcoholic cirrhosis versus NAFLD: A matched national cohort study
No full textBackground: This retrospective database study evaluates postoperative complications following total shoulder arthroplasty (TSA) in patients with non-alcoholic cirrhosis (NAC) versus non-alcoholic fatty liver disease (NAFLD) using a large matched national cohort.
Materials and methods: Among 266,263 patients who underwent TSA, 171,059 had continuous enrollment and were undergoing TSA for the first time. Out of this group, 1986 patients had NAC and 4240 had NAFLD. Propensity-score matching was conducted controlling for age, sex, Charlson Comorbidity Index, and key clinical covariates, resulting in a final cohort of 2170 total patients (1085 per group). Multivariable logistic regression was used to compare 90-day and one-year postoperative complication rates.
Results: Within 90 days postoperatively, patients with NAC had higher rates of acute kidney injury, blood transfusion, and any complication compared to patients with NAFLD. At one year, NAC patients continued to show higher odds of blood transfusion and overall complications, while NAFLD patients had significantly higher deep vein thrombosis incidence.
Discussion: Patients with NAC undergoing TSA are at increased risk for postoperative complications compared to those with NAFLD. Although NAFLD patients had fewer adverse outcomes, they exhibited elevated thromboembolic risk at one year. Tailored perioperative strategies to liver disease subtype patients are needed to help mitigate postoperative complications in this vulnerable population