Derek K. Smith

Background: Scarce scientific evidence is available on the association of cumulative coping challenges and periodontitis. The present retrospective cross‐sectional exploratory study explored a new method to depict the association between periodontal disease (extent/severity/rate of progression) and cumulative coping challenges. Methods: A population sample was identified with an array of periodontal disease levels and coping challenges within an academic setting. In January 2026, a total of 39 student‐led patient presentations were recorded using an ordinal scale based on the 2017 American Academy of Periodontology/European Federation of Periodontology framework of staging/grading and a cumulative coping challenges score, including biologic, sociologic, behavioral, demographic, and oral disability‐related factors. Spearman's correlation analysis was used to correlate periodontal disease level and cumulative coping challenges. Results: A distinct and significant association was found between level of periodontal disease and cumulative coping challenges (Spearman's ρ  = 0.77, 95% CI, p < 0.01). Clusters of patients depicted that those with mild forms of periodontal disease (Stages I–II) had few coping challenges and another group with severe forms of periodontal disease (Stages III–IV) harbored higher cumulative coping challenges. No person appeared with high disease and low cumulative coping challenges, and only one person appeared with low disease and high coping challenges. Conclusions: This retrospective cross‐sectional exploratory study quantified risk factors from a series of cumulative coping challenges resulting in a single metric to be correlated with disease level based on the current staging/grading system for periodontal diseases. Periodontitis was observed among individuals with minimal disease and few coping challenges, and another group with severe stages and extensive coping challenges. Despite the theoretical limitation of the proposed scale, the present model has a practical use in an educational setting as an exploratory composite index.

Acute kidney injury (AKI) is a serious complication after vascular surgery. Reduced synthesis of nicotinamide adenine dinucleotide (NAD+) from tryptophan is associated with an increased risk of AKI in critically ill patients, patients hospitalized with COVID-19, and cardiac surgery patients, and is marked by elevated urinary quinolinate and quinolinate to tryptophan ratios. We measured quinolinate concentrations in vascular surgery patients to determine if impaired NAD+ synthesis was associated with AKI in this patient population. Eight preoperative and eight postoperative vascular surgery patients who developed AKI were selected from a parent study to participate in this single-center case-control study. They were matched with controls who did not develop AKI based on age, sex, BMI, eGFR, hypertension, and diabetes. Urinary quinolinate and tryptophan concentrations were measured at anesthetic induction and on postoperative day one. Two-sided Mann–Whitney U tests were used to compare quinolinate and quinolinate to tryptophan ratios. Multivariate linear regression modeling was used to estimate the relationship between quinolinate and serum creatinine. There was no difference in preoperative or postoperative urine quinolinate concentrations or the preoperative quinolinate to tryptophan ratio between patients that did and did not develop AKI (p = 0.07, 0.50, and 0.32, respectively). However, postoperative quinolinate to tryptophan ratios were higher in AKI patients (p = 0.04). Further, after adjustment for AKI risk factors, higher preoperative quinolinate concentrations and higher postoperative quinolinate to tryptophan ratios were associated with greater postoperative creatinine increases (p = 0.04 and 0.04, respectively). These data suggest that impaired NAD+ synthesis may contribute to AKI development in vascular surgery patients.

Background: Commonly used predictive models for postoperative pulmonary complications (PPCs) do not perform when applied to head and neck cases. A head and neck‐specific risk prediction tool is needed.Methods: Data on 794 free flap head and neck surgery cases at a single center were abstracted from the electronic medical record. Each case was reviewed for the development of PPCs. A predictive model was developed and was then compared to existing predictive models for PPCs.Results: The least absolute shrinkage and selection operator procedure identified age, alcohol use, history of congestive heart failure, preoperative packed cell volume, preoperative oxygen saturation, and preoperative metabolic equivalents as predictors of PPCs in the head and neck population. The model demonstrated an area under the receiving operating characteristic curve of 0.75 (0.69–0.80) with moderately good calibration. Comparisons to the performance of existing models demonstrate superior performance.Conclusions: The model for the development of PPCs developed in this article displays superior performance to existing models.

Background: Radiation for patients with head and neck cancer (HNC) is associated with painful mucositis that impacts the delivery of treatment and contributes to high symptom burden.Methods: This was a prospective, randomized pilot trial. Eligible patients received primary or adjuvant chemoradiation. Patients were randomized to usual care vs usual care plus gabapentin titrated to drug tolerance during radiation. Patients completed a symptom survey at baseline and weekly during therapy.Results: Seventy‐nine patients were enrolled in the study (38 control, 41 treatment). At interim analysis, gabapentin use resulted in a decrease in pain (P= .004), with the biggest decreases being in the latter weeks of therapy. By week 7, the median pain score in the treatment group was below the 0.25 quantile of the control group.Conclusion: Prophylactic use of gabapentin during chemoradiation for HNC patients resulted in a decrease in pain, neurosensory symptoms, and general systemic symptoms.

A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant. We refer to this as the penetrance, the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance. The clinical implications for genetic variants, even definitively pathogenic variants, can vary strikingly across individuals. Lack of evidence to estimate the probability of disease from identified genetic variants, especially rare variants, presents a major barrier to integrating genotype information into clinical care. Here we advance an approach to estimate the penetrance, or positive predictive value of the discovery of a genetic variant, in service of advancing the use of genetic information in personalized medicine.