Objectives The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR). Methods Ninety-nine network general dentistry practices that used Dentrix or EagleSoft EDR shared de-identified data of patients who received PCR and/or RCT on permanent teeth through October 31, 2015. We evaluated the data completeness and correctness, summarized practice, and patient characteristics and summarized the two treatments by tooth type and arch location. Results Eighty-two percent of practitioners were male, with a mean age of 49 and 22.4 years of clinical experience. The final dataset comprised 217,887 patients and 11,289,594 observations, with the observation period ranging from 0 to 37 years. Most patients (73%) were 18 to 64 years old; 56% were female. The data were nearly 100% complete. Eight percent of observations had incorrect data, such as incorrect tooth number or surface, primary teeth, supernumerary teeth, and tooth ranges, indicating multitooth procedures instead of PCR or RCT. Seventy-three percent of patients had dental insurance information; 27% lacked any insurance information. While gender was documented for all patients, race/ethnicity was missing in the dataset. Conclusion This study established the feasibility of using EDR data integrated from multiple distinct solo and small-group network practices for longitudinal studies to assess treatment outcomes. The results laid the groundwork for a learning health system that enables practitioners to learn about their patients' outcomes by using data from their own practice. An internet site devoted to details about the nation's network is located at http://NationalDentalPBRN.org. We are grateful to the network's regional node coordinators, who function as the “face” of the network in recruiting, training, and interacting with the network's practitioners (Midwest Region: T.S., RDH, BSDH; Western Region: Stephanie Hodge, MA; Northeast Region: Christine O'Brien, RDH; South Atlantic Region: Hanna Knopf, BA, and Deborah McEdward, RDH, BS, CCRP; South Central Region: Shermetria Massengale, MPH, CHES, and Ellen Sowell, BA; Southwest Region: Stephanie Reyes, BA, Meredith Buchberg, MPH, and Colleen Dolan, MPH), and the network's program manager (Andrea Mathews, BS, RDH), and program coordinator (Terri Jones). Our study was determined to be nonhuman subject research by the Indiana University Institutional Review Board (IRB). We also received IRB approvals or exemptions separately from the six Network regions: the Midwest, South Central, Southwest, Northeast, Western, and South Atlantic regions. The informed consent of all human subjects who participated in this investigation was obtained after the nature of the procedures had been explained fully. ^* Dr. Thankam Paul Thyvalikakath and Dr. William D. Duncan are the first authors. ^** http://www.nationaldentalpbrn.org/collaborative-group.php.

Background: The use of social media platforms in health research is increasing, yet their application in studying rare diseases is limited. Hodgkin’s lymphoma (HL) is a rare malignancy with a high incidence in young adults. This study evaluates the feasibility of using social media data to study the disease and treatment characteristics of HL. Methods: We utilized the X (formerly Twitter) API v2 developer portal to download posts (formerly tweets) from January 2010 to October 2022. Annotation guidelines were developed from literature and a manual review of limited posts was performed to identify the class and attributes (characteristics) of HL discussed on X, and create a gold standard dataset. This dataset was subsequently employed to train, test, and validate a Named Entity Recognition (NER) Natural Language Processing (NLP) application. Results: After data preparation, 80,811 posts were collected: 500 for annotation guideline development, 2,000 for NLP application development, and the remaining 78,311 for deploying the application. We identified nine classes related to HL, such as HL classification, etiopathology, stages and progression, and treatment. The treatment class and HL stages and progression were the most frequently discussed, with 20,013 (25.56%) posts mentioning HL’s treatments and 17,177 (21.93%) mentioning HL stages and progression. The model exhibited robust performance, achieving 86% accuracy and an 87% F1 score. The etiopathology class demonstrated excellent performance, with 93% accuracy and a 95% F1 score. Discussion: The NLP application displayed high efficacy in extracting and characterizing HL-related information from social media posts, as evidenced by the high F1 score. Nonetheless, the data presented limitations in distinguishing between patients, providers, and caregivers and in establishing the temporal relationships between classes and attributes. Further research is necessary to bridge these gaps. Conclusion: Our study demonstrated potential of using social media as a valuable preliminary research source for understanding the characteristics of rare diseases such as Hodgkin’s Lymphoma.

Introduction: The prevalence of psychological distress (PD) among Asian Indians is unknown. This study estimated and compared moderate–serious PD in Asian Indians and non-Hispanic Whites (NHWs) in the United States. Methods: We used a cross-sectional design using the National Health Interview Survey (2012–2017). Adult (age >18 years) NHWs and Asian Indians (N=2,218) were included. PD was measured using the six-item Kessler (K6) scale. We used multivariable logistic regression to determine the association of Asian Indian ethnicity with PD. Results: In the analysis, 19.9% of NHWs and 11.0% of Asian Indians reported moderate–serious PD. Asian Indians were less likely to report PD in both unadjusted (unadjusted odds ratio=0.50; 95% confidence interval [CI]=0.42–0.58) and fully adjusted (adjusted odds ratio=0.7; 95% CI 0.59–0.82) models. Conclusions: Asian Indians had a lower prevalence of PD than NHWs, likely due to multiple protective factors such as high socioeconomic status and lower multimorbidity.

Objective: To evaluate chronic conditions as leading predictors of economic burden over time among older adults with incident primary Merkel Cell Carcinoma (MCC) using machine learning methods. Methods: We used a retrospective cohort of older adults (age ≥ 67 years) diagnosed with MCC between 2009 and 2019. For these elderly MCC patients, we derived three phases (pre-diagnosis, during-treatment, and post-treatment) anchored around cancer diagnosis date. All three phases had 12 months baseline and 12-months follow-up periods. Chronic conditions were identified in baseline and follow-up periods, whereas annual total and out-of-pocket (OOP) healthcare expenditures were measured during the 12-month follow-up. XGBoost regression models and SHapley Additive exPlanations (SHAP) methods were used to identify leading predictors and their associations with economic burden. Results: Congestive heart failure (CHF), chronic kidney disease (CKD) and depression had the highest average incremental total expenditures during pre-diagnosis, treatment, and post-treatment phases, respectively ($25,004, $24,221, and $16,277 (CHF); $22,524, $19,350, $20,556 (CKD); and $21,645, $22,055, $18,350 (depression)), whereas the average incremental OOP expenditures during the same periods were $3703, $3,013, $2,442 (CHF); $2,457, $2,518, $2,914 (CKD); and $3,278, $2,322, $2,783 (depression). Except for hypertension and HIV, all chronic conditions had higher expenditures compared to those without the chronic conditions. Predictive models across each of phases of care indicated that CHF, CKD, and heart diseases were among the top 10 leading predictors; however, their feature importance ranking declined over time. Although depression was one of the leading drivers of expenditures in unadjusted descriptive models, it was not among the top 10 predictors. Conclusion: Among older adults with MCC, cardiac and renal conditions were the leading drivers of total expenditures and OOP expenditures. Our findings suggest that managing cardiac and renal conditions may be important for cost containment efforts.

Purpose: The objective of this study was to evaluate and compare the effectiveness of a narrow-profile, small-diameter reload to a powered vascular stapler when used during VATs lobectomy. Patients and Methods: This was a retrospective, observational cohort study comparing outcomes after VATs lobectomy procedures in which either the Echelon Flex™ powered vascular stapler (PVS) or a Signia™ small-diameter reload (SDR) was used to transect pulmonary vasculature. Data from adult patients (≥ 18 years of age) who had lung cancer and underwent elective, primary VATs lobectomy in the 3 years between 2021 and 2023 were extracted from the PINC AI healthcare database. Two patient groups (PVS vs SDR) were propensity-score matched using a 1:1 nearest neighbor matching scheme with a caliper of 0.2 to adjust the baseline patient characteristic imbalance. Two sensitivity analyses were performed using multivariable regression models and propensity score inverse probability weighting. Results: Of 558 (34.1% SDR and 65.9% PVS) eligible inpatient cases, 68.5% age ≥ 65, 58.2% were female, 59.0% were married, 79.8% White, 68.5% were Medicare, 73.5% had comorbidity ≥ 3. One hundred and ninety pairs were matched. The incidence rate of blood transfusion was significantly higher in the PVS group than in the SDR group (PVS vs SDR: 6.4% vs 0.5% for matched pairs, difference: 5.8%, 95% confidence interval (CI) 2.2%– 9.5%, p = 0.002). The incidence rate of bleeding was not significantly different between the groups and no conversions were observed. Intensive care unit visits and length of stay were higher in the PVS group. Sensitivity analyses results were consistent with these findings. Conclusion: The narrow-profile SDR was an effective and beneficial tool for division and ligation during VATs lobectomy and is associated with fewer blood transfusions compared to PVS. Plain Language Summary: PLS Title: Comparing the performance of two surgical staplers during minimally invasive lung surgery. Surgical staplers are used for many kinds of surgeries. Some staplers are better for certain uses, depending on their design. In this article, we analyzed patient records from a US hospital-based, service-level, all-payer healthcare claims to help understand how two different surgical staplers performed in preventing bleeding during lobectomy (a type of lung surgery). One is called the “powered vascular stapler” (PVS) and the other is a “small-diameter reload” (SDR). We looked at lung cancer patients who underwent lobectomy in the years 2021 and 2023. Both staplers were effective tools during surgery. However, patients who had a procedure using the SDR stapler were less likely to need a blood transfusion compared to those where the PVS stapler was used. This suggests that patients in the SDR group may have lost less blood during surgery. Therefore, the SDR stapler might be a more suitable tool for minimally invasive lobectomy.

The cost of health care for patients with Hodgkin lymphoma (HL) is projected to rise, making it essential to understand expenditure drivers across different demographics, including the older adult population. Although older HL patients constitute a significant number of HL patients, the literature on health care expenditures in older HL patients is lacking. Predictive capabilities of machine learning (ML) methods enhance our ability to leverage a data-driven approach, which helps identify key predictors of expenditures and strategically plan future expenditures. To determine the leading predictors of health care expenditures among older HL survivors across prediagnosis, treatment, and posttreatment phases of care. The study uses a retrospective research design to identify the incident cases of HL diagnosed between 2009 and 2017 using Surveillance, Epidemiology, and End Results-Medicare data. Three phases of cancer care (prediagnosis, treatment, and posttreatment) were indexed around the diagnosis date, with each phase divided into 12 months of baseline and 12 months of follow-up. ML methods, including XGBoost, Random Forest, and Cross-Validated linear regressions, were used to identify the best regression model for predicting Medicare and out-of-pocket (OOP) health care expenditures. Interpretable ML SHapley Additive exPlanations method was used to identify the leading predictors of Medicare and OOP health care expenditures in each phase. The study analyzed 1,242 patients in the prediagnosis phase, 902 in the treatment phase, and 873 in the posttreatment phase. XGBoost regression outperformed Random Forest and Cross-Validated linear regression models with overall performance in predicting Medicare expenditures, with R-squared (root mean square error) values of 0.42 (1.39), 0.43 (0.56), and 0.46 (0.90) across the 3 phases of care, respectively. Interpretable ML methods highlighted baseline expenditures, number of prescription medications, and cardiac dysrhythmia as the leading predictors for Medicare and OOP expenditures in the prediagnosis phase. Chemotherapy and immunotherapy and surgical treatment and immunotherapy were the leading predictors of expenditures in the treatment and posttreatment phases, respectively. As ML applications increase in predicting health care expenditure, researchers should consider implementing models in different phases of care to identify the changes in the predictors. Leading predictors of health care expenditures can be targeted for informed policy development to address financial hardship in HL survivors.