Citations

Abstract Background Automated phlebotomy has the potential to improve patient outcomes and address phlebotomist workforce challenges. The Autonomous Blood Drawing Optimization and Performance Testing trial (ADOPT, ClinicalTrials.gov NCT05878483) assesses performance, safety, and patient experience of an autonomous robotic phlebotomy device (ARPD) that utilizes multimodal imaging. Methods Patients were recruited from outpatient phlebotomy departments in the Netherlands. In cohort 1, analytical equivalence was assessed by comparing within-subject test results from ARPD-collected and manually collected samples using Passing–Bablok regression analysis. In cohort 2, the ARPD was assessed in routine use. The primary performance endpoint was first-stick success rate; the primary safety endpoint was the rate of adverse events. Exploratory endpoints assessed pain and patient preference. Results One hundred fifty-three patients were enrolled in cohort 1. No statistically significant differences were observed in activated partial thromboplastin time, prothrombin time, lactate dehydrogenase, aspartate transaminase, or platelet count, indicating analytical equivalence. A total of 1633 patients were enrolled in cohort 2. The overall first-stick success rate, in patients where the ARPD could identify a suitable vein, was 94.5% (95% CI, 93.3–95.5) and remained high in patients with difficult venous access (92.7%), obesity (97.4%), and age ≥65 years (93.4%). Adverse events, all mild, occurred at a rate of 0.6%. Ninety percent of patients reported far less (19%), less (32%), or similar pain (39%) compared with manual phlebotomy. Eighty-two percent indicated they would strongly prefer (16%) or prefer (31%) use of the ARPD in the future or had no preference (35%). Conclusions Automated phlebotomy using the ARPD shows a favorable performance and safety profile and may improve patient experience. ClinicalTrials.gov Registration Number: NCT05878483

Abstract Context Osteoanabolic therapy for osteoporosis should be followed by antiresorptive treatment (“consolidation”) to preserve bone mass. Although osteoanabolic therapy is increasingly endorsed for primary fracture prevention, real-world implementation of consolidation remains unknown. Objective To assess patterns and predictors of timely consolidation after osteoanabolic therapy for primary fracture prevention. Methods We conducted a retrospective cohort study using US commercial, Medicare Advantage, and traditional Medicare claims data. Adults aged ≥50 years who initiated romosozumab, teriparatide, or abaloparatide between 2011 and 2022 were included. The primary outcome was timely consolidation, defined as initiation of an antiresorptive medication within 3 months after osteoanabolic therapy. Secondary outcomes included delayed consolidation (initiation >3 months after completion), osteoanabolic restart following a treatment gap, and no consolidation therapy. We further distinguished participants who completed the full osteoanabolic course, defined as ≥12 months for romosozumab and ≥18 months for teriparatide or abaloparatide. Multivariable logistic regression models were used to identify predictors of consolidation patterns. Results Among 15 389 patients (mean age 70.6 years; 89% women; 85% White), 30% completed osteoanabolic therapy (median, 12.9 months for romosozumab; 23.4 months for teriparatide/abaloparatide). Overall, 25% had timely consolidation, 24% delayed consolidation, 12% restarted osteoanabolics, and 40% had no consolidation; only 12% completed therapy with timely consolidation. Timely consolidation was more likely with endocrinology (OR 1.31; 95% CI, 1.17-1.48) or rheumatology (OR 1.45; 1.30-1.62) vs primary care, and less likely among patients aged 50-64 years (OR 0.67; 0.58-0.77) vs 65-74, those enrolled in Medicare Advantage (OR 0.85; 0.74-0.98) vs traditional Medicare, or those recently hospitalized (OR 0.88; 0.80-0.97). Odds increased over time (OR 4.27; 3.63-5.05 for 2021 vs 2011 starts). Conclusion Only 1 in 4 patients received timely consolidation therapy after osteoanabolic treatment, and 1 in 8 completed the full sequence. Strategies to improve consolidation in primary fracture prevention are needed.

Abstract Background Neuroendocrine tumors (NETs) are rare, heterogeneous neoplasms associated with prolonged survival and substantial symptom burden. However, patient-reported outcomes across NET subtypes remain poorly characterized, particularly in real-world settings. This study describes baseline health-related quality of life (HRQoL) and care experiences among patients with gastroenteropancreatic and lung NETs, examining differences by tumor site and time since diagnosis. Methods The Neuroendocrine Tumors–Patient Reported Outcomes study is a prospective, multi-institutional US cohort of adults (aged 18 years and older) with incident small intestinal, pancreatic, gastroenteropancreatic, or lung NETs diagnosed from January 2018 through September 2024, identified via a validated electronic medical record–based computable phenotype. Baseline surveys assessed HRQoL, symptoms, care experiences, and clinical characteristics using validated instruments. Descriptive statistics and standardized mean differences compared responses by NET site and time since diagnosis. Results Among 2367 participants (mean age = 57.8 years; 57.3% female), 1974 had gastroenteropancreatic NETs (659 small intestinal NET, 555 pancreatic NET) and 393 had lung NETs. Fatigue (mean = 33.0), insomnia (mean = 32.5), and diarrhea (mean = 25.7) were the most burdensome symptoms. Lung NET patients reported worse dyspnea (standardized mean difference = 0.58, P < .001) and lower physical, role, and global QoL scores than those with gastroenteropancreatic NETs, while pancreatic NET patients reported better functioning. Diarrhea worsened over time, especially in small intestinal NETs. Most rated care highly (75.3%) but cited concerns about treatment side effects (80.4%), costs (60.7%), and travel burden (58.8%). Conclusions This large US cohort reveals persistent symptom burden and HRQoL variation by tumor site and disease duration, underscoring the need for longitudinal HRQoL assessment in NET care.

Abstract Background Cardiomegaly is a clinically significant incidental finding on chest computed tomography (CT) associated with heart failure, arrhythmias, and sudden cardiac death. Qualitative radiologist assessment is variable, and automated AI tools may enable objective opportunistic cardiac volumetry. Purpose To evaluate whether AI-enabled total cardiac volume (TCVAI) derived from non-ECG-gated, non-contrast chest CT can identify cardiomegaly as defined by echocardiography. Materials and Methods This retrospective study included 307 consecutive patients (median age, 67 years; 56% male) who underwent non-contrast chest CT at a single center on 7 scanner types (4 vendors) and clinically indicated echocardiography within 31 days. A commercially available AI tool (AI-Rad Companion, Siemens Healthineers) automatically quantified TCVAI, indexed to body surface area (TCVAI/BSA). Echocardiography reports were reviewed for chamber dilation and left ventricular hypertrophy (LVH), collectively defined as cardiomegaly. Associations between TCVAI/BSA and echocardiographic findings were assessed using correlation, ordinal regression, and receiver operating characteristic (ROC). Interscan repeatability was evaluated in 248 patients with 544 repeat CT examinations. Prespecified sex-specific thresholds were tested in a temporally independent validation cohort of 50 patients. Results Median TCVAI was higher in patients with cardiomegaly than those without (1061.9 vs 798.4 mL; P < .001). TCVAI/BSA was associated with chamber dilation and LVH severity on univariate analysis and remained associated in multivariable ordinal models, except for right ventricular dilation. Discriminatory performance was fair to good, with area under the curve (AUC) 0.81 (95% CI, 0.75-0.87) in men and 0.77 (95% CI, 0.69-0.85) in women. Interscan repeatability was excellent (intraclass correlation coefficient [ICC]: 0.93). In independent validation, performance ranged from sensitivity 89.3%/specificity 27.3% at a high-sensitivity threshold to sensitivity 28.6%/specificity 100% at a high-specificity threshold. Conclusion AI-derived cardiac volume from routine chest CT shows fair to good performance for identifying echocardiography-defined cardiomegaly with high measurement repeatability, supporting a potential role for automated cardiac volumetry as an objective, opportunistic biomarker.

Abstract The practice patterns of fluorine 18–labeled fluorodeoxyglucose positron emission tomography/computed tomography ([18F] FDG PET/CT) use in cardiac implantable electronic device infection remain undefined. Among 258 Emerging Infections Network respondents, 69% reported never or rarely using [18F] FDG PET/CT. Limited access and insurance barriers were the most common obstacles.

(1) Background: Mammographic breast density (MBD) is a well-established predictor of breast cancer risk, yet the biological mechanisms underlying this association remain incompletely understood. MBD is characterized by alterations in breast stromal architecture, including increased collagen deposition and changes in immune cell composition. Given emerging evidence that the breast harbors a resident microbiome, we investigated whether the breast tissue microbiome correlates with MBD. (2) Methods: Adjacent normal breast tissue was collected under sterile conditions from 33 women undergoing surgery for benign or malignant breast disease. DNA was extracted and subjected to 16S rRNA gene sequencing (Illumina MiSeq). (3) Results: We observed a non-significant trend toward lower α-diversity in high-MBD samples compared to low-MBD samples, p = 0.13. β-Diversity analyses identified a modest association between MBD and microbial community composition (MiRKAT p = 0.049). A random forest-based model incorporating genus-level relative abundances improved prediction of MBD over clinical characteristics alone, identifying Corynebacterium (Actinobacteria) and other genera as key predictors. (4) Conclusions: Breast tissue microbial features vary with mammographic breast density, suggesting a potential association with density-associated breast cancer risk. These exploratory findings warrant validation in larger cohorts to better elucidate biological mechanisms and clinical relevance.

Abstract INTRODUCTION Guidelines recommend non-surgical biopsy for indeterminate pulmonary lesions > 8 mm. Shape-sensing robotic-assisted bronchoscopy (ssRAB) is growing in use to biopsy small pulmonary nodules; however current literature is limited to single center studies with limited follow-up. This study, PRECIsE, assessed safety and performance of the first ssRAB iteration across multiple sites and nascent users. METHODS Prospective, multicenter, observational study evaluating ssRAB without cone-beam CT guidance in patients with nodules 10–30 mm located in or beyond the sub-segmental airways. Patients were followed for 2 years; the primary endpoint was sensitivity for malignancy with diagnostic yield, and safety as secondary endpoints. Multilevel logistic regression models were used to control for factors associated with sensitivity and diagnostic yield. RESULTS A total of 305 procedures were performed across 6 centers. Median nodule size was 17.0 mm (IQR: 14.0–23.0) with bronchus sign present in 37% of cases. Sensitivity for malignancy through 2 years was 81.3% (95% CI: 75.7, 86.1). Multi-level modelling demonstrated female sex, smaller nodule size, lower lobe location, semi-solid density, and higher body-mass-index were associated with lower sensitivity. Diagnostic yield was 74.1% (95% CI: 68.8, 78.9) according to the ATS/ACCP criteria and 77.5% [95% CI: 72.4, 81.8] according to the intermediate criteria. Multi-level modeling demonstrated a non-significant site/center level effect on ATS/ACCP diagnostic yield (P = .36). Pneumothorax requiring intervention was 1.6% (5/305); bleeding was 1.0% (3/305) with two (2) Nashville grade 2 and one (1) Nashville grade 3 events. CONCLUSIONS The first iteration of ssRAB demonstrated encouraging performance and a strong safety profile among nascent users for the biopsy of small peripheral nodules.

Background Prior authorization (PA) is a major source of administrative burden, treatment delay, and clinician burnout. Artificial intelligence (AI), particularly large language models (LLMs), is increasingly used to assist with clinical documentation, yet its reliability for payer-facing administrative tasks remains uncertain. Objective To evaluate the quality of PA letters drafted by ChatGPT-5 for commonly used medications requiring PA in nephrology. Quality was evaluated based on correctness and strength of clinical reasoning. Methods We created a single standardized prompt and applied it across 29 nephrology scenarios to generate PA letters. Each PA letter was reviewed against four criteria: 1) absence of false statements or hallucinations, 2) correctness of ICD-10 coding, 3) presence and validity of citations, and 4) clinical reasoning, rated on a 4-point Likert scale (illogical, weak, adequate and strong). FDA drug labels, KDIGO guidelines and related randomized controlled trials were used as reference standards. Results Out of 29 letters, one letter (3.5%) contained false statements mentioning an irrelevant clinical trial. The ICD-10 diagnosis code was correct in 23 letters (79.3%), most errors were related to chronic kidney disease (CKD) staging or internal diagnostic inconsistencies. 27 letters (93.1%) cited valid references, with one letter citing an incorrect trial and another one citing a correct KDIGO guideline with inaccessible link. Twenty-six letters (89.7%) demonstrated strong clinical reasoning, supported by guideline-oriented or FDA label–aligned justification. The remaining 3 letters were rated as adequate reasoning. The main areas for improvement involved citing relevant references and emphasizing special considerations, for example Risk Evaluation and Mitigation Strategy (REMS) compliance for eculizumab. Conclusions ChatGPT-5 can generate clinically coherent PA drafts for nephrology medications, but limitations in coding precision and citation reliability persist. With appropriate oversight, AI-assisted documentation may reduce administrative burden while maintaining safety and accuracy.

Background Uncontrolled hypertension is common in chronic kidney disease (CKD) patients. Electro-hydraulic acoustic therapy (eHAT) is a non-invasive treatment that may lower blood pressure (BP), but its safety and efficacy in CKD remain unclear. This study aimed to evaluate eHAT's feasibility and safety in this population. Methods In this single-arm, prospective, single-center proof-of-concept study, CKD patients received six eHAT treatments over three weeks. BP was assessed in-office (attended and unattended) at baseline and at 4-, 12-, 24-, and 48-weeks post-treatment. Ambulatory BP monitoring (ABPM) was performed at baseline, 12, and 48 weeks. The primary endpoint was change in systolic office BP (OBP) from baseline to 12 weeks. Secondary endpoints included additional BP measurements, kidney function, and safety. Results Fifteen patients completed all follow-ups. At 12 weeks, mean systolic OBP decreased by 9.73 mmHg (SD 14.77; p = 0.0032) and remained significantly lower at 24 weeks (−7.67 mmHg; p = 0.0335) and 48 weeks (−19.40 mmHg; p < 0.0001). Diastolic OBP reductions were significant at 12 ( p = 0.0413) and 48 weeks ( p = 0.0022). By 12 weeks, 26% of participants reduced the number or dose of antihypertensive medications. Kidney function remained stable throughout. No safety signals were detected. Conclusion Our study highlights the feasibility and tolerability of eHAT in patients with CKD and HTN. Nonetheless, due to the small sample size and lack of a control group, these findings should be considered preliminary.