Citations

ABSTRACT Background and Objectives Among patient with early‐stage breast cancer, breast conservation surgery (BCS) is the most performed surgery, utilized in approximately two‐thirds of patients. While 5‐year overall‐survival rates are strong—typically above 90%—additional re‐excisions can occur in up to 25% of patients who undergo index BCS and some patients will require subsequent surgery for new breast cancer or recurrences. As such, these additional procedures can complicate patients’ pathway to survivorship, negatively impact quality of life, and lead to excess healthcare costs. Methods This retrospective cohort study used linked claims and an electronic healthcare record database to (1) characterize patients at higher risk for additional breast oncologic surgery (aBOS) following BCS, and (2) quantify the long‐term financial impact of aBOS after BCS. In total, 87,190 patients with breast cancer, who had an index BCS between January 1, 2012, to March 31, 2023, were followed for up to 5‐years post‐operatively. Patients with and without aBOS were analyzed, comparing demographics, tumor characteristics, healthcare utilization, and per‐person‐per‐month (PPPM) costs. Results Twenty‐three percent ( n = 20,212) of patients underwent aBOS. These patients were younger, had higher tumor grades and larger tumor sizes, and incurred greater healthcare costs compared to patients who did not undergo a secondary surgery. Conclusion This study highlights significant factors influencing BCS outcomes and healthcare utilization, underscoring the need for tailored therapeutic strategies for patients with breast cancer.

Abstract Patients with rare genetic disorders often wait years for a correct diagnosis, highlighting the urgent need for early and efficient triage for those at risk. Electronic health records (EHRs) are rich in useful information, yet the extent to which this data is sufficient to generate prediagnostic signals is uncertain. We trained machine learning models on billing codes and phenotypes extracted from clinical notes and applied these across eight conditions in a longitudinal cohort of roughly 3 million patient records from the Mayo Clinic, seeking early identification of conditions. The number of patients identified early varied by condition, ranging from 10% to 89% at 99% specificity. Median lead times exceeded one year prior to the first diagnostic billing code for most conditions, including Fabry disease (1,408 days), hereditary angioedema (2,755 days), neurofibromatosis type 1 (1,067 days), and hereditary hemorrhagic telangiectasia (2,320 days). We show that ML models trained on EHR data can prioritize patients for clinician review and confirmatory genetic testing and that the integration of diverse phenotypic data sources provides superior predictive value.

Background. Rare diseases collectively affect an estimated 300 million people worldwide, yet patients wait an average of 4-8 years for a correct diagnosis after visiting multiple physicians, accumulating unexplained findings, and suffering preventable disease progression before a unifying diagnosis is reached. Electronic health records (EHRs) capture this pre-diagnostic trajectory in rich detail, but conventional methods typically collapse years of longitudinal phenotypic signals into a single static snapshot; our approach instead models this temporal structure directly. Methods. We conducted a retrospective cohort study using longitudinal EHR data from approximately 3 million patients at Mayo Clinic Platform_Accelerate. Cases were patients with confirmed diagnoses of 10 rare diseases; controls (~5,200 per disease) had no record of any rare disease. HPO phenotype terms were extracted from clinical notes and laboratory results using a medspaCy NLP pipeline; ICD codes were grouped at three-character level. We developed and trained two temporal models, GRU-Attn and Conformer, that encode patient histories as quarterly time-binned sequences spanning up to 30 years, and compared these against three static baselines (CatBoost, XGBoost, logistic regression). Models were evaluated systematically at prediction horizons h = 1-10 years before diagnosis. Results. In the full held-out cohort benchmark, temporal models outperformed static baselines across the large majority of diseases and prediction horizons, with the advantage widening substantially as the horizon lengthened. At 1-2 years prior to diagnosis, mean AUROC was 0.928 (GRU-Attn) versus 0.871 (CatBoost); at 6-10 years prior to diagnosis, 0.726 versus 0.693, with static models degrading to near-chance for some diseases. Combining HPO and ICD features yielded the strongest performance, with a mean gain of +0.063 AUROC over the best single source at 1-2 years prior to diagnosis. Separately, in a manually curated cohort of 143 confirmed-undiagnosed cases with extended EHR histories, GRU-Attn identified 138 cases before first disease mention and all 143 cases before formal diagnosis at the prespecified 99th-percentile specificity threshold, with median lead times of 1.1-8.3 years and 1.9-21.7 years, respectively. Conclusions. By learning from the temporal trajectory of phenotypic signals rather than their static aggregate, temporal EHR models improved retrospective early detection performance across several rare diseases, most powerfully for conditions where pre-diagnostic findings accrue gradually and heterogeneously over years. These results show that longitudinal EHR trajectories carry rare-disease signal beyond static phenotypic burden and support prospective evaluation of temporal EHR modeling for rare-disease risk stratification.

Abstract Senescent cell accumulation and fibrosis play a fundamental role in kidney ageing. Our aim was to determine whether the angiotensin IV/insulin-regulated aminopeptidase (IRAP) axis of the renin-angiotensin system contributes to renal function decline, fibrosis, and senescent cell accumulation in aged mice. We studied the role of IRAP in kidney ageing using IRAP knockout (KO) mice, as well as in wild-type (WT) mice treated with an IRAP inhibitor. Complementary proximal tubule and collecting duct cell models were used to determine the mechanisms by which IRAP inhibition exerts protective effects. Glomerular filtration rate (GFR) declined by 25% in WT mice between 3 and 23 months of age, concomitant with marked increases in albumin excretion, glomerulosclerosis, tubulointerstitial fibrosis, and accumulation of senescent cells in the proximal tubules of the kidney. IRAP KO mice were protected against age-related decline in GFR, renal fibrosis, and senescence burden compared with age-matched WT mice. IRAP inhibition for 4 weeks (HFI-419, 500 ng/kg/min) in 28-month-old WT mice similarly prevented age-related decline in GFR and attenuated expression of cellular senescence markers. Using an in vitro model of cultured proximal tubule cells, IRAP inhibition mitigated the pro-senescent effects of oxidative stress. This was associated with shifts in the metabolic phenotype of proximal tubular cells, indicating the senoprotective effects of IRAP inhibition may be mediated in part via mitochondrial function. These studies provide evidence that IRAP deficiency is protective against chronic renal cellular senescence, fibrosis, and functional decline. Therefore, IRAP inhibition has therapeutic potential to attenuate pathological cardiorenal ageing through senoprotective mechanisms.

Importance Recent case reports suggest that B antigen–containing blood products may be associated with anaphylactic transfusion reactions in recipients with preformed immunoglobulin E to galactose- α -1,3-galactose (alpha-gal). Objective To test the association of local alpha-gal syndrome (AGS) prevalence and allergic transfusion reaction (ATR) in patients with blood type O. Design, Setting, and Participants In this international, multicenter, retrospective cohort study at academic medical centers, sites were assigned to AGS high- or low-prevalence clusters based on the known geographic distribution of AGS. Data were collected on platelet or plasma transfusions and ATRs during 2020 to 2024. Consecutive patients receiving platelet or plasma transfusions were included. Exposures Patients with blood type O who received B or AB units and patients with blood type O who received O units (reference group) in AGS high- and low-prevalence clusters were compared. Main Outcomes and Measures Before data collection, the hypothesis was that if transfusion-related alpha-gal syndrome (TRAGS) is a true clinical entity, excess ATRs to group B or AB units would be detected in AGS high-prevalence regions exclusively. The main measure was the overall risk ratio (RR) for ATRs to B or AB plasma or platelets in AGS high- vs low-prevalence clusters. Results In total, 558 823 platelet and plasma transfusions at 40 sites in 5 countries were analyzed, of which ATRs occurred in 1744 transfusions (0.3%). In the US AGS high-prevalence cluster (9 sites), significantly more ATRs of any severity occurred among patients with blood type O who received B or AB units vs patients with blood type O who received O units (overall RR, 3.93; 95% CI, 2.96-5.21; P < .001). In contrast, in the primary analysis of the AGS low-prevalence cluster (15 sites), excess ATRs to B or AB units were not detected. In subgroup analyses among patients with blood type O receiving B units, the RR for moderate to severe ATRs was 9.14 (95% CI, 5.39-15.52; P < .001) in the AGS high-prevalence cluster vs 2.15 (95% CI, 1.34-3.47; P = .002) in the AGS low-prevalence cluster. Outside the US, a signal consistent with TRAGS was not detected. Conclusions and Relevance This cohort study provides provocative epidemiologic evidence that TRAGS may be a unique manifestation of AGS and a previously unrecognized risk associated with transfusion. In AGS high-prevalence regions, blood collectors and hospital transfusion services should consider approaches to preventing severe ATRs from B or AB plasma or platelet units.

Abstract Objective Resective surgery achieves seizure freedom in approximately 60%–80% of patients with drug‐resistant temporal lobe epilepsy (TLE), yet the extent to which seizure cessation permits recovery of disrupted functional brain networks remains unclear. Increasing evidence suggests that recurrent seizures alter subcortical–cortical communication involved in arousal and cognition, contributing to chronic interictal deficits, such as in executive function. Prior work demonstrates that the nucleus basalis of Meynert (NBM), the primary source of cortical cholinergic input to the cortex, exhibits aberrant functional connectivity in patients with TLE. We examined NBM connectivity in patients with TLE before and after surgery using patient‐specific NBM segmentations. We sought to investigate whether seizure freedom is associated with recovery of NBM functional connectivity, consistent with previous observations of interictal network dysfunction reversal in other subcortical arousal structures. Methods Resting‐state functional magnetic resonance imaging was analyzed in 75 patients with TLE, including 34 patients postoperatively, and 106 controls. Patient‐specific NBM segmentations were used to compute functional connectivity between the NBM and frontoparietal association cortex (FPAC) and across the whole brain. Subcortical arousal network community structure was assessed using community detection. Postoperative network changes were evaluated relative to seizure outcome. Results Prior to surgery, patients exhibited reduced bilateral NBM connectivity to the FPAC and whole brain compared to controls, indicating widespread cholinergic network disruption beyond the ipsilateral hemisphere. Following surgery, NBM connectivity increased and no longer differed from controls. Seizure‐free patients demonstrated greater postoperative increases in ipsilateral NBM‐FPAC connectivity than non‐seizure‐free patients, which was not explained by time to postoperative scan or volumetric change. Subcortical arousal community structure remained altered postoperatively. Significance Successful TLE surgery is associated with partial restoration of cholinergic arousal network connectivity, supporting the idea that seizure cessation enables recovery of brain networks disrupted by recurrent seizures. These findings position the NBM as a potential biomarker of network recovery following epilepsy surgery.

ABSTRACT Introduction Postoperative mortality commonly follows complications that are not prevented, recognised or managed effectively. Ward‐based nursing care is central to surveillance, escalation and rescue, but population‐level evidence using shift‐level staffing data is limited. This national population‐based cohort study examined the association between surgical ward nurse staffing and postoperative outcomes. Methods Adults aged 18 years or older undergoing inpatient surgery under general or neuraxial anaesthesia in public hospitals in Aotearoa New Zealand between 2022 and 2024 were included. Prospectively recorded TrendCare staffing data were linked to administrative health datasets. The exposure was shift‐level staffing variance, defined as the difference between required and available nursing care hours on surgical wards in the treating hospital during admission. The primary outcome was 90‐day postoperative mortality. Secondary outcomes were postoperative complications and failure to rescue. Results The cohort included 202 428 patients undergoing 223 415 surgical admissions. Within 90 days, 6296 admissions resulted in death (2.8%). Complications occurred in 41 722 admissions (18.7%), with failure to rescue in 15.1%. Of 281 147 surgical ward shifts, 122 186 (43.5%) were understaffed. After adjustment, each one‐nurse reduction in staffing per shift was associated with higher odds of mortality (aOR: 1.13, 95% CI: 1.07–1.18), complications (aOR: 1.07, 95% CI: 1.05–1.10) and failure to rescue (aOR: 1.07, 95% CI: 1.00–1.14). Modelling estimated that correcting observed staffing deficits was associated with 182 fewer deaths annually. Conclusion Lower surgical ward nurse staffing was associated with increased postoperative mortality, complications and failure to rescue. Nurse staffing is a modifiable system factor associated with postoperative safety and should be prioritised to improve surgical outcomes.

Background : Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality. Optimal treatment decisions are challenging, and existing treatment allocation frameworks may not fully reflect the evolving therapeutic landscape or contemporary clinical practices in North America. Methods : Using a modified Delphi process, a multidisciplinary panel of 20 North American experts in hepatology, medical oncology, surgery, radiology, and radiation oncology developed a consensus-based treatment framework for HCC, termed the BEACON-HCC system. The framework was informed by current evidence and expert opinion through iterative discussion and voting. In a pilot study using 29 real-world patient cases, we assessed concordance between external expert recommendations and BEACON-HCC recommendations and Barcelona Clinic Liver Cancer (BCLC) 2025 treatment recommendations. Results : The BEACON-HCC treatment allocation framework diverges from prior frameworks by incorporating nuanced clinical features such as degree of intrahepatic tumor burden and vascular invasion, and adverse tumor prognostic markers, to align treatment allocation with tumor biology and therapeutic potential. Key innovations include the incorporation of emerging modalities such as external beam radiation therapy (EBRT), transarterial radioembolization (TARE), and systemic-locoregional combination therapies. In the pilot exercise, expert treatment decisions showed 96.6% concordance with BEACON-HCC recommendations and 72.4% concordance with BCLC recommendations. Conclusions : BEACON-HCC is a consensus-based framework for treatment allocation that incorporates the expanding range of therapeutic options available to patients in the North American HCC population. Expert treatment decisions showed a high concordance with the BEACON-HCC system; further validation using empiric clinical data is planned through the HCC-Live Consortium.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Background Hearing impairment (HI) has been identified as a potentially modifiable risk factor for dementia, yet its relationship with Alzheimer's disease (AD) neuropathology and the temporal directionality remain unclear, particularly in late life. Objective To examine cross-sectional and longitudinal associations between HI, clinical cognitive status, and AD biomarkers in community-dwelling older adults. Methods We included 474 DRIVES Project participants (mean age 73.5 ± 5.2 years). Hearing was assessed using the NIH Toolbox Words-in-Noise (WIN) test. Clinical cognitive status was assessed using Clinical Dementia Rating (CDR). AD biomarkers included cerebrospinal fluid (CSF) Aβ 42 /Aβ 40 , t-tau/Aβ 42 , and p-tau/Aβ 42 ratios and amyloid PET imaging. Multivariable linear, logistic, Cox proportional hazards, and Fine–Gray competing-risk models were used. Results HI was more prevalent among participants with mild cognitive impairment (MCI; defined as CDR = 0.5) than cognitively normal individuals (50% versus 24%, p < 0.001), and MCI status was independently associated with worse baseline WIN thresholds (β = 1.55 dB SNR; 95% CI 0.72–2.37). HI was not cross-sectionally associated with CSF amyloid or tau ratios or amyloid PET positivity. Longitudinally, baseline MCI was associated with a higher risk of incident HI (Cox HR = 2.63, 95% CI 1.48–4.67; Fine–Gray sHR = 2.79, 95% CI 1.06–7.40), whereas baseline HI was not associated with subsequent CDR progression. Conclusions In older adults, HI was associated with MCI but not core AD biomarkers or future CDR progression, suggesting that late-life HI may reflect cognitive vulnerability or broader brain aging rather than independently driving AD pathology.