Citations

Abstract Objective Low back pain (LBP) is a leading cause of pain and disability worldwide. Recent studies demonstrate a vertebrogenic model involving the basivertebral nerve (BVN) as a cause of chronic LBP. Vertebral endplates are highly innervated by the BVN, and literature shows type 1 and 2 Modic changes at vertebral endplates are a source of vertebrogenic low back pain (VBP). As a result, intraosseous radiofrequency ablation of the BVN is a minimally invasive outpatient procedure used to treat VBP. We conducted a retrospective review of 1,145 patients that have undergone BVN ablation to assess for safety and complication management. Methods Chart reviews were conducted to obtain demographic and pre-procedural information regarding patient gender, age at the time of BVN ablation, duration of LBP, and BMI. Post-procedure information derived from chart notes included vertebral levels ablated and post-BVN ablation complications, which were categorized as increased LBP > 7 days, increased radicular pain > 7 days, lower extremity weakness, infection, vertebral compression fracture, superficial bleeding, spinal or epidural hematoma, serious neurological injury, visit to the ER, and deep vein thrombosis/clot. Results 1,145 patient charts were reviewed showing an overall complication rate of 3.2% (37 complications). Increased radicular pain was the most common complication at 2.2% (25 complications) and resolved completely in all but one patient. The overall complication rate fell to 1.1% (13 complications) when the 24 cases of transient radicular pain were not counted as adverse events. Conclusions In this multicenter retrospective review, the overall rate of complications related to BVN ablation was low (3.2%). The most common complication was temporary radicular pain that resolved with conservative care. These findings suggest that BVN ablation is a safe treatment for VBP in real-world clinical settings.

ABSTRACT Background Experiencing weight stigmatization is linked to maladaptive eating, poorer mental health, and increased risk of weight gain, potentially impacting metabolic bariatric surgery (MBS) outcomes. This meta‐analysis aimed to synthesize patient experiences of weight stigmatization before and after MBS and to examine the association of presurgical weight stigma on postsurgical weight loss. Methods This preregistered study followed PRISMA guidelines. Six databases were searched through March 2025; eligible studies included published articles and dissertations/theses available in English that quantitatively measured presurgical weight stigma among MBS samples and included relevant outcomes. Meta‐analyses utilized random effects models with change scores converted to Hedge's g and associations between presurgical experienced weight stigma and post‐MBS weight change converted to coefficient r . Results Forty‐one studies were included, with 36 reporting on experienced weight stigma change pre‐ to post‐MBS and 10 studies reporting on the relationship between pre‐MBS experienced weight stigma and post‐MBS weight loss. Conclusions Results revealed weight stigmatization decreased substantially after MBS (6–12 months, g = 1.28; 13–24 months, g = 1.16; 25–36 months, g = 1.26). Presurgical weight stigma was not associated with weight loss post‐surgery ( r = 0.05). Findings are limited by minimal studies reporting on longer term relationships between baseline stigma, post‐MBS weight loss, and additional health outcomes.

Abstract Objectives Per National Comprehensive Cancer Network guidelines for acute myeloid leukemia (AML), bone marrow (BM) evaluation is recommended after starting induction chemotherapy at 14 to 21 days (mid-induction) and 30 to 35 days (postinduction). Timely assessment of residual morphologic and molecular disease is challenging at these time points, given the low cellularity, the lack of CD34 or CD117 expression by blasts in most NPM1-mutated (NPM1mut) AML, and the longer turnaround time for molecular testing. Methods NPM1 mut AML undergoing BM evaluation at mid- and postinduction with concurrent flow cytometry and NPM1 real-time polymerase chain reaction or next-generation sequencing was evaluated. Different cell types positive by NPM1 immunohistochemistry (IHC) were enumerated, and the results were compared with flow cytometry and molecular findings. Results NPM1 IHC demonstrated a sensitivity of 78% and 41% for detecting residual mutation at mid- and postinduction, respectively, with a positive predictive value ranging from 88% to 100%. Of 16 IHC-negative cases, 3 had no residual NPM1 mutation, 12 had very low-level mutation at a variant allele frequency of less than or equal to 21 transcripts, and only 1 had a high-level mutation in a markedly hypocellular BM. Of 24 IHC-positive cases, 22 had residual NPM1 mutation, and 2 showed false-positive staining in only a single cell with no residual NPM1 mutation. The level of IHC staining was mostly proportional to the level of residual mutation. Conclusions Mutation-specific NPM1 IHC is a valuable tool for a rapid assessment of residual morphologic and molecular disease in mid- and postinduction NPM1mut AML.

Abstract Real-time, proactive detection has become a necessity as health systems face converging pressures that legacy tools cannot solve: rising acuity, workforce strain, and persistent quality gaps that erode margins, capacity, and reputation. Clinical decision support has historically lagged because rudimentary rules-based systems drown clinicians in false alerts, eroding trust without reliably changing outcomes at the bedside. Bayesian Health’s real-time clinical intelligence platform was built to close this gap by continuously synthesizing multimodal EHR data (vitals, labs, medications, procedures, and longitudinal history) to learn patient-specific baselines and surface only the most actionable, high-precision signals to care teams. Across diverse health systems, this platform has been used to enable earlier recognition and intervention for a range of high-impact conditions, improving both bedside care and system performance. This talk will focus on concrete, real-world applications of the platform for sepsis, clinical deterioration, and palliative care needs, illustrating how earlier detection and workflow-integrated interventions drive measurable gains in clinical outcomes and margins, and why the value of early, precise detection is amplified in high-risk populations such as oncology patients, whose higher baseline acuity and treatment-related toxicities both elevate their risk of harm and make emerging deterioration harder to recognize. Case Study: Detecting Sepsis within a Oncology Patient Subpopulation. Background: Sepsis remains one of the leading causes of in-hospital mortality, and traditional rules-based alerts have struggled to deliver timely, actionable detection without overwhelming clinicians with false alarms. Oncology patients experience an elevated risk for sepsis and septic shock, and those who experience sepsis are more likely to suffer in-hospital mortality than non-cancer patients with sepsis. Early recognition of sepsis onset is challenging due to the overlap between signs of sepsis and inflammation and tumor- and treatment-related toxicities. We investigated the ability of Bayesian Health’s real-time artificial intelligence-based computational algorithm to perform timely detection of sepsis onset in hospitalized patients with cancer. This algorithm ingests data directly from the electronic health record, such as vital signs, laboratory results, medications, procedures, and patient-specific historical results, processing it in a continuous manner to flag presence of sepsis as early as possible. Methods: Analysis included 7480 adult emergency and inpatient encounters from three tertiary and quaternary academic centers (Cleveland Clinic Main Campus, Sinai Hospital of Baltimore, and Strong Memorial Hospital) and one community hospital (Cleveland Clinic Fairview Hospital) with sufficient data for inclusion (age ≥ 18, presence in an emergency or inpatient care unit, and at least one measurement of a panel of vital signs). Patients with cancer (including subgroups of solid tumor and hematologic malignancy) were identified by current or past recorded diagnoses (ICD-10 codes). All included patient records underwent a multi-tiered retrospective adjudication process. An automated screen labeled records with no evidence of infection or organ dysfunction as non-sepsis, removing them from need for manual review. Two independent physicians then adjudicated a sample of the eliminated records in addition to each remaining record for both presence of sepsis and time of onset, with a third physician reconciling disagreements. Evaluation of the algorithm included all discrete flags generated by the system during a period of time it was not in clinical use and not visible to care teams. Results: In the overall population, 3.1% of all patients and 6.0% of cancer patients were adjudicated to have had sepsis, including zero from a sample of 200 of the records marked as non-sepsis in automated screening. The detection algorithm identified sepsis with high and consistent accuracy across all patients and across cancer patients, including those with solid tumors and with hematologic malignancies (Table). A flag for sepsis occurred in 85.8% of all sepsis patients and 86.1% of cancer patients with sepsis within 4 days of sepsis onset. Sepsis was present in 24.2% of all cancer patients flagged (22.9% of solid tumor patients and 27.9% of hematologic malignancy patients). Compared to patients who were never flagged, all patients who were flagged, whether they had sepsis or not, were more likely to experience in-hospital mortality (7.9% vs 0.3%, p < 0.001) or ICU transfer (38.2% vs 7.9%, p < 0.001). This was also true within oncology patients (in-hospital mortality: 8.8% vs 0.4%, p < 0.001; ICU transfer: 34.7% vs 9.7%, p < 0.001). Implementation of an automated sepsis detection system with high accuracy in populations such as cancer patients, who have elevated risk of poor outcomes following sepsis, provides an opportunity to meaningfully improve timely delivery of care and reduce subsequent morbidity and mortality while minimizing alert fatigue for clinicians. Citation Format: Suchi Saria, Varesh Prasad, Nicasia Beebe-Wang, Ellie Zhang, Neri Cohen, David Graham, Conrad Gleber, James Morrison, Jacob Strand, Alexandra Tallman. Real-time multimodal AI enables individualized care in challenging subpopulations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr PL03-03.

Abstract Therapeutic resistance to chemotherapy is frequently the proximate cause of increased tumor burden, cancer treatment failure, and ultimately, patient mortality. The most successful therapy strategy derived from evolutionary practices to date, Adaptive Therapy (AT), leverages the fitness cost of developing therapeutic resistance. AT avoids treating cancer cells with the Maximum Tolerated Dose (MTD) of therapeutic drugs and instead incorporates a series of adaptive dosing schedules. Treatment-sensitive cells are bolstered by modulating or pausing treatment to out-compete and suppress treatment-resistant cell growth. We are evaluating AT for the first time in colorectal cancer, using a syngeneic orthotopic xenograft model. This work represents several key advances: it is the first to longitudinally and non-invasively track sensitive and resistant clones in vivo, to evaluate adaptive therapy in an immunocompetent mouse model, to incorporate multi-drug treatment regimens, and to integrate a targeted therapeutic agent. The dual-bioluminescent sensitive and resistant CT26 cell lines developed for this study allow real-time visualization and quantification of each population throughout tumor progression and treatment. Together, these advances establish a robust and clinically relevant platform for characterizing resistance dynamics and refining adaptive therapy strategies aimed at improving treatment outcomes in colorectal cancer. Citation Format: Harley I. Richker, Bailey Sierra Kane, Gissel Marquez Alcaraz, Ellie Pahl, Ryan Carr, Nagarajan Kannan, Carlo C. Maley. Harnessing clonal dynamics to overcome resistance: An adaptive therapy framework in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 702.

Purpose Survivors of critical illness often acquire significant physical, cognitive, and psychological burdens following their intensive care unit (ICU) stay, termed “post-intensive care syndrome” (PICS). Screening for PICS is generally not performed outside of research settings largely due to lack of a simple assessment tool and time constraints. Methods This was a prospective cohort study at a single academic medical center in the USA. We included adults (18 years or older) who spent greater than 48 h in any ICU within the medical center. Following transfer out of the ICU and at 3 months, psychological, cognitive, and physical functioning were assessed using the Hospital Anxiety and Depression Scale, Impact of Event Scale-Revised, Montreal Cognitive Assessment-blind, and Barthel Index. We compared these validated questionnaires to a brief, 15-item PICS screening tool (5 questions/domain) that was developed by our multidisciplinary study team and administered to study participants after ICU discharge to general care. The primary objectives of this study were to assess the ability of validated screening tools administered at ICU discharge to predict PICS at 3 months and develop a novel screening tool for identifying patients at risk of PICS. Major Findings 191 participants completed the initial questionnaire, and 109 (56%) completed the 3-month follow-up. PICS tool scores were compared to 3-month follow-up data from validated questionnaires to evaluate predictive performance. Overall, the screening tool weakly-to-moderately correlated with continuous outcomes of the validated measures at 3-month follow-up. When evaluating sensitivity and specificity for individual domains, the screening tool had an area under curve of >0.7 for all three domains, indicating moderate-to-good sensitivity and specificity. Conclusions We developed a brief PICS screening tool that is feasible to use without prior training that modestly predicted the development of PICS following ICU stay.

Introduction: Manual measurement of leg length (LL) and offset can be tedious. This study developed an automated algorithm for measuring LL and offset from pre- and postoperative AP pelvis radiographs in a large cohort of THA patients. Methods: Using a deep learning model trained on 1100 total AP pelvis radiographs, an algorithm was developed to calculate LL and offset. Algorithm measurements were compared with manual measurements by 4 raters on a sample of 100 pre- and postoperative image pairs. Inter- and intra-rater consistency was calculated using the intraclass correlation coefficient (ICC). The algorithm was applied to calculate the pre- and postoperative LL and offset discrepancies and the change in LL and offset bilaterally in a cohort of 15,951 image pairs. Results: ICC values between the algorithm and human raters ranged from 0.83 to 0.88 for offset measurements and 0.92 to 0.97 for LL measurements. Human raters demonstrated good-to-excellent inter-rater ICC and uniformly excellent intra-rater ICC. Entire database measurements demonstrated shorter LLs for arthritic joints versus the contralateral leg preoperatively and reduced LL discrepancy post-arthroplasty. Conclusions: We present a deep learning algorithm for calculating LL and offset using AP pelvis radiographs. This tool can support population-level studies and may assist operative management.

Abstract Use of Direct oral anticoagulants (DOAC) has increased exponentially since their introduction in 2010. Over the years they have been used without any systematic oversight, there have been ongoing concerns related to inappropriate or incorrect dosing, incorrect duration, and non-adherence to the drugs leading to adverse patient outcomes. Several factors have been attributed to DOAC non-adherence such as presence or fear of adverse effects, affordability, lack of perceived benefit, or concerns for reversibility. These barriers impact both primary (first time prescription) and secondary adherence (longer term prescription). Poor adherence can lead to adverse outcomes such as stroke, death, and higher medical resource utilization hence increased cost to the patient and payors. This study was conducted as a quality improvement (QI) initiative to improve the adherence to the prescription DOACS. We included patients 18 years old, a diagnosis of Atrial fibrillation and flutter who had a primary care provider writing the prescription for chronic DOAC therapy. Patients with venous thrombosis, pregnancy or who were institutionalized were excluded. The charts were reviewed for primary and secondary adherence using Surescripts functionality which can provide baseline information about prescription fills from the participating pharmacies. In the preimplementation phase we identified 1030 patients meeting inclusion criteria associated with the pilot sites within Mayo Clinic practice in preceding 6 months. For this population, Surescripts data found that the rates of primary and secondary adherence to be 84% and 86% respectively. We then implemented a 6-month pilot where we created workflow for a pharmacy led DOAC management unit. Herein, once a DOAC prescription was made for patients meeting inclusion criteria, they would enter a registry where the pharmacy team would review the charts for primary adherence by utilizing Surescripts data. If adherent these patients would be marked for follow up in the secondary adherence phase. However, if non adherent, the team would initiate intervention including engagement with patient online services which was further escalated to phone calls by the pharmacy team. Once the barriers were identified several remediation approaches were incorporated in the process, these included education about DOACS, medicare education, social work assistance to even switching to warfarin if necessary. The same process was followed for secondary adherence management. In the intervention cohort, we had 411 patients during the pilot, for primary adherence 87.1% of patients were adherent at baseline and an additional 3.4% became adherent with interventions, while 9.5% remain nonadherent. The primary causes of nonadherence, amongst 9.5%, were cost (86%) while 1% were concerned about side effects and 3% were concerned about lack of perceived benefit. The inverntions lead to impoved adherence in most patients and very few patients were transitioned to warfarin. For secondary adherence 92% of the patients were adherent without any intervention, an additional 4% of patients benefited with the interventions of the DOAC management unit with approaches to address cost considerations, while another 4% remained nonadherent where cost remained the primary reason for nonadherence, other reasons being lack of perceived benefit and side effect concerns. In summary, with active intervention, in our patient population we were able to improve the primary adherence from 84% to 91% and secondary adherence from 86% to 96% . Cost remained the biggest barrier for adherence followed by concern for side effects and lack of perceived benefit. Our engaged team of pharmacy staff was able to manage those concerns with the education and cost mitigation strategies leading to improved adherence. Based on previously published literature even a 5% difference in adherence to DOACS can make a huge difference in population-based outcomes. Our work was able to demonstrate a positive impact of the intervention, highlighting the need for a well-managed DOAC unit especially for complex care populations.

Abstract Introduction: The introduction of the Bruton tyrosine kinase inhibitor acalabrutinib combined with bendamustine and rituximab (ABR) as first-line (1L) therapy for mantle cell lymphoma (MCL) in older patients (pts) is reshaping the approach to treatment sequencing in this disease. The primary analysis of the phase 3 ECHO trial (NCT02972840) with data cutoff Feb 15, 2024 (median time on study 44.9 mo) demonstrated improved progression-free survival (PFS) with ABR vs placebo plus BR (PBR; hazard ratio [HR] 0.73; 95% confidence interval [CI] 0.57–0.94; P=0.0160) in pts ≥65 y with treatment-naive (TN) MCL. Here we present updated results after 50 mo of follow-up, including time to next treatment 2 (TTNT2; ie, initiation of third-line [3L] therapy). Methods: Pts aged ≥65 y with TN MCL and ECOG performance status ≤2 were randomly assigned 1:1 to receive ABR or PBR. Randomization was stratified according to geographic region and simplified MIPI score. BR was given for 6 cycles followed by rituximab maintenance for 2 y in pts achieving a partial or complete response (CR). Acalabrutinib (100 mg twice daily) or placebo was administered at study start until progressive disease (PD) or unacceptable toxicity. Crossover to acalabrutinib was permitted at PD. The primary endpoint was PFS per independent review committee. TTNT2, which was used as a surrogate for PFS2 (time from initial treatment to second disease progression), was defined as time from randomization to second subsequent (3L) antilymphoma therapy after discontinuation of randomized treatment or death. Results: In total, 299 pts were randomized to each arm; median age was 71 y. At data cutoff (February 15, 2025), median (range) time on study was 51.86 (0.03–93.04) mo and median (range) follow-up through reverse Kaplan-Meier method for PFS was 60.8 (0–88.5) mo. Median TTNT2 was not reached (NR) in the ABR arm and 73.8 mo in the PBR arm, establishing a 24% reduction in the risk of initiating 3L therapy or death with ABR vs PBR (stratified HR 0.76; 95% CI 0.59–0.98). In total, 33 (11.0%) and 100 (33.4%) pts received second-line [2L] treatment (median time to 2L therapy, 26.3 vs 19.6 mo) and 10 (3.3%) and 37 (12.4%) received 3L treatment in the ABR and PBR arms, respectively. When COVID-19 deaths were censored (in alignment with prespecified analyses from the primary study), the HR further improved (stratified HR 0.67; 95% CI 0.50–0.89; median TTNT2 NR in any arm). In total, 71.6% and 78.3% of pts discontinued acalabrutinib and placebo, respectively; the most common reasons (>5%) for discontinuation were adverse events (AEs; 44.5%, ABR; 32.4%, PBR) and objective PD (12.7%, ABR; 30.8%, PBR). Median PFS was 72.5 and 47.8 mo in the ABR and PBR arms, respectively (stratified HR 0.68; 95% CI 0.53–0.87; P=0.002). In total, 108 (36.1%) and 116 (38.8%) pts in each arm died; median overall survival (OS) was NR in both arms (stratified HR 0.87; 95% CI 0.67–1.13), with a 36-mo OS rate of 73.8% with ABR vs 68.3% with PBR. With 1 additional year of follow-up since the primary analysis, rates of grade ≥3 AEs and serious AEs in the ABR arm were comparable to previously reported rates, indicating that prolonged acalabrutinib exposure does not appear to result in cumulative toxicities. Three new cases of grade 3/4 neutropenia were reported, with no marked changes in the rates of grade ≥3 or serious infections. The rate of grade ≥3 cardiac events remained consistent with the primary analysis. In the ABR arm, 1 new case of grade 3/4 atrial fibrillation and 1 new case of grade 3/4 hypertension were reported. Rates of thrombocytopenia, major hemorrhage, and ventricular tachyarrhythmia remain unchanged. Conclusion: In this updated analysis of the ECHO trial, despite crossover to acalabrutinib being permitted in the PBR arm, fewer pts in the ABR arm were in need of subsequent treatment (2L and 3L), with longer TTNT2 in the ABR arm. These results suggest that 1L treatment with ABR provides a 24% reduction in risk of initiating 3L therapies or death. Further follow-up has demonstrated that ABR offers improved efficacy (ie, PFS) vs PBR, with no long-term safety concerns with acalabrutinib maintenance. The updated safety findings further support the favorable benefit–risk profile of acalabrutinib in TN MCL. Altogether, these data support the potential of acalabrutinib in combination with BR as 1L treatment, rather than in later lines, to modify long-term treatment outcomes in pts with TN MCL.

SUMMARY COVID-19 convalescent plasma (CCP) was the first specific therapy deployed for treating SARS-CoV-2 infection. CCP was successfully deployed in both resource-poor and resource-rich countries, establishing that convalescent plasma (CP) is a feasible option for combating the next pandemic. CCP reduced mortality and progression to hospitalization when used early in the disease with high-titer units. This knowledge was gained from a worldwide effort that included more than 50 countries. However, the deployment of CCP was haphazard and varied among countries. Clinical studies suffered from a lack of standardization regarding study design, CCP antibody dosing, timing of administration, and participant disease severity. Unfortunately, the hard-won knowledge from the serum therapy era in the early 20th century, which indicated that effective antibody therapy requires early use in the disease with a sufficient antibody dose, was largely forgotten. Many studies tested CCP late in the disease or without sufficient antibody titer and thus reported negative findings. Trial heterogeneity made it difficult to combine the results of studies. However, despite tremendous heterogeneity in study design and participant populations, meta-analysis revealed strong signals of efficacy when given early with high antiviral-specific antibody levels. When the next pandemic occurs, humanity is likely to resort to CP again. To avoid another chaotic rollout, planning for CP use should begin well before that emergency arrives and must involve both physician education on the principles of antibody therapy and clinical trial designs that test its efficacy in optimal conditions, which include early use with sufficient antibody doses.