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OBJECTIVE It is uncertain which cochlear dose parameters significantly impact hearing after stereotactic radiosurgery (SRS) for sporadic vestibular schwannoma (VS). The objective of this study was to determine the impact of cochlear dose parameters on hearing outcomes for patients with serviceable hearing (SH). METHODS This was a historical cohort study performed at a single tertiary center that included patients with sporadic VS and SH who underwent single-session Gamma Knife radiosurgery treatment from 2007 to 2022. Associations of cochlear dose parameters with time to non-SH and rates of change in pure-tone average (PTA) and word recognition score (WRS) following SRS were assessed. RESULTS A total of 205 patients with SH underwent SRS for a sporadic VS. At SRS, 54 (26%) tumors were confined to the internal auditory canal and 151 (74%) extended into the cerebellopontine angle. At 2, 5, and 10 years following SRS, 62%, 37%, and 15% of patients maintained SH, respectively. The median time to non-SH was 1.8 years. The median rates of change in PTA and WRS were 6.0 dB of hearing loss per year and −6.5% per year, respectively. In a multivariable analysis, each 1-Gy increase in minimum cochlear dose was significantly associated with time to non-SH (HR 1.5, 95% CI 1.2–1.9), rate of change in PTA decibel hearing loss per year (parameter estimate [PE] 3.4, 95% CI 0.6–6.2), and rate of change in WRS percentage per year (PE −6.4, 95% CI −11.2 to −1.5). The associations of each 1-Gy increase in mean cochlear dose with hearing outcomes were only significant among patients with class B hearing (time to non-SH HR 1.3, 95% CI 1.1–1.6; rate of change in PTA PE 3.6, 95% CI 1.2–5.9; and rate of change in WRS PE −5.7, 95% CI −9.7 to −1.7). CONCLUSIONS The minimum cochlear dose impacts hearing outcomes after SRS for VS and should be considered in radiosurgical treatment planning. In this cohort, the mean cochlear dose was only associated with hearing outcomes in the subgroup of patients with class B hearing at SRS.

Chronic myelomonocytic leukemia (CMML) is a severe myeloid malignancy with limited therapeutic options. Single-cell analysis of clonal architecture demonstrates early clonal dominance with few residual WT hematopoietic stem cells. Circulating myeloid cells of the leukemic clone and the cytokines they produce generate a deleterious inflammatory climate. Our hypothesis is that therapeutic control of the inflammatory component in CMML could contribute to stepping down disease progression. The present study explored the contribution of immature granulocytes (iGRANs) to CMML progression. iGRANs were detected and quantified in the peripheral blood of patients by spectral and conventional flow cytometry. Their accumulation was a potent and independent poor prognostic factor. These cells belong to the leukemic clone and behaved as myeloid-derived suppressor cells. Bulk and single-cell RNA-Seq revealed a proinflammatory status of iGRAN that secreted multiple cytokines of which CXCL8 was at the highest level. This cytokine inhibited the proliferation of WT but not CMML hematopoietic stem and progenitor cells (HSPCs) in which CXCL8 receptors were downregulated. CXCL8 receptor inhibitors and CXCL8 blockade restored WT HSPC proliferation, suggesting that relieving CXCL8 selective pressure on WT HSPCs is a potential strategy to slow CMML progression and restore some healthy hematopoiesis.

Introduction: Identifying patients at risk for ischemic events after percutaneous coronary intervention (PCI) relies on traditional analysis of limited clinical and imaging variables. Machine learning (ML) has shown promise in effectively predicting cardiovascular risk in population studies. While existing ML models mainly predict mortality and incorporate clinical variables, there is a lack of tools that have utilized genetic data and that predict ischemic events. Aims: This study aims to develop and validate a ML model incorporating genotyping and clinical data to enhance prediction of ischemic outcomes for PCI patients utilizing large prospectively derived diverse datasets. Methods: Patients from the TAILOR-PCI trial (n=5302) were utilized for model development. 50% of the sample was utilized for Boruta feature selection and 50% for training and testing using cross validation. Features included demographics, medical history, medications, PCI characteristics, and genetic data (specifically, CYP2C19 *2, *3, *17 alleles). The primary endpoint was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. Multiple ML classification algorithms, including Support Vector Machine (SVM) polynomial, Random Forest, Light gradient boost, XG Boost, among others, were benchmarked for their prediction performance on rare events. The top performing classifiers were externally validated on an independent dataset from the PRECISION PCI study (n=3,745). Results: Mean participant age of the training set was 64.2 ± 11.0 years, with 75.4% being male. During follow-up of 12 months, among 4,572 patients in the entire cohort 343 (7.5%) met the primary outcome. The SVM polynomial model demonstrated the highest area under the curve (AUC) of 0.67 for predicting the primary outcome with test dataset. The sensitivity, specificity, precision, and recall were 0.87, 0.28, 0.07, and 0.87 respectively (Figure). Peripheral arterial disease, body mass index, and age were among the top variables by feature importance. Conclusion: ML models incorporating both clinical and genetic data are feasible and highly promising in predicting major adverse cardiac events that may help guide use of anti-platelet drug therapy. The AUC values are reasonable given imbalances and misclassifications in datasets, and further model optimization with prospective utilization of the model will be paramount.

Background: Nationally, many ASCVD patients fail to achieve an LDL-C <70 mg/dL, and uptake of both statin and non-statin therapies is low. The degree to which this varies across health systems is less clear. Methods: A cross sectional analysis was performed where lipid levels and lipid lowering therapies (LLT) were assessed using electronic health record data in patients with a previous diagnosis of ASCVD. The data was obtained across 14 US healthcare systems between 1/1/2021-12/31/2022. Proportions of patients with an active prescription of any statin, high intensity statin, ezetimibe, PCSK9i, and combination therapy (two or more agents) within 395 days of the most recent LDL-C value (index date) was evaluated overall and by participating site. Additionally, the proportion of patients with an LDL-C <70 mg/dL at the index date was also assessed. Results: Across 14 health systems, 1,118,623 patients with ASCVD were identified (median 61,840 per health system, range 8,161-182,315). Overall, 675,776 (60.4%) had an LDL-C level in the past year (range 39.1% - 70.8%). Of those with a lipid level, achievement of LDL-C <70 mg/dL ranged from 34.6-47.2%. In total, 42.6% were on any statin, 20.1% were on a high intensity statin, 4.3% on ezetimibe, and 1.2% on a PCSK9i. Only 2.9% were on combination therapy of a statin with ezetimibe or a PCSK9i. Variability was seen across health systems in utilization of each of these therapies, however even in the highest performing health systems, LLT uptake and achievement of LDL-C < 70mg/dL remained low ( Figure). Conclusion: Variability in utilization of LLT in ASCVD patients between health systems suggests that system-level factors may impact achieving guideline-based LDL-C goals. Despite the variability, the highest proportion of patients achieving an LDL-C <70mg/dL remained under 50% indicating the need for aggressive implementation efforts.

Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure. Study Design: Cohort study; Level of evidence, 3. Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance. Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR. Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies. Registration: NCT00625885 (ClinicalTrials.gov identifier).

Background: Cardiomyopathies (CM) are a leading cause of heart failure. Identification of genetic risk preemptively may allow detection of the earliest phenotypic manifestations through implementation of interval-based cardiac screening. Previous population-based genetic screening studies have limited follow-up and inadequate phenotyping. Hypothesis: Disease penetrance of CM variants is significantly higher than previously reported. Aims: To determine the prevalence, penetrance, and disease expression of incidental CM-related genetic variants in an unselected, richly phenotyped, adult population. Methods: We analyzed a random sample of 10,000 patients from a quaternary medical center-based biobank cohort for reportable variants in 22-cardiomyopathy genes. Prioritization of genetic variants was performed using Semi-Automated Variant Interpretation, a novel software that has been previously developed and validated at our center. Prioritized variants were then manually curated by 2 independent variant interpretation specialists. Clinical phenotypes and outcomes of participants with likely pathogenic/pathogenic (LP/P) variants were determined. Results: LP/P cardiomyopathy variants were present in 1% of our cohort (n=104) within the following genes: TNNT2 (n=1), DES (n=2), LMNA (n=2), MYL2 (n=2), TNNI3 (n=2), DSG (n=4), FLNC (n=4), DSC2 (n=6), SCN5A (n=7), PKP2 (n=10), DSP (n=12), MYH7 (n=14), TTN (n=17), MYBPC3 (n=21). Only 1 participant carried two different LP/P variants. A definitive disease penetrance defined as heart failure or cardiomyopathy was found in 34% of genotype positive (G+) individuals. Possible disease penetrance defined as presence of structural heart disease identified by echocardiography was found in an additional 16% of participants (concentric left ventricular hypertrophy, diastolic dysfunction, biatrial and left atrial enlargement). Combined definitive and possible disease penetrance was found in 50% of G+ individuals. Arrhythmias and/or cardiac conduction disease (atrial fibrillation, atrioventricular block, ventricular arrhythmia) were present in 31% of G+ individuals. Conclusion: Cardiomyopathy associated LP/P variants are present in a small subset of a large quaternary medical center population as previously reported but unlike prior studies, disease penetrance manifesting as cardiac structural abnormalities and heart failure is high in G+ individuals.

Introduction: An elevated blood pressure (mean arterial pressure, MAP) response to exercise is an independent predictor of hypertension (HTN) development. HTN is highly prevalent in postmenopausal females. Postmenopausal, compared to premenopausal, females have a higher MAP at rest and during exercise primarily due to higher systemic vascular resistance (SVR). Studies using small muscle mass exercise have suggested that postmenopausal females have augmented increases in MAP from rest to exercise. However, it is unclear if postmenopausal females have augmented MAP responses from rest to exercise during whole-body exercise. Hypothesis: We hypothesized that postmenopausal females would have greater increases in MAP and smaller decreases in SVR from rest to peak exercise than premenopausal females. Methods: Normotensive premenopausal (n=16; age: 30±7 yrs; BMI: 23±4 kg/m 2 ) and postmenopausal (n=14; age: 57±7 yrs; BMI: 27±4 kg/m 2 ) female participants performed an incremental cycling test until task failure. Blood pressure was measured by manual sphygmomanometry, cardiac output (Q) via open-circuit acetylene wash-in technique, and SVR was calculated. Data are reported at rest and peak oxygen uptake (VO 2 ). Data are also reported as % change from rest to VO 2 peak. Results: Postmenopausal females had higher MAP and SVR at rest and at VO 2 peak than premenopausal females (all, p<0.05). Postmenopausal, compared to premenopausal, females had lower Q at VO 2 peak commensurate with the lower achieved VO 2 peak (p<0.01). Resting MAP was positively related to MAP at VO 2 peak in postmenopausal females (r=0.85, p<0.01), but not premenopausal females (p=0.22). % change in MAP from rest to VO 2 peak was not different between groups (Post: 18±8 vs. Pre: 18±9%) (p=0.94). However, postmenopausal females had smaller % decreases in SVR from rest to VO 2 peak (Post: -44±7 vs. Pre: -54±8%) and smaller % increases in Q compared to premenopausal females (Post: 113±31 vs. Pre: 162±50%) (both, p<0.01). Conclusion: Our findings indicate that postmenopausal and premenopausal females have similar increases in MAP from rest to VO 2 peak; however, the mechanisms contributing to the MAP response differed by menopausal status. Postmenopausal females relied on smaller decreases in SVR, whereas premenopausal females had greater increases in Q. These findings provide important insight into the mechanisms contributing to the clinically relevant higher MAP response during exercise in postmenopausal females.

Background: Despite expert opinion recommending patient-tailored cardiac rehabilitation (CR) after acute aortic dissection, both patient participation in CR and the associated risk factor modification after CR following aortic dissection repair are poorly understood. Objectives: To describe the characteristics of patients referred to CR, as well as the enrollment rate and outcomes of those referred to CR following thoracic aortic dissection. Methods: We reviewed electronic health records of adults (≥18) discharged alive following thoracic aortic dissection surgery across the Mayo Clinic Enterprise (01/01/2020-11/15/2022) with follow-up to 05/15/2024 (IRB #24-001141). Data are presented using summary statistics. Results: During the study period, 85 patients were included (median [IQR] age: 65 [50-75] years; 40% female; Table 1 ). Across 84% (N=71) who were referred to CR at discharge, 55% (N=39) enrolled. In those patients referred versus not referred to CR, the prevalence of type A dissection (75% vs. 43%), end-organ involvement (27% vs. 0%), and underlying CAD (54% vs. 21%) were greater while current tobacco use (9% vs. 29%) was lower. Of those enrolled at Mayo Clinic (N=16), CR was started 35 (28-65) days following surgery. These patients completed 21 (13-30) CR sessions at an exercise intensity of 59% (50-65%) maximum predicted heart rate. CR was discontinued in 44% (N=7) due to non-cardiac reasons ( Table 2 ). Of the 9 patients who completed CR, general health status (Dartmouth index: 19 [17-21] vs. 26 [22-34]), depressive symptoms (PHQ-9: 1 [0-1] vs. 4 [1-4]), and exercise capacity (6MWT: 214 [196-244] vs. 384 [305-597] meters) improved ( Table 2 ). There were no reported complications or deaths, with stable aortic sizes reported at 18 months. Conclusions: CR improves functional capacity and well-being without complications in patients after thoracic aortic dissection surgery. Further investigation is needed to better assess CR efficacy and usage in this population.

Background: New-onset atrial fibrillation (AF) is common among kidney transplant (KTx) recipients and is associated with reduced patient survival. Predictors of AF after KTx are not well understood, although AF can be associated with traditional and non-traditional risk factors. While artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise in predicting incident AF, its predictive and prognostic implications in the KTx population have not yet been evaluated. Hypothesis: AI-ECG can predict new-onset AF and carries prognostic implications in patients undergoing KTx. Aims: To evaluate the clinical implications of applying AI-ECG to the preoperative ECGs of recipients of a KTx. Methods: Patients without a history of AF who underwent KTx at three referral centers between 2011 and 2021, with at least one preoperative ECG, were included in this retrospective study. Preoperative ECGs were analyzed using a previously developed AI-ECG algorithm to estimate the probabilities of new-onset AF. Based on these probabilities, patients were categorized into two groups: high and low probability of incident AF. The optimal cut-off value for the AI-ECG tool was determined using ROC analysis. The incidence of new-onset AF and mortality at 5 years post KTx were compared between these two groups using univariate and multivariate Cox regression analyses. Results: In total, 6246 patients were included (mean age 52.9 ±14.3 years, 58.7% males). A pre-transplant AI-ECG probability of AF >10% was identified as the most accurate cutoff point to distinguish between patients at low risk and high risk of incident AF (ROC = 0.72). The study found that a preoperative AI-ECG high risk of AF demonstrated not only a strong association with new-onset AF (HR 2.54, 95%CI 2.02-3.19, p<0.001 on multivariable analysis, Figure 1A ) but was also associated with an increased risk of mortality both on univariate (HR 2.73, 95%CI 2.34-3.18, p<0.001) and multivariate (HR 1.57, 95%CI 1.29-1.91, p<0.001 ) analysis at 5-year follow-up ( Figure 1B ). Conclusion: The application of AI-ECG to a single widely available test may help assess the risk of new-onset AF and mortality in patients undergoing KTx. Accurate risk stratification may identify a high-risk subgroup of transplant recipients who can benefit from closer AF monitoring, risk factor modification, and early management.

AbstractObjectiveNeck metastases are a poor prognostic factor in olfactory neuroblastoma (ONB). Pathologic dural invasion (pathDI) may increase the risk of neck metastases due to invasion of dural lymphatics. We aim to evaluate the prognostic value of pathDI in predicting rates of neck metastases and recurrence using a large, multicenter database of ONB patients.Data sourcesRetrospective review of a prospective, multicenter database of electronic health records of all patients who presented with ONB between 2005 and 2021 at nine tertiary academic care centers within North America.Review methodsClinicopathologic features including modified Kadish staging systems, margin status, treatment modalities, Hyams grading, follow‐up time, and survival.ResultsOf 258 ONB patients, 189 patients met the inclusion criteria. The 10‐year neck recurrence‐free survival (neck‐RFS) rates were 85.7% (75.6‒97.3) and 61.8% (47.9‒79.8) for patients without and with pathDI, respectively (p = 0.018). Time‐to‐event multivariable regression analysis found pathDI to have an odds ratios of 9.7 (95% confidence interval [CI] 1.2–80.4, p = 0.04) for neck‐RFS and 9.5 for RFS at any site (95% CI 1.1–83.3, p = 0.04).ConclusionIn multivariable analysis, the presence of pathDI appears to be the strongest predictor of neck recurrence and recurrence at any site. Future studies exploring the benefit of elective neck dissection or radiation for patients with pathDI may impact disease management.