Citations

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: AI-ECG is a cost-effective tool for left ventricular dysfunction (LVD) screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, a drawback is low positive predictive value (PPV), which leads to high costs from unnecessary follow-up tests. Therefore, strategies to improve the yield of AI-ECG-based screening are needed. Aim: To evaluate the cost savings of a stepwise approach to SHD screening with AI-ECG followed by POCUS compared to AI-ECG alone. Methods: 286 adult outpatients undergoing AI-ECG were selected at random. Participants received same-day POCUS and had a recent TTE (our gold standard for SHD). We evaluated four SHDs: aortic stenosis (AS), cardiac amyloidosis (CA), HCM, and LVD. The costs of AI-ECG ($75) and TTE ($1,305) were obtained from Healthcare Bluebook. The cost of POCUS ($100) was estimated independently. Cost savings were analyzed for simultaneous screening for all forms of SHD and screening for individual SHDs. Results: AI-ECG identified potential SHD in 125 patients, but only 39 were true positives by TTE (31% PPV). In AI-ECG positive patients, POCUS demonstrated findings of SHD in 52/125. Compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV). The cost per patient diagnosed with SHD was $4,733 with AI-ECG alone but decreased to $3,182 with stepwise screening (33% cost savings). Screening for individual SHDs resulted in cost reduction from $18,724 to $6,315 (66% savings) for AS, $21,023 to $12,230 (42% savings) for CA, $9,883 to $6,175 (38% savings) for HCM, and $4,019 to $3,582 (11% savings) for LVD. Conclusions: Stepwise screening for SHD with AI-ECG followed by POCUS significantly reduces costs compared to AI-ECG alone. We also suggest a model for parallel screening for multiple SHDs, which is likely more cost-effective than screening for individual SHDs.

Background: Guidelines recommend shared decision-making (SDM) for patients with atrial fibrillation (AF) making decisions regarding anticoagulation for stroke prevention. Decision support tools (e.g., Decision Aids [DAs]) facilitate SDM. However, little research has compared the effectiveness of the two most common types of DAs: (1) encounter decision aid (EDA) and (2) pre-encounter patient decision aid (PDA). Our study assessed the comparative effectiveness of PDAs and EDAs on SDM during AF clinical encounters where stroke prevention strategies were discussed. Research Question: Do decision aids (PDA or EDA), alone or in combination, improve patients’ AF decision-making experience compared to usual care? Methods: A cluster randomized multi-center trial in patients with AF comparing usual care (no DA) vs. one DA (EDA or PDA) or both DAs. Clinicians and patients were randomized independently into 4 study arms. Clinicians were randomized to use or not use the EDA for all study visits, and patients were randomized to use or not use the PDA. Co-primary outcome domains were: (1) quality of SDM (2) patient knowledge, and (3) decisional conflict. Secondary outcomes included: (1) treatment choice, (2) treatment initiation, and (3) treatment persistence. Patient characteristics were assessed. We will present comparisons of the randomized DAs on the co-primary and secondary outcomes and subgroup analyses. Results: Between December 2020-July 2023, investigators in 6 U.S. healthcare systems enrolled 1117 patients (mean (M) age 69, 63% male, 89% White) and 107 clinicians. Compared to usual care (no DA, M=31.6), SDM was better in patients receiving PDA (M=35.4) and EDA (44.5) alone or together (M=43.7; p’s<0.001). Knowledge about AF was greatest for those receiving both DAs or the PDA alone (84% accurate responses for PDA and both vs 76% with no DA, p<0.001), followed closely by the EDA alone (82%, p=0.003). Finally, both DAs (M=19.5) and EDA alone (M=20.0) had better decisional conflict scores compared to usual care (M=25.8; p’s<0.001). We will present analyses that assess the impact of DAs on secondary outcomes and interactions with DA, patient, and clinical characteristics across each outcome. Conclusions: In one of the first randomized trials examining the comparative effectiveness of EDAs and PDAs on SDM in clinical medicine, both PDAs and EDAs improved shared decision-making, decisional conflict, and knowledge compared to patients receiving usual care.

Background: Left ventricular diastolic dysfunction (LVDD) predicts mortality in cardiac intensive care unit (CICU) patients. A novel artificial intelligence enhanced electrocardiogram (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in the cardiac intensive care unit (CICU). We aim to assess if LVDD by AI-ECG is associated with in-hospital and one-year mortality in CICU patients. Methods: In this retrospective cohort study, we included consecutive unique adults admitted to the Mayo Clinic CICU from 2007 to 2018 with an admission AIECG, which AI assigned LVDD grade (0 to 3). Medial mitral E/e’ ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. AIECG and TTE assessment of LVDD were used to assign patients to four groups based on TTE as gold standard: true and false negative, and true and false positive. In-hospital mortality was evaluated using multivariable logistic regression. One-year survival was evaluated using Kaplan-Meier curves and multivariable Cox proportional-hazards. Results: We included 11,868 patients (median age 69.5 years, 37.7% females); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grades were: grade 0 (normal), 33%; grade 1, 7%; grade 2, 39%; grade 3, 21%. In-hospital (adjusted OR) and one-year (adjusted HR) mortality increased in each higher AIECG LVDD grade (Figure A/B), before and after adjustment including TTE measurements. Patients with grade 2-3 LVDD by AIECG and medial mitral E/e’ ratio >15 by TTE (true positive) had the highest in-hospital (adjusted OR 2.5 [1.7-3.9]) and one-year (adjusted HR 1.9 [1.5-2.5]) mortality (Figure C/D), and mortality was elevated similarly in patients with either grade 2-3 LVDD by AIECG (false positive) or medial mitral E/e’ ratio >15 by TTE (false negative). Conclusions: The AIECG LVDD grade was strongly associated with in-hospital and one-year mortality in CICU patients, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures (true positive) were at highest risk.

Background: Etripamil nasal spray (NS) is a fast acting, self-administered calcium channel blocker in development for the termination of AV-nodal-dependent supraventricular tachycardia (SVT). Prior randomized, placebo-controlled and open-label studies have demonstrated favorable safety and efficacy of etripamil in converting paroxysmal supraventricular tachycardia (PSVT) to sinus rhythm (SR) self-administered without direct medical supervision. Research Question/Hypothesis: To assess patterns and annualized PSVT episode frequency among patients opting to self-administer acute treatment with etripamil. Methods: NODE-303 was an event-driven, multi-center, open-label Phase 3 study, conducted in North and South America to evaluate the safety and efficacy of etripamil in patients with documented PSVT over multiple episodes. Test dosing was not performed prior to at-home use. Enrolled patients, upon perceiving symptoms of PSVT: applied an ambulatory ECG monitor, performed a previously trained vagal maneuver and, if symptoms persisted, self-administered etripamil NS 70 mg. During the study, the protocol was amended to allow a repeat dose (70 mg) if symptoms persisted 10 min after the first dose. Each patient could self-treat up to 4 episodes. Results: Of 1,116 enrolled patients, 503 (45.1%) treated ≥1 perceived PSVT episode (safety population). Etripamil achieved conversion to SR in 60% of patients by 30 minutes and 70% by 60 minutes. A total of 220, 118, 62, and 103 patients completed the study with 1, 2, 3, and 4 etripamil-treated perceived episodes of PSVT, respectively, with an average time on study of 440 days. Among these patients, the average number of annualized etripamil-treated PSVT episodes was 3.2 (standard deviation 3.8). Annualized use: etripamil was self-administered for 0-2 PSVT episodes per year, 2-6 episodes, 6-12 episodes, and >12 episodes, in 54%, 32%, 10%, and 4% of patients, respectively (Figure). Conclusions: This analysis aimed to assess the annualized use of etripamil NS in a real-world setting, by analyzing how often patients would self-administer the drug for PSVT episodes. Of patients that self-administered etripamil (n=503), the majority treated >1 episode and the annualized frequency of episodes treated with etripamil was 3.2 episodes/yr.

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.

Background: Patients with spastic equinus, equinovarus, and claw toe deformities can experience marked pain and functional limitations in the ability to weight-bear comfortably, ambulate efficiently, or mobilize independently. Seen in 80% of patients with cerebral palsy and 18% of patients with stroke (1, 2), the spastic foot and ankle deformities, and its secondary sequelae of static joint contractures, osseous changes, and chronic pain, are unfortunately common. Methods: Adult and pediatric patients undergoing combined hyperselective and selective partial motor neurectomies for varus or claw toe deformities were reviewed. Patient demographics and complications were recorded. Pre- and postoperative Modified Ashworth Scale scores were compared. Results: Twenty-three patients (16 adults and seven pediatric) met inclusion criteria and were included in analysis. At early 6-month follow-up, the mean preoperative Modified Ashworth Score of 2.8 in adult patients and 3.0 in pediatric patients decreased to 0.6 postoperatively. Complications in three adult patients included one patient with temporary dysesthesias to the plantar foot, one with a popliteal abscess requiring incision and drainage, and one superficial wound dehiscence that was managed conservatively. Conclusions: A combined technique of hyperselective and partial motor neurectomies are effective in decreasing tone in the correction of spastic foot and ankle deformities in both adult and pediatric patients in short-term 6-month follow-up.

Abstract BACKGROUND Despite changes in national policy, women and minority patients remain under-accrued in glioma clinical trials. Supervised machine learning may serve as a novel approach to understanding which variables and their complex interplay most influence enrollment for underrepresented patients. We aimed to design and externally validate machine learning models to predict therapeutic clinical trial enrollment for a) all patients, b) women, and c) NIH-designated minority patients with low- and high-grade glioma. METHODS In a 20-year retrospective cohort of 1042 adult patients with low- and high-grade glioma, boosted neural network (BNN) models for the whole, women, and minority cohorts were used to analyze the combined effects of demographic, socioeconomic, and oncologic variables on clinical trial enrollment. These models were validated using an external cohort (n=103). RESULTS In the development cohort (445 [42.7%] women; 151 [14.5%] minorities; median age, 51.5 [interquartile range: 39.4-62.5] years), 350 patients (33.6%) enrolled in a therapeutic clinical trial. The whole cohort BNN (AUC: 0.827, misclassification rate (MR): 0.175), the women cohort BNN (AUC: 0.895, MR: 0.125), and the minority cohort BNN (AUC: 0.983, MR: 0.053) models accurately predicted trial enrollment on external validation. For the whole cohort and women models, several demographic (race, ethnicity, minority status, sex), socioeconomic (insurance, employment status), and oncologic variables (seizure, chemotherapy, KPS, WHO grade, tumor lobe) were equally influential (Main effect (ME): 1.000, Total effect (TE): 1.000) on enrollment. For the minority model, socioeconomic variables including insurance status (ME: 0.089, TE: 0.417) and occupation classification (ME: 0.087, TE: 0.381) drove enrollment. CONCLUSION We designed and externally validated the first machine learning model for predicting therapeutic clinical trial enrollment for patients with diffuse glioma. Model features driving trial enrollment varied for underrepresented patients. These results may help prioritize personalized patient-specific initiatives to address barriers to equitable patient accrual.

Abstract We have shown that members of the non-muscle myosin II (NMII) family of molecular motors are non-redundant drivers of both invasion and proliferation in glioblastoma (GBM), consistent with the canonical roles NMII isoforms play in cell motility and cytokinesis. However, we have also found that NMII also has a non-canonical role by regulating oncogenic signaling pathways relevant to GBM pathogenesis. This unexpected finding has led us to identify and characterize MT-125, a highly specific small molecule inhibitor of the two predominant NMII isoforms in GBM (NMIIA and NMIIB), and we have studied its effects in pre-clinical GBM models. MT-125 has high brain penetrance and retention; an excellent safety profile in rodents and canines with no changes in metabolic or hematologic indices after prolonged administration; is well-tolerated with daily dosing for at least 85 consecutive days; blocks GBM invasion and cytokinesis, consistent with the canonical roles of NMII; and prolongs median survival by 35% as a single agent in murine GBM models. MT-125 increases signaling along both the PDGFR- and MAPK-driven pathways through a mechanism that involves the upregulation of reactive oxygen species, leading to oncogene addiction to these pathways. As a consequence, MT-125 is synthetically lethal when combined with FDA-approved, CNS-permeant inhibitors of PDGFR and mTOR in vitro. Combining MT-125 with sunitinib, a PDGFR inhibitor, or paxalisib, a combined PI3 Kinase/mTOR inhibitor, in vivo doubles median survival in a highly aggressive murine orthotopic GBM model. Furthermore, combining MT-125 with sunitinib produces tumor free remissions of &gt;100 days--2.5-fold greater than median survival of either drug alone—in approximately 40% of mice. Our results provide a powerful rationale for developing NMII targeting strategies to treat cancer and demonstrate that MT-125 has strong clinical potential for the treatment of GBM.