Citations

ABSTRACT Aims Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. Methods and results A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85–0.92), a sensitivity of 0.85 (0.78–0.95) and a specificity of 0.76 (0.74–0.79) in the internal validation cohort and an AUC of 0.82 (0.73–0.91), a sensitivity of 0.74 (0.61–0.87) and a specificity of 0.79 (0.77–0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. Conclusion A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.

Background: Most strokes and cardiovascular diseases (CVDs) are potentially preventable if their risk factors are identified and well controlled. Digital platforms, such as the PreventS-MD web app (PreventS-MD) may aid health care professionals (HCPs) in assessing and managing risk factors and promoting lifestyle changes for their patients. Methods: This is a mixed-methods cross-sectional two-phase survey using a largely positivist (quantitative and qualitative) framework. During Phase 1, a prototype of PreventS-MD was tested internationally by 59 of 69 consenting HCPs of different backgrounds, age, sex, working experience, and specialties using hypothetical data. Collected comments/suggestions from the study HCPs in Phase 1 were reviewed and implemented. In Phase 2, a near-final version of PreventS-MD was developed and tested by 58 of 72 consenting HCPs using both hypothetical and real patient (n = 10) data. Qualitative semi-structured interviews with real patients (n = 10) were conducted, and 1 month adherence to the preventive recommendations was assessed by self-reporting. The four System Usability Scale (SUS) groups of scores (0–50 unacceptable; 51–68 poor; 68–80.3 good; >80.3 excellent) were used to determine usability of PreventS-MD. Findings: Ninety-nine HCPs from 27 countries (45% from low- to middle-income countries) participated in the study, and out of them, 10 HCPs were involved in the development of PreventS before the study, and therefore were not involved in the survey. Of the remaining 89 HCPs, 69 consented to the first phase of the survey, and 59 of them completed the first phase of the survey (response rate 86%), and 58 completed the second phase of the survey (response rate 84%). The SUS scores supported good usability of the prototype (mean score = 80.2; 95% CI [77.0–84.0]) and excellent usability of the final version of PreventS-MD (mean score = 81.7; 95% CI [79.1–84.3]) in the field. Scores were not affected by the age, sex, working experience, or specialty of the HCPs. One-month follow-up of the patients confirmed the high level of satisfaction/acceptability of PreventS-MD and (100%) adherence to the recommendations. Interpretation: The PreventS-MD web app has a high level of usability, feasibility, and satisfaction by HCPs and individuals at risk of stroke/CVD. Individuals at risk of stroke/CVD demonstrated a high level of confidence and motivation in following and adhering to preventive recommendations generated by PreventS-MD.

AbstractSynucleinopathies‐related disorders such as Lewy body dementia (LBD) and isolated/idiopathic REM sleep behavior disorder (iRBD) have been associated with neuroinflammation. In this study, we examined whether the human leukocyte antigen (HLA) locus plays a role in iRBD and LBD. In iRBD, HLA‐DRB1*11:01 was the only allele passing FDR correction (OR = 1.57, 95% CI = 1.27–1.93, p = 2.70e‐05). We also discovered associations between iRBD and HLA‐DRB1 70D (OR = 1.26, 95%CI = 1.12–1.41, p = 8.76e‐05), 70Q (OR = 0.81, 95%CI = 0.72–0.91, p = 3.65e‐04) and 71R (OR = 1.21, 95%CI = 1.08–1.35, p = 1.35e‐03). Position 71 (pomnibus = 0.00102) and 70 (pomnibus = 0.00125) were associated with iRBD. Our results suggest that the HLA locus may have different roles across synucleinopathies.

AbstractObjectivesAlthough hemispheric surgeries are among the most effective procedures for drug‐resistant epilepsy (DRE) in the pediatric population, there is a large variability in seizure outcomes at the group level. A recently developed HOPS score provides individualized estimation of likelihood of seizure freedom to complement clinical judgement. The objective of this study was to develop a freely accessible online calculator that accurately predicts the probability of seizure freedom for any patient at 1‐, 2‐, and 5‐years post‐hemispherectomy.MethodsRetrospective data of all pediatric patients with DRE and seizure outcome data from the original Hemispherectomy Outcome Prediction Scale (HOPS) study were included. The primary outcome of interest was time‐to‐seizure recurrence. A multivariate Cox proportional‐hazards regression model was developed to predict the likelihood of post‐hemispheric surgery seizure freedom at three time points (1‐, 2‐ and 5‐ years) based on a combination of variables identified by clinical judgment and inferential statistics predictive of the primary outcome. The final model from this study was encoded in a publicly accessible online calculator on the International Network for Epilepsy Surgery and Treatment (iNEST) website (https://hops‐calculator.com/).ResultsThe selected variables for inclusion in the final model included the five original HOPS variables (age at seizure onset, etiologic substrate, seizure semiology, prior non‐hemispheric resective surgery, and contralateral fluorodeoxyglucose–positron emission tomography [FDG‐PET] hypometabolism) and three additional variables (age at surgery, history of infantile spasms, and magnetic resonance imaging [MRI] lesion). Predictors of shorter time‐to‐seizure recurrence included younger age at seizure onset, prior resective surgery, generalized seizure semiology, FDG‐PET hypometabolism contralateral to the side of surgery, contralateral MRI lesion, non‐lesional MRI, non‐stroke etiologies, and a history of infantile spasms. The area under the curve (AUC) of the final model was 73.0%.SignificanceOnline calculators are useful, cost‐free tools that can assist physicians in risk estimation and inform joint decision‐making processes with patients and families, potentially leading to greater satisfaction. Although the HOPS data was validated in the original analysis, the authors encourage external validation of this new calculator.

Background: Atrial fibrillation (AF) in severe aortic stenosis (AS) is associated with worse outcomes compared to sinus rhythm (SR). The prevalence of AF and its impact on outcomes in very severe AS (vsAS) is unknown. Objectives: To determine the prevalence and prognostic significance of AF in vsAS. Methods: A total of 563 patients with vsAS (transaortic valve peak velocity ≥5 m/sec) were identified retrospectively out of 3139 with aortic valve area ≤1.0 cm 2 or ≤0.6 cm 2 /m 2 and left ventricular ejection fraction (LVEF) ≥50%. Patients were divided by rhythm (AF vs SR) at the time of index transthoracic echocardiogram. Outcomes in AF versus SR were compared in propensity-matched analyses (2 SR:1 AF). Results: AF was present in 50/563 patients (9%) and SR in 513/563 (91%). In propensity-matched analyses (100 SR:50 AF), the mean age was 83.2±7.3 years, 54% were female. There was no difference in comorbidity index (p=0.84). There was no difference in AF compared to SR in aortic valve area (0.7±0.1 vs 0.7±0.1 cm 2 , p=0.94), peak velocity (5.3±0.3 vs 5.3±0.3 m/sec, p=0.32), LVEF (63±7 vs 65±7%, p=0.14) or E/e’ (23±13 vs 21±12, p=0.48), but patients with AF had larger left atrial volume index (60±16 vs 48±15 mL/m 2 , p<0.001), higher right ventricular systolic pressure (52±15 vs 41±16 mmHg, p<0.001), and higher prevalence of moderate or more tricuspid regurgitation (33% vs 5%, p<0.001) and right ventricular systolic dysfunction (23% vs 6%, p=0.003). Age-, sex-, comorbidity index-adjusted all-cause mortality was higher in AF vs SR (HR 2.33 [1.40, 3.87], p=0.001) ( Figure: Observed overall survival ). Aortic valve replacement (AVR) was associated with improved survival (HR 0.44 [0.25, 0.76], p=0.004) with no evidence of interaction of AVR and rhythm (p=0.15). Conclusion: AF was not uncommon among patients with vsAS and identified a subset of patients with vsAS at much higher risk of mortality. Whether AVR should be considered earlier in vsAS when associated with AF needs to be investigated.

e24042 Background: Cancer is 2nd leading cause of death (1 in every 5) in the United States followed by heart disease. Use of ketogenic diet (KD)- high-fat & low-carbohydrate diet, allows fatty acids to become the obligatory source of cellular energy which can help in decreasing the tumor cell growth. Aim of this study was to evaluate role of KD in different types of cancer. Methods: We performed a systematic review using PRISMA protocol to identify articles describing the role of the ketogenic diet in cancer patients in PubMed. Keywords like cancer, neoplasm, tumor, KD, and intermittent fasting (IF) were used & filters like full text, English, human, observational studies, & clinical trials (CTs) were applied between 2018-2022. Results: Of the 362 studies after using filters only 22 studies were CTs. Of these 22, 12 studies were excluded & 10 studies were included in this study. [Table 1] We found CTs describing role of KD, IF or standard diet (SD) in brain cancer, non-metastasized rectal cancer, high-grade glioma, head and neck squamous cell carcinoma, locally advanced and metastatic breast cancer, ovarian and endometrial cancer. KD was describes as an accompanying measure for patients undergoing standard-of-care therapy that helps to improve the quality of life, survival & acceptable safety and tolerability. KD may augment the antitumor effects of classic chemotherapy and radiotherapy. Conclusions: Role of KD is described in depth, but majority RCT results were heterogeneous in nature to provide quantitative measures of survival timeline, outcomes, & recovery. Thus, more human prospective studies are warranted to evaluate the therapeutic effectiveness & safety of KD.[Table: see text]