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Social determinants of health, such as food insecurity, can significantly impact patient welfare, potentially increasing the prevalence of chronic illnesses while hindering their management, as shown in previous data collected by the National Health and Nutrition Examination Survey. This study aimed to investigate the association between food insecurity and other social determinants of health with hyperlipidemia, type 2 diabetes mellitus (T2DM), and hypertension. To that end, self-reported data on food security from clinical encounters and biological data from medical records were collected. This study utilized electronic medical record data from 349 patients aged between 18 and 85 years who answered two standard food insecurity screening questions. Each patient's current diagnoses and lab values, including blood pressure, fasting low-density lipoprotein (LDL) cholesterol, and hemoglobin A1c (HbA1c), were then collected. Among patients facing food insecurity (n = 48), 55% were diagnosed with hypertension (p = 0.019), 45% with hyperlipidemia, and 27% with T2DM (p = 0.005). By comparison, these values for food-secure patients were 39%, 54%, and 13%, respectively (n = 301, p > 0.05). Regarding control of these chronic illnesses, hypertension (defined as blood pressure >135/85 mmHg per American Academy of Family Physicians (AAFP) guidelines) was observed in 12% of food-secure patients (n = 301, p > 0.05) and 42% of food-insecure patients (n = 48, p = 0.0204), whereas differences in control of hyperlipidemia and T2DM were insignificant. These results suggest that food-insecure patients are more likely to be diagnosed with hypertension and T2DM but are less likely than food-secure patients to be diagnosed with hyperlipidemia. Consistent with previous research, this study highlights the potentially increased health risks for patients experiencing food insecurity and calls for further efforts to screen patients for social determinants of health.
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BACKGROUND: Atrial fibrillation and atrial flutter represent the most prevalent clinically significant cardiac arrhythmias. While the CHA2DS2-VASc score is commonly used to inform anticoagulation therapy decisions for patients with these conditions, its predictive power is limited. Therefore, we sought to improve risk prediction for left atrial appendage thrombus (LAAT), a known risk factor for stroke in these patients. METHODS: We developed and validated an explainable machine learning model using the eXtreme Gradient Boosting algorithm with 5 × 5 nested cross-validation. The primary outcome was to predict the probability of LAAT in patients with atrial fibrillation and atrial flutter who underwent transesophageal echocardiogram prior to cardioversion. Our algorithm used 37 demographic, comorbid, and transthoracic echocardiographic variables. RESULTS: A total of 795 patients were included in our analysis. LAAT was present in 11.3% of the patients. The average age of patients was 63.3 years and 34.7% were women. Patients with LAAT had significantly lower left ventricular ejection fraction (29.9% vs 43.5%; p < 0.001), lower E' lateral velocity (5.7 cm vs. 7.9 cm; p < 0.001) and higher E/A ratio (2.6 vs 1.8; p = 0.002). Our machine learning model achieved a high AUC of 0.79, with a high specificity of 0.82, and modest sensitivity of 0.57. Left ventricular ejection fraction was the most important variable in predicting LAAT. Patients were split into 10 buckets based on the percentile of their predicted probability of having thrombus. The lower the percentile (e.g., 10%), the lower the probability of having thrombus. Using a cutoff point of 0.16 which includes 10.0% of the patients, we can rule out thrombus with 100% confidence. CONCLUSION: Using machine learning, we refined the predictive power of predicting LAAT and explained the model. These results show promise in providing better guidance for anticoagulation therapy and cardioversion in AF and AFL patients.
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Background: Brain-computer interfaces (BCIs) are a rapidly advancing field which utilizes brain activity to control external devices for a myriad of functions, including the restoration of motor function. Clinically, BCIs have been especially impactful in patients who suffer from stroke-mediated damage. However, due to the rapid advancement in the field, there is a lack of accepted standards of practice. Therefore, the aim of this systematic review is to summarize the current literature published regarding the efficacy of BCI-based rehabilitation of motor dysfunction in stroke patients. Methodology: This systematic review was performed in accordance with the guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 statement. PubMed, Embase, and Cochrane Library were queried for relevant articles and screened for inclusion criteria by two authors. All discrepancies were resolved by discussion among both reviewers and subsequent consensus. Results: 11/12 (91.6%) of studies focused on upper extremity outcomes and reported larger initial improvements for participants in the treatment arm (using BCI) as compared to those in the control arm (no BCI). 2/2 studies focused on lower extremity outcomes reported improvements for the treatment arm compared to the control arm. Discussion/Conclusion: This systematic review illustrates the utility BCI has for the restoration of upper extremity and lower extremity motor function in stroke patients and supports further investigation of BCI for other clinical indications.