Fragmented Ventricular Complexes and Blood Pressure Variability Assessed by Ambulatory Blood Pressure Monitoring in Patients With Metabolic Syndrome.

Introduction Hypertension is a leading risk factor for the development of cardiovascular and metabolic derangements. In patients with metabolic syndrome (MetS), hypertension is one of the cornerstones showing high variability which is detected in ambulatory blood pressure monitoring. Fragmented ventricular complexes on ECG are seen as hypertensives and are a viable and easy measure of myocardial fibrosis even in the absence of obvious hypertrophy. Aim The present study was undertaken to study the blood pressure variability in patients of MetS with fragmented QRS (fQRS) versus normal ventricular complexes (QRS). Results Out of 100 patients, 22 (22%) had fQRS complexes. Hypertension and diabetes were the most prevalent associated in both groups but a difference was seen with coronary artery disease, which was significantly associated in the fQRS group (8.97% vs 95.45%, p<0.001) as compared to the non-fQRS group. Significant differences were observed in waist circumference (p=0.019), triglyceride (p=0.006) and left ventricular ejection fraction (p<0.001) between the two groups. There was a marked difference (p<0.05) between heart rate variability during day and night time between normal and fQRS sub-groups, being higher in the latter. A similar pattern of change was observed for systolic and diastolic blood pressures and associated dipping. Conclusion Significant differences exist between heart rate and blood pressure changes in patients with fQRS of MetS, thus making fQRS a potent indicator of cardiovascular status.

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype.

Predicting length of stay (LoS) and understanding its underlying factors is essential to minimizing the risk of hospital-acquired conditions, improving financial, operational, and clinical outcomes, and better managing future pandemics. The purpose of this study was to forecast patients' LoS using a deep learning model and to analyze cohorts of risk factors reducing or prolonging LoS. We employed various preprocessing techniques, SMOTE-N to balance data, and a TabTransformer model to forecast LoS. Finally, the Apriori algorithm was applied to analyze cohorts of risk factors influencing hospital LoS. The TabTransformer outperformed the base machine learning models in terms of F1 score (0.92), precision (0.83), recall (0.93), and accuracy (0.73) for the discharged dataset and F1 score (0.84), precision (0.75), recall (0.98), and accuracy (0.77) for the deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to laboratory, X-ray, and clinical data, such as elevated LDH and D-dimer levels, lymphocyte count, and comorbidities such as hypertension and diabetes. It also reveals what treatments have reduced the symptoms of COVID-19 patients, leading to a reduction in LoS, particularly when no vaccines or medication, such as Paxlovid, were available.

Toward Grading Subarachnoid Hemorrhage Risk Prediction: A Machine Learning-Based Aneurysm Rupture Score.

OBJECTIVE: Existing approaches neither provide an accurate prediction of subarachnoid hemorrhage (SAH) nor offer a quantitative comparison among a group of its risk factors. To evaluate the population, hypertension, age, size, earlier subarachnoid hemorrhage, and location (PHASES) and unruptured intracranial aneurysm treatment score (UIATS) scores and develop an Artificial Intelligence-based 5-year and lifetime aneurysmal rupture criticality prediction (ARCP) score for a set of risk factors. METHODS: We design various location-specific and ensemble learning models to develop lifetime rupture risk, employ the longitudinal data to develop a linear regression-based model to predict an aneurysm's growth score, and use the Apriori algorithm to identify risk factors strongly associated with SAH. We develop ARCP by integrating output of Apriori algorithm and ML models and compare with PHASES and UIATS scores along with the scores of a multidisciplinary team of neurosurgeons. RESULTS: The PHASES and UIATS scores show sensitivities of 22% and 35% and specificities of 76% and 79%, respectively. Location-specific models show precision and recall of 93% and 90% for the middle cerebral artery, 83% and 80% for the anterior communicating artery, and 80% and 80% for the supraclinoid internal carotid artery, respectively. The ensemble method shows both precision and recall of 80%. The validation of the models shows that ARCP performs better than our control group of neurosurgeons. Data-driven knowledge produces comparisons among 61 risk factor combinations, 11 ranked minor, 8 moderate, and 41 severe, and 1 of which is a critical factor. CONCLUSIONS: The PHASES and UIATS are weak predictors, and the ARCP score can identify, and grade, risk factors associated with SAH.

Introducing the Rupture Criticality Index to Compare Risk Factor Combinations Associated With Aneurysmal Rupture.

BACKGROUND: Relative risk is insufficient to guide treatment decision-making for unruptured intracranial aneurysms. Our objective was to introduce a novel risk assessment methodology called the Rupture Criticality Index (RCI), which allows for concurrent evaluation of groups of risk factors (RFs). METHODS: From a retrospective database of saccular aneurysms, we identify 915 patients and delineate 50 potential RFs for aneurysms in 11 unique locations. RF combinations for multivariable analysis were defined by aneurysm size, location, and a third variable from the study design. Data analysis was performed by applying frequency distribution methods to define the RCI of each RF combination. RESULTS: RF combinations at greatest risk were small (4.8-8.2 mm) or medium (8.3-14.5 mm) anterior communicating aneurysms (ACoA) in male individuals (RCI 9.87-10), small ACoA in those ≤37 years or 38-55 years (RCI 8.67-8.99), medium basilar tip aneurysms (BTAs) in male individuals (RCI 10), and large (14.6-22.5 mm) BTA in Caucasians or those aged 38-55 years (RCI 9.25, 9.35, respectively). CONCLUSIONS: We introduce the concept of RCI and compare how RF combinations are associated with aneurysmal rupture. This novel approach to aneurysmal rupture identifies high-risk clinical presentations and can be used to guide clinical decision-making in patients with non-traditional risks.

Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation.

BACKGROUND: Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization. OBJECTIVE: Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs. METHODS: In our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context-aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context-aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score. RESULTS: Evaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context-aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory. CONCLUSIONS: By employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.