Associations of Spiritual Well-Being and Hope with Health Anxiety Severity in Patients with Advanced Coronary Artery Disease

Background and Objectives: Health anxiety is one of the most common problems in patients with coronary artery disease. The present study tested whether health anxiety severity could be predicted by spiritual well-being and hope in patients with advanced coronary artery disease. Materials and Methods: In a cross-sectional study, 100 patients with advanced coronary artery disease were recruited from hospitals and healthcare centers in Iran. Patients completed self-report scales, including the Spiritual Well-Being Scale, Adult Hope Scale, and Short Health Anxiety Inventory. Hierarchical multiple regression analyses were used to empirically explore the relations among variables. Results: Results indicated that patients who reported higher levels of hope (β = 0.42, p < 0.01) and spiritual well-being (β = 0.20, p < 0.05) reported lower levels of health anxiety. Agency (β = 0.58, p < 0.01) scores were a significant negative predictor of health anxiety severity. Additionally, religious spirituality scores (β = 0.28, p < 0.01) were shown to significantly negatively predict health anxiety level. However, the pathways components of hope and existential spirituality were not significant predictors. Conclusion: The findings of the present study indicate that spiritual well-being and hope could be important factors in determining health anxiety for adults with coronary artery disease, and their role is worthy of further exploration to help improve health anxiety for patients with coronary artery disease.

A rule-based approach to identify patient eligibility criteria for clinical trials from narrative longitudinal records

Objective Achieving unbiased recognition of eligible patients for clinical trials from their narrative longitudinal clinical records can be time consuming. We describe and evaluate a knowledge-driven method that identifies whether a patient meets a selected set of 13 eligibility clinical trial criteria from their longitudinal clinical records, which was one of the tasks of the 2018 National NLP Clinical Challenges. Materials and Methods The approach developed uses rules combined with manually crafted dictionaries that characterize the domain. The rules are based on common syntactical patterns observed in text indicating or describing explicitly a criterion. Certain criteria were classified as “met” only when they occurred within a designated time period prior to the most recent narrative of a patient record and were dealt through their position in text. Results The system was applied to an evaluation set of 86 unseen clinical records and achieved a microaverage F1-score of 89.1% (with a micro F1-score of 87.0% and 91.2% for the patients that met and did not meet the criteria, respectively). Most criteria returned reliable results (drug abuse, 92.5%; Hba1c, 91.3%) while few (eg, advanced coronary artery disease, 72.0%; myocardial infarction within 6 months of the most recent narrative, 47.5%) proved challenging enough. Conclusion Overall, the results are encouraging and indicate that automated text mining methods can be used to process clinical records to recognize whether a patient meets a set of clinical trial criteria and could be leveraged to reduce the workload of humans screening patients for trials.