Kalathil, Ruth Ann Mathew

Abstract Background Transcatheter aortic valve replacement (TAVR) continues to be increasingly performed. While machine learning has demonstrated utility in predicting adverse outcomes in various cardiovascular procedures, its effectiveness in predicting in-hospital mortality in TAVR remains uncertain. Purpose We aimed to examine the accuracy of machine learning in predicting in-hospital mortality in TAVR. Methods We searched PubMed and Cochrane databases up to February 2024 for observational studies assessing the diagnostic accuracy of machine learning for predicting in-hospital mortality in TAVR. Extracted data included study characteristics, patient demographics, type of predictive model used, and area under the receiver operating characteristic curves (AUC). Predictive accuracy was assessed by pooling AUC of machine-learning models across studies. AUC values ranging from 0.80 to 0.90, 0.70 to 0.80, 0.60 to 0.70, and 0.50 to 0.60 corresponded to excellent, good, fair, and poor discrimination ability, respectively (1). We used a random effects model to estimate the overall effect size with a 95% confidence interval (CI). Heterogeneity was evaluated with the I2 test.We conducted subgroup analyses based on the type of machine-learning model utilized to assess for any potential differences in predictive accuracy. Results A total of three studies were identified, comprising a study population of 70,714 patients. Across studies, eight machine learning models were utilized, consisting of three artificial neural network models, one logistic regression model, one naive Bayes model, two random forest models, and one support vector machine model. Pooled AUC showed excellent accuracy of machine learning in predicting in-hospital mortality in TAVR (0.92; 95% CI 0.90 - 0.95). Subgroup analyses revealed similar yet excellent accuracy across all types of machine learning models utilized: artificial neural network (0.92; 95% CI 0.86-0.99), logistic regression (0.92; 95% CI 0.89-0.95), naive Bayes (0.90; 95% CI 0.88-0.92), random forest (0.94; 95% CI 0.87-1.00), and support vector machine (0.94; 95% CI 0.91-0.96). Conclusion Our study suggests that utilizing machine learning can accurately predict in-hospital mortality in TAVR. Operators of TAVR procedures may benefit from incorporating machine learning prediction models in their clinical practice. Further exploration of machine learning on outcomes in TAVR is needed to verify our findings.

Introduction and importance: Chest pain is a frequent reason patients seek medical attention. The broad spectrum of potential etiologies makes determining the underlying cause of chest pain complex. Among cardiovascular etiologies, aortitis is a rare but life-threatening possibility that should be considered in the differential diagnosis. Case presentation: A 53-year-old female with a history of smoking presented with progressively worsening chest and epigastric pain over several weeks. She had seen multiple physicians previously for the same symptoms with unremarkable work-ups. Physical examination was notable for severe tenderness upon palpation of her lower abdomen. The electrocardiogram and troponins were unremarkable. Computed tomography of the abdomen revealed aneurysmal dilatation of the abdominal aorta, soft tissue thickening, and surrounding inflammatory stranding, consistent with aortitis. Infectious and autoimmune work-ups were unremarkable. Intravenous steroids were initiated, and her symptoms improved significantly. Her aortitis was attributed to inflammation secondary to chronic smoking. Clinical discussion: Aortitis is a rare condition with varied clinical presentations. Etiologies of aortitis include infection and non-infectious inflammation. Diagnosis of aortitis requires a thorough clinical assessment and prompt imaging of the aorta, with computed tomography being the preferred imaging modality. Conclusion: Evaluation for cardiovascular chest pain must extend beyond an electrocardiogram and troponin level. Imaging should be considered in patients with atypical symptoms. Aortitis is a rare but important diagnosis requiring immediate treatment.