首页|Vascular and Thoracic Institute Reports Findings in Pericarditis (Predicting Lon g-Term Clinical Outcomes of Patients With Recurrent Pericarditis)
Vascular and Thoracic Institute Reports Findings in Pericarditis (Predicting Lon g-Term Clinical Outcomes of Patients With Recurrent Pericarditis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Pericarditis is the subject of a report. According to news reporting ou t of Cleveland, Ohio, by NewsRx editors, research stated, “Recurrent pericarditi s (RP) is a complex condition associated with significant morbidity. Prior studi es have evaluated which variables are associated with clinical remission.” Our news journalists obtained a quote from the research from Vascular and Thorac ic Institute, “However, there is currently no established risk-stratification mo del for predicting outcomes in these patients. We developed a risk stratificatio n model that can predict long-term outcomes in patients with RP and enable ident ification of patients with characteristics that portend poor outcomes. We retros pectively studied a total of 365 consecutive patients with RP from 2012 to 2019. The primary outcome was clinical remission (CR), defined as cessation of all an ti-inflammatory therapy with complete resolution of symptoms. Five machine learn ing survival models were used to calculate the likelihood of CR within 5 years a nd stratify patients into high-risk, intermediate-risk, and low-risk groups. Amo ng the cohort, the mean age was 46 ± 15 years, and 205 (56%) were w omen. CR was achieved in 118 (32%) patients. The final model includ ed steroid dependency, total number of recurrences, pericardial late gadolinium enhancement, age, etiology, sex, ejection fraction, and heart rate as the most i mportant parameters. The model predicted the outcome with a C-index of 0.800 on the test set and exhibited a significant ability in stratification of patients i nto low-risk, intermediate-risk, and high-risk groups (log-rank test; P<0.0001). We developed a novel risk-stratification model for predicting CR in RP . Our model can also aid in stratifying patients, with high discriminative abili ty.”
ClevelandOhioUnited StatesNorth an d Central AmericaCardiovascular Diseases and ConditionsCyborgsEmerging Tec hnologiesHealth and MedicineHeart DiseaseHeart Disorders and DiseasesMac hine LearningPericarditis
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