European journal of clinical investigation · 2021
A risk score for pericarditis recurrence.
СтатьяНазвание и аннотация: Перевод в очереди
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Lazarou Emilia, Lazaros George, Antonopoulos Alexios S, Imazio Massimo, Vasileiou Panagiotis, Karavidas Apostolos, Toutouzas Konstantinos, Vassilopoulos Dimitrios, Tsioufis Costas, Tousoulis Dimitris, Vlachopoulos Charalambos
Аннотация
BACKGROUND: Currently, we remain uncertain about which patients are at increased risk for recurrent pericarditis. We developed a risk score for pericarditis recurrence in patients with acute pericarditis.
MATERIALS AND METHODS: We prospectively recruited 262 patients with a first episode of acute pericarditis. Baseline patients' demographics, clinical, imaging and laboratory data were collected. Patients were followed up for a median of 51 months (interquartile range 21-71) for recurrence. Variables with <10% missingness were entered into multivariable logistic regression models with stepwise elimination to explore independent predictors of recurrence. The final model performance was assessed by the c-index whereas model's calibration and optimism-corrected c-index were evaluated after 10-fold cross-validation.
RESULTS: We identified six independent predictors for pericarditis recurrence, that is age, effusion size, platelet count (negative predictors) and reduced inferior vena cava collapse, in-hospital use of corticosteroids and heart rate (positive predictors). The final model had good performance for recurrence, c-index 0.783 (95% CI 0.725-0.842), while the optimism-corrected c-index after cross-validation was 0.752. Based on these variables, we developed a risk score point system for recurrence (0-22 points) with equally good performance (c-index 0.740, 95% CI 0.677-0.803). Patients with a low score (0-7 points) had 21.3% risk for recurrence, while those with high score (≥12 points) had a 69.8% risk for recurrence. The score was predictive of recurrence among most patient subgroups.
CONCLUSIONS: A simple risk score point system based on 6 variables can be used to predict the individualized risk for pericarditis recurrence among patients with a first episode of acute pericarditis.
MATERIALS AND METHODS: We prospectively recruited 262 patients with a first episode of acute pericarditis. Baseline patients' demographics, clinical, imaging and laboratory data were collected. Patients were followed up for a median of 51 months (interquartile range 21-71) for recurrence. Variables with <10% missingness were entered into multivariable logistic regression models with stepwise elimination to explore independent predictors of recurrence. The final model performance was assessed by the c-index whereas model's calibration and optimism-corrected c-index were evaluated after 10-fold cross-validation.
RESULTS: We identified six independent predictors for pericarditis recurrence, that is age, effusion size, platelet count (negative predictors) and reduced inferior vena cava collapse, in-hospital use of corticosteroids and heart rate (positive predictors). The final model had good performance for recurrence, c-index 0.783 (95% CI 0.725-0.842), while the optimism-corrected c-index after cross-validation was 0.752. Based on these variables, we developed a risk score point system for recurrence (0-22 points) with equally good performance (c-index 0.740, 95% CI 0.677-0.803). Patients with a low score (0-7 points) had 21.3% risk for recurrence, while those with high score (≥12 points) had a 69.8% risk for recurrence. The score was predictive of recurrence among most patient subgroups.
CONCLUSIONS: A simple risk score point system based on 6 variables can be used to predict the individualized risk for pericarditis recurrence among patients with a first episode of acute pericarditis.