Risk factors for benign paroxysmal positional vertigo and construction of a nomogram predictive model.
Аннотация
BACKGROUND
To analyze the risk factors for benign paroxysmal positional vertigo (BPPV) and to construct a predictive nomogram model.
METHODS
In this retrospective study, 312 participants were enrolled, including 164 BPPV patients and 148 healthy subjects without BPPV. Risk predictors for BPPV were identified using univariate and multivariate analyses, and a clinical nomogram was constructed. The predictive accuracy was assessed by unadjusted concordance index (C-index) and calibration plot.
RESULTS
Univariate and multivariate regression analysis identified stroke (95% CI, 0.575-5.954; P=0.022), hyperlipidemia (95% CI, 0.471-4.647; P=0.003), chronic suppurative otitis media (95% CI, 1.222-45.528; P=0.005), cervical spondylosis (95% CI, 1.232-3.017; P=0.005), and osteoporosis (95% CI, 1.130-3.071; P=0.001) were the independent risk factors for BPPV. These risk factors were used to construct a clinical predictive nomogram. The regression equation was: logit (P) = -6.820 + 0.450 * stroke + hyperlipidemia * 0.312 + chronic suppurative otitis media * 0.499 + cervical spondylosis * 0.916 + osteoporosis * 0.628. The calibration curves demonstrated excellent accuracy of the predictive nomogram. Decision curve analysis showed that the predictive model is clinically applicable when the threshold probability was between 20% and 60%.
CONCLUSIONS
Stroke, hyperlipidemia, chronic suppurative otitis media, cervical spondylosis and osteoporosis are independent risk predictors for BPPV. The developed nomogram is useful in predicting the risk of BPPV.
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.