Abstract：
BACKGROUND: Aspiration is a frequently observed complication in individuals diagnosed with acute ischemic stroke, leading to potentially severe consequences. However, the availability of predictive tools for assessing aspiration probabilities remains limited. Hence, our study aimed to develop and validate a nomogram for accurately predicting aspiration probability in patients with acute ischemic stroke.
METHODS: We analyzed 30 potential risk factors associated with aspiration in 359 adult patients diagnosed with acute ischemic stroke. Advanced statistical techniques, such as Least absolute shrinkage and selection operator (LASSO) and Multivariate Logistic regression, were employed to identify independent predictors. Subsequently, we developed a nomogram prediction model based on these predictors, which underwent internal validation through 1000 bootstrap resampling. Two additional cohorts (Cohort A n = 64; Cohort B, n = 105) were included for external validation. The discriminatory power and calibration performance of the nomogram were assessed using rigorous methods, including the Hosmer-Lemeshow test, area under the receiver operating characteristic curve (AUC), calibration curve analyses, and decision curve analyses (DCA).
RESULTS: The nomogram was established based on four variables: sputum suction, brain stem infarction, temporal lobe infarction, and Barthel Index score. The predictive model exhibited satisfactory discriminative ability, with an area under the receiver operating characteristic curve of 0.853 (95 % confidence interval, 0.795-0.910), which remained consistent at 0.852 (95 % confidence interval, 0.794-0.912) during the internal validation. The Hosmer-Lemeshow test (P = 0.394) and calibration curve demonstrated favorable consistency between the predicted and observed outcomes in the development cohort. The AUC was 0.872 (95 % confidence interval, 0.783-0.962) in validation cohort A and 0.877 (95 % confidence interval, 0.764-0.989) in validation cohort B, demonstrating sustained accuracy. DCA showed a good net clinical benefit of the nomogram.
CONCLUSIONS: A nomogram for predicting the probability of aspiration in patients with acute ischemia has been successfully developed and validated.
METHODS: We analyzed 30 potential risk factors associated with aspiration in 359 adult patients diagnosed with acute ischemic stroke. Advanced statistical techniques, such as Least absolute shrinkage and selection operator (LASSO) and Multivariate Logistic regression, were employed to identify independent predictors. Subsequently, we developed a nomogram prediction model based on these predictors, which underwent internal validation through 1000 bootstrap resampling. Two additional cohorts (Cohort A n = 64; Cohort B, n = 105) were included for external validation. The discriminatory power and calibration performance of the nomogram were assessed using rigorous methods, including the Hosmer-Lemeshow test, area under the receiver operating characteristic curve (AUC), calibration curve analyses, and decision curve analyses (DCA).
RESULTS: The nomogram was established based on four variables: sputum suction, brain stem infarction, temporal lobe infarction, and Barthel Index score. The predictive model exhibited satisfactory discriminative ability, with an area under the receiver operating characteristic curve of 0.853 (95 % confidence interval, 0.795-0.910), which remained consistent at 0.852 (95 % confidence interval, 0.794-0.912) during the internal validation. The Hosmer-Lemeshow test (P = 0.394) and calibration curve demonstrated favorable consistency between the predicted and observed outcomes in the development cohort. The AUC was 0.872 (95 % confidence interval, 0.783-0.962) in validation cohort A and 0.877 (95 % confidence interval, 0.764-0.989) in validation cohort B, demonstrating sustained accuracy. DCA showed a good net clinical benefit of the nomogram.
CONCLUSIONS: A nomogram for predicting the probability of aspiration in patients with acute ischemia has been successfully developed and validated.
摘要：
背景：在诊断为急性缺血性卒中的个体中，误吸是一种常见的并发症，导致潜在的严重后果。然而,评估期望概率的预测工具的可用性仍然有限.因此，我们的研究旨在建立并验证准确预测急性缺血性卒中患者误吸概率的列线图.
方法：我们分析了359例诊断为急性缺血性卒中的成年患者与误吸相关的30种潜在危险因素。先进的统计技术，如最小绝对收缩和选择算子(LASSO)和多变量Logistic回归，被用来识别独立的预测因子。随后，我们基于这些预测因子开发了一个列线图预测模型，通过1000个引导重采样进行了内部验证。另外两个队列(队列An=64；队列B，n=105)用于外部验证。使用严格的方法评估了列线图的判别力和校准性能，包括Hosmer-Lemeshow测试,接收器工作特性曲线下面积（AUC），校准曲线分析，和决策曲线分析(DCA)。
结果：根据四个变量建立了列线图：吸痰，脑干梗塞,颞叶梗死,和Barthel指数得分。预测模型表现出令人满意的判别能力，接收器工作特性曲线下面积为0.853(95%置信区间，0.795-0.910)，保持一致在0.852(95%置信区间，0.794-0.912)在内部验证期间。Hosmer-Lemeshow检验（P=0.394）和校准曲线显示出开发队列中预测和观察结果之间的良好一致性。AUC为0.872(95%置信区间,0.783-0.962)在验证队列A和0.877(95%置信区间，0.764-0.989)在验证队列B中，展示持续的准确性。DCA显示了列线图的良好净临床益处。
结论：已成功开发并验证了预测急性缺血患者误吸概率的列线图。
方法：我们分析了359例诊断为急性缺血性卒中的成年患者与误吸相关的30种潜在危险因素。先进的统计技术，如最小绝对收缩和选择算子(LASSO)和多变量Logistic回归，被用来识别独立的预测因子。随后，我们基于这些预测因子开发了一个列线图预测模型，通过1000个引导重采样进行了内部验证。另外两个队列(队列An=64；队列B，n=105)用于外部验证。使用严格的方法评估了列线图的判别力和校准性能，包括Hosmer-Lemeshow测试,接收器工作特性曲线下面积（AUC），校准曲线分析，和决策曲线分析(DCA)。
结果：根据四个变量建立了列线图：吸痰，脑干梗塞,颞叶梗死,和Barthel指数得分。预测模型表现出令人满意的判别能力，接收器工作特性曲线下面积为0.853(95%置信区间，0.795-0.910)，保持一致在0.852(95%置信区间，0.794-0.912)在内部验证期间。Hosmer-Lemeshow检验（P=0.394）和校准曲线显示出开发队列中预测和观察结果之间的良好一致性。AUC为0.872(95%置信区间,0.783-0.962)在验证队列A和0.877(95%置信区间，0.764-0.989)在验证队列B中，展示持续的准确性。DCA显示了列线图的良好净临床益处。
结论：已成功开发并验证了预测急性缺血患者误吸概率的列线图。
