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首页> 外文期刊>Journal of stroke and cerebrovascular diseases: The official journal of National Stroke Association >Development and Internal Validation of a Nomogram to Predict Post-Stroke Fatigue After Discharge
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Development and Internal Validation of a Nomogram to Predict Post-Stroke Fatigue After Discharge

机译：ROMO图表的开发和内部验证预测放电后卒中后疲劳

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Objectives: We aimed to develop and validate a nomogram for the individualized prediction of the risk of post-stroke fatigue (PSF) after discharge. Materials and methods: Fatigue was measured using the Fatigue Assessment Scale. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predictive model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was conducted using bootstrapping validation. Finally, a web application was developed to facilitate the use of the nomogram. Results: We developed a nomogram based on 95 stroke patients. The predictors included in the nomogram were sex, pre-stroke sarcopenia, acute phase fatigue, dysphagia, and depression. The model displayed good discrimination, with a C-index of 0.801 (95% confidence interval: 0.700-0.902) and good calibration. A high C-index value of 0.762 could still be reached in the interval validation. Decision curve analysis showed that the risk of PSF after discharge was clinically useful when the intervention was decided at the PSF risk possibility threshold of 10% to 90%. Conclusion: This nomogram could be conveniently used to provide an individual, visual, and precise prediction of the risk probability of PSF after being discharged home. Thus, as an aid in decision-making, physicians and other healthcare professionals can use this predictive method to provide early intervention or a discharge plan for stroke patients during the hospitalization period.
机译：目的：我们旨在开发和验证一个诺模图，用于个体化预测出院后卒中后疲劳（PSF）的风险。材料和方法：使用疲劳评估量表测量疲劳。应用多变量logistic回归分析，结合最小绝对收缩和选择算子回归模型中选择的特征，建立预测模型。使用C指数、校准图和决策曲线分析评估预测模型的识别、校准和临床实用性。使用引导验证进行内部验证。最后，开发了一个web应用程序，以方便诺模图的使用。结果：我们基于95名中风患者制定了列线图。列线图中的预测因素包括性别、卒中前肌肉减少、急性期疲劳、吞咽困难和抑郁。该模型显示出良好的分辨力，C指数为0.801（95%置信区间：0.700-0.902），校准良好。在区间验证中仍然可以达到高C指数0.762。决策曲线分析表明，当在10%至90%的PSF风险可能性阈值下决定干预措施时，出院后的PSF风险在临床上是有用的。结论：该列线图可以方便地为出院后PSF的风险概率提供个体化、可视化和精确的预测。因此，作为决策的一种辅助手段，医生和其他医疗专业人员可以使用这种预测方法，在住院期间为中风患者提供早期干预或出院计划。

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