评估风险评分以预测儿科重症哮喘急性发作
2022/04/14
摘要
背景：哮喘急性发作通常会导致额外的医疗保健使用，并且成本高昂，早期识别哮喘急性发作风险增加的儿童将允许采取积极的管理方法。
目的：我们使用来自电子病历(EMR)的数据评估常见的哮喘危险因素，以预测0-21岁儿童个体哮喘急性发作的概率。
方法：我们分析了7年期间在辛辛那提儿童医院医疗中心就诊的3000多例哮喘患者的纵向EMR数据。研究人群分为3个年龄组：0-4岁、5-11岁和12-21岁。每个年龄组分为推导队列和验证队列，用于构建风险评分模型。我们预测了未来12个月内急性发作的风险，按风险层验证了评分，并开发了一种临床工具来确定基于该模型的风险水平。
结果：风险模型结果通过日历年与年龄组的验证队列得到证实。种族、过敏和吸烟是0-4岁年龄组的重要危险因素，肺功能异常和肥胖是大于12岁儿童哮喘急性发作的更敏感预测因子，对于每个年龄组，较高的扩展评分与随后一年中较高的预测哮喘急性发作概率相关联。
结论：这种哮喘急性发作预测模型与相关的临床工具可以帮助临床医生识别出哮喘急性发作高风险儿童，这些儿童可能会从更积极的管理和有针对性的风险缓解中获益。
Evaluation of Risk Scores to Predict Pediatric Severe Asthma Exacerbations
Niu C, Xu Y, Schuler CL, et al.
J Allergy Clin Immunol Pract. 2021;9(12):4393-4401.e8.
ABSTRACT
BACKGROUND: Asthma exacerbations commonly lead to unplanned health care utilization and are costly. Early identification of children at increased risk of asthma exacerbations would allow a proactive management approach.
OBJECTIVE: We evaluated common asthma risk factors to predict the probability of exacerbation for individual children aged 0-21 years using data from the electronic medical record (EMR).
METHODS: We analyzed longitudinal EMR data for over 3000 participants with asthma seen at Cincinnati Children’s Hospital Medical Center over a 7-year period. The study population was divided into 3 age groups: 0-4, 5-11, and 12-21 years. Each age group was divided into a derivation cohort and a validation cohort, which were used to build a risk score model. We predicted risk of exacerbation in the next 12 months, validated the scores by risk stratum, and developed a clinical tool to determine the risk level based on this model.
RESULTS: Risk model results were confirmed with validation cohorts by calendar year and age groups. Race, allergic sensitization, and smoke exposure were each important risk factors in the 0-4 age group. Abnormal spirometry and obesity were more sensitive predictors of exacerbation in children >12 years. For each age group, a higher expanded score was associated with a higher predicted probability of an asthma exacerbation in the subsequent year.
CONCLUSION: This asthma exacerbation prediction model, and the associated clinical tool, may assist clinicians in identifying children at high risk for exacerbation that may benefit from more aggressive management and targeted risk mitigation.
背景：哮喘急性发作通常会导致额外的医疗保健使用，并且成本高昂，早期识别哮喘急性发作风险增加的儿童将允许采取积极的管理方法。
目的：我们使用来自电子病历(EMR)的数据评估常见的哮喘危险因素，以预测0-21岁儿童个体哮喘急性发作的概率。
方法：我们分析了7年期间在辛辛那提儿童医院医疗中心就诊的3000多例哮喘患者的纵向EMR数据。研究人群分为3个年龄组：0-4岁、5-11岁和12-21岁。每个年龄组分为推导队列和验证队列，用于构建风险评分模型。我们预测了未来12个月内急性发作的风险，按风险层验证了评分，并开发了一种临床工具来确定基于该模型的风险水平。
结果：风险模型结果通过日历年与年龄组的验证队列得到证实。种族、过敏和吸烟是0-4岁年龄组的重要危险因素，肺功能异常和肥胖是大于12岁儿童哮喘急性发作的更敏感预测因子，对于每个年龄组，较高的扩展评分与随后一年中较高的预测哮喘急性发作概率相关联。
结论：这种哮喘急性发作预测模型与相关的临床工具可以帮助临床医生识别出哮喘急性发作高风险儿童，这些儿童可能会从更积极的管理和有针对性的风险缓解中获益。
(张淑雯2 张红萍2 王刚1 四川大学华西医院中西医结合科呼吸病组 610041 摘译)
（J Allergy Clin Immunol Pract. 2021;9(12):4393-4401.e8.）
（J Allergy Clin Immunol Pract. 2021;9(12):4393-4401.e8.）
Evaluation of Risk Scores to Predict Pediatric Severe Asthma Exacerbations
Niu C, Xu Y, Schuler CL, et al.
J Allergy Clin Immunol Pract. 2021;9(12):4393-4401.e8.
ABSTRACT
BACKGROUND: Asthma exacerbations commonly lead to unplanned health care utilization and are costly. Early identification of children at increased risk of asthma exacerbations would allow a proactive management approach.
OBJECTIVE: We evaluated common asthma risk factors to predict the probability of exacerbation for individual children aged 0-21 years using data from the electronic medical record (EMR).
METHODS: We analyzed longitudinal EMR data for over 3000 participants with asthma seen at Cincinnati Children’s Hospital Medical Center over a 7-year period. The study population was divided into 3 age groups: 0-4, 5-11, and 12-21 years. Each age group was divided into a derivation cohort and a validation cohort, which were used to build a risk score model. We predicted risk of exacerbation in the next 12 months, validated the scores by risk stratum, and developed a clinical tool to determine the risk level based on this model.
RESULTS: Risk model results were confirmed with validation cohorts by calendar year and age groups. Race, allergic sensitization, and smoke exposure were each important risk factors in the 0-4 age group. Abnormal spirometry and obesity were more sensitive predictors of exacerbation in children >12 years. For each age group, a higher expanded score was associated with a higher predicted probability of an asthma exacerbation in the subsequent year.
CONCLUSION: This asthma exacerbation prediction model, and the associated clinical tool, may assist clinicians in identifying children at high risk for exacerbation that may benefit from more aggressive management and targeted risk mitigation.
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短效β2受体激动剂应用与重度哮喘发作：欧洲和北美的SABINA研究结果
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既往烟草暴露对重症哮喘患者气道嗜酸性粒细胞活化及自身免疫的影响
