Institute for Lung Health, NIHR Leicester Biomedical Research Centre, University of Leicester College of Life Sciences, Leicester, UK.
National Heart and Lung Institute, Imperial College London, London, UK.
BMJ Open. 2024 Aug 21;14(8):e079338. doi: 10.1136/bmjopen-2023-079338.
Longitudinal data are common in asthma studies, to assess asthma progression in patients and identify predictors of future outcomes, including asthma exacerbations and asthma control. Different methods can quantify temporal behaviour in prospective patient-collected diary variables to obtain predictive biomarkers of asthma outcomes. The aims of this systematic review were to evaluate methods for extracting biomarkers from longitudinally collected diary data in asthma and investigate associations between them and patient-reported outcomes (PROs) of patients with asthma.
Systematic review and narrative synthesis.
MEDLINE, EMBASE, CINAHL and the Cochrane Library were searched for studies published between January 2000 and July 2023.
Included studies generated biomarkers from prospective patient-collected peak expiratory flow, symptom scores, reliever use and nocturnal awakenings, and evaluated their associations with asthma PROs, namely asthma exacerbations, asthma control, asthma-related quality of life and asthma severity.
Two independent reviewers used standardised methods to screen and extract data from included studies. Study quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and the Prediction model Risk Of Bias ASessment Tool (PROBAST), respectively.
24 full-text articles met the inclusion criteria and were included in the review. Generally, higher levels of variability in the diary variables were associated with poorer outcomes, especially increased asthma exacerbation risk, and poor asthma control. There was increasing interest in non-parametric methods to quantify complex behaviour of diary variables (6/24). TRIPOD and PROBAST highlighted a lack of consistent reporting of model performance measures and potential for model bias.
Prospectively patient-collected diary variables aid in generating asthma assessment tools, including surrogate endpoints, for clinical trials and predictive biomarkers of adverse outcomes, warranting remote monitoring. Studies consistently lacked robust reporting of model performance. Future research should use diary variable-derived biomarkers.
纵向数据在哮喘研究中很常见，用于评估患者的哮喘进展并确定未来结果的预测因素，包括哮喘加重和哮喘控制。可以使用不同的方法来量化前瞻性患者收集的日记变量中的时间行为，以获得哮喘结果的预测生物标志物。本系统评价的目的是评估从哮喘纵向收集的日记数据中提取生物标志物的方法，并研究它们与哮喘患者报告的结局（PROs）之间的关联。
系统评价和叙述性综合。
在 2000 年 1 月至 2023 年 7 月期间，检索了 MEDLINE、EMBASE、CINAHL 和 Cochrane 图书馆中的研究。
纳入的研究从前瞻性患者收集的呼气峰流速、症状评分、缓解剂使用和夜间觉醒中生成生物标志物，并评估它们与哮喘 PROs（即哮喘加重、哮喘控制、哮喘相关生活质量和哮喘严重程度）的关联。
两名独立审查员使用标准方法从纳入的研究中筛选和提取数据。使用透明报告个体预后或诊断的多变量预测模型（TRIPOD）和预测模型风险偏倚评估工具（PROBAST）分别评估研究质量和偏倚风险。
24 篇全文文章符合纳入标准并包含在综述中。一般来说，日记变量的变异性较高与较差的结局相关，尤其是哮喘加重风险增加和哮喘控制不佳。人们越来越关注使用非参数方法来量化日记变量的复杂行为（6/24）。TRIPOD 和 PROBAST 强调了缺乏一致报告模型性能衡量标准和模型偏差的可能性。
前瞻性患者收集的日记变量有助于生成哮喘评估工具，包括临床试验中的替代终点和不良结局的预测生物标志物，需要远程监测。研究一致缺乏对模型性能的稳健报告。未来的研究应使用日记变量衍生的生物标志物。
