Department of Community Health Sciences, University of Manitoba, Max Rady College of Medicine, Winnipeg, Manitoba, Canada
Department of Community Health Sciences, University of Manitoba, Max Rady College of Medicine, Winnipeg, Manitoba, Canada.
BMJ Open. 2024 Aug 15;14(7):e083141. doi: 10.1136/bmjopen-2023-083141.
This study aims to (1) build and validate model-based case definitions for multiple sclerosis (MS) that use trends (ie, trend-based case definitions) and (2) to apply dynamic classification to identify the average number of data years needed for classification (ie, average trend needed).
Retrospective cohort study design.
608 MS cases and 59 620 MS non-cases.
Data from 1 April 2004 to 31 March 2022 were obtained from the Manitoba Population Research Data Repository. MS case status was ascertained from homecare records and linked to health data. Trend-based case definitions were constructed using multivariate generalised linear mixed models applied to annual numbers of general and specialist physician visits, hospitalisations and MS healthcare contacts or medication dispensations. Dynamic classification, which ascertains cases and non-cases annually, was used to estimate mean classification time. Classification accuracy performance measures, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), proportion correctly classified (PCC) and F1-scores, were compared for trend-based case definitions and a deterministic case definition of 3+MS healthcare contacts or medication dispensations.
When applied to the full study period, classification accuracy performance measure estimates for all case definitions exceeded 0.90, except sensitivity and PPV for the trend-based dynamic case definition (0.88, 0.64, respectively). PCC was high for all case definitions (0.94-0.99); F1-scores were lower for the trend-based case definitions compared with the deterministic case definition (0.74-0.93 vs 0.96). Dynamic classification identified 5 years as the average trend needed. When applied to the average trend windows, accuracy estimates for trend-based case definitions were lower than the estimates from the full study period (sensitivity: 0.77-0.89; specificity: 0.90-0.97; PPV: 0.54-0.81; NPV: 0.97-0.99; F1-score: 0.64-0.84). Accuracy estimates for the deterministic case definition remained high, except sensitivity (0.42-0.80). F1-score was variable (0.59-0.89).
Trend-based and deterministic case definitions classifications were similar to a population-based clinician assessment reference standard for multiple measures of classification accuracy. However, accuracy estimates for both trend-based and deterministic case definitions varied as the years of data used for classification were reduced. Dynamic classification appears to be a viable option for identifying the average trend needed for trend-based case definitions.
本研究旨在（1）建立和验证基于趋势的多发性硬化症（MS）病例定义模型（即趋势病例定义），（2）应用动态分类来确定分类所需的平均数据年限（即平均趋势）。
回顾性队列研究设计。
608 例 MS 病例和 59620 例 MS 非病例。
2004 年 4 月 1 日至 2022 年 3 月 31 日的数据来自马尼托巴人口研究数据存储库。通过家庭护理记录确定 MS 病例状态，并与健康数据相关联。使用多变量广义线性混合模型对年度普通和专科医生就诊次数、住院次数、MS 保健接触或药物配给次数进行趋势病例定义。动态分类每年确定病例和非病例，用于估计平均分类时间。对趋势病例定义和 3+MS 保健接触或药物配给的确定性病例定义进行比较，评估分类准确性性能指标，包括敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、正确分类比例（PCC）和 F1 评分。
当应用于整个研究期间时，所有病例定义的分类准确性性能指标估计值均超过 0.90，除了趋势动态病例定义的敏感性和 PPV（分别为 0.88、0.64）。所有病例定义的 PCC 均较高（0.94-0.99）；与确定性病例定义相比，趋势病例定义的 F1 评分较低（0.74-0.93 与 0.96）。动态分类确定 5 年为平均趋势所需。当应用于平均趋势窗口时，趋势病例定义的准确性估计值低于整个研究期间的估计值（敏感性：0.77-0.89；特异性：0.90-0.97；PPV：0.54-0.81；NPV：0.97-0.99；F1 评分：0.64-0.84）。确定性病例定义的准确性估计值仍然很高，除了敏感性（0.42-0.80）。F1 评分变化（0.59-0.89）。
基于趋势和确定性病例定义的分类与基于人群的临床医生评估参考标准相似，适用于多种分类准确性测量。然而，随着用于分类的年限减少，趋势和确定性病例定义的准确性估计值均有所变化。动态分类似乎是确定趋势病例定义平均趋势所需的可行选择。
