目的:本研究旨在(1)针对使用趋势(即,基于趋势的案例定义)和(2)应用动态分类来识别分类所需的平均数据年数(即,所需的平均趋势)。
方法:回顾性队列研究设计。
方法:608例MS和59620例非MS。
方法:2004年4月1日至2022年3月31日的数据来自马尼托巴省人口研究数据存储库。根据家庭护理记录确定MS病例状态,并与健康数据相关联。基于趋势的病例定义是使用多变量广义线性混合模型构建的,该模型适用于每年的普通和专科医师就诊次数。住院治疗和MS医疗保健联系人或药物分配。动态分类,每年确定案件和非案件,用于估计平均分类时间。分类准确性性能指标,包括灵敏度,特异性,阳性预测值(PPV),负预测值(NPV),正确分类的比例(PCC)和F1分数,比较了基于趋势的病例定义和3+MS医疗保健联系人或药物分配的确定性病例定义。
结果:当应用于整个研究期间时,除基于趋势的动态病例定义的敏感度和PPV(分别为0.88,0.64)外,所有病例定义的分类准确度性能测量估计值均超过0.90.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,基于趋势的病例定义的F1得分较低(0.74-0.93vs0.96)。动态分类确定5年为所需的平均趋势。当应用于平均趋势窗口时,基于趋势的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仍然很高,除了灵敏度(0.42-0.80)。F1评分是可变的(0.59-0.89)。
结论: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的多种分类准确性指标相似。然而,基于趋势和确定性病例定义的准确性估计随着用于分类的数据使用年限的减少而变化.动态分类似乎是确定基于趋势的案例定义所需的平均趋势的可行选择。
OBJECTIVE: 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).
METHODS: Retrospective cohort study design.
METHODS: 608 MS cases and 59 620 MS non-cases.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.