REM Sleep Behavior Disorder Diagnostic Code Accuracy and Implications in the Real-World Setting.

IF 2.3 Q3 CLINICAL NEUROLOGY
Neurology. Clinical practice Pub Date : 2025-02-01 Epub Date: 2024-11-07 DOI:10.1212/CPJ.0000000000200387
Lana M Chahine, Deena Ratner, Aaron Palmquist, Gayatri Dholakia, Anne B Newman, Richard D Boyce, Caterina Rosano, Maria Brooks
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引用次数: 0

Abstract

Background and objectives: Isolated REM sleep behavior disorder (iRBD) carries increased risk of neurodegenerative parkinsonian disorder or dementia (NPD) but is difficult to accurately screen for in the community. Health care data offer the opportunity to identify large numbers of iRBD cases among outpatients. We aimed to determine the positive predictive value (PPV) of an RBD International Classification of Disorders (ICD) code for actual iRBD based on manual review of the electronic health record (EHR), examine risk of NPD diagnosis, and explore whether a statistical model developed using selected EHR data can identify individuals with the RBD ICD code who have high probability for actual iRBD.

Methods: In this retrospective cohort study, a search of the EHR at a single health care system was conducted to identify outpatients who received the ICD9 or ICD10 RBD code in 2011-2021. The EHR for each case was manually reviewed. Secondary RBD cases were excluded. Remaining cases were classified as no iRBD or actual iRBD (possible, probable, or definite). Incident cases of NPD were identified. PPV of presence of the RBD ICD code for actual iRBD was calculated. Cumulative incidence of NPD with death as a competing event was compared in those with vs without iRBD. Least absolute shrinkage and selection operator (LASSO) regression was used to build a prediction model for iRBD, and the model was validated in an independent data set.

Results: Among 1,130 cases with the RBD ICD code, 499 had secondary causes of RBD. For the remaining 628 cases, EHR review indicated no iRBD in 168 (26.8%). PPV of the RBD ICD code was 73.25%. Over a median follow-up of 4.7 years, compared with the no iRBD group, the iRBD group had a higher risk of NPD (subdistribution hazard ratio = 10.4 [95% CI 2.5-43.1]). The LASSO prediction model for iRBD had an area under the receiver operating characteristic curve of 0.844 (95% CI 0.806-0.880).

Discussion: PPV of an RBD ICD code is moderate. In the real-world setting, patients with iRBD had a high risk of incident diagnosis of NPD over 4.7 years. Results indicate feasibility of using statistical models developed using EHR data to accurately predict iRBD.

快速眼动睡眠行为障碍诊断代码的准确性及其在现实世界中的意义。
背景和目的:孤立的快速眼动睡眠行为障碍(iRBD)会增加神经退行性帕金森氏症或痴呆症(NPD)的风险,但在社区中却很难准确筛查。医疗保健数据为在门诊患者中发现大量 iRBD 病例提供了机会。我们的目的是根据对电子健康记录(EHR)的人工审核,确定 RBD 国际疾病分类(ICD)代码对实际 iRBD 的阳性预测值(PPV),检查 NPD 诊断的风险,并探讨使用选定的 EHR 数据开发的统计模型是否能识别出具有 RBD ICD 代码且实际 iRBD 可能性高的个体:在这项回顾性队列研究中,我们搜索了一个医疗保健系统的电子病历,以确定在 2011-2021 年期间获得 ICD9 或 ICD10 RBD 代码的门诊患者。每个病例的电子病历都经过人工审核。排除了二次 RBD 病例。其余病例分为无 iRBD 或实际 iRBD(可能、可能或确定)。确定了 NPD 病例。计算了存在 RBD ICD 代码的实际 iRBD 的 PPV。比较了有 iRBD 和无 iRBD 的 NPD 累积发病率(死亡为竞争事件)。采用最小绝对收缩和选择算子(LASSO)回归法建立了 iRBD 预测模型,并在一个独立的数据集中对该模型进行了验证:结果:在 1,130 个有 RBD ICD 编码的病例中,499 个病例是继发性 RBD。其余 628 个病例中,有 168 个病例(26.8%)的电子病历审查结果显示没有 iRBD。RBD ICD 编码的 PPV 为 73.25%。在中位随访 4.7 年期间,与无 iRBD 组相比,iRBD 组的 NPD 风险更高(亚分布危险比 = 10.4 [95% CI 2.5-43.1])。iRBD的LASSO预测模型的接收者操作特征曲线下面积为0.844(95% CI 0.806-0.880):讨论:RBD ICD 编码的 PPV 值适中。在现实世界中,iRBD 患者在 4.7 年内被诊断为 NPD 的风险很高。结果表明,使用电子病历数据开发的统计模型来准确预测 iRBD 是可行的。
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来源期刊
Neurology. Clinical practice
Neurology. Clinical practice CLINICAL NEUROLOGY-
CiteScore
4.00
自引率
0.00%
发文量
77
期刊介绍: Neurology® Genetics is an online open access journal publishing peer-reviewed reports in the field of neurogenetics. The journal publishes original articles in all areas of neurogenetics including rare and common genetic variations, genotype-phenotype correlations, outlier phenotypes as a result of mutations in known disease genes, and genetic variations with a putative link to diseases. Articles include studies reporting on genetic disease risk, pharmacogenomics, and results of gene-based clinical trials (viral, ASO, etc.). Genetically engineered model systems are not a primary focus of Neurology® Genetics, but studies using model systems for treatment trials, including well-powered studies reporting negative results, are welcome.
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