Mehta Paul, Raymond Jaime, Han Moon, Punjani Reshma, Larson Theodore, Berry James D, Brooks Benjamin Rix, Oskarsson Björn, Goutman Stephen A, Horton Kevin
Agency for Toxic Substances and Disease Registry/Centers for Disease Control and Prevention, Atlanta, GA, USA.
Sean M Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA.
Amyotroph Lateral Scler Frontotemporal Degener. 2023 May;24(3-4):230-236. doi: 10.1080/21678421.2022.2121168. Epub 2022 Oct 4.
To evaluate the impact of 1) updating the existing algorithm to improve case-finding sensitivity and 2) reclassifying the Registry's diagnostic status nomenclature into four new categories ("confirmed ALS," "likely ALS," "undetermined ALS," or "not ALS") versus the current three ("definite ALS," "possible ALS," or "not ALS") to be more inclusive and descriptive of cases and individuals.
A retrospective analysis of Registry data from 2011-2017 was conducted to follow "possible ALS" individuals over time to determine what qualifier caused them to convert, if at all and when, to Registry-eligible cases (i.e. "confirmed ALS" or "likely ALS").
In 2011, 720 individuals were classified by the Registry algorithm as having "possible ALS". By 2017, 42% of these had converted to Registry-eligible ALS cases. Approximately 14% of those who were identified solely based on an ALS prescription drug never converted to Registry-eligible cases. This analysis indicates that "possible ALS" individuals with a single prescription for an ALS drug should be converted to Registry-eligible cases which would add between 300-500 cases per year on average.
The Registry's existing algorithm likely results in the under-ascertainment of ALS cases. However, updating the algorithm with the inclusion of patients having been prescribed ALS-specific drugs, even with a single prescription, leads to improved epidemiologic estimates of ALS in the US. This and future algorithmic updates will help the Registry more accurately depict the true disease burden of ALS in the US.
评估以下两方面的影响：1）更新现有算法以提高病例发现的敏感性；2）将登记处的诊断状态命名法重新分类为四个新类别（“确诊肌萎缩侧索硬化症”“可能是肌萎缩侧索硬化症”“未确定是否为肌萎缩侧索硬化症”或“不是肌萎缩侧索硬化症”），相较于当前的三个类别（“确诊肌萎缩侧索硬化症”“可能是肌萎缩侧索硬化症”或“不是肌萎缩侧索硬化症”），以便更具包容性并能更准确地描述病例和个体情况。
对2011年至2017年登记处的数据进行回顾性分析，以追踪“可能是肌萎缩侧索硬化症”的个体随时间的变化情况，确定是何种限定因素导致他们（如果有且何时）转变为符合登记处标准的病例（即“确诊肌萎缩侧索硬化症”或“可能是肌萎缩侧索硬化症”）。
2011年，登记处算法将720名个体归类为“可能是肌萎缩侧索硬化症”。到2017年，其中42%已转变为符合登记处标准的肌萎缩侧索硬化症病例。仅基于一种治疗肌萎缩侧索硬化症的处方药被识别出的个体中，约14%从未转变为符合登记处标准的病例。该分析表明，仅有一种肌萎缩侧索硬化症药物处方的“可能是肌萎缩侧索硬化症”个体应转变为符合登记处标准的病例，这平均每年将增加300 - 500例病例。
登记处现有的算法可能导致肌萎缩侧索硬化症病例的漏报。然而，通过纳入开具过肌萎缩侧索硬化症特异性药物处方的患者（即使只有一张处方）来更新算法，会使美国肌萎缩侧索硬化症的流行病学估计更准确。此次及未来的算法更新将有助于登记处更准确地描绘美国肌萎缩侧索硬化症的真实疾病负担。
