Predicting prognosis in amyotrophic lateral sclerosis: a simple algorithm.
Predicting prognosis in amyotrophic lateral sclerosis: a simple algorithm.
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DOI：
10.1007/s00415-015-7731-6
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发表时间：
2015-06
影响因子：
6
通讯作者：
Hardiman O
中科院分区：
文献类型：
--
作者：
Elamin M;Bede P;Montuschi A;Pender N;Chio A;Hardiman O
该研究的目的是利用首次临床咨询时获得的信息，为肌萎缩侧索硬化症 (ALS) 患者制定并验证实用的预后指数。我们询问了两个基于人口的项目（位于爱尔兰共和国和意大利）生成的数据集。爱尔兰患者队列分为训练和测试子队列。 Kaplan-Meier 方法和 Cox 比例风险回归用于识别训练集中预后的显着预测因子。使用加权分级系统，得出了区分三个风险组的预后指数。指数的有效性在爱尔兰测试子队列中进行了测试，并在意大利复制队列中得到了外部确认。在训练子队列 (n = 117) 中，预后的重要预测因素是疾病发作部位 (HR = 1.7，p = 0.012)；首次评估之前的 ALSFRS-R 斜率（HR = 2.8，p < 0.0001）和执行功能障碍（HR = 2.11，p = 0.001）。使用这些结果生成的风险组系统预测了训练集、测试集 (n = 87) 和意大利队列 (n = 122) 中的中位生存时间，95% CI 没有重叠 (p < 0.0001)。在验证队列中，高风险分类与不良预后的阳性预测值（73.3-85.7%）相关，与良好预后的阴性预测值（NPV）相关（93.3-100%）。分类为低风险组与 100% 不良预后的 NPV 相关。一种简单的算法使用可以在第一次遇到患者时收集的变量，并在独立的患者系列中进行验证，可以可靠地预测 ALS 患者的预后。
The objective of the study was to develop and validate a practical prognostic index for patients with amyotrophic lateral scleroses (ALS) using information available at the first clinical consultation. We interrogated datasets generated from two population-based projects (based in the Republic of Ireland and Italy). The Irish patient cohort was divided into Training and Test sub-cohorts. Kaplan–Meier methods and Cox proportional hazards regression were used to identify significant predictors of prognoses in the Training set. Using a weighted grading system, a prognostic index was derived that separated three risk groups. The validity of index was tested in the Irish Test sub-cohort and externally confirmed in the Italian replication cohort. In the Training sub-cohort (n = 117), significant predictors of prognoses were site of disease onset (HR = 1.7, p = 0.012); ALSFRS-R slope prior to first evaluation (HR = 2.8, p < 0.0001), and executive dysfunction (HR = 2.11, p = 0.001). The risk group system generated using these results predicted median survival time in the Training set, the Test set (n = 87) and the Italian cohort (n = 122) with no overlap of the 95 % CI (p < 0.0001). In the validation cohorts, a high-risk classification was associated with a positive predictive value for poor prognosis of 73.3–85.7 % and a negative predictive value (NPV) for good prognosis of 93.3–100 %. Classification into the low-risk group was associated with an NPV for bad prognosis of 100 %. A simple algorithm using variables that can be gathered at first patient encounter, validated in an independent patient series, reliably predicts prognoses in ALS patients.
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影响因子：
4
作者：
Kenna KP;McLaughlin RL;Byrne S;Elamin M;Heverin M;Kenny EM;Cormican P;Morris DW;Donaghy CG;Bradley DG;Hardiman O
通讯作者：
Hardiman O
影响因子：
4.4
作者：
Cedarbaum, JM;Stambler, N;Nakanishi, A
通讯作者：
Nakanishi, A
影响因子：
11
作者：
O'Toole, O.;Traynor, B. J.;Hardiman, O.
通讯作者：
Hardiman, O.
影响因子：
3.5
作者：
LOPEZVEGA, JM;CALLEJA, J;BERCIANO, J
通讯作者：
BERCIANO, J
影响因子：
6
作者：
Czaplinski, Adam;Yen, Albert A.;Appel, Stanley H.
通讯作者：
Appel, Stanley H.