Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease.
Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease.
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DOI：
10.1186/s12916-018-1013-y
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发表时间：
2018-03-02
期刊：
影响因子：
9.3
通讯作者：
3CIA collaboration
中科院分区：
文献类型：
--
作者：
Guerra B;Haile SR;Lamprecht B;Ramírez AS;Martinez-Camblor P;Kaiser B;Alfageme I;Almagro P;Casanova C;Esteban-González C;Soler-Cataluña JJ;de-Torres JP;Miravitlles M;Celli BR;Marin JM;Ter Riet G;Sobradillo P;Lange P;Garcia-Aymerich J;Antó JM;Turner AM;Han MK;Langhammer A;Leivseth L;Bakke P;Johannessen A;Oga T;Cosio B;Ancochea-Bermúdez J;Echazarreta A;Roche N;Burgel PR;Sin DD;Soriano JB;Puhan MA;3CIA collaboration
预后模型或评分的外部验证和比较是其在常规临床护理中使用的先决条件，但在包括慢性阻塞性肺疾病（COPD）在内的大多数医学领域缺乏。我们的目的是外部验证并同时比较多病COPD患者3年全因死亡率的预后评分。我们依赖于24项COPD队列合作国际评估联盟的队列研究，对应于欧洲、美洲和日本的初级、二级和三级护理。这些研究包括全球15762例COPD患者（1871例死亡，42203人年随访）。我们使用了适用于多重评分比较（MSC）的网络元分析，遵循频率主义两阶段方法；因此，我们能够在一个单一的分析框架中比较所有分数，考虑到队列内分数之间的相关性。我们评估了传递性、异质性和不一致性，并提供了预后评分的表现排名。根据数据的可用性，可以为每个队列计算2到9个预后评分。各队列的BODE评分（身体质量指数、气流阻塞、呼吸困难和运动能力）的曲线下面积（AUC）中位数为0.679[第1四分位数-第3四分位数= 0.655-0.733]。ADO评分（年龄、呼吸困难和气流阻塞）在预测死亡率方面表现最佳(差异AUCADO - AUCBODE = 0.015[95%可信区间(CI) = - 0.002 ~ 0.032]；p = 0.08)，其次是更新的BODE （AUCBODE更新- AUCBODE = 0.008 [95% CI = - 0.005至+0.022];p = 0.23）。并没有违背及物性的假设。直接比较的异质性很小，我们没有发现任何局部或全局的不一致。我们的分析显示，在COPD患者中，ADO评分和更新的BODE评分具有最佳的歧视性。在未来的研究中需要解决的一个限制是将MSC网络元分析扩展到校准措施。MSC网络荟萃分析可以应用于任何医学领域的预后评分，以确定最佳评分，可能为分层医学、公共卫生和研究铺平道路。本文的在线版本（10.1186/s12916-018-1013-y）包含补充材料，授权用户可以使用。
External validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD. We relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores. Depending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile–3rd quartile = 0.655–0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUCADO – AUCBODE = 0.015 [95% confidence interval (CI) = −0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUCBODE updated – AUCBODE = 0.008 [95% CI = −0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency. Our analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research. The online version of this article (10.1186/s12916-018-1013-y) contains supplementary material, which is available to authorized users.
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DOI：
10.1183/13993003.01485-2015
发表时间：
2016-06
期刊：
The European respiratory journal
影响因子：
--
作者：
Boeck L;Soriano JB;Brusse-Keizer M;Blasi F;Kostikas K;Boersma W;Milenkovic B;Louis R;Lacoma A;Djamin R;Aerts J;Torres A;Rohde G;Welte T;Martinez-Camblor P;Rakic J;Scherr A;Koller M;van der Palen J;Marin JM;Alfageme I;Almagro P;Casanova C;Esteban C;Soler-Cataluña JJ;de-Torres JP;Miravitlles M;Celli BR;Tamm M;Stolz D
通讯作者：
Stolz D
影响因子：
9.3
作者：
Hayes, Daniel F.;Markus, Hugh S.;Topol, Eric J.
通讯作者：
Topol, Eric J.
影响因子：
9.8
作者：
Franchini, A. J.;Dias, S.;Welton, N. J.
通讯作者：
Welton, N. J.
影响因子：
13.3
作者：
Esteban, C.;Quintana, J. M.;Capelastegui, A.
通讯作者：
Capelastegui, A.
DOI：
10.1016/0197-2456(86)90046-2
发表时间：
1986-09-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
DERSIMONIAN, R;LAIRD, N
通讯作者：
LAIRD, N