@article {6612, title = {Inflammatory markers and extent and progression of early atherosclerosis: Meta-analysis of individual-participant-data from 20 prospective studies of the PROG-IMT collaboration.}, journal = {Eur J Prev Cardiol}, volume = {23}, year = {2016}, month = {2016 Jan}, pages = {194-205}, abstract = {

BACKGROUND: Large-scale epidemiological evidence on the role of inflammation in early atherosclerosis, assessed by carotid ultrasound, is lacking. We aimed to quantify cross-sectional and longitudinal associations of inflammatory markers with common-carotid-artery intima-media thickness (CCA-IMT) in the general population.

METHODS: Information on high-sensitivity C-reactive protein, fibrinogen, leucocyte count and CCA-IMT was available in 20 prospective cohort studies of the PROG-IMT collaboration involving 49,097 participants free of pre-existing cardiovascular disease. Estimates of associations were calculated within each study and then combined using random-effects meta-analyses.

RESULTS: Mean baseline CCA-IMT amounted to 0.74 mm (SD = 0.18) and mean CCA-IMT progression over a mean of 3.9 years to 0.011 mm/year (SD = 0.039). Cross-sectional analyses showed positive linear associations between inflammatory markers and baseline CCA-IMT. After adjustment for traditional cardiovascular risk factors, mean differences in baseline CCA-IMT per one-SD higher inflammatory marker were: 0.0082 mm for high-sensitivity C-reactive protein (p < 0.001); 0.0072 mm for fibrinogen (p < 0.001); and 0.0025 mm for leucocyte count (p = 0.033). {\textquoteright}Inflammatory load{\textquoteright}, defined as the number of elevated inflammatory markers (i.e. in upper two quintiles), showed a positive linear association with baseline CCA-IMT (p < 0.001). Longitudinal associations of baseline inflammatory markers and changes therein with CCA-IMT progression were null or at most weak. Participants with the highest {\textquoteright}inflammatory load{\textquoteright} had a greater CCA-IMT progression (p = 0.015).

CONCLUSION: Inflammation was independently associated with CCA-IMT cross-sectionally. The lack of clear associations with CCA-IMT progression may be explained by imprecision in its assessment within a limited time period. Our findings for {\textquoteright}inflammatory load{\textquoteright} suggest important combined effects of the three inflammatory markers on early atherosclerosis.

}, issn = {2047-4881}, doi = {10.1177/2047487314560664}, author = {Willeit, Peter and Thompson, Simon G and Agewall, Stefan and Bergstr{\"o}m, G{\"o}ran and Bickel, Horst and Catapano, Alberico L and Chien, Kuo-Liong and de Groot, Eric and Empana, Jean-Philippe and Etgen, Thorleif and Franco, Oscar H and Iglseder, Bernhard and Johnsen, Stein H and Kavousi, Maryam and Lind, Lars and Liu, Jing and Mathiesen, Ellisiv B and Norata, Giuseppe D and Olsen, Michael H and Papagianni, Aikaterini and Poppert, Holger and Price, Jackie F and Sacco, Ralph L and Yanez, David N and Zhao, Dong and Schminke, Ulf and B{\"u}lb{\"u}l, Alpaslan and Polak, Joseph F and Sitzer, Matthias and Hofman, Albert and Grigore, Liliana and D{\"o}rr, Marcus and Su, Ta-Chen and Ducimetiere, Pierre and Xie, Wuxiang and Ronkainen, Kimmo and Kiechl, Stefan and Rundek, Tatjana and Robertson, Christine and Fagerberg, Bj{\"o}rn and Bokemark, Lena and Steinmetz, Helmuth and Ikram, M Arfan and V{\"o}lzke, Henry and Lin, Hung-Ju and Plichart, Matthieu and Tuomainen, Tomi-Pekka and Desvarieux, Mo{\"\i}se and McLachlan, Stela and Schmidt, Caroline and Kauhanen, Jussi and Willeit, Johann and Lorenz, Matthias W and Sander, Dirk} } @article {7574, title = {Automatic identification of variables in epidemiological datasets using logic regression.}, journal = {BMC Med Inform Decis Mak}, volume = {17}, year = {2017}, month = {2017 Apr 13}, pages = {40}, abstract = {

BACKGROUND: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable.

METHODS: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated.

RESULTS: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34\%, and a negative predictive value (NPV) of 95\%. In the validation sample, PPV was 33\%, whereas NPV remained at 94\%. In the construction sample, PPV was 50\% or less in 63\% of all variables, in the validation sample in 71\% of all variables.

CONCLUSIONS: We demonstrated that~the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.

}, issn = {1472-6947}, doi = {10.1186/s12911-017-0429-1}, author = {Lorenz, Matthias W and Abdi, Negin Ashtiani and Scheckenbach, Frank and Pflug, Anja and B{\"u}lb{\"u}l, Alpaslan and Catapano, Alberico L and Agewall, Stefan and Ezhov, Marat and Bots, Michiel L and Kiechl, Stefan and Orth, Andreas} }