2016-12-23T18:37:48

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Venous thromboembolism (VTE) occurs infrequently during pregnancy, and issues concerning its natural history, prevention and therapy remain unresolved. RIETE is an ongoing registry of consecutive patients with objectively confirmed, symptomatic acute VTE. In this analysis, we compared the clinical characteristics and outcome for all enrolled pregnant and postpartum women with acute VTE, and all non-pregnant women in the same age range. Up to May 2005, 11,630 patients were enrolled in RIETE, of whom 848 (7.3%) were women aged <47 years. Of them, 72 (8.5%) were pregnant, 64 (7.5%) postpartum. Pregnant women presented less often with symptomatic pulmonary embolism (11%) than non-pregnant women (39%). VTE developed during the first trimester in 29 (40%) pregnant patients; in the second in 13; in the third in 30. Thrombophilia tests were more often positive in women who had VTE during the first trimester (odds ratio [OR]: 4.4; 95% CI: 0.9-2.4; p=0.037). Most patients in all three groups were initially treated with low-molecular-weight heparin (LMWH). As for long-term therapy, 75% of pregnant women received LMWH until delivery. There were no maternal deaths, and no pregnant patient had recurrence or bled before delivery. However, after delivery one patient (1.4%) developed recurrent thrombosis, four (5.6%) had major bleeding. In conclusion, VTE developed during the first trimester in 40% of the pregnant women, thus suggesting that thromboprophylaxis, when indicated during pregnancy, should start in the first trimester. No patient showed recurrence or bled before delivery, but after delivery the risk of bleeding exceeded the risk of recurrences.

Diffuse reflectance spectroscopy (DRS) is emerging as a rapid and cost-effective alternative to routine laboratory analysis for many soil properties. However, it has primarily been applied in project-specific contexts. Here, we provide an assessment of DRS spectroscopy at the scale of the continental United States by utilizing the large (n > 50,000) USDA National Soil Survey Center mid-infrared spectral library and associated soil characterization database. We tested and optimized several advanced statistical approaches for providing routine predictions of numerous soil properties relevant to studying carbon cycling. On independent validation sets, the machine learning algorithms Cubist and memory-based learner (MBL) both outperformed random forest (RF) and partial least squares regressions (PLSR) and produced excellent overall models with a mean R2 of 0.92 (mean ratio of performance to deviation = 6.5) across all 10 soil properties. We found that the use of root-mean-square error (RMSE) was misleading for understanding the actual uncertainty about any particular prediction; therefore, we developed routines to assess the prediction uncertainty for all models except Cubist. The MBL models produced much more precise predictions compared with global PLSR and RF. Finally, we present several techniques that can be used to flag predictions of new samples that may not be reliable because their spectra fall outside of the calibration set.

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