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      Platelet count and indices as postpartum hemorrhage risk factors: a retrospective cohort study

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          Abstract

          Background

          Severe postpartum hemorrhage (SPPH) is the leading cause of maternal mortality and morbidity worldwide. Platelet anomalies frequently occur during pregnancy. However, their role in the etiology of SPPH is largely unknown.

          Objective

          To study the relation between platelet parameters and SPPH.

          Methods

          This retrospective single‐center cohort included deliveries between 2009 and 2017. SPPH was defined as ≥1000 ml blood loss within 24 h after delivery. Platelet parameters were measured within 72 h before delivery. Multiple imputation was performed for missing data. Odds ratios were adjusted (aORs) for maternal age, multiple gestation, macrosomia, induction of labor, preeclampsia, and hemolysis, elevated liver enzymes, and low platelets syndrome.

          Results

          A total of 23 205 deliveries were included. Of the 2402 (10.4%) women with thrombocytopenia (<150 × 10 9/L), 10.3% developed SPPH, compared with 7.6% of women with a normal platelet count (aOR: 1.34, 95% CI: 1.14–1.59). Women with a platelet count of <50 × 10 9/L were most at risk (aOR of 2.24 [1.01–4.94]) compared with the reference group with normal platelet counts; the aOR was 1.22 (0.77–1.93) for the 50–99 × 10 9/L platelet count group and 1.31 (1.10–1.56) for the 100–149 × 10 9/L platelet count group. Plateletcrit was associated with SPPH (aOR 1.15 [1.08–1.21] per 0.05% decrease), and, although rarely present, a platelet distribution width (PDW) ≥23% ( n = 22) also increased the odds of SPPH (aOR 6.05 [2.29–16.20]).

          Conclusion

          Different degrees of thrombocytopenia were independently associated with the occurrence of SPPH. Despite their relation to SPPH, plateletcrit and a PDW of ≥23% have limited additional value in addition to platelet count.

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          Most cited references65

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          How many imputations are really needed? Some practical clarifications of multiple imputation theory.

          Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most common approaches to missing data analysis. In theory, MI and FIML are equivalent when identical models are tested using the same variables, and when m, the number of imputations performed with MI, approaches infinity. However, it is important to know how many imputations are necessary before MI and FIML are sufficiently equivalent in ways that are important to prevention scientists. MI theory suggests that small values of m, even on the order of three to five imputations, yield excellent results. Previous guidelines for sufficient m are based on relative efficiency, which involves the fraction of missing information (gamma) for the parameter being estimated, and m. In the present study, we used a Monte Carlo simulation to test MI models across several scenarios in which gamma and m were varied. Standard errors and p-values for the regression coefficient of interest varied as a function of m, but not at the same rate as relative efficiency. Most importantly, statistical power for small effect sizes diminished as m became smaller, and the rate of this power falloff was much greater than predicted by changes in relative efficiency. Based our findings, we recommend that researchers using MI should perform many more imputations than previously considered sufficient. These recommendations are based on gamma, and take into consideration one's tolerance for a preventable power falloff (compared to FIML) due to using too few imputations.
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            Review: a gentle introduction to imputation of missing values.

            In most situations, simple techniques for handling missing data (such as complete case analysis, overall mean imputation, and the missing-indicator method) produce biased results, whereas imputation techniques yield valid results without complicating the analysis once the imputations are carried out. Imputation techniques are based on the idea that any subject in a study sample can be replaced by a new randomly chosen subject from the same source population. Imputation of missing data on a variable is replacing that missing by a value that is drawn from an estimate of the distribution of this variable. In single imputation, only one estimate is used. In multiple imputation, various estimates are used, reflecting the uncertainty in the estimation of this distribution. Under the general conditions of so-called missing at random and missing completely at random, both single and multiple imputations result in unbiased estimates of study associations. But single imputation results in too small estimated standard errors, whereas multiple imputation results in correctly estimated standard errors and confidence intervals. In this article we explain why all this is the case, and use a simple simulation study to demonstrate our explanations. We also explain and illustrate why two frequently used methods to handle missing data, i.e., overall mean imputation and the missing-indicator method, almost always result in biased estimates.
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              Pregnancy-Related Mortality in the United States, 2011–2013

              To update national population-level pregnancy-related mortality estimates and examine characteristics and causes of pregnancy-related deaths in the United States during 2011-2013.
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                Author and article information

                Contributors
                w.e.m.vandijk-16@umcutrecht.nl
                Journal
                J Thromb Haemost
                J Thromb Haemost
                10.1111/(ISSN)1538-7836
                JTH
                Journal of Thrombosis and Haemostasis
                John Wiley and Sons Inc. (Hoboken )
                1538-7933
                1538-7836
                20 August 2021
                November 2021
                : 19
                : 11 ( doiID: 10.1111/jth.v19.11 )
                : 2873-2883
                Affiliations
                [ 1 ] Benign Hematology, Van Creveldkliniek University Medical Center Utrecht The Netherlands
                [ 2 ] University Medical Center Utrecht Utrecht The Netherlands
                [ 3 ] Clinical Chemistry and Hematology University Medical Center Utrecht Utrecht The Netherlands
                [ 4 ] Obstetrics and Gynecology University Medical Center Utrecht Utrecht Netherlands
                Author notes
                [*] [* ] Correspondence

                Wobke E. M. van Dijk, Van Creveldkliniek, Department of Benign Hematology, University Medical Center Utrecht, Office C.01.428, Postbox 85500, 3508 GA Utrecht, The Netherlands.

                Email: w.e.m.vandijk-16@ 123456umcutrecht.nl

                Author information
                https://orcid.org/0000-0002-0821-7875
                https://orcid.org/0000-0002-9925-5921
                https://orcid.org/0000-0001-5465-4868
                https://orcid.org/0000-0002-5764-5788
                https://orcid.org/0000-0002-2291-2487
                https://orcid.org/0000-0002-1891-5255
                https://orcid.org/0000-0002-2762-6033
                https://orcid.org/0000-0001-6356-7707
                https://orcid.org/0000-0003-3251-8595
                Article
                JTH15481
                10.1111/jth.15481
                9292153
                34339085
                4c495b51-d8fe-44a8-b8ad-c66e232e5619
                © 2021 The Authors. Journal of Thrombosis and Haemostasis published by Wiley Periodicals LLC on behalf of International Society on Thrombosis and Haemostasis.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 27 July 2021
                : 24 February 2021
                : 29 July 2021
                Page count
                Figures: 3, Tables: 3, Pages: 11, Words: 7642
                Funding
                Funded by: Universitair Medisch Centrum Utrecht , doi 10.13039/501100003761;
                Categories
                Original Article
                PLATELETS
                Original Articles
                Custom metadata
                2.0
                November 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:18.07.2022

                Hematology
                mean platelet volume,platelet count,postpartum hemorrhage,pregnancy complications,thrombocytopenia

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