Background: The purpose of this meta-analysis was to examine the prevalence of hypertensive disorders of pregnancy (HDPs), particularly preeclampsia (PE), in peripartum cardiomyopathy (PPCM)-affected pregnancies, and to evaluate whether a HDP significantly alters the prognosis of PPCM, with specific reference to the recovery of left ventricular function (LVEF) and mortality. Methods: A total of 5468 potentially eligible studies were identified, and 104 were included in the meta-analysis. For pooling proportions, the inverse variance methods with logit transformation were used. Complete recovery of LVEF (>50%) and mortality were expressed by odds ratios (ORs), with 95% confidence intervals (CIs). The Peto OR (POR) was used in cases of rare events. Baseline LV function and baseline LV end-diastolic diameter (LVEDD) were summarized by the mean difference (MD) and 95% confidence interval (CI). Results: The summary estimate of the prevalence of HDPs and PE in women with PPCM was 36% and 25%, respectively. Patients with HDPs and, more specifically, PE with PPCM had a higher chance of complete recovery (OR = 1.87; 95%CI = 1.64 to 2.13; p < 0.001 and OR = 1.98; 95%CI 1.69 to 2.32; p < 0.001, respectively), a higher baseline LVEF (MD, 1.42; 95% CI 0.16 to 2.67; p = 0.03 and MD, 1.69; 95% CI 0.21 to 3.18; p = 0.03, respectively), and a smaller baseline LVEDD (MD, −1.31; 95% CI −2.50 to −0.13; p = 0.03 and MD, −2.63; 95% CI −3.75 to −1.51; p < 0.001, respectively). These results, however, did not translate into a significant difference in 12-month mortality (POR = 0.80; 95% CI = 0.57 to 1.13; p = 0.21 and POR = 1.56; 95% CI 0.90 to 2.73; p = 0.12, respectively). Conclusions: The findings of this study may contribute to evidence that can be utilized to aid in the risk stratification of patients with PPCM regarding their long-term prognoses.
Reports highlighting the problems with the standard practice of using bar graphs to show continuous data have prompted many journals to adopt new visualization policies. These policies encourage authors to avoid bar graphs and use graphics that show the data distribution; however, they provide little guidance on how to effectively display data. We conducted a systematic review of studies published in top peripheral vascular disease journals to determine what types of figures are used, and to assess the prevalence of suboptimal data visualization practices. Among papers with data figures, 47.7% of papers used bar graphs to present continuous data. This primer provides a detailed overview of strategies for addressing this issue by (1) outlining strategies for selecting the correct type of figure depending on the study design, sample size, and the type of variable; (2) examining techniques for making effective dot plots, box plots, and violin plots; and (3) illustrating how to avoid sending mixed messages by aligning the figure structure with the study design and statistical analysis. We also present solutions to other common problems identified in the systematic review. Resources include a list of free tools and templates that authors can use to create more informative figures and an online simulator that illustrates why summary statistics are meaningful only when there are enough data to summarize. Last, we consider steps that investigators can take to improve figures in the scientific literature.