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Item does not contain fulltext, BACKGROUND: Adenomyosis is a benign gynecologic condition arising from the uterine junctional zone. Recent studies suggest a relationship between adenomyosis and adverse obstetrical outcomes, but evidence remains conflicting. There is no large-scale study investigating obstetrical outcomes in women with adenomyosis using the gold standard of histopathologic diagnosis. OBJECTIVE: This study aimed to investigate the prevalence of adverse obstetrical and neonatal outcomes in women with histopathologic adenomyosis and that of the general (Dutch) population. STUDY DESIGN: This retrospective population-based study used 2 Dutch national databases (Perined, the perinatal registry, and the nationwide pathology databank [Pathologisch Anatomisch Landelijk Geautomiseerd Archief], from 1995 to 2018) to compare obstetrical outcomes in women before histopathologic adenomyosis diagnosis to the general Dutch population without registered histopathologic adenomyosis. The adjusted odds ratios (95% confidence interval) were calculated for adverse obstetrical outcomes. The outcomes were adjusted for maternal age, parity, ethnicity, year of registered birth, induction of labor, hypertensive disorders in previous pregnancies, multiple gestation, and low socioeconomic status. RESULTS: The pregnancy outcomes of 7925 women with histopathologic adenomyosis were compared with that of 4,615,803 women without registered adenomyosis. When adjusted for confounders, women with adenomyosis had adjusted odds ratios of 1.37 (95% confidence interval, 1.25-1.50) for hypertensive disorders, 1.37 (95% confidence interval, 1.25-1.51) for preeclampsia, 1.15 (95% confidence interval, 1.07-1.25) for small-for-gestational-age infants, 1.54 (95% confidence interval, 1.41-1.68) for emergency cesarean delivery, 1.24 (95% confidence interval, 1.12-1.37) for failure to progress, 1.29 (95% confidence interval, 1.10-1.48) for placental retention, and 1.23 (95% confidence interval, 1.10-1.38) for postpartum hemorrhage

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.

Objective. Appropriate adaptation of the maternal autonomic nervous system to progressing gestation is essential to a healthy pregnancy. This is partly evidenced by the association between pregnancy complications and autonomic dysfunction. Therefore, assessing maternal heart rate variability (HRV)—a proxy measure for autonomic activity—may offer insights into maternal health, potentially enabling the early detection of complications. However, identifying abnormal maternal HRV requires a thorough understanding of normal maternal HRV. While HRV in women of childbearing age has been extensively investigated, less is known concerning HRV during pregnancy. Subsequently, we investigate the differences in HRV between healthy pregnant women and their non-pregnant counterparts. Approach. We use a comprehensive suite of HRV features (assessing sympathetic and parasympathetic activity, heart rate (HR) complexity, HR fragmentation, and autonomic responsiveness) to quantify HRV in large groups of healthy pregnant (n = 258) and non-pregnant women (n = 252). We compare the statistical significance and effect size of the potential differences between the groups. Main results. We find significantly increased sympathetic and decreased parasympathetic activity during healthy pregnancy, along with significantly attenuated autonomic responsiveness, which we hypothesize serves as a protective mechanism against sympathetic overactivity. HRV differences between these groups typically had a large effect size (Cohen’s d > 0.8), with the largest effect accompanying the significantly reduced HR complexity and altered sympathovagal balance observed in pregnancy (Cohen’s d > 1.2). Significance. Healthy pregnant women are autonomically distinct from their non-pregnant counterparts. Subsequently, assumptions based on HRV research in non-pregnant women cannot be readily translated to pregnant women.

There is a clinical need for monitoring inspiratory effort to prevent lung- and diaphragm injury in patients who receive supportive mechanical ventilation in an Intensive Care Unit. Different pressure-based techniques are available to estimate this inspiratory effort at the bedside, but the accuracy of their effort estimation is uncertain since they are all based on a simplified linear model of the respiratory system, which omits gas compressibility of air, and the viscoelasticity and nonlinearities of the respiratory system. The aim of this in-silico study was to provide an overview of the pressure-based estimation techniques and to evaluate their accuracy using a more sophisticated model of the respiratory system and ventilator. The influence of the following parameters on the accuracy of the pressure-based estimation techniques was evaluated using the in-silico model: 1) the patient's respiratory mechanics 2) PEEP and the inspiratory pressure of the ventilator 3) gas compressibility of air 4) viscoelasticity of the respiratory system 5) the strength of the inspiratory effort. The best-performing technique in terms of accuracy was the whole breath occlusion. The average error and maximum error were the lowest for all patient archetypes. We found that the error was related to the expansion of gas in the breathing set and lungs and respiratory compliance. However, concerns exist that other factors not included in the model, such as a changed muscle-force relation during an occlusion, might influence the true accuracy. The estimation techniques based on the esophageal pressure showed an error related to the viscoelastic element in the model which leads to a higher error than the occlusion. The error of the esophageal pressure-based techniques is therefore highly dependent on the pathology of the patient and the settings of the ventilator and might change over time while a patient recovers or becomes more ill.