Effectiveness of routine third trimester ultrasonography to reduce adverse perinatal outcomes in low risk pregnancy (the IRIS study): nationwide, pragmatic, multicentre, stepped wedge cluster randomised trial

Objectives To investigate the effectiveness of routine ultrasonography in the third trimester in reducing adverse perinatal outcomes in low risk pregnancies compared with usual care and the effect of this policy on maternal outcomes and obstetric interventions. Design Pragmatic, multicentre, stepped wedge cluster randomised trial. Setting 60 midwifery practices in the Netherlands. Participants 13 046 women aged 16 years or older with a low risk singleton pregnancy. Interventions 60 midwifery practices offered usual care (serial fundal height measurements with clinically indicated ultrasonography). After 3, 7, and 10 months, a third of the practices were randomised to the intervention strategy. As well as receiving usual care, women in the intervention strategy were offered two routine biometry scans at 28-30 and 34-36 weeks’ gestation. The same multidisciplinary protocol for detecting and managing fetal growth restriction was used in both strategies. Main outcome measures The primary outcome measure was a composite of severe adverse perinatal outcomes: perinatal death, Apgar score <4, impaired consciousness, asphyxia, seizures, assisted ventilation, septicaemia, meningitis, bronchopulmonary dysplasia, intraventricular haemorrhage, periventricular leucomalacia, or necrotising enterocolitis. Secondary outcomes were two composite measures of severe maternal morbidity, and spontaneous labour and birth. Results Between 1 February 2015 and 29 February 2016, 60 midwifery practices enrolled 13 520 women in mid-pregnancy (mean 22.8 (SD 2.4) weeks’ gestation). 13 046 women (intervention n=7067, usual care n=5979) with data based on the national Dutch perinatal registry or hospital records were included in the analyses. Small for gestational age at birth was significantly more often detected in the intervention group than in the usual care group (179 of 556 (32%) v 78 of 407 (19%), P<0.001). The incidence of severe adverse perinatal outcomes was 1.7% (n=118) for the intervention strategy and 1.8% (n=106) for usual care. After adjustment for confounders, the difference between the groups was not significant (odds ratio 0.88, 95% confidence interval 0.70 to 1.20). The intervention strategy showed a higher incidence of induction of labour (1.16, 1.04 to 1.30) and a lower incidence of augmentation of labour (0.78, 0.71 to 0.85). Maternal outcomes and other obstetric interventions did not differ between the strategies. Conclusion In low risk pregnancies, routine ultrasonography in the third trimester along with clinically indicated ultrasonography was associated with higher antenatal detection of small for gestational age fetuses but not with a reduced incidence of severe adverse perinatal outcomes compared with usual care alone. The findings do not support routine ultrasonography in the third trimester for low risk pregnancies. Trial registration Netherlands Trial Register NTR4367

Use of Saliva for Early Dengue Diagnosis

The importance of laboratory diagnosis of dengue cannot be undermined. In recent years, many dengue diagnostic tools have become available for various stages of the disease, but the one limitation is that they require blood as a specimen for testing. In many incidences, phlebotomy in needle-phobic febrile individuals, especially children, can be challenging, and the tendency to forgo a dengue blood test is high. To circumvent this, we decided to work toward a saliva-based assay (antigen-capture anti-DENV IgA ELISA, ACA-ELISA) that has the necessary sensitivity and specificity to detect dengue early. Overall sensitivity of the ACA-ELISA, when tested on saliva collected from dengue-confirmed patients (EDEN study) at three time points, was 70% in the first 3 days after fever onset and 93% between 4 to 8 days after fever onset. In patients with secondary dengue infections, salivary IgA was detected on the first day of fever onset in all the dengue confirmed patients. This demonstrates the utility of saliva in the ACA-ELISA for early dengue diagnostics. This technique is easy to perform, cost effective, and is especially useful in dengue endemic countries

External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an individual participant data meta-analysis

Objective Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. Conclusions The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.Peer reviewe

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems

Planned delivery or expectant management in preeclampsia: an individual participant data meta-analysis.

OBJECTIVE: Pregnancy hypertension is a leading cause of maternal and perinatal mortality and morbidity. Between 34+0 and 36+6 weeks gestation, it is uncertain whether planned delivery could reduce maternal complications without serious neonatal consequences. In this individual participant data meta-analysis, we aimed to compare planned delivery to expectant management, focusing specifically on women with preeclampsia. DATA SOURCES: We performed an electronic database search using a prespecified search strategy, including trials published between January 1, 2000 and December 18, 2021. We sought individual participant-level data from all eligible trials. STUDY ELIGIBILITY CRITERIA: We included women with singleton or multifetal pregnancies with preeclampsia from 34 weeks gestation onward. METHODS: The primary maternal outcome was a composite of maternal mortality or morbidity. The primary perinatal outcome was a composite of perinatal mortality or morbidity. We analyzed all the available data for each prespecified outcome on an intention-to-treat basis. For primary individual patient data analyses, we used a 1-stage fixed effects model. RESULTS: We included 1790 participants from 6 trials in our analysis. Planned delivery from 34 weeks gestation onward significantly reduced the risk of maternal morbidity (2.6% vs 4.4%; adjusted risk ratio, 0.59; 95% confidence interval, 0.36-0.98) compared with expectant management. The primary composite perinatal outcome was increased by planned delivery (20.9% vs 17.1%; adjusted risk ratio, 1.22; 95% confidence interval, 1.01-1.47), driven by short-term neonatal respiratory morbidity. However, infants in the expectant management group were more likely to be born small for gestational age (7.8% vs 10.6%; risk ratio, 0.74; 95% confidence interval, 0.55-0.99). CONCLUSION: Planned early delivery in women with late preterm preeclampsia provides clear maternal benefits and may reduce the risk of the infant being born small for gestational age, with a possible increase in short-term neonatal respiratory morbidity. The potential benefits and risks of prolonging a pregnancy complicated by preeclampsia should be discussed with women as part of a shared decision-making process