Evaluation of a quality improvement intervention for labour and birth care in Brazilian private hospitals: a protocol

© 2018 The Author(s). Background: In Brazilian private hospitals, caesarean section (CS) is almost universal (88%) and is integrated into the model of birth care. A quality improvement intervention, “Adequate Birth” (PPA), based on four driving components (governance, participation of women and families, reorganisation of care, and monitoring), has been implemented to help 23 hospitals reduce their CS rate. This is a protocol designed to evaluate the implementation of PPA and its effectiveness at reducing CS as a primary outcome of birth care. Methods: Case study of PPA intervention conducted in 2017/2018. We integrated quantitative and qualitative methods into data collection and analysis. For the quantitative stage, we selected a convenient sample of twelve hospitals. In each of these hospitals, we included 400 women. This resulted in a total sample of 4800 women. We used this sample to detect a 2.5% reduction in CS rate. We interviewed managers and puerperal women, and extracted data from hospital records. In the qualitative stage, we evaluated a subsample of eight hospitals by means of systematic observation and semi-structured interviews with managers, health professionals and women. We used specific forms for each of the four PPA driving components. Forms for managers and professionals addressed the decision-making process, implemented strategies, participatory process in strategy design, and healthcare practice. Forms for women and neonatal care addressed socio-economic, demographic and health condition; prenatal and birth care; tour of the hospital before delivery; labour expectation vs. real experience; and satisfaction with care received. We will estimate the degree of implementation of PPA strategies related to two of the four driving components: “participation of women and families” and “reorganisation of care”. We will then assess its effect on CS rate and secondary outcomes for each of the twelve selected hospitals, and for the total sample. To allow for clinical, socio-demographic and obstetric characteristics in women, we will conduct multivariate analysis. Additionally, we will evaluate the influence of internal context variables (the PPA driving components “governance” and “monitoring”) on the degree of implementation of the components “participation of women and families” and “reorganisation of care”, by means of thematic content analysis. This analysis will include both quantitative and qualitative data. Discussion: The effectiveness of quality improvement interventions that reduce CS rates requires examination. This study will identify strategies that could promote healthier births

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