Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Implementation of the external cephalic version in breech delivery. Dutch national implementation study of external cephalic version

<p>Abstract</p> <p>Background</p> <p>Breech presentation occurs in 3 to 4% of all term pregnancies. External cephalic version (ECV) is proven effective to prevent vaginal breech deliveries and therefore it is recommended by clinical guidelines of the Royal Dutch Organisation for Midwives (KNOV) and the Dutch Society for Obstetrics and Gynaecology (NVOG). Implementation of ECV does not exceed 50 to 60% and probably less.</p> <p>We aim to improve the implementation of ECV to decrease maternal and neonatal morbidity and mortality due to breech presentations. This will be done by defining barriers and facilitators of implementation of ECV in the Netherlands. An innovative implementation strategy will be developed based on improved patient counselling and thorough instructions of health care providers for counselling.</p> <p>Method/design</p> <p>The ultimate purpose of this implementation study is to improve counselling of pregnant women and information of clinicians to realize a better implementation of ECV.</p> <p>The first phase of the project is to detect the barriers and facilitators of ECV. The next step is to develop an implementation strategy to inform and counsel pregnant women with a breech presentation, and to inform and educate care providers. In the third phase, the effectiveness of the developed implementation strategy will be evaluated in a randomised trial. The study population is a random selection of midwives and gynaecologists from 60 to 100 hospitals and practices. Primary endpoints are number of counselled women. Secondary endpoints are process indicators, the amount of fetes in cephalic presentation at birth, complications due to ECV, the number of caesarean sections and perinatal condition of mother and child. Cost effectiveness of the implementation strategy will be measured.</p> <p>Discussion</p> <p>This study will provide evidence for the cost effectiveness of a structural implementation of external cephalic versions to reduce the number of breech presentations at term.</p> <p>Trial Registration</p> <p>Dutch Trial Register (NTR): 1878</p

Decision aids for respite service choices by carers of people with dementia: development and pilot RCT

<p>Abstract</p> <p>Background</p> <p>Decision aids are often used to assist individuals confronted with a diagnosis of a serious illness to make decisions about treatment options. However, they are rarely utilised to help those with chronic or age related conditions to make decisions about care services. Decision aids should also be useful for carers of people with decreased decisional capacity. These carers' choices must balance health outcomes for themselves and for salient others with relational and value-based concerns, while relying on information from health professionals. This paper reports on a study that both developed and pilot tested a decision aid aimed at assisting carers to make evaluative judgements of community services, particularly respite care.</p> <p>Methods</p> <p>A mixed method sequential study, involving qualitative development and a pilot randomised controlled trial, was conducted in Tasmania, Australia. We undertook 13 semi-structured interviews and three focus groups to inform the development of the decision aid. For the randomised control trial we randomly assigned 31 carers of people with dementia to either receive the service decision aid at the start or end of the study. The primary outcome was measured by comparing the difference in carer burden between the two groups three months after the intervention group received the decision aid. Pilot data was collected from carers using interviewer-administered questionnaires at the commencement of the project, two weeks and 12 weeks later.</p> <p>Results</p> <p>The qualitative data strongly suggest that the intervention provides carers with needed decision support. Most carers felt that the decision aid was useful. The trial data demonstrated that, using the mean change between baseline and three month follow-up, the intervention group had less increase in burden, a decrease in decisional conflict and increased knowledge compared to control group participants.</p> <p>Conclusions</p> <p>While these results must be interpreted with caution due to the small sample size, all intervention results trend in a direction that is beneficial for carers and their decisional ability. Mixed method data suggest the decision aid provides decisional support that carers do not otherwise receive. Decision aids may prove useful in a community health services context.</p> <p>Trial registration number</p> <p>ISRCTN: <a href="http://www.controlled-trials.com/ISRCTN32163031">ISRCTN32163031</a></p

Pre-notification letter type and response rate to a postal survey among women who have recently given birth

Background: Surveys are commonly used in health research to assess patient satisfaction with hospital care. Achieving an adequate response rate, in the face of declining trends over time, threatens the quality and reliability of survey results. This paper reports on a postal satisfaction survey conducted with women who had recently given birth, and explores the effect of two strategies on response rates. Methods: A sample of 2048 Australian women who had recently given birth were invited to participate in a postal survey about their recent experiences with maternity care. The study design included two different strategies intended to increase response rates: a randomised controlled trial testing two types of pre-notification letter (with or without the option of opting out of the survey), and a request for consent to link survey data with existing routinely collected health data (omitting the latter data items from the survey reduced survey length and participant burden). Results: The survey had an overall response rate of 46%. Women receiving the pre-notification letter with the option of opting out of the survey were more likely to actively decline to participate than women receiving the letter without this option, although the overall numbers of women were small (27 versus 12). Letter type was not significantly associated with the return of a completed survey. Among women who completed the survey, 97% gave consent to link their survey data with existing health data. Conclusions: Seeking consent for record linkage was highly acceptable to women who completed the survey, and represents an important strategy to add to the arsenal for designing and implementing effective surveys. In addition to aspects of survey design, future research should explore how to more effectively influence personal constructs that contribute to the decision to participate in surveys.NHMR

Pain acceptance and personal control in pain relief in two maternity care models: a cross-national comparison of Belgium and the Netherlands

<p>Abstract</p> <p>Background</p> <p>A cross-national comparison of Belgian and Dutch childbearing women allows us to gain insight into the relative importance of pain acceptance and personal control in pain relief in 2 maternity care models. Although Belgium and the Netherlands are neighbouring countries sharing the same language, political system and geography, they are characterised by a different organisation of health care, particularly in maternity care. In Belgium the medical risks of childbirth are emphasised but neutralised by a strong belief in the merits of the medical model. Labour pain is perceived as a needless inconvenience easily resolved by means of pain medication. In the Netherlands the midwifery model of care defines childbirth as a normal physiological process and family event. Labour pain is perceived as an ally in the birth process.</p> <p>Methods</p> <p>Women were invited to participate in the study by independent midwives and obstetricians during antenatal visits in 2004-2005. Two questionnaires were filled out by 611 women, one at 30 weeks of pregnancy and one within the first 2 weeks after childbirth either at home or in a hospital. However, only women having a hospital birth without obstetric intervention (N = 327) were included in this analysis. A logistic regression analysis has been performed.</p> <p>Results</p> <p>Labour pain acceptance and personal control in pain relief render pain medication use during labour less likely, especially if they occur together. Apart from this general result, we also find large country differences. Dutch women with a normal hospital birth are six times less likely to use pain medication during labour, compared to their Belgian counterparts. This country difference cannot be explained by labour pain acceptance, since - in contrast to our working hypothesis - Dutch and Belgian women giving birth in a hospital setting are characterised by a similar labour pain acceptance. Our findings suggest that personal control in pain relief can partially explain the country differences in coping with labour pain. For Dutch women we find that the use of pain medication is lowest if women experience control over the reception of pain medication and have a positive attitude towards labour pain. In Belgium however, not personal control over the use of pain relief predicts the use of pain medication, but negative attitudes towards labour.</p> <p>Conclusions</p> <p>Apart from individual level determinants, such as length of labour or pain acceptance, our findings suggest that the maternity care context is of major importance in the study of the management of labour pain. The pain medication use in Belgian hospital maternity care is high and is very sensitive to negative attitudes towards labour pain. In the Netherlands, on the contrary, pain medication use is already low. This can partially be explained by a low degree of personal control in pain relief, especially when co-occurring with positive pain attitudes.</p