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

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

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

Prevalence and determinants of unintended pregnancies amongst women attending antenatal clinics in Pakistan

Background: Unintended pregnancies are a global public health concern and contribute significantly to adverse maternal and neonatal health, social and economic outcomes and increase the risks of maternal deaths and neonatal mortality. In countries like Pakistan where data for the unintended pregnancies is scarce, studies are required to estimate its accurate prevalence and predictors using more specific tools such as the London Measure of Unplanned Pregnancies (LMUP). Methods: We conducted a hospital based cross sectional survey in two tertiary care hospitals in Pakistan. We used a pre tested structured questionnaire to collect the data on socio-demographic characteristics, reproductive history, awareness and past experience with contraceptives and unintended pregnancies using six item the LMUP. We used Univariate and multivariate analysis to explore the association between unintended pregnancies and predictor variables and presented the association as adjusted odds ratios. We also evaluated the psychometric properties of the Urdu version of the LMUP. Results: Amongst 3010 pregnant women, 1150 (38.2%) pregnancies were reported as unintended. In the multivariate analysis age \u3c 20 years (AOR 3.5 1.1-6.5), being illiterate (AOR 1.9 1.1-3.4), living in a rural setting (1.7 1.2-2.3), having a pregnancy interval of = \u3c 12 months (AOR 1.7 1.4-2.2), having a parity of \u3e2 (AOR 1.4 1.2-1.8), having no knowledge about contraceptive methods (AOR 3.0 1.7-5.4) and never use of contraceptive methods (AOR 2.3 1.4-5.1) remained significantly associated with unintended pregnancy. The Urdu version of the LMUP scale was found to be acceptable, valid and reliable with the Cronbach\u27s alpha of 0.85. Conclusions: This study explores a high prevalence of unintended pregnancies and important factors especially those related to family planning. Integrated national family program that provides contraceptive services especially the modern methods to women during pre-conception and post-partum would be beneficial in averting unintended pregnancies and their related adverse outcomes in Pakistan