Breastfeeding and postpartum depression: state of the art review

Objective: To review the literature on the association between breastfeeding and postpartum depression. Sources: A review of literature found on MEDLINE/ PubMed database. Summary of findings: The literature consistently shows that breastfeeding provides a wide range of benefits for both the child and the mother. The psychological benefits for the mother are still in need of further research. Some studies point out that pregnancy depression is one of the factors that may contribute to breastfeeding failure. Others studies also suggest an association between breastfeeding and postpartum depression; the direction of this association is still unclear. Breastfeeding can promote hormonal processes that protect mothers against postpartum depression by attenuating cortisol response to stress. It can also reduce the risk of postpartum depression, by helping the regulation of sleep and wake patterns for mother and child, improving mother’s self efficacy and her emotional involvement with the child, reducing the child’s temperamental difficulties, and promoting a better interaction between mother and child. Conclusions: Studies demonstrate that breastfeeding can protect mothers from postpartum depression, and are starting to clarify which biological and psychological processes may explain this protection. However, there are still equivocal results in the literature that may be explained by the methodological limitations presented by some studies.

Anxiety and depression symptoms in women and men from early pregnancy to 3-months postpartum: parity differences and effects

This study aimed to investigate both anxiety and depression symptoms from early pregnancy to 3-months postpartum, comparing women and men and first and second-time parents. Methods: A sample of 260 Portuguese couples (N=520), first or second-time parents, recruited in an Obstetrics Out-patients Unit, filled in the State-Anxiety Inventory (STAI-S) and the Edinburgh Post-Natal Depression Scale (EPDS) at the 1st, 2nd and 3rd pregnancy trimesters, childbirth, and 3-months postpartum. Results: A decrease in anxiety and depression symptoms from early pregnancy to 3-months postpartum was found in both women and men, as well as in first and second-time parents. Men presented less anxiety and depression symptoms than women, but the same pattern of symptoms over time. Second-time parents showed more anxiety and depression symptoms than first-time parents and a different pattern of symptoms over time: an increase in anxiety and depression symptoms from the 3rd trimester to childbirth was observed in first-time parents versus a decrease in second-time parents. Limitations: The voluntary nature of the participation may have lead to a selection bias; women and men who agreed to participate could be those who presented fewer anxiety and depression symptoms. Moreover, the use of self-report symptom measures does not give us the level of possible disorder in participants. Conclusions: Anxiety and depression symptoms diminish from pregnancy to the postpartum period in all parents. Patterns of anxiety and depression symptoms from early pregnancy to 3-months postpartum are similar in women and men, but somewhat different in first and second time parents. Second-time parents should also be considered while studying and intervening during pregnancy and the postpartum.

Pregnancy massage reduces prematurity, low birthweight and postpartum depression

Pregnant women diagnosed with major depression were given 12 weeks of twice per week massage therapy by their significant other or only standard treatment as a control group. The massage therapy group women versus the control group women not only had reduced depression by the end of the therapy period, but they also had reduced depression and cortisol levels during the postpartum period. Their newborns were also less likely to be born prematurely and low birthweight, and they had lower cortisol levels and performed better on the Brazelton Neonatal Behavioral Assessment habituation, orientation and motor scales.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

A clinical recommendation system to maternity care

Nowadays in healthcare, the Clinical Decision Support Systems are used in order to help health professionals to take an evidence-based decision. An example is the Clinical Recommendation Systems. In this sense, it was developed and implemented in Centro Hospitalar do Porto a pre-triage system in order to group the patients on two levels (urgent or outpatient). However, although this system is calibrated and specific to the urgency of obstetrics and gynaecology, it does not meet all clinical requirements by the general department of the Portuguese HealthCare (Direção Geral de Saúde). The main requirement is the need of having priority triage system characterized by five levels. Thus some studies have been conducted with the aim of presenting a methodology able to evolve the pre-triage system on a Clinical Recommendation System with five levels. After some tests (using data mining and simulation techniques), it has been validated the possibility of transformation the pre-triage system in a Clinical Recommendation System in the obstetric context. This paper presents an overview of the Clinical Recommendation System for obstetric triage, the model developed and the main results achieved.