Clinical risk factors for preterm birth and evaluating maternal psychology in the postpartum period

Analysis of anxiety and depression status and related factors among mothers of children in neonatal intensive care unit

BACKGROUND

Newborns are immediately admitted to the neonatal intensive care unit (NICU) after birth, and thus mothers suffer from the pain of mother-infant separation. Some mothers worry about alterations in their child's condition and the uncertainty and high medical costs of possible sequelae, which frequently cause anxiety, depression, and other adverse emotions.

AIM

To investigate the anxiety and depression status of mothers of children in the NICU and its related factors.

METHODS

A convenient sampling method is adopted. The research objects included the mothers of 191 children in the NICU of Suzhou Ninth Hospital Affiliated with Suzhou University from January 2023 to July 2024. The general information questionnaire, personal control scale, self-rating anxiety scale, and self-rating depression scale were utilized for investigation. Anxiety and depression status in mothers of children in the NICU and its related factors were analyzed.

RESULTS

The incidences of maternal anxiety and depression among 191 hospitalized children in the NICU were 32.98% (63/191) and 23.56% (45/191), respectively. Single-factor analysis reveals that family monthly income, individual sense of control, gestational age of the child, and the number of diseases in the child are associated with the anxiety and depression experienced by the mother of the child in the NICU (P < 0.05). Multivariate logistic regression analysis revealed that family monthly income of < 5000 yuan (RMB), poor individual control, gestational age of < 32 weeks, and the number of diseases of ≥ 3 kinds are all related factors for anxiety and depression in mothers of children admitted to the NICU (P < 0.05).

CONCLUSION

Mothers of children admitted to the NICU demonstrated high anxiety and depression incidences. The nursing staff in the neonatal department established intervention measures for each related factor, strengthened communication and communication with the mother of the child, and did a good job in psychological counseling.

Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study

Background

Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.

Objective

This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models to achieve precise prediction of PPD, and interpret the model to reveal clinical implications.

Methods

This study recruited pregnant women who delivered at the West China Second University Hospital, Sichuan University. Various variables were collected from electronic medical record data and screened using least absolute shrinkage and selection operator penalty regression. Participants were divided into training (1358/2055, 66.1%) and validation (697/2055, 33.9%) sets by random sampling. Machine learning-based predictive models were developed in the training cohort. Models were validated in the validation cohort with receiver operating curve and decision curve analysis. Multiple model interpretation methods were implemented to explain the optimal model.

Results

We recruited 2055 participants in this study. The extreme gradient boosting model was the optimal predictive model with the area under the receiver operating curve of 0.849. Shapley Additive Explanation indicated that the most influential predictors of PPD were antepartum depression, lower fetal weight, elevated thyroid-stimulating hormone, declined thyroid peroxidase antibodies, elevated serum ferritin, and older age.

Conclusions

This study developed and validated a machine learning-based predictive model for PPD. Several significant risk factors and how they impact the prediction of PPD were revealed. These findings provide new insights into the early screening of individuals with high risk for PPD, emphasizing the need for comprehensive screening approaches that include both physiological and psychological factors.

Gong's acupuncture treatment for mid pregnancy insomnia and anxiety: A case report

RATIONALE

Gong's brain acupuncture (GBA) is a acupuncture technique that restores the balance of the central nervous system by stimulating specific acupoints on the skull to transmit stimulation to the nerves. Insomnia during pregnancy is an increasingly concerning issue, and GBA provides new solutions.

PATIENTS CONCERNS

The patient, a 26-year old woman at 26 + 1 weeks of pregnancy, presented with unexplained insomnia for 3 weeks. Her symptoms included difficulty initiating sleep, light sleep, and intermittent wakefulness throughout the night.

DIAGNOSES

The patient was diagnosed with primary insomnia, scoring 16 on the Pittsburgh sleep quality index, which indicates poor sleep quality. Her self-rating anxiety scale (SAS) score was 49 points, suggesting that she had not yet reached a state of mild anxiety.

INTERVENTIONS

The patient received 15 sessions of GBA treatment.

OUTCOMES

One month after treatment completion, the patient's Pittsburgh sleep quality index score improved to 3 points, indicating good sleep quality. Her self-rating anxiety scale score decreased to 39 points.

LESSONS

This case report demonstrated the successful treatment of mid pregnancy insomnia with GBA, providing preliminary evidence for further research on the therapeutic potential of GBA. However, further research is still needed to enhance persuasiveness.