RESUMEN
AIM: To clarify the concept of fear of childbirth among pregnant women and to examine its current measure tools. BACKGROUND: Fear of childbirth is a psychological symptom, prevalent among pregnant women, which negatively impacts women's health and well-being. It has become an increasingly concerning issue in perinatal mental health. However, due to its poor conceptualization, it presents difficulty in conducting reliable assessments and identifying risk factors. METHODS: The Walker and Avant approach to concept analysis guided this review. Six bibliographic databases were systematically searched for published research from their inception date to May 2023. Additional records were identified by manually searching the reference lists of relevant studies. Quantitative and qualitative studies investigating fear of childbirth in pregnant women were included. RESULTS: Three critical attributes have been identified: cognitive impairments, affective disorders and somatic symptoms. Antecedents include perceived a real or anticipated threat of pregnancy or its outcomes, low perceived self-coping ability and unmet social support needs. Consequences include processing and avoiding behaviours. This study also identified the dimensions of fear of childbirth, including 6 primary categories and 14 subcategories. The content of five scales was analysed and none covered all domains. CONCLUSIONS: The current analysis provides healthcare providers with a more comprehensive framework to assess and identify fear of childbirth. Further research is needed to develop a suitable instrument that covers all the attributes and dimensions of this concept and assesses its severity. IMPACT: This conceptual analysis provides a comprehensive insight into the phenomenon of fear of childbirth. This will help family members, healthcare providers and policymakers to identify the psychological needs of pregnant women and improve the quality of antenatal care. PATIENT OR PUBLIC CONTRIBUTION: Not applicable as no new data were generated.
RESUMEN
Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68-0.79] and 0.70 [95% CI 0.62-0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58-0.70] and 0.65 [95% CI 0.55-0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance.