Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal JNKI (Jurnal Ners dan Kebidanan Indonesia) (Indonesian Journal of Nursing and Midwifery)
Rosyidah, Rafhani
Universitas Muhamamdiyah Sidoarjo
Health Professions Medicine & Pharmacology Nursing Public Health Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Prediction instrument for obstructed labor Rosyidah, Rafhani; Azizah, Nurul; Rinata, Evi
JNKI (Jurnal Ners dan Kebidanan Indonesia) (Indonesian Journal of Nursing and Midwifery) Vol 13, No 2 (2025)
Publisher : Alma Ata University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/jnki.2025.13(2).226-237

Abstract

Background: Obstructed labor is a complication during childbirth that can increase maternal and neonatal mortality and morbidity, making early detection crucial for prevention. However, currently, no instrument is available to predict the occurrence of obstructed labor.Objectives: This study aims to develop an effective prediction instrument for the early identification of obstructed labor, enabling timely interventions to reduce associated risks. Methods: The study employs a case-control design for the development of the instrument and a cohort design for its testing. The research is conducted in three phases: Phase I involves a literature review to identify risk factors, Phase II focuses on the initial testing of the instrument, and Phase III includes the validation of the instrument in a clinical setting. The study population consists of 360 women in labor for instrument development and 40 women for instrument testing. Bivariate analysis is conducted using chi-square tests, while multivariate analysis is performed using logistic regression, followed by ROC curve analysis to determine the optimal cutoff point. Results: The results indicate significant associations between obstructed labor and factors such as infant birth weight, maternal height, age, obesity, upper arm circumference, anemia, history of abdominal surgery, and weight gain during pregnancy. The final predictive model demonstrated an AUC of 0.908, indicating excellent predictive performance with a sensitivity of 85.7% and specificity of 80.8%. Conclusions: This study highlights the importance of early risk detection and intervention and contributes to the literature by providing an instrument capable of predicting the risk of obstructed labor.
Risk prediction instrument for premature rupture of membranes based on clinical parameters and obstetric history Rosyidah, Rafhani; Azizah, Nurul; Cholifah, Siti
JNKI (Jurnal Ners dan Kebidanan Indonesia) (Indonesian Journal of Nursing and Midwifery) Vol 13, No 3 (2025)
Publisher : Alma Ata University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/jnki.2025.13(3).401-414

Abstract

Background: Premature rupture of membranes is one of the most significant obstetric complications, with a direct impact on increasing the risk of infection, preterm labor, and perinatal mortality. In Indonesia, the main challenge in managing premature rupture of membranes is the late identification of at-risk pregnant women due to the unavailability of objective and practical screening instruments.Objective: This study aims to develop and test the validity of a clinical and obstetric history-based early rupture risk prediction instrument that can be widely applied in primary health care.Methods: The research design used a case control approach with a total of 450 respondents, consisting of case and control groups. Data were obtained from medical records and analyzed through chi-square test and logistic regression to identify significant risk factors. A score prediction model was then developed and validated using ROC curve, Youden index, and sensitivity-specificity analysis.Results: Four variables proved significant in the final model: age beyond 20-35 years, at-risk parity (primipara or grandemultipara), anemia, and fetal abnormality. The prediction score constructed based on these four factors had an Area Under the Curve value of 0.887, with a sensitivity of 82.4% and specificity of 84.8% at a cut-off point of >2.50. The overall accuracy of the model reached 0.86, indicating excellent classification performance.Conclusions: This score-based prediction model was shown to be valid and can be used as a practical screening tool to detect early risk of premature rupture of membranes. Its application is expected to strengthen efforts to prevent obstetric complications and improve the quality of maternal care in Indonesia
Co-Authors Azizah, Nurul Cholifah, Siti Evi Rinata NURUL AZIZAH