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Determining gestational age for public health care users in Brazil: comparison of methods and algorithm creation
Ana Paula Esteves Pereira1, Marcos Augusto Bastos Dias, Maria Helena Bastos
1Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil. ana.pep@gmail.com
Insights
Accurate gestational age (GA) estimation is vital for classifying prematurity. Algorithms using available data, like ultrasound and last menstrual period, can improve GA accuracy in regions with limited early obstetric ultrasound access.
Area of Science:
- Perinatology
- Obstetrics
- Maternal-Fetal Medicine
Background:
- Accurate gestational age (GA) determination is critical for classifying prematurity and is a key issue in perinatology.
- This study assesses the validity of various GA estimation methods in Brazil.
- A follow-up study included 1483 participants interviewed during pregnancy and postpartum.
Purpose of the Study:
- To validate different methods for approximating gestational age (GA) at birth.
- To provide insights for developing algorithms for GA estimation in regions with characteristics similar to Brazil.
- To compare GA estimates from ultrasound (US) at different gestational windows, last menstrual period (LMP), and the Capurro method against a reference US standard.
Main Methods:
- A follow-up study in two Brazilian cities.
- Comparison of GA estimates from US (21-28 weeks, 29+ weeks), LMP, and Capurro method against a reference US (7-20 weeks).
- Calculation of Kappa, sensitivity, and specificity for preterm (<37 weeks) and post-term (>=42 weeks) births. Evaluation of GA estimate differences based on maternal and infant characteristics.
Main Results:
- For prematurity, US (21-28 weeks) showed the highest sensitivity (0.84), while the Capurro method had the highest specificity (0.97).
- For postmaturity, US (21-28 weeks) and the Capurro method demonstrated high sensitivity (0.98), but all methods had low specificity (≤0.50).
- Algorithm-derived GA estimates closely matched the reference US, yielding a preterm birth rate of 12.5%.
Conclusions:
- In settings like Brazil with less reliable LMP data and limited early US, developing algorithms using available information is recommended to minimize GA errors.
- Care is needed when comparing preterm birth rates across different locations using varying GA estimation methods.
- The study highlights the importance of methodological strategies to improve GA accuracy and comparability.
Background:
A valid, accurate method for determining gestational age (GA) is crucial in classifying early and late prematurity, and it is a relevant issue in perinatology. This study aimed at assessing the validity of different measures for approximating GA, and it provides an insight into the development of algorithms that can be adopted in places with similar characteristics to Brazil. A follow-up study was carried out in two cities in southeast Brazil. Participants were interviewed in the first trimester of pregnancy and in the postpartum period, with a final sample of 1483 participants after exclusions. The distribution of GA estimates at birth using ultrasound (US) at 21-28 weeks, US at 29+ weeks, last menstrual period (LMP), and the Capurro method were compared with GA estimates at birth using the reference US (at 7-20 weeks of gestation). Kappa, sensitivity, and specificity tests were calculated for preterm (<37 weeks of gestation) and post-term (>=42 weeks) birth rates. The difference in days in the GA estimates between the reference US and the LMP and between the reference US and the Capurro method were evaluated in terms of maternal and infant characteristics, respectively.
Results:
For prematurity, US at 21-28 weeks had the highest sensitivity (0.84) and the Capurro method the highest specificity (0.97). For postmaturity, US at 21-28 weeks and the Capurro method had a very high sensitivity (0.98). All methods of GA estimation had a very low specificity (≤0.50) for postmaturity. GA estimates at birth with the algorithm and the reference US produced very similar results, with a preterm birth rate of 12.5%.
Conclusions:
In countries such as Brazil, where there is less accurate information about the LMP and lower coverage of early obstetric US examinations, we recommend the development of algorithms that enable the use of available information using methodological strategies to reduce the chance of errors with GA. Thus, this study calls into attention the care needed when comparing preterm birth rates of different localities if they are calculated using different methods.
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