Developing and validating a risk prediction model for preterm birth at Felege Hiwot Comprehensive Specialized Hospital, North-West Ethiopia: a retrospective follow-up study

Abstract

ObjectiveTo develop and validate a risk prediction model for the prediction of preterm birth using maternal characteristics.DesignThis was a retrospective follow-up study. Data were coded and entered into EpiData, V.3.02, and were analysed using R statistical programming language V.4.0.4 for further processing and analysis. Bivariable logistic regression was used to identify the relationship between each predictor and preterm birth. Variables with p≤0.25 from the bivariable analysis were entered into a backward stepwise multivariable logistic regression model, and significant variables (p<0.05) were retained in the multivariable model. Model accuracy and goodness of fit were assessed by computing the area under the receiver operating characteristic curve (discrimination) and calibration plot (calibration), respectively.Setting and participantsThis retrospective study was conducted among 1260 pregnant women who did prenatal care and finally delivered at Felege Hiwot Comprehensive Specialised Hospital, Bahir Dar city, north-west Ethiopia, from 30 January 2019 to 30 January 2021.ResultsResidence, gravidity, haemoglobin <11 mg/dL, early rupture of membranes, antepartum haemorrhage and pregnancy-induced hypertension remained in the final multivariable prediction model. The area under the curve of the model was 0.816 (95% CI 0.779 to 0.856).ConclusionThis study showed the possibility of predicting preterm birth using maternal characteristics during pregnancy. Thus, use of this model could help identify pregnant women at a higher risk of having a preterm birth to be linked to a centre.

Development and validation of a nomogram for predicting preterm birth among pregnant women who had Antenatal care follow-up at University of Gondar Comprehensive Specialized Hospital using maternal and fetal characteristics: Retrospective follow-up study

“…This study showed that the incidence proportion of preterm birth was 14.15% (95% CI: 12.03-16.27), which is in line with studies conducted in Bahirdar (13.4%) [23], Axum (13.3%) [44], Butajira (15.5%) [45] .The similarity may be due to the fact that our country's health care system and services that are provided to mothers are essentially uniform across different parts of the nation and other reason might be due to related levels of socioeconomic status and lifestyle of the respondents.…”

“…,P is number of parameters (37), C statistics (AUC) which taken from previous study as 0.99 [36] as we could not nd study reporting R 2 cs, Φ incidence from previous study = 0.134 [23]. Taking the maximum sample size from the above the sample size was 986 and by adding 10% for missing records (charts) it will become 1084.…”

“…Existing models from developed nations may not be applied as their development is based on predictors like fetal brinogen (FN), cervical length, C-reactive protein, and interleukin-6 [21,22], which are not feasible to measure in resource-limited settings like Ethiopia. The previous prediction model from Ethiopia had a very good discriminative performance (AUC = 0.816) [23], but the model did not account for fetal characteristics like fetal sex, intrauterine growth retardation, and obstetric complications like polyhydramnios and oligohydramnios, which are known risks of preterm birth according to different studies [24][25][26][27][28][29].…”

Identifying predictors and developing a risk score for preterm birth among Kenyan pregnant women

“…20 One risk score developed among Ethiopian pregnant women to predict PTB performed well (AUROC: 0.816) and included some maternal factors readily collected in routine ANC (residence, gravidity) as well as others less-routinely collected in public sector ANC (hemoglobin < 11 mg/dL, early rupture of membranes, antepartum hemorrhage, pregnancy-induced hypertension). 21 Some of the variables included in this risk score were obtained at delivery, which limits utility for predicting women at-risk for PTB for care during the antenatal period. 21 These variables often occur simultaneously with PTB and may have the same pathophysiology which make them less useful for predicting PTB.…”

“…21 Some of the variables included in this risk score were obtained at delivery, which limits utility for predicting women at-risk for PTB for care during the antenatal period. 21 These variables often occur simultaneously with PTB and may have the same pathophysiology which make them less useful for predicting PTB. We attempted to develop a model for use in earlier antenatal care to identify women earlier in pregnancy who may be at risk for PTB, using factors easily collected by lower cadre healthcare workers in routine sub-Saharan African antenatal care settings.…”