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Birth weight is closely related to infant survival and future health, growth and development. In developing countries, the incidence of low birth weight is twice as high as in developed countries. Due to the low economic and medical level in northwest China, the problem of low birth weight needs to be solved urgently. We developed the predictive model based on data sets from a cross-sectional study conducted in northwest China, and data were collected from August 2013 to November 2013. A total of 27,233 patients were included in the study. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the optimal predictive characteristics among risk factors. The selected characteristics in the LASSO regression were used in multivariate logistic regression to build the prediction model. C-index and calibration plot were used to evaluate the degree of discrimination and calibration of the model. The decision curve is used to evaluate the net benefit rate of the application of the predictive tool. Bootstrapping validation was used for internal validation. Nomogram included gestational age, the sex of the attendance, the mother's education level, antenatal care, the mother's occupation, pregnancy-induced hypertension, family income, exposure to pesticides and nutritional supplements. The C-index of the predicted nomogram was 0.698(95% confidence interval: 0.671–0.725), C-index of internal verification was 0.694, indicating that the model had a good identification ability. Calibration plot showed that the model had good calibration. Decision curve indicated that patients with a threshold probability of low birth weight between 1% and 71% would benefit more from using the prediction tool. The use of this predictive model will contribute to clinicians and pregnant women to make personalized predictions easily and quickly so that early lifestyle detection and medical intervention can be undertaken by physicians and patients.

Development and validation of nomogram for prediction of low birth weight: a large-scale cross-sectional study in northwest China