Journal of perinatal medicine · 2022
Risk factors for postpartum hemorrhage in patients with retained placenta: building a predict model.
СтатьяНазвание и аннотация: Перевод в очереди
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Wei Xiaoning, Wei Jiangping, Wang Shaowei
Аннотация
OBJECTIVES: Among patients with placenta retention, the risk factors of massive blood loss remain unclear. In this study, a secondary data analysis was conducted to construct a predictive risk model for postpartum hemorrhage (PPH) in this particular population.
METHODS: A prediction model based on the data of 13 hospitals in the UK, Uganda, and Pakistan, from December 2004, to May 2008 was built. A total of 516 patients and 14 potential risk factors were analyzed. The least absolute shrinkage and selection operator regression (LASSO) model was used to optimize feature selection for the PPH risk model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the LASSO model. Discrimination and calibration were assessed using C-index and calibration plot.
RESULTS: Among patients with placenta retention, the incidence of PPH was 62.98% (325/526). Risk factors in the model were country, number of past deliveries, previous manual removal of placenta, place of placenta delivery, and how the placenta was delivered. In these factors, patients in the low-income country (i.e., Uganda) (OR: 1.753, 95% CI=1.055-2.915), retained placentas delivered in the theater (OR: 2.028, 95% CI=1.016-4.050), and having placentas partially removed by controlled cord traction (cct), completely removed manually (OR: 4.722, 95% CI=1.280-17.417) were independent risk factors. The C-statistics was 0.702.
CONCLUSIONS: By secondary data analysis, our study constructed a prediction model for PPH in patients with placenta retention, and identified the independent risk factors.
METHODS: A prediction model based on the data of 13 hospitals in the UK, Uganda, and Pakistan, from December 2004, to May 2008 was built. A total of 516 patients and 14 potential risk factors were analyzed. The least absolute shrinkage and selection operator regression (LASSO) model was used to optimize feature selection for the PPH risk model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the LASSO model. Discrimination and calibration were assessed using C-index and calibration plot.
RESULTS: Among patients with placenta retention, the incidence of PPH was 62.98% (325/526). Risk factors in the model were country, number of past deliveries, previous manual removal of placenta, place of placenta delivery, and how the placenta was delivered. In these factors, patients in the low-income country (i.e., Uganda) (OR: 1.753, 95% CI=1.055-2.915), retained placentas delivered in the theater (OR: 2.028, 95% CI=1.016-4.050), and having placentas partially removed by controlled cord traction (cct), completely removed manually (OR: 4.722, 95% CI=1.280-17.417) were independent risk factors. The C-statistics was 0.702.
CONCLUSIONS: By secondary data analysis, our study constructed a prediction model for PPH in patients with placenta retention, and identified the independent risk factors.