Researchers aim to create a sophisticated model to assess the risk of nosocomial infections among obstetric inpatients, with the goal of enhancing measures of infection prevention and control. Drawing on a large-scale retrospective study in China, they developed and authenticated a predictive tool known as a nomogram, designed to produce personalized estimates of postpartum infection risk for obstetric inpatients.
The study observed a cohort of 28,608 obstetric patients admitted for childbirth between 2017 and 2022. In essence, data from the year 2022, involving 4,153 inpatients, was utilized to validate the model. The researchers used univariable and multivariable stepwise logistic regression analyses to identify the factors influencing nosocomial infections among obstetric inpatients. Consequently, a nomogram was developed with the support of these findings.
A variety of aspects were used to predict the possible outcomes, these included gestational weeks greater than or equal to 37, prenatal anemia, prenatal hypoproteinemia, premature rupture of membranes, cesarean section, operative delivery, adverse birth outcomes, hospital stay over 5 days, central venous catheter use and ureter catheterization. The predictive accuracy of the model was estimated by calculating the area under the curve, with values of 0.828 and 0.855 seen in the training and validation datasets respectively.
This tool is of immense relevance owing to the severe physiological changes experienced by women during pregnancy, childbirth, and the postpartum period. Such changes can considerably weaken the immune system, making postnatal mothers highly susceptible to infections. Nosocomial infections not only hinder the recovery process for postnatal mothers but can also pose a significant risk to newborns, and in severe cases, result in fatalities.
The researchers highlighted that preventing nosocomial infections in obstetrics is of great significance. Key factors linked with nosocomial infections in obstetric inpatients include maternal age, pre-existing medical conditions like diabetes, hypertension, and anemia; type of delivery, premature rupture of membranes, preterm birth, type of surgical incision, prophylactic antibiotics, and hospitalization length.
Despite the extensiveness of this research, previous efforts to discern risk factors for nosocomial infections in obstetrics primarily relied on smaller sample sizes and lacked validation processes. This underpinned the necessity of a more reliable and practical predictive model like the nomogram developed in this study, offering a valuable reference for predicting and mitigating the risk of postpartum infections.
Emphasizing the nomogram’s robustness and its ability to provide bespoke postpartum infection risk estimates, it’s hoped that the tool can simplify the complex decision-making process in healthcare.
Source: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-024-09795-y