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Objective:To establish a risk prediction model for the occurrence of low 1 min Apgar scores in extremely premature infants (EPIs).Methods:From January 2017 to December 2021, EPIs delivered at our hospital were retrospectively analyzed and randomly assigned into training set group and validation set group in a 7∶3 ratio. 17 clinical indicators were selected as predictive variables and low Apgar scores after birth as outcome variables. Lasso regression and multi-factor logistic regression were used within the training set group to select the final predictors for the final model, and the calibration, distinguishability and clinical decision making curves of the final model were evaluated in the validation set group.Results:A total of 169 EPIs were enrolled, including 117 in the training set group and 52 in the validation set group. 4 indicators including gender, fetal distress, assisted conception and delivery time were selected as the final predictors in the final model. Both the training set group and the validation set group had good calibration curves. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.731, the sensitivity was 72.2%, the specificity was 60.5% and the AUC of the external validation curve was 0.704. The clinical decision making curve showed that the model had a greater benefit in predicting the occurrence of low Apgar score in EPIs within the threshold of 2% to 75%.Conclusions:The clinical prediction model established in this study has good distinguishability, calibration and clinical accessibility and can be used as a reference tool to predict low Apgar scores in EPIs.
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AIM: Through the Monte Carlo simulation to monitor the change of MIC in late-onset sepsis of gram-positive cocci in neonates, through the cumulative fraction of response to evaluate the changing trend of bacterial resistance in our center and analyze the possibility of inducing drug resistance of bacterial, to reduce the occurrence of bacterial drug resistance in clinical work. METHODS: This study retrospectively investigated the basic information, pathogen species and drug sensitivity results of neonatal late-onset sepsis of gram-positive cocci in Neonatal Intensive Care Units of Beijing Maternity Hospital from 2016 to 2019, and divided them into four groups by year. Crystal ball software was used to calculate the annual CFR of sensitive antibiotics (Vancomycin) against the gram-positive cocci by Monte Carlo simulation. RESULTS: From 2016 to 2019, there were 58 cases of late-onset sepsis caused by gram-positive cocci in neonates, and the number of pathogens detected each year showed no significant change, and there was no statistical difference in the affected population each year. Among them, the top three were 31 strains of Staphylococcus epidermidis (53.5%), 9 strains of Enterococcus faecium (15.5%), and 6 strains of Enterococcus faecalis (10.3%). Drug sensitivity tests showed that the resistance rates of Staphylococcus epidermidis, Enterococcus faecium and Enterococcus faecalis to Vancomycin and Linezolid were 0%. The CFR of Vancomycin against gram-positive cocci from 2016 to 2019 calculated by Monte Carlo simulation were 82%, 88.72%, 81.73% and 78.53%, respectively, which showed a downward trend. CONCLUSION: By using Monte Carlo simulation method, CFR can reflect the change of bacterial drug resistance with drug sensitivity test as the standard, and evaluate the current treatment plan, which should be paid attention to in clinical work.