Objective To construct and evaluate the risk prediction models for low Apgar score in very preterm infants (VPIs) using different machine learning algorithms. Methods From June 2023 to December 2024, a retrospective case‑control study was conducted among VPIs born in the Department of Obstetrics of our hospital. The patients were randomly assigned into a training set and a validation set with a ratio of 3:1. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the risk factors of low Apgar score in the training set. 1‑min Apgar score ≤7 was defined as the outcome indicator. Four machine learning algorithms: Logistic regression (LR), extreme gradient boosting (XGBoost), random forest (RF) and support vector machine (SVM) were used to construct the prediction model. Model performance was evaluated in the validation set using area under the receiver operating characteristic (ROC) curve (AUC), accuracy, precision, sensitivity, specificity and F1 score. The optimal model was further studied using Shapley additive explanations (SHAP) method. Results A total of 544 cases were enrolled, including 408 in the training set and 136 in the validation set. There were 126 cases with low Apgar scores and 282 cases with normal Apgar scores in the training set. LASSO regression identified 9 predictive factors: gestational age, maternal anemia, antenatal fever, cervical insufficiency, abnormal umbilical artery blood flow, fetal malpresentation, fetal distress, premature rupture of membranes (PROM) and placental abruption. There were 42 cases with low Apgar scores and 94 cases with normal Apgar scores in the validation set. The ROC curve analysis showed that AUCs of LR, XGBoost, RF and SVM were 0.804 (95%CI 0.759‑0.845), 0.795 (95%CI 0.715‑0.868), 0.817 (95%CI 0.730‑0.890) and 0.802 (95%CI 0.750‑0.840), respectively. RF had most balanced sensitivity (0.798, 95%CI 0.665‑0.845) and specificity (0.786, 95%CI 0.628‑0.891) with highest F1 score (0.843). SHAP analysis showed that the influencing factors in the model were ranked by importance as follows: fetal malpresentation, cervical insufficiency, fetal distress, maternal anemia, antenatal fever, gestational age, abnormal umbilical artery blood flow, PROM and placental abruption. Conclusions The model based on RF algorithm has a good predictive efficacy for low Apgar score in VPIs and maybe a useful tool for prenatal risk assessment.

OBJECTIVE:To establish a method for determining the plasma concentration of paeoniflorin and phillyrin and phar-macokinetic study before and after intragastric administration of Qianliean granules. METHODS:LC-MS/MS method was adopted. The column was Waters C18 with mobile phase consisted of acetonitrile(A)-2 mmol/L ammonium acetate(containing 0.05% formic acid)(B)(0-9 min:15%A→50%A;9-11 min:50%A→90%A;11-17 min:90%A;17-19 min:90%A→15%A;19-20 min:15%A),at the flow rate of 0.6 ml/min;column temperature was 35 ℃ and the volume was 20 μl;quantitative ions were paeoniflorin m/z 525.2 → m/z 449.0,phillyrin m/z 552.3 → m/z 355.3. 7 SD male rats were docked to collect blood 0.5 ml from angular vein 0.25,0.5,0.75,1,1.5,2,3,4,6,8,10,12,24 h after administration Qianliean granule solution 1 g(medicinal materials)/kg to determine the blood concentration of drugs. DAS 2.1.1 software was employed to calculate pharmacokinetic parameters. RE-SULTS:The linear range of paeoniflorin and phillyri were 5.0-2500.0 μg/L(r=0.9979)and 2.0-2000.0 μg/L(r=0.9982),re-spectively;RSD of precision test was less than 5.5%(n=5);the method recovery were 96.0%-104.0% and 92.0%-107.0%,the extration recovery were 71.4%-83.5% and 81.5%-92.3% and RSD of stability test was less than 5.0%(n=3). The pharmacokinet-ic parameters of paeoniflorin and phillyrin were as follows as t1/2 of (2.206 ± 0.631) and (1.355 ± 0.317) h;cmax of (1504.069 ± 620.885) and (79.043 ± 15.568)μg/L;tmax of (1.000 ± 0.250) and (1.214 ± 0.267) h;AUC0-24 h of (4897.645 ± 2207.577) and (263.475±54.795)μg·h/L;CL of(5.025±2.773)and(76.253±13.986)L/(h·kg). CONCLUSIONS:The method is highly sensi-tive,exclusive,simple,accurate and reliable,and can be applied to study the pharmacokinetic characteristics of paeoniflorin and phillyrin in rats in vivo.