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Objective:To investigate the effects of peripartum administration of low-dose corticosteroids or intravenous immunoglobulin (IVIG) on delivery outcomes in pregnant patients with primary immune thrombocytopenia (ITP).Methods:This prospective cohort study involved pregnant women (≥34 gestational weeks) who were diagnosed with ITP in Peking University People's Hospital from January 2017 to December 2021. Their platelet counts were between 20×10 9/L to 50×10 9/L without bleeding and none of them had been treated with any medications. All patients were divided into medication group (prednisone or IVIG) and platelet transfusion group based on their preference. Differences in vaginal delivery rate, postpartum hemorrhage rate and platelet transfusion volume between the two groups were compared using t-test, Wilcoxon rank sum test and Chi-square test. Binary logistic regression was used to investigate the factors influencing the rates of vaginal delivery and postpartum hemorrhage. Multiple linear regression was used to analyze the factors influencing the platelet transfusion volume. Results:A total of 96 patients with ITP were recruited with 70 in the medication group and 26 in the platelet transfusion group. The vaginal delivery rate in the medication group was higher than that in the platelet transfusion group [60.0% (42/70) vs 30.8% (8/26), χ 2=6.49, P=0.013]. After adjusted by the proportion of multiparae and the gestational age at delivery, binary logistic regression showed that the increased vaginal delivery rate in patients undergoing the peripartum treatment ( OR=4.937, 95% CI: 1.511-16.136, P=0.008). The incidence of postpartum hemorrhage in the two groups was 22.9% (16/70) and 26.9% (7/26), respectively, but no significant difference was shown ( χ 2=0.17, P=0.789). The median platelet transfusion volume was lower in the medication group than in the platelet transfusion group [1 U(0-4 U) vs 1 U(1-3 U), Z=－2.18, P=0.029]. After adjustment of related factors including the platelet count at enrollment, obstetrical complications and anemia, multiple linear regression showed that the platelet transfusion volume was also lower in the medication group (95% CI:0.053-0.911, P=0.028). Ninety-six newborns were delivered without intracranial hemorrhage. The overall incidence of neonatal thrombocytopenia was 26.0% (25/96). There was no significant difference in birth weight, and incidence of neonatal asphyxia or thrombocytopenia between the two groups. Conclusion:Peripartum therapy in ITP patients may increase vaginal delivery rate and reduce platelet transfusion volume without causing more postpartum hemorrhage.
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Objective:Severe trauma events are emergent, with low incidence and unpredictable. Current guideline does not provide precise recommendations on how the trauma centers should arrange the number of beds in trauma intensive care units while making rational use of medical resources. We analyzed the trauma intensive care unit bed requirement in the branch campus of our hospital to propose a reasonable assessment.Methods:Patients with severe trauma sent to the Intensive Care Unit of Peking University People's Hospital from January 2022 to June 2022 were collected. The daily number of patients received intensive care was counted. The bed requirement of the intensive care unit covering 99% of clinical needs was calculated based on the probability distribution function.Results:From January 2022 to June 2022, 103 patients with severe trauma [74 males and 29 females, aged (51.47±16.06) years, ranging 16 to 87 years] were included in the study. Among the 103 patients, 57 were injured in traffic accidents, 26 fell from a high altitude, 12 fell, 4 were hit by heavy objects, and 4 were stabbed. TISS ranged from 16 to 50. The range of the daily bed requirement in the intensive care unit was 0–10, which was consistent with the Poisson distribution. According to the probability distribution function, nine trauma intensive care beds could meet 99.19% of clinical needs.Conclusions:In severe traumatic events, patients need to be transferred to intensive care unit as soon as possible. For our branch campus, nine trauma intensive care beds can cover more than 99% of clinical needs. It follows that, in accordance with the basic requirements of trauma center construction, hospitals with trauma centers need at least 9 beds in intensive care units. However, traumatic events cannot be predicted; thus, the bed requirement needs to be regularly evaluated.
ABSTRACT
Objective@#The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. @*Materials and Methods@#Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. @*Results@#At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows:sensitivity 85.7% (95% confidence interval [CI]: 0.834–0.877), specificity 67.5% (95% CI: 0.636–0.712), PPV 82.1% (95% CI: 0.797–0.843), NPV 73.0% (95% CI: 0.691–0.766), and AUC 0.771 (95% CI: 0.750–0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541–0.995), specificity 70.0% (95% CI: 0.354–0.919), PPV 75.0% (95% CI: 0.428–0.933), NPV 87.5% (95% CI: 0.467–0.993), and AUC 0.800 (95% CI: 0.563–0.943). @*Conclusion@#We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.