Predictors of Adverse Pregnancy Outcomes Following Traumatic Injuries.

OBJECTIVE: After traumatic injury in pregnant women, providing timely and appropriate management for high-risk patients is crucial for both pregnant women and fetuses. This study aimed to identify risk factors that predict adverse pregnancy outcomes after traumatic injury. METHODS: A retrospective cohort study including 317 pregnant patients who experienced trauma was conducted. The collected data included general demographics, injury mechanisms and adverse pregnancy outcomes. Patients were divided into two subgroups based on the absence or presence of trauma-related adverse pregnancy outcomes. Univariate and multivariate logistic regressions were conducted to estimate the associations between clinical variables and adverse pregnancy outcomes. RESULTS: A total of 41 (12.93%) patients experienced adverse pregnancy outcomes within the first 24 h post-trauma. This study revealed that age >35 years (OR=14.995, 95% CI: 5.024-44.755, P<0.001), third trimester trauma (OR=3.878, 95% CI: 1.343-11.204, P=0.012), abdominal pain (OR=3.032, 95% CI: 1.221-7.527, P=0.017), vaginal bleeding (OR=3.226, 95% CI: 1.093-9.523, P=0.034), positive scan in focused assessment with sonography for trauma (FAST) positive (OR=8.496, 95% CI: 2.825-25.555, P<0.001), 9≤ injury severity score (ISS) <16 (OR=3.039, 95% CI: 1.046-8.835, P=0.041) and ISS≥16 (OR=5.553, 95% CI: 1.387-22.225, P=0.015) increased the probability of posttraumatic adverse pregnancy outcomes. Maternal age, gestational age at delivery, vaginal bleeding and positive FAST results were risk factors for abnormal delivery. CONCLUSION: Advanced maternal age, third trimester, and positive FAST results should alert multidisciplinary trauma teams to closely monitor patients to prevent adverse pregnancy outcomes.

Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.

Identification of Factors Influencing Out-of-county Hospitalizations in the New Cooperative Medical Scheme.

Throughout the duration of the New Cooperative Medical Scheme (NCMS), it was found that an increasing number of rural patients were seeking out-of-county medical treatment, which posed a great burden on the NCMS fund. Our study was conducted to examine the prevalence of out-of-county hospitalizations and its related factors, and to provide a scientific basis for follow-up health insurance policies. A total of 215 counties in central and western China from 2008 to 2016 were selected. The total out-of-county hospitalization rate in nine years was 16.95%, which increased from 12.37% in 2008 to 19.21% in 2016 with an average annual growth rate of 5.66%. Its related expenses and compensations were shown to increase each year, with those in the central region being higher than those in the western region. Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region (X1), rural population (X2), per capita per year net income (X3), per capita gross domestic product (GDP) (X4), per capita funding amount of NCMS (X5), compensation ratio of out-of-county hospitalization cost (X6), per time average in-county (X7) and out-of-county hospitalization cost (X8). According to Bayesian network (BN), the marginal probability of high out-of-county hospitalization rate was as high as 81.7%. Out-of-county hospitalizations were directly related to X8, X3, X4 and X6. The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors, economy factors, regional characteristics and NCMS policy factors was 95.7%, 91.1%, 93.0% and 88.8%, respectively. And how these factors affect out-of-county hospitalization and their interrelationships were found out. Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations, and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.