18 research outputs found

    Evaluating the Agreement of Risk Categorization for Fetal Down Syndrome Screening between Ultrasound-Based Gestational Age and Menstrual-Based Gestational Age by Maternal Serum Markers

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    Objective. To evaluate the agreement of risk categorization for Down syndrome screening between ultrasound scan-based gestational age (GA) and last menstrual period-based gestational age in both first and second trimesters by maternal serum markers. Methods. Data comprising 4,055 and 4,016 cases of first and second trimester screening were used. The maternal serum markers were analyzed using the ultrasound-based GA and menstrual age. The subjects whose menstrual age and ultrasound-based GA fell in different trimesters were excluded because the risk could not be calculated due to the different serum markers used in each trimester. The agreement of risk categorization for fetal Down syndrome was evaluated. Results. The agreement of Down syndrome screening in the first and the second trimesters were 92.7% and 89%, respectively. The study found a good agreement of risk categorization by Kappa index, which was 0.615 for the overall screening. The menstrual age had a slight decrease in the detection rate and a lower false-positive rate. Conclusion. Menstrual age is acceptable in cases of accurate last menstrual period. However, in places where ultrasonography is not readily available, gestational age estimation by menstrual age along with clinical examination that corresponds to the gestational age can be reliable

    The efficacy of ampicillin compared with ceftriaxone on preventing cesarean surgical site infections: an observational prospective cohort study

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    Abstract Background Cesarean surgical site infections (SSIs) can be prevented by proper preoperative antibiotic prophylaxis. Differences in antibiotic selection in clinical practice exist according to obstetricians’ preferences despite clear guidelines on preoperative antibiotic prophylaxis. This study aimed to compare the efficacy of ampicillin and ceftriaxone in preventing cesarean SSIs. Methods The observational prospective cohort study was conducted at a tertiary hospital in Thailand from 1 January 2007 to 31 December 2012. Propensity scores for ceftriaxone prophylaxis were calculated from potential influencing confounders. The cesarean SSI rates of the ceftriaxone group vs. those of the ampicillin prophylactic group were estimated by multilevel mixed-effects Poisson regression nested by propensity score. Results Data of 4149 cesarean patients were collected. Among these, 911 patients received ceftriaxone whereas 3238 patients received ampicillin as preoperative antibiotic prophylaxis. The incidence of incisional SSIs was (0.1% vs. 1.2%; p = 0.001) and organ space SSIs was (1.2% vs. 2.9%; p = 0.003) in the ceftriaxone group compared with the ampicillin group. After adjusting for confounders, the rate ratios of incisional and organ/space SSIs in the ceftriaxone compared with the ampicillin group did not differ (RR, 0.23; 95% CI 0.03–1.78), and (RR, 1.62; 95% CI 0.83–3.18), respectively. Conclusion These data indicate no difference exists between ampicillin and ceftriaxone to prevent SSIs after cesarean section. Ampicillin may be used as antibiotic prophylaxis in cesarean section

    DeepThal: A Deep Learning-Based Framework for the Large-Scale Prediction of the α+-Thalassemia Trait Using Red Blood Cell Parameters

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    Objectives: To develop a machine learning (ML)-based framework using red blood cell (RBC) parameters for the prediction of the α+-thalassemia trait (α+-thal trait) and to compare the diagnostic performance with a conventional method using a single RBC parameter or a combination of RBC parameters. Methods: A retrospective study was conducted on possible couples at risk for fetus with hemoglobin H (Hb H disease). Subjects with molecularly confirmed normal status (not thalassemia), α+-thal trait, and two-allele α-thalassemia mutation were included. Clinical parameters (age and gender) and RBC parameters (Hb, Hct, MCV, MCH, MCHC, RDW, and RBC count) obtained from their antenatal thalassemia screen were retrieved and analyzed using a machine learning (ML)-based framework and a conventional method. The performance of α+-thal trait prediction was evaluated. Results: In total, 594 cases (female/male: 330/264, mean age: 29.7 ± 6.6 years) were included in the analysis. There were 229 normal controls, 160 cases with the α+-thalassemia trait, and 205 cases in the two-allele α-thalassemia mutation category, respectively. The ML-derived model improved the diagnostic performance, giving a sensitivity of 80% and specificity of 81%. The experimental results indicated that DeepThal achieved a better performance compared with other ML-based methods in terms of the independent test dataset, with an accuracy of 80.77%, sensitivity of 70.59%, and the Matthews correlation coefficient (MCC) of 0.608. Of all the red blood cell parameters, MCH < 28.95 pg as a single parameter had the highest performance in predicting the α+-thal trait with the AUC of 0.857 and 95% CI of 0.816–0.899. The combination model derived from the binary logistic regression analysis exhibited improved performance with the AUC of 0.868 and 95% CI of 0.830–0.906, giving a sensitivity of 80.1% and specificity of 75.1%. Conclusions: The performance of DeepThal in terms of the independent test dataset is sufficient to demonstrate that DeepThal is capable of accurately predicting the α+-thal trait. It is anticipated that DeepThal will be a useful tool for the scientific community in the large-scale prediction of the α+-thal trait
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