Timing of Mycobacterium tuberculosis exposure explains variation in BCG effectiveness: a systematic review and meta-analysis.

RATIONALE: The heterogeneity in efficacy observed in studies of BCG vaccination is not fully explained by currently accepted hypotheses, such as latitudinal gradient in non-tuberculous mycobacteria exposure. METHODS: We updated previous systematic reviews of the effectiveness of BCG vaccination to 31 December 2020. We employed an identical search strategy and inclusion/exclusion criteria to these earlier reviews, but reclassified several studies, developed an alternative classification system and considered study demography, diagnostic approach and tuberculosis (TB)-related epidemiological context. MAIN RESULTS: Of 21 included trials, those recruiting neonates and children aged under 5 were consistent in demonstrating considerable protection against TB for several years. Trials in high-burden settings with shorter follow-up also showed considerable protection, as did most trials in settings of declining burden with longer follow-up. However, the few trials performed in high-burden settings with longer follow-up showed no protection, sometimes with higher case rates in the vaccinated than the controls in the later follow-up period. CONCLUSIONS: The most plausible explanatory hypothesis for these results is that BCG protects against TB that results from exposure shortly after vaccination. However, we found no evidence of protection when exposure occurs later from vaccination, which would be of greater importance in trials in high-burden settings with longer follow-up. In settings of declining burden, most exposure occurs shortly following vaccination and the sustained protection observed for many years thereafter represents continued protection against this early exposure. By contrast, in settings of continued intense transmission, initial protection subsequently declines with repeated exposure to Mycobacterium tuberculosis or other pathogens.

Endometrial thickness in the prediction of neonatal adverse outcomes in frozen cycles for singleton pregnancies.

RESEARCH QUESTION: Does endometrial thickness (EMT) predict adverse neonatal outcomes in singleton pregnancies after in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) frozen embryo transfer (FET)? DESIGN: This retrospective study involved 13,383 women undergoing IVF/ICSI FET cycles between January 2010 and December 2018 in Women's Hospital of Zhejiang University. The primary outcome was preterm delivery (PTD). The secondary outcomes were small for gestational age (SGA), large for gestational age (LGA) and low birthweight (LBW). RESULTS: A total of 13,383 FET cycles resulting in 5220 singleton live births and 8163 failed cycles were included. Multiple spline regression visualization showed an increasing risk of PTD and LBW for a thin EMT. By comparing multiple cut-off points using area under the curve, a cut-off point of 8 mm was identified, which was used to categorize EMT. A reference point of EMT greater than 8 mm was used; after adjusting for covariates, individuals with EMT less than 8 mm had an adjusted odds ratio of 1.75 (95% CI 1.30 to 2.34) for PTD, 1.57 (95% CI 1.09 to 2.26) for LBW, 0.97 (95% CI 0.63 to 1.50) for SGA and 1.04 (95% CI 0.79 to 1.37) for LGA. Additional analyses showed similar increasing risk with a thin endometrium for both PTD with and without caesarean section, and PTD with low and normal birthweight percentiles. CONCLUSION: A clinical cut-off point of 8 mm has been identified, below which risk of PTD and LBW increases in women undergoing IVF/ICSI.

Machine Learning Outperforms Existing Clinical Scoring Tools in the Prediction of Postoperative Atrial Fibrillation During Intensive Care Unit Admission After Cardiac Surgery.

OBJECTIVE(S): Using the Medical Information Mart for Intensive Care III (MIMIC-III) database, we compared the performance of machine learning (ML) to the to the established gold standard scoring tool (POAF Score) in predicting postoperative atrial fibrillation (POAF) during intensive care unit (ICU) admission after cardiac surgery. METHODS: Random forest classifier (RF), decision tree classifier (DT), logistic regression (LR), K neighbours classifier (KNN), support vector machine (SVM), and gradient boosted machine (GBM) were compared to the POAF Score. Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of ML models. POAF Score performance confidence intervals were generated using 1,000 bootstraps. Risk profiles for GBM were generated using Shapley additive values. RESULTS: A total of 6,349 ICU admissions encompassing 6,040 patients were included. POAF occurred in 1,364 of the 6,349 admissions (21.5%). For predicting POAF during ICU admission after cardiac surgery, GBM, LR, RF, KNN, SVM and DT achieved an AUC of 0.74 (0.71-0.77), 0.73 (0.71-0.75), 0.72 (0.69-0.75), 0.68 (0.67-0.69), 0.67 (0.66-0.68) and 0.59 (0.55-0.63) respectively. The POAF Score AUC was 0.63 (0.62-0.64). Shapley additive values analysis of GBM generated patient level explanations for each prediction. CONCLUSION: Machine learning models based on readily available preoperative data can outperform clinical scoring tools for predicting POAF during ICU admission after cardiac surgery. Explanatory models are shown to have potential in personalising POAF risk profiles for patients by illustrating probabilistic input variable contributions. Future research is required to evaluate the clinical utility and safety of implementing ML-driven tools for POAF prediction.

Neonatal outcome in 29 pregnant women with COVID-19: A retrospective study in Wuhan, China.

BACKGROUND: As of June 1, 2020, coronavirus disease 2019 (COVID-19) has caused more than 6,000,000 infected persons and 360,000 deaths globally. Previous studies revealed pregnant women with COVID-19 had similar clinical manifestations to nonpregnant women. However, little is known about the outcome of neonates born to infected women. METHODS AND FINDINGS: In this retrospective study, we studied 29 pregnant women with COVID-19 infection delivered in 2 designated general hospitals in Wuhan, China between January 30 and March 10, 2020, and 30 neonates (1 set of twins). Maternal demographic characteristics, delivery course, symptoms, and laboratory tests from hospital records were extracted. Neonates were hospitalized if they had symptoms (5 cases) or their guardians agreed to a hospitalized quarantine (13 cases), whereas symptom-free neonates also could be discharged after birth and followed up through telephone (12 cases). For hospitalized neonates, laboratory test results and chest X-ray or computed tomography (CT) were extracted from hospital records. The presence of antibody of SARS-CoV-2 was assessed in the serum of 4 neonates. Among 29 pregnant COVID-19-infected women (13 confirmed and 16 clinical diagnosed), the majority had higher education (56.6%), half were employed (51.7%), and their mean age was 29 years. Fourteen women experienced mild symptoms including fever (8), cough (9), shortness of breath (3), diarrhea (2), vomiting (1), and 15 were symptom-free. Eleven of 29 women had pregnancy complications, and 27 elected to have a cesarean section delivery. Of 30 neonates, 18 were admitted to Wuhan Children's Hospital for quarantine and care, whereas the other 12 neonates discharged after birth without any symptoms and had normal follow-up. Five hospitalized neonates were diagnosed as COVID-19 infection (2 confirmed and 3 suspected). In addition, 12 of 13 other hospitalized neonates presented with radiological features for pneumonia through X-ray or CT screening, 1 with occasional cough and the others without associated symptoms. SARS-CoV-2 specific serum immunoglobulin M (IgM) and immunoglobulin G (IgG) were measured in 4 neonates and 2 were positive. The limited sample size limited statistical comparison between groups. CONCLUSIONS: In this study, we observed COVID-19 or radiological features of pneumonia in some, but not all, neonates born to women with COVID-19 infection. These findings suggest that intrauterine or intrapartum transmission is possible and warrants clinical caution and further investigation. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2000031954 (Maternal and Perinatal Outcomes of Women with coronavirus disease 2019 (COVID-19): a multicenter retrospective cohort study).

Spatiotemporal analysis of regional TIA trends.

Background: There has been a decline in the stroke incidence across high income countries but such knowledge exists at Country or State rather than areal unit level such local government area (LGA). In this disease mapping study, we evaluate if there are local hot spots or temporal trends in TIA rate. Such knowledge will be of help in planning healthcare service delivery across regions. Methods: Linked hospital discharge data (Victorian Admitted Episodes Dataset or VAED) was used to collect TIA (defined by ICD-10-AM codes G450-G459) cases from 2001 to 2011. The State of Victoria is the second most populous state in Australia, with a population of 6.7 million and can be divided into 79 administrative units or LGA. The data is anonymized and contains residence of the patient in terms of LGA but not exact location. The date of the TIA event when the patient is admitted to hospital is provided in the dataset. The number of TIAs per year was aggregated for each LGA. Standardized TIA ratios were calculated by dividing actual over expected cases for each LGA per year. We used Integrated Nested Laplace Approximation (INLA) to perform spatial and spatiotemporal regression, adjusting for hypertension, sex and population, age (≥60), and socio-economic status (SES) decile within the LGA. The final model was chosen based on the lowest the Deviance Information Criterion (DIC) and Watanabe-Akaike information criteria (WAIC). Results: Choropleth maps showed a higher standardized TIA ratios in North-West rural region. Compared to the baseline model (DIC 13,159, WAIC 13,261), adding in a spatial random effect significantly improved the model (DIC 6,463, WAIC 6,667). However, adding a temporal component did not lead to a significant improvement (DIC 6,483, WAIC 6,707). Conclusion: Our finding suggests a statically significant spatial component to TIA rate over regional areas but no temporal changes or yearly trends. We propose that such exploratory method should be followed by evaluation of reasons for regional variations and which in turn can identify opportunities in primary prevention of stroke, and stroke care.

Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning.

CONTEXT: Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking. OBJECTIVES: This work aimed to establish effective models to predict early GDM. METHODS: Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively. RESULTS: A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5'-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66). CONCLUSIONS: We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.