BackgroundMost studies assessing the association between caesarean delivery (CD) and childhood overweight/obesity have failed to account for important confounders, such as maternal prepregnancy body mass index (BMI) or the indication of the CD. Furthermore, within-family analyses have reported contradictory results. We aimed at evaluating the association between CD and offspring’s risk of overweight/obesity while adjusting for important confounders and accounting for correlations between siblings.MethodsWomen in the ‘Seguimiento Universidad de Navarra’ cohort provided structured information regarding their pregnancy history and their children’s health through online cross-sectional questionnaires. We calculated adjusted differences in BMI z-score and risk ratios (RR) for offspring’s overweight/obesity associated with CD, with hierarchical models to account for correlations between siblings. We also performed a within-family analysis in 341 siblings who were discordant in delivery mode, using conditional multivariable logistic regression.ResultsAmong the 2791 children analysed, those born by CD had higher average BMI z-scores (difference: 0.17; 95% CI 0.07 to 0.27) and higher risk of overweight/obesity (RR: 1.32, 95% CI 1.05 to 1.65) than children born vaginally. The association did not differ by maternal characteristics or offspring’s age strata, and the results were consistent in sensitivity analyses. Furthermore, within-family analysis showed that children born by CD had 2.67-fold higher risk of overweight/obesity (95% CI 1.10 to 5.12) than their peers born vaginally.ConclusionChildren born by CD have higher average BMI z-scores and higher risk of overweight/obesity than children born vaginally. The consistency of these findings across multiple approaches to address potential residual confounding likely suggests a true biological effect.
Introduction. The boundaries between MDS and AML are still a matter of debate. In the 2001 WHO Classification, the myeloblast count distinguishing AML and MDS was lowered from 30% to 20% of the bone marrow (BM) cells or peripheral blood (PB) leukocytes. It was justified on the basis that treating patients with 20-29% BM blasts with intensive chemotherapy showed a similar outcome to those with > 30% BM blasts. However, the better knowledge of the biology of both diseases is showing that in several cases AML and high risk MDS share identical genetic profiles, as it is well known in AML with myelodysplasia- related changes (AML-MRC). Currently there are new therapeutic options, less toxic, and suitable for elderly people.The threshold of 20% BM blast is artificial, but it is still the main criterion used in clinical trials and also in real life to discriminate patients that probably belong to the spectrum of the same biological entity. Treatment of patients with MDS or AML is widely based in this relatively arbitrary condition. Objective: To study if the threshold of 20% bone marrow blasts, distinguishing MDS with excess of blasts type 2 (MDS EB 2) and AML, is reproducible among different observers. Methods. 120 bone marrow samples from patients previously diagnosed with MDS-EB-2, AML or therapy-related myeloid neoplasms (t-MN) according to 2016 WHO classification were included. The diagnosis of MDS required cytogenetics and/or FISH, and the cases with AML should have been classified following the 2017 ELN recommendations, regarding immunophenotyping, cytogenetics and molecular biology. The design of the study was established to include cases with <40% BM blasts, WBC <25x109/L and less than 20% myeloblasts in peripheral blood. The proportion of samples from each category was not predefined. Specimens were collected from 12 hospitals and were evaluated by 12 morphologists. Each observer evaluated 20 samples, and each sample was analyzed independently by two morphologists. The second observer was blinded to the clinical and laboratory data, except for the peripheral blood (PB) counts. The interobserver concordance was evaluated using the Cohen kappa test. Results. Finally 116/120 samples were considered suitable for the study. Regarding 2016 WHO categories, 55 cases showed MDS EB-2, 44 AML-MRC, 8 t-MN, 4 AML- NOS, 2 NPM1-mutated AML, 2 RUNX1-RUNX1T1 AML, 1 BCR-ABL1+ AML. Next generation sequencing was performed in 79 cases. Discordance was observed in 34/116 cases (29.3%). 14 cases with MDS-EB2 (1 NPM1+) were classified as AML-MRC by the second observer, 16 AML cases as MDS EB-2, 3 MDS EB-2 as MDS- EB1 and 1 AML as MDS- EB1. The genetic and /or molecular profile of the discordant cases was heterogeneous. Regarding the threshold of 20% BM blasts, discrepancies were 31/116 (26.7%, I Kappa test = 0.46, moderate agreement). The agreement between MDS-EB-2 and AML-MRC, with discordance in 28/98 cases (28.6%), was moderate-fair (Kappa test= 0.42). Conclusion. The threshold of 20% BM blasts did not accurately separate AML from MDS EB-2. Particularly less concordance was seen for AML-MRC. Incorporation of genetic and molecular characteristics to the morphologic diagnosis is needed to optimize the definition of both entities. Acknowledgment: Angel Cedillo, Secretaría Técnica AMHH. Disclosures Font Lopez: GILEAD: Membership on an entity's Board of Directors or advisory committees; CELGENE-BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. Loscertales: Janssen, Abbvie, Astra-Zeneca, Beigene, Roche, Gilead: Consultancy; Janssen, Abbvie, Roche, Gilead: Speakers Bureau. Cedena: Janssen, Celgene and Abbvie: Honoraria.
Introduction Hyperinflammatory response induced by the SARS-CoV19 (CV) coronavirus is the main cause of morbidity and mortality. Numerous studies have pointed-out the main role of monocyte activation. In addition neutrophils alterations appear to differ pathophysiologically from the changes that occur in Influenza Virus (IV) infection. Due to the overlap of symptoms between these two entities, the search of analytical markers that help in early diagnostic orientation is considered of crucial importance. Changes in cell function, phenotype, and morphology in circulating leukocytes can be translated into numerical data obtained from an automated analyzer. The objective of our study is to generate an Artificial Intelligence Model from conventional hematological blood count parameters which be able to discriminate between CV and IV infection, in a fast and efficient maner. Methods This is a retrospective single-center study, performed between January-April 2020. The patients (n = 816) were divided into two groups: Patients who came for suspected COVID and had a positive RT-PCR (n = 408) and patients with a diagnosis of influenza confirmed by RT-PCR (n = 408). The database was divided into two random subgroups (n = 654) to train the model and another (n = 162) to validate it. The first hemogram on admission to the Emergency Department of these patients was performed on a Beckman-Coulter® DXH-900 equipment. Total white blood cells, absolute neutrophils, absolute lymphocytes, absolute monocytes, monocyte distribution wide (MDW) and Cell Morphological Data (CMDs) based on the impedance, conductivity and light scattering of these leukocyte subpopulations have been used to construct the model. Five algorithms have been evaluated using the R studio Software and the Caret (Classification and Regression Training) package: Linear Discriminant Analysis (LDA), K-Nearest Neighbor (kNN), Neural Networks (NN), Support Vector Machines (SVM) and Recursive partitioning (Rpart). Results The evaluation of the different models was based on the comparison of the efficacy obtained through a cross validation (10x). It was decided to choose the SVM model by presenting a median of the area under the ROC curve of 0,841. No data preprocessing was performed, and the parameters chosen for the model were: sigma = 0,014, C = 1 and Number of Support Vectors = 458. Parameters with greater importance (>80%) in the model, were CMDs based on Neutrophil Light Scattering (SDLNE, SDLAN, SDMNE and MNLNE). The analysis of results was performed using a confusion matrix, where the model predicts the diagnosis of each patient in the validation subgroup (Table 2). A ROC curve with an area of 0,892 was obtained, with a sensitivity and specificity of 80% and 85%, respectively (Fig 1). Conclusions The creation of prediction algorithms from hemogram parameters allow to discriminate between COVID 19 infection and influenza A and B with a high specificity and sensitivity in a fast way. This could be a great advance for the early diagnostic orientation and guide clinical decisions as soon as possible with the consequent clinical benefit. Disclosures No relevant conflicts of interest to declare.
ObjectiveTo determine the sensitivity, specificity, and positive and negative predictive values of a cervical cancer screening program based on visual inspection with acetic acid and Lugol’s iodine using a smartphone in a sub-urban area of very low resources in Kinshasa (Democratic Republic of Congo).MethodsThis cross-sectional validation study was conducted at Monkole Hospital and it included women between the ages of 25–70 years after announcing a free cervical cancer screening campaign through posters placed in the region of our hospital. Questionnaires collected sociodemographic and behavioral patients characteristics. In the first consultation, we gathered liquid-based cytology samples from every woman. At that time, local health providers performed two combined visual inspection techniques (5% acetic acid and Lugol’s iodine) while a photograph was taken with a smartphone. Two international specialists evaluated the results of the smartphone cervicography. When a visual inspection was considered suspicious, patients were offered immediate cryotherapy. Cytological samples were sent to the Pathology Department of the University of Navarra for cytological assessment and human papillomavirus (HPV) DNA genotyping.ResultsA total of 480 women participated in the study. The mean age was 44.6 years (range 25–65). Of all the patients, only 18.7% were infected with HPV (75% had high-risk genotypes). The most frequent high-risk genotype found was 16 (12.2%). The majority (88%) of women had normal cytology. After comparing combined visual inspection results with cytology, we found a sensitivity of 66.0%, a specificity of 87.8%, a positive predictive value of 40.7%, and a negative predictive value of 95.3% for any cytological lesion. The negative predictive value for high-grade lesions was 99.7%.ConclusionsCervical cancer screening through combined visual inspection, conducted by non-specialized personnel and monitored by experts through smartphones, shows encouraging results, ruling out high-grade cytological lesions in most cases. This combined visual inspection test is a valid and affordable method for screening programs in low-income areas.
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