253875 research outputs found
Sort by
Competition for Materno-Fetal Resource Partitioning in a Rabbit Model of Undernourished Pregnancy
No full text
Effect of maternal death on child survival in rural West Africa: 25 years of prospective surveillance data in The Gambia
No full textThe death of a mother is a tragedy in itself but it can also have devastating effects for the survival of her children. We aim to explore the impact of a mother’s death on child survival in rural Gambia, West Africa.We used 25 years of prospective surveillance data from the Farafenni Health and Demographic surveillance system (FHDSS). Mortality rates per 1,000 child-years up to ten years of age were estimated and Kaplan-Meier survival curves plotted by maternal vital status. Cox proportional hazard models were used to examine factors associated with child survival. December 2014, a total of 2, 221 (7.8%) deaths occurred during 152,906 child-years of follow up. Overall mortality rate was 14.53 per 1,000 child-years (95% CI: 13.93–15.14). Amongst those whose mother died, the rate was 25.89 (95% CI: 17.99–37.25) compared to 14.44 (95% CI: 13.84–15.06) per 1,000 child-years for those whose mother did not die. Children were 4.66 (95% CI: 3.15–6.89) times more likely to die if their mother died compared to those with a surviving mother. Infants whose mothers died during delivery or shortly after were up to 7 times more likely to die within the first month of life compared to those whose mothers survived. Maternal vital status was significantly associated with the risk of dying within the first 2 years of life (p-value <0.05), while this was no longer observed for children over 2 years of age (P = 0.872). Other factors associated with an increased risk of dying were living in more rural areas, and birth spacing and year of birth.Mother’s survival is strongly associated with child survival. Our findings highlight the importance of the continuum of care for both the mother and child not only throughout pregnancy, and childbirth but beyond 6 weeks post-partum
Efficiency of a clinical prediction model for selective rapid testing in children with pharyngitis: A prospective, multicenter study
No full textThere is controversy whether physicians can rely on signs and symptoms to select children with pharyngitis who should undergo a rapid antigen detection test (RADT) for group A streptococcus (GAS). Our objective was to evaluate the efficiency of signs and symptoms in selectively testing children with pharyngitis.-index and calibration) and efficiency of the model-based strategy (proportion of participants in whom RADT could be avoided).-index of 0.73; calibration of the model was good. With the model-based strategy, RADT could be avoided in 6.6% of participants (95% confidence interval 4.7% to 8.5%), as compared to a RADT-for-all strategy.This study demonstrated that relying on signs and symptoms for selectively testing children with pharyngitis is not efficient. We recommend using a RADT in all children with pharyngitis
Comprehensive target capture/next-generation sequencing as a second-tier diagnostic approach for congenital muscular dystrophy in Taiwan
No full textCongenital muscular dystrophy (CMD) is a heterogeneous disease entity. The detailed clinical manifestation and causative gene for each subgroup of CMD are quite variable. This study aims to analyze the phenotypes and genotypes of Taiwanese patients with CMD as the epidemiology of CMD varies among populations and has been scantly described in Asia.A total of 48 patients suspected to have CMD were screened and categorized by histochemistry and immunohistochemistry studies. Different genetic analyses, including next-generation sequencing (NGS), were selected, based on the clinical and pathological findings. mutations were found in the two patients with inflammatory change on muscle pathology. They were clinically characterized by neck flexion limitation and early joint contracture, but no cardiac problem had developed yet.Muscle pathology remains helpful in guiding further molecular analyses by direct sequencing of certain genes or by target capture/NGS as a second-tier diagnostic tool, and is crucial for establishing the genotype-phenotype correlation. We also determined the frequencies of the different types of CMD in our cohort which is important for the development of a specific care system for each disease
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
No full textCurrent approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction.Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins).24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm.Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others
The Value of a Checklist for Child Abuse in Out-of-Hours Primary Care: To Screen or Not to Screen
No full textTo assess the diagnostic value of the screening instrument SPUTOVAMO-R2 (checklist, 5 questions) for child abuse at Out-of-hours Primary Care locations (OPC), by comparing the test outcome with information from Child Protection Services (CPS). Secondary, to determine whether reducing the length of the checklist compromises diagnostic value.All children (<18 years) attending one of the participating OPCs in the region of Utrecht, the Netherlands, in a year time, were included. The checklist is an obligatory field in the electronic patient file. CPS provided data on all checklist positives and a sample of 5500 checklist negatives (dataset). The checklist outcome was compared with a report to CPS in 10 months follow up after the OPC visit.The checklist was filled in for 50671 children; 108 (0.2%) checklists were positive. Within the dataset, 61 children were reported to CPS, with emotional neglect as the most frequent type of abuse (32.8%). The positive predictive value (PPV) of the checklist for child abuse was 8.3 (95% CI 3.9–15.2). The negative predictive value (NPV) was 99.1 (98.8–99.3), with 52 false negatives. When the length of the checklist was reduced to two questions closely related to the medical process (SPUTOVAMO-R3), the PPV was 9.1 (3.7–17.8) and the NPV 99.1 (98.7–99.3). These two questions are on the injury in relation to the history, and the interaction between child and parents.The checklist SPUTOVAMO-R2 has a low detection rate of child abuse within the OPC setting, and a high false positive rate. Therefore, we recommend to use the shortened checklist only as a tool to increase the awareness of child abuse and not as a diagnostic instrument
Follistatin attenuates radiation-induced fibrosis in a murine model
No full textFibrosis can be a disabling, severe side effect of radiotherapy that can occur in patients, and for which there is currently no effective treatment. The activins, proteins which are members of the TGFβ superfamily, have a major role in stimulating the inflammatory response and subsequent fibrosis. Follistatin is an endogenous protein that binds the activins virtually irreversibly and inhibits their actions. These studies test if follistatin can attenuate the fibrotic response using a murine model of radiation-induced fibrosis.C57BL/6 mice were subcutaneously injected with follistatin 24 hours prior to irradiation. Mice were irradiated in a 10 x 10 mm square area of the right hind leg with 35 Gy and were given follistatin 24 hours before radiation and three times a week for six months following. Leg extension was measured, and tissue was collected for histological and molecular analysis to evaluate the progression of the radiation-induced fibrosis.Leg extension was improved in follistatin treated mice compared to vehicle treated mice at six months after irradiation. Also, epidermal thickness and cell nucleus area of keratinocytes were decreased by the follistatin treatment compared to the cells in irradiated skin of control mice. Finally, the gene expression of transforming growth factor β1 (Tgfb1), and smooth muscle actin (Acta2) were decreased in the irradiated skin and Acta2 and inhibin βA subunit (Inhba) were decreased in the irradiated muscle of the follistatin treated mice.Follistatin attenuated the radiation-induced fibrotic response in irradiated mice. These studies provide the data to support further investigation of the use of follistatin to reduce radiation-induced fibrosis in patients undergoing radiotherapy for cancer
Development of a new risk model for predicting cardiovascular events among hemodialysis patients: Population-based hemodialysis patients from the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS)
No full textCardiovascular (CV) events are the primary cause of death and becoming bedridden among hemodialysis (HD) patients. The Framingham risk score (FRS) is useful for predicting incidence of CV events in the general population, but is considerd to be unsuitable for the prediction of the incidence of CV events in HD patients, given their characteristics due to atypical relationships between conventional risk factors and outcomes. We therefore aimed to develop a new prognostic prediction model for prevention and early detection of CV events among hemodialysis patients.We enrolled 3,601 maintenance HD patients based on their data from the Japan Dialysis Outcomes and Practice Patterns Study (J-DOPPS), phases 3 and 4. We longitudinaly assessed the association between several potential candidate predictors and composite CV events in the year after study initiation. Potential candidate predictors included the component factors of FRS and other HD-specific risk factors. We used multivariable logistic regression with backward stepwise selection to develop our new prediction model and generated a calibration plot. Additinially, we performed bootstrapping to assess the internal validity.We observed 328 composite CV events during 1-year follow-up. The final prediction model contained six variables: age, diabetes status, history of CV events, dialysis time per session, and serum phosphorus and albumin levels. The new model showed significantly better discrimination than the FRS, in both men (c-statistics: 0.76 for new model, 0.64 for FRS) and women (c-statistics: 0.77 for new model, 0.60 for FRS). Additionally, we confirmed the consistency between the observed results and predicted results using the calibration plot. Further, we found similar discrimination and calibration to the derivation model in the bootstrapping cohort.We developed a new risk model consisting of only six predictors. Our new model predicted CV events more accurately than the FRS