In-hospital outcomes of transapical versus surgical aortic valve replacement: from the U.S. national inpatient sample

OBJECTIVE To compare the outcomes of transapical transcatheter aortic valve replacement(TA-TAVR)and surgical aortic valve replacement(SAVR)using a large US population sample.METHODS The U.S.National Inpatient Sample was queried for all patients who underwent TA-TAVR or SAVR during the years2016-2017.The primary outcome was all-cause in-hospital mortality.Secondary outcomes were in-hospital stroke,pericardiocentesis,pacemaker insertion,mechanical ventilation,vascular complications,major bleeding,acute kidney injury,length of stay,and cost of hospitalization.Outcomes were modeled using multi-variable logistic regression for binary outcomes and generalized linear models for continuous outcomes.RESULTS A total of 1560 TA-TAVR and 44,280 SAVR patients were included.Patients who underwent TA-TAVR were older and frailer.Compared to SAVR,TA-TAVR correlated with a higher mortality(4.5%vs.2.7%,effect size(SMD)=0.1)and higher periprocedural complications.Following multivariable analysis,both TA-TAVR and SAVR had a similar adjusted risk for in-hospital mortality.TA-TAVR correlated with lower odds of bleeding with(adjusted OR(aOR)=0.26;95%CI:0.18-0.38;P<0.001),and a shorter length of stay(adjusted mean ratio(aMR)=0.77;95%CI:0.69-0.84;P<0.001),but higher cost(aMR=1.18;95%CI:1.10-1.28;P<0.001).No significant differences in other study outcomes.In subgroup analysis,TA-TAVR in patients with chronic lung disease had higher odds for mortality(aOR=3.11;95%CI:1.37-7.08;P=0.007).CONCLUSION The risk-adjusted analysis showed that TA-TAVR has no advantage over SAVR except for patients with chronic lung disease where TA-TAVR has higher mortality.

Fitting dynamic models to epidemic outbreaks with quantified uncertainty:A primer for parameter uncertainty,identifiability,and forecasts

Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.

Exploring predictive frameworks for malaria in Burundi

In Burundi,malaria infection has been increasing in the last decade despite efforts to increase access to health services,and several intervention programs.The use of heterogeneous data can help to build predictive models of malaria cases.We built predictive frameworks:the generalized linear model(GLM),and artificial neural network(ANN),to predict malaria cases in four sub-groups and the overall general population.Descriptive results showed that more than half of malaria infections are observed in pregnant women and children under 5 years,with high burden to children between 12 and 59 months.Modelling results showed that,ANN model performed better in predicting total cases compared to GLM.Both model frameworks showed that education rates and Insecticide Treated Bed Nets(ITNs)had decreasing effects on malaria cases,some other variables had an increasing effect.Thus,malaria control and prevention interventions program are encouraged to understand those variables,and take appropriate measures such as providing ITNs,sensitization in schools and the communities,starting within high dense communities,among others.Early prediction of cases can provide timely information needed to be proactive for intervention strategies,and it can help to mitigate the epidemics and reduce its impact on populations and the economy.