Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

Objective: Identifying high-risk children with a poor prognosis in pediatric intensive care units (PICUs) is critical. The aim of this study was to assess the predictive value of early plasma osmolality levels in determining the clinical outcomes of children in PICUs.Methods: We retrospectively assessed critically ill children in a pediatric intensive care database. The locally weighted-regression scatter-plot smoothing (LOWESS) method was used to explore the approximate relationship between plasma osmolality and in-hospital mortality. Linear spline functions and stepwise expansion models were applied in conjunction with a multivariate logistic regression to further analyze this relationship. A subgroup analysis by age and complications was performed.Results: In total, 5,620 pediatric patients were included in this study. An approximately “U”-shaped relationship between plasma osmolality and mortality was detected using LOWESS. In the logistic regression model using a linear spline function, plasma osmolality ≥ 290 mmol/L was significantly associated with in-hospital mortality [odds ratio (OR) 1.020, 95% confidence interval (CI) 1.010–1.031], while plasma osmolality <290 mmol/L was not significantly associated with in-hospital mortality (OR 0.990, 95% CI 0.966–1.014). In the logistic regression model with plasma osmolality as a tri-categorical variable, only high osmolality was significantly associated with in-hospital mortality (OR 1.90, 95% CI 1.38–2.64), whereas low osmolality was not associated with in-hospital mortality (OR 1.28, 95% CI 0.84–1.94). The interactions between plasma osmolality and age or complications were not significant.Conclusion: High osmolality, rather than low osmolality, can predict a poor prognosis in children in PICUs.

Pengaruh Indeks Massa Tubuh dan TrigliseridaTerhadap Gula Darah dengan Model Regresi Nonparametrik Spline Biprediktor

The regression approach can be carried out using three approaches namely parametric, nonparametric and semiparametric approaches. Nonparametric regression is a statistical method used to see the relationship between the response variable and the predictor variable when the shape of the data curve is unknown. Diabetes mellitus (DM) or commonly called diabetes is a disease that is found and observed in various parts of the world today. DM is often marked by a significant increase in blood sugar levels. In this study using blood sugar levels as response variables, body mass index and triglycerides as predictor variables. Data were analyzed using truncated linear spline with one, two and three point knots experiments. The best model is obtained based on the minimum generalized cross validation (GCV) value. The results obtained that the best model is linear spline using three point knots.

A numerical algorithm based on modified orthogonal linear spline for solving a coupled nonlinear inverse reaction-diffusion problem

In this paper, a modified orthogonal linear spline (OL-spline) is used for the numerical solution of a coupled nonlinear inverse reaction-diffusion problem to determine the unknown boundary conditions. The convergence properties of the new linear combination are obtained. A quasi-linearization technique is utilized to linearize the nonlinear term in the equations. This process produces a linear system of equations which can be solved easily. Using the new inequalities, error estimation and convergence of the proposed method are investigated. Two numerical examples are given to demonstrate the computational efficiency of the method and also the experimental convergence rate of examples are obtained.

Pemodelan Regresi Nonparametrik Spline Linear Persentase Penduduk Miskin di Kalimantan

Poverty is a social problem faced in almost every country. Based on BPS data published in 2018, East Kalimantan Province has a population of poor people of 222.39 thousand people or around 6.06 percent. In March 2018, the number of poor people was 218.90 thousand people or about 6.03 percent, which means the number of poor people had increased by an absolute 3.49 thousand people, this caused the percentage of poor people to rise 0.03 percent. In this study identified factors that influence the percentage of poor population using a linear spline nonparametric regression model. The data used in this study are data from the Central Statistics Agency (BPS) in 5 provinces in Kalimantan. In the nonparametric linear spline regression method using the optimal knot point based on the smallest GCV value. The results obtained an R2 value of 74.48% which shows that the model formed is feasible to be used to model the data pattern and there are 5 variables that have a significant effect on the Percentage of Poor Population, namely Population Growth Rate, School Length Average, School Old School Expectancy Rate, Level Open Unemployment, Labor Force Participation Rate.

A Linear Spline Markov Approximation Method for Random Maps with Position Dependent Probabilities

We present a rigorous convergence analysis of a linear spline Markov finite approximation method for computing stationary densities of random maps with position dependent probabilities, which consist of several chaotic maps. The whole analysis is based on a new Lasota–Yorke-type inequality for the Markov operator associated with the random map, which is better than the previous one in the literature and much simpler to obtain. We also present numerical results to support our theoretical analysis.