Acute Kidney Injury after Pediatric Cardiac Surgery: Risk Factors and Outcomes. Proposal for a Predictive Model

Objectives: To characterize the epidemiology and risk factors for acute kidney injury (AKI) after pediatric cardiac surgery in our center, to determine its association with poor short-term outcomes, and to develop a logistic regression model that will predict the risk of AKI for the study population. Methods: This single-center, retrospective study included consecutive pediatric patients with congenital heart disease who underwent cardiac surgery between January 2010 and December 2012. Exclusion criteria were a history of renal disease, dialysis or renal transplantation. Results: Of the 325 patients included, median age three years (1 day---18 years), AKI occurred in 40 (12.3%) on the first postoperative day. Overall mortality was 13 (4%), nine of whom were in the AKI group. AKI was significantly associated with length of intensive care unit stay, length of mechanical ventilation and in-hospital death (p<0.01). Patients’ age and postoperative serum creatinine, blood urea nitrogen and lactate levels were included in the logistic regression model as predictor variables. The model accurately predicted AKI in this population, with a maximum combined sensitivity of 82.1% and specificity of 75.4%. Conclusions: AKI is common and is associated with poor short-term outcomes in this setting. Younger age and higher postoperative serum creatinine, blood urea nitrogen and lactate levels were powerful predictors of renal injury in this population. The proposed model could be a useful tool for risk stratification of these patients.

A cumulative risk model of child physical maltreatment potential: findings from a community-based study

"Published online before print November 20, 2015"

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Validation of Logistic Regression Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.

Imputation in data fusion of heterogeneous data sets a model-based numerical experiment

Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.

Shrews in suburbia: an application of Goodman's extinction model

An equation is applied for calculating the expected persistence time of an unstructured population of the white-toothed shrew Crocidura russula from Preverenges, a suburban area in western Switzerland. Population abundance data from March and November between 1977 and 1988 were fit to the logistic density dependence model to estimate mean population growth rate as a function of population density. The variance in mean growth rate was approximated with two different models. The largest estimated persistence time was less than a few decades, the smallest less than 10 years. The results are sensitive to the magnitude of variance in population growth rate. Deviations from the logistic density dependence model in November are quite well explained by weather variables but those in March are uncorrelated with weather variables. Variability in population growth rates measured in winter months may be better explained by behavioural mechanisms. Environmental variability, dispersal of juveniles and refugia within the range of the population may contribute to its long-term survival.

Inventory-routing model, for a multi-period problem with stochastic and deterministic demand

The need for integration in the supply chain management leads us to considerthe coordination of two logistic planning functions: transportation andinventory. The coordination of these activities can be an extremely importantsource of competitive advantage in the supply chain management. The battle forcost reduction can pass through the equilibrium of transportation versusinventory managing costs. In this work, we study the specific case of aninventory-routing problem for a week planning period with different types ofdemand. A heuristic methodology, based on the Iterated Local Search, isproposed to solve the Multi-Period Inventory Routing Problem with stochasticand deterministic demand.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

A recoverable production planning model

Aware of the importance of developing new alternatives to improve the performance of the companies, our purpose in this paper is to develop a medium term production planning model that deals with the concepts of Partnership and Reverse Logistics. Our model takes advantage of the synergies of integration, developing a model for global production planning that generates the optimal production and purchasing schedule for all the companies integrating a logistic chain. In a second part of the paper we incorporate products returns to the first model proposed, and analyze the implications they have over this model. We use some examples with different configurations of supply chains varying the number of production plants, distribution centers and recovery plants. To solve the model we have combined optimization and simulation procedures.

Prediction of 18-month survival in patients with primary myelodysplastic syndrome. A regression model and scoring system based on the combination of chromosome findings and the Bournemouth score.

The predictive potential of six selected factors was assessed in 72 patients with primary myelodysplastic syndrome using univariate and multivariate logistic regression analysis of survival at 18 months. Factors were age (above median of 69 years), dysplastic features in the three myeloid bone marrow cell lineages, presence of chromosome defects, all metaphases abnormal, double or complex chromosome defects (C23), and a Bournemouth score of 2, 3, or 4 (B234). In the multivariate approach, B234 and C23 proved to be significantly associated with a reduction in the survival probability. The similarity of the regression coefficients associated with these two factors means that they have about the same weight. Consequently, the model was simplified by counting the number of factors (0, 1, or 2) present in each patient, thus generating a scoring system called the Lausanne-Bournemouth score (LB score). The LB score combines the well-recognized and easy-to-use Bournemouth score (B score) with the chromosome defect complexity, C23 constituting an additional indicator of patient outcome. The predicted risk of death within 18 months calculated from the model is as follows: 7.1% (confidence interval: 1.7-24.8) for patients with an LB score of 0, 60.1% (44.7-73.8) for an LB score of 1, and 96.8% (84.5-99.4) for an LB score of 2. The scoring system presented here has several interesting features. The LB score may improve the predictive value of the B score, as it is able to recognize two prognostic groups in the intermediate risk category of patients with B scores of 2 or 3. It has also the ability to identify two distinct prognostic subclasses among RAEB and possibly CMML patients. In addition to its above-described usefulness in the prognostic evaluation, the LB score may bring new insights into the understanding of evolution patterns in MDS. We used the combination of the B score and chromosome complexity to define four classes which may be considered four possible states of myelodysplasia and which describe two distinct evolutional pathways.

MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status.

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.

Model development for capercaillie (Tetrao urogallus) habitat in the Jura Mountains (Western Switzerland)

Capercaillie, Tetrao urogallus, is a threatened species in central Europe, and Swiss populations declined 40 to 50 % between 1970 and 1985. Capercaillie are sensitive to forest structure, and loss of habitat is a major cause of their decline. Knowledge of habitat characteristics is therefore essential for capercaillie conservation. Here, we present models predicting capercaillie probability of occurrence, based on relevant structural habitat variables. Models were built using multiple logistic regression analyses on capercaillie presence/absence data. Vegetation survey was carried out in July 1999 in a 170-km2 forested area (Jura mountains, canton de Vaud, western Switzerland) inhabited by capercaillie and presence/absence of the species was assessed according to dropping presence/absence. The survey was based on 10-m-radius sample plots each in a 1-km2 forest patch (n = 76 with capercaillie droppings, n = 80 without). A first model included seven out of 27 measured habitat variables and a second model only four. The latter model best represents practical needs. It includes three variables which had a negative impact on capercaillie presence: tree and shrub covers and spruce, Picea excelsa, shrub cover, and one which had a positive effect: bilberry, Vaccinium myrtillus, cover, highlighting that capaercaillie selected open forest with high bilberry abundance. The model can be used to map potential capercaillie habitat distribution and to manage the habitat in favour of capercaillie (protection and adapted forestry practices) in the Swiss Jura mountains.

Derivation and validation of a prognostic model for pulmonary embolism.

RATIONALE: An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment. OBJECTIVES: To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes. METHODS: We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France. MEASUREMENTS: We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. MAIN RESULTS: The prediction rule is based on 11 simple patient characteristics that were independently associated with mortality and stratifies patients with pulmonary embolism into five severity classes, with 30-day mortality rates of 0-1.6% in class I, 1.7-3.5% in class II, 3.2-7.1% in class III, 4.0-11.4% in class IV, and 10.0-24.5% in class V across the derivation and validation samples. Inpatient death and nonfatal complications were <or= 1.1% among patients in class I and <or= 1.9% among patients in class II. CONCLUSIONS: Our rule accurately classifies patients with pulmonary embolism into classes of increasing risk of mortality and other adverse medical outcomes. Further validation of the rule is important before its implementation as a decision aid to guide the initial management of patients with pulmonary embolism.

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed

Development of a weather-based model for Botrytis leaf blight of onion

ABSTRACT In the present study, onion plants were tested under controlled conditions for the development of a climate model based on the influence of temperature (10, 15, 20 and 25°C) and leaf wetness duration (6, 12, 24 and 48 hours) on the severity of Botrytis leaf blight of onion caused by Botrytis squamosa. The relative lesion density was influenced by temperature and leaf wetness duration (P <0.05). The disease was most severe at 20°C. Data were subjected to nonlinear regression analysis. Beta generalized function was used to adjust severity and temperature data, while a logistic function was chosen to represent the effect of leaf wetness on the severity of Botrytis leaf blight. The response surface obtained by the product of two functions was expressed as ES = 0.008192 * (((x-5)1.01089) * ((30-x)1.19052)) * (0.33859/(1+3.77989 * exp (-0.10923*y))), where ES represents the estimated severity value (0.1); x, the temperature (°C); and y, the leaf wetness (in hours). This climate model should be validated under field conditions to verify its use as a computational system for the forecasting of Botrytis leaf blight in onion.