scholarly journals Use of plasma lactate level to predict 28-day mortality in non-elderly and elderly sepsis patients based on the MIMIC-III database

2020 ◽  
Author(s):  
Yihua Dong ◽  
Xiaoyang Miao ◽  
Yufeng Hu ◽  
Yueyue Huang ◽  
Jie Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Elderly Patients ◽  
Lactate Level ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Plasma Lactate ◽  
Logistic Regression Models ◽  
Lactate Levels ◽  
Plasma Lactate Level

Abstract Purpose: We co mpared the use of lactate level for predicting 28-day mortality in non-elderly (<65 years) and elderly (≥65 years) sepsis patients who were admitted to an intensive care unit (ICU). A multivariate logistic regression model was established to predict 28-day mortality for each group. Methods: This retrospective study used the Medical Information Mart for Intensive Care Ⅲ, a publicly available database of ICUs. Eligible sepsis patients were at least 18 years-old, hospitalized for at least 24 h, and had lactate levels measured in the ICU. Univariate logistic regression analysis and step-wise multivariable logistic regression models were used to identify factors associated with 28-day mortality. Results: The 28-day mortality was 30.9% among the 2482 patients, and was significantly greater in elderly than non-elderly patients. Within each age group, the lactate level was greater for non-survivors than survivors. Among non-survivors, the lactate level was significantly higher for the non-elderly than the elderly. Adjusted logistic regression analysis showed that non-elderly patients with lactate levels of 2.0–4.0 mmol/L and above 4.0 mmol/L had greater risk of death than those with normal lactate levels. For all patients, the stepwise logistic regression model had an area under the receiver operating curve (AUROC) of 0.752; for non-elderly patients, the model had an AUROC of 0.793; for elderly patients, the model had an AUROC of 0.711. The Hosmer-Lemeshow test indicated acceptable goodness-of-fit for each group (P=0.206, P=0.646, and P= 0.482, respectively). Conclusion: In our population of sepsis patients, the lactate level was about 0.9 mmol/L lower in elderly non-survivors than non-elderly survivors. A plasma lactate level above 2.0 mmol/L was an independent risk factor for death at 28-days among non-elderly patients. Our logistic regression models effectively predicted 28-day mortality of sepsis patients in different age groups.

scholarly journals Use of plasma lactate level to predict 28-day mortality in non-elderly and elderly sepsis patients based on the MIMIC-III database

2019 ◽  
Author(s):  
Yihua Dong ◽  
Xiaoyang Miao ◽  
Yufeng Hu ◽  
Yueyue Huang ◽  
Jie Chen ◽  
...  
Keyword(s):  
Intensive Care ◽  
Elderly Patients ◽  
Lactate Level ◽  
Medical Information ◽  
Characteristic Curve ◽  
Age Group ◽  
Plasma Lactate ◽  
Risk Of Death ◽  
Lactate Levels ◽  
Plasma Lactate Level

Abstract Purpose We compared the use of lactate level for predicting 28-day mortality in non-elderly (<65 years) and elderly (≥65 years) sepsis patients who were admitted to an intensive care unit (ICU). Methods This retrospective study used the Medical Information Mart for Intensive Care III, a publicly available database of ICUs. Eligible sepsis patients were at least 18 years-old, hospitalized for at least 24 h, and had lactate levels measured in the ICU. The relationship of lactate level with 28-day mortality was determined. Results The 28-day mortality was 30.9% among the 2482 patients, and was significantly greater in elderly than non-elderly patients. Within each age group, the lactate level was greater for non-survivors than survivors. Among non-survivors, the lactate level was significantly higher for the non-elderly than the elderly. Adjusted logistic regression analysis showed that elderly and non-elderly patients with lactate levels of 2.0–4.0 mmol/L and above 4.0 mmol/L had greater risk of death than those with normal lactate. Cirrhosis, chronic renal failure, and malignancy were independent risk factors for 28-day mortality in each age group. Based on a lactate cut-off level of 2.1 mmol/L, the area under the receiver operating characteristic curve was 0.628 (overall), 0.707 (non-elderly), and 0.585 (elderly). Conclusion In our population of sepsis patients, a plasma lactate level above 2.0 mmol/L was an independent risk factor for death at 28-days. The lactate level among elderly non-survivors was about 0.9 mmol/L lower than among non-elderly survivors. Lactate was a better prognostic indicator for non-elderly than elderly patients.

scholarly journals Aplicación de modelos de regresión logística en metodología observacional: modalidades de competición en la iniciación al fútbol.

2015 ◽  
Vol 32 (1) ◽  
pp. 288 ◽  
Author(s):  
Daniel Lapresa ◽  
Javier Arana ◽  
M.Teresa Anguera ◽  
J.Ignacio Pérez-Castellanos ◽  
Mario Amatria
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Multiple Logistic Regression ◽  
Observational Methodology ◽  
Logistic Regression Models ◽  
Game Format ◽  
Simple Logistic

This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

scholarly journals Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1517
Author(s):  
Hao Yang Teng ◽  
Zhengjun Zhang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Treatment Effects ◽  
Real Data ◽  
Medical Data ◽  
Severe Problem ◽  
New Model ◽  
Logistic Regression Models

Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications, the model provides both significant and more meaningful results.

scholarly journals Prediction of independence in bowel function after spinal cord injury: validation of a logistic regression model

Spinal Cord ◽  
2020 ◽  
Author(s):  
Omar Khan ◽  
Jetan H. Badhiwala ◽  
Michael G. Fehlings
Keyword(s):  
Spinal Cord ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Clinical Utility ◽  
Logistic Regression Model ◽  
Bowel Function ◽  
Motor Score ◽  
Logistic Regression Models ◽  
Cord Injury

Abstract Study design Retrospective analysis of prospectively collected data. Objectives Recently, logistic regression models were developed to predict independence in bowel function 1 year after spinal cord injury (SCI) on a multicenter European SCI (EMSCI) dataset. Here, we evaluated the external validity of these models against a prospectively accrued North American SCI dataset. Setting Twenty-five SCI centers in the United States and Canada. Methods Two logistic regression models developed by the EMSCI group were applied to data for 277 patients derived from three prospective multicenter SCI studies based in North America. External validation was evaluated for both models by assessing their discrimination, calibration, and clinical utility. Discrimination and calibration were assessed using ROC curves and calibration curves, respectively, while clinical utility was assessed using decision curve analysis. Results The simplified logistic regression model, which used baseline total motor score as the predictor, demonstrated the best performance, with an area under the ROC curve of 0.869 (95% confidence interval: 0.826–0.911), a sensitivity of 75.5%, and a specificity of 88.5%. Moreover, the model was well calibrated across the full range of observed probabilities and displayed superior clinical benefit on the decision curve. Conclusions A logistic regression model using baseline total motor score as a predictor of independent bowel function 1 year after SCI was successfully validated against an external dataset. These findings provide evidence supporting the use of this model to enhance the care for individuals with SCI.

scholarly journals A-140 Single-Item Screening for Anxiety and Depression using the Neurobehavioral Symptom Inventory

2020 ◽  
Vol 35 (6) ◽  
pp. 933-933
Author(s):  
Rolin S ◽  
Kitchen Andren K ◽  
Mullen C ◽  
Kurniadi N ◽  
Davis J
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Anxiety And Depression ◽  
Average Score ◽  
Characteristic Analysis ◽  
Logistic Regression Models ◽  
Symptom Inventory

Abstract Objective Previous research in a Veterans Affairs sample proposed using single items on the Neurobehavioral Symptom Inventory (NSI) to screen for anxiety (item 19) and depression (item 20). This study examined the approach in an outpatient physical medicine and rehabilitation sample. Method Participants (N = 84) underwent outpatient neuropsychological evaluation using the NSI, BDI-II, GAD-7, MMPI-2-RF, and Memory Complaints Inventory (MCI) among other measures. Anxiety and depression were psychometrically determined via cutoffs on the GAD-7 (&gt;4) and MMPI-2-RF ANX (&gt;64 T), and BDI-II (&gt;13) and MMPI-2-RF RC2 (&gt;64 T), respectively. Analyses included receiver operating characteristic analysis (ROC) and logistic regression. Logistic regression models used dichotomous anxiety and depression as outcomes and relevant NSI items and MCI average score as predictors. Results ROC analysis using NSI items to classify cases showed area under the curve (AUC) values of .77 for anxiety and .85 for depression. The logistic regression model predicting anxiety correctly classified 80% of cases with AUC of .86. The logistic regression model predicting depression correctly classified 79% of cases with AUC of .88. Conclusion Findings support the utility of NSI anxiety and depression items as screening measures in a rehabilitation population. Consideration of symptom validity via the MCI improved classification accuracy of the regression models. The approach may be useful in other clinical settings for quick assessment of psychological issues warranting further evaluation.

Lactic Acidemia in Reye's Syndrome

PEDIATRICS ◽  
1982 ◽  
Vol 69 (1) ◽  
pp. 64-69
Author(s):  
James H. Tonsgard ◽  
Peter R. Huttenlocher ◽  
Ronald A. Thisted
Keyword(s):  
Lactate Level ◽  
Clinical Stage ◽  
Ammonia Level ◽  
Clinical State ◽  
Plasma Lactate ◽  
Reye's Syndrome ◽  
Metabolic Component ◽  
Lactate Levels ◽  
Lactic Acidemia ◽  
Plasma Lactate Level

Plasma lactate level was measured in 21 patients with Reye's syndrome and was compared with neurologic state as rated on a simple coma scale. Significant elevations in plasma lactate, ranging from 2 to 15 mEq/liter, were noted in all patients. There was a close correspondence between stage of coma at the time the sample was drawn and lactate levels. The correlation of plasma lactate level with clinical stage could not be accounted for by differences in glucose, Po2, Pco2, pH, blood pressure, or serum osmolality. In contrast, blood ammonia level correlated with the severity of the encephlopathy early in the course only and often returned to normal in patients with persistent coma. Other measurements of hepatic dysfunction such as SGOT and SGPT levels failed to correlate with clinical state. All patients had a metabolic acidosis; in five patients it was uncompensated. Lactate accounted for nearly all (mean 81%) of the observed base deficit. The findings suggest that lactic acidemia is an important metabolic component of Reye's syndrome.

Bootstrap internal validation command for predictive logistic regression models

2021 ◽  
Vol 21 (2) ◽  
pp. 498-509
Author(s):  
B. M. Fernandez-Felix ◽  
E. García-Esquinas ◽  
A. Muriel ◽  
A. Royuela ◽  
J. Zamora
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Predictive Model ◽  
Performance Measures ◽  
Predictive Models ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Model Performance ◽  
Internal Validation ◽  
Logistic Regression Models

Overfitting is a common problem in the development of predictive models. It leads to an optimistic estimation of apparent model performance. Internal validation using bootstrapping techniques allows one to quantify the optimism of a predictive model and provide a more realistic estimate of its performance measures. Our objective is to build an easy-to-use command, bsvalidation, aimed to perform a bootstrap internal validation of a logistic regression model.

scholarly journals Determinants of Contraceptive Practice in Oman by Using Multilevel Modeling

2015 ◽  
Vol 20 (1) ◽  
pp. 83 ◽  
Author(s):  
Moza S. Al-Balushi ◽  
Mohammed S. Ahmed ◽  
M. Mazharul Islam
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regional Variation ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Contraceptive Use ◽  
Multilevel Logistic Regression ◽  
Logistic Regression Models ◽  
Living Children ◽  
Multilevel Logistic Regression Model

In this paper, multilevel logistic regression models are developed for examining the hierarchical effects of contraceptive use and its selected determinants in Oman using the 2008 Oman National Reproductive Health Survey (ONRHS). Comparison between single level and multilevel logistic regression models has been made to examine the plausibility of multilevel effects of contraceptive use. From the multilevel logistic regression model analysis, it was found that there is real multilevel variation among contraceptive users in Oman. The results indicate that a multilevel logistic regression model is the best fit over ordinary multiple logistic regression models. Generally, this study revealed that women’s age, education, number of living children and region of residence are important factors that affect contraceptive use in Oman. The effect of regional variation for age of women, education of women and number of living children further implies that there exists considerable differences in modern contraceptive use among regions, and a model with a random coefficient or slope is more appropriate to explain the regional variation than a model with fixed coefficients or without random effects. The study suggests that researchers should use multilevel models rather than traditional regression methods when their data structure is hierarchal.  

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