Relevance of Brain Regions’ Eloquence Assessment in Patients With a Large Ischemic Core Treated With Mechanical Thrombectomy

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012863
Author(s):  
Basile Kerleroux ◽  
Joseph Benzakoun ◽  
Kévin Janot ◽  
Cyril Dargazanli ◽  
Dimitri Daly Eraya ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mechanical Thrombectomy ◽  
Predictive Accuracy ◽  
Unmet Need ◽  
C Statistic ◽  
Ischemic Core ◽  
Functional Relevance

ObjectiveIndividualized patient selection for mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large ischemic core (LIC) at baseline is an unmet need.We tested the hypothesis, that assessing the functional relevance of both the infarcted and hypo-perfused brain tissue, would improve the selection framework of patients with LIC for MT.MethodsMulticenter, retrospective, study of adult with LIC (ischemic core volume > 70ml on MR-DWI), with MRI perfusion, treated with MT or best medical management (BMM).Primary outcome was 3-month modified-Rankin-Scale (mRS), favourable if 0-3. Global and regional-eloquence-based core-perfusion mismatch ratios were derived. The predictive accuracy for clinical outcome of eloquent regions involvement was compared in multivariable and bootstrap-random-forest models.ResultsA total of 138 patients with baseline LIC were included (MT n=96 or BMM n=42; mean age±SD, 72.4±14.4years; 34.1% females; mRS=0-3: 45.1%). Mean core and critically-hypo-perfused volume were 100.4ml±36.3ml and 157.6±56.2ml respectively and did not differ between groups. Models considering the functional relevance of the infarct location showed a better accuracy for the prediction of mRS=0-3 with a c-Statistic of 0.76 and 0.83 for logistic regression model and bootstrap-random-forest testing sets respectively. In these models, the interaction between treatment effect of MT and the mismatch was significant (p=0.04). In comparison in the logistic regression model disregarding functional eloquence the c-Statistic was 0.67 and the interaction between MT and the mismatch was insignificant.ConclusionConsidering functional eloquence of hypo-perfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit.

scholarly journals Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Expert Opinion ◽  
Logistic Regression Model ◽  
Population Based ◽  
Regression Modelling ◽  
Preterm Babies ◽  
Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

Data Mining Medical Information

2011 ◽  
pp. 92-107
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

Data Mining Medical Information

2011 ◽  
pp. 2218-2231
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

scholarly journals Association of Common Genetic Risk Variants With Gestational Diabetes Mellitus and Their Role in GDM Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Polina V. Popova ◽  
Alexandra A. Klyushina ◽  
Lyudmila B. Vasilyeva ◽  
Alexandra S. Tkachuk ◽  
Elena A. Vasukova ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Regression Model ◽  
Genetic Risk ◽  
Logistic Regression Model ◽  
C Statistic ◽  
Risk Variants ◽  
Genetic Risk Variants

ObjectiveWe aimed to explore the associations between common genetic risk variants with gestational diabetes mellitus (GDM) risk in Russian women and to assess their utility in the identification of GDM cases.MethodsWe conducted a case-control study including 1,142 pregnant women (688 GDM cases and 454 controls) enrolled at Almazov National Medical Research Centre. The International Association of Diabetes and Pregnancy Study Groups criteria were used to diagnose GDM. A total of 11 single- nucleotide polymorphisms (SNPs), including those in HKDC1 (rs10762264), GCK (rs1799884), MTNR1B (rs10830963 and rs1387153), TCF7L2 (rs7903146 and rs12255372), KCNJ11 (rs5219), IGF2BP2 (rs4402960), IRS1 (rs1801278), FTO (rs9939609), and CDKAL1 (rs7754840) were genotyped using Taqman assays. A logistic regression model was used to calculate odds ratios (ORs) and their confidence intervals (CIs). A simple-count genetic risk score (GRS) was calculated using 6 SNPs. The area under the receiver operating characteristic curve (c-statistic) was calculated for the logistic regression model predicting the risk of GDM using clinical covariates, SNPs that had shown a significant association with GDM in our study, GRS, and their combinations.ResultsTwo variants in MTNR1B (rs1387153 and rs10830963) demonstrated a significant association with an increased risk of GDM. The association remained significant after adjustment for age, pre-gestational BMI, arterial hypertension, GDM in history, impaired glucose tolerance, polycystic ovary syndrome, family history of diabetes, and parity (P = 0.001 and P < 0.001, respectively). After being conditioned by each other, the effect of rs1387153 on GDM predisposition weakened while the effect of rs10830963 remained significant (P = 0.004). The risk of GDM was predicted by clinical variables (c-statistic 0.712, 95 % CI: 0.675 – 0.749), and the accuracy of prediction was modestly improved by adding GRS to the model (0.719, 95 % CI 0.682 – 0.755), and more by adding only rs10830963 (0.729, 95 % CI 0.693 – 0.764).ConclusionAmong 11 SNPs associated with T2D and/or GDM in other populations, we confirmed significant association with GDM for two variants in MTNR1B in Russian women. However, these variants showed limited value in the identification of GDM cases.

Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model

2019 ◽  
Vol 2673 (11) ◽  
pp. 901-914 ◽  
Author(s):  
Angela E. Kitali ◽  
Priyanka Alluri ◽  
Thobias Sando ◽  
Wensong Wu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Real Time ◽  
Logistic Regression Model ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Mitigation Strategies ◽  
Study Results ◽  
Highly Correlated ◽  
Penalized Logistic Regression

Secondary crashes (SCs) have increasingly been recognized as a major problem leading to reduced capacity and additional traffic delays. However, the limited knowledge on the nature and characteristics of SCs has largely impeded their mitigation strategies. There are two main issues with analyzing SCs. First, relevant variables are unknown, but, at the same time, most of the variables considered in the models are highly correlated. Second, only a small proportion of incidents results in SCs, making it an imbalanced classification problem. This study developed a reliable SC risk prediction model using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized logistic regression model with Synthetic Minority Oversampling TEchnique-Nominal Continuous (SMOTE-NC). The proposed model is considered to improve the predictive accuracy of the SC risk model because it accounts for the asymmetric nature of SCs, performs variable selection, and removes highly correlated variables. The study data were collected on a 35-mi I-95 section for 3 years in Jacksonville, Florida. SCs were identified based on real-time speed data. The results indicated that real-time traffic variables and primary incident characteristics significantly affect the likelihood of SCs. The most influential variables included mean of detector occupancy, coefficient of variation of equivalent hourly volume, mean of speed, primary incident type, percentage of lanes closed, incident occurrence time, shoulder blocked, number of responding agencies, incident impact duration, incident clearance duration, and roadway alignment. The study results can be used by agencies to develop SC mitigation strategies, and therefore improve the operational and safety performance of freeways.

Improving predictive accuracy of logistic regression model using ranked set samples

2015 ◽  
Vol 46 (1) ◽  
pp. 78-90 ◽  
Author(s):  
Kevin Carl P. Santos ◽  
Erniel B. Barrios
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Predictive Accuracy

Creation of a Venous Thromboembolism Risk Prediction Tool for Critically Ill Children - the Cleveland Score

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3803-3803
Author(s):  
Minh Q Tran ◽  
Steven L Shein ◽  
Xinge Ji ◽  
Kevin Chagin ◽  
Hilda Ding ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Critically Ill ◽  
Logistic Regression Model ◽  
Critically Ill Children ◽  
Net Benefit ◽  
Central Venous ◽  
Decision Curve Analysis ◽  
C Statistic

Abstract Introduction The incidence of Venous Thromboembolism (VTE) in critically ill children with central venous catheters (CVCs) is increasing. VTE in Pediatric Intensive Care Unit patients with CVCs (VTE-PICU) have been associated with fewer ventilation free days, prolonged length of stay (LOS) and higher risk of mortality. Screening and prophylaxis practices vary amongst clinicians due to the lack of a simple and reliable risk stratification tool. A validated bedside prediction tool could help clinicians improve outcomes by identifying patients who may benefit from screening and prophylaxis. Method With IRB approval, the Virtual Pediatric Systems, LLC database was interrogated for children < 18yo admitted between 01/2009-09/2014 to any of 130 participating PICUs who had a CVC present and developed VTE at some point during PICU care. This cohort was chosen due to an expected high prevalance of risk factors and a high incidence of VTE. From this cohort a prediction model was created using logistic regression. The logistic regression model was constructed using potential VTE risk factors and reduced to find the best fitting parsimonious model. The model reduction process was performed using Frank Harrell's "step down" model approximation method, where all the risk factors are ranked by their impact on the full model's R2 from the least impact to the most impact and removed from the model. The Model's performance was measured by discrimination, using c-statistic, which is the area under the receiver operator curve (ROC) describing the model's ability to distinguish between a patient at higher risk of the event from a patient who is at lower risk. The c-statistic ranges from 0 to 1, where 1 indicates that model has perfect discrimination and 0.5 indicates that the model does no better than chance. All reported measurements of accuracy were internally validated using a 200 bootstrap resampling process to correct for the bias of over fitting. The nomogram was then generated from the logistic regression model by rescaling the model's coefficients to a point scale to allow users of the model to make predictions of probability. Subjects whose risk score was in the lower third percentile were considered to have "normal risk," middle third percent tile "moderate risk" and those in the highest third percent tile were considered to have "high risk." Decision curve analysis was used to look at which strategy leads to the largest net benefit. VPS data was provided by Virtual Pediatric Systems (VPS, LLC). Results There were 1623 VTE-PICU amongst 158,299 PICU patients who had a CVC during their admission (10.3 per 1000 patients). A logistic regression model was generated and internally validated; Bootstrapped C-index was 0.832. Figure 1 shows the area under the receiver operator curve [82.55% (95% CI: 80.71%-84.38%)]. The decision curve analysis showed that across the range of reasonable threshold probabilities, the predicted model has a higher net benefit than "treat all" and "treat none" approach. Figure 2 shows the nomogram (the "Cleveland Score") generated from the logistic regression model by rescaling the model's coefficients to a point scale to allow users of the model to make predictions of probability. The major categories in the Cleveland Score are age, sex, race, primary diagnosis, post-op status and type of surgery, past medical history, type of central venous line, airway/ventilation status, and presence of cardiac catheterization. Using the Cleveland Score, a hypothetical 1-month-old white male, with a past medical history of sepsis, who remains intubated after a cardiac catheterization and congenital heart disease repair and has a percutaneous central venous catheter, has a high risk probability of 4.526% of developing a VTE. Conclusion Using a large multicenter database we were able to build and validate a nomogram to predict the risk of developing a VTE in critically ill hospitalized children. Furthermore, we created an easy to use bedside application for the prediction model - The Cleveland Score. We plan to prospectively validate the Cleveland score using the same database. Disclosures No relevant conflicts of interest to declare.

scholarly journals Characterizing the Spatial Distribution of Eragrostis Curvula (Weeping Lovegrass) in New Jersey (United States of America) Using Logistic Regression

Environments ◽  
2019 ◽  
Vol 6 (12) ◽  
pp. 125
Author(s):  
Kikombo Ilunga Ngoy ◽  
Daniela Shebitz
Keyword(s):  
Invasive Species ◽  
Logistic Regression ◽  
New Jersey ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Invasive Plant ◽  
Predictive Accuracy ◽  
Global Environmental Change ◽  
Eragrostis Curvula

The increasing spread of invasive plants has become a critical driver of global environmental change. Once established, invasive species are often impossible to eradicate. Therefore, predicting the spread has become a key element in fighting invasive species. In this study, we examined the efficiency of a logistic regression model as a tool to identify the spatial occurrence of an invasive plant species. We used Eragrostis curvula (Weeping Lovegrass) as the dependent variable. The independent variables included temperature, precipitation, soil types, and the road network. We randomly selected 68 georeferenced points to test the goodness of fit of the logistic regression model to predict the presence of E. curvula. We validated the model by selecting an additional 68 random points. Results showed that the probability to successfully predict the presence of E. Curvula was 82.35%. The overall predictive accuracy of the model for the presence or absence of E. Curvula was 80.88%. Additional tests including the Chi-square test, the Hosmer–Lemeshow (HL) test, and the area under the curve (AUC) values, all indicated that the model was the best fit. Our results showed that E. curvula was associated with the identified variables. This study suggests that the logistic regression model can be a useful tool in the identification of invasive species in New Jersey.

Enhancement of the predictive accuracy in logistic regression model by optimal threshold

2010 ◽  
Vol 13 (3) ◽  
pp. 501-513
Author(s):  
Tsoyu Calvin Lin ◽  
Hsien-Chueh Peter Yang ◽  
Tsung-Hao Chen
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Predictive Accuracy ◽  
Optimal Threshold

Data Mining Medical Information

2008 ◽  
pp. 2915-2927
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

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