197 The Relative Odds of Sustaining a Sport-Related Concussion: A Study of 12,320 Student-Athletes

Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 253-253
Author(s):  
Benjamin L Brett ◽  
Andrew W Kuhn ◽  
Aaron M Yengo-Kahn ◽  
Gary Solomon ◽  
Scott L Zuckerman
Keyword(s):  
Risk Score ◽  
Regression Models ◽  
Student Athletes ◽  
Area Under The Curve ◽  
Collegiate Athletes ◽  
Multivariable Model ◽  
Logistic Regression Models ◽  
Small Effect Size ◽  
History Of

Abstract INTRODUCTION Accurately quantifying the risk of sport-related concussion (SRC) can prove valuable in the management of student-athletes. Our objective was to develop and validate an aggregate risk score based on biopsychosocial factors to predict the odds of sustaining a SRC. METHODS An ambispective study was undertaken of 12,320 middle school, high school and collegiate athletes. Neurocognitive testing was completed at preseason (baseline) and post-SRC. Multiple univariate and multivariable logistic regression models were used to determine which pre-injury variables accurately predicted the occurrence of SRC. The score was validated utilizing bootstrapping resampling. RESULTS >Five variables maintained significance in the multivariable model, with corresponding risk score points: SRC history (21), prior headache treatment (6), contact sport (5), youth level of play (7), and history of ADHD/LD (2). Six groups were formed based on the differentiation of the probability of SRC. Classification of odds of SRC by these categories produced an area under the curve (AUC) of 0.71 (95% CI 0.69−0.72, P < 0.001). The scoring system was a significant predictor of SRC, X2 = 1112.75, P < 0.001, df = 7, although with small effect size. CONCLUSION An aggregate score was developed and internally validated to empirically assess factors associated with increased odds of sustaining a SRC. This summative score can be used as an adjunct to better conceptualize the odds of concussion for student-athletes. However, it is important to note that several other factors were not accounted for in the model and must be considered in the assessment of SRC risk.

scholarly journals Predicting drowning from sea and weather forecasts: development and validation of a model on surf beaches of southwestern France

2021 ◽  
pp. injuryprev-2020-044092
Author(s):  
Éric Tellier ◽  
Bruno Simonnet ◽  
Cédric Gil-Jardiné ◽  
Marion Lerouge-Bailhache ◽  
Bruno Castelle ◽  
...  
Keyword(s):  
Regression Models ◽  
Area Under The Curve ◽  
Temperature Wave ◽  
Medical Emergency ◽  
Emergency Center ◽  
Logistic Regression Models ◽  
Weather Forecasts ◽  
Step Model ◽  
Daily Risk ◽  
Development And Validation

ObjectiveTo predict the coast-wide risk of drowning along the surf beaches of Gironde, southwestern France.MethodsData on rescues and drownings were collected from the Medical Emergency Center of Gironde (SAMU 33). Seasonality, holidays, weekends, weather and metocean conditions were considered potentially predictive. Logistic regression models were fitted with data from 2011 to 2013 and used to predict 2015–2017 events employing weather and ocean forecasts.ResultsAir temperature, wave parameters, seasonality and holidays were associated with drownings. Prospective validation was performed on 617 days, covering 232 events (rescues and drownings) reported on 104 different days. The area under the curve (AUC) of the daily risk prediction model (combined with 3-day forecasts) was 0.82 (95% CI 0.79 to 0.86). The AUC of the 3-hour step model was 0.85 (95% CI 0.81 to 0.88).ConclusionsDrowning events along the Gironde surf coast can be anticipated up to 3 days in advance. Preventative messages and rescue preparations could be increased as the forecast risk increased, especially during the off-peak season, when the number of available rescuers is low.

Domestic Violence Reported to the Police: Correlates of Victims’ Reporting Behavior and Support to Legal Proceedings

2016 ◽  
Vol 31 (3) ◽  
pp. 402-415 ◽  
Author(s):  
Rémi Boivin ◽  
Chloé Leclerc
Keyword(s):  
Domestic Violence ◽  
Logistic Regression ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
The Other ◽  
Individual Factors ◽  
Initial Report ◽  
Logistic Regression Models ◽  
Legal Proceedings ◽  
History Of

This article analyzes reported incidents of domestic violence according to the source of the complaint and whether the victim initially supported judicial action against the offender. Almost three quarters of incidents studied were reported by the victim (72%), and a little more than half of victims initially wanted to press charges (55%). Using multinomial logistic regression models, situational and individual factors are used to distinguish 4 incident profiles. Incidents in which the victim made the initial report to the police and wished to press charges are the most distinct and involve partners who were already separated at the time of the incident or had a history of domestic violence. The other profiles also show important differences.

P1019Validation of a risk score to predict the need for pacemaker implantation after transcatheter aortic valve replacement

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Kiani ◽  
N Kamioka ◽  
H Caughron ◽  
A Dong ◽  
H Patel ◽  
...  
Keyword(s):  
Risk Score ◽  
Right Bundle Branch Block ◽  
Roc Analysis ◽  
Validation Cohort ◽  
Pacemaker Implantation ◽  
Area Under The Curve ◽  
Bundle Branch Block ◽  
Significant Difference ◽  
History Of ◽  
Qrs Duration

Abstract Background New conduction abnormalities necessitating pacemaker implantation (PMI) is a common occurrence after TAVR. There is an increased rate of PMI in the those receiving the most contemporary implanted valve, the Edwards Sapien-3 (S3), compared to prior generation balloon expandable valves. We previously described predictors of PMI in a large cohort. Herein we sought to validate these predictors of PMI in a subsequent validation cohort. Methods We evaluated all patients undergoing first time elective TAVR with S3 at our institution (n=326). We developed a risk score based on a predictive model we have previously described. Patients received one point for each of the following: history of syncope, oversizing of the valve >16%, baseline right bundle branch block morphology, and two points for a QRS duration >115 ms. We performed regression analysis of the risk score and need for PMI. We also evaluated the performance of the risk score using ROC analysis. Results Thirty patients (8%) of the total cohort had need for PMI after S3 implantation. Those with PMI had a higher rate of pre-existing infra-nodal conduction system disease – including QRS duration >115ms (57% vs. 20%, p<0.001) and right bundle branch block (RBBB) morphology (47% vs. 10%, p<0.001) - as well as more frequent valve oversizing >15.7% (47% vs. 23%, p<0.01). There was no significant difference in a history of syncope (10% vs. 8%, p=0.72) between groups. The PMI risk score had an area under the curve of 0.753 on ROC analysis. The PMI risk score was significantly associated with PMI (OR 2.37; 95% CI [1.64–3.34], p<0.001). Rate of PMI Stratified by Risk Score Conclusions The PMI risk score was strongly predictive of the need for PMI after implantation of the S3 valve in a large validation cohort. The PMI risk score performed well in sensitivity analysis. This PMI risk score represents a simple tool to help further risk stratify patients being considered for TAVR.

Progression of coal workers’ pneumoconiosis absent further exposure

2020 ◽  
Vol 77 (11) ◽  
pp. 748-751
Author(s):  
Kirsten S Almberg ◽  
Lee S Friedman ◽  
Cecile S Rose ◽  
Leonard H T Go ◽  
Robert A Cohen
Keyword(s):  
Disease Progression ◽  
Regression Models ◽  
Medical Surveillance ◽  
Coal Miners ◽  
Radiographic Evidence ◽  
Logistic Regression Models ◽  
Younger Age ◽  
History Of ◽  
Coal Workers Pneumoconiosis ◽  
Coal Workers

ObjectivesThe natural history of coal workers’ pneumoconiosis (CWP) after cessation of exposure remains poorly understood.MethodsWe characterised the development of and progression to radiographic progressive massive fibrosis (PMF) among former US coal miners who applied for US federal benefits at least two times between 1 January 2000 and 31 December 2013. International Labour Office classifications of chest radiographs (CXRs) were used to determine initial and subsequent disease severity. Multivariable logistic regression models were used to identify major predictors of disease progression.ResultsA total of 3351 former miners applying for benefits without evidence of PMF at the time of their initial evaluation had subsequent CXRs. On average, these miners were 59.7 years of age and had 22 years of coal mine employment. At the time of their first CXR, 46.7% of miners had evidence of simple CWP. At the time of their last CXR, 111 miners (3.3%) had radiographic evidence of PMF. Nearly half of all miners who progressed to PMF did so in 5 years or less. Main predictors of progression included younger age and severity of simple CWP at the time of initial CXR.ConclusionsThis study provides further evidence that radiographic CWP may develop and/or progress absent further exposure, even among miners with no evidence of radiographic pneumoconiosis after leaving the industry. Former miners should undergo regular medical surveillance because of the risk for disease progression.

Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI

2015 ◽  
Vol 25 (9) ◽  
pp. 2727-2737 ◽  
Author(s):  
Nikolaos Dikaios ◽  
Jokha Alkalbani ◽  
Mohamed Abd-Alazeez ◽  
Harbir Singh Sidhu ◽  
Alex Kirkham ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Models ◽  
Logistic Regression Models

scholarly journals The Occurrence of Seizure Clusters in Patients With Epilepsy Is Partly Determined by Epilepsy Severity: A Single-Center Retrospective Observational Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Rui Zhong ◽  
Qingling Chen ◽  
Xinyue Zhang ◽  
Weihong Lin
Keyword(s):  
Logistic Regression ◽  
Observational Study ◽  
Regression Models ◽  
Final Analysis ◽  
Retrospective Observational Study ◽  
Logistic Regression Models ◽  
History Of ◽  
Drug Resistant Epilepsy ◽  
The Individual ◽  
Patients With Epilepsy

Purpose: This retrospective observational study aimed to investigate the self-reported prevalence of seizure clusters (SCs) in patients with epilepsy (PWE) and its relationship with clinical characteristics.Methods: We retrospectively analyzed data from consecutive PWE from our hospital in northeastern China. Data were collected from the databank of a tertiary epilepsy center. Logistic regression models were employed to investigate the relationships between the individual patient demographic/clinical variables and the occurrence of SC.Results: In total, 606 consecutive PWE were included in the final analysis, and 268 (44.2%) patients experienced at least one seizure cluster. In multivariate logistic regression models, age (OR: 1.014; 95% CI: 1.002–1.027; p = 0.02), seizure frequency (OR: 2.08; 95% CI: 1.555–2.783; p &lt; 0.001), multiple seizure types (OR: 5.111; 95% CI: 1.737–15.043; p = 0.003), number of current anti-seizure medications (ASM) (OR: 1.533; 95% CI: 1.15–2.042; p = 0.004), drug-resistant epilepsy (OR: 1.987; 95% CI: 1.159–3.407; p = 0.013), and a history of status epilepticus (OR: 1.903; 95% CI: 1.24–2.922; p = 0.003) were independent variables associated with a history of SC in PWE.Conclusion: Seizure clusters (SCs) are common occurrences at our study center. The occurrence of SC in individuals with epilepsy, to some extent, is determined by the epilepsy severity.

scholarly journals Stopping when knowing: use of snus and nicotine during pregnancy in Scandinavia

2019 ◽  
Vol 5 (2) ◽  
pp. 00197-2018 ◽  
Author(s):  
Ina Kreyberg ◽  
Karen E.S. Bains ◽  
Kai-H. Carlsen ◽  
Berit Granum ◽  
Hrefna K. Gudmundsdóttir ◽  
...  
Keyword(s):  
Atopic Dermatitis ◽  
Young Women ◽  
Regression Models ◽  
Population Based ◽  
Maternal History ◽  
Urban Living ◽  
Logistic Regression Models ◽  
History Of ◽  
Electronic Questionnaires ◽  
In Pregnancy

In young women, the use of snus increases in parallel with decreasing smoking rates but the  use in pregnancy is unclear. Our aims were to determine the prevalence of snus use, smoking and other nicotine-containing product use during pregnancy, and to identify predictors for snus use in pregnancy.Prevalence was determined for 2528 women in Norway and Sweden based on the Preventing Atopic Dermatitis and ALLergies (PreventADALL) study, a population-based, mother–child birth cohort. Electronic questionnaires were completed in pregnancy week 18 and/or week 34, and potential predictors of snus use were analysed using logistic regression models.Ever use of any snus, tobacco or nicotine-containing products was reported by 35.7% of women, with similar rates of snus use (22.5%) and smoking (22.6%). Overall, 11.3% of women reported any use of nicotine-containing products in pregnancy up to 34 weeks, most often snus alone (6.5%). Most women (87.2%) stopped using snus by week 6 of pregnancy.Snus use in pregnancy was inversely associated with age and positively associated with urban living and personal or maternal history of smoking. While 11.3% of women used snus or other nicotine-containing products at some time, most stopped when recognising their pregnancy. Younger, urban living, previously smoking women were more likely to use snus in pregnancy.

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.

Abstract 039: Insomnia And Short Sleep Uniquely Predict Obesity, Hypertension, And Diabetes

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Adam Knowlden ◽  
Michael A Grandner
Keyword(s):  
Regression Models ◽  
Sleep Problems ◽  
Population Level ◽  
Cardiometabolic Health ◽  
Epidemiological Evidence ◽  
Logistic Regression Models ◽  
Short Sleep ◽  
Health And Nutrition ◽  
History Of ◽  
Obesity Hypertension

Background: Epidemiological evidence of short sleep’s (<6 hours) association with negative cardiometabolic health outcomes continues to mount; yet, the complex relationship between sleep and health is still not well-understood. Sleep problems, such as short sleep (SS) and insomnia (IN), are often analyzed as a singular construct at the population level; however, it has been proposed that, although these two sleep problems likely overlap, they are separate phenomena. The purpose of this study was to: (1) determine if SS and IN were independent constructs; and to (2) evaluate whether SS and IN predicted obesity, hypertension, and diabetes. Methods: Analyses were based on the 2015-2016 National Health and Nutrition Examination Survey (NHANES). NHANES employs a complex, multistage, probability sampling design to survey a representative sample of non-institutionalized U.S. adults (≥18 years). Data related to short (<6), normal (7-8), and long (9+) sleep duration, insomnia (present: mild, moderate, severe), hypertension (present: previous hypertension/hypertension medications/blood pressure in the hypertensive range), and diabetes (present: history of diabetes/fasting blood sugar of 130+) were extracted for analysis. Age, sex, and obesity (body mass index, 30.0+) were entered as covariates into the models. Results: Among the subjects, 0.08% were normal sleepers with IN; 0.21% were SS with insomnia; and, 0.59% had IN with SS. Table 1 summarizes the multivariate and stratified logistic regression models of SS and IN predicting obesity, hypertension, and diabetes. Conclusions: Findings from this study suggested SS and IN are independent constructs, uniquely predicting obesity, hypertension, and diabetes. SS and IN neither mediated nor moderated one another, implying these two sleep outcomes are not additive in nature, but are instead separate health problems. The distinction between SS and IN may have important epidemiological and clinical implications.

scholarly journals Exploring grip strength as a predictor of depression in middle-aged and older adults

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adilson Marques ◽  
Duarte Henriques-Neto ◽  
Miguel Peralta ◽  
Priscila Marconcin ◽  
Élvio R. Gouveia ◽  
...  
Keyword(s):  
Older Adults ◽  
Depressive Symptoms ◽  
Grip Strength ◽  
Regression Models ◽  
Area Under The Curve ◽  
Age Group ◽  
Middle Aged ◽  
Logistic Regression Models ◽  
Depression Risk ◽  
Group Data

AbstractGrip strength (GS) is an indicator of health and vulnerability and inversely associated with depressive symptoms. The aim of this study was to explore GS discrimination capacity for depression; and possible GS cut-off values for depression by sex and age group. Data from 2011 and 2015 on 20,598 (10,416 women) middle-aged and older adults from 14 European countries was analysed. GS was assessed by dynamometer, and depressive symptoms using the EURO-D scale. GS cut-off values for depression were calculated and logistic regression models were used to quantify the odds of having depression in 2011 and in 2015 according to being bellow or above the cut-off value. GS had a weak discriminant capacity for depression, with the area under the curve varying between 0.54 and 0.60 (p < 0.001). Sensitivity varied between 0.57 and 0.74; specificity varied between 0.46 and 0.66. GS cut-off values for discriminating depression were 43.5 kg for men and 29.5 kg for women aged 50–64 years, 39.5 kg for men and 22.5 kg for women aged ≥ 65 years. Having GS above the cut-off represents significant lower odds of depression in 2011 and 4 years later, in 2015. Healthcare practitioners and epidemiologic researchers may consider the low GS cut-off values to screen for potential depression risk. However, due to its weak discriminant values these cut-offs should not be used to identify depression.

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