scholarly journals Revised Injury Severity Classification II (RISC II) is a predictor of mortality in REBOA-managed severe trauma patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246127
Author(s):  
Peter Hibert-Carius ◽  
David T. McGreevy ◽  
Fikri M. Abu-Zidan ◽  
Tal M. Hörer ◽  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Predictive Value ◽  
Logistic Regression Model ◽  
Injury Severity ◽  
Univariate Analysis ◽  
Severe Trauma ◽  
Trauma Patients ◽  
Revised Injury Severity Classification ◽  
Significant Factors

The evidence supporting the use of Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) in severely injured patients is still debatable. Using the ABOTrauma Registry, we aimed to define factors affecting mortality in trauma REBOA patients. Data from the ABOTrauma Registry collected between 2014 and 2020 from 22 centers in 13 countries globally were analysed. Of 189 patients, 93 died (49%) and 96 survived (51%). The demographic, clinical, REBOA criteria, and laboratory variables of these two groups were compared using non-parametric methods. Significant factors were then entered into a backward logistic regression model. The univariate analysis showed numerous significant factors that predicted death including mechanism of injury, ongoing cardiopulmonary resuscitation, GCS, dilated pupils, systolic blood pressure, SPO2, ISS, serum lactate level and Revised Injury Severity Classification (RISCII). RISCII was the only significant factor in the backward logistic regression model (p < 0.0001). The odds of survival increased by 4% for each increase of 1% in the RISCII. The best RISCII that predicted 30-day survival in the REBOA treated patients was 53.7%, having a sensitivity of 82.3%, specificity of 64.5%, positive predictive value of 70.5%, negative predictive value of 77.9%, and usefulness index of 0.385. Although there are multiple significant factors shown in the univariate analysis, the only factor that predicted 30-day mortality in REBOA trauma patients in a logistic regression model was RISCII. Our results clearly demonstrate that single variables may not do well in predicting mortality in severe trauma patients and that a complex score such as the RISC II is needed. Although a complex score may be useful for benchmarking, its clinical utility can be hindered by its complexity.

Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy

2021 ◽  
Author(s):  
Cuiping Zhou ◽  
Xiaohua Ban ◽  
Huijun Hu ◽  
Qiuxia Yang ◽  
Rong Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Partial Hepatectomy ◽  
Logistic Regression Model ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Roc Curve Analysis ◽  
Hepatocellular Carcinomas ◽  
Ct Features

Abstract Background: Hepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. Partial hepatectomy is one of the most effective therapies for HCC but suffer from the high recurrence rate. At present, the studies of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are still limited. The purpose of this study is to determine the predictive CT features and establish a model for predicting relapse or metastasis in patients with primary hepatocellular carcinomas (HCCs) undergoing partial hepatectomy.Methods: The clinical data and CT features of 112 patients with histopathologically confirmed primary HCCs were retrospectively reviewed. The clinical outcomes were categorized into two groups according to whether relapse or metastasis occurred within 2 years after partial hepatectomy. The association between clinical outcomes and CT features including tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis was analyzed using univariate analysis and binary logistic regression. Then establish logistic regression model, followed by receiver operating characteristic (ROC) curve analysis.Results: CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcomes, as determined by univariate analysis (P<0.05). Only tumor margin and VI remained independent risk factors in binary logistic regression analysis (OR=6.41 and 10.92 respectively). The logistic regression model was logit(p)=-1.55+1.86 margin +2.39 VI. ROC curve analysis showed that the area under curve of the obtained logistic regression model was 0.887(95% CI：0.827-0.947).Conclusion: Patients with ill-defined margin or VI of HCCs were independent risk predictors of poor clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes.

scholarly journals Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors

2020 ◽  
Vol 17 (13) ◽  
pp. 4852
Author(s):  
Pouya Gholizadeh ◽  
Behzad Esmaeili
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Odds Ratio ◽  
Logistic Regression Model ◽  
The Body ◽  
Occupational Accidents ◽  
Multivariate Logistic Regression Model ◽  
Ratio Analysis ◽  
Accident Scenarios ◽  
Significant Factors

The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a “vehicle” than when it is a “tool, instrument, or equipment”. After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in “fencing, installing lights, signs, etc.” tasks in “alteration and rehabilitation” projects where the source of injury was “parts and materials”. The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model.

scholarly journals Assessment of factors that affect the bankruptcy of an organization

2020 ◽  
Vol 19 (1) ◽  
pp. 57-71
Author(s):  
Irina Vinnikova
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Timely Manner ◽  
Financial Situation ◽  
Significant Factors

Analysis of factors that influence the company's bankruptcy is one of the main tasks for companies that want to assess their financial situation and prevent possible bankruptcy in a timely manner. This article analyzes the factors that affect the company's bankruptcy. A logistic regression model was constructed based on the indicators of both bankrupt and financially stable companies. During the development of the model, significant factors were identified for predicting the bankruptcy of the organization. The results will be useful both for future bankruptcy researchers and for those companies that want to assess their financial situation.

scholarly journals Predictors of "occult" intra-abdominal injuries in blunt trauma patients

2015 ◽  
Vol 42 (5) ◽  
pp. 311-317 ◽  
Author(s):  
José Gustavo Parreira ◽  
Juliano Mangini Dias Malpaga ◽  
Camilla Bilac Olliari ◽  
Jacqueline A. G. Perlingeiro ◽  
Silvia C. Soldá ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Abdominal Pain ◽  
Physical Examination ◽  
Regression Model ◽  
Blunt Trauma ◽  
Logistic Regression Model ◽  
Trauma Patients ◽  
Trauma Mechanism ◽  
Abdominal Injuries ◽  
Group I

Objective: to assess predictors of intra-abdominal injuries in blunt trauma patients admitted without abdominal pain or abnormalities on the abdomen physical examination. Methods: We conducted a retrospective analysis of trauma registry data, including adult blunt trauma patients admitted from 2008 to 2010 who sustained no abdominal pain or abnormalities on physical examination of the abdomen at admission and were submitted to computed tomography of the abdomen and/or exploratory laparotomy. Patients were assigned into: Group 1 (with intra-abdominal injuries) or Group 2 (without intra-abdominal injuries). Variables were compared between groups to identify those significantly associated with the presence of intra-abdominal injuries, adopting p<0.05 as significant. Subsequently, the variables with p<0.20 on bivariate analysis were selected to create a logistic regression model using the forward stepwise method. Results: A total of 268 cases met the inclusion criteria. Patients in Group I were characterized as having significantly (p<0.05) lower mean AIS score for the head segment (1.0±1.4 vs. 1.8±1.9), as well as higher mean AIS thorax score (1.6±1.7 vs. 0.9±1.5) and ISS (25.7±14.5 vs. 17,1±13,1). The rate of abdominal injuries was significantly higher in run-over pedestrians (37.3%) and in motorcyclists (36.0%) (p<0.001). The resultant logistic regression model provided 73.5% accuracy for identifying abdominal injuries. The variables included were: motorcyclist accident as trauma mechanism (p<0.001 - OR 5.51; 95%CI 2.40-12.64), presence of rib fractures (p<0.003 - OR 3.00; 95%CI 1.47-6.14), run-over pedestrian as trauma mechanism (p=0.008 - OR 2.85; 95%CI 1.13-6.22) and abnormal neurological physical exam at admission (p=0.015 - OR 0.44; 95%CI 0.22-0.85). Conclusion Intra-abdominal injuries were predominantly associated with trauma mechanism and presence of chest injuries.

scholarly journals The Effect of Urine pH and Urinary Uric Acid Levels on the Development of Contrast Nephropathy

2019 ◽  
Vol 45 (1) ◽  
pp. 131-141 ◽  
Author(s):  
Gamze Aslan ◽  
Baris Afsar ◽  
Alan A. Sag ◽  
Volkan Camkiran ◽  
Nihan Erden ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Uric Acid ◽  
Coronary Angiography ◽  
Regression Model ◽  
Serum Sodium ◽  
Logistic Regression Model ◽  
Univariate Analysis ◽  
Crystal Formation ◽  
Urine Ph ◽  
Contrast Volume

Background: Hyperuricemia may cause acute kidney injury by activating inflammatory, pro-oxidative and vasoconstrictive pathways. In addition, radiocontrast causes an acute uricosuria, potentially leading to crystal formation. We therefore aimed to investigate the effect of urine acidity and urine uric acid level on the development of contrast-induced nephropathy (CIN) in patients undergoing elective coronary angiography. Methods: We enrolled 175 patients who underwent elective coronary angiography. CIN was defined as a >25% increase in the serum creatinine levels relative to basal values 48–72 h after contrast use. Prior to coronary angiography and 48–72 h later, serum uric acid, urea, creatinine, bicarbonate levels, and spot uric acid to creatinine ratio (UACR) were measured. Results: Of the 175 subjects included, 29 (16.6%) developed CIN. Those who developed CIN had a higher prevalence of diabetes, higher UACR (0.60 vs. 0.44, p = 0.014), higher contrast volume, and lower serum sodium level. With univariate analysis of a logistic regression model, the risk of CIN was found to be associated with diabetes (p = 0.0016, OR = 3.8 [95% CI: 1.7–8.7]), urine UACR (p = 0.0027, OR = 9.6 [95% CI: 2.2–42.2]), serum sodium (p = 0.0079, OR = 0.8 [95% CI: 0.77–0.96]), and contrast volume (p = 0.0385, OR = 1.8 [95% CI: 1.03–3.09]). In a multiple logistic regression model with stepwise method of selection, diabetes (p = 0.0120, OR = 3.2 [95% CI: 1.3–8.1]) and UACR (p = 0.0163, OR = 6.9 [95% CI: 1.4–33.4]) were the 2 risk factors finally identified. Conclusions: We have demonstrated that higher urine UACR is associated with the development of CIN in patients undergoing elective coronary angiography.

scholarly journals Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Daiquan Xiao ◽  
Xuecai Xu ◽  
Li Duan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Injury Severity ◽  
Temporal Analysis ◽  
Human Beings ◽  
Temporal Perspective ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials.

scholarly journals MATHEMATICAL MODELING OF THE DEVELOPMENT OF CHRONIC FLUORINE INTOXICATION IN ALUMINIUM INDUSTRY WORKERS

2020 ◽  
Vol 99 (1) ◽  
pp. 115-119
Author(s):  
L. N. Budkar ◽  
Tatyana Yu. Obukhova ◽  
S. I. Solodushkin ◽  
A. A. Fedoruk ◽  
O. G. Shmonina ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Aluminum Industry ◽  
Univariate Analysis ◽  
Predictive Ability ◽  
Occupational Exposures ◽  
Exposure Limits ◽  
X Ray

Introduction. Chronic fluorine intoxication prevails among the newly discovered occupational diseases in aluminum industry workers. Mathematical modeling is one of the helpful tools in ensuring better risk management with respect to the development of occupational fluorosis. Objective. Developing a logistic regression model predicting a probability of occupational fluorosis development in an occupational staff of aluminum plants in order to suggest adequate prophylactic strategies. Material and methods. A logistic regression model predicting a probability of the development of occupational fluorosis in aluminum industry workers of the Sverdlovsk region was constructed. The model embraced the results of a univariate analysis conducted with respect to major occupational exposures and health characteristics of 201 workers. Results. Six major factors were identified as being predictive of occupational fluorosis development in aluminum industry workers: age (fluorosis risk increases with age); type 2 diabetes mellitus; atrophic gastritis; kidney cysts; X-ray examination data (fluorosis risk increases with the stage as determined by X-ray); the hydro fluoride concentration increases by more than 2 occupational exposure limits. The developed model was verified by clinical cases and showed a high predictive ability (86.2 %). Both sensitivity (true positive rate) and specificity (true negative rate) of the model amounted to 86.2 %. Conclusion. By multivariate analysis the significant, mutually independent factors were identified, their combination being associated with chronic fluorine intoxication in an occupational staff of aluminum plants. The developed mathematical model has a high predictive ability and can be recommended as a sure tool to forecast the course of occupational fluorosis development in the workers at the aluminum industry.

scholarly journals Incidence of Elevated Procalcitonin and Presepsin Levels after Severe Trauma: A Pilot Cohort Study

2017 ◽  
Vol 45 (5) ◽  
pp. 600-604 ◽  
Author(s):  
K. Hoshino ◽  
Y. Irie ◽  
M. Mizunuma ◽  
K. Kawano ◽  
T. Kitamura ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Risk Factor ◽  
Independent Risk Factor ◽  
Injury Severity ◽  
Severe Trauma ◽  
Trauma Patients ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Traumatic Injuries ◽  
Early Phases

Procalcitonin (PCT) and presepsin (PSEP) are useful biomarkers for diagnosing sepsis; however, elevated PCT and PSEP levels may be observed in conditions other than sepsis. We hypothesised that PCT and PSEP levels could increase after severe traumatic injuries. Trauma patients with an Injury Severity Score of ≥16 from October 2013 to September 2015 were enrolled in our study. We examined PCT and PSEP levels and their positive rates on days 0 and 1. PCT and PSEP levels on days 0 and 1 were compared. Risk factors for increasing sepsis biomarker levels were identified by multivariate logistic regression analyses. In this study, 75 patients were included. PCT levels on days 0 and 1 were 0.1±0.4 and 1.8±6.3 ng/ml, respectively (P=0.02). PSEP levels on days 0 and 1 were 221±261 and 222±207 pg/ml, respectively (P=0.98). As per multivariate logistic regression analyses, packed red blood cell (PRBC) transfusion was the only independent risk factor for higher PCT levels on day 1 (P=0.04). Using PCT to diagnose sepsis in trauma patients on day 1 requires caution. PRBC transfusion was found to be a risk factor for increasing PCT levels. On the other hand, PSEP levels were not affected by trauma during the early phases.

scholarly journals Machine learning to predict mortality after rehabilitation among patients with severe stroke

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Domenico Scrutinio ◽  
Carlo Ricciardi ◽  
Leandro Donisi ◽  
Ernesto Losavio ◽  
Petronilla Battista ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Predictive Value ◽  
Logistic Regression Model ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Severe Disability ◽  
Operating Characteristics ◽  
Receiver Operating Characteristics Curve

AbstractStroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.

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