• Journal of Korean Neurosurgical Society
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• v.67 no.2
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• pp.177-185
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• 2024

Objective : Delayed cerebral ischemia (DCI) is a major cause of disability in patients who survive aneurysmal subarachnoid hemorrhage (aSAH). Systemic inflammatory markers, such as peripheral leukocyte count and systemic immune-inflammatory index (SII) score, have been considered predictors of DCI in previous studies. This study aims to investigate which systemic biomarkers are significant predictors of DCI. Methods : We conducted a retrospective, observational, single-center study of 170 patients with SAH admitted between May 2018 and March 2022. We analyzed the patients' clinical and laboratory parameters within 1 hour and 3-4 and 5-7 days after admission. The DCI and non-DCI groups were compared. Variables showing statistical significance in the univariate logistic analysis (p<0.05) were entered into a multivariate regression model. Results : Hunt-Hess grade "4-5" at admission, modified Fisher scale grade "3-4" at admission, hydrocephalus, intraventricular hemorrhage, and infection showed statistical significance (p<0.05) on a univariate logistic regression. Lymphocyte and monocyte count at admission, SII scores and C-reactive protein levels on days 3-4, and leukocyte and neutrophil counts on days 5-7 exhibited statistical significance on the univariate logistic regression. Multivariate logistic regression analysis revealed that monocyte count at admission (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.04-2.65; p=0.036) and SII score at days 3-4 (OR, 1.55; 95% CI, 1.02-2.47; p=0.049) were independent predictors of DCI. Conclusion : Monocyte count at admission and SII score 3-4 days after rupture are independent predictors of clinical deterioration caused by DCI after aSAH. Peripheral monocytosis may be the primer for the innate immune reaction, and the SII score at days 3-4 can promptly represent the propagated systemic immune reaction toward DCI.

• Clinics in Orthopedic Surgery
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• v.10 no.4
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• pp.448-454
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• 2018

Background: Traumatic spinal cord injury (SCI) is a tragic event that has a major impact on individuals and society as well as the healthcare system. The purpose of this study was to investigate the strength of association between surgical treatment timing and neurological improvement. Methods: Fifty-six patients with neurological impairment due to traumatic SCI were included in this study. From January 2013 to June 2017, all their medical records were reviewed. Initially, to identify the factors affecting the recovery of neurological deficit after an acute SCI, we performed univariate logistic regression analyses for various variables. Then, we performed a multivariate logistic regression analysis for variables that showed a p-value of < 0.2 in the univariate analyses. The Hosmer-Lemeshow test was used to determine the goodness of fit for the multivariate logistic regression model. Results: In the univariate analysis on the strength of associations between various factors and neurological improvement, the following factors had a p-value of < 0.2: surgical timing (early, < 8 hours; late, 8-24 hours; p = 0.033), completeness of SCI (complete/incomplete; p = 0.033), and smoking (p = 0.095). In the multivariate analysis, only two variables were significant: surgical timing (odds ratio [OR], 0.128; p = 0.004) and completeness of SCI (OR, 9.611; p = 0.009). Conclusions: Early surgical decompression within 8 hours after traumatic SCI appeared to improve neurological recovery. Furthermore, incomplete SCI was more closely related to favorable neurological improvement than complete SCI. Therefore, we recommend early decompression as an effective treatment for traumatic SCI.

• Clinics in Shoulder and Elbow
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• v.26 no.3
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• pp.252-259
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• 2023

Background: The purpose of this study was to identify demographics and risk factors associated with unplanned 30-day readmission and reoperation following open procedures for shoulder instability and examine recent trends in open shoulder instability procedures. Methods: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried using current procedural terminology (CPT) codes 23455, 23460, and 23462 to find patients who underwent shoulder instability surgery from 2015 to 2019. Independent sample Student t-tests and chi-square tests were used in univariate analyses to identify demographic, lifestyle, and perioperative variables related to 30-day readmission following repair for shoulder instability. Multivariate logistic regression modeling was subsequently performed. Results: In total, 1,942 cases of open surgical procedures for shoulder instability were identified. Within our study sample, 1.27% of patients were readmitted within 30 days of surgery, and 0.85% required reoperation. Multivariate logistic regression modeling confirmed that the following patient variables were associated with a statistically significant increase in the odds of readmission: open anterior bone block/Latarjet-Bristow procedure, being a current smoker, and a long hospital stay (all P<0.05). Multivariate logistic regression modeling confirmed statistically significant increased odds of reoperation with an open anterior bone block or Latarjet-Bristow procedure (P<0.05). Conclusions: Unplanned 30-day readmission and reoperation after open shoulder instability surgery is infrequent. Patients who are current smokers, have an open anterior bone block or Latarjet-Bristow procedure, or a longer than average hospital stay have higher odds of readmission than others. Patients who undergo an open anterior bone block or Latarjet-Bristow procedure have higher odds of reoperation than those who undergo an open soft-tissue procedure. Level of evidence: III.

• Clinics in Shoulder and Elbow
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• v.16 no.1
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• pp.63-72
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• 2013

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3967-3971
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• 2012

Aims: To explore the relationship between various molecular makers and liver metastasis of colorectal cancer (CRC). Method: Using immunohistochemistry, protein expression of CEA, nm23, c-met, MMP2, COX-2, VEGF, EGFR, and CD44 was assessed in 80 CRC cases. The Chi-square test and logistic regression were performed to analyze the relationship between these indicators and CRC liver metastasis. Results: There were significant differences in expression of CEA, MMP2, CD44, VEGF and EGFR between the liver metastasis and non metastasis groups (P < 0.05); no significant differences were noted for nm23, c-met, and COX-2 expression. Logistic regression analysis showed that only CEA, VEGF, and EGFR entered into the regression equation, and had significant correlations with CRC liver metastasis (${\alpha}$ inclusion= 0.10, ${\alpha}$ elimination = 0.15, R2 = 0.718). Conclusions: Combination detection of CEA, VEGF, and EGFR may be an effective means to predict CRC liver metastasis. Nm23, c-met, MMP2, COX-2, and CD44, in contrast, are not suitable as prognostic markers.

• Research in Community and Public Health Nursing
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• v.28 no.3
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• pp.302-312
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• 2017

Purpose: The purpose of this study was to identify associated factors of chewing discomfort among community-dwelling elderly. Methods: The study was cross-sectional design and secondary data analysis using the 6th Korea National Health and Nutrition Examination Survey. Among the total of 7,550 participants, data was analyzed with 1,126 adults aged 65 years and over. Chewing discomfort was assessed by the perceived chewing discomfort. Multivariate logistic regression analysis was used to find the associated factors of chewing discomfort. Results: Along with 61.7% of the participants reported having chewing discomfort, 85.2% reported to perceive poor oral health and 35.0% had oral pain. In multivariate logistic regression, perceived oral health (OR 3.22, 95% CI 2.24~4.63), oral pain (OR 2.46, 95% CI 1.76~3.43), activity limitation (OR 1.71, 95% CI 1.05~2.80), teeth requiring treatment (OR 1.61, 95% CI 1.14~2.26), number of remaining teeth (OR 1.60, 95% CI 1.22~2.10) and educational level (OR 1.56, 95% CI 1.15~2.12) were the significant predictors of chewing discomfort. Conclusion: The prevalence in chewing discomfort was high in elderly Koreans and various factors were associated with chewing discomfort. To improve chewing ability, it is suggested that the national level of policies offer strategical oral health programs in this population.

• Journal of Korean Biological Nursing Science
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• v.16 no.4
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• pp.259-266
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• 2014

Purpose: The purpose of this study was to analyze risk factors in predicting medical patients transferred to Intensive Care Unit (ICU) on the general ward. Methods: We reviewed retrospectively clinical data of 120 medical patients on the general ward and a Modified Early Warning Score (MEWS) between ICU group and general ward group. Data were analyzed with multivariate logistic regression and the area under the receiver operating characteristic curves using SPSS/WIN 18.0 program. Results: Fifty-two ICU patients and 68 general ward patients were included. In multivariate logistic regression, the MEWSs (Odds Ratio [OR], 1.91; 95% confidence interval [CI], 1.32-2.76), sequential organ failure assessment score (OR, 1.28; 95% CI, 1.10-1.72), $PaO_2/FiO_2$ ratio (OR, 0.98; 95% CI, 0.98-0.99), and saturation (OR, 0.93; 95% CI, 0.88-0.99) were predictive of ICU transfer. The sensitivity and the specificity of the MEWSs used with a cut-off value of six were 80.8% and 70.6% respectively for ICU transfer. Conclusion: These findings suggest that early prediction and treatment of patients with high risk of ICU transfer may improve the prognosis of patients.

• Journal of Ecology and Environment
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• v.32 no.4
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• pp.229-236
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• 2009

A habitat model was developed to delineate potential habitat of the leopard cat (Prionailurus bengalensis) in a mountainous region of Kangwon Province, Korea. Between 1997 and 2005, 224 leopard cat presence sites were recorded in the province in the Nationwide Survey on Natural Environments. Fifty percent of the sites were used to develop a habitat model, and the remaining sites were used to test the model. Fourteen environmental variables related to topographic features, water resources, vegetation and human disturbance were quantified for 112 of the leopard cat presence sites and an equal number of randomly selected sites. Statistical analyses (e.g., t-tests, and Pearson correlation analysis) showed that elevation, ridges, plains, % water cover, distance to water source, vegetated area, deciduous forest, coniferous forest, and distance to paved road differed significantly (P < 0.01) between presence and random sites. Stepwise logistic regression was used to develop a habitat model. Landform type (e.g., ridges vs. plains) is the major topographic factor affecting leopard cat presence. The species also appears to prefer deciduous forests and areas far from paved roads. The habitat map derived from the model correctly classified 93.75% of data from an independent sample of leopard cat presence sites, and the map at a regional scale showed that the cat's habitats are highly fragmented. Protection and restoration of connectivity of critical habitats should be implemented to preserve the leopard cat in mountainous regions of Korea.

• Korean Journal of Health Education and Promotion
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• v.28 no.1
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• pp.69-80
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• 2011

Objectives: This study aims to identify the factors associated with colorectal cancer screening behaviors. Methods: The nation-wide representative samples of 2,928 adults aged ${\geq}50$ years for colorectal cancer screening were derived from the fourth Korea National Health and Nutrition Examination Survey (KNHANES IV). This study investigated socio-demographic, health behavioral and contextual factors associated with colorectal cancer screening using descriptive statistics and multivariate logistic regression analysis. Results: In terms of socio-demographic factors, gender, age, marital status, occupation, monthly income, and resident region were significantly different between screening group and non-screening group. Among health behavioral and contextual factors, regular physical checkup, weight control, physical activity, smoking, drinking and having other cancers were significantly different. From the multivariate logistic regression analysis, marital status, education level, regular physical checkup and weight control were associated with colorectal cancer screening behavior. Conclusions: It is necessary to understand the importance of early detection and cancer screening. Appropriate health education and active promotion about the cancer screening should be developed based on the study findings in order to motivate people to have cancer screening. Also, these findings should be reflected in the health policy.

• Information Systems Review
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• v.8 no.3
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• pp.125-134
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• 2006

This article adopts a decision tree algorithm(C5.0) to predict customer churning in online auto insurance environment. Using a sample of on-line auto insurance customers contracts sold between 2003 and 2004, we test how decision tree-based model(C5.0) works on the prediction of customer churning. We compare the result of C5.0 with those of logistic regression model(LRM), multivariate discriminant analysis(MDA) model. The result shows C5.0 outperforms other models in the predictability. Based on the result, this study suggests a way of setting marketing strategy and of developing online auto insurance business.