AIM To investigate the incidence and the determinants of cardiovascular morbidity in Greek renal transplant recipients(RTRs) expressed as major advance cardiac event(MACE) rate. METHODS Two hundred and forty-two adult...AIM To investigate the incidence and the determinants of cardiovascular morbidity in Greek renal transplant recipients(RTRs) expressed as major advance cardiac event(MACE) rate. METHODS Two hundred and forty-two adult patients with a functioning graft for at least three months and availabledata that were followed up on the August 31, 2015 at two transplant centers of Western Greece were included in this study. Baseline recipients' data elements included demographics, clinical characteristics, history of comorbid conditions and laboratory parameters. Follow-up data regarding MACE occurrence were collected retrospectively from the patients' records and MACE risk score was calculated for each patient. RESULTS The mean age was 53 years(63.6% males) and 47 patients(19.4%) had a pre-existing cardiovascular disease(CVD) before transplantation. The mean estimated glomerular filtration rate was 52 ± 17 mL /min per 1.73 m2. During follow-up 36 patients(14.9%) suffered a MACE with a median time to MACE 5 years(interquartile range: 2.2-10 years). Recipients with a MACE compared to recipients without a MACE had a significantly higher mean age(59 years vs 52 years, P < 0.001) and a higher prevalence of pre-existing CVD(44.4% vs 15%, P < 0.001). The 7-year predicted mean risk for MACE was 14.6% ± 12.5% overall. In RTRs who experienced a MACE, the predicted risk was 22.3% ± 17.1% and was significantly higher than in RTRs without an event 13.3% ± 11.1%(P = 0.003). The discrimination ability of the model in the Greek database of RTRs was good with an area under the receiver operating characteristics curve of 0.68(95%CI: 0.58-0.78).CONCLUSION In this Greek cohort of RTRs, MACE occurred in 14.9% of the patients, pre-existing CVD was the main risk factor, while MACE risk model was proved a dependable utility in predicting CVD post RT.展开更多
In this editorial,we comment on the article by Chen et al.We specifically focus on the risk factors,prognostic factors,and management of brain metastasis(BM)in breast cancer(BC).BC is the second most common cancer to ...In this editorial,we comment on the article by Chen et al.We specifically focus on the risk factors,prognostic factors,and management of brain metastasis(BM)in breast cancer(BC).BC is the second most common cancer to have BM after lung cancer.Independent risk factors for BM in BC are:HER-2 positive BC,triplenegative BC,and germline BRCA mutation.Other factors associated with BM are lung metastasis,age less than 40 years,and African and American ancestry.Even though risk factors associated with BM in BC are elucidated,there is a lack of data on predictive models for BM in BC.Few studies have been made to formulate predictive models or nomograms to address this issue,where age,grade of tumor,HER-2 receptor status,and number of metastatic sites(1 vs>1)were predictive of BM in metastatic BC.However,none have been used in clinical practice.National Comprehensive Cancer Network recommends screening of BM in advanced BC only when the patient is symptomatic or suspicious of central nervous system symptoms;routine screening for BM in BC is not recommended in the guidelines.BM decreases the quality of life and will have a significant psychological impact.Further studies are required for designing validated nomograms or predictive models for BM in BC;these models can be used in the future to develop treatment approaches to prevent BM,which improves the quality of life and overall survival.展开更多
Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when ...Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.展开更多
BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for...BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.展开更多
Background: Kidney (renal) diseases and dialysis are among the most costly disorders and represent a worldwide burden. In this study, we evaluate the medical costs for individuals with kidney diseases and risk factors...Background: Kidney (renal) diseases and dialysis are among the most costly disorders and represent a worldwide burden. In this study, we evaluate the medical costs for individuals with kidney diseases and risk factors for the diseases in Japan. Data and Methods: The dataset used contained 113,979 medical checkups and 3,172,066 medical cost records obtained from 48,022 individuals in one health insurance society. The sample period was April 2013 to March 2016. We evaluated the distribution of all medical costs, and those of kidney diseases specifically. Then the power transformation Tobit model was used to remove the effects of other variables. Finally, a probit analysis was used to analyze the risk factors. Results: In 0.25% of all cases, individuals were diagnosed with kidney diseases. An individual with kidney disease cost 14.5 times more than those without kidney disease. If the diseases progressed into chronic kidney disease (CKD), the medical costs increased substantially. Even disregarding various characteristics of individuals, this conclusion did not vary. We found important risk factors included diabetes and blood pressure problems. In particular, an individual with both factors had a high probability of developing kidney disease. Conclusion: Kidney diseases are much costlier than other diseases. Screening high-risk individuals, educating patients, and ensuring that treatment begins at an early stage are critically important to controlling medical costs. Limitations: The dataset was observatory, and the sample period was only 3 years.展开更多
BACKGROUND： Parkinson disease （PD） results from the reduce of neurotransmitter dopamine that transmits intracellular information in brain caused by some reasons, then leads to the dynamic disequilibrium with anothe...BACKGROUND： Parkinson disease （PD） results from the reduce of neurotransmitter dopamine that transmits intracellular information in brain caused by some reasons, then leads to the dynamic disequilibrium with another neurotransmitter of acetylcholine which is relatively hyperactive. The main causes for PD are still unclear. OBJECTIVE： To screen out the risk factors of PD by means of univariate analysis and multivariate Logistic regression analysis, and investigate the manner of actions between various factors and PD, so as to provide clues for the etiological study of PD. DESIGN： A paired design, Logistic regression analysis, path analysis. SETTING： Department of Scientific Research, Shandong Institute of Physical Education. PARTICIPANTS： Totally 157 PD patients were selected from the Department of Neurology, Qilu Hospital of Shandong University from November 2001 to October 2002. Inclusive criteria： PD was diagnosed according to the standard set by the Fourth National Seminar on Extrapyramidal Disease, Parkinsonian syndromes caused by stroke, carbon monoxide poisoning, encephalitis, drugs, etc. were excluded. Another 157 patients treated in the same department at the same period were selected as the control group, they were the same in sex as those in the patient group, within 3 years older or younger than those in the patient group, and without PD or other extrapyramidal diseases. METHODS： （1） The general conditions were investigated in all the subjects, including general conditions, social behavioral factor, environmental factor, genetic factor, life events, and previous disease; There were 12 main variables, including educational level, family history, mental labour, contact to insecticides, living place before school-age, smoking index, drinking index, tea-drinking index, history of brain trauma, history of cardiovascular disease, history of diabetes mellitus, and history of depression. （2） SAS6.12 software and SPSS 10.0 software were used in the conditional Logistic regression analysis and path analysis respectively. MAIN OUTCOME MEASURES： The results of 12-variable univariate and multivariate analyses; Correlation between main variables and PD; Effects of the factors. RESULTS： All the subjects were involved in the analysis of results. （1） The results of Logistic regression analysis showed that family history, mental labour, insecticides, drinking index and history of depression all had significant positive correlations with PD （univariate analysis： OR=1.405- 5.429, P 〈 0.05- 0.01; multivariate analysis： OR=2.029- 6.754, P 〈 0.05- 0.01）, whereas smoking had significant negative correlations with PD [univariate analysis： odd ratio （OR）=0.765, P 〈 0.05; multivariate analysis： OR =0.489, P 〈 0.01]. （2） The path analysis showed that family history, mental labour, insecticides, smoking, drinking and history of depression had direct effects on PD occurrence [（path coefficient= - 0.218 to 0.204, P 〈 0.05 -0.01）]; Insecticides could cause PD indirectly on the basis of family history （genetic susceptibility） （path coefficient=0.946, P 〈 0.01）; Insecticides could also cause PD by drinking （path coefficient=0.165, P 〈 0.01） Drinking could cause PD indirectly on the basis of family history （path coefficient=0.043, P 〈 0.01 ）. CONCLUSION： The main risk factors of PD are family history, history of depression, drinking, mental labour and insecticides, whereas the protective factor is smoking. PD attack has genetic susceptibility, insecticides and drinking can cause PD on the basis of PD family history. The risk of PD can be decreased by reducing the occasion for contacting the environmental risk factors.展开更多
Objectives: To identify independent risk factors for abdominal wound dehiscence and develop a risk model to recognize high risk patients. Methods: The samples studied were patients who underwent midline laparotomy in ...Objectives: To identify independent risk factors for abdominal wound dehiscence and develop a risk model to recognize high risk patients. Methods: The samples studied were patients who underwent midline laparotomy in the department of surgery, SMHS Hospital Srinagar from March 2009 to April 2015. For each case of abdominal wound dehiscence, three controls were selected from a group of patients who had undergone open abdominal surgery as close as possible in time. Preoperative, perioperative, and postoperative variables and in-hospital mortality were studied for all patients. Cases were compared with controls using the chi-square test or the Mann-Whitney U-test for categorical or continuous data, respectively. Subsequently, multivariate stepwise logistic regression with backwards elimination test used to identify main independent risk factors of abdominal wound dehiscence. The resulting regression coefficients for the major risk factors were used as weights for these variables to calculate a risk score for abdominal wound dehiscence. Results: 140 cases of abdominal wound dehiscence were reported and compared with 420 selected controls. All variables that were significant in univariate analyses were entered in a multivariate stepwise logistic regression to determine which variables were significant independent risk factors. Major independent risk factors were male gender, chronic pulmonary disease, corticosteroid use, smoking, obesity, anemia, jaundice, ascites, and sepsis, type of surgery, postoperative coughing, and wound infection. Based on these findings, a risk model was developed. Conclusions: The model can give an estimate of the risk of abdominal wound dehiscence for individual patients. High-risk patients may be planned preventive wound closing with reinforcements as mesh.展开更多
The objective of the present study is to propose a risk evaluation statistical model for a given vulnerability by examining the Vulnerability Life Cycle and the CVSS score. Having a better understanding of the behavio...The objective of the present study is to propose a risk evaluation statistical model for a given vulnerability by examining the Vulnerability Life Cycle and the CVSS score. Having a better understanding of the behavior of vulnerability with respect to time will give us a great advantage. Such understanding will help us to avoid exploitations and introduce patches for a particular vulnerability before the attacker takes the advantage. Utilizing the proposed model one can identify the risk factor of a specific vulnerability being exploited as a function of time. Measuring of the risk factor of a given vulnerability will also help to improve the security level of software and to make appropriate decisions to patch the vulnerability before an exploitation takes place.展开更多
文摘AIM To investigate the incidence and the determinants of cardiovascular morbidity in Greek renal transplant recipients(RTRs) expressed as major advance cardiac event(MACE) rate. METHODS Two hundred and forty-two adult patients with a functioning graft for at least three months and availabledata that were followed up on the August 31, 2015 at two transplant centers of Western Greece were included in this study. Baseline recipients' data elements included demographics, clinical characteristics, history of comorbid conditions and laboratory parameters. Follow-up data regarding MACE occurrence were collected retrospectively from the patients' records and MACE risk score was calculated for each patient. RESULTS The mean age was 53 years(63.6% males) and 47 patients(19.4%) had a pre-existing cardiovascular disease(CVD) before transplantation. The mean estimated glomerular filtration rate was 52 ± 17 mL /min per 1.73 m2. During follow-up 36 patients(14.9%) suffered a MACE with a median time to MACE 5 years(interquartile range: 2.2-10 years). Recipients with a MACE compared to recipients without a MACE had a significantly higher mean age(59 years vs 52 years, P < 0.001) and a higher prevalence of pre-existing CVD(44.4% vs 15%, P < 0.001). The 7-year predicted mean risk for MACE was 14.6% ± 12.5% overall. In RTRs who experienced a MACE, the predicted risk was 22.3% ± 17.1% and was significantly higher than in RTRs without an event 13.3% ± 11.1%(P = 0.003). The discrimination ability of the model in the Greek database of RTRs was good with an area under the receiver operating characteristics curve of 0.68(95%CI: 0.58-0.78).CONCLUSION In this Greek cohort of RTRs, MACE occurred in 14.9% of the patients, pre-existing CVD was the main risk factor, while MACE risk model was proved a dependable utility in predicting CVD post RT.
文摘In this editorial,we comment on the article by Chen et al.We specifically focus on the risk factors,prognostic factors,and management of brain metastasis(BM)in breast cancer(BC).BC is the second most common cancer to have BM after lung cancer.Independent risk factors for BM in BC are:HER-2 positive BC,triplenegative BC,and germline BRCA mutation.Other factors associated with BM are lung metastasis,age less than 40 years,and African and American ancestry.Even though risk factors associated with BM in BC are elucidated,there is a lack of data on predictive models for BM in BC.Few studies have been made to formulate predictive models or nomograms to address this issue,where age,grade of tumor,HER-2 receptor status,and number of metastatic sites(1 vs>1)were predictive of BM in metastatic BC.However,none have been used in clinical practice.National Comprehensive Cancer Network recommends screening of BM in advanced BC only when the patient is symptomatic or suspicious of central nervous system symptoms;routine screening for BM in BC is not recommended in the guidelines.BM decreases the quality of life and will have a significant psychological impact.Further studies are required for designing validated nomograms or predictive models for BM in BC;these models can be used in the future to develop treatment approaches to prevent BM,which improves the quality of life and overall survival.
基金supported by the National Nature Science Foundation of China(Grant No.71401052)the National Social Science Foundation of China(Grant No.17BGL156)the Key Project of the National Social Science Foundation of China(Grant No.14AZD024)
文摘Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.
文摘BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.
文摘Background: Kidney (renal) diseases and dialysis are among the most costly disorders and represent a worldwide burden. In this study, we evaluate the medical costs for individuals with kidney diseases and risk factors for the diseases in Japan. Data and Methods: The dataset used contained 113,979 medical checkups and 3,172,066 medical cost records obtained from 48,022 individuals in one health insurance society. The sample period was April 2013 to March 2016. We evaluated the distribution of all medical costs, and those of kidney diseases specifically. Then the power transformation Tobit model was used to remove the effects of other variables. Finally, a probit analysis was used to analyze the risk factors. Results: In 0.25% of all cases, individuals were diagnosed with kidney diseases. An individual with kidney disease cost 14.5 times more than those without kidney disease. If the diseases progressed into chronic kidney disease (CKD), the medical costs increased substantially. Even disregarding various characteristics of individuals, this conclusion did not vary. We found important risk factors included diabetes and blood pressure problems. In particular, an individual with both factors had a high probability of developing kidney disease. Conclusion: Kidney diseases are much costlier than other diseases. Screening high-risk individuals, educating patients, and ensuring that treatment begins at an early stage are critically important to controlling medical costs. Limitations: The dataset was observatory, and the sample period was only 3 years.
文摘BACKGROUND： Parkinson disease （PD） results from the reduce of neurotransmitter dopamine that transmits intracellular information in brain caused by some reasons, then leads to the dynamic disequilibrium with another neurotransmitter of acetylcholine which is relatively hyperactive. The main causes for PD are still unclear. OBJECTIVE： To screen out the risk factors of PD by means of univariate analysis and multivariate Logistic regression analysis, and investigate the manner of actions between various factors and PD, so as to provide clues for the etiological study of PD. DESIGN： A paired design, Logistic regression analysis, path analysis. SETTING： Department of Scientific Research, Shandong Institute of Physical Education. PARTICIPANTS： Totally 157 PD patients were selected from the Department of Neurology, Qilu Hospital of Shandong University from November 2001 to October 2002. Inclusive criteria： PD was diagnosed according to the standard set by the Fourth National Seminar on Extrapyramidal Disease, Parkinsonian syndromes caused by stroke, carbon monoxide poisoning, encephalitis, drugs, etc. were excluded. Another 157 patients treated in the same department at the same period were selected as the control group, they were the same in sex as those in the patient group, within 3 years older or younger than those in the patient group, and without PD or other extrapyramidal diseases. METHODS： （1） The general conditions were investigated in all the subjects, including general conditions, social behavioral factor, environmental factor, genetic factor, life events, and previous disease; There were 12 main variables, including educational level, family history, mental labour, contact to insecticides, living place before school-age, smoking index, drinking index, tea-drinking index, history of brain trauma, history of cardiovascular disease, history of diabetes mellitus, and history of depression. （2） SAS6.12 software and SPSS 10.0 software were used in the conditional Logistic regression analysis and path analysis respectively. MAIN OUTCOME MEASURES： The results of 12-variable univariate and multivariate analyses; Correlation between main variables and PD; Effects of the factors. RESULTS： All the subjects were involved in the analysis of results. （1） The results of Logistic regression analysis showed that family history, mental labour, insecticides, drinking index and history of depression all had significant positive correlations with PD （univariate analysis： OR=1.405- 5.429, P 〈 0.05- 0.01; multivariate analysis： OR=2.029- 6.754, P 〈 0.05- 0.01）, whereas smoking had significant negative correlations with PD [univariate analysis： odd ratio （OR）=0.765, P 〈 0.05; multivariate analysis： OR =0.489, P 〈 0.01]. （2） The path analysis showed that family history, mental labour, insecticides, smoking, drinking and history of depression had direct effects on PD occurrence [（path coefficient= - 0.218 to 0.204, P 〈 0.05 -0.01）]; Insecticides could cause PD indirectly on the basis of family history （genetic susceptibility） （path coefficient=0.946, P 〈 0.01）; Insecticides could also cause PD by drinking （path coefficient=0.165, P 〈 0.01） Drinking could cause PD indirectly on the basis of family history （path coefficient=0.043, P 〈 0.01 ）. CONCLUSION： The main risk factors of PD are family history, history of depression, drinking, mental labour and insecticides, whereas the protective factor is smoking. PD attack has genetic susceptibility, insecticides and drinking can cause PD on the basis of PD family history. The risk of PD can be decreased by reducing the occasion for contacting the environmental risk factors.
文摘Objectives: To identify independent risk factors for abdominal wound dehiscence and develop a risk model to recognize high risk patients. Methods: The samples studied were patients who underwent midline laparotomy in the department of surgery, SMHS Hospital Srinagar from March 2009 to April 2015. For each case of abdominal wound dehiscence, three controls were selected from a group of patients who had undergone open abdominal surgery as close as possible in time. Preoperative, perioperative, and postoperative variables and in-hospital mortality were studied for all patients. Cases were compared with controls using the chi-square test or the Mann-Whitney U-test for categorical or continuous data, respectively. Subsequently, multivariate stepwise logistic regression with backwards elimination test used to identify main independent risk factors of abdominal wound dehiscence. The resulting regression coefficients for the major risk factors were used as weights for these variables to calculate a risk score for abdominal wound dehiscence. Results: 140 cases of abdominal wound dehiscence were reported and compared with 420 selected controls. All variables that were significant in univariate analyses were entered in a multivariate stepwise logistic regression to determine which variables were significant independent risk factors. Major independent risk factors were male gender, chronic pulmonary disease, corticosteroid use, smoking, obesity, anemia, jaundice, ascites, and sepsis, type of surgery, postoperative coughing, and wound infection. Based on these findings, a risk model was developed. Conclusions: The model can give an estimate of the risk of abdominal wound dehiscence for individual patients. High-risk patients may be planned preventive wound closing with reinforcements as mesh.
文摘The objective of the present study is to propose a risk evaluation statistical model for a given vulnerability by examining the Vulnerability Life Cycle and the CVSS score. Having a better understanding of the behavior of vulnerability with respect to time will give us a great advantage. Such understanding will help us to avoid exploitations and introduce patches for a particular vulnerability before the attacker takes the advantage. Utilizing the proposed model one can identify the risk factor of a specific vulnerability being exploited as a function of time. Measuring of the risk factor of a given vulnerability will also help to improve the security level of software and to make appropriate decisions to patch the vulnerability before an exploitation takes place.
