Obesity, type 2 diabetes mellitus (DM) and metabolic syndrome (MS) are common in patients with heart failure (HF). Studies investigating the association between known biomarkers and adiposity in patient populations are limited. The aim of the present study was to investigate the association between C-reactive protein (CRP) and leptin with adiposity in a sub-group of overweight/obese patients with HF, DM and/or MS. A total of 36 patients  were enrolled and their height, weight, waist circumference and body composition (e.g. percentage body fat and lean mass), as well as the levels of CRP and leptin, were assessed. The results demonstrated that there was a significant association between CRP and leptin, CRP and body mass index (BMI) and gender and percentage body fat . Analysis of leptin and CRP levels revealed that patients in the highest BMI quartile  had higher CRP levels  and higher leptin levels  compared with patients in the lower BMI quartile . In conclusion, among obese patients with HF, DM and/or MS, an association between CRP and leptin was identified, providing further evidence that metabolic and inflammatory mechanisms are involved in these diseases. Future investigation to assess the potential impact of inflammation and adiposity, and the role of dietary interventions and weight loss on clinical outcomes in this population of chronically ill patients is warranted. Heart failure (HF), obesity and type 2 diabetes mellitus (DM) are disease conditions associated with the progression of cardiovascular disease, functional disability and a diminished quality of life as reported in our recent study . PreviouCRP is a marker of systemic inflammation and the levels of CRP markedly increase as part of the acute-phase response. CRP is synthesized by the liver and regulated by cytokines, particularly interleukin (IL)-6 . ElevateLeptin, an adipocyte-derived hormone is involved in appetite regulation, insulin homeostasis and obesity through the effects of the hypothalamus ,14. LeptThe aim of the present study was to determine the circulating levels of CRP and leptin and to examine their correlation with each other as well as with body mass index (BMI) and adiposity in overweight/obese patients with HF, DM and/or MS. Investigation of these biomarkers may provide insight into the association between inflammatory and metabolic mechanisms involved in disease onset and pathophysiology in this patient population.A total of 36 subjects (>18 years old) were recruited and provided informed consent to participate in a randomized controlled clinical trial for overweight/obese patients with HF, DM and/or MS. The present study was approved by the Institutional Human Subjects Review Committee. Inclusion criteria included a BMI >27, New York Heart Association (NYHA) functional class II or III HF (either with preserved left ventricular ejection fraction [HFpEF] or reduced ejection fraction [HFrEF]) and a history of DM (e.g. impaired fasting glucose of 100–125 mg/dl), or meeting three or more of the criteria for MS. Participants were excluded if they were pregnant, had a serum creatinine level >1.5 mg/dl or had a history of clinically significant illness, including acute myocardial infarction or sustained ventricular arrhythmia three months prior to the start of the experiment. In addition, patients were also excluded if they were currently suffering from liver, respiratory and/or gastrointestinal disease and malignancy, or a history of gout. Participants were interviewed by nurses to provide information regarding sociodemographic and clinical variables .Height, weight, waist circumference and vital signs were measured by the research staff as previously described . Body coFollowing an overnight fast, venous blood samples were obtained and the levels of total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, fasting blood glucose and insulin were measured. High sensitivity CRP and leptin levels were measured using Quantikine Elisa Assay kits purchased from R&D Systems . CRP was measured with lower and upper detection limits of 0.78 ng/ml and 50 ng/ml, respectively, using an assay sensitivity of 0.022 ng/ml. The assay for leptin measured between 16 and 1,000 pg/ml, with a sensitivity of 7.8 pg/ml.2) categories based on quartiles of all BMI data collected in the present study: i) BMI 28.6–32.4, ii) BMI 32.5–35.8, iii) BMI 35.9–40.2 and iv) BMI 40.3–61.2. Data were analyzed using SPSS statistical software version 19.0 for Windows . Analysis of variance was used to compare sociodemographic and clinical variables of the entire sample size (n=36), as well as each of the BMI quartiles. Comparison of CRP and leptin levels within gender or BMI groups was performed using the independent samples t-test. The association between serum CRP and leptin levels and age, gender, waist circumference, total percentage fat and total cholesterol were evaluated using Pearson’s product moment correlation coefficient and Spearman’s Rho, depending on the level of measurement. Multivariate analysis was performed to test the independent association between CRP and leptin using leptin as the dependent variable and CRP, age, gender, BMI, waist circumference and statin as covariates.Patients were classified into four BMI (kg/m2) and had a mean total cholesterol of 158.5±39.6 and a total body fat percentage of 36.9±7.0%. With the exception of the expected weight-associated variables , the participants in the four BMI quartiles only differed significantly in the mean age (due to the inclusion of a 27 year old in the highest quartile).Univariate analyses for variables of interest are illustrated in The different BMI quartiles were then compared in order to further evaluate the association between obesity and levels of CRP and leptin. While the association between leptin and CRP was independent of obesity level, there was a statistically significant increase in the concentration of these markers as the level of obesity increased . In the To the best of our knowledge, the present study is the first to examine the association between CRP, leptin and adiposity and other cardiovascular risk factors in overweight/obese patients with HF, DM and/or MS. The moderately strong association between CRP and adiposity observed in the present study is in accordance with a previous study that linked adiposity with a chronic state of inflammation, which may be involved in the development of multiple chronic illnesses, including cardiovascular disease and MS . FurtherIn the present study, a statistically significant correlation between CRP and leptin was identified, independent of BMI in the present study population of overweight/obese patients with HF, and this has also been confirmed in a number of other patient populations and healthy adults –25. The The mechanisms linking CRP with leptin in different disease models is not well understood. Since CRP synthesis is considered to be regulated by IL-6 , and sinet al  found that CRP levels were almost twice as high in females compared with males  . In a reet al  investiget al ,36. Gendet al . Future The present study was limited by the number of participants enrolled and the fewer numbers of females compared with males. Furthermore, there is always a limitation in analyzing multiple factors in biological systems  due to the adjustments that have to be made for common disease pathways. Finally, analysis of the current diet and physical activity of each participant may potentially alter the risk estimates used in the present study. In future, a larger sample size is required and the effect of weight loss regimens need to be evaluated to further reveal the link between inflammation, adiposity and adipokines, and their association with risk factors and outcome in overweight/obese patients with HF, DM and/or MS.


P < 0.1 entered a logistic regression model. The prevalence of recurrent falls was 30% (95% CI 24%–38%). In multivariate analysis, independent risk factors for recurrent falls were (P < 0.05) levodopa equivalent dose , UPDRS-ADL > 16 points , FES-I > 30 points , and Berg ≤ 48 points .We encourage the utilization of these modifiable risk factors in the screening of fall risk.Falls can be considered a disabling feature in Parkinson's disease. We aimed to identify risk factors for falling, testing simultaneously the ability of disease-specific and balance-related measures. We evaluated 171 patients, collecting demographic and clinical data, including standardized assessments with the Unified Parkinson's Disease Rating Scale (UPDRS), activities of daily living (ADL) and motor sections, modified Hoehn and Yahr Scale, Schwab and England, eight-item Parkinson's Disease Questionnaire, Activities-specific Balance Confidence Scale, Falls Efficacy Scale-International (FES-I), Berg Balance Scale, Dynamic Gait Index, Functional Reach, and Timed Up and Go. ROC curves were constructed to determine the cutoff scores for all measures. Variables with  Parkinson's disease (PD) is a common neurodegenerative disorder in older adults and postural instability is well known to occur in these individuals, increasing the risk for falling –4. FallsThe consequences of falls are numerous and can affect not only fallers, but also their family and community. Falls can result in injuries such as head trauma and hip fractures and may also contribute to increased fear of falling, reduced level of activity, and functional impairment, leading to decreased quality of life in these individuals –16. The Several risk factors of falls have been proposed, although we can note that some of them have limited use in the general clinical practice, such as longer disease duration and dementia ; abnormaIn a meta-analysis of prospective studies of falling in PD, the best predictor of falls was the presence of two or more falls in the previous year . AlthougConsidering the wide range and scope of falls consequences, it is of paramount importance to increase our knowledge of factors associated with recurrent falls, in particular, those identifiable through common clinical measures. This will provide useful information for identification of individuals at risk of falling and may contribute to the development of specific interventions to prevent falls.The goals of this study were to report the frequency of falls in a sample of community-dwelling individuals with PD and to identify risk factors for recurrent falls, testing simultaneously the ability of disease-specific and balance-related measures to predict falls.The study sample consisted of consecutive individuals with PD with independent walking ability who attended the Movement Disorders Clinic at the State of Bahia Centre for the Elderly Health Attention, Brazil. Independent walking ability was defined when the patient was able to walk without assistance of another person, with or without a walking aid. The movement disorders clinic is a reference service for the state of Bahia, where approximately 500 elderly (≥60 years old) patients with PD and other parkinsonian syndromes are followed, mostly from the public health system. The diagnosis of idiopathic PD was made by a certified neurologist, in accordance with the UK Parkinson's Disease Society Brain Bank Diagnostic Criteria . ExclusiPatients were recruited between April of 2010 and January of 2012. The study was approved by the local research ethics committee. Initially, potential participants were approached by a physical therapist (L.R.S.A) and explanations related to the study were given to them. After that, each individual who decided to participate in the study provided written informed consent.Demographic and clinical data were recorded including the following information: age, gender, disease duration, history of falls, antiparkinsonian medications taken, presence of motor fluctuations, dyskinesia, FOG, and comorbidities . The definition of each comorbidity was based on self-report complemented by chart review. The levodopa equivalent dose (LED) was calculated based on a previously reported conversion factor . A fall The UPDRS was completed, including the activities of daily living (ADL) and motor sections, to assess both motor disability and motor impairment. The ADL section ranges from 0 to 52 points (greater disability) and the motor section from 0 to 108 (greater impairment) . The modFear of falling was evaluated with two scales: the Activities-specific Balance Confidence Scale (ABC), which measures self-perceived balance confidence for completing daily living tasks, ranging from 0 to 100% (full confidence) , 20, andBalance and gait were assessed with four tests: Berg Balance Scale (BBS) , FunctioAll patients were assessed in the movement disorder clinic on the same occasion as they were recruited. They were evaluated during the “on” phase of the medication cycle and the balance measures were administered in the order described above by a trained physical therapist (L.R.S.A.). Participants were allowed to rest as needed during the balance assessments. Only the TUG was performed twice, and the first trial was considered as practice . All ther ≥ 0.75 was set. Rates of falling were reported with 95% confidence intervals (CI). A significance level of 0.05 was set for these statistical tests.Statistics were performed using SPSS for Windows (version 16.0). Descriptive statistics were calculated for demographic and clinical variables, while comparison of recurrent fallers and nonfallers was completed using the Mann-Whitney test for continuous variables and the Pearson's chi-square test for categorical variables. To identify collinearity between the functional capacity scales, the fear of falling measures, and the balance tests, the Spearman correlation was performed and a value of P < 0.1 were selected to enter into the multivariate regression analysis. To improve the clinical utility of the disease-specific, fear of falling, and balance measures, receiver operating characteristic (ROC) curves were constructed to determine the cutoff scores that would be best related to recurrent fallers, based on Youden indices [R-squared.Variables with  indices . The aren = 58), essential tremor (n = 73), dystonia (n = 56), spinocerebellar ataxia (n = 2), and chorea (n = 2). One hundred and two people with PD were excluded due to other neurological conditions in addition to idiopathic PD (n = 10), cognitive impairment (n = 32), musculoskeletal problems that affected balance (n = 15), severe visual disturbance (n = 3), and vestibular dysfunction (n = 5) and because they were unable to walk independently (n = 37). An additional 51 individuals with PD were approached, but 16 were not regularly taking antiparkinsonian medication and 35 were unwilling to consent to participate in the study. This number (n = 51) corresponds to 23% of all participants who met all the eligibility criteria (n = 222).During the recruitment period, a total of 515 people attended the movement disorders clinic and 171 individuals with PD were enrolled in the study. One hundred and ninety-one patients were excluded because they did not meet the inclusion criteria for the following reasons: atypical parkinsonism  and 89 (52%) were female. Patients had a median H&Y stage of 2.5 (range 1.0–4.0) and the median LED was 600 mg (range 300–1621.9). Seventy-four  patients reported one or more falls in the previous 12 months. Most patients  reported two or more falls and were classified as recurrent fallers.P < 0.001), as shown in P = 0.001), FOG (P < 0.001), urinary incontinence (P < 0.001), and constipation (P = 0.002) were associated with recurrent falls. Motor fluctuations were not associated with recurrent falls, but we found a borderline P value of 0.084.Demographic and disease-specific characteristics of the sample are shown in The chosen cutoff scores and validity indices were as follows for the disease-specific measures: H&Y > 2.5 ; UPDRS-motor > 33 points ; S&E ≤ 70% ; and UPDRS-ADL > 16 points .In relation to the fear of falling scales, the cutoff scores and the corresponding values were FES-I > 30 points  and ABC ≤ 46% .Regarding the balance measures, the determined cutoff scores and validity indices were BBS ≤ 48 points ; DGI ≤ 18 points ; FRT ≤ 17 centimeters ; TUG > 16.6 seconds .In the multivariate analysis, 164 patients were included due to missing data related to the UPDRS motor score, which was entered into the model. The H&Y score and the LED were also used as measures of disease severity rather than disease duration. Collinearity was found between the UPDRS-ADL and S&E scale, and the first one was selected to enter into the model due to its higher AUC. To remove redundancy, FOG, also used as a dichotomous variable, was excluded from the model as it is an item from the UPDRS-ADL. As both the ABC and FES-I scales assess fear of falling during everyday tasks and they had similar values of AUC, we chose to enter the FES-I into the model, due to its higher value of sensitivity. As we found collinearity between all balance measures, the BBS was entered into the model due to its higher AUC.The independent risk factors for recurrent falls in the multivariate model were LED, UPDRS-ADL > 16 points, FES-I > 30 points, and BBS ≤ 48 points . The finThis study aimed to report the frequency of falls in a sample of individuals with PD and to identify risk factors for recurrent falls. The reported frequency of one or more falls was 43% and of two or more falls 30%. These findings confirm that falling is a common feature of PD. When analyzing retrospective studies, our reported frequency of one or more falls was lower than that found by Landers et al. 51%)  and Dibb1%  and DOur main results were the independent association between recurrent fallers and higher LED and the association between recurrent fallers and three clinical scales . Recurrent fallers had longer disease duration and increased disease severity based on the H&Y and UPDRS motor sections than nonfallers, as well as higher LED. Recurrent fallers also showed reduced quality of life, higher degree of fear of falling and functional impairment, and decreased performance on all balance measures. These results are in agreement with those reported in previous studies , 40, 41.Previous research found that H&Y stage, UPDRS motor score, and disease duration were considered risk factors for falls in multivariate analyses , 43, 44.The UPDRS-ADL > 16 points was also found to be an independent risk factor for recurrent falls. This subscale comprises some activities that can be difficult to perform not only because of motor limitations, but also due to postural instability, such as dressing and hygiene. Moreover, it rates walking difficulty and occurrence of FOG, factors linked to balance impairment, in addition to frequency of falling. FOG is a disabling feature of PD that occurs more often with disease progression and is related to falls in these patients , 47. TheThe BBS, with a cutoff score of ≤ 48 points, remained an independent risk factor for falling. The BBS is a well-established tool to assess postural instability in people with PD –52 and pRecurrent fallers had higher degree of fear of falling on both ABC and FES-I. ABC scores have been determined to be independently associated with increased risk of falling in individuals with PD , 54. In Other variables were associated with recurrent falls, although they were not found to be independent risk factors in the presence of the other factors included in our analysis. Recurrent fallers presented more dyskinesia than nonfallers. This high frequency of dyskinesia could be explained by the higher intake of levodopa showed by recurrent fallers . It has Urinary incontinence and constipation, both considered signs of autonomic dysfunction in PD, were associated with falls. Although we have not investigated thoroughly urinary incontinence, it is known that nocturia and urinary urgency are common events in PD . UsuallyP value [Motor fluctuations might be associated with postural instability and higher risk of falling, as it generates reduction of motor performance , leadingP value , similarThe main strength of our study was to simultaneously test disease-specific and balance-related measures in the same sample population, suggesting the importance of using common clinical scales to assess risk of falling. The utilization of the scales that we found to be associated with recurrent falls, with their respective cutoff scores, may be useful in clinical practice. These scales provide detailed information about impaired functional activities and balance-related tasks and can be used not only in the assessment of the individuals with PD, but also during treatment, and to evaluate treatment outcomes. Our results also provide evidence for an independent validation sample using these scales, selecting the best combination of measures to determine the risk of recurrent falls. Although our score performed well in our patient sample, an independent validation sample is important before incorporation into clinical practice.Differences found in this study may be due to different methods applied by previous authors to identify risk factors associated with falls, different classification of fallers and nonfallers, and different sample characteristics. One limitation of this study is the retrospective classification of recurrent fallers and nonfallers. However, our rate of recurrent fallers was similar to that reported by previous prospective studies. We are currently carrying out a one-year prospective study and further analysis may differentiate predictors of single and recurrent falls.The occurrence of falls in PD is high and recurrent falls, especially, can play an important role in the functional status of patients. The identification of risk factors associated with recurrent falls in people with PD, especially those that are modifiable, could be useful to target intervention programs to improve balance and possibly reduce the risk of falls and related injuries. We encourage the use of the UPDRS-ADL section, BBS, and FES-I in screening for falls risk in individuals with PD, measures that could be easily performed either in a home or outpatient clinical setting.


It is well-recognized that diabetes represents a powerful independent risk factor for cardiovascular diseases. However, very few studies have investigated the relationship between diabetes and risk of aortic dissection (AD).The aim of this case-control study was to evaluate the association between diabetes and risk of AD in Chinese population.A hospital-based case-control study, consisting of 2160 AD patients and 4320 controls, was conducted in a Chinese population. Demographic, clinical characteristics and risk factors were collected. Diabetes rate of patients with overall AD, Stanford type A AD and type B AD group was compared with that of corresponding matched control groups. Logistic regression analysis was used to estimate the odds ratios (OR) and 95% confidence intervals (95% CI) for relationship between diabetes and AD risk.The prevalence of diabetes was lower in AD cases than that of control subjects, whether it is the overall AD, type A AD or type B AD group . Furthermore, in multivariate model, diabetes was found to be associated with lower AD risk, which not only applies to the overall AD , but also type A AD  and type B AD .We observed the paradoxical inverse relationship between DM and risk of AD in the Chinese population. These results suggest diabetes may play a protective role in the development of AD. However, further studies are needed to enrich related evidence, especially with regard to underlying mechanisms for these trends. Aortic dissection (AD) is a potentially critical break in the lining of the main arterial outflow from the heart . As a reDiabetes is a high risk factor for the development of cardiovascular diseases (CVD)and atherosclerosis , 8. NumeHowever, opposed to the assumption, a few recent studies indicated that diabetes has a protective effect against aortic diseases, including AD –15. The The study was approved by the Ethics Committee of the Tongji hospital of Huazhong University of Science and Technology. All aspects of the study comply with the Declaration of Helsinki. Ethics Committee of Tongji hospital of Huazhong University of Science and Technology specially approved that not informed consent was required because data were going to be analyzed anonymously.This project was designed as a hospital-based retrospective 1:2 case—control study. Between 1 January 2003 and 1 December 2013, a total of 2160 consecutive AD patients at Tongji Hospital  were enrolled in the study. Cases were diagnosed by imaging, surgical visualization, or autopsy. Patients with traumatic aortic dissection and iatrogenic aortic dissection were excluded. According to the Stanford classification system [In addition to describing case subject’s characteristics, a case—control approach was performed. 4,320 controls  were selected at random by frequency matching age (plus or minus 1 year) and gender from 22,430 people , who had received a health examination in Tongji hospital in 2011. All patients with previous history of aortic dissection, aneurysm and active inflammatory disease were excluded to remove elements of confounding bias. The ratio of cases to controls was 1:2. The controls were divided in groups according to the different case groups. The corresponding controls for overall AD group were controls group , for type A AD group were control A group  and for type B AD group were control B group . Information on demographic and other variables, such as diabetes mellitus (type 2), hypertension, hyperlipidemia, history of smoking, Marfan syndrome, bicuspid aortic valve, COPD, etc, were abstracted from hospital charts. The prevalence of diabetes in each AD group was compared with that of the corresponding control groups, respectively. Finally, the odds ratios (OR) and 95% confidence intervals (95% CI) for relationship between diabetes and risk of AD in each case/control group were calculated.History of smoking was assumed if the patient had smoked within the last 10 years. Hypertension was defined as blood pressure ≥140/90 mmHg or treatment for hypertension before admission; hyperlipidemia was defined as cholesterol level >220 mg/dl or treatment for hyperlipidemia; According to the WHO criteria , diabeteContinuous variables are presented as mean ± SD, and categorical variables are presented as frequencies. All comparisons between two groups of continuous variables were made using a two-sample t-test or nonparametric test; the Chi-square test was employed when comparing categorical variables. To study the association between diabetes and AD risk, logistic regression method was used for the calculation of odds ratio (OR) as well as its 95% confident interval (95% CI). Adjusted ORs were computed using multivariate logistic regression with adjustment for history of smoking, hyperlipidemia, hypertension, coronary heart disease, peripheral vascular disease, drug abuse, Marfan syndrome, bicuspid aortic valve and COPD. P<0.05 was taken to indicate statistical significance. The statistical analyses above were performed with the SPSS Software version 16.0  and were based on two-tailed probability.The baseline characteristics of the case/control samples are shown in We further divided cases into two subgroups, Stanford type A and type B. The corresponding controls for type A AD group were control A group and for type B AD group were control B group. Similarly, the prevalence of diabetes in Stanford type A AD and type B AD group were lower than that of control A and control B group .In multivariate logistic regression analysis, after adjustment for hypertension, history of smoking, Marfan syndrome, hyperlipidemia, bicuspid aortic valve, drug abuse, CHD, PVD and COPD, DM was related to the reduced risk of AD . Furthermore, the inverse association also remained significant in the Stanford type A/case A group  and type B/case B group . Other factors that were independently associated with risk of AD are shown in Finally, we compared the incidence of in-hospital death between diabetes and non-diabetes in AD patients. As shown in Diabetes has been known to be a powerful risk factor for associated cardiovascular diseases (CVD) and the development of atherosclerosis. Patients with type 2 diabetes have a 2- to 4- fold higher risk of CVD death compared with patients without . FurtherPrevious studies have shown that for patients with high risk of atherosclerosis, forbidding smoking and controlling the blood pressure and blood lipid in normal level can significantly reduce cardiovascular morbidity and mortality. However, with the hemoglobin A1c (HbA1c) controlled at normal level for patients with diabetes and high risk of atherosclerosis, the incidence of micro-vascular disease may be reduced, but a significant increase in mortality of macro-vascular can be brought about , 21. In Recently, increasing evidence has suggested the inverse relationship between diabetes and aortic diseases, including aortic aneurysm and AD. population study suggest that the overall pooled incidence rate of thoracic aortic aneurysm (TAA) and AAA was 15% lower in the type 2 diabetes cohort than non-diabetes cohort (3.85 vs 4.51 per 10 000 person-years), with an adjusted HR of 0.65 (95% CI: 0.56–0.74). In the One thing meriting our consideration, however, is that only the inverse relationship between diabetes and risk of AD was shown and the exact mechanism for the beneficial effect by which diabetes may protect against the development of AD is still ambiguous. One of the possible mechanisms is that the biologic of aortic wall may be changed by diabetes. According to the recent studies, hyperglycemia associated with diabetes plays the role of stabilizing the collagen network through giving rise to cross-linking of collagen network in the aortic wall media. As for this cross-linking, it resists proteolysis and inhibits secretion of matrix metalloprotei-nases (MMPs) which is deemed as with regulating effect on aortic aneurysm formation and promoting effect on atherosclerotic plaque rupture . AdditioA key strength of this study is that it is one of the first to assess the correlation between AD risk and diabetes in a non-western case-control study with sizeable samples. Drawn from the same geography and population, both AD cases and control subjects were likely to be representative of the general population. Furthermore, effective methods were used to control biases which may influence the quality of case-control study. For controlling selection bias, all the AD cases were strictly chosen according to the inclusive criteria while control subjects were randomly selected. For decreasing confounding bias, all controls were matched by age and gender. Moreover, a 1: 2 matching design method was adopted to increase the statistical power. More importantly, our study will leave a foundation for future studies about the relationship between diabetes and risk of aortic dissection. Future research might focus on further identifying the exact mechanisms related to beneficial effect with a view to developing targeted pharmacological therapy or precaution for aortic dissection.Nevertheless, a few limitations of our study deserve our consideration. First, as this is a retrospective case-control study, it is not appropriate for any casual association to be established between variables. Also, it was difficult to avoid residual unrecognized confounding variables influence and thus a large, multi-center, prospective study is necessary. Second, it is possible that control patients had been receiving regular medical care and thus were more likely to have diabetes diagnosed. Yet, compared with control patients, the acute aortic dissection may not have been previously cared for or diagnosed. Therefore, diabetes prevalence in aortic dissection group may be underestimated. Finally, due to the lack of research on relevant mechanisms in our part, further researches involving underlying mechanism related to the protective role of hyperglycemia in AD are required to reach a more definitive conclusion. Despite these limitations, our study may leave a foundation for future studies about the relationship between diabetes and risk of aortic diseases.In conclusion, our findings indicated that diabetes was significantly associated with decreased risk of AD in our Chinese subjects. Further confirmatory results from other research conducted in different populations with different research designs (e.g. cohort studies) are required to establish this association; however, results from this investigation have demonstrated that diabetes may play a protective role in the development of AD and that relevant mechanisms related to the beneficial effect may provide a new insight into the causes, prevention and treatment of AD.S1 Appendix(XLSX)Click here for additional data file.S2 Appendix(XLSX)Click here for additional data file.


Self esteem (SE) and organizational commitment (OC) have significant impact on the quality of work life.This study aims to gain a better understanding of the relationships between SE and OC among health information management staff in tertiary care hospitals in Tehran (Iran).This was a descriptive correlational and cross sectional study conducted on the health information management staff of tertiary care hospitals in Tehran, Iran. A total of 155 participants were randomly selected from 400 staff. Data were collected by two standard questionnaires. The SE and OC was measured using Eysenck SE scale and Meyer and Allen’s three component model, respectively. The collected data were analyzed with the SPSS (version 16) using statistical tests of of independent T-test, Pearson Correlation coefficient, one way ANOVA and F tests.The OC and SE of the employees’ were 67.8, out of 120 (weak and 21.0 out of 30 (moderate), respectively. The values for affective commitment, normative commitment, and continuance commitment were respectively 21.3 out of 40 (moderate), 23.9 out of 40 (moderate), and 22.7 out of 40 (moderate). The Pearson correlation coefficient test showed a significant OC and SE was statistically significant (P<0.05). The one way ANOVA test (P<0.05) did not show any significant difference between educational degree and work experience with SE and OC.This research showed that SE and OC are moderate. SE and OC have strong correlation with turnover, critical thinking, job satisfaction, and individual and organizational improvement. Therefore, applying appropriate human resource policies is crucial to reinforce these measures. Previous studies have indicated the main factors influencing the productivity of organizations at macro or micro levels, where the human resources plays a crucial role in these factors . The proSE is one of the most challenging issues in the psychology, considered as behavior predictor, strong relief for anxiety and substantial solution to organizational problems . SE can OC is more related to social interactions of people so that they reveal it in the work environment according to their preferences, talents, attitudes and cognitions. In addition, it plays a promising role in the success of organization . OC has OC has received considerable interest from organizational researchers since its inception. Therefore, it has been tested in a variety of research fields such as: job satisfaction , learninTherefore, SE and OC representing individual and social aspects can detect the efficiency rate of employees. Previous studies on Iranian hospitals have demonstrated different occupational problems including occupational stress, staff shortage, lack of promotion, and turnover .Human resources in health information technology have important role in quality, efficiency, and reducing medical errors  to gain a better understanding of the relationships between SE and OC.This study was a descriptive correlational and cross sectional survey conducted on 400 employees of health information technology department of the tertiary care hospitals of Tehran in 2010. The sample size of 155 was determined by Morgan Table and 155 155 participate in research. Eighteen hospitals were selected through a random sampling method. One hundred and fifty five questionnaires were used for all participants. The SE and OC was measured using Eysenck SE scale and Meyer and Allen’s three component model, respectively. These questionnaires are standard. Validity and reliability of SE questionnaire had been verified by previous studies . In addiThe staff involved in the study had the mean age below 35 years old (75%). The majority of them (75%) had a bachelor’s degree in Health Information Technology major.Total score for SE and OC measured by Eysenck and Meyer and Allen scale is 30 and 120, respectively.Total score for affective, continuance and normative commitment is 40. Employees’ scores are showed in Findings show that the employees’ OC and SE were 67.8, out of 120 (weak) and 21.0 out of 30 (moderate), respectively. The values for affective commitment, normative commitment, and continuance commitment were respectively 21.3 out of 40 (moderate), 23.9 out of 40 (moderate), and 22.7 out of 40 (moderate) .The independent T-test (p<0.05) was conducted to compare the differences the marital status and OC (t=.045 df=150 sig.964), continuance commitment (t=.927 df=150 sig.355) and SE (t=1.188 df=150 sig.237). The Pearson correlation coefficient (r) test showed no statistically significant relationship between affective, normative and continuance commitment with SE. However, the relationship between OC and SE was statically significant (p<0.05). The detailed results of the statistical tests are presented in The ANOVA statistical test show significant effect of age on OC , whereas no significant effect of age was found on SE  .The findings show no significant difference between age and affective, normative and continuance commitment and SE. Also statistical test (P<0.05) did not show any significant difference between educational degree and work experience with SE, affective commitment, normative commitment, continuance commitment and OC.The present study evaluated the relationships between SE and OC among health information management staff in tertiary care hospitals in Tehran (Iran). The average scores of SE were frequently moderate (22.3 out of 30) Haghirosadat reported this score as 21.8 . In addiIn this study, normative commitment of employees acquired higher score than continuance and affective commitment and mean score of OC was 67 from 120. The OC is a psychological status in the three component model of Meyer and Allen that includes: desire, need and obligation named affective commitment, continuance commitment and normative commitment, respectively. Meyer and Allen mapped affective and normative commitments with attitudinal dimension and continuance commitment with behavioral dimension . AffectiSE is a growing field . New resThe findings of this study showed that SE and OC of the employees of health information technology department in the tertiary hospitals in Tehran are moderate. SE is a growing field and a very important feature among other features related to mental health. In other words, it is one of the most fundamental factors affecting mental health. In addition, SE is effective in the success and compatibility of people. The social aspect of SE is the OC that a person shows it in the workplace based on their preferences, talents, attitudes and cognitions. The OC has a key role in the employee’s loyalty and is the most important related factor defining performance, values and attitudes of the employees. Especially it is a kind of belief and internal feelings of a person affecting performance, adherence and judgment of him/her to the organization. In general, SE and OC affect the organization performance through improvement of job quality, job abandonment, job satisfaction, job stress, human resources output, critical thinking, stimulus for progress, education success rate, stress reducing and job conflict. Because of the importance of health information technology department, managers and policy makers of health systems should fulfill a proper planning to improve human resources management. In this regard, insufficient attention can lead to low rates of return on investment.Our findings recommend further studies on attitudinal level, job satisfaction relations, SE and OC, organizational health and organizational justice of the employees of health information technology department.


To compare the distribution of the intrinsic molecular subtypes of breast cancer based on immunohistochemical profile in the five major geographic regions of Brazil, a country of continental dimension, with a wide racial variation of people.The study was retrospective observational. We classified 5,687 invasive breast cancers by molecular subtype based on immunohistochemical expression of estrogen-receptor (ER), progesterone-receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 proliferation index. Cases were classified as luminal A , luminal B , triple-positive (ER and/or PR positive and HER2 positive), HER2-enriched , and triple-negative (TN) . Comparisons of the ages of patients and molecular subtypes between different geographic regions were performed.South and Southeast regions with a higher percentage of European ancestry and higher socioeconomic status presented with the highest proportion of luminal tumors. The North region presented with more aggressive subtypes (HER2-enriched and triple-negative), while the Central-West region predominated triple-positive carcinomas. The Northeast—a region with a high African influence—presented intermediate frequency of the different molecular subtypes. The differences persisted in subgroups of patients under and over 50 years.The geographic regions differ according to the distribution of molecular subtypes of breast cancer. However, other differences, beside those related to African ancestry, such as socioeconomic, climatic, nutritional, and geographic, have to be considered to explain our results. The knowledge of the differences in breast cancer characteristics among the geographic regions may help to organize healthcare programs in large countries like Brazil with diverse economic and race composition among different geographic regions. Breast cancer remains a major health problem responsible for 458,400 deaths worldwide in 2008 . The molRacial/ethnic differences have been observed among the different molecular subtypes -12 with th century. Although there is a tendency of one race predominant over another in these regions , there are some ethnic variations in the Brazilian population mainly due to a high rate of interracial marriage. Anti-miscegenation and segregation laws have not been part of the Brazilian culture, so Brazil is a home to a population characterized by a color continuum between white and black races. This leads to a well-known difficulty in categorizing races because they lack a precise legal definition [We investigated whether a similar effect of race could be present in Brazil, a country of a continental size with a wide variation in the distribution of people from various racial backgrounds in its five major geographic regions. Brazil is a large country with the fifth largest area and the fifth largest population in the world with a total of more than 190 million inhabitants according to the 2010 official census . It has finition .Previous studies actually demonstrated that in Brazil color, as determined by a physical evaluation, is a poor predictor of genetic ancestry estimated by molecular markers ,18. TherIn Brazil, federal law regulates mammographic screening and it has been offered to all women over 50 years of age since 2009. The number of new cases of breast cancer in Brazil estimated in 2014 was 57,120 . CurrentAll patient data were obtained from the files of Consultoria em Patologia, a private surgical pathology laboratory located in the city of Botucatu, State of São Paulo, Brazil. Consultoria em Patologia is a reference laboratory and analyzes over 5,000 breast cancer cases per year. These cases usually are sent by pathologists and oncologists from all five geographic regions of Brazil in order to be evaluated for predictive and prognostic immunohistochemical markers, i.e., estrogen and progesterone receptors (ER and PR), human epidermal growth factor receptor 2 (HER2) and Ki-67 proliferation index. Age at diagnosis and the state of origin of the patients were obtained from the pathological report. Geographic regions of Brazil were classified as North, Northeast, Central-West, South, and Southeast. We selected consecutive cases of invasive breast cancer from July 2009 to March 2011 with assessable immunohistochemical study of ER, PR, HER2 and Ki-67. One of the reasons this period of time was chosen is that all these cases had the immunohistochemistry study performed using exactly the same immunohistochemical protocol. Another reason was that we included only the cases sent for routine prognostic and predictive factors determination. We excluded in situ and microinvasive carcinomas as well the cases, which were sent for second opinion.ER, PR, and HER2 status and Ki-67 proliferation index were determined at the time of the patient’s cancer assessment by an immunohistochemical method on a selected tumor block. ER and PR were considered positive with >1% of the nuclear staining in tumor cells, although in all cases except in three, ER and PR results showed > 10% of positive cells. HER2 was considered positive with a 3+ score and negative with a 0+ or 1+ immunoreactivity using the previous ASCO/CAP recommendation . Also, bImmunohistochemical surrogate markers  were used to determine the molecular category of breast cancer as previously described in the following groups: luminal A , luminal B , triple-positive (TP) , HER2-enriched (ER- and PR- and HER2+), and triple-negative (TN)  ,22. HER2p-value less than 0.05 was considered significant.Descriptive statistical analysis was used to characterize the distribution of the patient’s age at diagnosis, hormonal receptor status, HER2 status, and molecular subtypes for the total sample and by the geographic region. Comparisons of the age of patients between different geographic regions, molecular subtypes, ER/PR status, and HER2 status were performed using the Kruskal-Wallis test. Associations between molecular subtypes, hormonal receptor status, HER2 expression, and categories of patient’s age with geographic regions were tested by a chi-square test. Missing values were not included in our statistical analysis and were deleted list-wise. Statistical analyses were performed using MedCalc for Windows , and a The study was approved by the Scientific Committee of the Department of Pathology of the Faculdade de Medicina da Universidade de Sao Paulo, and also by the Ethical Committee for Research Projects of the Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (CAPPesq) (protocol 311/10).This study has adhered to the STROBE guidelines for observational studies.p < 0.0001). Actually, the Southeast and South regions presented the highest proportion of ER/PR-positive tumors (p = 0.0003) (Table p < 0.0001) (Table p = 0.0001) and over 50 years (p = 0.0012) . The distribution of age at diagnosis, hormonal and HER2 status, and the molecular subtypes are summarized in Table Table 2 and with a population of 190,755,799. It is the most populous country in Latin America as well as one of the most populous in the world. The female population, according to the official 2010 census, is 97,348,809 [Brazil is a large country with a surface area of 8,511,960 km,348,809 . We founThe Brazilian racial composition is very heterogeneous with most populations descending from Europeans, Africans, Amerindians of various ethnic groups, and Asians. Most importantly, Brazil has experienced a high rate of intermarriage, and consequently, a mixed-race population has been created along five centuries of admixture.A racial/ethnic influence on the presentation of breast cancer has been suggested in various studies ,14,23-26The high prevalence of basal-like tumors in premenopausal African Americans is pointed to as an important factor to the poorer prognosis in this group of patients ,28, justIn our study, Northern Brazil presented the highest proportion of TN carcinomas (20.3%). This region, largely covered by the Amazon rainforest, presents the largest Amerindian influences, both in culture and ethnicity. Indians represent 0.4% of the total Brazilian population , but theWe must consider, however, the difficulty in defining race in Brazil and the need of a genomic classification. Pena et al. estimated the proportion of European, African, and Amerindian ancestry using a panel of 40 validated ancestry-informative insertion-deletion DNA polymorphisms in a population composed by black, white, and brown people from the four most populous Brazilian geographic regions  . The resThe South region of Brazil houses the largest percentage of European descents, including German, Italian and Polish ancestry. White races comprise 80.6% of the population in this region compared to 24% in the North, 29.5% in the Northeast, 58.5% in the Southeast and 43.5% in the Central-West. According to our results, the South region showed the highest frequency of luminal A carcinomas—the more favorable molecular subtype—as well as a higher frequency of ER/PR-positive tumors, which is consistent with other studies ,30,31. AThe second region with a high proportion of luminal A and ER/PR-positive carcinomas was the Southeast, which was also the region with the second largest white population. The lowest incidence of luminal A was documented in the North, which presented a rate of breast cancer varying from 10 cases/100,000 women in the state of Acre to 26 cases/100,000 women in the state of Rondonia . AccordiIn addition to luminal A carcinomas, the luminal B category also was more prevalent in the South and Southeast and showed a lower frequency in the North region. It is important to stress that our luminal B did not include HER2-coexpression, like some authors have classified ,11. We aThe distribution of HER2 carcinomas among different races/ethnic groups is more difficult to analyze because some authors included TP within the HER2 group , and othThe differences in molecular subtypes that we observed among the five geographic regions persisted in the subgroups of patients under and over 50 years, suggesting the existence of a geographic phenomenon more than effect of age itself. Similar results have been reported by others . Clarke Although we found significant differences in the distribution of molecular subtypes between the five geographic regions, we could not associate them to African ancestry, as other studies have been able to do in primarily the USA, despite the important African influence in the Brazilian racial composition. The reasons for this finding remain to be explored. One possible explanation is that the influence of African ancestry in Brazil may be different from that seen in the USA and Europe due to high rate of interracial marriage seen in Brazil. However, as we discussed above, there are other important factor to be considered,CDH13 in breast tumor samples from African-American women when compared with European-American patients. The hypermethylation of the CDH13 gene probably is related to early onset ER-negative breast cancer [FGFR2, LSP1, H19, TLR1/TLR6 and RELN for African-Americans and in FGFR2, TNRC9, H19 and MAP3K1 for whites [There are studies that explored some genetic conditions implicated with racial/ethnic differences in breast cancer. Wang et al. observed a significantly higher methylation of the suppressor gene t cancer . Severalt cancer . Associar whites . It is hHealth disparities, either by racial/ethnic, socioeconomic, cultural, climatic, nutritional, or geographic are very complex to decode since there is a significant overlap between these factors . UnderstThe distribution of molecular subtypes of breast cancer differs by geographic region in Brazil, a country of continental dimension and a wide racial variation of people. These differences are multifactorial and must be taken into account for public health policy.The authors declare that they have no competing interests.CEB: concept, performance of histological and immunohistochemical studies, and revision of the manuscript. LMB and KMP: collect of data and revision of the manuscript. MVR: concept and revision of manuscript. FMC: analysis and interpretation of data; writing of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6874/14/102/prepub


Rheumatoid arthritis (RA) is a chronic autoimmune rheumatic disease that can cause painful swelling in the joint lining, morning stiffness, and joint deformation/destruction. These symptoms decrease both quality of life and life expectancy. However, if RA can be diagnosed in the early stages, it can be controlled with pharmacotherapy. Although many studies have examined the possibility of early assessment and diagnosis, few have considered the relationship between significant risk factors and the early assessment of RA. In this paper, we present a novel framework for early RA assessment that utilizes data preprocessing, risk pattern mining, validation, and analysis. Under our proposed framework, two risk patterns can be discovered. Type I refers to well-known risk patterns that have been identified by existing studies, whereas Type II denotes unknown relationship risk patterns that have rarely or never been reported in the literature. These Type II patterns are very valuable in supporting novel hypotheses in clinical trials of RA, and constitute the main contribution of this work. To ensure the robustness of our experimental evaluation, we use a nationwide clinical database containing information on 1,314 RA-diagnosed patients over a 12-year follow-up period (1997–2008) and 965,279 non-RA patients. Our proposed framework is employed on this large-scale population-based dataset, and is shown to effectively discover rich RA risk patterns. These patterns may assist physicians in patient assessment, and enhance opportunities for early detection of RA. The proposed framework is broadly applicable to the mining of risk patterns for major disease assessments. This enables the identification of early risk patterns that are significantly associated with a target disease. Rheumatoid arthritis (RA) is a chronic autoimmune rheumatic disease most commonly occurring in older patients and females. Severe symptoms include painful swelling in the lining of the joints, morning stiffness, and joint deformation and destruction. The prevalence of RA is 1% in the global population . The patTo improve treatment quality, medical organizations have accumulated vast amounts of clinical information. The effective use of this information in medical decision-making requires analysis software to mine the considerable expertise. Many data mining techniques use treatment decisions for disease assessment models; moreover, related system applications and their algorithms are designed based on differing medical data characteristics.Recent work on data mining in medical applications has involved hepatitis B surface antigen (HBsAg) immunoassay prediction , survivaCurrently, the diagnosis of RA involves an evaluation of the patient based on clinical experience using certain RA disease classification criteria (such as the 1987 and 2010 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) Classification Criteria for Rheumatoid Arthritis ). If earVarious methods for the early diagnosis of RA have been proposed, such as the aforementioned 1987 and 2010 ACR/EULAR Classification Criteria , the vank-fold validation method that uses measured sensitivity and specificity values. The risk patterns can assist physicians in patient assessment, and enhance opportunities for the early detection of RA. Consequently, it is possible to provide early treatment to RA patients, reducing damage to bones and joints, and improving patient quality of life.For the present retrospective cohort study, we designed a novel framework that enables the analysis of a vast nationwide clinical database. To support RA disease assessment and analysis, our approach mines the class association rules (CARs) of RA before making a definite diagnosis. The effectiveness of the patterns obtained in this way is evaluated by a In this disease assessment analysis, we investigate the factors involved in RA disease prediction, data mining applications in the prediction of other diseases, data mining of the Taiwan National Health Insurance Research Database (NHIRD), and associated classification techniques.The prediction of RA is highly feasible. Previous studies have considered various physiological attributes and statistical regression to establish disease prediction models. For instance, Shadick et al.  used C-rCurrently, many data mining techniques are used in disease prediction to address related problems. In recent years, the data mining of electronic medical records has become quite popular, and further results are highly anticipated. The use of data mining is expected to decrease costs and the public health burden, and should improve diagnostic accuracy, prevention, treatment, and quality of care. Mathias et al.  used preThe objective of data mining is to extract important information and reveal implicit patterns. If source data is not readily available, and the data size is very large, there are numerous opportunities for mining with appropriate rules. The Taiwan NHIRD, a nationwide clinical database, is such a case. In recent years, a growing number of studies have applied data mining techniques to the NHIRD. This research includes an analysis of comorbidities (other diseases occurring with the primary disease) and analysis of specific disease treatment drugs (associations among drugs). Chen et al.  used assAssociative classification integrates features of association rule mining and classification rule mining. The function of classification rule mining is to discover a small set of rules that forms an accurate classifier. Association rule mining identifies all rules in the database that satisfy specific minimum support and minimum confidence constraints .Classification based on multiple association rules (CMAR)  is an asIn this study, we describe a novel framework for the mining and analysis of RA risk patterns with the aim of facilitating the early detection of RA disease. The framework has four phases: data preprocessing, risk pattern mining, risk pattern validation, and analysis see . In the In the analysis phase, we use the National Center for Biotechnology Information (NCBI) E-utilities Web Service (SOAP) to design an evaluation and automation mechanism for risk pattern-related literature. The inferential RA disease risk patterns that we identify are novel and build a new, potentially strong hypothesis, a point based on the small amount of related literature. Finally, to provide further characterization information and pattern distributions for comparative analysis by medical experts, we describe the Risk Pattern Category Viewer, a tool that represents and organizes risk patterns based on a tree structure. In the following, we describe the details of the proposed framework.Data in which coding errors may occur for the ID, sex, date of birth, or diagnosis code fields are removed.Patient data with fewer than four diagnostic records are filtered. If the number of diagnostic data records in a patient’s medical history is too small, it is essentially meaningless, as there is little recognition or representation. Hence, such patients are removed from the cohort.Patient grouping is based on the cohort-extracted clinical data of patients from the NHIRD who were confirmed as having an RA diagnosis and catastrophic illness record. The patient data are divided into an RA group and a non-RA group. The RA patient definition is provided below.For our purposes, the definition of an RA patient is one whose first RA diagnosis was between 1997 and 2008, and whose RA catastrophic illness was confirmed in the registry record with ICD code 714.0 and A-code 430 . In the NHIRD, the A-code was used before December 31, 1999, and the ICD-9-CM code was used during the period 2000–2008. The cohort-collected clinical records are from before the first diagnosis of RA for each patient. The timeline for data collection of the RA group is shown in Non-RA patients are those without RA; that is, a patient from the cohort with no catastrophic illness record of RA diagnosis during the period 1997–2008. These non-RA patients comprise the non-RA group.T. Let object obj = , where D1, …, Dn are the patient diagnosis records. The diagnosis adheres to ICD-9-CM. Pattern P = D1, …, Dn is a set of disease codes that occur in the same patient. Let c be class labels that indicate an RA group or non-RA group.The proposed framework integrates efficient associative classifiers to mine RA risk patterns from a large, nationwide clinical database. The risk patterns are rule-based, easy to understand, and contribute to clinical decision support with disease assessment. In this phase, we extract risk patterns that occur frequently, and classify the disease status of the patient. A given database of NHIRD diagnosis records is denoted as transaction R: P ➔ c, the number of data objects in T matching pattern. These have the class label c, which is called the support of R and is denoted as sup(R). The ratio of the number of objects of matching pattern P that have class label c versus the total number of objects with matching pattern P is called the confidence of R, denoted as conf(R). For example, if 91% of patients have been diagnosed with Sjogren’s syndrome before a definite diagnosis of RA—i.e., the confidence of rule R: Sjogren’s syndrome ➔ RA is 91%—then we can use many rules R to assess the patient clinical data. When a new (unknown) patient enters the cohort, the classifier selects the rule that matches the new patient clinical data and has the highest confidence with support. This information is used to predict the class label of the new patient.For rule x2 to integrate correlation and popularity information into the measure. This improves the objectivity of the results. In our experiments, we tested different parameter adjustments, and set a suitable threshold for the support and confidence so that meaningless rules would be filtered. We also allowed CMAR to locate the complete set of CARs, and enhanced the sensitivity and specificity of the models.To prevent important rules from being ignored—i.e., those that are slightly low in confidence but high in support—CMAR proposes a weighted For validation purposes, we applied ten-fold cross-validation. The cohort was randomly split into ten subsamples: one formed the test dataset for verifying the effectiveness of the model, and the others formed the training dataset to determine RA disease risk patterns for the model. The cross-validation process was repeated ten times, with each of the ten subsamples used as the validation data once.To evaluate the models and analyze their assessment capabilities, we measured the sensitivity and specificity of each model. The sensitivity of a model is its ability to correctly classify an individual as “diseased.” We evaluate this metric asThe NHIRD mining framework can be used to mine interesting and effective patterns if the partial of discovered patterns are well known and consistent with known medical knowledge. This finding can be new significant discoveries or supports related literature based on population, particularly if the patterns have not yet been reported or have been less frequently reported. In this case, they meet a minimum support value with a high confidence value, and enhance the accuracy of the RA assessment.disease 1 of pattern[TIAB] ANDdisease 2 of pattern[TIAB] ANDdisease 3 of pattern[TIAB] …, until a specific pattern was matched. “[TIAB]” refers to the literature title, collection title, abstract, and other excerpts of a citation.Therefore, to analyze the novelty of risk patterns in previous studies, we applied SOAP , which pWe designed a tree-structure-based Risk Pattern Viewer to enable physicians to further analyze risk pattern characteristics and distributions. The user interface is split into two parts see , which aPart I: The Patterns Distribution View shows a list with segments assigned by the ICD-9-CM coding system. ICD-9-CM has different segments for various disease classifications. The interface aligns with these segments, and provides relevant information:Field 1: Ratio of all related patterns in this ICD-9-CM coding segment.Field 2: Distribution of patterns among all patients, the RA group, and the non-RA group in the specific ICD-9-CM coding region.Field 3: Number of patterns of unique diseases in the RA group in the specific ICD-9-CM coding region.Field 4: Specific ICD-9-CM coding region.Field 5: Description of the specific ICD-9-CM coding region.Part II: The Pattern Properties View is employed when a user selects a specific field of the ICD-9-CM coding region in the Patterns Distribution View. The Pattern Properties View shows the pattern characteristics, which can be sorted by the following attributes:Group: The group to which the pattern applies.Eczema, asthma, urinary tract infections➔ RA} has a high confidence of 86.55%, which represents that a patient diagnosed with the disease/symptom items of eczema, asthma, and urinary tract infections has a high risk of being diagnosed with RA.Confidence (Conf.): Confidence is defined as the probability of seeing the risk pattern’s consequences under the condition that the medical transactions also contain the antecedent . The forSup. %: Support supp(X) of item set X, defined as the proportion of transactions in the specific group that contains the item set.Lift : The lift of a risk pattern is defined as ependent . If the ICD-9-CM and the description field are the ICD-9-CM code region and disease or symptom description, respectively.The cohorts were gathered from Taiwan NHIRD clinical records, provided by the Taiwan National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The NHI coverage rate at the end of 2007 was 99.3% , , . The supA small number of patient diagnosis records were unrepresentative and required removal. Patients who lacked a sufficient number of diagnosis records provided insufficient information to implicate a disease risk model, and could therefore not provide implicit information for early disease assessment analysis. The distribution of RA patients under different numbers of diagnosis records is illustrated in Following discussions with physicians and experiments with different diagnosis record filter values, we determined that the removal of patients with fewer than nine diagnostic records was appropriate; this number is approximately equivalent to three visits to a doctor. Therefore, to maintain the validity of the experiments, the minimum number of diagnosis records was set to 15. The trend of RA assessment effects under different diagnosis record filter numbers is shown in disease diagnosis ➔ RA or non-RA, confidence value, support value, relative risk value}, where “➔” denotes implication. The relative risk value is the risk ratio of the pattern occurring in the implied group compared to another group.In our study, the format of disease risk patterns was {To analyze these important risk patterns in terms of combinations of diseases or symptoms, we screened disease types from the risk pattern set. The number of disease occurrences of the RA group was 3,169 before mining the 250 million NHIRD outpatient records. After mining, we had 32,560 RA risk patterns. From The distribution of disease risk patterns according to the ICD-9-CM coding system is summarized in To compare the different distribution rates of disease risk patterns in the RA group and non-RA group, we applied a relative risk ratio analysis. The ratio was calculated by dividing the disease risk pattern rate of the RA group by that of the non-RA group in the same ICD-9-CM categories. The significance test was based on a new standard deviation derived from the standard deviations of the RA group and non-RA group. The threshold of alpha (Type I) error, although generally accepted as 0.05, was found to be rather sensitive to the sample size. This analysis demonstrates the potential for RA early detection through segments that strongly differentiate the RA group from the non-RA group.The risk pattern distribution of the RA group and non-RA group for all diseases categorized in ICD-9-CM is given in We searched related literature in the PubMed database to analyze which disease risk patterns were known to relate to RA. Our objective was to comprehend the novelty of the patterns and the degree to which they were known.related_disease ➔ RA, with the query term related_disease[TIAB] AND Rheumatoid arthritis[TIAB]. The related disease name was a specific disease word extracted from the risk pattern, such as Meniere’s disease. The literature in PubMed exhibits an exponential trend for single diseases, as shown in The format of the risk patterns was As depicted in mitral valve AND chronic conjunctivitis ➔ RA showed a confidence level of 83.57% with high lift dependencies, which indicates that these two diseases have occurred in the same patient before a definite diagnosis of RA. Therefore, based on these strong assumptions, this disease risk pattern could provide clinical researchers with multiple new research directions.As shown by Figs. A pattern contains multiple symptoms or diseases prior to a definite diagnosis of RA. These symptoms appear more often in the RA group, and rarely appear in the non-RA group. When a new patient’s diagnostic records contain these patterns, the patient has a higher risk of developing RA.The RA early risk models were evaluated using ten-fold cross-validation, measured by sensitivity and specificity. The results are listed in The disease risk patterns mined from the NHIRD can generally be categorized into two types. The relationship of Type I patterns is well known, as they are supported by ample research and are primarily single diseases associated with RA. The Type II patterns have no related research discussing their relationship with multiple diseases in a risk pattern.rheumatism ➔ RA, 82.97% confidence, 9.16% support, 4.9 relative risk}, {autoimmune disease, not elsewhere classified ➔ RA, 89.81% confidence, 7.02 relative risk}, {arteritis, unspecified ➔ RA, 72.58% confidence, 2.13% support, 2.64 relative risk} [systemic lupus erythematosus (SLE) ➔ RA, 85.01% confidence, 3.6% support, 5.67 relative risk}  [carpal tunnel syndrome (CTS) ➔ RA, 73.95% confidence, 9.49% support, 2.8 relative risk} (CTS is one of the most frequent extra-articular manifestations of RA) [This type is primarily composed of a single disease or symptom that is typically known as an RA risk pattern and is covered in the literature. Examples of this pattern include {ve risk} , {systemfrom RA) , and {cas of RA) . The minmitral valve disorders, chronic conjunctivitis, menopausal ➔ RA, 83.57% confidence, 1.96% support, 5.1 relative risk}. In the literature, there is more discussion on the relationship between a single disease and RA; the relationship between multiple diseases and RA has been addressed far less often. Dismantling a portion of risk pattern mining from past studies may yield some relevant analysis, such as on mitral valve disorders and conjunctivitis.The majority of such patterns include multiple diseases or symptoms that combine to imply an RA risk pattern. Their joint relationship is unknown, and has rarely or never been discussed in the literature. These patterns are supported by high confidence, support, relative risk, and lift values, which are clinically valid hypotheses for the further study of their underlying medical significance. Related patterns include {laryngopharyngitis, periodontitis, rheumatism ➔ RA, with a high confidence value of 85.29%, lift > 1 positive correlation, support of 2.45%, and high relative risk of 5.8}. This example shows that the disease risk patterns are consistent with previous literature; moreover, they can be used to make an assessment in the early disease phases, before a definite RA diagnosis has been made.Periodontal disease, an inflammatory problem of the tooth-supporting structures, may be an environmental trigger for RA ; indeed,The relationship between single diseases and RA has frequently been discussed in the literature. For instance, many inflammatory disorders have been discussed alongside RA, such as asthma , atherosunspecified disorder of joint OR rheumatism) AND autoimmune-related disease ➔ RA} or {multiple autoimmune-related disease ➔ RA}, as shown in The experimental analysis of the NHIRD data indicates that a variety of autoimmune-related diseases could be considered RA assessment factors. This pattern is {(Previous studies have rarely explored the causation of multiple simultaneous symptoms or diseases in patients with RA. Therefore, in the present study, we mined RA disease risk patterns from NHIRD. The disease risk patterns can each be supported by ample previous literature; however, they are rarely discussed together. Our results provide a strong topology for RA-related diseases. This topology enables population-based disease assessment prior to a definite diagnosis of RA, and thereby improves the clinical decision support available to physicians.In this paper, we have presented an effective framework for assessing RA premorbidity by discovering risk patterns in large-scale real data. The main contributions of this paper are as follows. First, we have illustrated the benefits of using a large-scale, nationally representative dataset, namely the Taiwanese NHIRD. Second, we integrated the associative classification technique with this dataset to derive an RA disease assessment framework for the analysis of RA disease risk. Our method is suitable and effective for large-scale population-based outpatient data, such as those in the NHIRD. The proposed framework assesses RA disease risk in patients before making a definite diagnosis of RA. This enables the discovery of risk patterns that allow for early diagnosis. Comprehensive experiments demonstrated the effectiveness of our framework, with an average sensitivity of 80.3% and an average specificity of 72.6%. In summary, the proposed framework facilitates the identification of many early risk patterns, including hidden risk factors, which are significantly associated with the target disease. Third, by summarizing disease risk patterns, we discovered a number of patterns that demonstrate a high correlation with RA prior to its diagnosis. In addition, we designed the Pattern Viewer, which provides physicians with further analysis of pattern characteristics by determining the comparative pattern distributions of RA and non-RA groups. The highest relative risk was found to occur in the psychiatric disorders category, and the highest proportion of RA risk patterns occurred in the musculoskeletal system category. These categories have higher relative ratios in the RA group. The risk ratios of discovered risk patterns were significant in the “240–279 endocrine, metabolic, and immunity,” “280–289 blood and blood-forming organs,” and “390–459 circulatory system” categories. Further, we developed an automated mechanism for searching the PubMed literature. This tool uses the NCBI e-utilities API to enable rapid confirmation of the degree of novelty and supporting knowledge of many disease risk patterns. Overall, our findings show that risk patterns mined in this study can be divided into two types, namely well-known patterns and unknown patterns. The well-known patterns are supported by previous literature, whereas the unknown patterns have rarely or never been discussed in past studies. This latter class has the potential to form a number of novel clinical research hypotheses.In future work, when sequential risk pattern mining technique is used, it is very likely that more precise assessments could be provided by considering the development order of diseases. The proposed framework also has the versatility to be applied to the discovery of risk patterns in many major diseases by changing the target disease criteria in the patient grouping component.


The following were risk factors: female gender ; one additional hour of work at home ; smoking ; health dissatisfaction ; income dissatisfaction ; living alone ; having a garden . Awareness of the need for safety at home could be boosted by information campaigns on the risk, and its social cost could be reduced by specific prevention schemes. Developing tools for assessing the risk at home and for removing the main hazards would be useful for both informative and prevention interventions.Home injuries are an important public health issue in both developed and developing countries. This study focused on the Italian epidemiological framework between 1999 and 2006, using a nation-representative sample provided by the National Institute of Statistics. Every year, about 3,000,000 Italian residents reported at least one home injury, with an overall annual rate of 5.2/100 (95% CI 5.1–5.4); 3.2/100 (3.0–3.4) for males and 7.2/100 (6.9–7.4) for females. Poisson regression models were used for different age-specific populations , to evaluate the effects of socio-demographic, health/income satisfaction and housing variables. For children, non-applicable variables  were taken as those of the head of family, while housework time was taken the family mean time. Evidence of decreasing time trend in risk of home injury was found only among young/adults ( Home injuries are a significant public health problem in both developed and developing countries. In the USA during the 1997–2007 period most injuries occurred at home and more than 40% of reported medically treated injuries occurred in and around the home . BetweenChildren aged 0–14 years and the older people aged over 70 spend a lot of time at home, so they are more exposed to the risks of home injury. Their injuries need the most specialized health care and, in countries with a welfare and public health system , this implies high social costs. An estimated 6% of Australian children require emergency department treatment for a new injury each year and 15% of presentations lead to hospital admission . AnnuallIn Italy, housing health and safety were regulated by Law No. 493/1999 (3 December 1999); it established institutional roles and marked the starting point for national coordinated actions concerning home injuries . The Minet al. [et al. [et al. [et al. [et al. [Several studies have analyzed the SINIACA data on a regional level. Farchi et al.  tested t [et al.  investig [et al.  analyzed [et al.  and Ferr [et al. . The forSo far, Italian studies have focused on specific features and they do not provide a broad overview to the public health authorities for planning interventions. With a view to suggesting opportunities for efficient prevention, this study investigated multiple aspects of the phenomenon such as magnitude, time trends after the current law about prevention  was pasi.e., those remaining. For discriminating among SR and NSR, the population cutoff was determined at regional level by the populations’ ratio region/Italy and the mean number of families’ components. In the SR, each municipality was considered an independent layer and cluster sampling was applied. The primary sampling units comprised households entered in the Public Registry. In the NSR, a two-stage pattern was adopted: municipalities themselves and the households in the Public Registry were respectively the primary and secondary units. Both units were sampled without replacement, the primary ones with probabilities proportional to their population size and the secondary ones by a uniform distribution; sample details can be found elsewhere [The DL section of the ISTAT Italian multipurpose survey was the data source 1999–2006). For each family member, the survey was based on face-to-face interviews and a self-administrated questionnaire; when needed children were supported by guardians, information regarding unavailable persons were provided by a relative (proxy interviews). The two-stage sample covers 141,583 families and 374,471 interviews  and is representative of the whole nation. The response rate was over 95%, the percentage of proxy interviews was 25.6%, 61.4% among children (0–14 years), 21.2% among young/adults (15–64 years) and 13.3% among older people (≥ 65 years). In the first stage, municipalities were divided into two groups: self-representative municipalities (SR), with larger populations, and non-self-representative municipalities (NSR), 99–2006. lsewhere ,23.The home injury item was: In the last three months, have you experienced at least one accident at home with injuries? As the recorded events referred to a quarterly value, the annual number (AN) and the annual rate (AR) per 100 inhabitants of home injured were calculated by multiplying the occurrences by four. Confidence intervals (95%) for AR were computed by sample properties. Regional age-adjusted rates were assessed and presented as map . Three mThe explanatory variables were the same for all the models: year , macro-region , sex , age , marital status , educational level , employment status , number of people in family , housework (weekly hours), number of rooms , home ownership , smoker , income dissatisfaction  and health dissatisfaction . Reference modalities were those with the lowest values detected. Finally, interaction between sex and age was included. As children’s safety depends on their parents, for the non-applicable variables marital status, education, employment status, smoking, income/health dissatisfaction and home ownership were considered those of the Head of Family (HoF) while Housework weekly time was taken the mean of the family. The HoF was indicated by the family as the reference member. With respect to the child, he/she was generally the father (84.6%), sometimes the mother (10.8%), rarely the grandfather (2.9%) or the grandmother (1.3%) or anyone else (0.4%).i.e., socio-demographic, income/health satisfaction and housing variables) are presented in Relative risks adjusted for all independent variables included in the Poisson model  was twice the male one (AR 3.2). The age-class distribution of risk rates differed by sex: babies (0–5 years) and people over 80 years were the groups most at risk among respectively the male and female populations .p = 0.01) than the reference year (2005) and evidence of decreasing time trend in risk of injuries was found (p < 0.001). Home safety was best in the North Western regions; the risks of injury for children and older people were highest in the North East (NE), and for young/adults in the Southern and central regions and the Islands. Married individuals were more exposed to risk of injury than single young/adults , divorced/separated  and widowed people ; housewives  and the unemployed  were more exposed to risk of injury than employed people. Children with a student HoF had four times the risk of those with an employed HoF . Living alone is associated with an increased risk only for young/adults , but after dropping marital status from the model its effect also appeared as significant among the older people  did not change in a significant way; RR 1.2, p = 0.02). A garden increased the risk of injury by the same magnitude for all three populations (RR 1.1), but its effect was significant only for young/adults (p < 0.01). The number of rooms, home ownership and the presence/absence of a balcony did not change the risk. Young/adults current and ex-smokers had a greater risk of injury  than non-smokers; children living with smokers were also more strongly exposed to the risk of injury .Throughout the whole period, age-adjusted injuries incidence rates were highest in the Islands, South and North East, while the North West and center had the lowest rates . For thep = 0.05 for children; RR 1.6, p < 0.01 for young/adults; RR 1.7, p < 0.01 for the older people) as did income dissatisfaction for young/adults . Each weekly hour of work at home increased the relative risk by 16% for children (p = 0.01) and by 9% for young/adults (p < 0.01). Sex differences are summarized in Health dissatisfaction (of HoF for children) increased the probability of injury . Surveys contained a non-negligible percentage of proxy responses (25.6%). The annual estimates of occurrence and rates did not consider seasonal patterns: collected injuries were relative to the first/last quarters of the year, and in the summer the scenario might be different. Finally, income and health were assessed by perceived measures and not through observed data.i.e., reducing exposure time.Except for the North East, there was a territorial gradient, with home safety increasing from south to north . As noteAnalysis by year provides information about the effects of the current law from its establishment (3 December 1999) up to seven years later. Poisson regression results showed a high risk in 1999 for young/adults and evidenced a decreasing time trend among them; as the time effect was adjusted for all the other independent variables of the model, this finding suggests the need to improve age-specific policies for children and older people. Age patterns of rates differed for the sexes, indicating different probabilities for types and consequences of injury: males are exposed mainly to cuts and bumps while females are prone to burns and falls . Living These results highlight the importance of information campaigns , above aSimple interventions for children and parents could be done by schools, where validated scales of risks could be distributed. Annual assessments of injury risks at home could be assigned to the students and decreasing trends among years could be rewarded. Housewives and older people could be reached in their homes by television. Advice for improving safety at home could be provided in popular entertainment programs and, as for traffic accidents, annual national statistics could be divulged by news programs in prime time. Places of worship could be effective as a promoting communication channel as most exposed people  go there very regularly . SpecifiSupporting the relation between household income and home injuries ,37, incoAmong the housing variables, attention needs to focus on the garden where the risk of injury was significantly high only for young/adults; specific garden hazard studies have been done, and measures for prevention suggested . As men To follow the progress of these home risks and integrate them with the injury patterns , the surChildren and older people are the most exposed people.Risk distribution among ages are different by gender.What is already known on this topic:We estimate the magnitude and trend of home injuries in Italy in an broad period (1999–2006) starting just after the establishment of the current law (No. 493/1999) about home injury.We examine injury risks for different age-specific populations  and assess the effects of socio-demographic, health/income satisfaction and housing variables.We find a decreasing trend of risk only in young/adults suggesting specific information campaigns.What this study adds: