898 resultados para Switching regression models


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OBJECTIVES: Alcohol use is associated with self-reported health status. However, little is known about the concurrent association between alcohol screening scores and patient perception of health. We evaluated this association in a sample of primarily older male veterans.METHODS: This secondary, cross-sectional analysis included male general medicine outpatients from 7 VA medical centers who returned mailed questionnaires. Screening scores from the Alcohol Use Disorders Identification Test Consumption (AUDIT-C) questionnaire were divided into 6 categories (0, 1­3, 4­5, 6­7, 8­9, and 10­12). Outcomes included scores on the 8 subscales and 2 component scores of the 36-item Short Form Health Survey (SF-36). Unadjusted and adjusted linear regression models were fit to characterize the association between AUDIT-C categories and SF-36 scores. Models were adjusted for demographic characteristics, smoking, and site?both alone and in combination with 14 self-reported comorbid conditions.RESULTS: Male respondents (n = 24,531; mean age = 63.6 years) represented 69% of those surveyed with the SF-36. After adjustment, a quadratic (inverted U-shaped) relationship was demonstrated between AUDIT-C categories and all SF-36 scores such that patients with AUDIT-C scores 4­5 or 6­7 reported the highest health status, and patients with AUDIT-C scores 0, 8­9, and =10 reported the lowest health status.CONCLUSIONS: Across all measures of health status, patients with the most severe alcohol misuse had significantly poorer health status than those who screened positive for alcohol misuse at mild or moderate levels of severity. The relatively good health status reported by patients with mild-moderate alcohol misuse might interfere with clinicians' acceptance and adoption of guidelines recommending that they counsel these patients about their drinking.

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BACKGROUND Health-related quality of life (HRQoL) is gaining importance as a valuable outcome measure in oral cancer area. The aim of this study was to assess the general and oral HRQoL of oral and oropharyngeal cancer patients 6 or more months after treatment and compare them with a population free from this disease. METHODS A cross-sectional study was carried out with patients treated for oral cancer at least 6 months post-treatment and a gender and age group matched control group. HRQoL was measured with the 12-Item Short Form Health Survey (SF-12); oral HRQoL (OHRQoL) was evaluated using the Oral Health Impact Profile (OHIP-14) and the Oral Impacts on Daily Performances (OIDP). Multivariable regression models assessed the association between the outcomes (SF-12, OHIP-14 and OIDP) and the exposure (patients versus controls), adjusting for sex, age, social class, functional tooth units and presence of illness. RESULTS For patients (n = 142) and controls (n = 142), 64.1% were males. The mean age was 65.2 (standard deviation (sd): 12.9) years in patients and 67.5 (sd: 13.7) years in controls. Patients had worse SF-12 Physical Component Summary scores than controls even in fully the adjusted model [β-coefficient = -0.11 (95% CI: -5.12-(-0.16)]. The differences in SF-12 Mental Component Summary were not statistically significant. Regarding OHRQoL patients had 11.63 (95% CI: 6.77-20.01) higher odds for the OHIP-14 and 21.26 (95% CI: 11.54-39.13) higher odds for OIDP of being in a worse category of OHRQoL compared to controls in the fully adjusted model. CONCLUSION At least 6 months after treatment, oral cancer patients had worse OHRQoL, worse physical HRQoL and similar psychological HRQoL than the general population.

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BACKGROUND In this study, we evaluated the ability of gene expression profiles to predict chemotherapy response and survival in triple-negative breast cancer (TNBC). METHODS Gene expression and clinical-pathological data were evaluated in five independent cohorts, including three randomised clinical trials for a total of 1055 patients with TNBC, basal-like disease (BLBC) or both. Previously defined intrinsic molecular subtype and a proliferation signature were determined and tested. Each signature was tested using multivariable logistic regression models (for pCR (pathological complete response)) and Cox models (for survival). Within TNBC, interactions between each signature and the basal-like subtype (vs other subtypes) for predicting either pCR or survival were investigated. RESULTS Within TNBC, all intrinsic subtypes were identified but BLBC predominated (55-81%). Significant associations between genomic signatures and response and survival after chemotherapy were only identified within BLBC and not within TNBC as a whole. In particular, high expression of a previously identified proliferation signature, or low expression of the luminal A signature, was found independently associated with pCR and improved survival following chemotherapy across different cohorts. Significant interaction tests were only obtained between each signature and the BLBC subtype for prediction of chemotherapy response or survival. CONCLUSIONS The proliferation signature predicts response and improved survival after chemotherapy, but only within BLBC. This highlights the clinical implications of TNBC heterogeneity, and suggests that future clinical trials focused on this phenotypic subtype should consider stratifying patients as having BLBC or not.

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The restoration of body composition (BC) parameters is considered to be one of the most important goals in the treatment of patients with anorexia nervosa (AN). However, little is known about differences between AN diagnostic subtypes [restricting (AN-R) and binge/purging (AN-BP)] and weekly changes in BC during refeeding treatment. Therefore, the main objectives of our study were twofold: 1) to assess the changes in BC throughout nutritional treatment in an AN sample and 2) to analyze predictors of BC changes during treatment, as well as predictors of treatment outcome. The whole sample comprised 261 participants [118 adult females with AN (70 AN-R vs. 48 AN-BP), and 143 healthy controls]. BC was measured weekly during 15 weeks of day-hospital treatment using bioelectrical impedance analysis (BIA). Assessment measures also included the Eating Disorders Inventory-2, as well as a number of other clinical indices. Overall, the results showed that AN-R and AN-BP patients statistically differed in all BC measures at admission. However, no significant time×group interaction was found for almost all BC parameters. Significant time×group interactions were only found for basal metabolic rate (p = .041) and body mass index (BMI) (p = .035). Multiple regression models showed that the best predictors of pre-post changes in BC parameters (namely fat-free mass, muscular mass, total body water and BMI) were the baseline values of BC parameters. Stepwise predictive logistic regressions showed that only BMI and age were significantly associated with outcome, but not with the percentage of body fat. In conclusion, these data suggest that although AN patients tended to restore all BC parameters during nutritional treatment, only AN-BP patients obtained the same fat mass values as healthy controls. Put succinctly, the best predictors of changes in BC were baseline BC values, which did not, however, seem to influence treatment outcome.

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BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.

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BACKGROUND/AIMS: While several risk factors for the histological progression of chronic hepatitis C have been identified, the contribution of HCV genotypes to liver fibrosis evolution remains controversial. The aim of this study was to assess independent predictors for fibrosis progression. METHODS: We identified 1189 patients from the Swiss Hepatitis C Cohort database with at least one biopsy prior to antiviral treatment and assessable date of infection. Stage-constant fibrosis progression rate was assessed using the ratio of fibrosis Metavir score to duration of infection. Stage-specific fibrosis progression rates were obtained using a Markov model. Risk factors were assessed by univariate and multivariate regression models. RESULTS: Independent risk factors for accelerated stage-constant fibrosis progression (>0.083 fibrosis units/year) included male sex (OR=1.60, [95% CI 1.21-2.12], P<0.001), age at infection (OR=1.08, [1.06-1.09], P<0.001), histological activity (OR=2.03, [1.54-2.68], P<0.001) and genotype 3 (OR=1.89, [1.37-2.61], P<0.001). Slower progression rates were observed in patients infected by blood transfusion (P=0.02) and invasive procedures or needle stick (P=0.03), compared to those infected by intravenous drug use. Maximum likelihood estimates (95% CI) of stage-specific progression rates (fibrosis units/year) for genotype 3 versus the other genotypes were: F0-->F1: 0.126 (0.106-0.145) versus 0.091 (0.083-0.100), F1-->F2: 0.099 (0.080-0.117) versus 0.065 (0.058-0.073), F2-->F3: 0.077 (0.058-0.096) versus 0.068 (0.057-0.080) and F3-->F4: 0.171 (0.106-0.236) versus 0.112 (0.083-0.142, overall P<0.001). CONCLUSIONS: This study shows a significant association of genotype 3 with accelerated fibrosis using both stage-constant and stage-specific estimates of fibrosis progression rates. This observation may have important consequences for the management of patients infected with this genotype.

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INTRODUCTION: The aim of the present study was to assess the association between remembered previous work place environment and return to work (RTW) after hospitalisation in a rehabilitation hospital. METHODS: A cohort of 291 orthopedic trauma patients discharged from hospital between 15 December 2004 and 31 December 2005 was included in a study addressing quality of life and work-related questions. Remembered previous work environment was measured by Karasek's 31-item Job Content Questionnaire (JCQ), given to the patients during hospitalisation. Post-hospitalisation work status was assessed 3 months, 1, and 2 years after discharge, using a questionnaire sent to the ex-patients. Logistic regression models were used to test the role of four JCQ variables on RTW at each time point while controlling for relevant confounders. RESULTS: Subjects perceiving a higher physical demand were less likely to return to work 1 year after hospital discharge. Social support at work was positively associated with RTW at all time points. A high job strain appeared to be positively associated with RTW 1 year after rehabilitation, with limitations due to large confidence intervals. CONCLUSIONS: Perceptions of previous work environment may influence the probability of RTW. In a rehabilitation setting, efforts should be made to assess those perceptions and, if needed, interventions to modify them should be applied.

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INTRODUCTION AND AIMS: This study investigated the associations of alcohol outlet density with specific alcohol outcomes (consumption and consequences) among young men in Switzerland and assessed the possible geographically related variations. DESIGN AND METHODS: Alcohol consumption and drinking consequences were measured in a 2010-2011 study assessing substance use risk factors (Cohort Study on Substance Use Risk Factors) among 5519 young Swiss men. Outlet density was based on the number of on- and off-premise outlets in the district of residence. Linear regression models were run separately for drinking level, heavy episodic drinking (HED) and drinking consequences. Geographically weighted regression models were estimated when variations were recorded at the district level. RESULTS: No consistent association was found between outlet density and drinking consequences. A positive association between drinking level and HED with on-premise outlet density was found. Geographically weighted regressions were run for drinking level and HED. The predicted values for HED were higher in the southwest part of Switzerland (French-speaking part). DISCUSSION AND CONCLUSIONS: Among Swiss young men, the density of outlets and, in particular, the abundance of bars, clubs and other on-premise outlets was associated with drinking level and HED, even when drinking consequences were not significantly affected. These findings support the idea that outlet density needs to be considered when developing and implementing regional-based prevention initiatives. [Astudillo M, Kuendig H, Centeno-Gil A, Wicki M, Gmel G. Regional abundance of on-premise outlets and drinking patterns among Swiss young men: District level analyses and geographic adjustments. Drug Alcohol Rev 2014;33:526-33].

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The relative contributions of Alzheimer disease (AD) and vascular lesion burden to the occurrence of cognitive decline are more difficult to define in the oldest-old than they are in younger cohorts. To address this issue, we examined 93 prospectively documented autopsy cases from 90 to 103 years with various degrees of AD lesions, lacunes, and microvascular pathology. Cognitive assessment was performed prospectively using the Clinical Dementia Rating scale. Neuropathologic evaluation included the Braak neurofibrillary tangle (NFT) and β-amyloid (Aβ) protein deposition staging and bilateral semiquantitative assessment of vascular lesions. Statistics included regression models and receiver operating characteristic analyses. Braak NFTs, Aβ deposition, and cortical microinfarcts (CMIs) predicted 30% of Clinical Dementia Rating variability and 49% of the presence of dementia. Braak NFT and CMI thresholds yielded 0.82 sensitivity, 0.91 specificity, and 0.84 correct classification rates for dementia. Using these threshold values, we could distinguish 3 groups of demented cases and propose criteria for neuropathologic definition of mixed dementia, pure vascular dementia, and AD in very old age. Braak NFT staging and severity of CMI allow for defining most of demented cases in the oldest-old. Most importantly, single cutoff scores for these variables that could be used in the future to formulate neuropathologic criteria for mixed dementia in this age group were identified.

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The EORTC 22881-10882 trial in 5178 conservatively treated early breast cancer patients showed that a 16 Gy boost dose significantly improved local control, but increased the risk of breast fibrosis. To investigate predictors for the long-term risk of fibrosis, Cox regression models of the time to moderate or severe fibrosis were developed on a random set of 1797 patients with and 1827 patients without a boost, and validated in the remaining set. The median follow-up was 10.7 years. The risk of fibrosis significantly increased (P<0.01) with increasing maximum whole breast irradiation (WBI) dose and with concomitant chemotherapy, but was independent of age. In the boost arm, the risk further increased (P<0.01) if patients had post-operative breast oedema or haematoma, but it decreased (P<0.01) if WBI was given with >6 MV photons. The c-index was around 0.62. Nomograms with these factors are proposed to forecast the long-term risk of moderate or severe fibrosis.

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In this article we compare regression models obtained to predict PhD students’ academic performance in the universities of Girona (Spain) and Slovenia. Explanatory variables are characteristics of PhD student’s research group understood as an egocentered social network, background and attitudinal characteristics of the PhD students and some characteristics of the supervisors. Academic performance was measured by the weighted number of publications. Two web questionnaires were designed, one for PhD students and one for their supervisors and other research group members. Most of the variables were easily comparable across universities due to the careful translation procedure and pre-tests. When direct comparison was notpossible we created comparable indicators. We used a regression model in which the country was introduced as a dummy coded variable including all possible interaction effects. The optimal transformations of the main and interaction variables are discussed. Some differences between Slovenian and Girona universities emerge. Some variables like supervisor’s performance and motivation for autonomy prior to starting the PhD have the same positive effect on the PhD student’s performance in both countries. On the other hand, variables like too close supervision by the supervisor and having children have a negative influence in both countries. However, we find differences between countries when we observe the motivation for research prior to starting the PhD which increases performance in Slovenia but not in Girona. As regards network variables, frequency of supervisor advice increases performance in Slovenia and decreases it in Girona. The negative effect in Girona could be explained by the fact that additional contacts of the PhD student with his/her supervisor might indicate a higher workload in addition to or instead of a better advice about the dissertation. The number of external student’s advice relationships and social support mean contact intensity are not significant in Girona, but they have a negative effect in Slovenia. We might explain the negative effect of external advice relationships in Slovenia by saying that a lot of external advice may actually result from a lack of the more relevant internal advice

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Several cross-sectional studies have shown the ability of the TBS to discriminate between those with and without fractures in European populations. The aim of this study was to assess the ability of TBS to discriminate between those with and without fractures in a large female Caucasian population in the USA. This was a case-control study of 2,165 Caucasian American women aged 40 and older. Patients with illness or taking medications known to affect bone metabolism were excluded. Those in the fracture group (n = 289) had at least one low-energy fracture. BMD was measured at L1-L4, TBS calculated directly from the same DXA image. Descriptive statistics and inferential tests for difference were used. Univariate and multivariate logistic regression models were created to investigate possible association between independent variables and the status of fracture. Odds ratios per standard deviation decrease (OR) and areas under the ROC curve were calculated for discriminating parameters. Weak correlations were observed between TBS and BMD and between TBS and BMI (r = 0.33 and -0.17, respectively, p < 0.01). Mean age, weight, BMD and TBS were significantly different between control and fracture groups (all p ≤ 0.05), whereas no difference was noted for BMI or height. After adjusting for age, weight, BMD, smoking, and maternal and family history of fracture, TBS (but not BMD) remained a significant predictor of fracture: OR 1.28[1.13-1.46] even after adjustment. In a US female population, TBS again was able to discriminate between those with and those without fractures, even after adjusting for other clinical risk factors.

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OBJECTIVE: To comprehensively assess pre-, intra-, and postoperative delirium risk factors as potential targets for intervention. BACKGROUND: Delirium after cardiac surgery is associated with longer intensive care unit (ICU) stay, and poorer functional and cognitive outcomes. Reports on delirium risk factors so far did not cover the full range of patients' presurgical conditions, intraoperative factors, and postoperative course. METHODS: After written informed consent, 221 consecutive patients ≥ 50 years scheduled for cardiac surgery were assessed for preoperative cognitive performance, and functional and physical status. Clinical and biochemical data were systematically recorded perioperatively. RESULTS: Of the 215 patients remaining for analysis, 31% developed delirium in the intensive care unit. Using logistic regression models, older age [73.3 (71.2-75.4) vs 68.5 (67.0-70.0); P = 0.016], higher Charlson's comorbidity index [3.0 (1.5-4.0) vs 2.0 (1.0-3.0) points; P = 0.009], lower Mini-Mental State Examination (MMSE) score (MMSE, [27 (23-29) vs 28 (27-30) points; P = 0.021], length of cardiopulmonary bypass (CPB) [CPB; 133 (112-163) vs 119 (99-143) min; P = 0.004], and systemic inflammatory response syndrome in the intensive care unit [25 (36.2%) vs 13 (8.9%); P = 0.001] were independently associated with delirium. Combining age, MMSE score, Charlson's comorbidity index, and length of CPB in a regression equation allowed for a prediction of postoperative delirium with a sensitivity of 71.19% and a specificity of 76.26% (receiver operating analysis, area under the curve: 0.791; 95% confidence interval: 0.727-0.845). CONCLUSIONS: Further research will evaluate if modification of these risk factors prevents delirium and improves outcomes.

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Topological indices have been applied to build QSAR models for a set of 20 antimalarial cyclic peroxy cetals. In order to evaluate the reliability of the proposed linear models leave-n-out and Internal Test Sets (ITS) approaches have been considered. The proposed procedure resulted in a robust and consensued prediction equation and here it is shown why it is superior to the employed standard cross-validation algorithms involving multilinear regression models

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INTRODUCTION: Several studies have shown an increased risk of type 2 diabetes among smokers. Therefore, the aim of this analysis was to assess the relationship between smoking, cumulative smoking exposure and nicotine dependence with pre-diabetes. METHODS: We performed a cross-sectional analysis of healthy adults aged 25-41 in the Principality of Liechtenstein. Individuals with known diabetes, Body Mass Index (BMI) >35 kg/m² and prevalent cardiovascular disease were excluded. Smoking behaviour was assessed by self-report. Pre-diabetes was defined as glycosylated haemoglobin between 5.7% and 6.4%. Multivariable logistic regression models were done. RESULTS: Of the 2142 participants (median age 37 years), 499 (23.3%) had pre-diabetes. There were 1,168 (55%) never smokers, 503 (23%) past smokers and 471 (22%) current smokers, with a prevalence of pre-diabetes of 21.2%, 20.9% and 31.2%, respectively (p <0.0001). In multivariable regression models, current smokers had an odds ratio (OR) of pre-diabetes of 1.82 (95% confidential interval (CI) 1.39; 2.38, p <0.0001). Individuals with a smoking exposure of <5, 5-10 and >10 pack-years had an OR (95% CI) for pre-diabetes of 1.34 (0.90; 2.00), 1.80 (1.07; 3.01) and 2.51 (1.80; 3.59) (p linear trend <0.0001) compared with never smokers. A Fagerström score of 2, 3-5 and >5 among current smokers was associated with an OR (95% CI) for pre-diabetes of 1.27 (0.89; 1.82), 2.15 (1.48; 3.13) and 3.35 (1.73; 6.48) (p linear trend <0.0001). DISCUSSION: Smoking is strongly associated with pre-diabetes in young adults with a low burden of smoking exposure. Nicotine dependence could be a potential mechanism of this relationship.