Development and validation of a clinical prediction rule for chest wall syndrome in primary care.

ABSTRACT: BACKGROUND: Chest wall syndrome (CWS), the main cause of chest pain in primary care practice, is most often an exclusion diagnosis. We developed and evaluated a clinical prediction rule for CWS. METHODS: Data from a multicenter clinical cohort of consecutive primary care patients with chest pain were used (59 general practitioners, 672 patients). A final diagnosis was determined after 12 months of follow-up. We used the literature and bivariate analyses to identify candidate predictors, and multivariate logistic regression was used to develop a clinical prediction rule for CWS. We used data from a German cohort (n = 1212) for external validation. RESULTS: From bivariate analyses, we identified six variables characterizing CWS: thoracic pain (neither retrosternal nor oppressive), stabbing, well localized pain, no history of coronary heart disease, absence of general practitioner's concern, and pain reproducible by palpation. This last variable accounted for 2 points in the clinical prediction rule, the others for 1 point each; the total score ranged from 0 to 7 points. The area under the receiver operating characteristic (ROC) curve was 0.80 (95% confidence interval 0.76-0.83) in the derivation cohort (specificity: 89%; sensitivity: 45%; cut-off set at 6 points). Among all patients presenting CWS (n = 284), 71% (n = 201) had a pain reproducible by palpation and 45% (n = 127) were correctly diagnosed. For a subset (n = 43) of these correctly classified CWS patients, 65 additional investigations (30 electrocardiograms, 16 thoracic radiographies, 10 laboratory tests, eight specialist referrals, one thoracic computed tomography) had been performed to achieve diagnosis. False positives (n = 41) included three patients with stable angina (1.8% of all positives). External validation revealed the ROC curve to be 0.76 (95% confidence interval 0.73-0.79) with a sensitivity of 22% and a specificity of 93%. CONCLUSIONS: This CWS score offers a useful complement to the usual CWS exclusion diagnosing process. Indeed, for the 127 patients presenting CWS and correctly classified by our clinical prediction rule, 65 additional tests and exams could have been avoided. However, the reproduction of chest pain by palpation, the most important characteristic to diagnose CWS, is not pathognomonic.

Compliance with the Swiss Society for Nutrition's dietary recommendations in the population of Geneva, Switzerland: a 10-year trend study (1999-2009).

The trends in compliance with the dietary recommendations of the Swiss Society for Nutrition in the Geneva population were assessed for the period from 1999 to 2009 using 10 cross-sectional, population-based surveys (Bus Santé study) with a total of 9,320 participants aged 35 to 75 years (50% women). Dietary intake was assessed using a self-administered, validated, semi-quantitative food frequency questionnaire. Trends were assessed by logistic regression adjusting for age, smoking status, education, and nationality using survey year as the independent variable. After excluding participants with extreme intakes, the percentage of participants with a cholesterol intake of <300 mg/day increased from 40.8% in 1999 to 43.6% in 2009 for men (multivariate-adjusted P for trend=0.04) and from 57.8% to 61.4% in women (multivariate-adjusted P for trend=0.06). Calcium intake >1 g/day decreased from 53.3% to 46% in men and from 47.6% to 40.7% in women (multivariate-adjusted P for trend<0.001). Adequate iron intake decreased from 68.3% to 65.3% in men and from 13.3% to 8.4% in women (multivariate-adjusted P for trend<0.001). Conversely, no significant changes were observed for carbohydrates, protein, total fat (including saturated, monounsaturated, and polyunsaturated fatty acids), fiber, and vitamins D and A. We conclude that the quality of the Swiss diet did not improve between 1999 and 2009 and that intakes deviate substantially from expert recommendations for health promotion and chronic disease risk reduction.

The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients.

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.

Prospective association of the SHARE-operationalized frailty phenotype with adverse health outcomes: evidence from 60+ community-dwelling Europeans living in 11 countries.

BACKGROUND: Among the many definitions of frailty, the frailty phenotype defined by Fried et al. is one of few constructs that has been repeatedly validated: first in the Cardiovascular Health Study (CHS) and subsequently in other large cohorts in the North America. In Europe, the Survey of Health, Aging and Retirement in Europe (SHARE) is a gold mine of individual, economic and health information that can provide insight into better understanding of frailty across diverse population settings. A recent adaptation of the original five CHS-frailty criteria was proposed to make use of SHARE data and measure frailty in the European population. To test the validity of the SHARE operationalized frailty phenotype, this study aims to evaluate its prospective association with adverse health outcomes. METHODS: Data are from 11,015 community-dwelling men and women aged 60+ participating in wave 1 and 2 of the Survey of Health, Aging and Retirement in Europe, a population-based survey. Multivariate logistic regression analyses were used to assess the 2-year follow up effect of SHARE-operationalized frailty phenotype on the incidence of disability (disability-free at baseline) and on worsening disability and morbidity, adjusting for age, sex, income and baseline morbidity and disability. RESULTS: At 2-year follow up, frail individuals were at increased risk for: developing mobility (OR 3.07, 95% CI, 1.02-9.36), IADL (OR 5.52, 95% CI, 3.76-8.10) and BADL (OR 5.13, 95% CI, 3.53-7.44) disability; worsening mobility (OR 2.94, 95% CI, 2.19- 3.93) IADL (OR 4.43, 95% CI, 3.19-6.15) and BADL disability (OR 4.53, 95% CI, 3.14-6.54); and worsening morbidity (OR 1.77, 95% CI, 1.35-2.32). These associations were significant even among the prefrail, but with a lower magnitude of effect. CONCLUSIONS: The SHARE-operationalized frailty phenotype is significantly associated with all tested health outcomes independent of baseline morbidity and disability in community-dwelling men and women aged 60 and older living in Europe. The robustness of results validate the use of this phenotype in the SHARE survey for future research on frailty in Europe.

An integer-based score to predict functional outcome in acute ischemic stroke: The ASTRAL score.

OBJECTIVE: To develop and validate a simple, integer-based score to predict functional outcome in acute ischemic stroke (AIS) using variables readily available after emergency room admission. METHODS: Logistic regression was performed in the derivation cohort of previously independent patients with AIS (Acute Stroke Registry and Analysis of Lausanne [ASTRAL]) to identify predictors of unfavorable outcome (3-month modified Rankin Scale score >2). An integer-based point-scoring system for each covariate of the fitted multivariate model was generated by their β-coefficients; the overall score was calculated as the sum of the weighted scores. The model was validated internally using a 2-fold cross-validation technique and externally in 2 independent cohorts (Athens and Vienna Stroke Registries). RESULTS: Age (A), severity of stroke (S) measured by admission NIH Stroke Scale score, stroke onset to admission time (T), range of visual fields (R), acute glucose (A), and level of consciousness (L) were identified as independent predictors of unfavorable outcome in 1,645 patients in ASTRAL. Their β-coefficients were multiplied by 4 and rounded to the closest integer to generate the score. The area under the receiver operating characteristic curve (AUC) of the score in the ASTRAL cohort was 0.850. The score was well calibrated in the derivation (p = 0.43) and validation cohorts (0.22 [Athens, n = 1,659] and 0.49 [Vienna, n = 653]). AUCs were 0.937 (Athens), 0.771 (Vienna), and 0.902 (when pooled). An ASTRAL score of 31 indicates a 50% likelihood of unfavorable outcome. CONCLUSIONS: The ASTRAL score is a simple integer-based score to predict functional outcome using 6 readily available items at hospital admission. It performed well in double external validation and may be a useful tool for clinical practice and stroke research.

Risk factors of sunburns from occupational sun exposure in outdoor workers

Question: Outdoor workers can be exposed to intense ultraviolet (UV) solar radiation likely to results to sunburns. As sunburn is an important risk factor for skin cancer, in particular melanoma, we investigated the causes of occupational sunburns (OS) in French outdoor workers. Methods: A population-based survey was conducted in May-June 2012 through computer-assisted telephonic interviews in population 25 to 69 years of age. History of sunburn from occupational exposure within the year preceding interview was collected. We analysed the risk of OS in multivariate logistic regression. Results: Out of 1442 individuals who declared having an occupational exposure to solar UV radiation, 403 (27.9%) reported a sunburn from occupational exposure in the year preceding the interview. Sunburns were more frequent in women (30% vs. 26.4% in men although not significant p = 0.14), in younger workers (p = 0.0099), in sensitive phototype (40% in phototype I/II vs. 23% in phototype III/IV, p < 0.001) and in workers taking lunch outdoor (p = 0.0355). Some occupations were more associated with OS (more than 30%): health occupations, managing, research/engineering, construction workers and culture/art/social sciences workers. In multivariate analysis, risk factors for OS are phototype (I vs. IV, OR = 4.30 95% CI [2.65-6.98]), sunburn during leisure time (OR = 3.46 95% CI [2.62-4.59]), seasonality of exposure (seasonal vs. constant exposure OR = 1.36 95% CI [1.02-1.81] and annual UVA exposure (OR for 10J/m² daily average increment 1.08 95% CI [1.02-1.14]). In multivariate analysis the type of occupation was not associated with increased OS. Conclusion: Sunburns from occupation was also observed in non sensitive population, phototype IV, which shows that outdoor workers are potentially exposed to intense UV radiations. This study suggests that prevention should target UV sensitive outdoor workers as well as those cumulating intense UV exposure.

Exploring the periodicity of cardiovascular events in Switzerland: variation in deaths and hospitalizations across seasons, day of the week and hour of the day.

PURPOSE: The purpose of this study is to explore the periodical patterns of events and deaths related to cardiovascular disease (CVD), acute myocardial infarction (AMI) and stroke in Swiss adults (≥ 18 years). METHODS: Mortality data for period 1969-2007 (N=869,863 CVD events) and hospitalization data for period 1997-2008 (N=959,990 CVD events) were used. The annual, weekly and circadian distribution of CVD-related deaths and events were assessed. Multivariate analysis was conducted using multinomial logistic regression adjusting for age, gender and calendar year and considering deaths from respiratory diseases, accidents or other causes as competitive events. RESULTS: CVD deaths and hospitalizations occurred less frequently in the summer months. Similar patterns were found for AMI and stroke. No significant weekly variation for CVD deaths was found. Stratification by age and gender showed subjects aged <65 years to present a higher probability of dying on Mondays and Saturday, only for men. This finding was confirmed after multivariate adjustment. Finally, a circadian variation in CVD mortality was observed, with a first peak in the morning (8-12 am) and a smaller second peak in the late afternoon (2-6 pm). This pattern persisted after multivariate adjustment and was more pronounced for AMI than for stroke. CONCLUSION: There is a periodicity of hospitalizations and deaths related to CVD, AMI and stroke in Switzerland. This pattern changes slightly according to the age and sex of the subjects. Although the underlying mechanisms are not fully identified, preventive measures should take into account these aspects to develop better strategies of prevention and management of CVD.

Use of NSAIDS compared to paracetamol as potential risk factors for asthma

Introduction: Use of paracetamol has been associated with an increased risk of asthma in several epidemiological studies. In contrast, it has been suggested that non-steroidal anti-inflammatory drugs (NSAIDs) might be protective (Kanabar, Clin Ther 2007), but data relating to these drugs are scarce. Methods: Prevalence of asthma and intake of analgesics in the past 2 years were assessed by questionnaire in 2008 in young adults (≥;16 years) diagnosed with cancer between 1976 and 2003 (Swiss Childhood Cancer Survivor Study). In a multivariate logistic regression we analysed the association between asthma and intake of paracetamol only, NSAIDs only or their combination, adjusting for age, sex, cancer diagnosis, cancer therapy and time since diagnosis. Results: Of the 1293 participants (response rate 68%), 83 (6%) reported asthma and 845 (65%) intake of analgesics in the past 2 years. Of these, 257 (29%) took paracetamol only, 224 (25%) NSAIDs only, 312 (35%) a combination of both and 52 (6%) other analgesics. Adjusted Odds ratios for asthma were 2.2 (95% CI 1.0-4.7; p = 0.04), 1.9 (0.9-4.3; p = 0.12) and 2.9 (1.4-6.1; p <0.01) in those using paracetamol only, NSAIDs only or their combination respectively. Conclusion: These cross-sectional data in a selected population do not support a protective effect of NSAIDs against asthma, neither taken alone nor in combination with paracetamol. All analgesics were positively associated with reported asthma episodes in the past two years. This can be explained by reverse causation, with intake of analgesics being a result rather than a cause of asthma events. Randomised controlled trials in unselected populations are needed to clarify the direction of causation.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Evaluation of a multicomponent worksite health promotion program for cardiovascular risk factors-correcting for the regression towards the mean effect.

BACKGROUND: We assessed the impact of a multicomponent worksite health promotion program for0 reducing cardiovascular risk factors (CVRF) with short intervention, adjusting for regression towards the mean (RTM) affecting such nonexperimental study without control group. METHODS: A cohort of 4,198 workers (aged 42 +/- 10 years, range 16-76 years, 27% women) were analyzed at 3.7-year interval and stratified by each CVRF risk category (low/medium/high blood pressure [BP], total cholesterol [TC], body mass index [BMI], and smoking) with RTM and secular trend adjustments. Intervention consisted of 15 min CVRF screening and individualized counseling by health professionals to medium- and high-risk individuals, with eventual physician referral. RESULTS: High-risk groups participants improved diastolic BP (-3.4 mm Hg [95%CI: -5.1, -1.7]) in 190 hypertensive patients, TC (-0.58 mmol/l [-0.71, -0.44]) in 693 hypercholesterolemic patients, and smoking (-3.1 cig/day [-3.9, -2.3]) in 808 smokers, while systolic BP changes reflected RTM. Low-risk individuals without counseling deteriorated TC and BMI. Body weight increased uniformly in all risk groups (+0.35 kg/year). CONCLUSIONS: In real-world conditions, short intervention program participants in high-risk groups for diastolic BP, TC, and smoking improved their CVRF, whereas low-risk TC and BMI groups deteriorated. Future programs may include specific advises to low-risk groups to maintain a favorable CVRF profile.

A simple score for estimating ischemic heart disease in patients with non-traumatic chest pain in primary care

Background: Modelling epidemiological knowledge in validated clinical scores is a practical mean of integrating EBM to usual care. Existing scores about cardiovascular disease have been largely developed in emergency settings, but few in primary care. Such a toll is needed for general practitioners (GP) to evaluate the probability of ischemic heart disease (IHD) in patients with non-traumatic chest pain. Objective: To develop a predictive model to use as a clinical score for detecting IHD in patients with non-traumatic chest-pain in primary care. Methods: A post-hoc secondary analysis on data from an observational study including 672 patients with chest pain of which 85 had IHD diagnosed by their GP during the year following their inclusion. Best subset method was used to select 8 predictive variables from univariate analysis and fitted in a multivariate logistic regression model to define the score. Reliability of the model was assessed using split-group method. Results: Significant predictors were: age (0-3 points), gender (1 point), having at least one cardiovascular risks factor (hypertension, dyslipidemia, diabetes, smoking, family history of CVD; 3 points), personal history of cardiovascular disease (1 point), duration of chest pain from 1 to 60 minutes (2 points), substernal chest pain (1 point), pain increasing with exertion (1 point) and absence of tenderness at palpation (1 point). Area under the ROC curve for the score was of 0.95 (IC95% 0.93; 0.97). Patients were categorised in three groups, low risk of IHD (score under 6; n = 360), moderate risk of IHD (score from 6 to 8; n = 187) and high risk of IHD (score from 9-13; n = 125). Prevalence of IHD in each group was respectively of 0%, 6.7%, 58.5%. Reliability of the model seems satisfactory as the model developed from the derivation set predicted perfectly (p = 0.948) the number of patients in each group in the validation set. Conclusion: This clinical score based only on history and physical exams can be an important tool in the practice of the general physician for the prediction of ischemic heart disease in patients complaining of chest pain. The score below 6 points (in more than half of our population) can avoid demanding complementary exams for selected patients (ECG, laboratory tests) because of the very low risk of IHD. Score above 6 points needs investigation to detect or rule out IHD. Further external validation is required in ambulatory settings.

IL28B polymorphisms predict response to therapy among chronic hepatitis C patients with HCV genotype 4.

Summary.  Genetic polymorphisms near IL28B are associated with spontaneous and treatment-induced clearance of hepatitis C virus (HCV). Our objective was to assess the predictive value of IL28B polymorphisms in the treatment of chronic hepatitis C of patients with HCV genotypes 4, for which data are currently limited. We analysed the association of IL28B polymorphisms with the virological response to treatment among 182 naïve chronic hepatitis C patients with HCV genotype 4, all from Syria. Associations of alleles with the response patterns were evaluated by univariate analysis and multivariate logistic regression, accounting for all relevant covariates. Sustained virological response (SVR) was achieved in 26% of rs8099917 TG/GG carriers compared with 60% of TT carriers (P < 0.0001) and 35% of rs12979860 CT/TT carriers compared with 62% of CC carriers (P = 0.0011). By multivariate analysis, the association between rs8099917 and SVR remained significant (OR = 0.19, 95% CI 0.07-0.50, for TG/GG vs TT, P = 0.0007), with the only significant covariate being advanced fibrosis (OR = 0.13, 95% CI 0.04-0.37, P = 0.0002). In conclusion, IL28B polymorphisms are the strongest predictors of response to therapy among chronic hepatitis C patients with HCV genotype 4.

EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.