Detecting and measuring deprivation in primary care: development, validity and reliability of a self-reported questionnaire - the DiPCare-Q

Background: General practitioners play a central role in taking deprivation into consideration when caring for patients in primary care. Validated questions to identify deprivation in primary-care practices are still lacking. For both clinical and research purposes, this study therefore aims to develop and validate a standardized instrument measuring both material and social deprivation at an individual level. Methods: The Deprivation in Primary Care Questionnaire (DiPCare-Q) was developed using qualitative and quantitative approaches between 2008 and 2011. A systematic review identified 199 questions related to deprivation. Using judgmental item quality, these were reduced to 38 questions. Two focus groups (primary-care physicians, and primary-care researchers), structured interviews (10 laymen), and think aloud interviews (eight cleaning staff) assured face validity. Item response theory analysis was then used to derive the DiPCare-Q index using data obtained from a random sample of 200 patients who were to complete the questionnaire a second time over the phone. For construct and criterion validity, the final 16 questions were administered to a random sample of 1,898 patients attending one of 47 different private primary-care practices in western Switzerland (validation set) along with questions on subjective social status (subjective SES ladder), education, source of income, welfare status, and subjective poverty. Results: Deprivation was defined in three distinct dimensions (table); material deprivation (eight items), social deprivation (five items) and health deprivation (three items). Item consistency was high in both the derivation (KR20 = 0.827) and the validation set (KR20 = 0.778). The DiPCare-Q index was reliable (ICC = 0.847). For construct validity, we showed the DiPCare-Q index to be correlated to patients' estimation of their position on the subjective SES ladder (rs = 0.539). This position was correlated to both material and social deprivation independently suggesting two separate mechanisms enhancing the feeling of deprivation. Conclusion: The DiPCare-Q is a rapid, reliable and validated instrument useful for measuring both material and social deprivation in primary care. Questions from the DiPCare-Q are easy to use when investigating patients' social history and could improve clinicians' ability to detect underlying social distress related to deprivation.

A Study of the Reliability of the ASTM C-666 Freeze-Thaw Test, MLR-72-01 and R-252, 1972

The Iowa State Highway Commission purchased a Conrad automatic freeze and thaw machine and placed it in operation during October 1961. There were a few problems, but considering, the many electrical and mechanical devices used in the automatic system it has always functioned quite well. Rapid freezing and thawing of 4"x4"xl8" concrete beams has been conducted primarily in accordance with ASTM C-29l (now ASTM C-666 procedure B) at the rate of one beam per day. Over 4000 beams have been tested since 1961, with determination of the resulting durability factors. Various methods of curing were used and a standard 90 day moist cure was selected. This cure seemed to yield durability factors that correlated very well with ratings of coarse aggregates based on service records. Some concrete beams had been made using the same coarse aggregate and the durability factors compared relatively well with previous tests. Durability factors seemed to yield reasonable results until large variations in durability factors were noted from beams of identical concrete mix proportions in research projects R-234 and R-247. This then presents the question "How reliable is the durability as determined by ASTM C-666?" This question became increasingly more important when a specification requiring a minimum durability factor for P.C. concrete made from coarse aggregates was incorporated into the 1972 Standard Specification for coarse aggregates for concrete.

Prediction of Recanalization Trumps Prediction of Tissue Fate: The Penumbra: A Dual-edged Sword.

BACKGROUND AND PURPOSE: To determine whether infarct core or penumbra is the more significant predictor of outcome in acute ischemic stroke, and whether the results are affected by the statistical method used. METHODS: Clinical and imaging data were collected in 165 patients with acute ischemic stroke. We reviewed the noncontrast head computed tomography (CT) to determine the Alberta Score Program Early CT score and assess for hyperdense middle cerebral artery. We reviewed CT-angiogram for site of occlusion and collateral flow score. From perfusion-CT, we calculated the volumes of infarct core and ischemic penumbra. Recanalization status was assessed on early follow-up imaging. Clinical data included age, several time points, National Institutes of Health Stroke Scale at admission, treatment type, and modified Rankin score at 90 days. Two multivariate regression analyses were conducted to determine which variables predicted outcome best. In the first analysis, we did not include recanalization status among the potential predicting variables. In the second, we included recanalization status and its interaction between perfusion-CT variables. RESULTS: Among the 165 study patients, 76 had a good outcome (modified Rankin score ≤2) and 89 had a poor outcome (modified Rankin score >2). In our first analysis, the most important predictors were age (P<0.001) and National Institutes of Health Stroke Scale at admission (P=0.001). The imaging variables were not important predictors of outcome (P>0.05). In the second analysis, when the recanalization status and its interaction with perfusion-CT variables were included, recanalization status and perfusion-CT penumbra volume became the significant predictors (P<0.001). CONCLUSIONS: Imaging prediction of tissue fate, more specifically imaging of the ischemic penumbra, matters only if recanalization can also be predicted.

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.

Détermination de l'antigène HLA-G soluble dans le liquide folliculaire et dans le milieu de culture d'embryons comme indicateur du succès d'un traitement par FIV-ICSI [Soluble human leukocyte antigen-G (sHLA-G) in follicular fluid and embryo culture medium and its impact on pregnancy prediction in IVF-ICSI treatment]

In IVF around 70% of embryos fail to implant. Often more than one embryo is transferred in order to enhance the chances of pregnancy, but this is at the price of an increased multiple pregnancy risk. In the aim to increase the success rate with a single embryo, research projects on prognostic factors of embryo viability have been initiated, but no marker has found a routine clinical application to date. Effects of soluble human leukocyte antigen-G (sHLA-G) on both NK cell activity and on Th1/Th2 cytokine balance suggest a role in the embryo implantation process, but the relevance of sHLA-G measurements in embryo culture medium and in follicular fluid (FF) are inconsistent to date. In this study, we have investigated the potential of sHLA-G in predicting the achievement of a pregnancy after IVF-ICSI in a large number of patients (n = 221). sHLA-G was determined in media and in FF by ELISA. In both FF and embryo medium, no significant differences in sHLA-G concentrations were observed between the groups "pregnancy" and "implantation failure", or between the groups "ongoing" versus "miscarried pregnancies". Our results do not favour routine sHLA-G determinations in the FF nor in embryo conditioned media, with the current assay technology available.

The family alliance assessment scales : steps toward validity and reliability of an observational assessment tool for early family interactions

We present the first steps in the validation of an observational tool for father-mother-infant interactions: the FAAS (Family Alliance Assessment Scales). Family-level variables are acknowledged as unique contributors to the understanding of the socio-affective development of the child, yet producing reliable assessments of family-level interactions poses a methodological challenge. There is, therefore, a clear need for a validated and clinically relevant tool. This validation study has been carried out on three samples: one non-referred sample, of families taking part in a study on the transition to parenthood (normative sample; n = 30), one referred for medically assisted procreation (infertility sample; n = 30) and one referred for a psychiatric condition in one parent (clinical sample; n = 15). Results show that the FAAS scales have (1) good inter-rater reliability and (2) good validity, as assessed through known-group validity by comparing the three samples and through concurrent validity by checking family interactions against parents' self-reported marital satisfaction.

Geostatistical prediction of clay percentage based on soil survey data

Selostus: Viljelymaiden savespitoisuuden alueellistaminen geostatistiikan ja pistemäisen tiedon avulla

Improvement of therapeutic drug monitoring of imatinib by Bayesian prediction of trough levels. Personalized drug dosage

Aims: Plasma concentrations of imatinib differ largely between patients despite same dosage, owing to large inter-individual variability in pharmacokinetic (PK) parameters. As the drug concentration at the end of the dosage interval (Cmin) correlates with treatment response and tolerability, monitoring of Cmin is suggested for therapeutic drug monitoring (TDM) of imatinib. Due to logistic difficulties, random sampling during the dosage interval is however often performed in clinical practice, thus rendering the respective results not informative regarding Cmin values.Objectives: (I) To extrapolate randomly measured imatinib concentrations to more informative Cmin using classical Bayesian forecasting. (II) To extend the classical Bayesian method to account for correlation between PK parameters. (III) To evaluate the predictive performance of both methods.Methods: 31 paired blood samples (random and trough levels) were obtained from 19 cancer patients under imatinib. Two Bayesian maximum a posteriori (MAP) methods were implemented: (A) a classical method ignoring correlation between PK parameters, and (B) an extended one accounting for correlation. Both methods were applied to estimate individual PK parameters, conditional on random observations and covariate-adjusted priors from a population PK model. The PK parameter estimates were used to calculate trough levels. Relative prediction errors (PE) were analyzed to evaluate accuracy (one-sample t-test) and to compare precision between the methods (F-test to compare variances).Results: Both Bayesian MAP methods allowed non-biased predictions of individual Cmin compared to observations: (A) - 7% mean PE (CI95% - 18 to 4 %, p = 0.15) and (B) - 4% mean PE (CI95% - 18 to 10 %, p = 0.69). Relative standard deviations of actual observations from predictions were 22% (A) and 30% (B), i.e. comparable to the intraindividual variability reported. Precision was not improved by taking into account correlation between PK parameters (p = 0.22).Conclusion: Clinical interpretation of randomly measured imatinib concentrations can be assisted by Bayesian extrapolation to maximum likelihood Cmin. Classical Bayesian estimation can be applied for TDM without the need to include correlation between PK parameters. Both methods could be adapted in the future to evaluate other individual pharmacokinetic measures correlated to clinical outcomes, such as area under the curve(AUC).

Prediction of free imatinib concentrations based on total plasma concentrations in patients with gastrointestinal stromal tumours.

AIM: Total imatinib concentrations are currently measured for the therapeutic drug monitoring of imatinib, whereas only free drug equilibrates with cells for pharmacological action. Due to technical and cost limitations, routine measurement of free concentrations is generally not performed. In this study, free and total imatinib concentrations were measured to establish a model allowing the confident prediction of imatinib free concentrations based on total concentrations and plasma proteins measurements. METHODS: One hundred and fifty total and free plasma concentrations of imatinib were measured in 49 patients with gastrointestinal stromal tumours. A population pharmacokinetic model was built up to characterize mean total and free concentrations with inter-patient and intrapatient variability, while taking into account α1 -acid glycoprotein (AGP) and human serum albumin (HSA) concentrations, in addition to other demographic and environmental covariates. RESULTS: A one compartment model with first order absorption was used to characterize total and free imatinib concentrations. Only AGP influenced imatinib total clearance. Imatinib free concentrations were best predicted using a non-linear binding model to AGP, with a dissociation constant Kd of 319 ng ml(-1) , assuming a 1:1 molar binding ratio. The addition of HSA in the equation did not improve the prediction of imatinib unbound concentrations. CONCLUSION: Although free concentration monitoring is probably more appropriate than total concentrations, it requires an additional ultrafiltration step and sensitive analytical technology, not always available in clinical laboratories. The model proposed might represent a convenient approach to estimate imatinib free concentrations. However, therapeutic ranges for free imatinib concentrations remain to be established before it enters into routine practice.

Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation

High-throughput prioritization of cancer-causing mutations (drivers) is a key challenge of cancer genome projects, due to the number of somatic variants detected in tumors. One important step in this task is to assess the functional impact of tumor somatic mutations. A number of computational methods have been employed for that purpose, although most were originally developed to distinguish disease-related nonsynonymous single nucleotide variants (nsSNVs) from polymorphisms. Our new method, transformed Functional Impact score for Cancer (transFIC), improves the assessment of the functional impact of tumor nsSNVs by taking into account the baseline tolerance of genes to functional variants.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Assessing inter-rater reliability when the raters are fixed: two concepts and two estimates.

Intraclass correlation (ICC) is an established tool to assess inter-rater reliability. In a seminal paper published in 1979, Shrout and Fleiss considered three statistical models for inter-rater reliability data with a balanced design. In their first two models, an infinite population of raters was considered, whereas in their third model, the raters in the sample were considered to be the whole population of raters. In the present paper, we show that the two distinct estimates of ICC developed for the first two models can both be applied to the third model and we discuss their different interpretations in this context.

Optimizing Pavement Base, Subbase, and Subgrade Layers for Cost and Performance of Local Roads, TR-640

This report is one of two products for this project with the other being a design guide. This report describes test results and comparative analysis from 16 different portland cement concrete (PCC) pavement sites on local city and county roads in Iowa. At each site the surface conditions of the pavement (i.e., crack survey) and foundation layer strength, stiffness, and hydraulic conductivity properties were documented. The field test results were used to calculate in situ parameters used in pavement design per SUDAS and AASHTO (1993) design methodologies. Overall, the results of this study demonstrate how in situ and lab testing can be used to assess the support conditions and design values for pavement foundation layers and how the measurements compare to the assumed design values. The measurements show that in Iowa, a wide range of pavement conditions and foundation layer support values exist. The calculated design input values for the test sites (modulus of subgrade reaction, coefficient of drainage, and loss of support) were found to be different than typically assumed. This finding was true for the full range of materials tested. The findings of this study support the recommendation to incorporate field testing as part of the process to field verify pavement design values and to consider the foundation as a design element in the pavement system. Recommendations are provided in the form of a simple matrix for alternative foundation treatment options if the existing foundation materials do not meet the design intent. The PCI prediction model developed from multi-variate analysis in this study demonstrated a link between pavement foundation conditions and PCI. The model analysis shows that by measuring properties of the pavement foundation, the engineer will be able to predict long term performance with higher reliability than by considering age alone. This prediction can be used as motivation to then control the engineering properties of the pavement foundation for new or re-constructed PCC pavements to achieve some desired level of performance (i.e., PCI) with time.