Validation of a standardized mapping system of the hip joint for radial MRA sequencing

OBJECTIVE Intraarticular gadolinium-enhanced magnetic resonance arthrography (MRA) is commonly applied to characterize morphological disorders of the hip. However, the reproducibility of retrieving anatomic landmarks on MRA scans and their correlation with intraarticular pathologies is unknown. A precise mapping system for the exact localization of hip pathomorphologies with radial MRA sequences is lacking. Therefore, the purpose of the study was the establishment and validation of a reproducible mapping system for radial sequences of hip MRA. MATERIALS AND METHODS Sixty-nine consecutive intraarticular gadolinium-enhanced hip MRAs were evaluated. Radial sequencing consisted of 14 cuts orientated along the axis of the femoral neck. Three orthopedic surgeons read the radial sequences independently. Each MRI was read twice with a minimum interval of 7 days from the first reading. The intra- and inter-observer reliability of the mapping procedure was determined. RESULTS A clockwise system for hip MRA was established. The teardrop figure served to determine the 6 o'clock position of the acetabulum; the center of the greater trochanter served to determine the 12 o'clock position of the femoral head-neck junction. The intra- and inter-observer ICCs to retrieve the correct 6/12 o'clock positions were 0.906-0.996 and 0.978-0.988, respectively. CONCLUSIONS The established mapping system for radial sequences of hip joint MRA is reproducible and easy to perform.

Predicting recurrence after unprovoked venous thromboembolism: prospective validation of the updated Vienna Prediction Model.

The updated Vienna Prediction Model for estimating recurrence risk after an unprovoked venous thromboembolism (VTE) has been developed to identify individuals at low risk for VTE recurrence in whom anticoagulation (AC) therapy may be stopped after 3 months. We externally validated the accuracy of the model to predict recurrent VTE in a prospective multicenter cohort of 156 patients aged ≥65 years with acute symptomatic unprovoked VTE who had received 3 to 12 months of AC. Patients with a predicted 12-month risk within the lowest quartile based on the updated Vienna Prediction Model were classified as low risk. The risk of recurrent VTE did not differ between low- vs higher-risk patients at 12 months (13% vs 10%; P = .77) and 24 months (15% vs 17%; P = 1.0). The area under the receiver operating characteristic curve for predicting VTE recurrence was 0.39 (95% confidence interval [CI], 0.25-0.52) at 12 months and 0.43 (95% CI, 0.31-0.54) at 24 months. In conclusion, in elderly patients with unprovoked VTE who have stopped AC, the updated Vienna Prediction Model does not discriminate between patients who develop recurrent VTE and those who do not. This study was registered at www.clinicaltrials.gov as #NCT00973596.

External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission

BACKGROUND Predicting long-term survival after admission to hospital is helpful for clinical, administrative and research purposes. The Hospital-patient One-year Mortality Risk (HOMR) model was derived and internally validated to predict the risk of death within 1 year after admission. We conducted an external validation of the model in a large multicentre study. METHODS We used administrative data for all nonpsychiatric admissions of adult patients to hospitals in the provinces of Ontario (2003-2010) and Alberta (2011-2012), and to the Brigham and Women's Hospital in Boston (2010-2012) to calculate each patient's HOMR score at admission. The HOMR score is based on a set of parameters that captures patient demographics, health burden and severity of acute illness. We determined patient status (alive or dead) 1 year after admission using population-based registries. RESULTS The 3 validation cohorts (n = 2,862,996 in Ontario, 210 595 in Alberta and 66,683 in Boston) were distinct from each other and from the derivation cohort. The overall risk of death within 1 year after admission was 8.7% (95% confidence interval [CI] 8.7% to 8.8%). The HOMR score was strongly and significantly associated with risk of death in all populations and was highly discriminative, with a C statistic ranging from 0.89 (95% CI 0.87 to 0.91) to 0.92 (95% CI 0.91 to 0.92). Observed and expected outcome risks were similar (median absolute difference in percent dying in 1 yr 0.3%, interquartile range 0.05%-2.5%). INTERPRETATION The HOMR score, calculated using routinely collected administrative data, accurately predicted the risk of death among adult patients within 1 year after admission to hospital for nonpsychiatric indications. Similar performance was seen when the score was used in geographically and temporally diverse populations. The HOMR model can be used for risk adjustment in analyses of health administrative data to predict long-term survival among hospital patients.

Validation of an enzyme-linked immunosorbent assay (ELISA) for the measurement of canine S100A12.

BACKGROUND Canine S100 calcium-binding protein A12 (cS100A12) shows promise as biomarker of inflammation in dogs. A previously developed cS100A12-radioimmunoassay (RIA) requires radioactive tracers and is not sensitive enough for fecal cS100A12 concentrations in 79% of tested healthy dogs. An ELISA assay may be more sensitive than RIA and does not require radioactive tracers. OBJECTIVE The purpose of the study was to establish a sandwich ELISA for serum and fecal cS100A12, and to establish reference intervals (RI) for normal healthy canine serum and feces. METHODS Polyclonal rabbit anti-cS100A12 antibodies were generated and tested by Western blotting and immunohistochemistry. A sandwich ELISA was developed and validated, including accuracy and precision, and agreement with cS100A12-RIA. The RI, stability, and biologic variation in fecal cS100A12, and the effect of corticosteroids on serum cS100A12 were evaluated. RESULTS Lower detection limits were 5 μg/L (serum) and 1 ng/g (fecal), respectively. Intra- and inter-assay coefficients of variation were ≤ 4.4% and ≤ 10.9%, respectively. Observed-to-expected ratios for linearity and spiking recovery were 98.2 ± 9.8% (mean ± SD) and 93.0 ± 6.1%, respectively. There was a significant bias between the ELISA and the RIA. The RI was 49-320 μg/L for serum and 2-484 ng/g for fecal cS100A12. Fecal cS100A12 was stable for 7 days at 23, 4, -20, and -80°C; biologic variation was negligible but variation within one fecal sample was significant. Corticosteroid treatment had no clinically significant effect on serum cS100A12 concentrations. CONCLUSIONS The cS100A12-ELISA is a precise and accurate assay for serum and fecal cS100A12 in dogs.

A multinational case-control study on childhood brain tumours, anthropogenic factors, birth characteristics and prenatal exposures: A validation of interview data.

Little is known about the aetiology of childhood brain tumours. We investigated anthropometric factors (birth weight, length, maternal age), birth characteristics (e.g. vacuum extraction, preterm delivery, birth order) and exposures during pregnancy (e.g. maternal: smoking, working, dietary supplement intake) in relation to risk of brain tumour diagnosis among 7-19 year olds. The multinational case-control study in Denmark, Sweden, Norway and Switzerland (CEFALO) included interviews with 352 (participation rate=83.2%) eligible cases and 646 (71.1%) population-based controls. Interview data were complemented with data from birth registries and validated by assessing agreement (Cohen's Kappa). We used conditional logistic regression models matched on age, sex and geographical region (adjusted for maternal age and parental education) to explore associations between birth factors and childhood brain tumour risk. Agreement between interview and birth registry data ranged from moderate (Kappa=0.54; worked during pregnancy) to almost perfect (Kappa=0.98; birth weight). Neither anthropogenic factors nor birth characteristics were associated with childhood brain tumour risk. Maternal vitamin intake during pregnancy was indicative of a protective effect (OR 0.75, 95%-CI: 0.56-1.01). No association was seen for maternal smoking during pregnancy or working during pregnancy. We found little evidence that the considered birth factors were related to brain tumour risk among children and adolescents.

Validation of a Spatially Continuous EDEN Water-Surface Model for the Everglades, Florida

The Everglades Depth Estimation Network (EDEN) is an integrated network of realtime water-level monitoring, ground-elevation modeling, and water-surface modeling that provides scientists and managers with current (2000-present), online water-stage and water-depth information for the entire freshwater portion of the Greater Everglades. Continuous daily spatial interpolations of the EDEN network stage data are presented on grid with 400-square-meter spacing. EDEN offers a consistent and documented dataset that can be used by scientists and managers to: (1) guide large-scale field operations, (2) integrate hydrologic and ecological responses, and (3) support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan (CERP) (U.S. Army Corps of Engineers, 1999). The target users are biologists and ecologists examining trophic level responses to hydrodynamic changes in the Everglades. The first objective of this report is to validate the spatially continuous EDEN water-surface model for the Everglades, Florida developed by Pearlstine et al. (2007) by using an independent field-measured data-set. The second objective is to demonstrate two applications of the EDEN water-surface model: to estimate site-specific ground elevation by using the validated EDEN water-surface model and observed water depth data; and to create water-depth hydrographs for tree islands. We found that there are no statistically significant differences between model-predicted and field-observed water-stage data in both southern Water Conservation Area (WCA) 3A and WCA 3B. Tree island elevations were derived by subtracting field water-depth measurements from the predicted EDEN water-surface. Water-depth hydrographs were then computed by subtracting tree island elevations from the EDEN water stage. Overall, the model is reliable by a root mean square error (RMSE) of 3.31 cm. By region, the RMSE is 2.49 cm and 7.77 cm in WCA 3A and 3B, respectively. This new landscape-scale hydrological model has wide applications for ongoing research and management efforts that are vital to restoration of the Florida Everglades. The accurate, high-resolution hydrological data, generated over broad spatial and temporal scales by the EDEN model, provides a previously missing key to understanding the habitat requirements and linkages among native and invasive populations, including fish, wildlife, wading birds, and plants. The EDEN model is a powerful tool that could be adapted for other ecosystem-scale restoration and management programs worldwide.

Development and validation of the Neuro Symptom Inventory in the primary brain tumor patient population

Symptoms has been shown to predict quality of life, treatment course and survival in solid tumor patients. Currently, no instrument exists that measures both cancer-related symptoms and the neurologic symptoms that are unique to persons with primary brain tumors (PBT). The aim of this study was to develop and validate an instrument to measure symptoms in patients who have PBT. A conceptual analysis of symptoms and symptom theories led to defining the symptoms experience as the perception of the frequency, intensity, distress, and meaning that occurs as symptoms are produced, perceived, and expressed. The M.D. Anderson Symptom Inventory (MDASI) measures both symptoms and how they interfere with daily functioning in patients with cancer, which is similar to the situational meaning defined in the analysis. A list of symptoms pertinent to the PBT population was added to the core MDASI and reviewed by a group of experts for validity. As a result, 18 items were added to the core MDASI (the MDASI-BT) for the next phase of instrument development, establishing validity and reliability through a descriptive, cross-sectional approach with PBT patients. Data were collected with a patient completed demographic data sheet, an investigator completed clinician checklist, and the MDASI-BT. Analysis evaluated the reliability and validity of the MDASI-BT in PBT patients. Data were obtained from 201 patients. The number of items was reduced to 22 by evaluation of symptom severity as well as cluster analysis. Regression analysis showed more than half (56%) of the variability in symptom severity was explained by the brain tumor module items. Factor analysis confirmed that the 22 item MDASI-BT measured six underlying constructs: (a) affective; (b) cognitive; (c) focal neurologic deficits; (d) constitutional symptoms; (e) treatment-related symptoms; and (f) gastrointestinal symptoms. The MDASI-BT was sensitive to disease severity and if the patient was hospitalized. The MDASI-BT is the first instrument to measure symptoms in PBT patients that has demonstrated reliability and validity. It is the first step in a program of research to evaluate the occurrence of symptoms and plan and evaluate interventions for PBT patients. ^

The Spanish translation, cultural adaptation and validation of the Powe Fatalism Inventory

Background/significance. The scarcity of reliable and valid Spanish language instruments for health related research has hindered research with the Hispanic population. Research suggests that fatalistic attitudes are related to poor cancer screening behaviors and may be one reason for low participation of Mexican-Americans in cancer screening. This problem is of major concern because Mexican-Americans constitute the largest Hispanic subgroup in the U.S.^ Purpose. The purposes of this study were: (1) To translate the Powe Fatalism Inventory, (PFI) into Spanish, and culturally adapt the instrument to the Mexican-American culture as found along the U.S.-Mexico border and (2) To test the equivalence between the Spanish translated, culturally adapted version of the PFI and the English version of the PFI to include clarity, content validity, reading level and reliability.^ Design. Descriptive, cross-sectional.^ Methods. The Spanish language translation used a translation model which incorporates a cultural adaptation process. The SPFI was administered to 175 bilingual participants residing in a midsize, U.S-Mexico border city. Data analysis included estimation of Cronbach's alpha, factor analysis, paired samples t-test comparison and multiple regression analysis using SPSS software, as well as measurement of content validity and reading level of the SPFI. ^ Findings. A reliability estimate using Cronbach's alpha coefficient was 0.81 for the SPFI compared to 0.80 for the PFI in this study. Factor Analysis extracted four factors which explained 59% of the variance. Paired t-test comparison revealed no statistically significant differences between the SPFI and PFI total or individual item scores. Content Validity Index was determined to be 1.0. Reading Level was assessed to be less than a 6th grade reading level. The correlation coefficient between the SPFI and PFI was 0.95.^ Conclusions. This study provided strong psychometric evidence that the Spanish translated, culturally adapted SPFI is an equivalent tool to the English version of the PFI in measuring cancer fatalism. This indicates that the two forms of the instrument can be used interchangeably in a single study to accommodate reading and speaking abilities of respondents. ^

Development and preliminary validation of the cancer family impact scale for colorectal cancer

This study aimed to develop and validate The Cancer Family Impact Scale (CFIS), an instrument for use in studies investigating relationships among family factors and colorectal cancer (CRC) screening when family history is a risk factor. We used existing data to develop the measure from 1,285 participants (637 families) across the United States who were in the Johns Hopkins Colon Cancer Genetic Testing study. Participants were 94% white with an average age of 50.1 years, and 60% were women. None had a personal CRC history, and eighty percent had 1 FDR with CRC and 20% had more than one FDR with CRC. The study had three aims: (1) to identify the latent factors underlying the CFIS via exploratory factor analysis (EFA); (2) to confirm the findings of the EFA via confirmatory factor analysis (CFA); and (3) to assess the reliability of the scale via Cronbach's alpha. Exploratory analyses were performed on a split half of the sample, and the final model was confirmed on the other half. The EFA suggested the CFIS was an 18-item measure with 5 latent constructs: (1) NEGATIVE: negative effects of cancer on the family; (2) POSITIVE: positive effects of cancer on the family; (3) COMMUNICATE: how families communicate about cancer; (4) FLOW: how information about cancer is conveyed in families; and (5) NORM: how individuals react to family norms about cancer. CFA on the holdout sample showed the CFIS to have a reasonably good fit (Chi-square = 389.977, df = 122, RMSEA= 0.058 (.052-.065), CFI=.902, TLI=.877, GF1=.939). The overall reliability of the scale was α=0.65. The reliability of the subscales was: (1) NEGATIVE α = 0.682; (2) POSITIVE α = 0.686; (3) COMMUNICATE α = 0.723; (4) FLOW α = 0.467; and (5) NORM α = 0.732. ^ We concluded the CFIS to be a good measure with most fit levels over 0.90. The CFIS could be used to compare theoretically driven hypotheses about the pathways through which family factors could influence health behavior among unaffected individuals at risk due to family history, and also aid in the development and evaluation of cancer prevention interventions including a family component. ^

Validation of an instrument for assessing the medicolegal death investigation system: A public health approach

Background. This study validated the content of an instrument designed to assess the performance of the medicolegal death investigation system. The instrument was modified from Version 2.0 of the Local Public Health System Performance Assessment Instrument (CDC) and is based on the 10 Essential Public Health Services. ^ Aims. The aims were to employ a cognitive testing process to interview a randomized sample of medicolegal death investigation office leaders, qualitatively describe the results, and revise the instrument accordingly. ^ Methods. A cognitive testing process was used to validate the survey instrument's content in terms of the how well participants could respond to and interpret the questions. Twelve randomly selected medicolegal death investigation chiefs (or equivalent) that represented the seven types of medicolegal death investigation systems and six different state mandates were interviewed by telephone. The respondents also were representative of the educational diversity within medicolegal death investigation leadership. Based on respondent comments, themes were identified that permitted improvement of the instrument toward collecting valid and reliable information when ultimately used in a field survey format. ^ Results. Responses were coded and classified, which permitted the identification of themes related to Comprehension/Interpretation, Retrieval, Estimate/Judgment, and Response. The majority of respondent comments related to Comprehension/Interpretation of the questions. Respondents identified 67 questions and 6 section explanations that merited rephrasing, adding, or deleting examples or words. In addition, five questions were added based on respondent comments. ^ Conclusion. The content of the instrument was validated by cognitive testing method design. The respondents agreed that the instrument would be a useful and relevant tool for assessing system performance. ^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

DISPRO datasets for validation of coastal hydrodynamic models

Appropriate field data are required to check the reliability of hydrodynamic models simulating the dispersion of soluble substances in the marine environment. This study deals with the collection of physical measurements and soluble tracer data intended specifically for this kind of validation. The intensity of currents as well as the complexity of topography and tides around the Cap de La Hague in the center of the English Channel makes it one of the most difficult areas to represent in terms of hydrodynamics and dispersion. Controlled releases of tritium - in the form of HTO - are carried out in this area by the AREVA-NC plant, providing an excellent soluble tracer. A total of 14 493 measurements were acquired to track dispersion in the hours and days following a release. These data, supplementing previously gathered data and physical measurements (bathymetry, water-surface levels, Eulerian and Lagrangian current studies) allow us to test dispersion models from the hour following release to periods of several years which are not accessible with dye experiments. The dispersion characteristics are described and methods are proposed for comparing models against measurements. An application is proposed for a 2 dimensions high-resolution numerical model. It shows how an extensive dataset can be used to build, calibrate and validate several aspects of the model in a highly dynamic and macrotidal area: tidal cycle timing, tidal amplitude, fixed-point current data, hodographs. This study presents results concerning the model's ability to reproduce residual Lagrangian currents, along with a comparison between simulation and high-frequency measurements of tracer dispersion. Physical and tracer data are available from the SISMER database of IFREMER (www.ifremer.fr/sismer/catal). This tool for validation of models in macro-tidal seas is intended to be an open and evolving resource, which could provide a benchmark for dispersion model validation.