Developmental Dataset Research Articles

Quantitative proteomics of Xenopus laevis embryos: expression kinetics of nearly 4000 proteins during early development

While there is a rich literature on transcription dynamics during the development of many organisms, protein data is limited. We used iTRAQ isotopic labeling and mass spectrometry to generate the largest developmental proteomic dataset for any animal. Expression dynamics of nearly 4,000 proteins of Xenopus laevis was generated from fertilized egg to neurula embryo. Expression clusters into groups. The cluster profiles accurately reflect the major events that mark changes in gene expression patterns during early Xenopus development. We observed decline in the expression of ten DNA replication factors after the midblastula transition (MBT), including a marked decline of the licensing factor XCdc6. Ectopic expression of XCdc6 leads to apoptosis; temporal changes in this protein are critical for proper development. Measurement of expression in single embryos provided no evidence for significant protein heterogeneity between embryos at the same stage of development.

Sepsis severity score: an internationally derived scoring system from the surviving sepsis campaign database*.

As the Surviving Sepsis Campaign was assessing patient-level data over multiple countries, we sought to evaluate the use of a pragmatic and parsimonious severity-of-illness scoring system for patients with sepsis in an attempt to provide appropriate comparisons with practical application. Prospective, observational evaluation. Data from 23,438 patients with suspected or confirmed sepsis from 218 hospitals in 18 countries were evaluated. This analysis was conducted on prospective data submitted to a database from January 2005 through March 2010. None. Maximum likelihood logistic regression was used to estimate model coefficients, and these were then used to develop a Sepsis Severity Score. The probability of hospital mortality was estimated using the Sepsis Severity Score as the sole variable in a logistic regression model. Univariable logistic regression determined which variables were included in the multivariable predictor model. The scale of continuous variables was assessed using fractional polynomials. Two-way interactions between variables were considered for model inclusion if the interaction p value is less than 0.05. The prediction model was developed based on randomly selecting 90% of available patients and was validated on the remaining 10%, as well as by using a bootstrapping technique. The p values for the Hosmer-Lemeshow goodnessof-fit statistic in the developmental and validation datasets were considerably greater than 0.05, suggesting good calibration. Development and validation areas under the receiver operator curve curves were 0.736 and 0.748, respectively. Observed and estimated probabilities of hospital mortality for the total population were both 0.334. The validation and the developmental datasets were gradually compared over deciles of predicted mortality and found to be very similar. The Sepsis Severity Score accurately estimated the probability of hospital mortality in severe sepsis and septic shock patients. It performed well with respect to calibration and discrimination, which remained consistent over deciles. It functioned well over international geographic regions. This robust, population-specific evaluation of international severe sepsis patients provides an effective and accurate mortality estimate allowing for appropriate quality comparisons with practical clinical and research application.

DYNAMIC BAYESIAN CLUSTERING

Clusters of time series data may change location and memberships over time; in gene expression data, this occurs as groups of genes or samples respond differently to stimuli or experimental conditions at different times. In order to uncover this underlying temporal structure, we consider dynamic clusters with time-dependent parameters which split and merge over time, enabling cluster memberships to change. These interesting time-dependent structures are useful in understanding the development of organisms or complex organs, and could not be identified using traditional clustering methods. In cell cycle data, these time-dependent structure may provide links between genes and stages of the cell cycle, whilst in developmental data sets they may highlight key developmental transitions.

Improved identification of out-of-hospital-cardiac arrest during emergency call to a medical dispatch centre

Background: Out-of-hospital cardiac arrest (OHCA) carries a very poor prognosis. Despite advancements in management, mortality remains high and a few severity of illness scoring systems have been calibrated for this pathology. Provision of novel prognostication algorithms is a priority for patient risk stratification, treatment decisions or screening for clinical studies in patients admitted in ICU after OHCA. Objective: To construct a new scoring system for the prediction of poor neurological outcome inOHCApatients admitted in ICI after initial resuscitation, on the basis of objective risk factors. Materials and methods: The CAHP score database was divided into a developmental cohort coming from the SuddenDeath ExpertiseCenter registry (SDEC, Paris, France) and intoavalidationcohort coming fromanother database (Parisian CARegistry). In the former, objective risk factors were weighted on the basis of regression analysis. The primary outcome was poor neurological outcome defined as Cerebral Performance Category [CPC]≥3. An additive score of predicted poor neurological outcome was constructed. Its calibration and discrimination characteristics were assessed in the validation dataset. Thresholds were defined to distinguish low, moderate and high risk groups. Results: The developmental dataset had 767 patients coming from 36 hospitals in Paris and her inner suburbs. The CAHP score was calculated using 6 variables readily obtainable at the scene and 1 variable collected at ICU admission. These items were age, shockable rhythm, no flow and low flow duration, place of the cardiac arrest, epinephrine dosages and arterial pH at admission. In the development cohort, the calibration byHosmer–LemeshowChi squarewas=13 (P=0.11) and the discrimination by area under ROC curve was 0.93. In the validation cohort (367 patients), calibration Chi square (8)was =9.39 (P<0.31) and theareaunder theROCcurve was 0.86. Over the 2 datasets, the scoring system identified 3 risk groups. The low risk group (CAHP score≤10) had a 16% rate of poor outcome, the medium risk group (CAHP score 10–40) had a 74% rate of poor outcome and the high-risk group had a 98% rate of poor outcome. Conclusion: CAHP score is a simple and objective system for assessing prognosis of out-of-hospital cardiac arrest at the admission of ICU. It identifies three risk groups and could be used for treatment allocation, stratification in randomized studies or for comparing cohorts in epidemiological studies.

The CAHP (Cardiac Arrest Hospital Prognosis) SCORE: Predicting neurological outcome after out-of-hospital cardiac arrest

Background: Out-of-hospital cardiac arrest (OHCA) carries a very poor prognosis. Early prognostication of patients admitted in ICU after resuscitated OHCA is a key issue but remains challenging. The aim of that study was to establish a new scoring system to predict poor neurological outcome in these patients. Materials and Methods: The CAHP (Cardiac Arrest Hospital Prognosis) score was developed from the Sudden Death Expertise Center registry (SDEC, Paris, France). Objective risk factors were weighted on the basis of a logistic regression analysis. The primary outcome was poor neurological outcome defined as Cerebral Performance Category 3, 4 or 5. Thresholds were defined to distinguish low, moderate and high-risk groups. The CAHP score was then validated in an external dataset (Parisian OHCA Registry). Score calibration and discrimination characteristics were assessed in the validation dataset. Results: The developmental dataset included 819 patients admitted in ICU from May 2011 to December 2012. After logistic regression, 7 variables were independently associated with poor neurological outcome: age, initial shockable rhythm, time form collapse to basic life support (BLS), time from BLS to return of spontaneous circulation (ROSC), location of cardiac arrest, epinephrine dose during resuscitation and arterial pH at admission. These variables were included in the CAHP score. 3 risks groups were identified: a low risk group (score ≤ 150, 39 % of unfavorable outcome), medium risk group (score 150-200, 81% of unfavorable outcome) and high-risk group (CAHP score ≥ 200, 100 % of unfavorable outcome). AUC of the CAHP score was 0.93. In the external validation dataset, discrimination value of the CAHP score was consistent with an AUC of 0.85. Conclusion: The CAHP score is a simple and objective tool for early assessment of prognosis in patients admitted to ICU after OHCA. Moreover it allows to stratify the probability of poor neurological outcome by identifying a very high-risk category of patients (score ≥ 200).

SinoSCORE: a logistically derived additive prediction model for post-coronary artery bypass grafting in-hospital mortality in a Chinese population

This study aims to construct a logistically derived additive score for predicting in-hospital mortality risk in Chinese patients undergoing coronary artery bypass surgery (CABG). Data from 9839 consecutive CABG patients in 43 Chinese centers were collected between 2007 and 2008 from the Chinese Coronary Artery Bypass Grafting Registry. This database was randomly divided into developmental and validation subsets (9:1). The data in the developmental dataset were used to develop the model using logistic regression. Calibration and discrimination characteristics were assessed using the validation dataset. Thresholds were defined for each model to distinguish different risk groups. After excluding 275 patients with incomplete information, the overall mortality rate of the remaining 9564 patients was 2.5%. The SinoSCORE model was constructed based on 11 variables: age, preoperative NYHA stage III or IV, chronic renal failure, extracardiac arteriopathy, chronic obstructive pulmonary disease, preoperative atrial fibrillation or flutter (within 2 weeks), left ventricular ejection fraction, other elective surgery, combined valve procedures, preoperative critical state, and BMI. In the developmental dataset, calibration using a Hosmer-Lemeshow (HL) test was at P = 0.44 and discrimination based on the area under the receiver operating characteristic curve (ROC) was 0.80. In the validation dataset, the HL test was at P = 0.34 and the area under the ROC (AUC) was 0.78. A logistically derived additive model for predicting in-hospital mortality among Chinese patients undergoing CABG was developed based on the most up-to-date multi-center data from China.

Recursive partitioning for new classification of patients with esophageal cancer treated by chemoradiotherapy.

e14500 Background: The 7th edition of the American Joint Committee on Cancerstaging system does not include lymph node size in the guidelines for staging patients with esophageal cancer. The objectives of this study were to determine the prognostic impact of the largest lymph node diameter (ND) on survival and to develop and validate a new staging system for patients with esophageal squamous cell cancer who were treated with definitive chemoradiotherapy (CRT). Methods: Information on 402 patients with esophageal cancer undergoing CRT at 2 institutions was reviewed. Univariate and multivariate analyses of data from 1 institution were used to assess the impact of clinical factors on survival, and recursive partitioning analysis was performed to develop the new staging classification. To assess its clinical utility, the new classification was validated using data from the second institution. Results: According to RPA, ND stages were best when classified as ND0 (the absence of lymph node metastases), ND1 (&lt; 2.8 cm), and ND2 (≥ 2.8 cm). By multivariate analysis, gender, T, N, and ND stages were independently and significantly associated with survival (p&lt;0.05). The resulting new staging classification showed the following: T1-2ND0 as Group I; T3-4ND0 or T1-2ND1 as Group II; T3-4ND1 as Group III; and TanyND2 as Group IV. The 4 new stages led to good separation of survival curves in both the developmental and validation datasets (p&lt;0.05). Conclusions: Our results showed that lymph node size is a strong independent prognostic factor and that the new staging system, which incorporated lymph node size, provided good prognostic power and discriminated effectively for patients with esophageal cancer undergoing CRT.

Reducing patient burden to the FACT-Melanoma quality-of-life questionnaire

Respondent burden has been defined as the cumulative demand placed on study participants related to the use of questionnaires or measurement instruments. The aim of this study was to reduce respondent burden associated with the Functional Assessment of Cancer Therapy-Melanoma (FACT-M), a melanoma-specific quality-of-life questionnaire, through item reduction using multiple psychometric approaches. Data for this study were pooled from three institutional review board-approved protocols. Poorly performing items were identified through distributional and correlation analyses, confirmatory factor analysis, reliability estimation, and Rasch-based approaches in a developmental dataset, and the reduced scale was assessed in a separate testing cohort. Validity, reliability, goodness of fit, and Rasch-based testing were carried out for both the full and the reduced scales. The clinical characteristics of the development (n=198) and testing (n=204) cohorts were similar. Three items identified through classical psychometric approaches and three items identified by Rasch-based analyses were excluded from the FACT-M subscale. Two additional items were identified for potential reduction but were ultimately maintained due to the adverse consequences to the psychometric integrity of the reduced instrument. The reduced FACT-M module contains 18 items. In addition to psychometric assessment, expert consultation was essential when examining areas of content redundancy and was critical when considering specific items for removal. This methodological approach reduced respondent burden by 25% while maintaining the psychometric integrity of the FACT-M.

Recursive Partitioning Analysis for New Classification of Patients With Esophageal Cancer Treated by Chemoradiotherapy

The 7th edition of the American Joint Committee on Cancer staging system does not include lymph node size in the guidelines for staging patients with esophageal cancer. The objectives of this study were to determine the prognostic impact of the maximum metastatic lymph node diameter (ND) on survival and to develop and validate a new staging system for patients with esophageal squamous cell cancer who were treated with definitive chemoradiotherapy (CRT). Information on 402 patients with esophageal cancer undergoing CRT at two institutions was reviewed. Univariate and multivariate analyses of data from one institution were used to assess the impact of clinical factors on survival, and recursive partitioning analysis was performed to develop the new staging classification. To assess its clinical utility, the new classification was validated using data from the second institution. By multivariate analysis, gender, T, N, and ND stages were independently and significantly associated with survival (p < 0.05). The resulting new staging classification was based on the T and ND. The four new stages led to good separation of survival curves in both the developmental and validation datasets (p < 0.05). Our results showed that lymph node size is a strong independent prognostic factor and that the new staging system, which incorporated lymph node size, provided good prognostic power, and discriminated effectively for patients with esophageal cancer undergoing CRT.

A gene regulatory network for root epidermis cell differentiation in Arabidopsis.

The root epidermis of Arabidopsis provides an exceptional model for studying the molecular basis of cell fate and differentiation. To obtain a systems-level view of root epidermal cell differentiation, we used a genome-wide transcriptome approach to define and organize a large set of genes into a transcriptional regulatory network. Using cell fate mutants that produce only one of the two epidermal cell types, together with fluorescence-activated cell-sorting to preferentially analyze the root epidermis transcriptome, we identified 1,582 genes differentially expressed in the root-hair or non-hair cell types, including a set of 208 “core” root epidermal genes. The organization of the core genes into a network was accomplished by using 17 distinct root epidermis mutants and 2 hormone treatments to perturb the system and assess the effects on each gene's transcript accumulation. In addition, temporal gene expression information from a developmental time series dataset and predicted gene associations derived from a Bayesian modeling approach were used to aid the positioning of genes within the network. Further, a detailed functional analysis of likely bHLH regulatory genes within the network, including MYC1, bHLH54, bHLH66, and bHLH82, showed that three distinct subfamilies of bHLH proteins participate in root epidermis development in a stage-specific manner. The integration of genetic, genomic, and computational analyses provides a new view of the composition, architecture, and logic of the root epidermal transcriptional network, and it demonstrates the utility of a comprehensive systems approach for dissecting a complex regulatory network.

Mapping FACT-Melanoma Quality-of-Life Scores to EQ-5D Health Utility Weights

ObjectivesWe sought to develop a mapping function from functional assessment of cancer therapy-melanoma (FACT-M) quality of life scores to the EuroQol-5D (EQ-5D) utility scores. MethodsFACT-M and EQ-5D scores were collected during a prospective study of melanoma-related quality of life at a tertiary cancer care center in the United States. The study sample was divided into development and validation datasets with equal distributions by cancer stage and treatment status. Censored Least Absolute Deviation (CLAD) and Ordinary Least Squares (OLS) regression analyses were performed using the developmental dataset to derive mapping functions, and model performance was examined through comparisons of residuals and measures of fit in the validation dataset. Exploratory analyses examined the predictive ability of clinical factors and individual subscales. ResultsOf 273 patients, 75 were undergoing treatment with 198 in follow-up surveillance. Relatively even distributions were observed by melanoma stage: I/II (n = 102), III (n = 100), and IV (n = 71). OLS regression resulted in a mapping function of EQ-5D = 0.0037*FACT-M+0.2238 with an R2 0.499. CLAD regression resulted in a mapping function of EQ-5D = 0.0042*FACT-M+0.1648 with pseudo R2 0.328. When applied to the validation dataset, correlations between observed and predicted values resulted in identical coefficients (r = 0.824, P < 0.001). Though the mapping functions were similar, residuals were smaller at the 20th, 40th, and 60th percentiles using the OLS model. The CLAD derived mapping function resulted in smaller residuals only for patients whose EQ-5D = 1. ConclusionsThe OLS mapping function demonstrated better predictive ability and will facilitate the derivation of utilities when direct population preference measures are not available.

High spatial resolution interpolation of monthly temperatures of Sardinia

AbstractInterpolation of monthly averages of maximum and minimum temperatures for 1 year data on a 250 m grid by multilinear regression his presented here. The principal aim is to find a suitable parameter for interpolation of minimum temperatures in cases of nocturnal inversion. For this purpose a geostatistical parameter has been calculated and tested. It is related to the height relative to the nearest valley. The second aim is to find how the regression equation depends on the sea distance, if in a linear or non linear form and, if nonlinear, what is the best exponent. The procedure has been tested on a 1 year data set from 60 meteorological stations on Sardinia Island. The selection of parameters has been made by the forward selection method. The interpolation errors (RMSE) on independent stations (i.e. not used to calculate the regression coefficient) have been calculated by the cross‐validation method using a developmental data set of size n − 1. The parameter that contains most of the variance is the height: the second one, for minimum temperatures, is the relative height. For maximum temperatures the second parameter is the sea distance, but only in summer months. The RMSE on the independent data ranges from 1.0 to 1.5 °C for minimum temperatures and from 0.5 °C (winter months) to 1.4 °C (summer months) for maximum temperatures. The effect of relative elevation in the regression is a 15% increase of the coefficient of determination. At the same time it lowers the RMSE significantly. Copyright © 2011 Royal Meteorological Society

1795 COMPARATIVE ANALYSIS OF PREDICTIVE ACCURACY OF RISK ASSESSMENT TOOLS IN AUSTRALIAN PROSTATE CANCER PATIENTS

You have accessJournal of UrologyProstate Cancer: Localized1 Apr 20111795 COMPARATIVE ANALYSIS OF PREDICTIVE ACCURACY OF RISK ASSESSMENT TOOLS IN AUSTRALIAN PROSTATE CANCER PATIENTS Samarth Chopra, David Tamblyn, Tina Kopsaftis, Carole Pinnock, Yu Changhong, and Michael Kattan Samarth ChopraSamarth Chopra Adelaide, Australia More articles by this author , David TamblynDavid Tamblyn Adelaide, Australia More articles by this author , Tina KopsaftisTina Kopsaftis Adelaide, Australia More articles by this author , Carole PinnockCarole Pinnock Adelaide, Australia More articles by this author , Yu ChanghongYu Changhong Cleveland, OH More articles by this author , and Michael KattanMichael Kattan Cleveland, OH More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.2145AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Risk stratification of newly diagnosed prostate cancer (PCa) patients is an important step in treatment decision making. We compared performance of the three commonly used risk assessment tools the 1998 Kattan Nomogram with the Stephenson Nomogram (modification of Kattan 2006) and the CAPRA score in a SA patient cohort. METHODS We analysed prospectively collected data on 635 men, who had all the necessary variables required for predicting Biochemical Recurrence (BR) post Radical Prostatectomy (RP) between 1998 and May 2009. BR was defined as PSA ° Ý 0.2ng/ml or secondary treatment for a rising PSA. For calculation of BR-free probability (BRFP), patients were censored at the time of last recorded PSA. Performance of the risk assessment tools were tested using claibration plots and Harrell's concordance or c-index. RESULTS Our cohort was different from the developmental data sets used for 1998 Kattan Nomogram, Stephenson Nomogram (modification of Kattan 2006) and the CAPRA score. The Average age was 62 years and mean follow up was 2.8 years. Freedom from biochemical recurrence for the entire cohort was 84.5% and 77.3% at 3 and 5 years, respectively. Harrell's C Index. For the 3 tools was Kattan Nomogram 0.744, Stephenson Nomogram 0.791 and CAPRA Score 0.787 respectively. At 3 years the agreement between predicted and observed BCR-free rates was close to ideal prediction for the earlier Kattan nomogram. However the Stephenson and CAPRA models tended to underestimate and overestimate the risk of BCR respectively. CONCLUSIONS This study demonstrated that all three models have reasonably comparable discrimination in our dataset, however absolute risk predictions vary. Differences in definitions of recurrence, patient selection and surgical volume may contribute to such variation. Despite the limitations described, the results of the present study are promising. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185Issue 4SApril 2011Page: e721 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.MetricsAuthor Information Samarth Chopra Adelaide, Australia More articles by this author David Tamblyn Adelaide, Australia More articles by this author Tina Kopsaftis Adelaide, Australia More articles by this author Carole Pinnock Adelaide, Australia More articles by this author Yu Changhong Cleveland, OH More articles by this author Michael Kattan Cleveland, OH More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Core Symptoms That Discriminate Premenstrual Syndrome

To identify core symptoms that discriminate premenstrual syndrome (PMS) in prospective daily diary ratings and determine the association of these symptoms with functional impairment. The study analyzed prospective daily symptom ratings and functional impairment data provided by 1081 women who requested PMS treatment at an academic medical center. The data were obtained before any treatment procedures. A random-split sample design provided separate developmental and validation datasets. Logistic regression was used to identify a reduced set of symptoms that best discriminated PMS. The results were validated in a separate dataset. Optimal cutoff points in the symptom scores were identified for clinical use. Statistical modeling identified 6 symptoms that discriminated PMS and not PMS as well as 17 symptoms in daily diary ratings. The identified core symptoms included anxiety/tension, mood swings, aches, appetite/food cravings, cramps, and decreased interest in activities. The area under the curve (AUC) was 0.84 in both models. The sums of the premenstrual symptom scores also discriminated PMS and not PMS and correctly classified 84%-86% of the cases. Six symptoms rated in daily diaries discriminate between PMS and not PMS among women seeking treatment and are significantly associated with functional impairment. The findings suggest that the burden of daily diaries to confirm PMS can be reduced to a smaller number of symptoms that distinguish the patients who meet this requirement. Results also support the concept that a clinical diagnosis of PMS can be developed around a core symptom group.

Mild renal dysfunction in patients undergoing cardiac surgery as a new risk factor for EuroSCORE

BackgroundIt has recently been suggested that mild renal dysfunction is associated with increased mortality in cardiac surgery; however, this risk factor is not accounted for in the European System for...

The Fracture and Immobilization Score (FRISC) for risk assessment of osteoporotic fracture and immobilization in postmenopausal women―A joint analysis of the Nagano, Miyama, and Taiji Cohorts

Introduction We aimed to (i) explore risk factors for major osteoporotic fracture or immobilization; (ii) develop a prediction model that can be used to assess the risk of fracture and immobilization; and (iii) assess external validity of the final model. Methods A total of 1787 postmenopausal Japanese women were followed in a hospital-based cohort study. Endpoints included the annual incidence of major osteoporotic fracture and immobilization. For each endpoint, multivariate Poisson regression models were fitted separately and risk factors were screened through backward variable selection. The predictive accuracy of the final model (FRISC) was evaluated in two independent community-based cohorts. Results Over a median follow-up of 5.3 years, a total of 383 major osteoporotic fractures (279 clinical vertebral, 44 hip, 60 distal forearm) and 83 immobilizations occurred in the developmental dataset. Backward variable selection confirmed that the following are risk factors for major osteoporotic fracture: age, weight, prior fracture, back pain, and lumbar bone mineral density (BMD). Age, prior fracture and dementia were significant risk factors for immobilization. Hosmer–Lemeshow tests did not indicate any significant deviation between the observed fracture frequency and prediction from the FRISC in the independent validation dataset. The C statistic for the FRISC was 0.727 (95% confidence interval: 0.660 to 0.794) and was higher than that for BMD alone significantly ( p = 0.03). Conclusions We developed a novel prediction model for fracture and immobilization, FRISC, and the clinical risk factors in the FRISC allows better identification of populations at high risk of fracture than BMD alone. A web application is available at http://www.biostatistics.jp/prediction/frisc.

Detecting separate time scales in genetic expression data

BackgroundBiological processes occur on a vast range of time scales, and many of them occur concurrently. As a result, system-wide measurements of gene expression have the potential to capture many of these processes simultaneously. The challenge however, is to separate these processes and time scales in the data. In many cases the number of processes and their time scales is unknown. This issue is particularly relevant to developmental biologists, who are interested in processes such as growth, segmentation and differentiation, which can all take place simultaneously, but on different time scales.ResultsWe introduce a flexible and statistically rigorous method for detecting different time scales in time-series gene expression data, by identifying expression patterns that are temporally shifted between replicate datasets. We apply our approach to a Saccharomyces cerevisiae cell-cycle dataset and an Arabidopsis thaliana root developmental dataset. In both datasets our method successfully detects processes operating on several different time scales. Furthermore we show that many of these time scales can be associated with particular biological functions.ConclusionsThe spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast. Using similar large-scale expression datasets, the identification of biological processes acting at multiple time scales in many organisms is now possible.

295 INTERNATIONAL VALIDATION OF THE CAPRA SCORE FOR PREDICTING RECURRENCE FOLLOWING RADICAL PROSTATECTOMY (RP): RESULTS FROM AN AUSTRALIAN COHORT

You have accessJournal of UrologyProstate Cancer: Localized I1 Apr 2010295 INTERNATIONAL VALIDATION OF THE CAPRA SCORE FOR PREDICTING RECURRENCE FOLLOWING RADICAL PROSTATECTOMY (RP): RESULTS FROM AN AUSTRALIAN COHORT Samarth Chopra, Carole Pinnock, David Tamblyn, Tina Kopsaftis, and Peter Sutherland Samarth ChopraSamarth Chopra More articles by this author , Carole PinnockCarole Pinnock More articles by this author , David TamblynDavid Tamblyn More articles by this author , Tina KopsaftisTina Kopsaftis More articles by this author , and Peter SutherlandPeter Sutherland More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2010.02.357AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Risk stratification of newly diagnosed prostate cancer (PCa) patients is an important step in rational treatment decision making. The cancer of the prostate risk assessment score (CAPRA) performs well in US and European settings for this purpose. We report its validation in the South Australian Prostate Cancer Outcomes Database (PCCOD), an Australian multi-institutional cohort with higher clinical risk profile than published US cohorts. METHODS We studied 1361 men diagnosed with localised PCa and treated with RP between January 1996 and May 2009. Data was complete or missing only clinical stage for 1039 men. Clinical stage was imputed [5]for 308 men using SPSS Statistics 17.0. Biochemical recurrence (BR) is defined as PSA ¡Ý 0.2ng/ml or secondary treatment for a rising PSA. For calculation of BR-free survival (BRFS), patients were censored at the time of last recorded prostate specific antigen (PSA) test and excluded if less than 90 days from RP (remaining n=920). Accuracy of prediction of BRFS by CAPRA (as a 7 group score) was analysed with proportional hazards regression and Harrell¡–s C concordance (c) index using STATA 10.0 RESULTS Our cohort differed from the CAPRA development dataset with higher PSA (66.7% vs 50% > 6 ng/ml) and Gleason sum (38.5% vs 25.8% > 6) respectively. Median follow-up was 33 months and 133 (14.5%) patients had BR. CAPRA score predicted increasing risk of recurrence with a hazard ratio for each single point increase of 1.46 (95% CI 1.34-1.59). CAPRA performed better in this cohort (c index 0.74) than the original developmental and validation datasets (c indices 0.66 and 0.68 respectively). This is consistent with results of the European validation study (c index 0.78)[3] which also reported a higher clinical risk profile than the US series CONCLUSIONS The UCSF-CAPRA score performs well in an Australian dataset with a different clinical risk profile to the developmental and validation datasets. To the best of our knowledge, this is the first external validation of CAPRA outside Europe and USA. Given the differences in PCa detection, diagnosis and treatment approaches in these three diverse regions, our results highlight the general applicability of this risk stratification tool. Adelaide, Australia© 2010 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 183Issue 4SApril 2010Page: e117 Advertisement Copyright & Permissions© 2010 by American Urological Association Education and Research, Inc.MetricsAuthor Information Samarth Chopra More articles by this author Carole Pinnock More articles by this author David Tamblyn More articles by this author Tina Kopsaftis More articles by this author Peter Sutherland More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Defining reference genes in Oryza sativausing organ, development, biotic and abiotic transcriptome datasets

BackgroundReference genes are widely used to normalise transcript abundance data determined by quantitative RT-PCR and microarrays. However, the approaches taken to define reference genes can be variable. Although Oryza sativa (rice) is a widely used model plant and important crop specie, there has been no comprehensive analysis carried out to define superior reference genes.ResultsAnalysis of 136 Affymetrix transcriptome datasets comprising of 373 genome microarrays from studies in rice that encompass tissue, developmental, abiotic, biotic and hormonal transcriptome datasets identified 151 genes whose expression was considered relatively stable under all conditions. A sub-set of 12 of these genes were validated by quantitative RT-PCR and were seen to be stable under a number of conditions. All except one gene that has been previously proposed as a stably expressed gene for rice, were observed to change significantly under some treatment.ConclusionA new set of reference genes that are stable across tissue, development, stress and hormonal treatments have been identified in rice. This provides a superior set of reference genes for future studies in rice. It confirms the approach of mining large scale datasets as a robust method to define reference genes, but cautions against using gene orthology or counterparts of reference genes in other plant species as a means of defining reference genes.

Gastrointestinal complications after cardiac surgery – improved risk stratification using a new scoring model

Gastrointestinal (GI) complications are serious consequences of cardiac surgery. The aim of this study was to develop, evaluate and validate a new risk score model for GI complications after cardiac surgery. The risk score model, named gastrointestinal complication score (GICS), was developed using prospectively collected data from 5593 patients who underwent 5636 cardiac surgical procedures between 1996 and 2001. The model was validated on 1031 cardiac surgery patients between 2005 and 2006. The scoring system's ability to predict GI complications was estimated by receiver operating characteristic (ROC)-curves and Hosmer-Lemeshow test. Fifty GI complications were identified in 47 patients (0.8%) in the developmental data set and eight (0.8%) in the validation data set. The ROC area in the developmental data set was 0.81 with a good calibration estimated by Hosmer-Lemeshow test (P=0.89). In the validation data set, the area under the curve was 0.83. The estimated probability for the patient to develop a GI complication after cardiac surgery at a GICS >or=15 is >20% and at a GICS <or=5 is <0.4%. Risk stratification according to GICS, specifically developed to predict GI complications after cardiac surgery, showed a good predictive ability.