Model Development Dataset Research Articles

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

BackgroundPREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in ‘step’ changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status.MethodsMultivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT.ResultsIn the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease.The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40.ConclusionsThe PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

Batı Akdeniz Yöresi doğal sedir meşcereleri için hacim denklemleri

Taurus cedar ( Cedrus libani A. Rich.) is one of the most important tree species in Turkey. Therefore, the information is necessary about growth and yield of the species for developing future management and planning strategies. The one of the essential building blocks in forest growth and yield prediction models is the equations for estimating individual tree volume. In this study, tree volume equations were developed for Taurus cedar stands in West Mediterranean Region. The tested models were compared using six performance criteria (Fit index, average bias, average absolute residuals, absolute maximum bias, and Akaike Information criteria) for model development and validation dataset. According to relative ranks of models, the best volume equations are Takata (1958) and Schumacher-Hall (1933) for Taurus cedar. As a result, tree volume can be estimated with high precision using Schumacher-Hall (1933)’s equation for natural cedar stands in this region.

Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study.

The prognosis of early-onset pre-eclampsia (before 34 weeks' gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women. To develop and validate prediction models for outcomes in early-onset pre-eclampsia. Prospective cohort for model development, with validation in two external data sets. Model development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies. Pregnant women with early-onset pre-eclampsia. Nine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets. The predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey. The primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications. We developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). The PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with anoptimism-adjusted c-statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with a c-statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had a c-statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications. The PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high- or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation. Current Controlled Trials ISRCTN40384046. The National Institute for Health Research Health Technology Assessment programme.

Improving Risk Adjustment for Mortality After Pediatric Cardiac Surgery: The UK PRAiS2 Model

Partial Risk Adjustment in Surgery (PRAiS), a risk model for 30-day mortality after children's heart surgery, has been used by the UK National Congenital Heart Disease Audit to report expected risk-adjusted survival since 2013. This study aimed to improve the model by incorporating additional comorbidity and diagnostic information. The model development dataset was all procedures performed between 2009 and 2014 in all UK and Ireland congenital cardiac centers. The outcome measure was death within each 30-day surgical episode. Model development followed an iterative process of clinical discussion and development and assessment of models using logistic regression under 25× 5 cross-validation. Performance was measured using Akaike information criterion, the area under the receiver-operating characteristic curve (AUC), and calibration. The final model was assessed in an external 2014 to 2015 validation dataset. The development dataset comprised 21,838 30-day surgical episodes, with 539 deaths (mortality, 2.5%). The validation dataset comprised 4,207 episodes, with97deaths (mortality, 2.3%). The updated risk model included 15 procedural, 11 diagnostic, and 4 comorbidity groupings, and nonlinear functions of age and weight. Performance under cross-validation was: median AUC of 0.83 (range, 0.82 to 0.83), median calibration slope and intercept of 0.92 (range, 0.64 to 1.25) and -0.23 (range, -1.08 to 0.85) respectively. In the validation dataset, the AUC was 0.86 (95% confidence interval [CI], 0.82 to 0.89), and the calibration slope andintercept were 1.01 (95% CI, 0.83 to 1.18) and 0.11 (95% CI,-0.45 to 0.67), respectively, showing excellent performance. A more sophisticated PRAiS2 risk model for UK use was developed with additional comorbidity and diagnostic information, alongside age and weight as nonlinear variables.

Development of a Gestational Age–Specific Case Definition for Neonatal Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is a major cause of neonatal morbidity and mortality. Preventive and therapeutic research, surveillance, and quality improvement initiatives are hindered by variations in case definitions. To develop a gestational age (GA)-specific case definition for NEC. We conducted a prospective 34-month population study using clinician-recorded findings from the UK National Neonatal Research Database between December 2011 and September 2014 across all 163 neonatal units in England. We split study data into model development and validation data sets and categorized GA into groups (group 1, less than 26 weeks' GA; group 2, 26 to less than 30 weeks' GA; group 3, 30 to less than 37 weeks' GA; group 4, 37 or more weeks' GA). We entered GA, birth weight z score, and clinical and abdominal radiography findings as candidate variables in a logistic regression model, performed model fitting 1000 times, averaged the predictions, and used estimates from the fitted model to develop an ordinal NEC score and cut points to develop a dichotomous case definition based on the highest area under the receiver operating characteristic curves [AUCs] and positive predictive values [PPVs]. Abdominal radiography performed to investigate clinical concerns. Ordinal NEC likelihood score, dichotomous case definition, and GA-specific probability plots. Of the 3866 infants, the mean (SD) birth weight was 2049.1 (1941.7) g and mean (SD) GA was 32 (5) weeks; 2032 of 3663 (55.5%) were male. The total included 2978 infants (77.0%) without NEC and 888 (23.0%) with NEC. Infants with NEC in group 1 were less likely to present with pneumatosis (31.1% vs 47.2%; P = .01), blood in stool (11.8% vs 29.6%; P < .001), or mucus in stool (2.1% vs 5.6%; P = .048) but more likely to present with gasless abdominal radiography findings (6.3% vs 0.9%; P = .009) compared with infants with NEC in group 3. In the ordinal NEC score analysis, we allocated 3 points to pneumatosis, 2 points to blood in stool, and 1 point each to abdominal tenderness and abdominal discoloration; 1 point was assigned if 1 or more of pneumoperitoneum, fixed loop, and portal venous gas were present, and 1 point was assigned if both increased and/or bilious aspirates and abdominal distension were present. The cutoff scores for the dichotomous GA-specific case definition were 2 or greater for infants in groups 1 and 2, 3 or greater for infants in group 3, and 4 or greater for infants in group 4. The ordinal NEC score and dichotomous case definition discriminated well between infants with (AUC, 87%) and without (AUC, 80%) NEC. The case definition has a sensitivity of 66.2% (95% CI, 63.0-69.4), a specificity of 94.4% (95% CI, 93.2-95.4), an AUC of 80.0% (95% CI, 79-82), and a PPV of 85.5% (95% CI, 82.6-88.1). Applying the cut points to the 431 infants who underwent a laparotomy yielded a sensitivity of 76.5% (95% CI, 70.0-82.1), a specificity of 74.4% (95% CI, 68.3-80.0), an AUC of 75.0% (95% CI, 71.0- 80.0), and a PPV of 72.9% (95% CI, 66.4-78.7). The risk of NEC and clinical presentation are associated with GA. Adoption of a consistent GA-specific case definition would strengthen global efforts to reduce the population burden of this devastating neonatal disease.

Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows

Milk fatty acid (MFA) have already been used to model methane (CH4) emissions from dairy cows. However, the data sets used to develop these models covered limited variation in dietary conditions, reducing the robustness of the predictions. In this study, a data set containing 140 observations from nine experiments (41 Holstein cows) was used to develop models predicting CH4 expressed as g/day, g/kg dry matter intake (DMI) and g/kg milk. The data set was divided into a training (n=112) and a test data set (n=28) for model development and validation, respectively. A generalized linear mixed model was fitted to the data using the marginal R2(m) and the Akaike information criterion to evaluate the models. The coefficient of determination of validation (R2(v)) for different models developed ranged between 0.18 and 0.41. Form the intake-related parameters, only inclusion of total DMI improved the prediction (R2(v)=0.58). In addition, in an attempt to further explore the relationships between MFA and CH4 emissions, the data set was split into three categories according to CH4 emissions: LOW (lowest 25% CH4 emissions); HIGH (highest 25% CH4 emissions); and MEDIUM (50% remaining observations). An ANOVA revealed that concentrations of several MFA differed for observations in HIGH compared with observations in LOW. Furthermore, the Gini coefficient was used to describe the MFA distribution for groups of MFA in each CH4 emission category. The relative distribution of the MFA, particularly of the odd- and branched-chain fatty acids and mono-unsaturated fatty acids of observations in category HIGH differed from those in the other categories. Finally, in an attempt to validate the potential of MFA to identify cases of high or low emissions, the observations were re-classified into HIGH, MEDIUM and LOW according to the proportion of each individual MFA. The proportion of observations correctly classified were recorded. This was done for each individual MFA and for the calculated Gini coefficients, finding that a maximum of 67% of observations were correctly classified as HIGH CH4 (trans-12 C18:1) and a maximum of 58% of observations correctly classified as LOW CH4 (cis-9 C17:1). Gini coefficients did not improve this classification. These results suggest that MFA are not yet reliable predictors of specific amounts of CH4 emitted by a cow, while holding a modest potential to differentiate cases of high or low emissions.

Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Viet Nam

For mitigating climate change through carbon sequestration and for reporting, Viet Nam needs to develop biomass equations at a national scale. These equations need to be accurate and provide quantifiable uncertainty. Using data from 968 trees across five ecoregions of Viet Nam, we developed a set of models to estimate tree aboveground biomass (AGB) in evergreen broadleaf forests (EBLF) at the national level. Diameter at breast height (DBH), tree height (H), wood density (WD), and combination of these three tree characteristics were used as covariates of the biomass models. Effect of ecoregion, wood density, plant family on AGB were examined. Best models were selected based on AIC, Adjusted R2, and visual interpretation of model diagnostics. Cross-validation statistics of percent bias, root mean square percentage error (RMSPE), and mean absolute percent error (MAPE) were computed by randomly splitting data 200 times into model development (80%) and validation (20%) datasets and averaging over the 200 realizations. Effects models were used, the best results were obtained by using a combined variable (DBH2HWD (kg)=(DBH (cm)/100)2×H (m)×WD (g/cm3)×1000) model AGB=a×(DBH2HWD)b. Including a categorical WD variable as a random effect reduced AIC, percent bias, RMSPE, MAPE of models AGB=a×DBHb and AGB=a×(DBH2H)b; ecoregion as a random effect reduced the AIC of models AGB=DBHb×WD, AGB=a×(DBH2H)b, and AGB=a×(DBH2HWD)b. For models that did not include WD variable, including plant family as a random effect reduced AIC, RMSE, and MAPE; recommendations are provided for models with specific parameters for main families and without WD if this variable is not available. The overall best model for estimating AGB was the equation form AGB=a×(DBH2HWD)b with ecoregion as a random effect.

A Simple Nearest-Neighbor Technique to Predict the Soil Water Retention Curve

Abstract. Knowing the soil water retention curve (WRC) is essential for analyzing soil hydraulic behavior within the vadose zone. The van Genuchten (VG) soil hydraulic equation is one of the most frequently adopted models to parameterize the WRC. Some measured water retention points are needed to fit the VG model, but direct measurement of water content versus matric potential is expensive and time consuming. A pedotransfer function (PTF) enables indirect determination of a WRC from basic soil information. The typical method employed to derive a PTF using the VG model (VG-PTF) is to establish a mathematical relationship between the parameters of the VG model and basic soil data. However, both establishing and reusing a VG-PTF for new soils are challenging due to several reasons, such as over-parameterization, low correlation between basic soil data and VG parameters, and interdependency among parameters. In this study, a nonparametric approach based on the k nearest neighbor technique was designed and tested to establish a VG-PTF. A subset of soils from the UNSODA database (n = 554)) and a data set from Belgium (n = 69) were used as the model development and validation data sets, respectively. The proposed PTF showed reasonable accuracy and reliability and was comparable to well-known parametric VG-PTFs available in the literature.

PTH-144 Risk adjustment for surgical short-term outcomes for oesophago-gastric cancer patients

Introduction Health outcomes are increasingly compared between providers with the aim to benchmark quality of care. In such benchmarks adjusting for patient characteristics is of major importance to avoid unfair comparisons between providers. We developed a case-mix adjustment model for comparing 30-, 90-day mortality and anastomotic leakage rates after oesophagus gastric (O-G) cancer resections between NHS hospitals. Method We reviewed the literature for existing prediction models for 30-, 90-day mortality and anastomotic leakage after oesophago-gastric cancer resections. Based on this review we selected predictors that were consequently used to develop a case-mix adjustment model in the national audit data of 4,882 O-G cancer patients undergoing surgery in the years 2012 and 2013. Models were developed with logistic regression analysis. Discriminative ability was quantified with the Area under the ROC curve and internal validity was assessed using a bootstrap procedure. Results The risk prediction models identified in the literature were not suitable for case-mix adjustment in the NHS audit data as they required detailed laboratory data that are not available at the national level. Moreover, the majority of existing models were methodologically weak and developed based on data pooled over long periods of time during which outcomes improved considerably in the UK. In the data used for model development, 30-day mortality was 2.3%, 90-day mortality was 4.4% and 6.2% of the patients developed an anastomotic leakage. Discriminative ability in the model development dataset was 0.70, 0.69 and 0.63 for 30-day mortality, 90-day mortality and anastomotic leakage, respectively. The internally validated AUCs were well calibrated and showed moderate discriminative ability (area under ROC curve 0.65 for 30-day mortality, 0.66 for 90-day mortality, yet only 0.59 for anastomotic leakage). Conclusion Based on nationally available data, we developed three case mix adjustment models for postoperative outcomes in the largest contemporary cohort of O-G patients undergoing curative surgery. These models should be used for the risk adjustment when assessing hospital performance in the NHS. Disclosure of interest None Declared.

비선형 회귀분석, 인공신경망, 구조방정식을 이용한 지방부 4지 신호교차로 교통사고 예측모형 성능 비교 연구

도로의 안전성을 평가하기 위한 방법으로서 교통사고 자료를 이용하는 방법, 사전-사후평가를 통한 방법 또는 전문가 의견이나 기존 문헌을 통한 방법 등 다양한 방법들이 존재한다. 특히, 교차로 교통 안전성을 평가하는 경우 많은 연구들이 교통사고예측모형 개발을 통하여 교통사고와 관련한 원인과 안전성을 평가하고 있다. 교통사고예측모형 개발에 있어서 모형의 예측력과 전용성을 확보하는 것이 중요하다. 즉, 예측력을 확보함으로써 교통사고 건수나 교통 안전성 판단의 지표를 예측하는데 오차를 줄일 수 있고, 전용성을 확보함으로써 개발된 모형이 다른 지점이나 구간에 적용하더라도 문제없이 적용될 수 있는 대표성을 가질 수 있다. 따라서 본 연구에서는 교통사고예측모형 개발에 주로 사용되는 회귀모형과 인공신경망, 구조방정식을 이용하여 교통사고예측모형을 각각 개발하였으며, 개발될 모형의 예측력과 전용성을 평균절대오차와 평균제곱예측오차를 기준으로 확인하였다. 90개소 신호교차로의 모형개발자료를 이용하여 세 가지 방법으로 교통사고예측모형을 개발 후 개발데이터를 통해 예측력을 비교한 결과 인공신경망이 가장 높은 예측력을 보였다. 또한 모형의 전용성 검증을 위하여 별도로 수집한 33개소 신호교차로의 모형검증자료를 이용하여 개발된 모형을 검증한 결과 비선형 회귀모형이 가장 적합한 것으로 나타났다. 모형개발 과정에서 가장 높은 예측력을 보인 인공신경망의 경우 다른 대상지에서 수집된 모형검증 자료를 적용하였을 때 예측력에 큰 변화를 보여 전용성이 떨어진 것으로 분석되었다. For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.

Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows

Lameness is a major problem in modern dairy husbandry and has welfare implications and other negative consequences. The behavior of dairy cows is influenced by lameness. Automated lameness detection can, among other methods, be based on day-to-day variation in animal behavior. Activity sensors that measure lying time, number of lying bouts, and other parameters were used to record behavior per cow per day. The objective of this research was to develop and validate a lameness detection model based on daily activity data. Besides the activity data, milking data and data from the computerized concentrate feeders were available as input data. Locomotion scores were available as reference data. Data from up to 100 cows collected at an experimental farm during 23mo in 2010 and 2011 were available for model development. Behavior is cow-dependent, and therefore quadratic trend models were fitted with a dynamic linear model on-line per cow for 7 activity variables and 2 other variables (milk yield per day and concentrate leftovers per day). It is assumed that lameness develops gradually; therefore, a lameness alert was given when the linear trend in 2 or more of the 9 models differed significantly from zero in a direction that corresponded with lameness symptoms. The developed model was validated during the first 4mo of 2012 with almost 100 cows on the same farm by generating lameness alerts each week. Performance on the model validation data set was comparable with performance on the model development data set. The overall sensitivity (percentage of detected lameness cases) was 85.5% combined with specificity (percentage of nonlame cow-days that were not alerted) of 88.8%. All variables contributed to this performance. These results indicate that automated lameness detection based on day-to-day variation in behavior is a useful tool for dairy management.

Abstract P4-12-03: Towards a risk prediction model for breast cancer that utilizes breast tissue risk features

Abstract Background: Optimal early detection and prevention strategies for breast cancer are predicated on our ability to accurately identify women at increased risk for disease. Current breast cancer risk prediction models rely on clinical and epidemiologic features. As demonstrated with other cancers, such as those of the cervix and colon, cancer risk assessment is enhanced when the tissue at risk is examined for evidence of premalignant abnormalities. Here, we present a multivariate model for individualized breast cancer risk prediction that incorporates tissue features identified from biopsies from women with benign breast disease (BBD). Methods: We used our cohort of more than 9000 women diagnosed with BBD at the Mayo Clinic from 1967–1991, and identified a nested case:control series of 377 women who developed cancer (cases) matched with 734 women who did not (controls). We identified a set of variables that either alone or in two-way interactions contributed to a multivariable risk prediction model. We combined this relative risk model with the baseline incidence of breast cancer and the baseline hazard of death from our full BBD cohort to develop a risk prediction tool that computes absolute risk estimates for breast cancer. We calculated 5-year risk predictions for these cases and controls using our model and using the Gail model and compared c-statistics from these risk predictions. We validated our model using an independent set of 378 breast cancer cases and 728 matched controls drawn from our BBD cohort. Results: Cases and controls in the model development data set differed significantly on a number of features when examined individually. Those included in a multivariable relative risk model for breast cancer among women with BBD are shown below. After including interactions between lobular involution, and histologic impression, with age at first live birth/number of children, and degree of involution with sclerosing adenosis, we devised a model that predicts the absolute risk of breast cancer. Predicted five-year risks in the model development series, we observed a c-statistic of 0.66, higher than the c-statistic of 0.61 for the Gail model. With risk prediction estimates from our validation series of cases and matched controls, the c-statistic of our model decreased to 0.59 and the c-statistic for the Gail model was 0.55. Conclusion: We have identified an initial risk prediction model for breast cancer in women with BBD. The identified model includes basic demographic information as well as a number of histologic features of the tissue samples. Its initial performance is promising, providing improved discrimination over the Gail model in our replication data set. Further work is needed to identify features which might provide additional predictive information for breast cancer risk. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-12-03.

A new method for real‐time monitoring of soil CO2 efflux

Summary1. A better understanding of temporal and spatial variability of soil CO2 fluxes is essential to improve model predictions of soil effluxes. To accomplish that goal, high‐frequency and long‐term data sets for model development and validation are needed. However, the cost and high maintenance associated with the current technology make high‐frequency measurements for small or large spatially distributed grids difficult to achieve. Here, we describe a new observational infrastructure for monitoring soil CO2 efflux, which is attractive because of its low cost and low power consumption compared to traditional methods.2. Three observational stations equipped with forced diffusion (FD) chambers were deployed in the summer of 2010 across a 1000‐km transect in Atlantic Canada. At half‐hourly resolution, each observational station recorded soil carbon dioxide (CO2) efflux from two flux chambers and from a suite of meteorological sensors and peripherals. Each station was equipped with telemetry, and data were continuously downloaded for c. 1 year.3. The average power consumption for each station was roughly a third of a LI‐COR LI‐8100 system. The FD chambers were approximately four times more affordable than conventional equipment and were also reliable with &lt;1% of the data lost because of power failure.4. High‐frequency observations from the three sites showed that the systems were extremely dynamic, with CO2 efflux dependency to temperature and moisture on many time‐scales. For instance, the data showed pronounced increases in soil CO2 efflux after major rain events. The results from the FD chambers also highlighted the role of other biological and physical factors on soil CO2 efflux.5. Overall, this new method was very successful in key areas including survivability, management intensity and cost. The high‐frequency data hold many interesting features that are not captured in synoptic data sets and which will be useful for tuning our understanding of soil carbon dynamics.

Photochemical mineralization of dissolved organic nitrogen to ammonia in prairie lakes

We examined the rate of photoammonifi- cation in 16 lakes from Saskatchewan, Canada. Lakes were selected to encompass a broad range in dissolved organic nitrogen (DON) (269-1,435 l gl -1 ). Lake filtrate (\0.2 lm) was exposed to artificial solar radiation for 4 h. Rates of photoammonification were significant in 7 of the 16 study lakes. Ammonia (NH3) concentrations increased 0.84-2.85 l gl -1 over con- trol values. This is a 4-92% increase in NH3 concen- tration and a conversion of 0.18-0.3% of the DON pool to NH3. We developed an empirical model to predict photoammonification rates across aquatic ecosystems. Photoammonification rates and ancillary parameters (i.e., pH, DOC and DON concentrations, DOC:DON ratios, and a350:DOC) were obtained from published studies to expand our dataset for model development. Model selection was conducted with Akaike's Infor- mation Criterion (AIC). DON concentration and pH were selected as model predictors by AIC. Our model explains 49% of the variance in photoammonification rate across a diverse set of aquatic systems. This model may be useful in estimating photoammonification rates in other aquatic systems.

Hyperspectral Aquatic Radiative Transfer Modeling Using a High-Performance Cluster Computing-Based Approach

For aquatic studies, radiative transfer (RT) modeling can be used to compute hyperspectral above-surface remote sensing reflectance that can be utilized for inverse model development. Inverse models can provide bathymetry and inherent-and bottom-optical property estimation. Because measured oceanic field/organic datasets are often spatio-temporally sparse, synthetic data generation is useful in yielding sufficiently large datasets for inversion model development; however, these forward-modeled data are computationally expensive and time-consuming to generate. This study establishes the magnitude of wall-clock-time savings achieved for performing large, aquatic RT batch-runs using parallel computing versus a sequential approach. Given 2,600 simulations and identical compute-node characteristics, sequential architecture required ~100 hours until termination, whereas a parallel approach required only ~2.5 hours (42 compute nodes)—a 40x speed-up. Tools developed for this parallel execution are discussed.

A Score for the Prediction of Cardiovascular Events in the Hypertensive Aged

With few exceptions, tools used to estimate cardiovascular disease (CVD) risk in those without prior events are based mainly on data from middle-aged subjects. Given the ever increasing number of older people, many with hypertension, a risk score relevant to this group is warranted. Our aim was to develop a cardiovascular risk equation suitable for risk prediction in elderly, hypertensive populations. We utilized cardiovascular end point data from 4.1 years median follow-up in 5,426 hypertensive subjects without previous CVD from the Second Australian National Blood Pressure Study (ANBP2). Our risk model, based on Cox regression, was developed using 75% of subjects without evident CVD (n = 4,072), randomly selected and stratified by age and gender, and internally validated using the remaining 25%. The model was also externally validated against the Dubbo Study dataset. The final model included sex, age, physical activity in the 2 weeks prior to entry into study, family history, use of anticoagulants, centrally acting antihypertensive agents or diabetes medication, and an interaction term for sex and diabetes medication. The C-statistic was 0.65 (0.62-0.67) for our predictive model on the model development dataset and 0.62 (0.57-0.67) on the internal validation dataset. The Dubbo Data C-statistic for CVD was 0.68 (95% CI 0.65-0.71). All models performed similarly. Because of greater ease of implementation, we recommend that existing algorithms be extended into older age groups.

Predicting Mortality in Incident Dialysis Patients: An Analysis of the United Kingdom Renal Registry

The risk of death in dialysis patients is high, but varies significantly among patients. No prediction tool is used widely in current clinical practice. We aimed to predict long-term mortality in incident dialysis patients using easily obtainable variables. Prospective nationwide multicenter cohort study in the United Kingdom (UK Renal Registry); models were developed using Cox proportional hazards. Patients initiating hemodialysis or peritoneal dialysis therapy in 2002-2004 who survived at least 3 months on dialysis treatment were followed up for 3 years. Analyses were restricted to participants for whom information for comorbid conditions and laboratory measurements were available (n = 5,447). The data set was divided into data sets for model development (n = 3,631; training) and validation (n = 1,816) using random selection. Basic patient characteristics, comorbid conditions, and laboratory variables. All-cause mortality censored for kidney transplant, recovery of kidney function, and loss to follow-up. In the training data set, 1,078 patients (29.7%) died within the observation period. The final model for the training data set included patient characteristics (age, race, primary kidney disease, and treatment modality), comorbid conditions (diabetes, history of cardiovascular disease, and smoking), and laboratory variables (hemoglobin, serum albumin, creatinine, and calcium levels); reached a C statistic of 0.75 (95% CI, 0.73-0.77); and could discriminate accurately among patients with low (6%), intermediate (19%), high (33%), and very high (59%) mortality risk. The model was applied further to the validation data set and achieved a C statistic of 0.73 (95% CI, 0.71-0.76). Number of missing comorbidity data and lack of an external validation data set. Basic patient characteristics, comorbid conditions, and laboratory variables can predict 3-year mortality in incident dialysis patients with sufficient accuracy. Identification of subgroups of patients according to mortality risk can guide future research and subsequently target treatment decisions in individual patients.

Models to Predict Organic Contaminant Removal by RO and NF Membranes

AbstractThis article summarizes work recently completed and funded by the WateReuse Research Foundation—Predictive Models to Aid in the Design of Membrane Systems for Organic Micropollutant Removal. The project's goal was to develop a model to predict organic contaminant removal during water reuse applications employing high-pressure membranes. Various modeling approaches were evaluated, including a phenomenological model, hydrodynamic model, Donnan steric-pore model, solution-diffusion model, surface force pore model, and quantitative structure property relationship models. In bench-scale experiments, approximately 110 organic compounds and two membranes (one reverse osmosis membrane and one nanofiltration membrane) were used to develop a robust dataset for model development and validation. Modeling approaches developed using bench-scale experimental data were incorporated into mass balance models to describe rejection performance for pilot- and full-scale membrane systems. Developed modeling approaches ...

Analysis of meteorology and emission in haze episode prevalence over mountain-bounded region for early warning

This study investigated the main causes of haze episodes in the northwestern Thailand to provide early warning and prediction. In an absence of emission input data required for chemical transport modeling to predict the haze, the climatological approach in combination with statistical analysis was used. An automatic meteorological classification scheme was developed using regional meteorological station data of 8 years (2001–2008) which classified the prevailing synoptic patterns over Northern Thailand into 4 patterns. Pattern 2, occurring with high frequency in March, was found to associate with the highest levels of 24 h PM 10 in Chiangmai, the largest city in Northern Thailand. Typical features of this pattern were the dominance of thermal lows over India, Western China and Northern Thailand with hot, dry and stagnant air in Northern Thailand. March 2007, the month with the most severe haze episode in Chiangmai, was found to have a high frequency of occurrence of pattern 2 coupled with the highest emission intensities from biomass open burning. Backward trajectories showed that, on haze episode days, air masses passed over the region of dense biomass fire hotspots before arriving at Chiangmai. A stepwise regression model was developed to predict 24 h PM 10 for days of meteorology pattern 2 using February–April data of 2007–2009 and tested with 2004–2010 data. The model performed satisfactorily for the model development dataset (R 2 = 87%) and test dataset (R 2 = 81%), which appeared to be superior over a simple persistence regression of 24 h PM 10 (R 2 = 76%). Our developed model had an accuracy over 90% for the categorical forecast of PM 10 > 120 μg/m 3. The episode warning procedure would identify synoptic pattern 2 and predict 24 h PM 10 in Chiangmai 24 h in advance. This approach would be applicable for air pollution episode management in other areas with complex terrain where similar conditions exist.

Predicting Survival Outcome of Localized Melanoma: An Electronic Prediction Tool Based on the AJCC Melanoma Database

We sought to develop a reliable and reproducible statistical model to predict the survival outcome of patients with localized melanoma. A total of 25,734 patients with localized melanoma from the 2008 American Joint Committee on Cancer (AJCC) Melanoma Database were used for the model development and validation. The predictive model was developed from the model development data set (n = 14,760) contributed by nine major institutions and study groups and was validated on an independent model validation data set (n = 10,974) consisting of patients from a separate melanoma center. Multivariate analyses based on the Cox model were performed for the model development, and the concordance correlation coefficients were calculated to assess the adequacy of the predictive model. Patient characteristics in both data sets were virtually identical, and tumor thickness was the single most important prognostic factor. Other key prognostic factors identified by stratified analyses included ulceration, lesion site, and patient age. Direct comparisons of the predicted 5- and 10-year survival rates calculated from the predictive model and the observed Kaplan-Meier 5- and 10-year survival rates estimated from the validation data set yielded high concordance correlation coefficients of 0.90 and 0.93, respectively. A Web-based electronic prediction tool was also developed ( http://www.melanomaprognosis.org/ ). This is the first predictive model for localized melanoma that was developed based on a very large data set and was successfully validated on an independent data set. The high concordance correlation coefficients demonstrated the accuracy of the predicted model. This predictive model provides a clinically useful tool for making treatment decisions, for assessing patient risk, and for planning and analyzing clinical trials.