Validation R2 Research Articles

Determining α-tocopherol in refined bleached and deodorized palm olein by Fourier transform infrared spectroscopy

Fourier transform infrared (FTIR) spectroscopy was developed for the determination of α-tocopherol in refined bleached and deodorized (RBD) palm olein. The calibration and validation samples were prepared by spiking known amount of α-tocopherol to produce a wide range of α-tocopherol up to 2000 ppm. The method was based on the sodium chloride (NaCl) windows, and the transmission path was fixed at 50 μm at room temperature. The partial least squares (PLS) calibration models for predicting α-tocopherol were developed by using the FTIR spectral region at 3100–2750 cm−1. The accuracy of the method was comparable to that of the high-performance liquid chromatography (HPLC), with coefficients of determination (R2) from calibration samples of 0.9922. The models were validated and the R2 of validation and the standard errors of prediction (SEP) computed. The standard deviation of difference for repeatability (SDDr) for the method was comparable to that for HPLC. With its speed and ease of data manipulation by the computer software, FTIR spectroscopy is advantageous as a simple and rapid quantitative determining analytical tool for α-tocopherol in RBD palm olein.

QSAR modeling based on the bias/variance compromise: a harmonious

Modeling quantitative structure-activity relationships (QSAR) is considered with an emphasis on prediction. An abundance of methods are available to develop such models. Using a harmonious approach that balances the bias and variance of predictions, the best calibration models are identified relative to the bias and variance criteria used. Criteria utilized to determine the adequacy of models are the root mean square error of calibration (RMSEC) and validation (RMSEV), respective R2 values, and the norm of the regression vector. QSAR data from the literature are used to demonstrate concepts. For these data sets and criteria used, it is suggested that models obtained by ridge regression (RR) are more harmonious and parsimonious than models obtained by partial least squares (PLS) and principal component regression (PCR) when the data is mean-centered. The most harmonious RR models have the best bias/variance tradeoff, reflected by the smallest RMSEC, RMSEV, and regression vector norms and the largest calibration and validation R2 values. The most parsimonious RR models have the smallest effective rank.

PREDICTING RUNOFF FROM A YARD WASTE WINDROW COMPOSTING PAD

Composting is utilized increasingly as an aerobic treatment alternative to landfilling for bioconverting organicwastes such as municipal biosolids and yard wastes into soil amendments or landscaping materials. However, little informationis available to estimate composting operation runoff to corresponding design rainfall events. The objective of this workwas to evaluate dynamic versions of the simple . index and NRCS curve number runoff models currently used in agriculturalor urban stormwater management for predicting runoff from windrow composting operations. A 1.4 ha (approximately 48 300 m) windrow pad was constructed by placing 25 mm crusher-run aggregate on a tightly compacted Cecil sandy clay loam.The pad was sloped (<1% on the 300 m axis and 0% on the 48 m axis) and bermed to direct all runoff to a 0.2 ha 2 m deepcatchment pond with less than 3.175 mm/day seepage constructed with a regular trapezoidal shape, thus enabling astraightforward relationship between pond depth and volume. Precipitation, pond stage, and compost amount were monitoreddaily in accordance with environmental permit requirements. A dynamic NRCS curve number daily runoff model, in whichthe precipitation was adjusted with pad fraction covered and 7-day antecedent moisture, resulted in a validation R2 of 0.56and had a modeled vs. observed slope of 0.93 when applied to the validation data set. Applied to monthly data, the dynamiccurve number approach, which adjusted precipitation with average fraction covered with compost for the month, gave avalidation R2 of 0.75 and a validation slope of 0.76. Based on daily and monthly results, the dynamic NRCS-CN models withprecipitation adjustments yielded the best validation results. The more conventional dynamic NRCS-CN modeling approachwhere the curve number was modified performed nearly as well with daily and monthly runoff prediction. Theprecipitation-modified NRCS-CN equation tended to overpredict daily runoff at low precipitation rates, whereas theCN-modified version of the daily NRCS-CN equation was more consistent across the observed precipitation range. Bothdynamic NRCS-CN equations tended to overpredict monthly runoff.

3D-QSAR Studies on natural acetylcholinesterase inhibitors of Sarcococca saligna by comparative molecular field analysis (CoMFA)

We have derived a comprehensive structure–activity relationship (SAR) picture for a new series of natural acetylcholinesterase inhibitors isolated from Sarcococca saligna. A set of 32 previously isolated and tested pregnane-type steroidal alkaloids inhibitors were investigated with respect to their IC50 values (pIC50) against the AChE enzyme in order to derive CoMFA models using atom-based alignment. A highly significant CoMFA model was obtained with r2 value of 0.974. The q2 (cross validation r2) value also confirms the statistical significance of our model.

Rapid Characterization of Organic Resource Quality for Soil and Livestock Management in Tropical Agroecosystems Using Near‐Infrared Spectroscopy

Organic resources constitute a major source of nutrient inputs to both soils and livestock in smallholder tropical production systems. Determination of resource quality attributes using current laboratory methods is both timely and costly. This study tested visible and near‐infrared (wavelengths from 0.35–2.50 μm) reflectance spectroscopy (NIRS) for rapid prediction of quality attributes for a diverse range of organic resources. A spectral library was constructed for 319 samples of oven‐dried, ground plant material originating from green leaf (186 samples), litter (33), root (25), and stem (21) samples from 83 species including tropical crops and trees used for agroforestry and manure samples (39). Organic resource attributes were calibrated to first‐derivative reflectance using regression trees with stochastic gradient boosting, and screening tests were developed for separating various organic resource quality classes using classification trees. Validation r2 values for actual vs. predicted values using a 25% holdout sample were 0.91 for N, 0.90 for total soluble polyphenol, and 0.64 for lignin concentration. Screening tests gave validation prediction efficiencies of 96% for detecting samples with high N concentration, 91% for low total soluble polyphenol, and 86% for low lignin concentration. The spectral screening tests were robust even at small (n = 48) calibrations sample sizes. Screening tests for detecting samples with low or high levels of P, K, Ca, and Mg gave prediction efficiencies of 74 to 92%. Near‐infrared reflectance spectroscopy can be used to rapidly screen organic resource quality. Global spectral calibration libraries should be established for a range of resource quality attributes.

Determination of hexane residues in vegetable oils with FTIR spectroscopy

AbstractThe FTIR spectroscopy method was developed for the determination of hexane residues in palm and groundnut (peanut) oils. The method was based on horizontal attenuated total reflectance with a ZnSe crystal at 45° at room temperature, and partial least squares (PLS) statistics were used to derive calibration models. The accuracy of the method was comparable to that of the AOCS Method Ca 3b‐87, with coefficients of determination (R2) of 0.9866 and 0.9810 for palm and groundnut oils, respectively, and SE of calibration of 3.83 and 4.91, respectively. The calibration models were validated, and the R2 of validation and the SE of prediction computed. The SD of the difference for repeatability for the method was comparable to that for the standard AOCS method when used for palm and groundnut oils. With its speed and ease of data manipulation by computer software, FTIR spectroscopy has an advantage over present chemical methods, which require preparation of the oil using toxic solvents before GC.

Long-Range Forecasting of the Nile River Flows Using Climatic Forcing

Correlation analysis is used to determine the linear relationship between the Nile River flows and leading climatic indicators, such as SST and precipitation, in an effort to establish a basis for quantitative long-term streamflow prediction. The analysis of the lead–lag correlations between the Blue Nile River flows during the “flood season” [July–August–September–October (JASO)] and SSTs led to the identification of a number of regions in the oceans that are significantly correlated and suggests that the SSTs may be useful for predicting the Blue Nile flows. The significant correlation regions between SST in the Pacific and Blue Nile JASO flows evolve through time in a manner that is consistent with the ENSO development; that is, the evolution of the ENSO signal in the Pacific Ocean is reflected in the evolution of the referred cross-correlation field. In addition, the Blue Nile River JASO flow is significantly correlated with the previous year August–November Guinea precipitation, which suggests that the Guinea precipitation is another potential predictor of the Blue Nile River flows with 11 months of lead time. Furthermore, models based on multiple linear regression (MLR) and principal component analysis (PCA) are used to forecast the Blue Nile flows based on SST in the three oceans and the previous year of Guinea precipitation. The models based on PCA showed significant improvement in forecast accuracy over MLR models that were developed in terms of the original variables. The predictability is shown to be the highest for forecasts made in the preceding season and decreases as the lead time increases. The coefficients of multiple determination R2 for validation based on PCA models vary in the range 84%–59% for forecast lead times of 4–16 months. Further analysis using only SST predictors for the period 1913–89 indicates that the predictability of the Blue Nile River JASO flows is more affected by the variability of SSTs in the Pacific Ocean than by those of the other oceans. The conclusion is that long-range forecasting of the Blue Nile River flows with lead times over 1 yr is possible with a high degree of explained variance by using SST in a few regions in the Pacific Ocean and the previous year of Guinea precipitation.

Application of FTIR spectroscopy in determining sesamol in sesame seed oil

AbstractA new analytical method was developed for determining sesamol in sesame seed oil by FTIR spectroscopy. Sesamol was also spiked at 0 to 1000 mg/kg in freshly refined, bleached, and deodorized palm olein (RBDPOo) and groundnut (peanut) oil. FTIR spectra were recorded using a transmission (NaCl) cell accessory at room temperature, and the partial least squares regression statistical method was used to derive calibration models for each oil. The standard errors of calibration were 6.07, 5.88, and 4.24 mg/100 g for sesame, RBDPOo, and groundnut oils, with coefficients of determination (R2) of 0.9947, 0.9940, and 0.9662, respectively. The calibration models were validated by the “leave‐one‐out” cross‐validation method, and the R2 of validation, the standard errors of prediction, and SD of the differences for repeatability and accuracy were computed. Our results support the premise that FTIR spectroscopy is an efficient and accurate method for determining minor components such as sesamol in edible oils.

Modeling administrative outcomes in fever and neutropenia: clinical variables significantly influence length of stay and hospital charges.

Administrative outcomes such as length of stay and charges are used to compare the quality of care across institutions and among individual providers. Clinical variables representing disease severity may explain some of the variability in these outcomes. To determine the extent to which readily available clinical data can explain the variability in length of stay and charges for children with cancer admitted to the hospital for fever and neutropenia, and to assess the appropriateness of using a time-efficient electronic case-finding strategy for the development of administrative outcome models. A retrospective cohort of 157 fever and neutropenia encounters in a single institution during 11 months in 1997 was identified using a largely automated case-finding strategy followed by independent, blinded review of the selected discharge summaries. Models of admission variables predicting log length of stay and log charges were developed using multiple linear regression. The "smearing" technique of Duan adjusted for logarithmic retransformation was used in calculating each subject's predicted length of stay and charges. R2 values were calculated. There were two secondary analyses. In one, the result of admission blood culture was entered as a potential covariate. In the second, to evaluate the appropriateness of basing models on automated case-finding without discharge summary review, the authors rederived the models using all of the encounters (n = 160) identified by the algorithm, which had included three false-positive cases. Mean length of stay was 6.45 days. Mean charges were $11,967. Absolute monocyte count at admission was a significant, independent negative predictor of length of stay and charges. Underlying cancer diagnosis also was significant. Charges were highest for acute myeloid leukemia, followed by central nervous system tumors, other solid tumors, and acute lymphoblastic leukemia and lymphomas. Length of stay was highest for acute myeloid leukemia, followed by central nervous system tumors, acute lymphoblastic leukemia and lymphomas, and other solid tumors. Absolute monocyte count and tumor type were the major components of the model, but admission temperature (for both administrative outcomes) and the presence of localized infection (for length of stay) also were significant predictors. R2 values were 35.3% (charges) and 38.5% (length of stay), with validation R2 values of 26.6% and 29.2%, respectively. Entering bacteremia as a covariate improved the models. Inclusion of the three false-positive cases generated models with only a modest loss of accuracy; it introduced over-and underreporting of some of the less significant predictors but did not disrupt the ability to identify the major predictors, absolute monocyte count and tumor type. The clinical variables that were significant in this study account, in validation R2 estimates, for more than 25% of the variability in administrative outcomes for encounters of fever and neutropenia. Adjusting length of stay and charges for these clinical variables would allow for a fairer comparison of institutions and individual providers. The electronic case-finding algorithm served as an efficient way to identify absolute monocyte count and tumor type as the major predictors and provided a conservative estimate of R2.

Development of Reflectance Spectral Libraries for Characterization of Soil Properties

Methods for rapid estimation of soil properties are needed for quantitative assessments of land management problems. We developed a scheme for development and use of soil spectral libraries for rapid nondestructive estimation of soil properties based on analysis of diffuse reflectance spectroscopy. A diverse library of over 1000 archived topsoils from eastern and southern Africa was used to test the approach. Air‐dried soils were scanned using a portable spectrometer (0.35–2.5 μm) with an artificial light source. Soil properties were calibrated to soil reflectance using multivariate adaptive regression splines (MARS), and screening tests were developed for various soil fertility constraints using classification trees. A random sample of one‐third of the soils was withheld for validation purposes. Validationr2values for regressions were: exchangeable Ca, 0.88; effective cation‐exchange capacity (ECEC), 0.88; exchangeable Mg, 0.81; organic C concentration, 0.80; clay content, 0.80; sand content, 0.76; and soil pH, 0.70. Validation likelihood ratios for diagnostic screening tests were: ECEC &lt;4.0 cmolckg−1, 10.8; pH &lt;5.5, 5.6; potential N mineralization &gt;4.1 mg kg−1d−1, 2.9; extractable P &lt;7 mg kg−1, 2.9; exchangeable K &lt;0.2 cmolckg−1, 2.6. We show the response of prediction accuracy to sample size and demonstrate how the predictive value of spectral libraries can be iteratively increased through detection of spectral outliers among new samples. The spectral library approach opens up new possibilities for modeling, assessment and management of risk in soil evaluations in agricultural, environmental, and engineering applications. Further research should test the use of soil reflectance in pedotransfer functions for prediction of soil functional attributes.

Development of Reflectance Spectral Libraries for Characterization of Soil Properties

Methods for rapid estimation of soil properties are needed for quantitative assessments of land management problems. We developed a scheme for development and use of soil spectral libraries for rapid nondestructive estimation of soil properties based on analysis of diffuse reflectance spectroscopy. A diverse library of over 1000 archived topsoils from eastern and southern Africa was used to test the approach. Air-dried soils were scanned using a portable spectrometer (0.35–2.5 μm) with an artificial light source. Soil properties were calibrated to soil reflectance using multivariate adaptive regression splines (MARS), and screening tests were developed for various soil fertility constraints using classification trees. A random sample of one-third of the soils was withheld for validation purposes. Validation r2 values for regressions were: exchangeable Ca, 0.88; effective cation-exchange capacity (ECEC), 0.88; exchangeable Mg, 0.81; organic C concentration, 0.80; clay content, 0.80; sand content, 0.76; and soil pH, 0.70. Validation likelihood ratios for diagnostic screening tests were: ECEC <4.0 cmolc kg−1, 10.8; pH <5.5, 5.6; potential N mineralization >4.1 mg kg−1 d−1, 2.9; extractable P <7 mg kg−1, 2.9; exchangeable K <0.2 cmolc kg−1, 2.6. We show the response of prediction accuracy to sample size and demonstrate how the predictive value of spectral libraries can be iteratively increased through detection of spectral outliers among new samples. The spectral library approach opens up new possibilities for modeling, assessment and management of risk in soil evaluations in agricultural, environmental, and engineering applications. Further research should test the use of soil reflectance in pedotransfer functions for prediction of soil functional attributes.

Development and estimation of a pediatric chronic disease score using automated pharmacy data.

Although risk assessment models for specific adult populations such as the elderly have been developed, little work has focused on developing pediatric-specific models. The lack of pediatric models may result in incorrect estimates of relative disease severity among children, in reduced reimbursement for health plans and providers, and in inadequate health care for chronically ill children. To develop and to evaluate a pediatric risk assessment model using automated pharmacy data. Retrospective, case-cohort study using automated data. All children continuously enrolled in Group Health Cooperative of Puget Sound during 1992 and 1993. The Pediatric Chronic Disease Score (PCDS), an algorithm that classified children into chronic disease categories by prescription drug fills, was compared with the ICD-9-CM-based Ambulatory Care Groups (ACG) model and a demographic model for prediction of total, ambulatory, or primary care costs and primary care visits. Forecast models were estimated using linear regression and they were evaluated with R2, mean prediction error, mean squared prediction error, and Mincer-Zarnowitz tests. The pharmacy-based PCDS performed significantly better on each of the four forecasting accuracy tests than did a demographic model (eg, R2s averaging fourfold higher). Compared with the ACG model, the PCDS model performed similarly on mean squared prediction error tests; however, the ACG generally had higher validation R2 values. A pharmacy-based pediatric risk assessment model performs better than a demographic model and represents a viable alternative to ICD-9-CM-based models. Further research is necessary to determine if children must be considered separately from adults when conducting population-based risk assessments.

Nondestructive Assessment of Sinapic Acid Esters in Brassica Species: I. Analysis by Near Infrared Reflectance Spectroscopy

Sinapic acid esters (SAE) are important antinutritional compounds of the seed of rapeseed (Brassica napus L.) and related Brassica species. The objective of this work was to study the potential of near infrared reflectance spectroscopy (NIRS) to analyze SAE content intact‐seed samples of the genus Brassica. A total of 400 samples belonging to 17 species of Brassica were used to develop an initial calibration equation for this trait by using modified partial least squares regression on second derivative‐transformed and scatter‐corrected spectra. The r2 in cross validation was 0.91, and the standard error of cross validation was 1.1 g kg−1 seed (mean = 10.0). The calibration set was expanded with 187 further samples, selected for low SAE content with the initial equation, and the accuracy of the method was tested through both external and independent validation. In the first case, the analysis of samples not included but belonging to species represented in the calibration set led to an r2 of 0.92 and standard error of performance (SEP) of 1.1 g kg−1 (mean = 9.2). In the second case, the analysis of 57 samples of B. juncea (L.) Czern., not represented with any sample in the calibration, resulted in a SEP of 1.1 g kg−1 (mean = 7.3). The results demonstrated the robustness of the calibration equations to estimate SAE content in a wide range of species of the genus Brassica, even of those not used for calibration. NIRS discrimination of this trait was associated with eight spectral regions, all related to chemical absorbers in the sinapic acid molecule.

Determination of free fatty acids in palm oil by near‐infrared reflectance spectroscopy

AbstractA near‐infrared (NIR) spectroscopy calibration was developed for the determination of free fatty acids (FFA) in crude palm oil and its fractions based on the NIR reflectance approach. A range of FFA concentrations was prepared by hydrolyzing oil with 0.15% (w/w) lipase in an incubator at 60°C (200 rpm). Sample preparation was performed in Dutch cup, and the spectra were measured in duplicate for each sample. The optimized calibration models were constructed with multiple linear regression analysis based on C=O overtone regions from 1850–2050 nm. The best wavelength combinations were 1882, 2010, and 2040 nm. Multiple correlation coefficients squared (R2) were: 0.994 for crude palm oil, 0.961 for refined‐bleached‐deodorized (RBD) palm olein, and 0.971 for RBD palm oil. Calibrations were validated with an independent set of 8–10 samples. R2 of validation were 0.997, 0.943, and 0.945, respectively. The developed method was rapid, with a total analysis time of 5 min, and environmentally friendly, and its accuracy was generally good for raw‐material quality control.

Determination of seed oil content and fatty acid composition in sunflower through the analysis of intact seeds, husked seeds, meal and oil by near‐infrared reflectance spectroscopy

AbstractA methodological study was conducted to test the potential of near‐infrared reflectance spectroscopy (NIRS) to estimate the oil content and fatty acid composition of sunflower seeds. A set of 387 intact‐seed samples, each from a single plant, were scanned by NIRS, and 120 of them were selected and further scanned as husked seed, meal, and oil. All samples were analyzed for oil content (nuclear magnetic resonance) and fatty acid composition (gas chromatography), and calibration equations for oil content and individual fatty acids (C16:0, C16:1, C18:0, C18:1, and C18:2) were developed for intact seed, husked seed, meal, and oil. For intact seed, the performance of the calibration equations was evaluated through both cross‐ and external validation, while cross‐validation was used in the rest. The results showed that NIRS is a reliable and accurate technique to estimate these traits in sunflower oil (validation r2 ranged from 0.97 to 0.99), meal (r2 from 0.92 to 0.98), and husked seeds (r2 from 0.90 to 0.97). According to these results, there is no need to grind the seeds to scan the meal; similarly accurate results are obtained by analyzing husked seeds. The analysis of intact seeds was less accurate (r2 from 0.76 to 0.85), although it is reliable enough to use for pre‐screening purposes to identify variants with significantly different fatty acid compositions from standard phenotypes. Screening of intact sunflower seeds by NIRS represents a rapid, simple, and cost‐effective alternative that may be of great utility for users who need to analyze a large number of samples.

Estimating the fatty acid composition of the oil in intact-seed rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy

The objective of this work was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid method to estimate the fatty acid composition of the oil in intact-seed samples of rapeseed. A total of 549 samples (3 g intact seed) from selected mutant and breeding lines were scanned by NIRS, and 220 of them were selected and scanned again by using two different adapters, which reduced the sample size to 300 and 60 mg, respectively. Selected samples were analysed by gas liquid chromatography and calibration equations for individual fatty acids were developed. Calibrations for oleic, linoleic, linolenic, and erucic acid were highly accurate, with values of r2 in cross validation from 0.95 to 0.98 (samples of 3 g), from 0.93 to 0.97 (300 mg), and from 0.84 to 0.96 (60 mg). Calibrations for palmitic and stearic acid were less accurate, with values of r2 in cross validation always lower than 0.8, probably because of the narrow range available for these fatty acids. The accuracy of the calibration equations for eicosenoic acid was very low (r2 = 0.69 in 3 g samples), although improved equations were developed (r2 from 0.78 to 0.91) when the relationship between erucic and eicosenoic acid was taken into account. We conclude that NIRS is a powerful technique to estimate the fatty acid composition of the oil in rapeseed, provided that samples covering a wide range of fatty acid levels are available, with the advantage that such estimation is possible with few additional costs when NIRS is used for the determination of other seed quality traits.

Determination of the fatty acid composition of the oil in intact‐seed mustard by near‐infrared reflectance spectroscopy

AbstractNear‐infrared reflectance spectroscopy (NIRS) was used to estimate the fatty acid composition of the oil in intact‐seed samples of Ethiopian mustard (Brassica carinata Braun) within a mutation breeding program that produced seeds with variable fatty acid compositions. Five populations, from 1992 to 1996 crops, were included in this study; and NIRS calibration equations for major fatty acids (palmitic, stearic, oleic, linoleic, linolenic, eicosenoic, and erucic) were developed within each single population. Furthermore, global calibration equations, including samples from the five populations, were developed. After external validation, the NIRS technique permitted us to obtain a reliable and accurate nondestructive estimation of the fatty acid composition of the oil, especially for the major acids—oleic, linoleic, linolenic, and erucic. For these, the r2 in external validation was higher than 0.95 by using both single‐and multipopulation equations, and higher than 0.85 for the remaining fatty acids. Moreover, the multipopulation equations provided an accurate estimation of samples from a population not represented in the calibration data set, with values of coefficient of determination in validation (r2) from 0.80 (palmitic and eicosenoic acids) to 0.97 (erucic acid). The ability of NIRS to discriminate among different fatty acid profiles was mainly due to changes within six spectral regions, 1140–1240, 1350–1400, 1650–1800, 1880–1920, 2140–2200, and 2240–2380 nm, all of them associated with fatty acid absorbers. Thus, NIRS can be used to estimate the fatty acid composition of Ethiopian mustard seeds with a high degree of accuracy, provided that calibration equations be developed from calibration sets that include large variability for the fatty acid composition of the oil.

Determining the Fat Acidity of Rough Rice by Near‐Infrared Reflectance Spectroscopy

ABSTRACTNear‐infrared reflectance spectroscopy (NIRS) was used to develop calibration curves for determining the fat acidity of whole‐kernel and ground rough rice with 13% moisture content at 25°C. Partial‐leastsquares regression (PLSR) uses the optimal calibration curve for wholekernel rough rice to measure the coefficient of determination (r2) of validation and standard error of prediction (SEP) of 0.87 and 0.83 mg of KOH/100 g of dry matter, respectively. However, the optimal calibration curve for ground rough rice has a higher r2 of validation and lower SEP of 0.94 and 0.73 mg of KOH/100 g of dry matter, respectively. From 10 to 40°C, the temperature effect causes an increase of 0.24 mg of KOH/100 g of dry matter/°C in the predicted fat acidity of whole‐kernel rough rice.

Development and Validation of Computed Tomography Derived Anthropometric Regression Equations for Estimating Abdominal Adipose Tissue Distribution

The purpose of this study was twofold: (1) to develop multiple regression equations for predicting computed tomography (CT) derived intra-abdominal (IAF), subcutaneous (SCF), and total (TOTF= IAF+SCF) abdominal adipose tissue areas from anthropometric measures in adult white males with a large range of age (18-71 years) and percent body fat (2.0-40.6); and (2) to validate the new and existing equations that used similar Hounsfield Units (HU) for determining IAF for estimating these fat depots. One hundred fifty-one white male subjects had IAF, SCF, and TOTF determined by a single CT scan, skinfold and circumference measures taken and body density determined. Linear intra-correlations and factor analysis procedures were used to identify variables for inclusion in stepwise multiple regression solutions. IAF was estimated from age, waist circumference, the sum of mid-thigh and lower thigh circumferences, and vertical abdominal skinfold. SCF was estimated from age, umbilicus circumference, chest and suprailiac skinfolds. TOTF was estimated from age, body mass index (BMI), chest skinfold, and umbilicus circumference. R2 for IAF, SCF, and TOTF was .73, .77, and .86 respectively. The existing and the new equations were validated on an independent sub-sample of 51 subjects. The only existing equation that met validation criteria had a validation R2 = .67 for IAF. All three new equations met validation criteria with R2 validations of .75, .79, and .85 for IAF, SCF, and TOTF respectively. It is concluded that the new equations might be used as an inexpensive estimation of IAF, SCF, and TOTF in adult white males varying greatly in age and percent body fat.

Case-mix groups for VA hospital-based home care.

The purpose of this study is to group hospital-based home care (HBHC) patients homogeneously by their characteristics with respect to cost of care to develop alternative case mix methods for management and reimbursement (allocation) purposes. Six Veterans Affairs (VA) HBHC programs in Fiscal Year (FY) 1986 that maximized patient, program, and regional variation were selected, all of which agreed to participate. All HBHC patients active in each program on October 1, 1987, in addition to all new admissions through September 30, 1988 (FY88), comprised the sample of 874 unique patients. Statistical methods include the use of classification and regression trees (CART software: Statistical Software; Lafayette, CA), analysis of variance, and multiple linear regression techniques. The resulting algorithm is a three-factor model that explains 20% of the cost variance (R2 = 20%, with a cross validation R2 of 12%). Similar classifications such as the RUG-II, which is utilized for VA nursing home and intermediate care, the VA outpatient resource allocation model, and the RUG-HHC, utilized in some states for reimbursing home health care in the private sector, explained less of the cost variance and, therefore, are less adequate for VA home care resource allocation.