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At least 1,063 records · Page 59Linked to original sources
PubMed2006

Changes in aortic blood flow induced by passive leg raising predict fluid responsiveness in critically ill patients.

INTRODUCTION: Esophageal Doppler provides a continuous and non-invasive estimate of descending aortic blood flow (ABF) and corrected left ventricular ejection time (LVETc). Considering passive leg raising (PLR) as a reversible volume expansion (VE), we compared the relative abilities of PLR-induced ABF variations, LVETc and respiratory pulsed pressure variations (DeltaPP) to predict fluid responsiveness. METHODS: We studied 22 critically ill patients in acute circulatory failure in the supine position, during PLR, back to the supine position and after two consecutive VEs of 250 ml of saline. Responders were defined by an increase in ABF induced by 500 ml VE of more than 15%. RESULTS: Ten patients were responders and 12 were non-responders. In responders, the increase in ABF induced by PLR was similar to that induced by a 250 ml VE (16% versus 20%; p = 0.15). A PLR-induced increase in ABF of more than 8% predicted fluid responsiveness with a sensitivity of 90% and a specificity of 83%. Corresponding positive and negative predictive values (PPV and NPV, respectively) were 82% and 91%, respectively. A DeltaPP threshold value of 12% predicted fluid responsiveness with a sensitivity of 70% and a specificity of 92%. Corresponding PPV and NPV were 87% and 78%, respectively. A LVETc of 245 ms or less predicted fluid responsiveness with a sensitivity of 70%, and a specificity of 67%. Corresponding PPV and NPV were 60% and 66%, respectively. CONCLUSION: The PLR-induced increase in ABF and a DeltaPP of more than 12% offer similar predictive values in predicting fluid responsiveness. An isolated basal LVETc value is not a reliable criterion for predicting response to fluid loading.

Aged↗
PubMed2004

Prediction of peak oxygen uptake in patients fulfilling the 1994 CDC criteria for chronic fatigue syndrome.

PURPOSE: To establish an inexpensive, simple method of predicting peak oxygen uptake (VO2peak) in patients fulfilling the 1994 Centers for Disease Control and Prevention (CDC) criteria for chronic fatigue syndrome (CFS). DESIGN: A retrospective observational study. SETTING: An outpatient tertiary care chronic fatigue clinic. SUBJECTS: Two hundred and forty consecutive patients fulfilling the 1994 CDC criteria for CFS. INTERVENTIONS: Heart rate, metabolic and ventilatory parameters were measured continuously during a maximal exercise stress test on a bicycle ergometer. Using the equation peak oxygen uptake = 13.1 x peak workload +284 (used by Mullis et al., Br J Sports Med 1999; 33: 352-56), VO2peak was predicted from the peak workload of a maximal exercise capacity test. Pearson correlation coefficient and linear regression analysis were used to establish the most accurate way to predict VO2peak. RESULTS: Percentage error encountered when comparing actual measured VO2peak with predicted value was 17.3% (+/-10.0). A strong correlation between VO2peak and peak workload was observed (r= 0.89, p < 0.001). A regression analysis established the relation as VO2peak = 10.47 x peak workload +284.1, where VO2peak is given in ml/min and peak workload in W (error in prediction = 11.0+/-9.5%). CONCLUSIONS: Monitoring of the peak workload during a maximal, graded bicycle ergometric test suffices to predict the VO2peak. When predicting VO2peak the used operational definition for the diagnosis of CFS could be taken into account. Compared with the equation used by Mullis et al., peak workload is multiplied by 10.47 in order to predict peak oxygen uptake in CDC-defined CFS patients.

Adult↗
PubMed2005

Gene expression profile predicts patient survival of gastric cancer after surgical resection.

PURPOSE: This study was conducted to characterize gene expression profile of survival in patients with surgically curable gastric cancer by using an in-house membrane microarray and developing a survival prediction model. MATERIALS AND METHODS: Data of cDNA microarrays were obtained from 18 pairs of cancerous and noncancerous gastric tissues. Nine patients who survived > 30 months were identified as good survival, and the other nine, who survived < 12 months, were identified as poor survival. Supervised analysis was performed to identify a gene expression profile by good and poor survival. Semiquantitative reverse transcriptase polymerase chain reaction (RT-PCR) was used to confirm the microarray data in 10 patients with sufficient RNA. Using these 10 patients and another 10 patients selected randomly from 40 newly enrolled patients as the training group, the RT-PCR status of the confirmed genes was used for predicting good versus poor survival. Finally, the prediction model was tested in the remaining 30 newly enrolled gastric cancer patients. RESULTS: A survival prediction model consisting of three genes (CD36, SLAM, PIM-1) was developed. This model could correctly predict poor or good survival in 23 (76.7%) of 30 newly enrolled patients, and yielded a specificity of 80% and a sensitivity of 73.3%. The survival rate of the patients predicted to have good survival was significantly higher than that of those predicted to have poor survival in the test group as a whole (N = 30; P = .00531) and in stage III patients (n = 16; P = .04467). CONCLUSION: The semiquantitative RT-PCR gene expression profiling of three genes extracted from microarray study can accurately predict surgery-related outcome in gastric cancer patients.

Gastrectomy↗
PubMed2005

Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer.

PURPOSE: To combine clinical variables associated with pathologic complete response (pCR) and distant metastasis-free survival (DMFS) after preoperative chemotherapy (PC) into a prediction nomogram. PATIENTS AND METHODS: Data from 496 patients treated with anthracycline PC at the Institut Gustave Roussy were used to develop and calibrate a nomogram for pCR based on multivariate logistic regression. This nomogram was tested on two independent cohorts of patients treated at the M.D. Anderson Cancer Center. The first cohort (n = 337) received anthracycline; the second cohort (n = 237) received a combination of paclitaxel and anthracycline PC. A separate nomogram to predict DMFS was developed using Cox proportional hazards regression model. RESULTS: The pCR nomogram based on clinical stage, estrogen receptor status, histologic grade, and number of preoperative chemotherapy cycles had good discrimination and calibration in the training and the anthracycline-treated validation sets (concordance indices, 0.77, 0.79). In the paclitaxel plus anthracycline group, when the predicted pCR rate was less than 14%, the observed rate was 7.5%; for a predicted rate of > or = 38%, the actual rate was 85%. For a predicted rate between 14% to 38%, the observed rates were 50% with weekly and 27% with 3-weekly paclitaxel. This indicates that patients with intermediate chemotherapy sensitivity benefit the most from the optimized schedule of paclitaxel. Patients unlikely to achieve pCR to anthracylines remain at low probability for pCR, even after inclusion of paclitaxel. The nomogram for DMFS had a concordance index of 0.72 in the validation set and outperformed other prediction tools (P = .02). CONCLUSION: Our nomograms predict pCR accurately and can serve as a basis to integrate future molecular markers into a clinical prediction model.

Anthracyclines↗
PubMed2005

A new equivalence based metric for predictive check to qualify mixed-effects models.

The main objective of any modeling exercise is to provide a rationale for effective decision making during drug development. The aim of the current simulation experiment was to evaluate the properties of predictive check as a covariate model qualification technique and, more importantly, to introduce and evaluate alternative criteria to qualify models.Original concentration-time profiles (yod) were simulated using a 1-compartment model for an intravenous drug administered to 25 men and 25 women. The typical clearance for male subjects (TVCLm) was assumed to be 5-fold higher than that for female subjects (TVCLf). Fifty such trials under the same design were generated randomly. Predictive check was used as the model qualification tool to study predictive performance of true (males not equal females) and false (males = females) models in the context of maximum likelihood estimation. For each yod, 200 replications were generated to study the properties of a discrepancy variable, a statistic that depends on the model, and a test statistic, a statistic that does not depend on the model. Several qualification criteria were evaluated in assessing predictive performance, such as, predictive p-value (Pp), probability of equivalence (peqv), and probability of rejecting the null hypothesis (data = model) using the Kolmogorov-Smirnov test (pks). The Pp value was calculated using sum of squared errors as a discrepancy variable. For both of the models, the Pp values uniformly ranged between 0 and 1. The pattern of Pp values suggests that qualification of the false model is unlikely. For both of the models, the range of peqv is about 0.95 to 1.0 for concentration at 0.5 hours. However, this is not the case for the concentration at 4 hours, which is primarily dependent on the clearance. The false model (0.35 to 0.50) has poor predictive performance compared with the true model (0.65 to 0.80) using peqv. The pks suggests no difference in the distributions of replicated and original concentrations at all of the time points for both of the models. Discrepancy variables cannot aid in rejecting false models, whereas the use of a test statistic can aid in rejecting false models. However, selection of an informative test statistic is challenging. As far as the qualification criteria are considered, the equivalence-based comparison of a test statistic is more informative than a significance-based comparison. No convincing evidence exists in the literature demonstrating the added advantages of predictive check as a routine model qualification tool over the existing tools, such as diagnostic plots or mechanistic reasoning. However, when a model is to be used for designing a trial, it should at least be able to regenerate the data used to build the model. In such cases, predictive check might offer insights into potential inconsistencies.

Humans↗
PubMed2007

Can surgical oncologists reliably predict the likelihood for non-SLN metastases in breast cancer patients?

BACKGROUND: In approximately 40% of the breast cancer patients with sentinel lymph node (SLN) metastases, additional nodal metastases are detected in the completion axillary lymph node dissection (cALND). The MSKCC nomogram can help to quantify a patient's individual risk for non-SLN metastases with fairly accurate predicted probability. The aim of this study was to compare the predictions of surgical oncologists for non-SLN metastases with nomogram results and to clarify the impact of nomogram results on clinical decision-making. METHODS: Questionnaires, containing patient scenarios, were sent to surgical oncologists involved in breast cancer care. The surgeon was asked to predict the probability for non-SLN metastases for the first five scenarios. For the remaining scenarios, the patient's actuarial likelihood, calculated by the nomogram, was supplied. The surgeon was asked whether or not (s)he would perform a cALND. The type of hospital and the surgeon's experience were registered. RESULTS: The concordance-index amounted to 0.78, indicating moderate concurrence between the surgical predictions and nomogram results. The intersurgeon variation was important. About 25% of the surgeons was influenced by nomogram information and decided in one or more patients to abandon the cALND. Neither the type of hospital nor experience influenced predicting abilities or the clinical decision-making process. CONCLUSION: Individual predictions of surgical oncologists for non-SLN metastases do not correlate well with the MSKCC nomogram. The distribution between intersurgeon predictions for one scenario is important. Therefore, the nomogram is superior to clinical estimations for predicting the likelihood for non-SLN metastases.

Axilla↗
PubMed2004

Using decision forest to classify prostate cancer samples on the basis of SELDI-TOF MS data: assessing chance correlation and prediction confidence.

Class prediction using "omics" data is playing an increasing role in toxicogenomics, diagnosis/prognosis, and risk assessment. These data are usually noisy and represented by relatively few samples and a very large number of predictor variables (e.g., genes of DNA microarray data or m/z peaks of mass spectrometry data). These characteristics manifest the importance of assessing potential random correlation and overfitting of noise for a classification model based on omics data. We present a novel classification method, decision forest (DF), for class prediction using omics data. DF combines the results of multiple heterogeneous but comparable decision tree (DT) models to produce a consensus prediction. The method is less prone to overfitting of noise and chance correlation. A DF model was developed to predict presence of prostate cancer using a proteomic data set generated from surface-enhanced laser deposition/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The degree of chance correlation and prediction confidence of the model was rigorously assessed by extensive cross-validation and randomization testing. Comparison of model prediction with imposed random correlation demonstrated biologic relevance of the model and the reduction of overfitting in DF. Furthermore, two confidence levels (high and low confidences) were assigned to each prediction, where most misclassifications were associated with the low-confidence region. For the high-confidence prediction, the model achieved 99.2% sensitivity and 98.2% specificity. The model also identified a list of significant peaks that could be useful for biomarker identification. DF should be equally applicable to other omics data such as gene expression data or metabolomic data. The DF algorithm is available upon request.

Decision Support Techniques↗
PubMed1998

Predictive performance of a vancomycin-aminoglycoside population model.

OBJECTIVE: To evaluate the Wragge-Cooper method of predicting vancomycin serum concentrations utilizing knowledge of aminoglycoside pharmacokinetic parameters in general medicine and intensive care unit populations, and to develop a revised model if necessary. DESIGN: This study consists of two phases evaluating 50 adults receiving concurrent vancomycin and aminoglycoside therapy. Patients were identified by a retrospective review of medical records. Bayesian analysis of measured serum aminoglycoside and vancomycin concentrations was performed to determine the individualized pharmacokinetic parameters. Phase I of the study tested the predictive performance of a published model incorporating aminoglycoside elimination (Wragge-Cooper) in 25 patients (group 1), and a revised model was developed. Phase II determined the predictive performance of the revised model (revised) and its performance relative to the Wragge-Cooper model and a traditional model incorporating estimated creatinine clearance (traditional) in an additional 25 patients (group 2). SETTING: Two tertiary care university teaching hospitals. MAIN OUTCOME MEASURES: The predictive performance of the models was determined by comparing predicted with measured vancomycin serum concentrations. Bias and precision were evaluated by calculating the mean prediction error (ME) and mean absolute error (MAE), respectively. Linear regression was performed to determine relationships between parameters. RESULTS: The Wragge-Cooper model consistently underpredicts vancomycin serum concentrations in general medicine and intensive care unit populations (ME = -5.18, MAE = 6.63). Relative predictive performance analysis indicates no significant difference in bias or precision between the traditional and Wragge-Cooper models (delta ME 1.17, delta MAE -0.80). Regression analysis of individualized aminoglycoside and vancomycin elimination derived from patients in group 1 reveals the following relationship: vancomycin k10 (1/h) = 0.081 + 1.037ke,amg, r = 0.73. The revised model is significantly less biased and more precise compared with the traditional model (delta ME -4.48; delta MAE 1.22), and is significantly less biased (delta ME 4.29) but no more precise than the Wragge-Cooper model (delta MAE -0.58), using patients from group 2. CONCLUSIONS: The revised model is an accurate method of predicting vancomycin serum concentrations in both general medicine and intensive care unit populations. Use of this model enables individualization of vancomycin dosage in patients receiving concurrent aminoglycoside therapy and minimizes vancomycin serum concentration monitoring.

Adult↗
PubMed1999

How accurate is spirometry at predicting restrictive pulmonary impairment?

OBJECTIVE: To determine the accuracy with which spirometric measurements of FVC and expiratory flow rates can diagnose the presence of a restrictive impairment. DESIGN: The pulmonary function tests of 1,831 consecutive white adult patients who had undergone both spirometry and lung volume measurements on the same visit over a 2-year period were analyzed. The probability of restrictive pulmonary impairment, defined as a reduced total lung capacity (TLC) below the lower limit of the 95% confidence interval, was determined for each of several categoric classifications of the spirometric data, and additionally for each of several interval levels of the FVC and the FEV1/FVC ratio. SETTING: A large clinical laboratory in a university teaching hospital using quality-assured and standardized spirometry and lung volume measurement techniques according to American Thoracic Society standards. RESULTS: Two hundred twenty-five of 1,831 patients (12.3%) had a restrictive defect. The positive predictive value of spirometry for predicting restriction was relatively low; of 470 patients with a low FVC on spirometry, only 41% had restriction confirmed on lung volume measurements. When the analysis was confined to the 264 patients with a restrictive pattern on spirometry (ie, low FVC and normal or above normal FEV1/FVC ratio), the positive predictive value was 58%. Conversely, spirometry had a very favorable negative predictive value; only 2.4% of patients (32 of 1,361) with a normal vital capacity (VC) on spirometry had a restrictive defect by TLC measurement. The probability of a restrictive defect was directly and linearly related to the degree of reduction of FVC when the FVC was < 80% of predicted (p = 6.002). Combining the FVC and the FEV1/FVC ratio improved the predictive ability of spirometry; for all values of FVC < 80% of the predicted amount, the likelihood of restrictive disease increased as the FEV1/FVC ratio increased. CONCLUSIONS: Spirometry is very useful at excluding a restrictive defect. When the VC is within the normal range, the probability of a restrictive defect is < 3%, and unless restrictive lung disease is suspected a priori, measurement of lung volumes can be avoided. However, spirometry is not able to accurately predict lung restriction; < 60% of patients with a classical spirometric restrictive pattern had pulmonary restriction confirmed on lung volume measurements. For these patients, measurement of the TLC is needed to confirm a true restrictive defect.

Adult↗
PubMed2005

Limited predictive value of an acute test with subcutaneous octreotide for long-term IGF-I normalization with Sandostatin LAR in acromegaly.

OBJECTIVES: To study whether the growth hormone (GH) response after the subcutaneous administration 50 microg of octreotide (acute octreotide test) has any predictive value for long-term IGF-I normalization with Sandostatin LAR. DESIGN: Twenty four therapy-naive patients with active acromegaly were studied. RESULTS: > 75% GH decrease in the acute octreotide test predicted long-term IGF-I normalization with Sandostatin LAR in 8/11 (73%) of patients. 3/13 (23%) patients with < 75% GH decrease in the acute octreotide test were long-term biochemically controlled with Sandostatin LAR. Using the > 75% GH reduction criterion, the sensitivity and specificity of this test for predicting long-term normalization of serum IGF-I with Sandostatin LAR treatment were 73% and 77%, respectively (positive and negative predictive values: 73% and 77%, respectively). 6/8 (75%) patients with GH suppression to levels < 1.1 microg/l and 9/16 (56%) patients with GH suppression to levels < 2 microg/l in the acute octreotide test showed normalization of serum IGF-I with long-term Sandostatin LAR treatment. The sensitivity and specificity of GH suppression < 1.1 microg/l for predicting of the long-term normalization of serum IGF-I with Sandostatin LAR therapy were 55% and 85%, respectively (positive and negative predictive values: 75% and 69%, respectively). The sensitivity and specificity of GH suppression < 2 microg/l for predicting of the long-term normalization of serum IGF-I with Sandostatin LAR therapy were 82% and 46%, respectively (positive and negative predictive values: 56% and 75%, respectively). CONCLUSION: The acute octreotide is not recommended for clinical decision making with regard to long-term treatment using the long-acting somatostatin analog Sandostatin LAR in acromegaly.

Acromegaly↗
PubMed2002

An early (sixth-hour) serum bilirubin measurement is useful in predicting the development of significant hyperbilirubinemia and severe ABO hemolytic disease in a selective high-risk population of newborns with ABO incompatibility.

OBJECTIVE: In the era of early discharge of newborns from the hospital, newborns with ABO incompatibility are at especially greater risk for developing a subsequent significant hyperbilirubinemia because some of these infants also may present with some degree of ABO isoimmune disease. In this study, we aimed to determine prospectively the critical serum total bilirubin level to predict significant hyperbilirubinemia and severe hemolytic disease in healthy term newborns with ABO incompatibility based on a serum bilirubin measurement made at a postnatal age at which all newborns are at the hospital before discharge and at which any therapeutic intervention, if necessary, could be started as early as possible. METHODS: A total of 136 healthy term newborns with ABO (O-A or O-B) blood group incompatibility were followed prospectively with daily serum total bilirubin measurements for the first 5 days of life. Newborns with serum total bilirubin levels of > or =5 mg/dL and an increase in serum total bilirubin concentration of >0.5 mg/dL/h in the first 24 hours, > or =12 mg/dL on day 2, > or =15 mg/dL on day 3, and > or =17 mg/dL on days 4 and 5 were defined to have significant hyperbilirubinemia and were started on phototherapy treatment. Additional treatment modalities, including intense phototherapy, intravenous immunoglobulin treatment, and exchange transfusion, were used when serum bilirubin concentrations exceeded 20 mg/dL or increased by >1 mg/dL/h despite a phototherapy treatment of at least 4 hours. The additional assessment of the predictive ability of the sixth-hour serum total bilirubin value in determining the development of significant hyperbilirubinemia was made on the basis of the placement of any of the first 5 days' serum bilirubin measurements in the > or =90th percentile of the study population. On the basis of the percentile tracks constructed from the 10th, 35th, 50th, 60th, and 90th percentiles of serum total bilirubin values, a nomogram demonstrating the 3 percentile tracks as risk zone demarcators with divided risk zones was produced. RESULTS: Twenty-nine newborns (21.3%) had significant hyperbilirubinemia. There were significant differences between the newborns who did and the newborns who did not develop significant hyperbilirubinemia with respect to the reticulocyte count (4.39 +/- 3.46% vs 2.95 +/- 1.63) and the presence of a direct antiglobulin test positivity (6 of 23 vs 0 of 107) and a sibling with neonatal jaundice (6 of 23 vs 5 of 102). A mean serum bilirubin level of > or =4 mg/dL at the sixth hour of life was determined to have the highest sensitivity (86.2%) and negative predictive value (94.5%) and a positive predictive value of 39.7% to predict the newborns who would develop significant hyperbilirubinemia. At the mean serum bilirubin level of 6 mg/dL, the sensitivity, specificity, and negative and positive predictive values were 100%, 91.5%, 100%, and 35.3%, respectively, in diagnosing 6 cases of severe ABO hemolytic disease. On the hour (age)-specific percentile-based nomogram, the zone above the 90th percentile was determined as high risk and that below the 35th percentile as low risk. CONCLUSIONS: The reticulocyte count, a positive direct antiglobulin test, and the presence of a sibling with neonatal jaundice were determined to be the good predictors for the development of significant hyperbilirubinemia and severe hemolytic disease of the newborn. A serum bilirubin measurement and the use of the critical bilirubin levels of 4 mg/dL and 6 mg/dL at the sixth hour of life will predict nearly all newborns who will have significant hyperbilirubinemia and those who will develop severe hemolytic disease of the newborn, respectively. An hour (age)-specific percentile-based nomogram can be used to predict which newborn is at high risk (> or =90th percentile), intermediate risk (35th-90th percentiles), and low risk (<35th percentile) for developing significant hyperbilirubinemia. The 35th and 90th percentile tracks, approximating the serum bilirubin levels of 3.3 mg/dL and 6.5 mg/dL at the sixth hour of life, respectively, can be used as safe risk demarcators in deciding about the time of discharge of ABO-incompatible newborns from the hospital.

ABO Blood-Group System↗
PubMed2006

Brief hospitalization and pulse oximetry for predicting amoxicillin treatment failure in children with severe pneumonia.

OBJECTIVE: In settings with limited assessment tools, we sought to determine whether early clinical signs and symptoms and blood oxygen saturation would predict amoxicillin treatment failure in children with severe pneumonia (as defined by the World Health Organization). METHODS: Data were from a previously reported, multinational trial of orally administered amoxicillin versus injectable penicillin for the treatment of World Health Organization-defined severe pneumonia in children 3 to 59 months of age. We assessed all 857 participants assigned randomly to the experimental amoxicillin arm. Six multivariate logistic regression models were created and evaluated for their ability to predict failure after 48 hours of therapy. Regression models included vital signs, symptoms, and laboratory data collected at baseline and after 12 or 24 hours of observation. Oxygen saturation data were included in 3 models. RESULTS: Clinical treatment failure occurred for 18% of children. Younger age, increased initial respiratory rate, and baseline hypoxia predicted treatment failure in all models. Data available after 24 hours improved the ability to predict failure compared with data available at baseline or 12 hours. The inclusion of oximetry data improved the predictive ability at baseline, 12 hours, and 24 hours. The ability to predict failure after 12 hours of observation with oximetry data was similar to the predictive ability after 24 hours without pulse oximetry data. CONCLUSIONS: Assessment of clinical parameters at presentation and after 24 hours improved the ability to predict clinical failure of oral amoxicillin therapy, compared with assessment at presentation alone or at presentation and after only 12 hours, for children with World Health Organization-defined severe pneumonia.

Amoxicillin↗
PubMed1999

Prediction of hepatic metabolic clearance based on interspecies allometric scaling techniques and in vitro-in vivo correlations.

This article reviews the methods available for predicting hepatic metabolic clearance in humans, and discusses their application to the processes of drug discovery and development. The application of these techniques has increased markedly during the past few years because of the improved availability of human liver samples, which has increased the opportunities to use in vitro studies to predict human clearance. The techniques available involve both empirical and physiologically based approaches. Allometric scaling using in vitro data from animals and humans combines certain aspects of both approaches. An evaluation of data retrieved from the literature indicates that, together with in vitro human data, allometric scaling based on a combination of in vitro and in vivo preclinical data can accurately predict clearance in humans. With this approach, 80% of the predictions were within a 2-fold factor of actual human clearance values, with an overall accuracy of 1.6-fold. The uncertainties and inaccuracies in predicting human clearance are related to: (i) the specific method that is used to make the prediction; (ii) the experimental design and the model used to determine the in vitro clearance; (iii) protein binding within the in vitro test system; and (iv) various in vivo factors such as the involvement of extrahepatic metabolism and active transport processes, interindividual variability and nonlinearity in pharmacokinetics. In contrast to purely empirical approaches, the physiological approach to predicting clearance gives an opportunity to integrate some of these complexities and, therefore, should provide more confidence in the prediction of clearance in humans.

Animals↗
PubMed2001

The use of in vitro methods to predict in vivo pharmacokinetics and drug interactions.

With the dramatic change underway in the process of drug discovery and development it has become increasingly important to define, both qualitatively and quantitatively, the dispositional features of new chemical entities (NCEs) as early in the process as possible. To that end strategies have emerged that are designed to enable reasonable predictions about a NCE's absorption from the gastrointestinal tract, systemic bioavailability and likelihood for significant pre-systemic clearance, character of metabolic processing both within the gastrointestinal tract and the liver, in vivo pharmacokinetics (PK), and likelihood for clinically significant interactions with other drugs. To some extent these strategies have embraced interspecies allometric scaling in which findings in animals are extrapolated to predict outcomes in humans. However, a greater emphasis in recent years has been placed on predicting human PK and the likelihood of clinically significant drug-drug interactions for NCEs solely from in vitro experiments. These general strategies have been methodologically streamlined so that hundreds or even thousands of experiments on a given NCE can be conducted within several days. Dispositional data from these pre-clinical experiments is useful for rapidly identifying potential marketing advantages for NCEs, and for screening out those substances that should not be placed into more expensive and labor-intensive animal experiments or brought to clinical trial. The key issue in these strategies is the accuracy with which pre-clinical findings predict clinical outcomes. Based largely on retrospective analyses the current state of the art exhibits a high percentage of useful predictions. However, there are many examples in which the prediction of either human PK or clinical drug-drug interactions from pre-clinical data has failed. The reasons for inaccurate predictions are manifold, and may include the actual in vitro methodology used, inappropriate model selection, and errant scale-up factors. Additionally, in vitro methods may fail to account for complex hepatobiliary processing including transport phenomena and Phase II metabolism. Progress has been made in establishing humanized methodologies that accurately describe these processes, with a view toward reconstituting the contributions of each into a more complex and accurate depiction and prediction of in vivo PK and drug-interaction potential.

Animals↗
PubMed2002

Prediction of postoperative lung function in patients with lung cancer: comparison of quantitative CT with perfusion scintigraphy.

OBJECTIVE: Prediction of postoperative lung function is important in preoperative evaluation of patients with lung cancer. Perfusion scintigraphy is the current method to assess the fractional contribution of lung function of the remaining lung. We developed a quantitative CT method and compared it with perfusion scintigraphy for predictions of postoperative forced expiratory volume in 1 sec (FEV1) in patients with lung cancer. SUBJECTS AND METHODS: Forty-four patients with lung cancer undergoing lung resection with preoperative CT and perfusion scintigraphy were enrolled. Quantitative CT used a dual threshold (-500 and -910 H) on standard preoperative CT to semiautomatically extract lung volume without emphysema or tumor and atelectasis, which we defined as "functional lung volume." Prediction was calculated from preoperative FEV1 multiplied by the fractional contribution of functional lung volume of the remaining lung by quantitative CT. Perfusion scintigraphy was the standard method. Predictions were correlated with postoperatively measured FEV1. RESULTS: Both quantitative CT and perfusion scintigraphy predicted postoperative FEV1 well in patients who underwent pneumonectomy (n = 28, r = 0.88 vs r = 0.86) and in lobectomy (n = 16, r = 0.90 vs r = 0.80) (both, p < 0.001). There was good agreement between the two methods by the Bland-Altman method. In the four patients with low measured postoperative FEV1 (<40% predicted normal), quantitative CT had true-positive prediction in four and perfusion scintigraphy, in only two. CONCLUSION: Given its simplicity, we proposed that quantitative CT be widely used in predicting postoperative FEV1.

Carcinoma, Non-Small-Cell Lung↗
PubMed2007

Increasing the accuracy of oral glucose tolerance testing and extending its application to individuals with normal glucose tolerance for the prediction of type 1 diabetes: the Diabetes Prevention Trial-Type 1.

OBJECTIVE: We assessed the extent to which both standard and alternative indexes from 2-h oral glucose tolerance testing predict type 1 diabetes and whether oral glucose tolerance tests (OGTTs) predict type 1 diabetes in individuals with normal glucose tolerance. RESEARCH DESIGN AND METHODS: The prediction of type 1 diabetes from baseline OGTTs was studied in 704 Diabetes Prevention Trial-Type 1 participants (islet-cell autoantibody [ICA]-positive relatives of type 1 diabetic patients). The maximum follow-up was 7.4 years. Analyses utilized receiver-operator curves (ROCs), proportional hazards models, and survival curves. RESULTS: ROC areas under the curve (ROCAUCs) for both the AUC glucose (0.73 +/- 0.02) and an OGTT prediction index (0.78 +/- 0.02) were higher (P < 0.001) than those for the fasting (0.53 +/- 0.02) and 2-h glucose (0.66 +/- 0.02). ROCAUCs for the 60- and 90-min glucose (0.71 +/- 0.02 and 0.72 +/- 0.02, respectively) were also higher (P < 0.01) than those for the fasting and 2-h glucose. Among individuals with normal glucose tolerance, OGTTs were highly predictive, with 4th versus 1st quartile hazard ratios for the 2-h glucose, AUC glucose, and OGTT prediction index ranging from 3.77 to 5.30 (P < 0.001 for all). CONCLUSIONS: Certain alternative OGTT indexes appear to better predict type 1 diabetes than standard OGTT indexes in ICA-positive relatives of type 1 diabetic patients. Moreover, even among those with normal glucose tolerance, OGTTs are strongly predictive. This suggests that subtle metabolic abnormalities are present several years before the diagnosis of type 1 diabetes.

Area Under Curve↗
PubMed2001

Gluteus medius and rump fat depths as additional live animal ultrasound measurements for predicting retail product and trimmable fat in beef carcasses.

This study was conducted to determine the ability of additional ultrasound measures to enhance the prediction accuracy of retail product and trimmable fat yields based on weight and percentage. Thirty-two Hereford-sired steers were ultrasonically measured for 12th-rib fat thickness, longissimus muscle area, rump fat thickness, and gluteus medius depth immediately before slaughter. Chilled carcasses were evaluated for USDA yield grade factors and then fabricated into closely trimmed, boneless subprimals with 0.32 cm s.c. fat. The kilogram weight of end-point product included the weight of trimmed, boneless subprimals plus lean trim weights, chemically adjusted to 20% fat, whereas the fat included the weight of trimmed fat plus the weight of fat in the lean trim. Prediction equations for carcass yield end points were developed using live animal or carcass measurements, and live animal equations were developed including ultrasound ribeye area or using only linear measurements. Multiple regression equations, with and without ultrasound rump fat thickness and gluteus medius depth, had similar R2 values when predicting kilograms of product and percentages of product, suggesting that these alternative variables explained little additional variation. Final unshrunk weight and ultrasound 12th-rib fat thickness explained most of the variation when predicting kilograms of fat. Rump fat and gluteus medius depth accounted for an additional 10% of the variation in kilograms of fat, compared with the equation containing final weight, ultrasound ribeye area, and ultrasound 12th-rib fat thickness; however, the two equations were not significantly different. Prediction equations for the cutability end points had similar R2 values whether live animal ultrasound measurements or actual carcass measurements were used. However, when ultrasound ribeye area was excluded from live animal predictions, lower R2 values were obtained for kilograms of product (0.81 vs 0.67) and percentages of product (0.41 vs 0.17). Conversely, the exclusion of ultrasound ribeye area had little effect on the prediction accuracy for kilograms of fat (0.75 vs 0.74) and percentage fat (0.50 vs 0.40). These data substantiate the ability of live animal ultrasound measures to accurately assess beef carcass composition and suggest that the alternative ultrasound measures, rump fat and gluteus medius depth, improve the accuracy of predicting fat-based carcass yields.

Adipose Tissue↗
PubMed1991

Prediction tree for severely head-injured patients.

Prediction tree techniques are employed in the analysis of data from 555 patients admitted to the Medical College of Virginia hospitals with severe head injuries. Twenty-three prognostic indicators are examined to predict the distribution of 12-month outcomes among the five Glasgow Outcome Scale categories. A tree diagram, illustrating the prognostic pattern, provides critical threshold levels that split the patients into subgroups with varying degrees of risk. It is a visually useful way to look at the prognosis of head-injured patients. In previous analyses addressing this prediction problem, the same set of prognostic factors (age, motor score, and pupillary response) was used for all patients. These approaches might be considered inflexible because more informative prediction may be achieved by somewhat different combinations of factors for different patients. Tree analysis reveals that the pattern of important prognostic factors differs among various patient subgroups, although the three previously mentioned factors are still of primary importance. For example, it is noted that information concerning intracerebral lesions is useful in predicting outcome for certain patients. The overall predictive accuracy of the tree technique for these data is 77.7%, which is somewhat higher than that obtained via standard prediction methods. The predictive accuracy is highest among patients who have a good recovery or die; it is lower for patients having intermediate outcomes.

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