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PubMed2006

[Bispectral index, cerebral state index and the predicted effect-site concentration at different clinical end-points during target-controlled infusion of propofol].

OBJECTIVE: To examine the predicted effect-site concentration of propofol at two clinical end-points: loss of verbal contact (LVC) and loss of consciousness (LOC), and to explore the relationship between bispectral index (BIS) values, cerebral state index (CSI) values and the predicted effect-site concentration during the target-controlled infusion of propofol. METHODS: In 20 patients during the target-controlled infusion of propofol, the propofol infusion was set at an initial effect-site concentration of 0.5 mg/L, and increased by 0.5 mg/L every 5 min until 5 min after the modified observer's assessment of alertness/sedation scale (OAA/S) values reached zero. The predicted effect-site concentration of propofol, the values of CSI and BIS were recorded, and the sedation level was examined by the modified OAA/S every 20s. The predicted effect-site concentrations of propofol in target-controlled infusion (TCI) system were recorded when they increased by more than 0.1 mg/L. The predicted effect-site concentrations of propofol and the values of BIS and CSI at LVC and LOC in 5%, 50% and 95% of the patients were calculated. RESULTS: There was good linearity between BIS and the predicted effect-site concentration of propofol (R(2)=0.787), as well as between CSI and the predicted effect-site concentration of propofol (R(2)=0.792). The predicted effect-site concentrations of propofol at LVC in 5%, 50% and 95% of the patients were 1.1,1.8 and 2.4 mg/L, respectively. The values of BIS and CSI at LVC in 5%, 50% and 95% of the patients were 79.2, 69.2 and 59.2; 74.9, 65.9 and 56.8, respectively. The predicted effect-site concentrations of propofol at LOC in 5%, 50% and 95% of the patients were 1.5, 2.5 and 3.4 mg/L, respectively. At LOC, the values of BIS and CSI in 5%, 50% and 95% of the patient were 73.6, 57.1 and 40.6; 65.2, 54.8 and 44.3, respectively. CONCLUSION: During target-controlled infusion of propofol, LVC and LOC occur within a definite range of predicted effect-site concentrations. There is the good linearity between BIS, CSI and the predicted effect-site concentrations of propofol. CSI may be more useful than BIS in predicting LVC and LOC.

Adult↗
PubMed1997

Does modification of the Innsbruck and the Glasgow Coma Scales improve their ability to predict functional outcome?

BACKGROUND: The accurate prediction of functional outcome requires the development of multivariate models. To enhance their contribution to such models, the predictive power of each component must be optimized. OBJECTIVES: To improve the predictive power of coma scales as the first step in building more sophisticated multivariate models to predict specific levels of functional outcome. DESIGN: Prospective descriptive study. SETTING: Neurology and neurosurgery intensive care unit (NNICU) in a tertiary care academic center. PATIENTS: Eighty-four patients with acute traumatic brain injury, intracerebral hemorrhage, subarachnoid hemorrhage, or ischemic stroke. INTERVENTIONS: None. MAIN OUTCOME MEASURES: The Glasgow Coma Scale (GCS) and Innsbruck Coma Scale (ICS) were administered within 24 hours of admission to the NNICU and then at 48-hour intervals until discharge of the patient from the NNICU. The assessments were performed by 3 occupational therapy graduate students working under the supervision of the medical director of the NNICU. The functional outcome at 3 months after discharge from the hospital was assessed by telephone by the same nurse using the following categories: (1) dead, (2) receiving nursing home or custodial care, (3) home with help, or (4) independent. Cronbach's alpha estimates of reliability for each scale were computed using all scores obtained during the study. The analyses indicated that the verbal response item of the GCS and the oral automatisms item of the ICS were less reliable in this patient population. The scales were modified by deleting those items, and predictive validity for the original and modified scales was computed using a discriminant function of the admission scores. RESULTS: Before modification, both scales were best at predicting independence (GCS and ICS, 71% correct) and mortality (GCS, 60% correct; ICS, 56% correct). The modifications produced a modest improvement in the ability of both scales to better predict levels of outcome (modified GCS: home with help, 33% correct, independent, 71% correct; modified ICS: home with help, 0% correct, independent, 74% correct). CONCLUSIONS: By deleting items with low reliability from the ICS and the GCS we achieved improved reliability and predictive validity. The improvement in predictive power, however, was inadequate to accurately predict functional outcome. Combining clinical scales with other demographic, physiological, functional, and radiographic data will be needed to achieve useful predictions of functional outcome.

Adult↗
PubMed2004

Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical molecules.

Computational models are currently being used by regulatory agencies and within the pharmaceutical industry to predict the mutagenic potential of new chemical entities. These models rely heavily, although not exclusively, on bacterial mutagenicity data of nonpharmaceutical-type molecules as the primary knowledge base. To what extent, if any, this has limited the ability of these programs to predict genotoxicity of pharmaceuticals is not clear. In order to address this question, a panel of 394 marketed pharmaceuticals with Ames Salmonella reversion assay and other genetic toxicology findings was extracted from the 2000-2002 Physicians' Desk Reference and evaluated using MCASE, TOPKAT, and DEREK, the three most commonly used computational databases. These evaluations indicate a generally poor sensitivity of all systems for predicting Ames positivity (43.4-51.9% sensitivity) and even poorer sensitivity in prediction of other genotoxicities (e.g., in vitro cytogenetics positive; 21.3-31.9%). As might be expected, all three programs were more highly predictive for molecules containing carcinogenicity structural alerts (i.e., the so-called Ashby alerts; 61% +/- 14% sensitivity) than for those without such alerts (12% +/- 6% sensitivity). Taking all genotoxicity assay findings into consideration, there were 84 instances in which positive genotoxicity results could not be explained in terms of structural alerts, suggesting the possibility of alternative mechanisms of genotoxicity not relating to covalent drug-DNA interaction. These observations suggest that the current computational systems when applied in a traditional global sense do not provide sufficient predictivity of bacterial mutagenicity (and are even less accurate at predicting genotoxicity in tests other than the Salmonella reversion assay) to be of significant value in routine drug safety applications. This relative inability of all three programs to predict the genotoxicity of drugs not carrying obvious DNA-reactive moieties is discussed with respect to the nature of the drugs whose positive responses were not predicted and to expectations of improving the predictivity of these programs. Limitations are primarily a consequence of incomplete understanding of the fundamental genotoxic mechanisms of nonstructurally alerting drugs rather than inherent deficiencies in the computational programs. Irrespective of their predictive power, however, these programs are valuable repositories of structure-activity relationship mutagenicity data that can be useful in directing chemical synthesis in early drug discovery.

Computer Simulation↗
PubMed2006

Exploring predictive and reproducible modeling with the single-subject FIAC dataset.

Predictive modeling of functional magnetic resonance imaging (fMRI) has the potential to expand the amount of information extracted and to enhance our understanding of brain systems by predicting brain states, rather than emphasizing the standard spatial mapping. Based on the block datasets of Functional Imaging Analysis Contest (FIAC) Subject 3, we demonstrate the potential and pitfalls of predictive modeling in fMRI analysis by investigating the performance of five models (linear discriminant analysis, logistic regression, linear support vector machine, Gaussian naive Bayes, and a variant) as a function of preprocessing steps and feature selection methods. We found that: (1) independent of the model, temporal detrending and feature selection assisted in building a more accurate predictive model; (2) the linear support vector machine and logistic regression often performed better than either of the Gaussian naive Bayes models in terms of the optimal prediction accuracy; and (3) the optimal prediction accuracy obtained in a feature space using principal components was typically lower than that obtained in a voxel space, given the same model and same preprocessing. We show that due to the existence of artifacts from different sources, high prediction accuracy alone does not guarantee that a classifier is learning a pattern of brain activity that might be usefully visualized, although cross-validation methods do provide fairly unbiased estimates of true prediction accuracy. The trade-off between the prediction accuracy and the reproducibility of the spatial pattern should be carefully considered in predictive modeling of fMRI. We suggest that unless the experimental goal is brain-state classification of new scans on well-defined spatial features, prediction alone should not be used as an optimization procedure in fMRI data analysis.

Artifacts↗
PubMed2005

Home blood glucose prediction: clinical feasibility and validation in islet cell transplantation candidates.

AIMS/HYPOTHESIS: Diabetic subjects do home monitoring to substantiate their success (or failure) in meeting blood glucose targets set by their providers. To succeed, patients require decision support, which, until now, has not included knowledge of future blood glucose levels or of hypoglycaemia. To remedy this, we devised a glucose prediction engine. This study validates its predictions. METHODS: The prediction engine is a computer program that accesses a central database in which daily records of self-monitored blood glucose data and life-style parameters are stored. New data are captured by an interactive voice response server on-line 24 h a day, 7 days a week. Study subjects included 24 patients with debilitating hypoglycaemia (unawareness), which qualified them for islet cell transplantation. Comparison of each prediction with the actually observed data was done using a Clarke Error Grid (CEG). Patients and providers were blinded as to the predictions. RESULTS: Prior to transplantation, a total of 31,878 blood glucose levels were reported by the study subjects. Some 31,353 blood glucose predictions were made by the engine on a total of 8,733 days-used. Of these, 79.4% were in the clinically acceptable Zones of the CEG. Of 728 observed episodes of hypoglycaemia, 384 were predicted. After transplantation, a total of 45,529 glucose measurements were reported on a total of 12,906 days-used. Some 42,316 glucose predictions were made, of which 97.5% were in the acceptable CEG Zones A and B. Successful transplantation eliminated hypoglycaemia, improved glycaemic control, lowered HbA(1)c and freed 10 of 24 patients from daily insulin therapy. CONCLUSIONS/INTERPRETATION: It is clinically feasible to generate valid predictions of future blood glucose levels. Prediction accuracy is related to glycaemic stability. Risk of hypoglycaemia can be predicted. Such knowledge may be useful in self-management.

Blood Glucose Self-Monitoring↗
PubMed1996

Does prior knowledge of safety effect help to predict how effective a measure will be?

Studies evaluating the effects of traffic safety measures are often done for the purpose of predicting the effects of future applications of the measures. The predictive value of evaluation studies is unknown. Some general arguments for and against attributing a general predictive value to the results of evaluation studies are discussed. Predictability is shown to depend on many factors. Meta-analyses of evidence from evaluation studies can be used as a basis for testing the predictive performance of such studies. The predictive performance of studies that have evaluated the safety effects of road lighting and traffic separation is tested. Predictive performance is found to depend mainly on whether the results of evaluation studies are stable over time or exhibit a trend. In the latter case, predictions based on evidence accumulated before the trend became apparent can be very erroneous. It is shown that increasing the amount of evidence that predictions are based on does not necessarily make the predictions more accurate. More research does not always improve predictive performance.

Accidents, Traffic↗
PubMed2004

Usefulness of clinical prediction rules for the diagnosis of venous thromboembolism: a systematic review.

PURPOSE: To summarize the evidence on the predictive value of clinical prediction rules for the diagnosis of venous thromboembolism. METHODS: We selected all studies in the English literature in which a clinical prediction rule was prospectively validated against a reference standard, and calculated likelihood ratios, predictive values, and the area under the receiver operating characteristic (ROC) curve for each prediction rule. RESULTS: Twenty-three studies met our eligibility criteria: 17 evaluated prediction rules for the diagnosis of deep venous thrombosis and six evaluated rules for pulmonary embolism. The most frequently evaluated prediction rule for deep vein thrombosis was the Wells rule, which had median positive likelihood ratios of 6.62 for patients with a high pretest probability, 1 for moderate pretest probability, and 0.22 for low pretest probability. The median area under the ROC curve was 0.82. Addition of the D-dimer test to the prediction rule increased the median area under the curve to 0.90. The Wells prediction rule was the most commonly studied for pulmonary embolus and had median positive likelihood ratios of 6.75 for those with high pretest probability, 1.82 for moderate pretest probability, and 0.13 for low pretest probability. The median area under the ROC curve was 0.82. CONCLUSION: The Wells prediction rule is useful in identifying patients at low risk of being diagnosed with venous thromboembolism. The addition of a rapid latex D-dimer assay improved the overall performance of the prediction rule.

Clinical Trials as Topic↗
PubMed2002

Comparison of energy prediction equations with measured resting energy expenditure in children with sickle cell anemia.

OBJECTIVE: To determine the accuracy of energy prediction equations when compared with measured resting energy expenditure (REE) in children with sickle cell anemia. To develop a modified equation that more accurately estimates the energy needs of children with sickle cell anemia and to cross-validate these on a different set of patients (test patients). DESIGN: REE was measured in children using indirect calorimetry and compared with predicted values using the Harris-Benedict and the Food and Agriculture Organization/World Health Organization/United Nations University equations (WHO). SUBJECTS/SETTING: Eighteen patients participated in the original sample that compared predicted with measured energy expenditure. The modified equations were developed using the original 18 patients. A test population of 20 different patients was used to validate the modified equations. STATISTICAL ANALYSIS: Wilcoxon signed-rank test was performed to compare measured with predicted REE. The correlation analysis method and multiple linear regression method were used to develop 2 modified versions for the Harris-Benedict and WHO prediction equations. RESULTS: When compared with the mean predicted REE using the Harris-Benedict and WHO equations, the mean measured REE was 14% and 12% greater than both (P=.005 and P=.014, respectively). Two modified equations were developed from the Harris-Benedict and WHO equations. Based on the data from the test patients, the mean measured REE was 15% greater than the mean predicted REE based on the Harris-Benedict and WHO equations (P=.0001 for both). When the modified Harris-Benedict and WHO equations were used, there was almost no difference in the mean measured REE and the mean predicted REE (mean difference using Harris-Benedict = 14, P = .9273; mean difference using WHO = -13, P = .6215). CONCLUSION: Both energy prediction equations underestimated REE in children with sickle cell anemia. The 2 modified versions of the energy prediction equations that we propose predicted the energy needs of these children much more accurately; however, the modified equations need to be validated through application to other children with sickle cell anemia.

Analysis of Variance↗
PubMed1999

Low-dose dobutamine testing using contrast left ventriculography in the same session as coronary angiography predicts the improvement of left ventricular function after coronary angioplasty in postinfarction patients.

The role of percutaneous transluminal coronary angioplasty (PTCA) in the subacute or chronic phases of myocardial infarction remains controversial. This study investigates the usefulness of dobutamine contrast left ventriculography in a single session with coronary angiography for predicting the improvement of ventricular function after PTCA. The study group consisted of 30 patients in whom a contrast left ventricular angiogram and PTCA were performed after a first myocardial infarction. The centerline method was used to calculate dysfunction extent at baseline and its variation during dobutamine infusion at 7.5 microg/kg/min; contractile reserve was defined as a significant (> or = 15%) reduction of dysfunction extent. A second ventricular angiogram was performed 6 months later in all patients. Abnormal wall motion extent decreased at 6 months after PTCA (84+/-21% vs 70+/-29%, p = 0.0001). Wall motion improvement after PTCA correlated with the response to dobutamine (r = 0.54, p = 0.002). Ten patients showed a significant reduction (> or = 15%) of dysfunction extent at 6 months; dobutamine testing had a 80% sensitivity, 84% specificity, 67% positive predictive value, and 89% negative predictive value in detecting regional function improvement. In the subgroup of 21 patients without restenosis, both the correlation between dysfunction improvement after PTCA and response to dobutamine (r = 0.72, p = 0.0001) and the accuracy of dobutamine testing (sensitivity 88%, specificity 92%, positive predictive value 88%, and negative predictive value 92%) increased. The ejection fraction significantly increased (>5%) after PTCA in 6 patients; dobutamine testing had a 67% sensitivity, 74% specificity, 44% positive predictive value, and 88% negative predictive value in predicting the increase in the ejection fraction. In the subgroup without restenosis the improvement of the ejection fraction correlated with the response to dobutamine (r = 0.63, p = 0.007), and the sensitivity of dobutamine testing was 80%, specificity 83%, positive predictive value 67%, and negative predictive value 91%. In conclusion, dobutamine contrast left ventriculography testing in the same session as coronary angiography predicts regional function and ejection fraction improvement after PTCA in postinfarction patients, particularly when restenosis does not develop.

Aged↗
PubMed2003

Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection.

BACKGROUND: Hepatocellular carcinoma has a poor prognosis because of the high intrahepatic recurrence rate. There are technological limitations to traditional methods such as TNM staging for accurate prediction of recurrence, suggesting that new techniques are needed. METHODS: We investigated mRNA expression profiles in tissue specimens from a training set, comprising 33 patients with hepatocellular carcinoma, with high-density oligonucleotide microarrays representing about 6000 genes. We used this training set in a supervised learning manner to construct a predictive system, consisting of 12 genes, with the Fisher linear classifier. We then compared the predictive performance of our system with that of a predictive system with a support vector machine (SVM-based system) on a blinded set of samples from 27 newly enrolled patients. FINDINGS: Early intrahepatic recurrence within 1 year after curative surgery occurred in 12 (36%) and eight (30%) patients in the training and blinded sets, respectively. Our system correctly predicted early intrahepatic recurrence or non-recurrence in 25 (93%) of 27 samples in the blinded set and had a positive predictive value of 88% and a negative predictive value of 95%. By contrast, the SVM-based system predicted early intrahepatic recurrence or non-recurrence correctly in only 16 (60%) individuals in the blinded set, and the result yielded a positive predictive value of only 38% and a negative predictive value of 79%. INTERPRETATION: Our system predicted early intrahepatic recurrence or non-recurrence for patients with hepatocellular carcinoma much more accurately than the SVM-based system, suggesting that our system could serve as a new method for characterising the metastatic potential of hepatocellular carcinoma.

Carcinoma, Hepatocellular↗
PubMed1997

Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions.

BACKGROUND: Artificial neural networks are computer programs that can be used to discover complex relations within data sets. They permit the recognition of patterns in complex biological data sets that cannot be detected with conventional linear statistical analysis. One such complex problem is the prediction of outcome for individual patients treated for colorectal cancer. Predictions of outcome in such patients have traditionally been based on population statistics. However, these predictions have little meaning for the individual patient. We report the training of neural networks to predict outcome for individual patients from one institution and their predictive performance on data from a different institution in another region. METHODS: 5-year follow-up data from 334 patients treated for colorectal cancer were used to train and validate six neural networks designed for the prediction of death within 9, 12, 15, 18, 21, and 24 months. The previously trained 12-month neural network was then applied to 2-year follow-up data from patients from a second institution; outcome was concealed. No further training of the neural network was undertaken. The network's predictions were compared with those of two consultant colorectal surgeons supplied with the same data. FINDINGS: All six neural networks were able to achieve overall accuracy greater than 80% for the prediction of death for individual patients at institution 1 within 9, 12, 15, 18, 21, and 24 months. The mean sensitivity and specificity were 60% and 88%. When the neural network trained to predict death within 12 months was applied to data from the second institution, overall accuracy of 90% (95% CI 84-96) was achieved, compared with the overall accuracy of the colorectal surgeons of 79% (71-87) and 75% (66-84). INTERPRETATION: The neural networks were able to predict outcome for individual patients with colorectal cancer much more accurately than the currently available clinicopathological methods. Once trained on data from one institution, the neural networks were able to predict outcome for patients from an unrelated institution.

Bias↗
PubMedDate not supplied

A validation of two preoperative nomograms predicting recurrence following radical prostatectomy in a cohort of European men.

Kattan et al. at Baylor College of Medicine and D'Amico et al. at Harvard Medical School have each developed preoperative nomograms for prostate cancer recurrence after radical prostatectomy based on readily available clinical variables. Calibration and validation of those tools was achieved using North American patient cohorts, and their validity has not yet been shown in patients from other continents. We investigated the predictive accuracy of these nomograms when applied to European men with localized prostate cancer. Clinical data from patients who underwent radical prostatectomy at the University-Hospital Hamburg and fitted the respective derivation criteria were used for external validation (n = 1003 for the Kattan-Nomogram, n = 932 men for the D'Amico-Nomogram). Nomogram predictions of the probability for 2-years and 5-years freedom from recurrence predicted by the D'Amico-Nomogram and the Kattan-Nomogram respectively were compared with actual follow-up. The predictive accuracy of the nomograms was tested using areas under the receiver-operating-characteristic curves (AUC). The D'Amico-Nomogram AUC predicting 2-years probability of freedom from PSA recurrence was 0.80 vs. Kattan-Nomogram 5-years prediction with an AUC of 0.83. Using the 932 patients who exactly fit the derivation criteria of both nomograms, the predictive accuracy of the Kattan-Nomogram was 0.81. The superiority in predictive accuracy of the Kattan-Nomogram was statistically significant (p = 0.0274) but of unclear clinical significance. The two nomograms predicted recurrence with similar accuracy when applied to men diagnosed with localized prostate cancer in Germany. The high predictive accuracy of both nomograms demonstrates that these predictive tools derived in the U.S. can be applied to non-U.S. patients.

Cohort Studies↗
PubMed2003

A multiple in silico program approach for the prediction of mutagenicity from chemical structure.

We have conducted an evaluation of three of the most widely used commercial toxicity prediction programs, Toxicity Prediction by Komputer Assisted Technology (TOPKAT), Deductive Estimation of Risk from Existing Knowledge (DEREK) for Windows (DfW) and CASETOX. The three programs were evaluated for their ability to predict Ames test mutagenicity using 520 proprietary drug candidate (Test set 1) and 94 commercial (Test set 2) compounds. The study demonstrates that these three commercially available programs are useful, with limitations in their ability to predict mutagenicity over a wide range of chemical space, i.e. global predictivity. Individually, each of the programs performed at an acceptable level for overall accuracy, i.e. the ability to predict the correct outcome. However, analysis of the predictions indicates that the overall accuracy figure is heavily weighted by the ability of the programs to correctly predict non-mutagens, whereas none of the programs individually performed well in the prediction of novel mutagenic structures, i.e. Ames positive compounds. The performance of these programs' in predicting Ames positive mutagens appeared to be independent of the chemical utility of the compound, i.e. industrial, agricultural or pharmaceutical. The combination of program predictions provided some improvement in overall accuracy, sensitivity and specificity.

Mutagenicity Tests↗
PubMed2003

Reduction and lumping of physiologically based pharmacokinetic models: prediction of the disposition of fentanyl and pethidine in humans by successively simplified models.

Physiologically based pharmacokinetic (PBPK) models can be used to predict drug disposition in humans from animal data and the influence of disease or other changes in physiology on the pharmacokinetics of a drug. The potential usefulness of a PBPK model must however be balanced against the considerable effort needed for its development. Proposed methods to simplify PBPK modeling include predicting the necessary tissue:blood partition coefficients (kp) from physicochemical data on the drug instead of determining them in vivo, formal lumping of model compartments, and replacing the various kp values of the organs and tissues by only two values, for "fat" and "lean" tissues, respectively. The aim of this study was to investigate the effects of simplifying complex PBPK models on their ability to predict drug disposition in humans. Arterial plasma concentration curves of fentanyl and pethidine were simulated by means of a number of successively reduced models. Median absolute prediction errors were used to evaluate the performance of each model, in relation to arterial plasma concentration data from clinical studies, and the Wilcoxon matched pairs test was used for comparison of predictions. An originally diffusion-limited model for fentanyl was simplified to perfusion-limitation, and this model was either lumped, reducing 11 organ/tissue compartments to six, or changed to a model based on only two kp values, those of fat (used for fat and lungs) and muscle (used for all other tissues). None of these simplifications appreciably changed the predictions of arterial drug concentrations in the 10 patients. Perfusion-limited models for pethidine were set up using either experimentally determined [Gabrielsson et al. 1986] or theoretically calculated [Davis and Mapleson 1993] kp values, and predictions using the former were found to be significantly better. Lumping of the models did not appreciably change the predictions; however, going from a full set of kp values to only two ("fat" and "lean") had an adverse effect. Using a kp for lungs determined either in rats or indirectly in humans [Persson et al. 1988], i.e., a total of three kp values, improved these predictions. In conclusion, this study strongly suggested that complex PBPK models for lipophilic basic drugs may be considerably reduced with marginal loss of power to predict standard plasma pharmacokinetics in humans. Determination of only two or three kp values instead of a "full" set can mean an important reduction of experimental work to define a basic model. Organs of particular pharmacological or toxicological interest should of course be investigated separately as needed. This study also suggests and applies a simple method for statistical evaluation of the predictions of PBPK models.

Adult↗
PubMed2000

Comparing the predictive validity of DUI risk screening instruments: development of validation standards.

AIMS: This study compares the predictive efficacy of driving under the influence (DUI) screening instruments validated in previous studies, illustrates how variations in base rates of failure and selection ratios affect conclusions concerning the efficacy of different instruments, and develops evaluation standards to ensure valid comparisons of risk prediction instruments. DESIGN: The study: (1) examines a sample of 4815 DUI offenders to illustrate how variations in base rates of failure and selection ratios affect traditional measures of predictive efficacy, (2) uses such measures to compare the predictive efficacy of 10 instruments validated in previous studies, and (3) demonstrates the use of a measure of predictive efficacy which is relatively insensitive to the aforementioned variations. FINDINGS: While three instruments examined at specific cut-points consistently ranked highest on several measures of predictive efficacy, use of different evaluation standards produced substantively different conclusions regarding the efficacy of different instruments. Based on the analyses, standards for validation of risk prediction instruments were developed. CONCLUSIONS: The findings illustrate how failure to use equivalent standards have led to erroneous conclusions concerning the relative predictive efficacy of different risk prediction instruments. The standards developed in this study should facilitate equivalent comparisons of the predictive efficacy of risk prediction instruments.

Alcohol Drinking↗
PubMed2000

A prospective study of laboratory and clinical measures of postural stability to predict community-dwelling fallers.

BACKGROUND: The identification of specific risk factors for falls in community-dwelling elderly persons is required to detect early changes and permit a preventative approach to management. This study determines the ability of various laboratory measures and clinical tests of postural stability to prospectively predict fallers in community-dwelling elderly women. METHODS: One hundred elderly women (65-86 years, mean age 73 +/- 5 years) performed a reaction-time step task, a limits of stability, and a quiet stance balance task. Postural muscle timing and movement speed were recorded during the step task. Center of pressure (COP) motion was recorded in quiet stance and at the limits of stability. Four common clinical balance tests were performed, and balance confidence, medical and activity history questionnaires were completed. Subjects were followed up regularly for a 6-month period following testing to determine the frequency and characteristics of any falls that occurred. Predictive capabilities of the balance measures to determine fallers were determined through logistic regression models. RESULTS: The clinical balance tests investigated were not able to predict fallers in this community-dwelling elderly population. A combination of variables from the laboratory tasks provided the best overall prediction rate (77%) of fallers (sensitivity 51%) and nonfallers (specificity 91%) from laboratory measures. Of these, step movement time and gluteus medius onset times were the factors best able to predict fallers. Alone, measures of COP motion in quiet stance and at the limits of stability had a poor ability to predict fallers, although they could correctly identify most nonfallers. Prediction was not significantly improved when clinical balance test results were added to the most predictive laboratory measures. CONCLUSIONS: Not all older adults with a reduction in balance ability reported a fall over a 6-month period. Of those who did, a combination of measures reflective of different aspects of mediolateral postural stability during a rapid step task, quiet stance, and movement to the limits of stability were best able to predict faller status, with nonfallers better predicted than fallers. These results emphasize the importance of the multifactorial nature of falls in the community-dwelling elderly population in that the clinical and laboratory measures did not predict a high proportion of fallers.

Accidental Falls↗
PubMed2005

GFR prediction using the MDRD and Cockcroft and Gault equations in patients with end-stage renal disease.

BACKGROUND: Although prediction equations are recommended to determine GFR and creatinine clearance (CrCl), neither the MDRD equations nor the Cockcroft and Gault formula have been validated for the low levels of GFR present in end-stage renal disease (ESRD). The accuracy of the MDRD equations and the Cockcroft and Gault formula in predicting GFR and CrCl, respectively, was examined in patients with ESRD and its relationship to the basal GFR and two markers of malnutrition, urinary creatinine and body fat determined. METHODS: Inulin clearance (C(in)) was measured in 26 non-diabetic patients with ESRD and the 24 h CrCl determined. GFR was predicted using three equations derived from the MDRD study population containing four to six variables. Both CrCl and GFR were predicted from the Cockcroft and Gault formula. Estimates of bias and precision were obtained and Bland and Altman analysis performed. Body fat was measured by DEXA scan. RESULTS: The predicted GFR (MDRD) was 10% lower than C(in) (8.83+/-0.71 ml/min/1.73 m2) with all three MDRD equations, showing a similar degree of precision and bias. C(in) gave a negative correlation with the difference between the predicted GFR (MDRD) and the measured GFR. The predicted GFR (MDRD) underestimated GFR when C(in) >8 ml/min/1.73 m2 but overestimated GFR when C(in) <8 ml/min/1.73 m2. The Cockcroft and Gault formula overestimated CrCl by 14% and overestimated C(in) by 35%. C(in) gave a negative correlation with the difference between the predicted GFR (Cockcroft and Gault) and measured GFR, overestimating GFR when C(in) <13 ml/min/1.73 m2. The overestimation of GFR by the MDRD equation was not associated with urinary creatinine excretion. However, both Cockcroft and Gault and the MDRD predictions showed a positive, but weak, correlation with body fat. CONCLUSION: The MDRD equations were more accurate in predicting the group mean GFR in patients with ESRD than the Cockcroft and Gault formula. However, the predicted GFR using either formula was related to the basal GFR and percentage body fat.

Adolescent↗
PubMed1992

Effect of epinephrine on the ability of end-tidal carbon dioxide readings to predict initial resuscitation from cardiac arrest.

OBJECTIVE: To determine if the administration of epinephrine changes the partial pressure of end-tidal CO2 during cardiac arrest, as previously reported. Such a change could diminish the demonstrated ability of end-tidal CO2 measurements to predict resuscitation from cardiac arrest. DESIGN: The partial pressures of end-tidal CO2 of adult cardiac arrest patients who received i.v. epinephrine in doses from 1 to 15 mg were monitored throughout arrest. SETTING: Emergency department of a university hospital. PATIENTS: Adults (n = 64) in cardiac arrest with a mean age of 70 +/- 12 yrs, of whom 35 were males and 15 had a mean time of return of spontaneous circulation of 6.5 +/- 11 hrs. INTERVENTIONS: End-tidal CO2 (in torr) was analyzed on arrival, before the first dose of epinephrine, and 4 mins after epinephrine was administered in varying doses chosen by the supervising physician. MEASUREMENTS AND RESULTS: The end-tidal CO2 decreased an average of 0.3 torr (0.04 kPa) after epinephrine was administered. Patients with a return of pulse had a decrease of -2 torr (-0.3 kPa) vs. an increase of 0.3 torr (0.04 kPa) for those patients with no return of pulse (p = .07). In 33% of patients, there was no change; in 28%, the partial pressure of end-tidal CO2 increased, and in 39%, it decreased. There was no correlation between the change in end-tidal CO2 after epinephrine and whether or not patients regained a pulse (r2 = .08, p = .07), although a decrease in end-tidal CO2 was most often associated with return of pulse. At a threshold of 10 torr (1.3 kPa), the first end-tidal CO2 had a positive predictive value for return of pulse of 50% and a negative predictive value of 82%. Just before epinephrine administration, the positive predictive value was 71% and the negative predictive value was 83%; 4 mins after epinephrine administration, the positive predictive value was 64% and the negative predictive value was 86%. A decrease in end-tidal CO2 after epinephrine had a positive predictive value of 53% and a negative predictive value of 92%. End-tidal CO2 readings predicted resuscitation most accurately when taken after initial stabilization and before administration of epinephrine. CONCLUSIONS: Although epinephrine administration may decrease end-tidal CO2 tensions in cardiac arrest, it does so unpredictably in individual patients, and it does not eliminate the predictive value of this measurement.

Aged↗