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PubMed1991

Is it time to use evoked potentials to predict outcome in comatose children and adults?

OBJECTIVE: To determine the value of multimodality-evoked potential recordings in predicting outcome in comatose children. DESIGN: Prospective series and literature review. SETTING: Pediatric ICU in a university hospital. PATIENTS: Forty-one children with a Glasgow Coma Scale score of less than 8, who were admitted to the pediatric ICU between 1984 and 1989. INTERVENTIONS: Forty-one patients underwent brainstem auditory-evoked potential testing within 72 hrs of admission. Of these patients, 37 also had somatosensory-evoked potential testing at the same time. Four patients did not receive somatosensory-evoked potential testing for various nonmedical reasons. MEASUREMENTS AND MAIN RESULTS: Multimodality-evoked potential recordings were used to predict outcome in these comatose children. Outcomes were categorized as bad (death or chronic vegetative state) or good (all other outcomes). Survivor outcomes were determined at discharge and on subsequent follow-up visits from 1 to 3 yrs later. There were no false pessimistic predictions, and two false optimistic predictions in this series. A comprehensive literature review of coma outcome prediction, using multimodality-evoked potential recordings, revealed 20 series with 982 additional patients in whom the predictive errors of false optimism and false pessimism could be determined. Five cases of false pessimism and 99 cases of false optimism were identified in the 982 additional patients. If neonates are excluded, the false pessimism number is reduced to three. CONCLUSIONS: A bad outcome can be reliably predicted using multimodality-evoked potential recordings with little chance of a false pessimistic prediction. The acceptable error of false optimism occurs frequently, since patients often die of progressive neurologic and nonneurologic problems that may or may not be present at the time of the evoked potential recordings. Thus, in comatose children, multimodality-evoked potential recordings are a useful adjunct to clinical examination and other diagnostic aids in predicting outcome and in making decisions regarding the degree of intervention to offer.

Adolescent↗

PubMed2000

Acuteness of preoperative factors to predict hearing preservation in acoustic neuroma surgery.

OBJECTIVES: To determine in patients with acoustic neuromas the predictive factors of hearing preservation according to clinical, radiological, and electrophysiological parameters and to evaluate, for each of these predictive factors, the percentage of patients with preserved hearing. STUDY DESIGN: The study involved 107 candidates for hearing preservation attempt. Mean age was 49.7 +/- 11.4 years. Quantitative and qualitative parameters were prospectively studied. Quantitative parameters were age, duration of functional complaints, hearing loss assessed by pure tone and speech audiometry, and auditory brainstem responses (ABRs). Qualitative parameters (expressed in percentage of presence) were sex, functional complaints, vestibular deficit revealed by vestibular testings, well-shaped ABRs, wave I, III, or V of ABRs, and transient evoked otoacoustic emissions (TEOAEs). METHODS: Patients were divided into two groups according to whether their hearing was preserved (52.3%) or not preserved (47.7%). First, quantitative and qualitative factors were compared between both groups to identify predictive factors. Second, all patients were considered together and the percentage of hearing preservation was determined according to the presence of each predictive factor. RESULTS: The results confirmed the predictive value of classic parameters such as preoperative hearing level, radiological data, and trace of ABRs. They also emphasized the predictive role of other parameters such as short duration of hearing loss, presence of wave III in ABRs, and presence of TEOAEs. CONCLUSIONS: The size of the tumor and the preoperative hearing levels are longstanding predictive factors of hearing preservation in acoustic neuroma surgery, and candidates for hearing preservation are therefore now selected according to these factors. This study added more recent predictive factors and, among the 10 factors identified as predictive, the most relevant to hearing preservation were the presence of TEOAEs (69.7%), short duration of hearing loss (66.7%), and presence of wave III in ABRs (66.7%).

Acute Disease↗

PubMed1985

Computer prediction of serum theophylline concentrations in ambulatory patients.

This study assessed the ability of a computer program Simulated Kinetics (SIMKIN) to predict serum theophylline concentrations in ambulatory patients receiving oral theophylline. Data were collected by retrospective review of prospectively obtained data. A total of only 20 measured serum theophylline concentrations could be included in the study, although records of 195 patients were reviewed. An estimated patient compliance of 90-110% was required and was computed using prescription refill information. Predicted serum theophylline concentrations were generated for each patient by entering into the SIMKIN program the characteristics pertinent to theophylline disposition and the patient's theophylline dosing regimen. Actual and SIMKIN-predicted theophylline concentrations were compared by using simple linear regression and by constructing a 95% confidence interval around the mean prediction error and root mean squared error. The ability of SIMKIN to predict therapeutic category, i.e., subtherapeutic, therapeutic, or toxic, was assessed using Fisher's exact test. SIMKIN predictions of individual theophylline concentration were insufficiently accurate to replace confirmatory followup monitoring of actual levels. However, SIMKIN was able to predict the therapeutic category with 70% accuracy. We conclude that SIMKIN may be useful for categorizing a dose regimen of theophylline as therapeutic, but that it is of little use in predicting individual concentrations in outpatients when literature-averaged pharmacokinetic parameters are the sole criteria for prediction, and compliance cannot be accurately assessed.

Adolescent↗

PubMed2003

Bayesian forecasting and prediction of tacrolimus concentrations in pediatric liver and adult renal transplant recipients.

AIM: To test the predictive capacity of two recently derived population pharmacokinetic models and the usefulness of Bayesian forecasting to predict tacrolimus blood concentrations in pediatric liver and adult kidney transplant recipients. MATERIALS AND METHODS: New databases were added to the Abbottbase PKS (Bayesian dosage prediction) program to incorporate the population pharmacokinetic models developed for tacrolimus. Two independent populations of transplant recipients were used to predict tacrolimus trough concentrations. Pharmacokinetic, demographic, and covariate data were collected from patient records. Different time weighting factors were tested (1, 1.005, 1.01) and the influence of excluding data collected in the first 5 days post-transplant examined. Concentrations were predicted until the 10th tacrolimus measurement. Actual tacrolimus concentrations were compared with those predicted by the PKS program and bias and precision determined. RESULTS: Tacrolimus concentrations predicted by the PKS program were, on average, unbiased for the pediatric liver population, but were over-predicted (9%) for the adult renal population. In both populations predictions were not precise (imprecision ranged from 39 to 50%). CONCLUSIONS: Due to the imprecision seen in this study, these models could not be used in clinical practice in the immediate post-transplant period. Poor precision may be due to reliance on routine drug monitoring data alone, difficulties with expression of covariates in continuous modeling relationships in the PKS program, lack of accurate quantitative measures of liver function, or large, random intraindividual variability in the bioavailability of tacrolimus.

Adolescent↗

PubMed2004

An evaluation of two scoring systems to predict instability in fractures of the distal radius.

BACKGROUND: Various scoring scales have been introduced in the management of patients with multiple injuries and lower extremity injuries. Two scoring systems have been introduced to predict instability in distal radius fractures. The purpose of this investigation was to evaluate the accuracy of these two models in predicting instability. METHODS: A prospective study of 105 consecutive patients sustaining unilateral closed distal radius fractures was performed. Two scoring systems--the MacKenney formula and the Adolphson formula--were used to calculate the probability of fracture instability on the basis of initial presentation and injury films. The predicted probability of instability calculated from both models was then compared with actual results of instability on the basis of specific radiographic criteria at follow-up. RESULTS: Final follow-up information was available on 80 patients. There were 44 unstable fractures and 36 stable fractures at final follow-up. Using the MacKenney formula, of the 38 fractures predicted to have a low probability of instability (Pinstability < 30%), 18 (47.4%) were found to be unstable. Using the Adolphson formula, of the 28 fractures predicted to have a low probability of instability (Pstability > 70%), 14 (50%) were actually unstable. CONCLUSION: Both scoring systems were found to underestimate the degree of fracture instability and to have a negative predictive value between 47 and 50% in a prospective series of patients. In fractures predicted to have a low probability of instability in both models, we found a poor correlation between predicted instability and actual instability. Our results demonstrate the limitations of two scoring systems in predicting fracture stability and in making clinical decisions on the basis of their results.

Adult↗

PubMed2001

Depletion models can predict shorebird distribution at different spatial scales.

Predicting the impact of habitat change on populations requires an understanding of the number of animals that a given area can support. Depletion models enable predictions of the numbers of individuals an area can support from prey density and predator searching efficiency and handling time. Depletion models have been successfully employed to predict patterns of abundance over small spatial scales, but most environmental change occurs over large spatial scales. We test the ability of depletion models to predict abundance at a range of scales with black-tailed godwits, Limosa limosa islandica. From the type II functional response of godwits to their prey, we calculated the handling time and searching efficiency associated with these prey. These were incorporated in a depletion model, together with the density of available prey determined from surveys, in order to predict godwit abundance. Tests of these predictions with Wetland Bird Survey data from the British Trust for Ornithology showed significant correlations between predicted and observed densities at three scales: within mudflats, within estuaries and between estuaries. Depletion models can thus be powerful tools for predicting the population size that can be supported on sites at a range of scales. This greatly enhances our confidence in predictions of the consequences of environmental change.

Animals↗

PubMed2005

Assessing the limits of genomic data integration for predicting protein networks.

Genomic data integration--the process of statistically combining diverse sources of information from functional genomics experiments to make large-scale predictions--is becoming increasingly prevalent. One might expect that this process should become progressively more powerful with the integration of more evidence. Here, we explore the limits of genomic data integration, assessing the degree to which predictive power increases with the addition of more features. We focus on a predictive context that has been extensively investigated and benchmarked in the past-the prediction of protein-protein interactions in yeast. We start by using a simple Naive Bayes classifier for integrating diverse sources of genomic evidence, ranging from coexpression relationships to similar phylogenetic profiles. We expand the number of features considered for prediction to 16, significantly more than previous studies. Overall, we observe a small, but measurable improvement in prediction performance over previous benchmarks, based on four strong features. This allows us to identify new yeast interactions with high confidence. It also allows us to quantitatively assess the inter-relations amongst different genomic features. It is known that subtle correlations and dependencies between features can confound the strength of interaction predictions. We investigate this issue in detail through calculating mutual information. To our surprise, we find no appreciable statistical dependence between the many possible pairs of features. We further explore feature dependencies by comparing the performance of our simple Naive Bayes classifier with a boosted version of the same classifier, which is fairly resistant to feature dependence. We find that boosting does not improve performance, indicating that, at least for prediction purposes, our genomic features are essentially independent. In summary, by integrating a few (i.e., four) good features, we approach the maximal predictive power of current genomic data integration; moreover, this limitation does not reflect (potentially removable) inter-relationships between the features.

Algorithms↗

PubMed2006

Iterative gene prediction and pseudogene removal improves genome annotation.

Correct gene prediction is impaired by the presence of processed pseudogenes: nonfunctional, intronless copies of real genes found elsewhere in the genome. Gene prediction programs frequently mistake processed pseudogenes for real genes or exons, leading to biologically irrelevant gene predictions. While methods exist to identify processed pseudogenes in genomes, no attempt has been made to integrate pseudogene removal with gene prediction, or even to provide a freestanding tool that identifies such erroneous gene predictions. We have created PPFINDER (for Processed Pseudogene finder), a program that integrates several methods of processed pseudogene finding in mammalian gene annotations. We used PPFINDER to remove pseudogenes from N-SCAN gene predictions, and show that gene prediction improves substantially when gene prediction and pseudogene masking are interleaved. In addition, we used PPFINDER with gene predictions as a parent database, eliminating the need for libraries of known genes. This allows us to run the gene prediction/PPFINDER procedure on newly sequenced genomes for which few genes are known.

Animals↗

PubMed2006

A Consensus Data Mining secondary structure prediction by combining GOR V and Fragment Database Mining.

The major aim of tertiary structure prediction is to obtain protein models with the highest possible accuracy. Fold recognition, homology modeling, and de novo prediction methods typically use predicted secondary structures as input, and all of these methods may significantly benefit from more accurate secondary structure predictions. Although there are many different secondary structure prediction methods available in the literature, their cross-validated prediction accuracy is generally <80%. In order to increase the prediction accuracy, we developed a novel hybrid algorithm called Consensus Data Mining (CDM) that combines our two previous successful methods: (1) Fragment Database Mining (FDM), which exploits the Protein Data Bank structures, and (2) GOR V, which is based on information theory, Bayesian statistics, and multiple sequence alignments (MSA). In CDM, the target sequence is dissected into smaller fragments that are compared with fragments obtained from related sequences in the PDB. For fragments with a sequence identity above a certain sequence identity threshold, the FDM method is applied for the prediction. The remainder of the fragments are predicted by GOR V. The results of the CDM are provided as a function of the upper sequence identities of aligned fragments and the sequence identity threshold. We observe that the value 50% is the optimum sequence identity threshold, and that the accuracy of the CDM method measured by Q(3) ranges from 67.5% to 93.2%, depending on the availability of known structural fragments with sufficiently high sequence identity. As the Protein Data Bank grows, it is anticipated that this consensus method will improve because it will rely more upon the structural fragments.

Algorithms↗

PubMed2005

Computer-aided disease prediction system: development of application software with SAS component language.

AIMS: The intricacy of predictive models associated with prognosis and risk classification of disease often discourages medical personnel who are interested in this field. The aim of this study was therefore to develop a computer-aided disease prediction model underpinning a step-by-step statistics-guided approach including five components: (1) data management; (2) exploratory analysis; (3) type of predictive model; (4) model verification; (5) interactive mode of disease prediction using SAS 8.02 Windows 2000 as a platform. METHODS: The application of this system was illustrated by using data from the Swedish Two-County Trial on breast cancer screening. The effects of tumour size, node status, and histological grade on breast cancer death using logistic regression model or survival models were predicted. A total of 20 questions were designed to exemplify the usefulness of each component. We also evaluated the system using a controlled randomized trial. Times to finish the above 20 questions were used as endpoint to evaluate the performance of the current system. User satisfaction with the current system such as easy to use, the efficiency of risk prediction, and the reduction of barrier to predictive model was also evaluated. RESULTS: The intervention group not only performed more efficiently than the control group but also satisfied with this application software. CONCLUSIONS: The MD-DP-SOS system characterized by menu-driven style, comprehensiveness, accuracy and adequacy assessment, and interactive mode of disease prediction is helpful for medical personnel who are involved in disease prediction.

Decision Support Techniques↗

PubMed1993

The accuracy of predicting treadmill VO2max for adults with mental retardation, with and without Down's syndrome, using ACSM gender- and activity-specific regression equations.

The purpose of this study was to examine the validity of the American College of Sports Medicine's (ACSM) prediction equations for calculating peak oxygen consumption (VO2max) in young adults with mental retardation. A total of 32 subjects with mental retardation participated in this study: 15 young adults with Down's syndrome (DS) and 17 non-DS young adults (NDS). Subjects were matched for age, gender and intelligence quotient (IQ). Subjects were given a standard treadmill-graded exercise test to determine peak heart rate (HR) and peak oxygen consumption (VO2max). Subjects were connected to a metabolic cart during the test. Peak VO2 was predicted using ACSM's prediction equations where predicted VO2max is: men, 57.8-0.445 (age); and women, 42.3-0.356 (age). Statistical significance between groups was determined using a two-tail t-test, with alpha set a priori at 0.05. The DS group had a significantly (P = 0.0003) lower peak HR (DS 155.90 +/- 12.12 vs NDS 175.38 +/- 9.87) and per cent HR achieved (P = 0.0007) (DS 80.26 +/- 6.76 vs NDS 89.39 +/- 4.46) as compared to the NDS group. Differences were also found between groups with respect to peak oxygen consumption. The DS had a significantly (P = 0.006) lower peak oxygen uptake (ml kg-1 min-1) as compared to the NDS group (23.68 +/- 4.01 vs 31.00 +/- 7.11, respectively). Significant differences (P = 0.007) were accordingly observed with respect to per cent predicted oxygen uptake achieved (DS 55.22 +/- 10.61 vs NDS 73.27 +/- 19.15). A nearly two-fold difference (P = 0.01) was observed with respect to the functional aerobic impairment between the DS (44.79 +/- 10.61) and NDS (28.29 +/- 18.63) groups, further illustrating the impaired peak cardiovascular capacities of both groups. The results of this study indicated that use of the ACSM gender and activity specific prediction equations in young adults with mental retardation (DS and NDS), peak VO2 is significantly over-predicted (83.9 and 39.2%, respectively). Therefore, peak oxygen consumption and derived exercise prescriptions must be based on actual measurements, rather than via ACSM prediction equations. Otherwise, training intensities may be over-predicted and impose possible health risks.

Adult↗

PubMed2006

Accurate prediction of autologous stem cell apheresis yields using a double variable-dependent method assures systematic efficiency control of continuous flow collection procedures.

BACKGROUND AND OBJECTIVES: Stem cell collection is a standard procedure for the procurement of autologous grafts to rescue myelosuppression induced by high-dose treatments. Accurate prediction of collection yields may contribute to optimize planning and quality control of collection. MATERIALS AND METHODS: Data of 313 autologous haematopoietic stem cell (AHSC) evaluable collections performed in 208 patients with haematologic and non-haematologic neoplasms from seven centres were prospectively analysed to test the accuracy of yield predictions generated by a formula that required the input of peripheral blood (PB) CD34+ cell precount and desired PB volume to be processed. Data were matched in a standard linear regression, in a zero-point regression analysis and tested for prediction accuracy. Further 165 AHSC collections were analysed on a single-centre basis, using yield predictions as reference standards. RESULTS: Analysis showed high levels of correlation between measured collection yields (my) and predictions (py) (R = 0.85; P = 0.000000) as well as high degree of prediction accuracy (my vs. py at paired t-test: P = 0.114781; median my/py ratio = 1.23). Analysis of additional 165 AHSC collections on a single-centre basis showed that the analysed centres had 70% or more measured yields comprising the 0.6-1.8 interval of the my/py ratio. The observance of the 'efficiency' my/py interval assured collection quality control in these centres confirming the reliability of the method. CONCLUSIONS: This prediction method generates accurate and immediate yield predictions allowing collection planning and rapid efficiency control. As a consequence of our study, four centres out of seven use the described method to plan both leukapheresis number and single-procedure blood processing volume while the remaining three centres plan leukapheresis number on the basis of our predictions, maintaining a fixed single-procedure 200 ml/kg blood volume processing, according to their centre AHSC collection policy.

Adolescent↗

PubMed2006

The use of the Framingham equation to predict myocardial infarctions in HIV-infected patients: comparison with observed events in the D:A:D Study.

BACKGROUND: The D:A:D (Data Collection on Adverse Events of Anti-HIV Drugs) Study, a prospective observational study on a cohort of 23 468 patients with HIV infection, indicated that the incidence of myocardial infarction (MI) increased by 26% per year of exposure to combination antiretroviral treatment (CART). However, it remains unclear whether the observed increase in the rate of MI in this population can be attributed to changes in conventional cardiovascular risk factors. OBJECTIVE: To compare the number of MIs observed among participants in the D:A:D Study with the number predicted by assuming that conventional cardiovascular risk equations apply to patients with HIV infection. METHODS: The Framingham equation, a conventional cardiovascular risk algorithm, was applied to individual patient data in the D:A:D Study to predict rates of MI by duration of CART. A series of sensitivity analyses were performed to assess the effect of model and data assumptions. Predictions were extrapolated to provide 10-year risk estimates, and various scenarios were modelled to assess the expected effect of different interventions. RESULTS: In patients receiving CART, the observed numbers of MIs during D:A:D follow up were similar to or somewhat higher than predicted numbers: 9 observed vs 5.5 events predicted, 14 vs 9.8, 22 vs 14.9, 31 vs 23.2 and 47 vs 37.0 for<1 year, 1-2 years, 2-3 years, 3-4 years and >4 years CART exposure, respectively. In patients who had not received CART, the observed number of MIs was fewer than predicted (3 observed vs 7.6 predicted). Nine per cent of the study population have a predicted 10-year risk of MI above 10%, a level usually associated with initiation of intervention on risk factors. CONCLUSIONS: A consistent feature of all analyses was that observed and predicted rates of MI increased in a parallel fashion with increased CART duration, suggesting that the observed increase in risk of MI may at least in part be explained by CART-induced changes in conventional risk factors. These findings provide guidance in terms of choosing lifestyle or therapeutic interventions to decrease those risk factors in much the same way as in persons without HIV infection.

Adult↗

PubMed1981

Prediction of the secondary structure of myelin basic protein.

An investigation into the probable secondary structure of the myelin basic protein was carried out by the application of three procedures currently in use to predict the secondary structures of proteins from knowledge of their amino acid sequences. In order to increase the accuracy of the predictions, the amino acid substitutions that occur in the basic protein from different species were incorporated into the predictive algorithms. It was possible to locate regions of probable alpha-helix, beta-structure, beta-turn, and unordered conformation (coil) in the protein. One of the predictive methods introduces a bias into the algorithm to maximize or minimize the amounts of alpha-helix and/or beta-structure present; this made it possible to assess how conditions such as pH and protein concentration or the presence of anionic amphiphilic molecules could influence the protein's secondary structure. The predictions made by the three methods were in reasonably good agreement with one another. They were consistent with experimental data, provided that the stabilizing or destabilizing effects of the environment were taken into account. According to the predictions, the extent of possible alpha-helix and beta-structure formation in the protein s severely restricted by the low frequency and extensive scattering of hydrophobic residues, along with a high frequency and extensive scattering of residues that favor the formation of beta-turns and coils. Neither prolyl residues nor cationic residues per se are responsible for the low content of alpha-helix predicted in the protein. The principal ordered conformation predicted is the beta-turn. Many of the predicted beta-turns overlap extensively, involving in some cases up to 10 residues. In some of these structures it is possible for the peptide backbone to oscillate in a sinusoidal manner, generating a flat, pleated sheetlike structure. Cationic residues located in these structures would appear to be ideally oriented for interaction with lipid phosphate groups located at the cytoplasmic surface of the myelin membrane. An analysis of possible and probable conformations that the triproline sequence could assume questions the popular notion that this sequence produces a hairpin turn in the basic protein.

Amino Acid Sequence↗

PubMed2005

Abnormal heart rate turbulence predicts the initiation of ventricular arrhythmias.

BACKGROUND: Abnormal heart rate turbulence (HRT) reflects autonomic derangements predicting all-cause mortality, yet has not been shown to predict ventricular arrhythmias in at-risk patients. We hypothesized that HRT at programmed ventricular stimulation (PVS) would predict arrhythmia initiation in patients with left ventricular dysfunction. METHODS: We studied 27 patients with coronary disease, left ventricular ejection fraction (LVEF) 26.7 +/- 9.1%, and plasma B-type natriuretic peptide (BNP) 461 +/- 561 pg/mL. Prior to arrhythmia induction at PVS, we measured sinus cycles after spontaneous or paced premature ventricular contractions (PVCs) for turbulence onset (TO; % cycle length change following PVC) and slope (TS; greatest slope of return to baseline cycle). T-wave alternans (TWA) was also measured during atrial pacing. RESULTS: At PVS, abnormal TO (> or =0%) predicted inducible ventricular tachycardia (VT; n = 10 patients; P < 0.05). TO was greater in inducible than in noninducible patients (2.3 +/- 3.1% vs -0.02 +/- 2.8%, P < 0.05) and correlated with LVEF (P < 0.05) but not with BNP. TS did not differ between groups. Conversely, ambulatory HRT differed significantly from HRT at PVS (TO -0.55 +/- 1.08% vs 0.85 +/- 3.02%, P < 0.05; TS 2.63 +/- 2.09 ms/RR vs 8.70 +/- 6.56 ms/RR, P < 0.01), and did not predict inducible VT but trended (P = 0.05) to predict sustained VT on 739 +/- 179 days follow-up. TWA predicted inducible (P < 0.05) and spontaneous (P = 0.0001) VT but did not co-migrate with HRT. CONCLUSIONS: Abnormal HRT measured at PVS predicted the induction of sustained ventricular arrhythmias in patients with ischemic cardiomyopathy. However, HRT at PVS did not correlate with ambulatory HRT, nor with TWA, both of which predicted spontaneous ventricular arrhythmias. Thus, HRT may reflect the influence of autonomic milieu on arrhythmic susceptibility and is likely complementary to traditional arrhythmic indices.

Aged↗

PubMed2000

Predicting recovery from acute asthma in an emergency diagnostic and treatment unit.

OBJECTIVE: Optimal use of emergency diagnostic and treatment unit (EDTU) resources for treatment of acute asthma should be facilitated by the selection of patients with a high probability of discharge from the EDTU. The study goal was to identify characteristics of the patient or exacerbation that could be used to predict recovery of pulmonary function within 12 hours. METHODS: Comprehensive cohort design in an urban public hospital. The subjects were 269 patients with moderately severe asthma exacerbations. Data were collected for historical and presenting features and response to treatment over 12 hours. Two outcomes were examined: 1) discharge from the EDTU and 2) achieving 50% predicted peak expiratory flow rate (PEFR) within 12 hours. RESULTS: The two outcomes showed good concordance. The third-treatment PEFR was found to be predictive of both discharge and reaching 50% predicted PEFR within 12 hours. Since the objective measure of reaching 50% predicted PEFR is more readily defined and thus more generalizable, the authors focused on this outcome when describing prediction zones. Patients with 40% or higher PEFR after third treatment had an 89% probability of reaching 50% predicted in 12 hours, while those with a third-treatment PEFR lower than 32% predicted had only a 22% probability. CONCLUSIONS: A simple objective measure of pulmonary function early in treatment discriminated among those with high, low, and intermediate probabilities of achieving a specified level of PEFR within 12 hours. Awareness of this probability could assist clinicians attempting to predict discharge from the EDTU and facilitate decision making regarding utilization of EDTU resources.

Adult↗

PubMed2000

The accuracy of oral predictive and infrared emission detection tympanic thermometers in an emergency department setting.

OBJECTIVE: To assess the accuracy of an oral predictive thermometer and an infrared emission detection (IRED) tympanic thermometer in detecting fever in an adult emergency department (ED) population, using an oral glass mercury thermometer as the criterion standard. METHODS: This was a single-center, nonrandomized trial performed in the ED of a metropolitan tertiary referral hospital with a convenience sample of 500 subjects. The temperature of each subject was taken by an oral predictive thermometer, an IRED tympanic thermometer set to "oral" equivalent, and an oral glass mercury thermometer (used as the criterion standard). A fever was defined as a temperature of 37.8 degrees C or higher. The subject's age, sex, triage category, and diagnostic group were also recorded. Sensitivity, specificity, positive and negative likelihood ratios, positive and negative predictive values, and corresponding 95% confidence intervals were calculated. Logistic regression was used to identify predictors of fever. RESULTS: The sensitivities and specificities for detection of fever of the predictive and the IRED tympanic thermometers were similar (sensitivity 85.7%/88.1% and specificity 98.7%/95.8%, respectively). The predictive thermometer had a better positive predictive value (85.7%) compared with the IRED tympanic thermometer (66.1%). The positive and negative likelihood ratios for the predictive oral thermometer were 65 and 0.14, respectively, and for the IRED tympanic thermometer 21 and 0.12, respectively, indicating that the predictive thermometer will "miss" 1 in about 7 fevers and the IRED tympanic thermometer will "miss" 1 in about 8 fevers. CONCLUSIONS: Although quick and convenient, oral predictive and IRED tympanic thermometers give readings that cannot always be relied on in the detection of fever. If we are to continue using electronic thermometers in the ED setting, we need to recognize their limitations and maintain the importance of our clinical judgment.

Emergency Service, Hospital↗

PubMed2004

Pollutant dispersion in a large indoor space. Part 2: Computational fluid dynamics predictions and comparison with a scale model experiment for isothermal flow.

UNLABELLED: This paper reports on an investigation of the adequacy of computational fluid dynamics (CFD), using a standard Reynolds Averaged Navier-Stokes (RANS) model, for predicting dispersion of neutrally buoyant gas in a large indoor space. We used CFD to predict pollutant (dye) concentration distribution in a water-filled scale model of an atrium with a continuous pollutant source in the absence of furniture and occupants. Predictions from the RANS formulation are comparable with an ensemble average of independent identical experiments. Model results were compared with pollutant concentration data in a horizontal plane from experiments in a scale model atrium. Predictions were made for steady-state (fully developed) and transient (developing) pollutant concentrations. Agreement between CFD predictions and ensemble averaged experimental measurements is quantified using the ratios of CFD-predicted and experimentally measured dye concentration at a large number of points in the measurement plane. Agreement is considered good if these ratios fall between 0.5 and 2.0 at all points in the plane. The standard k-epsilon two-equation turbulence model obtains this level of agreement and predicts pollutant arrival time to the measurement plane within a few seconds. These results suggest that this modeling approach is adequate for predicting isothermal pollutant transport in a large room with simple geometry. PRACTICAL IMPLICATIONS: CFD modeling of pollutant transport is becoming increasingly common but high quality comparisons between CFD and experiment remain rare. Our results provide such a comparison. We demonstrate that the standard k-epsilon model provides good predictions for both transient and fully developed pollutant concentrations for an isothermal large space where furnishings are unimportant. This model is less computationally intensive than a large eddy simulation or low Reynolds number k-epsilon model.

Air Movements↗